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Quaisquer fotografias de pré-requisitos de seguro automóvel usadas neste site não são reais e são usadas apenas para ilustrar os resultados que alguns podem alcançar. O proprietário deste site não é uma companhia de seguros ou agente. O proprietário deste site e dos produtos e serviços referidos neste site apenas fornece um serviço onde os consumidores podem obter e comparar cotações de seguros de terceiros fornecedores. O proprietário não recomenda ou endossa qualquer companhia de seguros específica. As informações fornecidas neste site não se destinam a ser seguro ou aconselhamento financeiro. Coberturas de seguros reais e descontos estão sujeitos a elegibilidade e podem ser diferentes ou não disponíveis em todos os estados. Compartilhando Apresentando Características Clínicas em 48 Crianças com Poligangite Microscópica a 183 Crianças que Têm Granulomatose com Polianite (Wegeners): Um Estudo de Coorte de ARChiVe


















































Maurizio, Cutolo, Correspondente Endereço de e-mail: mcutolounige. it Universidade de Génova e IRCCS Azienda Ospedaliera Universitaria San Martino, Génova, Itália Correspondência de endereço Maurizio Cutolo, MD, Laboratório de Pesquisa e Divisão Acadêmica de Clínica Reumatologia, Departamento de Medicina Interna, Universidade de Gênova, Viale Benedetto XV, 6, 16132 Gênova, Itália (e-mail: mcutolounige. it) ou Vanessa Smith, MD, PhD, Departamento de Reumatologia, Hospital Universitário de Ghent, 0K12-IB, De Pintelaan 185, 9000 Ghent, Bélgica (E-mail: vanessa. smithugent. be). Ariane L. Herrick, Universidade de Manchester, Salford Royal NHS Fundação Trust, NIHR Manchester Unidade de Pesquisa Biomédica Musculoskeletal, Central Manchester NHS Foundation Trust e Manchester Académico Health Science Center, Manchester, Reino Unido Oliver Distler, Mike O. Becker , Hospital Universitário, Zurique, Suíça, e Hospital Universitário Chariteacute, Berlim, Alemanha Beltran, apoiado por Actelion Pharmaceuticals. Dr. Smiths trabalho foi apoiado por FWO investigação subvenção 1.5.217.13N ela também é beneficiário de um FWO Senior Investigador Clínico fellowship. Dr. Cutolo recebeu financiamento de pesquisa através da Universidade de Gênova da Actelion Pharmaceuticals, Bristol-Myers Squibb e Horizon. Dr. Herrick recebeu honorários de consultoria e / ou falando honorários de Actelion Pharmaceuticals e Apricus (menos de 10.000 cada) e bolsas de pesquisa da Actelion Pharmaceuticals. A Dra. Distler recebeu honorários de consultoria da 4D Science, da Active Biotec, da Bristol-Myers Squibb, da EpiPharm, da Biogen Idec, da Genentech / Roche, da GlaxoSmithKline, da Inventiva, da Lilly, da Pfizer, da Serodapharm, da Sinoxa, da ErgoNex, da Pharmacyclics e da Sanofi (menos de 10.000 Cada um) e da Actelion Pharmaceuticals, Bayer, Boehringer Ingelheim e Medac (mais de 10.000 cada) e tem recebido bolsas de investigação dessas empresas, ele detém uma patente para o uso de microRNA-29 no tratamento da esclerose sistêmica. Dr. Becker recebeu honorários de oradores e / ou honorários de, e tem servido como consultor para, Actelion Pharmaceuticals (menos de 10.000 não relacionados ao presente estudo). O Dr. Carpentier é membro do Comitê Científico sobre úlceras digitais da Actelion Pharmaceuticals SAS France. O Dr. Inanccedil recebeu honorários de consultoria e honorários de fala da Actelion Pharmaceuticals (menos de 10.000) e recebeu subsídios de pesquisa desta empresa. Dr. Chadha-Boreham, Sra. Cottreel, e Drs. Pfister e Rosenberg são empregados de, e ações próprias ou opções de ações em, Actelion Pharmaceuticals. O Sr. Torres recebeu honorários de consultoria da Actelion Pharmaceuticals (mais de 10.000). Dr. Smith recebeu honorários de consultoria e / ou taxas de fala da Actelion Pharmaceuticals, Boehringer Ingelheim e Galapagos (menos de 10.000 cada) e recebeu subsídios de pesquisa da Actelion Pharmaceuticals e Galápagos. Resumo Objetivo Identificar características videocapilaroscópicas unilaterais e outros fatores de risco clínicos para novas úlceras digitais (DUs) durante um período de 6 meses em pacientes com esclerose sistêmica (SSc). Métodos Neste estudo multicêntrico, prospectivo, de coorte observacional, o estudo videoCapillaroscopy (CAP), avaliamos 623 pacientes com SSc de 59 centros (14 países). Os pacientes foram estratificados em dois grupos: um grupo de história de DU e um grupo de história de DU não. No momento da inscrição, os pacientes foram submetidos a uma avaliação videocapilaroscópica detalhada da unha e avaliação das características demográficas, status do DU, e características clínicas e SSc. Os fatores de risco para o desenvolvimento de novas DUs foram avaliados por meio de análises de regressão logística univariada e multivariada (MLR). Resultados Dos 468 pacientes do grupo de história do DU (significava que a idade entre 54 e 30 anos era de 54,0 anos), 79,5 eram do sexo feminino, 59,8 tinham uma SS cutânea limitada e 22 desenvolveram um novo DU durante o seguimento. Os fatores de risco mais fortes para os DUs novos identificados pelo MLR no grupo de história de DU incluíram o número médio de capilares por milímetro no dedo médio da mão dominante, o número de DUs (categorizados como 0, 1, 2 ou ge3) e A presença de isquemia digital crítica. A característica de operação do receptor (ROC) da área sob a curva (AUC) do modelo MLR final foi de 0,738 (intervalo de confiança 95 95 IC 0,681 ± 0,795). A validação interna através de bootstrap gerou uma AUC ROC de 0,633 (95 IC 0,510ndash0,756). Conclusão Este estudo prospectivo internacional, que incluiu uma avaliação videocapilaroscópica detalhada e caracterização clínica extensiva de pacientes com SSc, identificou o número médio de capilares por milímetro no dedo médio da mão dominante, o número de DUs no momento da inscrição e a presença de fatores críticos Isquemia digital na inscrição como fatores de risco para o desenvolvimento de novas UDs. A esclerose sistêmica (SSc) é uma doença de tecido conectivo multissistêmica rara caracterizada por dano microvascular, fibrose da pele e órgãos internos, e anormalidades imunológicas específicas. A ulceração digital, que representa uma manifestação visível da vasculopatia periférica, é uma complicação freqüente da ES, com uma prevalência estimada de 50 1, 2. As úlceras digitais (DUs) freqüentemente ocorrem relativamente cedo no curso da doença, causando dor severa e comprometimento funcional, e têm um grande impacto na qualidade de vida dos pacientes 3-11. DUs também pode resultar em desfiguração significativa e infecção e pode levar a gangrena, osteomielite e, eventualmente, amputação 4. Além disso, os DUs são freqüentemente persistentes, recorrentes e demoram a cicatrizar, exigindo recursos consideráveis ​​para o tratamento de feridas e cuidados de enfermagem 2, 12. Dada a carga clínica e financeira, bem como a disponibilidade de terapias para prevenir DUs em pacientes com SSc 13, é necessário identificar fatores de risco para o desenvolvimento de novas DUs. Além do papel estabelecido da capilaroscopia no diagnóstico da ESc 14-16 e da avaliação do seu possível papel no monitoramento da SSc, alguns estudos relataram que as anormalidades observadas na capilaroscopia estão associadas aos DUs 17-25. O objetivo deste estudo foi identificar potenciais fatores de risco para a ocorrência de novas DUs durante um período de 6 meses em pacientes com SSc, com base em videocapilaroscopia (NVC) e outras características clínicas. PACIENTES E MÉTODOS Desenho do estudo O estudo videoCapillaroscopy (CAP) foi um estudo de coorte multicêntrico, prospectivo e observacional com registro estratificado na história do DU e nenhum grupo de história do DU. Os fatores de risco potenciais para o desenvolvimento de DUs foram avaliados no grupo de história de DU. O grupo de história de DU não foi incluído apenas para fins exploratórios, uma vez que se esperava que a incidência de novas DUs fosse baixa. A matrícula ocorreu durante um período de 1 ano para minimizar os efeitos sazonais. Os pacientes foram monitorados desde o momento da inscrição até a ocorrência de um novo DU ou um máximo de 6 meses, o que ocorrer primeiro. No momento da inscrição, os pacientes receberam um folheto educativo sobre a identificação dos DUs, e os funcionários de cada centro telefonaram mensalmente aos pacientes para se informar sobre a ocorrência de novas DUs. Se um DU foi relatado, uma visita paciente foi organizada de modo que o médico pudesse confirmar ou excluir a presença de um DU. O gerenciamento dos dados foi realizado de forma centralizada e a qualidade dos dados foi rigorosamente monitorada. A consistência dos dados de origem com a base de dados clínicos foi verificada para variáveis ​​críticas para 3 pacientes selecionados aleatoriamente por local (ou menos, se menos pacientes tivessem sido incluídos). Os dados foram revistos regularmente. O estudo da CAP foi conduzido por um comitê de direção independente (ver os Materiais Complementares, disponíveis no site da artrite e reumatologia em onlinelibrary. wiley / doi / 10.1002 / art.39718 / abstract). A lista completa de investigadores do estudo CAP também é dada nos Materiais Complementares. Este estudo foi conduzido de acordo com a Declaração de Helsinque e suas emendas, seguiu as Diretrizes para Boas Práticas Clínicas da Conferência Internacional de Harmonização e foi aprovado pelas comissões locais de revisão institucional e comitês de ética. Todos os pacientes forneceram consentimento informado por escrito. Estudo população Cinqüenta e nove centros SSc em 14 países (12 países europeus e Turquia e Israel) participaram no estudo entre 31 de janeiro de 2011 e 26 de julho de 2012. Pacientes idades ge18 anos com um diagnóstico de SSc de acordo com o American College of Rheumatology (ACR) 26 e / ou os critérios de LeRoy e Medsger 14 foram elegíveis para inclusão. Os critérios de inclusão foram amplos para permitir a generalização para uma população mais ampla de SSc. Para enriquecer a ocorrência de novas DUs na população estudada, os pacientes tiveram que atender a 1 dos 2 critérios seguintes: 1) história de DUs ou DU na matrícula (grupo de história do DU), ou 2) duração da doença de le2 Anos (nenhum grupo de história do DU), definido como o tempo desde o primeiro médico-documentado nonndashRaynauds fenômeno característica clínica de SSc 3. Como o estudo foi conduzido para permitir a extrapolação dos resultados para o ambiente real, os pacientes foram autorizados a continuar seus tratamentos em curso. Pacientes incapazes de submeter-se à avaliação da NVC não eram elegíveis para inclusão no estudo. Os pacientes com SSc esclerodermia sistêmica foram excluídos porque não se esperava que desenvolvessem DUs freqüentemente durante o período de observação de 6 meses. Além disso, pacientes que haviam sido submetidos a transplante de células-tronco ou que haviam participado de um ensaio clínico intervencionista nos três meses anteriores à inscrição foram excluídos, uma vez que essas intervenções podem ter efeitos desconhecidos na ocorrência de novas DUs. Recolha de dados As covariáveis ​​das características demográficas, características clínicas