Machine Learning Prediction of Death in Critically Ill Patients With Coronavirus Disease 2019

Matthew M Churpek, Shruti Gupta, Alexandra B Spicer, Salim S Hayek, Anand Srivastava, Lili Chan, Michal L Melamed, Samantha K Brenner, Jared Radbel, Farah Madhani-Lovely, Pavan K Bhatraju, Anip Bansal, Adam Green, Nitender Goyal, Shahzad Shaefi, Chirag R Parikh, Matthew W Semler, David E Leaf, STOP-COVID Investigators, Carol P. Walther, Samaya J. Anumudu, Justin Arunthamakun, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, ThuyDuyen Nguyen, Shahzad Shaefi, Megan L. Krajewski, Sidharth Shankar, Ameeka Pannu, Juan D. Valencia, Sushrut S. Waikar, Zoe A. Kibbelaar, Ambarish M. Athavale, Peter Hart, Oyintayo Ajiboye, Matthew Itteera, Adam Green, Jean-Sebastien Rachoin, Christa A. Schorr, Lisa Shea, Daniel L. Edmonston, Christopher L. Mosher, Alexandre M. Shehata, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Metha, Aquino Williams, Samantha K. Brenner, Patricia Walters, Ronaldo C. Go, Keith M. Rose, Miguel A. Hernán, Amy M. Zhou, Ethan C. Kim, Rebecca Lisk, Lili Chan, Kusum S. Mathews, Steven G. Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily Leven, Jing G. Wang, Gohar Mosoyan, Girish N. Nadkarni, Allon N. Friedman, John Guirguis, Rajat Kapoor, Christopher Meshberger, Chirag R. Parikh, Brian T. Garibaldi, Celia P. Corona-Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Carmen Elena Cervantes, Samir C. Gautam, Crystal Chang, H. Bryant Nguyen, Afshin Ahoubim, Leslie F. Thomas, Pramod K. Guru, Paul A. Bergl, Yan Zhou, Jesus Rodriguez, Jatan A. Shah, Mrigank S. Gupta, Princy N. Kumar, Deepa G. Lazarous, Seble G. Kassaye, Michal L. Melamed, Tanya S. Johns, Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V. Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Jyotsana Thakkar, Neelja Kumar, Michael J. Ross, Michael Chang, Ritesh Raichoudhury, Edward J. Schenck, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen, David Charytan, Ashley Macina, Daniel W. Ross, Anand Srivastava, Alexander S. Leidner, Carlos Martinez, Jacqueline M. Kruser, Richard G. Wunderink, Alexander J. Hodakowski, Juan Carlos Q. Velez, Eboni G. Price-Haywood, Luis A. Matute-Trochez, Anna E. Hasty, Muner MB Mohamed, Rupali S. Avasare, David Zonies, David E. Leaf, Shruti Gupta, Rebecca M. Baron, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K. Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa Gaviria, Tanveer Shaukat, Omer Kamal, Wei Wang, Heather Yang, Jeffery O. Boateng, Meghan Lee, Ian A. Strohbehn, Jiahua Li, Saif A. Muhsin, Ernest I. Mandel, Ariel L. Mueller, Nicholas S. Cairl, Farah Madhani-Lovely, Chris Rowan, Vasil Peev, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes, Anne K. Sutherland, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Jared Radbel, Sonika Puri, Jag Sunderram, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim, Jayanth Vatson, Shuchi Anand, Joseph E. Levitt, Pablo Garcia, Suzanne M. Boyle, Rui Song, Zhang Jingjing, Moh’d A. Sharshir, Vadym V. Rusnak, Anip Bansal, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham, Arash Rashidi, Rana Hejal, Eric Judd, Laura Latta, Ashita Tolwani, Timothy E. Albertson, Jason Y. Adams, Steven Y. Chang, Rebecca M. Beutler, Carl E. Schulze, Etienne Macedo, Harin Rhee, Kathleen D Liu, Vasantha