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Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

医学 接收机工作特性 回顾性队列研究 急性呼吸窘迫综合征 机器学习 观察研究 内科学 重症监护医学 人工智能 计算机科学
作者
Manoj V Maddali,Matthew M Churpek,Tài Pham,Emanuele Rezoagli,Hanjing Zhuo,Wendi Zhao,June He,Kevin Delucchi,Chunxue Wang,Nancy Wickersham,J. Brennan McNeil,Alejandra Jauregui,Serena Ke,Kathryn Vessel,Antonio Artigas,Carolyn M. Hendrickson,Kirsten N. Kangelaris,Aartik Sarma,Aleksandra Leligdowicz,Kathleen D. Liu,Michael A. Matthay,Lorraine B. Ware,John G. Laffey,Giacomo Bellani,Carolyn S. Calfee,Pratik Sinha,Fernando Ríos,Frank Van Haren,Thierry Sottiaux,Fredy S Lora,Luciano C. P. Azevedo,Pieter Depuydt,Eddy Fan,Guillermo Bugedo,Haibo Qiu,Marcos Gonzalez,Juan Silesky,Vladimír Černý,Jonas Vestergaard Nielsen,Manuel Jibaja,Tài Pham,Hermann Wrigge,Dimitrios Matamis,Jorge Luis Ranero,Seyed Mohammadreza Hashemian,Pravin Amin,Kevin Clarkson,Giacomo Bellani,Kiyoyasu Kurahashi,Asisclo Villagómez,Amine Ali Zeggwagh,Leo Heunks,Jon Henrik Laake,Jose Emmanuel Palo,Antero Fernandes,Dorel Săndesc,Y Arabi,Vesna Bumbasierevic,Nicolás Nín,José A. Lorente,Anders Larsson,Lise Piquilloud,Fékri Abroug,Daniel F. McAuley,Lia McNamee,Javier Hurtado,Ednan K. Bajwa,Gabriel D’Empaire,Guy François,Hektor Sula,Lordian Nunci,Alma Cani,Alan Zazu,Christian Dellera,Carolina S Insaurralde,Risso V Alejandro,Julio Daldin,Mauricio Vinzio,Ruben O Fernandez,Luis Pablo Cardonnet,Lisandro Bettini,Mariano Carboni Bisso,Emilio M. Osman,Mariano Setten,Pablo Lovazzano,Javier Álvarez,Veronica Villar,César Milstein,Norberto C. Pozo,Nicolas Grubissich,Gustavo Plotnikow,Daniela N. Vásquez,Santiago Ilutovich,Norberto Tiribelli,Ariel Chena,Carlos A. Pellegrini,María Gabriela Sáenz,Jan Bakker,Matías Brizuela,Hernan Gianinetto,Pablo Eduardo Gomez,Valeria I. Cerrato,Marco Bezzi,Silvina Borello,Flavia Loiácono,Adriana Fernández,Serena Knowles,Claire Reynolds,Deborah Inskip,Jennene Miller,Jing Kong,Christina Whitehead,Shailesh Bihari,Aylin Seven,Amanda Krstevski,Helen Rodgers,Rebecca Millar,Toni McKenna,Irene Bailey,Gabrielle Hanlon,Anders Åneman,Joan Lynch,Raman Azad,John Neal,Paul Woods,Brigit Roberts,Mark Kol,Helen Wong,Katharina Riss,Thomas Staudinger,Xavier Wittebole,Caroline Berghe,Pierre Bulpa,Alain Dive,Rik Verstraete,Herve Lebbinck,Pieter Depuydt,Joris Vermassen,Philippe Meersseman,Helga Ceunen,Jonas Rosa,Daniel Beraldo,Cláudio Piras,Adenilton M R Ampinelli,Antônio Paulo Nassar,Sérgio Elia Mataloun,Marcelo Moock,Marlus M. Thompson,Claudio Gonçalves,Ana Carolina Peçanha Antônio,Aline Maria Ascoli,Rodrigo Biondi,Danielle Fontenele,Danielle Nóbrega,Vanessa Sales,Suresh Shindhe,Dk Maizatul Aiman B Pg Hj Ismail,John G. Laffey,François Beloncle,Kyle Davies,Rob Cirone,Venika Manoharan,Mehvish Ismail,Ewan C. Goligher,Mandeep S. 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Adams,Andrew Burtenshaw,Gareth Sellors,Ingeborg Welters,Karen Williams,Robert Hessell,Matthew Oldroyd,Ceri Battle,Suresh Pillai,Istvan Kajtor,Mageswaran Sivashanmugave,Sinéad O’Kane,Adrian Donnelly,Aniko Frigyik,Jon Careless,Martin May,Richard Stewart,T John Trinder,Samantha Hagan,Matt P. Wise,Jade Cole,Caroline Macfie,Anna Dowling,Javier Hurtado,Javier Hurtado,Edgardo Nuñez,Gustavo Pittini,Ruben Rodriguez,María Imperio,Cristina Santos,Ana Graciela França,Alejandro Ebeid,Alberto Deicas,Carolina Serra,Aditya Uppalapati,Ghassan Kamel,Valerie Banner‐Goodspeed,Jeremy R. Beitler,Satyanarayana Reddy Mukkera,Shreedhar Kulkarni,Jarone Lee,Tomaž Mesar,John O Shinn,Dina Gomaa,Christopher Tainter,Tomaž Mesar,R. Adams Cowley,Dale Yeatts,Jessica Warren,Michael J. Lanspa,R. Miller,Colin K. Grissom,Samuel Brown,Philippe R. Bauer,Ryan Gosselin,Barrett T. Kitch,Jason Cohen,Scott Beegle,Renaud Gueret,Aiman Tulaimat,Shazia Choudry,William S. Stigler,Hitesh Batra,Nidhi G. Huff,Keith Lamb,Trevor W Oetting,Nicholas M. Mohr,Claine Judy,Shigeki Saito,Fayez Kheir,Adam Schlichting,Angela Delsing,Mary Elmasri,Daniel R. Crouch
出处
期刊:The Lancet Respiratory Medicine [Elsevier]
卷期号:10 (4): 367-377 被引量:71
标识
DOI:10.1016/s2213-2600(21)00461-6
摘要

Background Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. Findings The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94] vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). Interpretation Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. Funding US National Institutes of Health and European Society of Intensive Care Medicine.
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