da ES, DUs e outras características clínicas, bem como os resultados da NVC foram recolhidos no momento da inscrição e estão resumidos na Tabela 1 e nas Tabelas Suplementares 1ndash4 (covariables não NVC) e na Tabela 2 e As Tabelas Suplementares 5 e 6 (covariables NVC) (Materiais Suplementares estão disponíveis on-line em onlinelibrary. wiley / doi / 10.1002 / art.39718 / abstract). As co-variáveis ​​de características do DU incluíram uma história de DUs (avaliados à discrição dos investigadores), a presença de DUs (número e localização) e as complicações / intervenções anteriores e actuais associadas à DU, incluindo a isquemia digital crítica (definida como uma doença prolongada, Persistente na perfusão de tecidos digitais sem reaquecimento ver Tabela Suplementar 7 e Figura 1 Suplementar). Tabela 1. Covariables de videocapilaroscopia não selecionadas selecionadas no estágio de análise de ULR e transportadas para a análise de MLR dentro do bundle no grupo de história de DU Os dados são estatísticas sumárias de covariables de videocapilaroscopia (NVC) unilateral selecionadas e transportadas para a regressão logística multivariada (MLR) Intra-bundle em pacientes que desenvolveram (casos) e aqueles que não desenvolveram (noncases) uma úlcera digital (DU) durante o período de observação. As características quantitativas de NVC transferidas para as análises MLR dentro do feixe e em feixes transversais para sub-feixes 5,1 e 5,4 e sub-feixe 5,6 são mostradas na Tabela Suplementar 5 (na Seção 3 dos Materiais Complementares, disponível no Arthritis amp Reumatology Web site em onlinelibrary. wiley / doi / 10.1002 / art.39718 / abstract). Índice de confiança CIthinspthinsp95 ULRthinspthinspunivariable regressão logística. Pelo teste do chi-quadrado de Walds. O valor médio no nível do dedo é a contagem média por medida de 2 imagens capilaroscópicas. A variável também foi levada adiante para a análise MLR através de bundles. O odds ratio (OR) não é dado uma vez que a relação funcional é quadrática. O valor de P associado ea área característica de operação do receptor (ROC) sob a curva (AUC) são termos quadráticos. Usado em um modelo final alternativo como ldquosurrogaterdquo para variáveis ​​quantitativas de NVC. Padrão ativo versus normal / precoce. Padrão tardio versus normal / precoce. As informações sobre o uso de medicamentos nos três meses anteriores à inscrição, no momento da inscrição e durante o período de observação foram registradas como classes predefinidas de medicamentos, incluindo medicamentos vasoativos e imunossupressores. Resultado do estudo O DU foi definido clinicamente como uma área desnudada localizada nos dedos e com uma borda definida e perda de epitelização e perda de epiderme e derme. A definição excluía fissuras, paroníquia, cicatrizes de picadas ou úlceras localizadas sobre as articulações metacarpofalângicas ou cotovelos (ver Tabela Complementar 7, disponível on-line onlinelibrary. wiley / doi / 10.1002 / art.39718 / abstract). Os DUs distal à articulação metacarpofalângica e nos aspectos volar e dorsal da mão foram incluídos (ver Figura 1). As úlceras induzidas por calcinose não foram especificamente excluídas. O resultado do DU dos pacientes foi registrado como uma variável binária: ou a ocorrência ou não ocorrência de um novo DU. Os casos foram definidos como pacientes que experimentaram um novo DU que foi confirmado pelo investigador durante o período de observação de 6 meses. Os não-casos foram definidos como pacientes que não experimentaram um DU novo durante o período de observação de 6 meses. Para noncases, o nonoccurrence de um DU novo foi reconhecido somente se o paciente tinha sido contatado com sucesso pelo pessoal para pelo menos 3 dos telefonemas mensais, including o atendimento de telefone do mês 6, e não tinha relatado nenhum DUs novo ou se os noncases tinham relatado Um novo DU que não tinha sido confirmado pelos investigadores. Médicos avaliar a presença de DUs não foram cegos para os dados coletados na matrícula. Recolha e avaliação das imagens NVC Foram examinadas, em cada paciente, as unhas do segundo, terceiro, quarto e quinto dedos de ambas as mãos com o uso de um videocapilaroscope equipado com uma lente de ampliação de 200x que é comumente utilizada para NVC 5,27 , 28, e conectado ao software de análise de imagem. Dois campos adjacentes que se estendem sobre 1 mm, no meio da prega, e correspondendo à fila distal de capilares foram estudados [27]. As imagens foram avaliadas utilizando técnicas de avaliação qualitativa e quantitativa 18, 27, 29. A avaliação qualitativa das imagens (1 covariável) (ver Tabela Suplementar 6) classificou o paciente como tendo o padrão NVC normal, precoce, ativo ou tardio de esclerodermia de acordo com Cutolo e cols. 29 (ver folheto sobre Materiais Suplementares, Investigador, disponível on-line onlinelibrary. Wiley / doi / 10.1002 / art.39718 / abstract). A avaliação quantitativa das imagens (6 covariáveis ​​em cada um dos 8 dedos) consistiu em contar as seguintes 5 covariáveis ​​por milímetro linear: capilares, capilares gigantes (em forma de ferradura ou em forma de ferradura, capilar grosso homogêneo com diâmetro gt50 mum) Capilares (diâmetro gt20 morfologia mamã pode ser em forma de gancho, tortuoso, ou atravessar uma vez), microhemorragias (massas escuras devido a depósitos de hemossiderina, que pode ser ligado a um capilar desaparecendo), e neoangiogeneses (meandering, ramificado, ramificado, Capilares e capilares com cruzamentos gt2), mais uma sexta covariável consistindo em medir o diâmetro capilar máximo em capilares com um diâmetro gt50 mum (ver folheto de Materiais Complementares, Investigador). Para garantir uma confiabilidade ótima na avaliação, os funcionários de todos os centros foram treinados na avaliação de imagens capilaroscópicas em um workshop interativo com uso prático dos dispositivos de capilaroscopia e foram fornecidos com um folheto contendo definições ilustradas das características capilaroscópicas (ver Materiais Complementares, Livreto do investigador). Para refletir a prática clínica do mundo real neste estudo observacional, as imagens foram analisadas em cada centro participante. A qualidade da imagem foi avaliada para os primeiros pacientes em cada centro por 3 membros do comitê de direção (MC, ALH e VS). Se a qualidade da imagem não era a mais adequada, foi dada formação adicional sobre a utilização correcta da NVC. Análise estatística Tamanho da amostra O tamanho da amostra baseou-se em considerações de viabilidade, onde 350 pacientes inscritos no grupo de história de DU forneceriam um número razoável de casos (nthinspthinsp150) para a construção de modelos usando um processo de fase para explorar associações e discriminação de fatores de risco 30-32. As análises estatísticas exploratórias foram planejadas para 150 pacientes no grupo de história sem DU, onde se esperava que o número de casos fosse baixo. A estratificação foi utilizada para aumentar a homogeneidade dentro da história do DU e nenhum grupo de história do DU, uma vez que os fatores associados à ocorrência de DUs novos nos dois grupos deveriam ser diferentes. Análise de fatores de risco para a ocorrência de novas DUs Covariables foram descritas para casos e noncases usando estatísticas de resumo. As associações entre as covariáveis ​​categóricas individuais e os novos resultados de DU foram inicialmente exploradas utilizando o teste qui-quadrado ou o teste exato de Fisher, conforme apropriado. Foram fornecidas estatísticas resumidas para as variáveis ​​contínuas nos grupos caso e noncase e para as diferenças entre os 2 grupos. A modelagem de regressão logística foi o principal método analítico para examinar as associações ea capacidade discriminatória de potenciais fatores de risco para a ocorrência de novas DUs, incluindo relações funcionais lineares e quadráticas. A força da associação entre um fator de risco e o novo resultado do DU foi dada pelo odds ratio (OR) com o seu intervalo de confiança de 95 (IC95) significância estatística foi dada via teste de Qui-quadrado de Walds. A calibração do modelo na análise de regressão logística multivariada (MLR) foi avaliada através do teste Qui-quadrado de Hosmer-Lemeshow. O desempenho discriminatório dos vários fatores de risco (individual e combinado) foi dado pela área característica de operação do receptor (ROC) sob a curva (AUC) e seu correspondente IC95. A estratégia estatística para a seleção dos fatores de risco de melhor desempenho para o modelo MLR final foi um processo escalonado em 3 estágios amplos utilizando ldquobundlesrdquo de covariáveis ​​33, 34 (ver Tabelas Suplementares 1ndash6), onde estágio 1 é análise de regressão logística univariável (ULR) O estágio 2 é MLR dentro da análise de feixe, ea fase 3 é MLR across-bundles análise. O número de covariáveis ​​foi reduzido em cada fase, e as covariáveis ​​de melhor desempenho dos feixes foram transportadas para a fase seguinte. As covariáveis ​​não-NVC com características semelhantes (de acordo com os domínios nas formas de relato de caso) foram organizadas em 4 feixes: bundle 1 para dados demográficos, bundle 2 para características clínicas SSc, bundle 3 para DU e bundle 4 para outras características clínicas . Seis sub-feixes de covariables de NVC (feixes 5,1 e 5,5) foram derivados de várias maneiras a partir das 6 covariáveis ​​avaliadas para os 4 dedos em cada mão. Os sub-feixes de NVC foram organizados em 3 níveis para a construção de modelos MLR concorrentes: os níveis de doente, mão e dedo (ver Tabela 5). A covariabilidade qualitativa de padrão 1 NVC foi utilizada em um modelo final alternativo como um substituto para as covariáveis ​​quantitativas de NVC. O Pacote 6 foi formado para a investigação estatística de interações entre covariáveis, as quais foram pré-especificadas por consenso clínico (ver Tabela Complementar 8, disponível on-line em onlinelibrary. wiley / doi / 10.1002 / art.39718 / abstract). O padrão de uso de medicamentos (por classe) variou de um país para outro, e os pacientes tomaram uma ampla gama de combinações de diferentes classes de medicação. Uma análise mostrou que o uso de medicação dentro de 3 meses antes e no momento da inscrição estava associado com a gravidade da doença de DU antes e na matrícula (número de DUs, pelo teste de tendência de Cochran-Armitage e hospitalizações anteriores devido a DUs, pelo teste de qui-quadrado) (Tabela 3). Especificamente, a gravidade da DO ea utilização de medicação foram colineares, o que significa que um poderia ser predito a partir do outro com razoável precisão. Portanto, devido à colinearidade na regressão multivariada e à complexidade dos padrões de uso de medicamentos em todos os países, decidiu-se não incluir o uso de medicação como fator de risco potencial, mas sim incluir a gravidade da doença no processo de construção do modelo. Os critérios estatísticos para a seleção de covariáveis ​​de bom desempenho através da seleção passo-a-passo direta (FSS) no estágio ULR foram a estatística do teste do qui-quadrado de Walds, P thinspltthinsp0.15 para termos lineares, ou P thinspltthinsp0 .05 para termos quadráticos. As variáveis ​​categóricas com uma frequência de doentes com