K. Jotwani, Jay L. Koyner, Chintan V. Shah, Vishal Jaikaransingh, Stephanie M. Toth-Manikowski, Min J. Joo, James P. Lash, Javier A. Neyra, Nourhan Chaaban, Alfredo Iardino, Elizabeth H. Au, Jill H. Sharma, Marie Anne Sosa, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Hayley B. Gershengorn, Salim S. Hayek, Pennelope Blakely, Hanna Berlin, Tariq U. Azam, Husam Shadid, Michael Pan, Patrick O’ Hayer, Chelsea Meloche, Rafey Feroze, Kishan J. Padalia, Jeff Leya, John P. Donnelly, Andrew J. Admon, Jennifer E. Flythe, Matthew J. Tugman, Brent R. Brown, Amanda K. Leonberg-Yoo, Ryan C. Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez, Amar D. Bansal, Natalie C. Ernecoff, Csaba P. Kovesdy, Miklos Z. Molnar, S. Susan Hedayati, Mridula V. Nadamuni, Sadaf S. Khan, Duwayne L. Willett, Samuel A.P. Short, Amanda D. Renaghan, Pavan Bhatraju, A. Bilal Malik, Matthew W. Semler, Anitha Vijayan, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao, Greg L. Schumaker, Nitender Goyal, Anthony J. Faugno, Greg L. Schumaker, Caroline M. Hsu, Asma Tariq, Leah Meyer, Marta Christov, Francis P. Wilson, Tanima Arora, Ugochukwu Ugwuowo, Matthew M Churpek, Shruti Gupta, Alexandra B Spicer, Salim S Hayek, Anand Srivastava, Lili Chan, Michal L Melamed, Samantha K Brenner, Jared Radbel, Farah Madhani-Lovely, Pavan K Bhatraju, Anip Bansal, Adam Green, Nitender Goyal, Shahzad Shaefi, Chirag R Parikh, Matthew W Semler, David E Leaf, STOP-COVID Investigators, Carol P. Walther, Samaya J. Anumudu, Justin Arunthamakun, Kathleen F. Kopecky, Gregory P. Milligan, Peter A. McCullough, ThuyDuyen Nguyen, Shahzad Shaefi, Megan L. Krajewski, Sidharth Shankar, Ameeka Pannu, Juan D. Valencia, Sushrut S. Waikar, Zoe A. Kibbelaar, Ambarish M. Athavale, Peter Hart, Oyintayo Ajiboye, Matthew Itteera, Adam Green, Jean-Sebastien Rachoin, Christa A. Schorr, Lisa Shea, Daniel L. Edmonston, Christopher L. Mosher, Alexandre M. Shehata, Zaza Cohen, Valerie Allusson, Gabriela Bambrick-Santoyo, Noor ul aain Bhatti, Bijal Metha, Aquino Williams, Samantha K. Brenner, Patricia Walters, Ronaldo C. Go, Keith M. Rose, Miguel A. Hernán, Amy M. Zhou, Ethan C. Kim, Rebecca Lisk, Lili Chan, Kusum S. Mathews, Steven G. Coca, Deena R. Altman, Aparna Saha, Howard Soh, Huei Hsun Wen, Sonali Bose, Emily Leven, Jing G. Wang, Gohar Mosoyan, Girish N. Nadkarni, Allon N. Friedman, John Guirguis, Rajat Kapoor, Christopher Meshberger, Chirag R. Parikh, Brian T. Garibaldi, Celia P. Corona-Villalobos, Yumeng Wen, Steven Menez, Rubab F. Malik, Carmen Elena Cervantes, Samir C. Gautam, Crystal Chang, H. Bryant Nguyen, Afshin Ahoubim, Leslie F. Thomas, Pramod K. Guru, Paul A. Bergl, Yan Zhou, Jesus Rodriguez, Jatan A. Shah, Mrigank S. Gupta, Princy N. Kumar, Deepa G. Lazarous, Seble G. Kassaye, Michal L. Melamed, Tanya S. Johns, Ryan Mocerino, Kalyan Prudhvi, Denzel Zhu, Rebecca V. Levy, Yorg Azzi, Molly Fisher, Milagros Yunes, Kaltrina Sedaliu, Ladan Golestaneh, Maureen Brogan, Jyotsana Thakkar, Neelja Kumar, Michael J. Ross, Michael Chang, Ritesh Raichoudhury, Edward J. Schenck, Soo Jung Cho, Maria Plataki, Sergio L. Alvarez-Mulett, Luis G. Gomez-Escobar, Di Pan, Stefi Lee, Jamuna Krishnan, William Whalen, David Charytan, Ashley Macina, Daniel W. Ross, Anand Srivastava, Alexander S. Leidner, Carlos Martinez, Jacqueline M. