Learning from past respiratory failure patients to triage COVID-19 patient ventilator needs: A multi-institutional study

COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/map.html (accessed July 28, 2020). Boccia, 2020, What other countries can learn from Italy during the COVID-19 pandemic, JAMA - J. Am. Med. Assoc., 180, 927 Cummings, 2020, Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study, Lancet., 395, 1763, 10.1016/S0140-6736(20)31189-2 Zhu, 2020, Clinical characteristics of 3,062 COVID-19 patients: a meta-analysis, J. Med. Virol., 1 Spellberg, 2020, Community prevalence of SARS-CoV-2 among patients with influenzalike Illnesses presenting to a Los Angeles Medical Center in March 2020, Jama., 1 S. Sang, R. Sun, J. Coquet, H. Carmichael, T. Seto, T. Hernandez-Boussard, Learning from past respiratory infections to predict COVID-19 outcomes: retrospective study. J. Med. Int. Res. 23(2) e23026. https://doi.org/10.2196/23026. Collins, 2015, Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) the TRIPOD statement, Circulation., 131, 211, 10.1161/CIRCULATIONAHA.114.014508 Liang, 2020, Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19, JAMA Intern. Med., 10.1001/jamainternmed.2020.2033 Zhou, 2020, Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study, Lancet., 6736, 1 Wu, 2019, Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention, JAMA - J. Am. Med. Assoc., 2020, 3 N. Chen, M. Zhou, X. Dong, J. Qu, F. Gong, Y. Han, Y. Qiu, J. Wang, Y. Liu, Y. Wei, J. Xia, T. Yu, X. Zhang, L. Zhang, Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study, Lancet 395 (2020) 507–513. https://doi.org/10.1016/S0140-6736(20)30211-7. Gattinoni, 2020, Covid-19 does not lead to a “typical” acute respiratory distress syndrome, Am. J. Respir. Crit. Care Med., 1 Yang, 2020, A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parameters, PLoS One., 15, 1 Vaid, 2020, Machine Learning to predict mortality and critical events in COVID-19 positive New York City patients, MedRxiv. Ma, 2020, Length-of-stay prediction for pediatric patients with respiratory diseases using decision tree methods, IEEE J. Biomed. Heal. Inform., 10.1109/JBHI.2020.2973285 Lundberg, 2017, A unified approach to interpreting model predictions, Nips., 16, 426 Varghese, 2017, Comparison of acute physiology and chronic health evaluation II (APACHE II) and acute physiology and chronic health evaluation IV (APACHE IV) severity of illness scoring systems, in a multidisciplinary ICU, J. Anaesthesiol. Clin. Pharmacol., 33, 248, 10.4103/0970-9185.209741 Jehi, 2020, Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19, PLoS One., 15, 10.1371/journal.pone.0237419 Horby, 2020, Dexamethasone in hospitalized patients with covid-19 — preliminary report, N. Engl. J. Med., 1 L. Wynants, B. Van Calster, G.S. Collins, R.D. Riley, G. Heinze, E. Schuit, M.M.J. Bonten, J.A.A. Damen, T.P.A. Debray, M. De Vos, P. Dhiman, M.C. Haller, M.O. Harhay, L. Henckaerts, N. Kreuzberger, A. Lohmann, K. Luijken, J. Ma, C.L. Andaur Navarro, J.B. Reitsma, J.C. Sergeant, C. Shi, N. Skoetz, L.J.M. Smits, K.I.E. Snell, M. Sperrin, R. Spijker, E.W. Steyerberg, T. Takada, S.M.J. Van Kuijk, F.S. Van Royen, C. Wallisch, L. Hooft, K.G.M. Moons, M. Van Smeden, Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal, BMJ. 369 (2020). https://doi.org/10.1136/bmj.m1328. Yan, 2020, An interpretable mortality prediction model for COVID-19 patients, Nat. Mach. Intell., 1 Shang, 2020, Scoring systems for predicting mortality for severe patients with COVID-19, EClinicalMedicine., 24, 1, 10.1016/j.eclinm.2020.100426 Burdick, 2020, Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial, Comput. Biol. Med., 124, 10.1016/j.compbiomed.2020.103949 Röösli, 2020, Bias at warp speed: How AI may contribute to the disparities gap in the time of COVID-19, J. Am. Med. Informatics Assoc. Hernandez-Boussard, 2020, MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care, J. Am. Med. Informatics Assoc., 00, 1 Barish, 2021, External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19, Nature Mach. Intell., 3, 25, 10.1038/s42256-020-00254-2