lt20 não foram movidas para a frente de ULR para MLR (ver Tabela Suplementar 9, disponível on-line em onlinelibrary. wiley / doi / 10.1002 / art.39718 / abstract). Além do FSS, a técnica de grupo nominal foi utilizada pelo comitê de direção para excluir algumas covariáveis ​​baseadas na falta de plausibilidade clínica e / ou viabilidade, com especial atenção ao uso na prática padrão (ver Tabela 10). Os critérios estatísticos para a selecção de covariáveis ​​através do FSS durante a análise de MLR fase 2 (dentro do feixe) foram introduzidos se P thinspltthinsp0.15 e reter se P thinspltthinsp0.10. Na análise MLR fase 3 (across-bundles), critérios estatísticos para a seleção de covariáveis ​​via FSS foram para entrar se P thinspltthinsp0.15 e reter se P thinspltthinsp0.05. A redução das covariáveis ​​em cada um dos 3 estádios resultou no modelo MLR final. Modelagem dentro de cada um dos sub-bundles NVC resultou em 6 modelos concorrentes MLR. A fim de reter apenas 1 tipo de avaliação de NVC no modelo final entre as diversas opções de avaliação para o NVC (qualitativo ou quantitativo nos níveis de paciente, mão e dedo), decidiu-se levar adiante o subgrupo com o Maior AUC ROC no estágio de across-bundles. Validação do modelo A validação interna do modelo MLR final foi realizada através do método bootstrap 35, com 2.000 re-amostras usando os mesmos procedimentos de construção de modelos e covariáveis ​​do que no modelo final. O software SAS, versão 9.1.3, foi utilizado para a análise estatística e para o relato de dados clínicos. RESULTADOS Os achados no resultado do DU da população de estudo foram conhecidos para 591 dos 623 pacientes inscritos. Dentre os 591 pacientes, 468 (79,2) pertenciam ao grupo de história do DU (Figura 1), dos quais 103 (22,0) desenvolveram DU durante o período de observação. Dos 123 pacientes no grupo sem DU, apenas 5 (4,1) desenvolveram um novo DU (Figura 1). Como a incidência de novas DUs no grupo de história de DU não era baixa, este relatório concentra-se no grupo de história de DU. A distribuição do número total de pacientes eo número de pacientes que desenvolveram um novo DU (casos) foram altamente variáveis ​​entre países e centros (ver Figura 2, disponível on-line onlinelibrary. wiley / doi / 10.1002 / art.39718 / abstract ). Os resultados do processo de seleção covariável para os fatores de risco com melhor desempenho para o novo resultado de DU para cada feixe nos estágios 1, 2 e 3 do desenvolvimento do modelo são apresentados em detalhes nas Tabelas Suplementares 1 e 6. Figura 1. Fluxograma mostrando a distribuição dos pacientes do estudo e estratificação do conjunto de desfechos de úlcera digital (DU). No total, 637 pacientes com esclerose sistêmica foram selecionados. Após a exclusão de falhas de triagem, pacientes inelegíveis, descontinuações prematuras e pacientes não atribuídos a categorias de casos ou não, foi obtido o conjunto avaliável de DU (nthinspthinsp591). Dado que as variáveis ​​que influenciam a ocorrência de um novo DU foram pensadas como sendo diferentes no grupo de história de DU (aqueles com história de DUs ou com DUs no momento da inscrição) versus o grupo de história de DU sem história de DUs, o A análise dos dados foi estratificada nos mesmos dois grupos de estudo. Características demográficas, úlceras digitais e características clínicas Do grupo de história do DU, 79,5 dos pacientes eram do sexo feminino, 280 (59,8) foram classificados como tendo SSc cutânea limitada e 188 (40,2) com SS cutânea difusa. As características demográficas, DU e clínicas, de acordo com o resultado do DU, são relatadas na Tabela 1. A presença e o número de DUs na matrícula foram significativamente associados com a ocorrência de novas DUs (P thinspltthinsp0.001), com as RUP aumentando com um Aumentando o número de DUs na matrícula. A OR de ter um novo DU foi de 2.691 (95 IC 1.507ndash4.803) em pacientes com 1 DU na matrícula, 3.787 (95 IC 1.879ndash7.630) naqueles com 2 DUs e 7.399 (95 IC 3.687ndash14.848) Naqueles com ge3 DUs. As AUCs de ROC para covariables individuais não-NVC por ULR (fase 1) tinham um intervalo entre 0,520 e 0,678 (Tabela 1). The highest ROC AUC for the non-NVC covariables selected by MLR within-bundle (stage 2) was for the DU characteristics bundle, at 0.694 (95 CI 0.637ndash0.751) (see Supplementary Tables 11ndash14, available online at onlinelibrary. wiley/doi/10.1002/art.39718/abstract ). Calcinosis was present in 20.4 of cases and 18.4 of noncases. On ULR analysis, calcinosis was not significantly associated with the development of new DUs (OR 1.090 95 CI 0.637ndash1.866). The vasomodulating and immunosuppressive medications taken during the 3 months prior to or at enrollment and their association with DU disease severity, are described in Table 3. During the observation period, medication use was stable for the drug classes recorded. NVC covariables, qualitative and quantitative measurements Descriptive analysis of the NVC covariables selected at the ULR stage and carried forward to the MLR within-bundle analysis, are reported in Table 2 and in Supplementary Tables 5 and 6. The NVC pattern was significantly associated with the DU outcome ( P thinspltthinsp0.002) the proportion of patients with a late SSc NVC pattern was higher in the cases than in the noncases (71.8 versus 54.0), and the OR for a late versus normal/early SSc NVC pattern was 4.150 (95 CI 1.441ndash11.950), which is consistent with previous reports 20, 21 . The mean number of capillaries per millimeter was significantly reduced in cases versus noncases regardless of the NVC quantitative assessment type used (patient, hand, or finger level). The NVC covariables selected by MLR within-bundle analysis (stage 2) are depicted in Supplementary Tables 15 and 16 (available online at onlinelibrary. wiley/doi/10.1002/art.39718/abstract ). The NVC sub-bundle encompassing analysis at the finger level of the dominant hand in the 4 individual fingers (sub-bundle 5.5) had the highest ROC AUC among the 6 competing NVC sub-bundle models (ROC AUC 0.677 95 CI 0.614ndash0.740) and was carried forward to the MLR across-bundles analysis (stage 3). The complete list of NVC covariables carried forward from MLR within-bundle to MLR across-bundles analysis is presented in Supplementary Table 17 (available online at onlinelibrary. wiley/doi/10.1002/art.39718/abstract ). Interactions between covariables Analysis of the interactions bundle 6 showed that none of the 8 interaction terms were statistically significant. Therefore, no interaction terms were carried forward to the MLR across-bundles analysis. Non-NVC and NVC covariables in MLR across-bundles analysis MLR across-bundles analysis (stage 3) resulted in the final model with 3 risk factors: 1) the mean number of capillaries/mm in the middle finger of the dominant hand (evaluated on 2 adjacent fields in the middle of the nailfold), with an OR of 0.838 (95 CI 0.735ndash0.955), 2) the number of DUs at the enrollment visit (categorized as 0, 1, 2, or ge3), with an OR for ge3 DUs versus 0 DUs at enrollment of 6.160 (95 CI 2.999ndash12.653), and 3) the presence of critical digital ischemia at enrollment, with an OR of 3.194 (95 CI 1.284ndash7.945) (Table 4 ). The ROC AUC for the model was 0.738 (95 CI 0.681ndash0.795), and the trade-off between sensitivity and specificity can be seen in the crossover curves (see Supplementary Figure 3, available online at onlinelibrary. wiley/doi/10.1002/art.39718/abstract ). The Hosmer-Lemeshow test indicated that the final model did not show a significant lack of fit ( P thinspthinsp0.751). Internal validation of the final MLR model through bootstrap generated a ROC AUC of 0.633 (95 CI 0.510ndash0.756). Table 4. Final multivariable logistic regression modela A supplementary analysis in which the NVC pattern was used as the NVC covariable in the final model yielded similar model performance, with a ROC AUC of 0.715 (95 CI 0.658ndash0.771). DISCUSSION The CAP study was the first large, prospective, international, multicenter study to evaluate capillaroscopic and other clinical characteristics to determine risk factors for the development of new DUs during a 6-month period in patients with SSc. A very low number of cases were reported in the no DU history group during the 6-month observation period therefore, risk factor analysis was performed only on the DU history group. The strongest performing risk factors for the occurrence of new DUs identified by MLR analysis were the mean number of capillaries per millimeter in the middle finger of the dominant hand, the number of DUs at enrollment, and the presence of critical digital ischemia at enrollment. The characteristics of our study population were similar to those in the European League Against Rheumatism (EULAR) Scleroderma Trials and Research Group (EUSTAR) cohort of patients with SSc 36 . As expected, it appeared that the number of DUs had a large influence on the risk of future DUs, which is consistent with data reported previously 37, 38 , and may be linked to increased severity of SSc disease, since patients with DUs are more likely to have an earlier onset of SSc and more extensive skin involvement 3 . There are a number of therapies available for the prevention of DUs 32, 39, 40 , and it is therefore important to identify patients who are at risk of developing DUs so that they can receive preventive management. Consistent with previous studies, the CAP study revealed capillary density (number of capillaries/mm) as the most robust NVC risk factor for new DUs. Of note, loss of capillaries has been linked to an increased risk of developing SSc and may therefore predict an early diagnosis, more severe skin involvement, and a poorer prognosis 21, 41-46 . While the current study found that the capillary density on the third digit of the dominant hand was sufficient for predicting the risk of new DUs, it is still necessary to evaluate at least 4 digits per hand for the diagnosis of SSc in patients with Raynauds phenomenon. The study also indicated that the NVC pattern may play a role in predicting DUs, confirming the results of smaller studies that have shown that the late SSc NVC pattern is associated with an increased risk of DUs 20, 47, 48 . Ideally, future studies would encompass NVC-based indices assessing eventual disease-modifying characteristics of treatment on the clinical complications of SSc such as DUs 49, 50 . With regard to the individual performance of each of the final models 3 variables in the ULR analysis, the number of DUs was the strongest risk factor (ROC AUC 0.678), followed by the number of capillaries/mm in the middle finger of the dominant hand (ROC AUC 0.614), and then the presence of critical digital ischemia at enrollment (ROC AUC 0.556). The combination of the 3 variables in the final MLR model improved the models discriminatory ability (ROC AUC 0.738). Furthermore, the relative weights of the Walds chi-square values for the 3 variables (Table 4 ) demonstrated that both the number of capillaries/mm in the middle finger of the dominant hand (NVC variable) and critical digital ischemia at enrollment make important additional contributions to the number of DUs for determining the risk of new DUs in the final MLR model. Interestingly, whereas a 50 incidence of new DUs in the DU history group had been assumed in the sample size estimation of our study, in reality, there was only a 22 incidence. The lower-than-expected incidence of new DUs may be the result of patients already receiving best practice standard of care for the management of their disease at enrollment. Medication use was not restricted, varied between countries, and was found to be associated with DU disease severity, which is consistent with findings from the Digital Ulcer Outcome Registry showing that patients with chronic and/or recurrent DUs have a shorter time to new DUs as compared with patients with no or episodic DUs 51 . Therefore, medication use was not considered to be an independent variable and was not included as a potential risk factor for new DUs. Because the no DU history subgroup was considered exploratory, it was not possible to identify variables that predicted DUs because of the small number of cases in this subgroup. We had anticipated at least 20 of cases, based on the report that 50 of patients with SSc experience DUs 1 and the fact that the first DU usually occurs early in the disease course 3 however, we observed only 4.1 of cases of new DUs in this subgroup. This may have 2 explanations. First, the SSc population may have changed in the last few years: patients with SSc may be diagnosed earlier than in the past, and preventive and efficient measures are now more widely used. Second, it might have been beneficial to restrict patients in the no DU history group to those with a first nonndashRaynauds phenomenon symptom within 1 year (instead of 2 years) in order to be closer to the population described by Hachulla et al 3 , in which 43 of patients had their first DU within 1 year of their first clinical sign of SSc. NVC has the potential to be a useful tool for monitoring the progression of microvascular disease associated with SSc and for measuring the response to treatment 52, 53 . It is a well-established, noninvasive technique that allows for higher-magnification analysis compared with the older widefield capillaroscopic method 53, 54 . The training needed for the device is minimal (sim5 days 55 ) and the required examination time is short (sim10 minutes including image recording). An NVC apparatus is more costly than handheld devices such as dermatoscopes, but NVC has been shown to permit more-detailed assessments 56 and the grading of more images 54 . During patient follow-up, detailed assessment and quantification of abnormalities is important, and therefore, handheld tools may not be as useful in this setting 53 . Of note, capillaroscopy has been recently introduced as a criterion in the ACR/EULAR classification criteria for SSc 16 , thereby increasing their sensitivity and specificity. Overall, there are several key strengths of this study. First is its generalizability, ensured by the broad distribution of participating centers and patients representing current standard of care. Second is its applicability in real-world clinical practice, owing to the broad study population, the simplicity and ease of clinical evaluation of the NVC, and the clinical risk factors that built the final model. Third is its value in the management of patients with a history or presence of DUs. The scope of the CAP study was to determine risk factors for developing DUs by using NVC and other clinical characteristics in routine clinical practice and health care environments, including centers with different levels of NVC experience. Thus, the ldquocenterrdquo was not regarded as a potential risk factor, which could allow generalizability of the study findings for patients with SSc outside of the study centers. The interrater variability of NVC assessments has been of concern, and the capillary density has been identified as the NVC variable with the best interrater agreement in earlier, smaller studies 28, 57 . In the CAP study, interrater variability was addressed by practical training and teaching booklets that were offered to the investigators. Despite these efforts, the ambitious intention to allow extrapolation to the real-world setting may have introduced a large amount of noise that was detrimental to obtaining a final model with high discriminatory ability. Limitations of this study include the fact that the diagnosis of DUs and critical digital ischemia is not always unequivocal 58, 59 , although definitions were provided in the protocol and/or the case report form. Investigators were not blinded to the NVC and other clinical characteristics assessed at enrollment and may therefore have been biased in favor of a diagnosis of a new DU. However, this was inherent to the real-world nature of the study, as physicians in clinical practice are not blinded to the results of other assessments. Although the wide geographic spread of centers permits generalizability, it was conducive to introducing heterogeneity in the data. The center effect could not be explored in depth because of the wide distribution of patients across a large number of centers, with a small number of cases per center. Although it was estimated that 150 cases would be a reasonable number for exploring risk factor associations with the development of DUs, only 103 cases were observed in this cohort and, as such, the study was underpowered. Nevertheless, the modeling strategy (ULR, MLR within-bundle, MLR across-bundles analyses), together with the reduction in the number of variables entered into the model and the bootstrap validation demonstrated the robustness of the variables in the final model, thereby compensating for the lower-than-prespecified number of cases. Laboratory biomarkers were not included in this study, which could have helped to improve the discriminatory ability of the final model. Biomarkers have previously been shown to be useful for predicting DUs 60 and may be useful to include in future studies. Given the nature of this real-world observational study, it was not feasible to determine the presence of an association between medication use and development of DUs. Future larger studies could be designed to explore this association by controlling for confounding factors such as DU disease severity and the use of medications (individually and in combination) in different countries. In conclusion, this longitudinal, multicenter study of almost 500 patients with SSc has shown that the mean number of capillaries/mm on the middle finger of the dominant hand, the number of DUs at enrollment, and the presence of critical digital ischemia at enrollment are risk factors for the development of new DUs. The risk factors identified are simple to evaluate in the clinic, and the real-world nature of the study allows the results to be generalized to a wider SSc population, thereby providing the physician with useful information when considering a patients risk of future DUs. ACKNOWLEDGMENTS We thank our colleagues for sending their patients to our Scleroderma Clinics and our patients for their trust and unconditional commitment. We also thank Harald Heinzl (CeMSIIS, Medical University of Vienna, Vienna, Austria) for statistical consultation and assistance with the bootstrap analysis, as well as the wider group of statisticians for their contributions (see Supplementary Materials, available on the Arthritis amp Rheumatology web site at onlinelibrary. wiley/doi/10.1002/art.39718/abstract ). AUTHOR CONTRIBUTIONS All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Drs. Cutolo and Smith had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study conception and design Cutolo, Herrick, Distler, Chadha-Boreham, Cottreel, Pfister, Rosenberg, Smith. Acquisition of data Cutolo, Herrick, Distler, Beltran, Carpentier, Inanccedil, Vlachoyiannopoulos, Smith. Analysis and interpretation of data Cutolo, Herrick, Distler, Becker, Beltran, Carpentier, Ferri, Inanccedil, Vlachoyiannopoulos, Chadha-Boreham, Cottreel, Pfister, Rosenberg, Torres, Smith. ROLE OF THE STUDY SPONSOR Actelion Pharmaceuticals funded the study and was responsible for the study protocol design, data collection, and statistical analysis, with, and under the leadership of, an independent study steering committee (non-Actelion authors). Actelion Pharmaceuticals also paid for editorial assistance (provided by Drs. Marion James and Lynda McEvoy, ApotheCom, a medical communications company). The steering committee members, all of whom are authors of this article, and the 4 authors employed by Actelion Pharmaceuticals were involved in the decision to submit the article for publication. Publication of this article was not contingent upon approval by Actelion Pharmaceuticals. Ancillary Article Information DOI Format Available Full text: HTML PDF copy 2016 The Authors. Arthritis amp Rheumatology published by Wiley Periodicals, Inc. on behalf of the American College of Rheumatology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. Publication History Issue online: 28 September 2016 Version of record online: 28 September 2016 Accepted manuscript online: 25 April 2016 Manuscript Accepted: 7 April 2016 Manuscript Received: 27 November 2015 Supporting Information Additional Supporting Information may be found in the online version of this article. Supplementary Table S1. Bundle 1: Demographics Supplementary Table S2. Bundle 2: Systemic sclerosis clinical characteristics Supplementary Table S3. Bundle 3: Digital ulcer characteristics Supplementary Table S4. Bundle 4: Other clinical characteristics Supplementary Table S5. Bundle 5: Nailfold videocapillaroscopic characteristics: quantitative assessment (6 sub-bundles) Supplementary Table S6. Nailfold videocapillaroscopic characteristics: qualitative assessment (1 covariable) Supplementary Table S7. Definitions of digital ulcer (DU), critical digital ischemia, and other (than DU) Supplementary Figure S1. Coding of the location of digital ulcers Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. REFERENCES 1 Walker UA. Tyndall A. Czirjaacutek L. Denton C. Farge-Bancel D. Kowal-Bielecka O. et al. Clinical risk assessment of organ manifestations in systemic sclerosis: a report from the EULAR Scleroderma Trials and Research group database. Ann Rheum Dis 2007 66. 754 ndash 63. CrossRef PubMed CAS Web of Sciencereg Times Cited: 267 2 Steen V. Denton CP. Pope JE. Matucci-Cerinic M. Digital ulcers: overt vascular disease in systemic sclerosis. Rheumatology (Oxford) 2009 48 Suppl 3. iii19 ndash 24. PubMed 3 Hachulla E. Clerson P. Launay D. Lambert M. Morell-Dubois S. Queyrel V. et al. Natural history of ischemic digital ulcers in systemic sclerosis: single-center retrospective longitudinal study. J Rheumatol 2007 34. 2423 ndash 30. PubMed Web of Sciencereg Times Cited: 70 4 Nihtyanova SI. Brough GM. Black CM. Denton CP. Clinical burden of digital vasculopathy in limited and diffuse cutaneous systemic sclerosis. Ann Rheum Dis 2008 67. 120 ndash 3. CrossRef PubMed Web of Sciencereg Times Cited: 54 5 Cutolo M. Sulli A. Smith V. Assessing microvascular changes in systemic sclerosis diagnosis and management. Nat Rev Rheumatol 2010 6. 578 ndash 87. CrossRef PubMed Web of Sciencereg Times Cited: 79 6 Beacuterezneacute A. Seror R. Morell-Dubois S. de Menthon M. Fois E. Dzeing-Ella A. et al. Impact of systemic sclerosis on occupational and professional activity with attention to patients with digital ulcers. Arthritis Care Res (Hoboken) 2011 63. 277 ndash 85. Wiley Online Library PubMed Web of Sciencereg Times Cited: 31 7 Guillevin L. Hunsche E. Denton CP. Krieg T. Schwierin B. Rosenberg D. et al. 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A prospective study of systemic sclerosis-related digital ulcers: prevalence, location, and functional impact. Scand J Rheumatol 2013 42. 483 ndash 6. CrossRef PubMed Web of Sciencereg Times Cited: 14 11 Mouthon L. Carpentier PH. Lok C. Clerson P. Gressin V. Hachulla E. et al. Ischemic digital ulcers affect hand disability and pain in systemic sclerosis. J Rheumatol 2014 41. 1317 ndash 23. CrossRef PubMed Web of Sciencereg Times Cited: 3 12 Matucci-Cerinic M. Steen V. Nash P. Hachulla E. The complexity of managing systemic sclerosis: screening and diagnosis. Rheumatology (Oxford) 2009 48 Suppl 3. iii8 ndash 13. PubMed 13 Kowal-Bielecka O. Landeweacute R. Avouac J. Chwiesko S. Miniati I. Czirjak L. et al. EULAR recommendations for the treatment of systemic sclerosis: a report from the EULAR Scleroderma Trials and Research group (EUSTAR). Ann Rheum Dis 2009 68. 620 ndash 8. CrossRef PubMed Web of Sciencereg Times Cited: 209 14 LeRoy EC. Medsger TA Jr. Criteria for the classification of early systemic sclerosis. J Rheumatol 2001 28. 1573 ndash 6. PubMed Web of Sciencereg Times Cited: 582 15 Avouac J. Fransen J. Walker UA. Riccieri V. Smith V. Muller C. et al. Preliminary criteria for the very early diagnosis of systemic sclerosis: results of a Delphi consensus study from EULAR Scleroderma Trials and Research Group. Ann Rheum Dis 2011 70. 476 ndash 81. CrossRef PubMed Web of Sciencereg Times Cited: 106 16 Van den Hoogen F. Khanna D. Fransen J. Johnson SR. Baron M. Tyndall A. et al. 2013 classification criteria for systemic sclerosis: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum 2013 65. 2737 ndash 47. Wiley Online Library PubMed Web of Sciencereg Times Cited: 236 17 Sebastiani M. Manfredi A. Vukatana G. Moscatelli S. Riato L. Bocci M. et al. Predictive role of capillaroscopic skin ulcer risk index in systemic sclerosis: a multicentre validation study. Ann Rheum Dis 2012 71. 67 ndash 70. CrossRef PubMed Web of Sciencereg Times Cited: 36 18 Sebastiani M. Manfredi A. Colaci M. DAmico R. Malagoli V. Giuggioli D. et al. Capillaroscopic skin ulcer risk index: a new prognostic tool for digital skin ulcer development in systemic sclerosis patients. Arthritis Rheum 2009 61. 688 ndash 94. Wiley Online Library PubMed CAS Web of Sciencereg Times Cited: 54 19 Smith V. Riccieri V. Pizzorni C. Decuman S. Deschepper E. Bonroy C. et al. Nailfold capillaroscopy for prediction of novel future severe organ involvement in systemic sclerosis. J Rheumatol 2013 40. 2023 ndash 8. CrossRef PubMed Web of Sciencereg Times Cited: 29 20 Smith V. Decuman S. Sulli A. Bonroy C. Piette Y. Deschepper E. et al. Do worsening scleroderma capillaroscopic patterns predict future severe organ involvement A pilot study. Ann Rheum Dis 2012 71. 1636 ndash 9. CrossRef PubMed Web of Sciencereg Times Cited: 50 21 Smith V. De Keyser F. Pizzorni C. Van Praet JT. Decumnan S. Sulli A. et al. Nailfold capillaroscopy for day-to-day clinical use: construction of a simple scoring modality as a clinical prognostic index for digital trophic lesions. Ann Rheum Dis 2011 70. 180 ndash 3. CrossRef PubMed Web of Sciencereg Times Cited: 42 22 Ennis H. Moore T. Murray A. Vail A. Herrick AL. Further confirmation that digital ulcers are associated with the severity of abnormality on nailfold capillaroscopy in patients with systemic sclerosis. Rheumatology (Oxford) 2014 53. 376 ndash 7. CrossRef PubMed Web of Sciencereg Times Cited: 8 23 Manfredi A. Sebastiani M. Carraro V. Iudici M. Bocci M. Vukatana G. et al. Prediction risk chart for scleroderma digital ulcers: a composite predictive model based on capillaroscopic, demographic and clinico-serological parameters. Clin Hemorheol Microcirc 2015 59. 133 ndash 43. PubMed Web of Sciencereg Times Cited: 7 24 Brand M. Hollaender R. Rosenberg D. Scott M. Hunsche E. Tyndall A. et al. An observational cohort study of patients with newly diagnosed digital ulcer disease secondary to systemic sclerosis registered in the EUSTAR database. Clin Exp Rheumatol 2015 33 Suppl 91. S47 ndash 54. PubMed Web of Sciencereg Times Cited: 1 25 Hunzelmann N. Riemekasten G. Becker M. Moinzadeh P. Kreuter A. Melchers I. et al. The Predict Study: Low risk for digital ulcer development in systemic sclerosis patients with increasing disease duration and lack of topoisomerase-1 antibodies. Br J Dermatol 2016 174. 1384 ndash 7. Wiley Online Library PubMed Web of Sciencereg 26 Subcommittee for Scleroderma Criteria of the American Rheumatism Association Diagnostic and Therapeutic Criteria Committee. Preliminary criteria for the classification of systemic sclerosis (scleroderma). Arthritis Rheum 1980 23. 581 ndash 90. Wiley Online Library PubMed 27 Sulli A. Secchi ME. Pizzorni C. Cutolo M. Scoring the nailfold microvascular changes during the capillaroscopic analysis in systemic sclerosis patients. Ann Rheum Dis 2008 67. 885 ndash 7. CrossRef PubMed Web of Sciencereg Times Cited: 101 28 Smith V. Pizzorni C. De Keyser F. Decuman S. Van Praet JT. Deschepper E. et al. Reliability of the qualitative and semiquantitative nailfold videocapillaroscopy assessment in a systemic sclerosis cohort: a two-centre study. Ann Rheum Dis 2010 69. 1092 ndash 6. CrossRef PubMed Web of Sciencereg Times Cited: 58 29 Cutolo M. Sulli A. Pizzorni C. Accardo S. Nailfold videocapillaroscopy assessment of microvascular damage in systemic sclerosis. J Rheumatol 2000 27. 155 ndash 60. PubMed Web of Sciencereg Times Cited: 262 30 Harrell FE Jr. Lee KL. Califf RM. Pryor DB. Rosati RA. Regression modelling strategies for improved prognostic prediction. Stat Med 1984 3. 143 ndash 52. Wiley Online Library PubMed Web of Sciencereg Times Cited: 691 31 Steyerberg E. Harrell F. Statistical models for prognostication. Bethesda (MD). National Institutes of Health 2010 . 32 Korn JH. Mayes M. Matucci Cerinic M. Rainisio M. Pope J. Hachulla E. et al, for the RAPIDS-1 Study Group. Digital ulcers in systemic sclerosis: prevention by treatment with bosentan, an oral endothelin receptor antagonist. Arthritis Rheum 2004 50. 3985 ndash 93. Wiley Online Library PubMed CAS Web of Sciencereg Times Cited: 333 33 Blettner M. Sauerbrei W. Influence of model-building strategies on the results of a case-control study. Stat Med 1993 12. 1325 ndash 38. Wiley Online Library PubMed CAS Web of Sciencereg Times Cited: 20 34 Coghlan JG. Denton CP. Gruumlnig E. Bonderman D. Distler O. Khanna D. et al. Evidence-based detection of pulmonary arterial hypertension in systemic sclerosis: the DETECT study. Ann Rheum Dis 2014 73. 1340 ndash 9. CrossRef PubMed Web of Sciencereg Times Cited: 82 35 Efron B. Tibshirani RJ. An introduction to the bootstrap. Boca Raton (FL). CRC Press 1993. CrossRef 36 Ingegnoli F. Ardoino I. Boracchi P. Cutolo M. Airograve P. Ananieva LP. Et ai. Nailfold capillaroscopy in systemic sclerosis: data from the EULAR Scleroderma Trials and Research (EUSTAR) database. Microvasc Res 2013 89. 122 ndash 8. CrossRef PubMed Web of Sciencereg Times Cited: 21 37 Sebastiani M. Manfredi A. Lo Monaco A. Praino E. Riccieri V. Grattagliano V. et al. Capillaroscopic Skin Ulcers Risk Index (CSURI) calculated with different videocapillaroscopy devices: how its predictive values change. Clin Exp Rheumatol 2013 31 Suppl 76. 115 ndash 7. PubMed 38 Mihai C. Landeweacute R. van der Heijde D. Walker UA. Constantin PI. Gherge AM. Et ai. Digital ulcers predict a worse disease course in patients with systemic sclerosis. Ann Rheum Dis 2016 75. 681 ndash 6. CrossRef PubMed Web of Sciencereg Times Cited: 2 39 Chung L. Therapeutic options for digital ulcers in patients with systemic sclerosis. J Dtsch Dermatol Ges 2007 5. 460 ndash 5. Wiley Online Library PubMed 40 Tingey T. Shu J. Smuczek J. Pope J. Meta-analysis of healing and prevention of digital ulcers in systemic sclerosis. Arthritis Care Res (Hoboken) 2013 65. 1460 ndash 71. Wiley Online Library PubMed CAS Web of Sciencereg Times Cited: 20 41 Cutolo M. Sulli A. Pizzorni C. Smith V. Capillaroscopy as an outcome measure for clinical trials on the peripheral vasculopathy in SScmdashis it useful Int J Rheumatol 2010 2010. 784947. PubMed 42 Hofstee HM. Vonk NA. Voskuyl AE. Dijkmans BA. Postmus PE. Smulders YM. Et ai. Nailfold capillary density is associated with the presence and severity of pulmonary arterial hypertension in systemic sclerosis. Ann Rheum Dis 2009 68. 191 ndash 5. CrossRef PubMed Web of Sciencereg Times Cited: 40 43 Koenig M. Joyal F. Fritzler MJ. Roussin A. Abrahamowicz M. Boire G. et al. Autoantibodies and microvascular damage are independent predictive factors for the progression of Raynauds phenomenon to systemic sclerosis: a twenty-year prospective study of 586 patients, with validation of proposed criteria for early systemic sclerosis. Arthritis Rheum 2008 58. 3902 ndash 12. Wiley Online Library PubMed Web of Sciencereg Times Cited: 185 44 Riccieri V. Vasile M. Iannace N. Stefanantoni K. Sciarra I. Vizza CD. Et ai. Systemic sclerosis patients with and without pulmonary arterial hypertension: a nailfold capillaroscopy study. Rheumatology (Oxford) 2013 52. 1525 ndash 8. CrossRef PubMed CAS Web of Sciencereg Times Cited: 8 45 Kayser C. Sekiyama JY. Proacutespero LC. Camargo CZ. Andrade LE. Nailfold capillaroscopy abnormalities as predictors of mortality in patients with systemic sclerosis. Clin Exp Rheumatol 2013 31 Suppl 76. 103 ndash 8. PubMed 46 Ingegnoli F. Boracchi P. Gualtierotti R. Biganzoli EM. Zeni S. Lubatti C. et al. Improving outcome prediction of systemic sclerosis from isolated Raynauds phenomenon: role of autoantibodies and nail-fold capillaroscopy. Rheumatology (Oxford) 2010 49. 797 ndash 805. CrossRef PubMed CAS Web of Sciencereg Times Cited: 32 47 Caramaschi P. Canestrini S. Martinelli N. Volpe A. Pieropan S. Ferrari M. et al. Scleroderma patients nailfold videocapillaroscopic patterns are associated with disease subset and disease severity. Rheumatology (Oxford) 2007 46. 1566 ndash 9. CrossRef PubMed CAS Web of Sciencereg Times Cited: 44 48 Silva I. Teixeira A. Oliveira J. Almeida I. Almeida R. Aacuteguas A. et al. Endothelial dysfunction and nailfold videocapillaroscopy pattern as predictors of digital ulcers in systemic sclerosis: a cohort study and review of the literature. Clin Rev Allergy Immunol 2015 49. 240 ndash 52. CrossRef PubMed CAS Web of Sciencereg Times Cited: 1 49 Cutolo M. Ruaro B. Pizzorni C. Ravera F. Smith V. Zampogna G. et al. Longterm treatment with endothelin receptor antagonist bosentan and iloprost improves fingertip blood perfusion in systemic sclerosis. J Rheumatol 2014 41. 881 ndash 6. CrossRef PubMed CAS Web of Sciencereg Times Cited: 10 50 Cutolo M. Zampogna G. Vremis L. Smith V. Pizzorni C. Sulli A. Longterm effects of endothelin receptor antagonism on microvascular damage evaluated by nailfold capillaroscopic analysis in systemic sclerosis. J Rheumatol 2013 40. 40 ndash 5. CrossRef PubMed CAS Web of Sciencereg Times Cited: 29 51 Matucci-Cerinic M. Krieg T. Guillevin L. Schwierin B. Rosenberg D. Cornelisse P. et al. Elucidating the burden of recurrent and chronic digital ulcers in systemic sclerosis: long-term results from the DUO Registry. Ann Rheum Dis 2015. E-pub ahead of print. 52 Anderson ME. Allen PD. Moore T. Hillier V. Taylor CJ. Herrick AL. Computerized nailfold video capillaroscopymdasha new tool for assessment of Raynauds phenomenon. J Rheumatol 2005 32. 841 ndash 8. PubMed Web of Sciencereg Times Cited: 62 53 Herrick AL. Cutolo M. Clinical implications from capillaroscopic analysis in patients with Raynauds phenomenon and systemic sclerosis review. Arthritis Rheum 2010 62. 2595 ndash 604. Wiley Online Library PubMed Web of Sciencereg Times Cited: 40 54 Hughes M. Moore T. OLeary N. Tracey A. Ennis H. Dinsdale G. et al. A study comparing videocapillaroscopy and dermoscopy in the assessment of nailfold capillaries in patients with systemic sclerosis-spectrum disorders. Rheumatology (Oxford) 2015 54. 1435 ndash 42. CrossRef PubMed Web of Sciencereg Times Cited: 3 55 Cutolo M. Smith V. Sulli A. Training in capillaroscopy: a growing interest. J Rheumatol 2012 39. 1113 ndash 6. CrossRef PubMed Web of Sciencereg Times Cited: 3 56 Dogan S. Akdogan A. Atakan N. Nailfold capillaroscopy in systemic sclerosis: is there any difference between videocapillaroscopy and dermatoscopy Skin Res Technol 2013 19. 446 ndash 9. PubMed Web of Sciencereg Times Cited: 4 57 Hudson M. Masetto A. Steele R. Arthurs E. Baron M. Reliability of widefield capillary microscopy to measure nailfold capillary density in systemic sclerosis. Clin Exp Rheumatol 2010 28 Suppl 62. S36 ndash 41. PubMed Web of Sciencereg Times Cited: 3 58 Baron M. Chung L. Gyger G. Hummers L. Khanna D. Mayes MD. Et ai. Consensus opinion of a North American Working Group regarding the classification of digital ulcers in systemic sclerosis. Clin Rheumatol 2014 33. 207 ndash 14. CrossRef PubMed Web of Sciencereg Times Cited: 3 59 Herrick AL. Roberts C. Tracey A. Silman A. Anderson M. Goodfield M. et al. Lack of agreement between rheumatologists in defining digital ulceration in systemic sclerosis. Arthritis Rheum 2009 60. 878 ndash 82. Wiley Online Library PubMed Web of Sciencereg Times Cited: 14 60 Silva I. Teixeira A. Oliveira J. Almeida I. Almeida R. Vasconcelos C. Predictive value of vascular disease biomarkers for digital ulcers in systemic sclerosis patients. Clin Exp Rheumatol 2015 33 Suppl 91. S127 ndash 30. PubMed Web of Sciencereg 61 Leffondreacute K. Abrahamowicz M. Xiao Y. Siemiatycki J. Modelling smoking history using a comprehensive smoking index: application to lung cancer. Stat Med 2006 25. 4132 ndash 46. Wiley Online Library PubMed Web of Sciencereg Times Cited: 42 Related content Articles related to the one you are viewing Please enable Javascript to view the related content of this article. Citing LiteratureJessica Meijs 1 , Anne A Schouffoer 1. 2 , Nina Ajmone Marsan 3 , Theo Stijnen 4 , Hein Putter 4 , Maarten K Ninaber 5 , Tom W J Huizinga 1 and Jeska K de Vries-Bouwstra 1 1 Department of Rheumatology. Leiden University Medical Center. Leiden. The Netherlands 2 Haga Hospital. The Hague. The Netherlands 3 Department of Cardiology. Leiden University Medical Center. Leiden. The Netherlands 4 Department of Medical Statistics. Leiden University Medical Center. Leiden. The Netherlands 5 Department of Pulmonology. Leiden University Medical Center. Leiden. The Netherlands Correspondence to Jessica Meijs J. Meijs lumc. nl Abstract Objective To develop a model that assesses the risk for progressive disease in patients with systemic sclerosis (SSc) over the short term, in order to guide clinical management. Methods Baseline characteristics and 1 year follow-up results of 163 patients with SSc referred to a multidisciplinary healthcare programme were evaluated. Progressive disease was defined as: death, 10 decrease in forced vital capacity, 15 decrease in diffusing capacity for carbon monoxide, 10 decrease in body weight, 30 decrease in estimated-glomerular filtration rate, 30 increase in modified Rodnan Skin Score (with 5) or 0.25 increase in Scleroderma Health Assessment Questionnaire. The number of patients with progressive disease was determined. Univariable and multivariable logistic regression analyses were used to assess the probability of progressive disease for each individual patient. Performance of the prediction model was evaluated using a calibration plot and area under the receiver operating characteristic curve. Results 63 patients had progressive disease, including 8 patients who died 18 months after first evaluation. Multivariable analysis showed that friction rubs, proximal muscular weakness and decreased maximum oxygen uptake as predicted, adjusted for age, gender and use of immunosuppressive therapy at baseline, were significantly associated with progressive disease. Using the prediction model, the predicted chance for progressive disease increased from a pretest chance of 37 to 6789. Conclusions Using the prediction model, the chance for progressive disease for individual patients could be doubled. Friction rubs, proximal muscular weakness and maximum oxygen uptake as predicted were identified as relevant parameters. Few studies have described algorithms on an individualised basis to predict mortality after 2 to 15 years of follow-up in systemic sclerosis (SSc). What does this study add A prediction model assessing the chance for progressive disease for individual patients at short term was currently developed. How might this impact on clinical practice Using the prediction model, the predicted chance for progressive disease could be doubled from a pretest chance of 37 to 67-89. Maximum oxygen uptake as measured by CPET is identified as biomarker for progressive SSc. Introduction Individualised management and treatment is one of the most important challenges in medicine. Systemic sclerosis (SSc) is a rare multisystem disease which is highly heterogeneous in presentation and disease course. Recent evidence suggests that earlier initiation of adequate treatment based on regular screening for organ involvement contributes to improved survival.1 The availability of new treatment options, such as autologous haematopoietic stem cell transplantation (HSCT), offers the chance for prolonged event-free survival.2 For optimal efficacy of this treatment, careful timing in the disease course is of pivotal importance. Given the associated treatment-related mortality during the first year, this treatment option underlines the need to identify patients with a high risk of severe organ involvement in the short term. Numerous attempts have been made to identify predictors for severe organ involvement and mortality in SSc.311 Only a few studies have described algorithms to predict outcome on a more individualised basis.1216 The aforementioned studies defined outcome of interest after 215 years of follow-up. In contrast, a recently published study containing observational data from the EUSTAR database described a model identifying, among patients with diffuse cutaneous SSc, those with a 44 chance of skin fibrosis progression during the first year, as compared to 9.7 in the whole cohort.17 Ideally, in order to guide individualised management of patients with SSc, a model combining outcome parameters for several organ systems and mortality predicting disease course in the short term should be available. Whether it is possible to reliably identify patients at risk using such a model, given the heterogeneous nature of SSc, remains to be determined. The present study aimed to develop a model that predicts progressive disease in the short term, defined by either deterioration of organ functions, or mortality, in patients with SSc. The derived prediction model is evaluated for discriminative performance, and a cut-off value is determined in order to evaluate utility in clinical practice. Patients and methods Study design This study is performed using data from a prospective cohort study in patients with SSc who participated in an annual 2-day multidisciplinary healthcare programme aiming to structure screening for organ involvement and to provide multidisciplinary care for patients with SSc. Ethical approval was obtained from the Institutional Review Board of the Leiden University Medical Centre (LUMC). All participants gave written informed consent. Patients Data from all patients referred to the multidisciplinary healthcare programme between April 2009 and January 2014 were collected. Patients were included if they had a diagnosis of SSc according to the American Rheumatism Association,18 the LeRoy criteria19 or the ACR/EULAR 2013 classification criteria.20 On the basis of the degree of skin involvement, three subtypes of patients were classified: Diffuse cutaneous SSc (DcSSc) with skin involvement proximal to the elbows and knees. Limited cutaneous SSc (LcSSc) with skin involvement distal to the elbows and knees. Limited non-cutaneous SSc (LSSc) without skin involvement. Skin scores were all performed by experienced rheumatologists (AS, JVB, AAS). Patients were classified on the basis of their maximum skin score ever. For example, if a patient had had a skin score of 30 and underwent HSCT after which the skin score decreased to 6, the patient was still classified as DcSSc. For the current analysis, selected patients had to have participated in the care programme at least twice, with the second visit 1 year after the baseline visit (range 1023 months). Multidisciplinary healthcare programme All patients participated in the healthcare programme that combines annual extensive organ screening with multidisciplinary team care. Cardiopulmonary investigations included: high-resolution CT (HRCT) of the thorax, pulmonary function tests (including analyses of forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO)), cardiopulmonary exercise test (CPET including analyses of maximum heart rate, maximum wattage, maximum oxygen consumption (VO2) and maximum ventilation), echocardiography and 24 h Holter electrocardiography (ECG). Furthermore, laboratory investigations including measurement of autoantibodies and nailfold videocapillaroscopy were performed. In addition, the patients completed the Scleroderma Health Assessment Questionnaire (SHAQ) for assessment of physical functioning and the Short Form-36 (SF-36) for quality of life. Diagnosis of interstitial lung disease (ILD) was determined on the basis of the presence of a non-specific interstitial pneumonia pattern or usual interstitial pneumonia pattern on the HRCT-thorax,21 as reported by the radiologist. The systolic pulmonary artery pressure (SPAP) was estimated using echocardiography by an experienced cardiologist and elevated pulmonary pressure is defined using a cut-off value of 35 mm Hg.22 Left ventricular end-diastolic and end-systolic volumes were measured using the biplane modified Simpsons rule. Left ventricular ejection fraction was calculated as left ventricular end-diastolicLV end-systolic volume/left ventricular end-diastolic volume. Furthermore, the presence of pericardial effusion was noted. The presence of arrhythmias was defined as the presence of multiform ventricular extrasystole gt100 per day, couplets or runs of ventricular tachycardia or supraventricular tachycardia of at least 30 s on 24 h Holter ECG monitoring.23 Conduction abnormalities were defined as a complete left bundle branch block or right bundle branch block, atrioventricular block (first, second or third degree) or pacemaker rhythm for sinus node dysfunction.23 Change of treatment Initiation of new immunosuppressive treatment is mainly considered in case of extensive and/or progressive skin involvement, relevant decline in VC and/or DLCO (without using an absolute threshold) in combination with the presence of non-specific interstitial pneumonia or usual interstitial pneumonia on HRCT. Autologous HSCT is applied according to the inclusion criteria and treatment regimen as described in the ASTIS trial (24). Azathioprine (AZA) is prescribed in case of primary biliary cirrhosis and hydroxychloroquine (HCQ) in case of SSc overlap syndrome with rheumatoid arthritis (RA). Rituximab is given as part of a randomised placebo-controlled clinical trial (RITIS), registered at www. clinicaltrialsregister. eu/ EudraCT Number: 2008-007180-16. Progressive disease Since we aimed to define risk for progressive disease in general, in order to guide clinical management, several variables were chosen, each reflecting a different organ system. Cut-offs for these variables were based on reported values for minimal important difference (MID). Selected variables were: (1) death before the second visit (2) decrease of 10 in FVC (percentage of predicted)16 (3) decrease of 15 in DLCO (percentage of predicted)16 (4) decrease of 10 in body weight24 (5) decrease of 30 in estimated-glomerular filtration rate (eGFR)25 (6) increase of 30 in modified Rodnan Skin Score (mRSS) with a minimum of 526. 27 or 7 0.25 increase in SHAQ.27 Overall progressive disease was defined as the occurrence of at least one of the above prespecified outcomes during 1 year of follow-up. Statistical analysis Associations between baseline variables and the presence of progressive disease were evaluated and expressed as ORs with the 95 CIs and p values. Missing values of variables used to define overall progressive disease and baseline predictors were replaced by multiple imputation using multiple regression modelling by the multiple imputations by chained equations procedure as implemented in SPSS.28 Missing CPET were considered missing not at random, as an inability to perform CPET most likely reflects severe impaired cardiopulmonary performance status. Therefore, VO2 max was not imputed and the missing-indicator method was used.28 When CPET was missing, an indicator variable, with value 1 if the CPET was missing and 0 if the result was present, was created. Univariable logistic regression analysis was used to determine the independent association between baseline characteristics and overall progressive disease after 1 year of follow-up. Possible correlations between all variables which were significantly contributing in the univariable logistic regression analyses were checked for multicollinearity using a variance inflation factor (VIF) of 10.29 In case of multicollinearity between variables (VIF gt10), the most significant variable was selected for further analysis. For the multivariable model, all predictor variables with a p value smaller than 0.05 in the univariable analysis, indicating an important association with progressive disease, were selected using a predictor selection approach (forward selection). Univariable and multivariable logistic regression analyses were always adjusted for previous and current immunosuppressive therapy (including cyclophosphamide, methotrexate and HSCT) at baseline. Multivariable logistic regression analysis was adjusted for age and gender. The predicted probability of progressive disease was calculated for every patient. The predicted probabilities were compared with the observed percentage of patients with progressive disease. The positive predictive value (PPV) and negative predictive value (NPV) were determined for several cut-off values of the predicted probability. The predictive performance of the model was assessed by examining measures of calibration and discrimination. Calibration refers to how close predicted progressive disease agrees with observed progressive disease and was assessed with a calibration plot.30 Since progressive disease is a binary outcome, a loess algorithm was used as a smoothing technique to estimate the observed probability.31 The discrimination of the prediction model was assessed by receiver operating characteristic (ROC) curve analysis. For internal validation, a bootstrap procedure was performed for control for overfitting.32 All statistical analyses were executed using SPSS V.20.0 software (SPSS Inc, Chicago, USA), except that bootstrap validation was performed by using R V.3.1.1. Results Patient population By January 2014, 163 patients with SSc had had a second evaluation after a mean period of 13.5 months (SD 2.5). Eight patients died before the second visit could have been performed. Baseline characteristics of the 171 included patients are presented in table 1. The patients were mostly women (80), Caucasian (70) and, on average, 53 years (SD 14). Patients had a median disease duration of 2 years. The disease subset at baseline was classified as DcSSc in 61 patients, LcSSc in 75 patients and LSSc in 28 patients. Baseline characteristics of the systemic sclerosis population with a baseline visit and 1 year follow-up At baseline, 63 (39) patients were treated with immunosuppressive medication, including 55 (32) patients who were previously treated with one or more immunosuppressive medications including autologous HSCT (n13), cyclophosphamide (n18), corticosteroids (n28), methotrexate (MTX n28) and AZA (n2), HCQ (n2) and 60 (35) patients currently being treated with immunosuppressive medication, including mycophenolate mofetil (MMF n6), corticosteroids (n24), MTX (n22), AZA (n5) and HCQ (n7). In total, 65 (38) patients were previously, or are currently, treated at baseline evaluation with cyclophosphamide, MTX or HSCT. Change of treatment On the basis of the findings during the multidisciplinary healthcare programme, new immunosuppressive treatment (one or more medications) was started at baseline in 37 patients (22). Newly prescribed treatment included autologous HSCT (n2), cyclophosphamide (n10), MMF (n5), corticosteroids (n4), MTX (n7), AZA (n1), HCQ (n2) and rituximab/placebo (n8). In none of the patients with previous HSCT was new immunosuppressive medication started. Mortality Within 1 year after the first visit, eight patients (mean age 62.6 years) died, including four patients with DcSSc, three patients with LcSS and one patient with LSSc. Three patients died due to ILD, one due to PAH, one due to adenocarcinoma of the lung (in a non-smoker), one due to cardiac failure and one due to cytomegalovirus pneumonitis after allogeneic SCT. In one patient (died at age 87 years after suffering from renal disease), the exact cause of death could not be determined. All patients were classified as those with progressive disease, since for none of the patients could an association between SSc and death be ruled out with absolute certainty. Progressive disease Sixty-three patients showed overall progressive disease at follow-up evaluation according to the predefined criteria, including eight patients who died (table 2 ). Overall progressive disease was found in 25 (39) patients with DcSSc, in 30 (38) with LcSSc and in eight (29) with LSSc. One patient with LcSSC and one patient with LSSc progressed to DcSSc within 1 year of follow-up. Three patients with LSSc evolved to LcSSc based on development of sclerodactyly. Progressive disease in patients with LSSc was primarily based on a decrease of pulmonary function FVC decreased in four patients and DLCO in one patient. Incidence of progressive disease in SSc according to SSc subtype Progressive disease, N () The organ systems and number of prespecified outcomes contributing to overall progressive disease are demonstrated in online supplementary figure 1. The majority of the patients (71) had overall progressive disease based on one event, while in 13 of the patients two events and in 3 of the patients three events contributed to overall progressive disease. Missing values Age, gender, disease subset, SSc-related autoantibodies, friction rubs, proximal muscular weakness, eGFR, ESR and body weight were available for all patients. The following baseline variables were missing and imputed: mRSS (N3), FVC (N3) and DLCO (N4), urine protein (N7), SHAQ (N4) and physical component summary score (according to SF-36 (PCSS) N12). CPET was not performed in 11 patients due to an inability to cycle based on bad physical performance (N4) and musculoskeletal disability (N2). At follow-up, the following outcome parameters were missing and imputed: mRSS (N2), FVC (N3), DLCO (N5) and SHAQ (N22). Prediction of progressive disease Univariable analyses Table 3 shows results of the univariable logistic regression analysis, adjusted for previous and current immunosuppressive therapy. After adjusting for immunosuppressive therapy, friction rubs, proximal muscular weakness, pulmonary crackles, mRSS, DLCO, VO2 max, SHAQ and PCSS according to SF-36 were significantly associated with progressive disease after 1 year of follow-up. FVC and gender were borderline significant (p value lt0.10). Baseline characteristics of patients with progressive and stable disease No multicollinearity was found between friction rubs, proximal muscular weakness, pulmonary crackles, mRSS, DLCO, VO2 max, SHAQ and PCSS. Prediction in subpopulations with DcSSc and LcSSc No significant predictors for progressive disease in patients with DcSSc were identified. In patients with LcSSc, VO2 max predicted and PCSS of SF-36 were identified as significant predictors for progressive disease. However, the multivariable logistic regression analysis for patients with LcSSc did not identify significant predictors for progressive disease (see online supplementary material file). Multivariable analyses and derivation of the prediction model In the multivariable logistic regression analysis, independent predictive variables for progressive disease were friction rubs, proximal muscular weakness, VO2 max predicted and immunosuppressive therapy. OR for progressive disease increased with 12.462 (95 CI 1.253 to 123.905) in the presence of friction rubs and 5.550 (95 CI 1.000 to 30.796) in the presence of proximal muscular weakness, and decreased with 0.979 (95 CI 0.964 to 0.995) per unit increase in VO2 max predicted. A missing VO2 max was accompanied by its corresponding missing indicator variable. The coefficients for the prediction model are listed in table 4 . Independent predictive variables for progressive disease based on multivariable logistic regression analysis The multivariable model remained unchanged when adding the borderline significant variable in the univariable analysis: FVC was excluded from the final model resulting from the forward selection. Predictive performance of the prediction model Table 5 shows the predictive performance of several cut-off values for the predicted probability and the number of observed patients with progressive disease. Using cut-off values 0.25 and 0.75, 80 of the patients who had a score of lt0.25 did not develop progressive disease (NPV 80, 95 CI 69 to 91), and 89 of the patients who had a score of gt0.75 did develop progressive disease (PPV 89, 95 CI 84 to 94). The calibration plot of the prediction model is shown in the online supplementary file (figure 2). The prediction model showed a reliable calibration, predicting progressive disease in agreement with the observed progressive disease. The calibration plot showed that for predicted probabilities smaller than 0.55, the prediction model is overestimating the observed overall disease progression. For the probabilities higher than 0.60, the model slightly underestimates the chance for progressive disease. Predictive performance of several cut-off values for predicted probability of progressive disease The discriminative ability of the model was evaluated with an ROC curve (see online supplementary file, figure 3), showing an AUC of 0.707 (95 CI 0.682 to 0.732). The optimum cut-off value of the prediction model, as verified by the ROC curve, showed a sensitivity of 60 and a specificity of 85, corresponding to a cut-off value of 0.38 for the predicted probability, with a PPV of 42 and an NPV of 76. Internal validation The AUC of the bootstrap predictions equalled the AUC value of the prediction model (0.72, 95 CI 0.64 to 0.81), indicating that overfitting was not a problem. Discussion This study is the first attempt to develop a clinical model to assess the chance for overall progressive disease in the short term in patients with SSc in order to guide clinical management. Our study shows that even in a cohort of patients with SSc not selected for disease duration or subtype, overall disease progression is frequently observed. By applying the model, the expected chance for progression could be increased from 37 to 6789, depending on the chosen cut-off value, indicating that improved discrimination of patients is a reasonable possibility. The current prediction model is not externally validated and should therefore be validated in other cohorts. However, internal validation showed that overfitting was not a problem, and results seem to be robust. A broad set of variables was available for evaluation of association with overall disease and its role in pulmonary involvement. Friction rubs, proximal muscular weakness and VO2 max predicted as determined by CPET were the relevant predicting variables included in the model, after correction for age, gender and immunosuppressive treatment. This suggests that friction rubs, proximal muscular weakness and VO2 max are relatively sensitive variables in measuring overall progressive disease. Recently, a EUSTAR study concerning predictors of progressive disease has been published,4 identifying joint synovitis and tendon friction rubs as parameters independently associated with disease progression after 2 years of follow-up. In our population, we did not find an association between synovitis and progressive disease however, friction rubs were significantly associated with progressive disease, confirming the relevance of this finding. As compared to this EUSTAR study, we used a different definition of progressive disease, generally identifying patients at an earlier disease stage. CPET evaluates patients during exercise, so it is likely to detect cardiopulmonary abnormalities not measurable at rest. Different aspects of pulmonary involvement can be evaluated during CPET (lung parenchymal damage as well as vascular abnormalities). Furthermore, it is highly reproducible, non-invasive and operator-independent. The role of the CPET in organ involvement screening is a relatively new finding. Most studies focused on the role of CPET in identifying PAH33. 34 Very few studies have investigated CPET as a possible biomarker for active/progressive disease in SSc. Cuomo et al 35 suggested including CPET in screening programmes for severity of SSc. They found that an impaired maximum oxygen uptake was present in 93 of the patients and independently associated with the severity of lung involvement. Our study confirmed the importance of CPET in identifying patients at risk for progressive disease in general, including progressive disease based on other parameters than pulmonary involvement or PAH. Our study has several limitations which should be taken into account. First, no validated definition of progressive disease is available, and therefore overall progressive disease was defined as a combination of MIDs as used in randomised clinical trials in SSc. Whether this definition is a useful outcome parameter should be evaluated in future studies. We have chosen to define outcome parameters reflecting the different organ systems (skin, lungs, kidneys), as well as parameters reflecting health in general (weight loss, mortality, functional ability). The cut-off values have been based on defined MIDs, as our intention is to select patients with a high risk for significant deterioration for more stringent annual follow-up. Second, the accuracy of our model is moderate. However, it is in line with other prediction models.1215. 17 We hypothesise that the poor discriminative ability is at least partially caused by our decision to define a broad outcome parameter describing several organ systems in the relatively short term. Third, while other prediction models specifically have focused on patients with DcSSc,14. 17 we were interested in predicting progressive disease in the whole patient population, as regular follow-up of all patients with SSC is being advocated.36. 37 In line with this, recently developed diagnostic criteria also aim at classifying patients earlier in the disease course,20. 38 in order to identify significant organ involvement at an earlier time point. As a matter of interest, we did evaluate possible predictive factors in subpopulations with DcSSc and LcSSc, but no significantly strong predictors were identified. This can possibly be explained by the low number of patients when only selecting either DcSSc (n64) or LcSSc (n79). In addition, since most patients with DcSSc had long-standing disease (mean disease duration of 5.16 years), discrimination of those patients who still progress is even more difficult, and larger patient groups are needed. Including patients with L(c)SSc and patients with DcSSc with longer disease duration naturally decreased the overall percentage of patients with progressive disease in our cohort as patients presenting with early DcSSc have a different natural history.39 Within 1 year of follow-up, progression to DcSSc was found in 2 of patients with L(c)SSc. Strikingly, although all subpopulations of SSc, including LSSc, LcSSc and DcSSc, were included, mRSS was not selected for the final prediction model. We believe that this is explained by the fact that part of the patients with DcSSc had been treated successfully before the baseline visit and had stable low skin scores during the time frame under study. However, since our intention was to develop a prediction rule which can be used to guide clinical practice, for all patients with SSc currently in follow-up irrespective of disease duration or previous treatment, we explicitly chose to also include these patients. Lastly, 38 of the patients were previously or currently treated with cyclophosphamide, MTX or autologous HSCT at baseline evaluation, which can have influenced our findings. Therefore, all logistic regression analyses were repeated including only patients who had not been treated before. Multivariable logistic regression analyses in these patients identified VO2 max predicted as the only significant predictor (data not shown). Since our aim was to develop a prediction rule which can be used to guide clinical practice, also in patients who have been treated before, we explicitly chose to develop our model based on a population including untreated patients and treated patients. This study explored the possibility of prediction of progressive disease in the short term in a heterogeneous population with SSc. The advantages of our study are that the data were prospectively derived from a single centre cohort of patients with SSc. The number of missing values was very low. Since all patients fulfilling SSc classification criteria that visit in the healthcare programme are scheduled for a follow-up visit, independent of disease duration and subtype, the study population reflects the whole population present in a tertiary care centre and therefore the risk of selection bias is low. As compared to other cohorts with SSc, the sociodemographic characteristics, disease severity and functional status of our cohort are comparable to those of other cohorts.40. 41 In conclusion, our study shows that individualised management in patients with SSc is a reasonable possibility. Using the developed prediction model, the chance for progressive disease could be increased from 39 to 6789, advocating annual stringent follow-up at least in patients with friction rubs, proximal muscle weakness and low maximum oxygen uptake at baseline. Future studies are needed to further optimise prediction of disease progression for the individual patient. In addition, maximum oxygen uptake as measured by CPET was identified as a possible new biomarker for progressive disease in SSc. This finding should be replicated in different cohorts of patients with SSc. Received April 7, 2015. Revision received August 19, 2015. Accepted October 3, 2015. Footnotes Contributors JM collected the data, was involved in statistical analysis, writing and critically reviewing the manuscript, and approved the final version. AAS, NAM and MKN collected the data, were involved in critically reviewing the manuscript and approved the final version. TS was involved in statistical analysis and in critically reviewing the manuscript, and approved the final version. HP was involved in statistical analysis and approved the final version. TWJH was involved in critically reviewing the manuscript and approved the final version. JKdV-B collected the data, reviewed the data statistics, was involved in writing and critically reviewing the manuscript, and approved the final version. Funding JM was supported by an unrestricted educational grant of Actelion Pharmaceuticals Nederland BV (Woerden, The Netherlands). Competing interests None declared Patient consent Obtained. Acknowledgements We would like to thank Annemie J. M. Schuerwegh for her involvement in the clinical care of the multidisciplinary health care program. Ethics approval Leiden, The Netherlands. Provenance and peer review Not commissioned externally peer reviewed. Data sharing statement No additional data are available. Nihtyanova SI , Tang EC , Coghlan JG. et al . 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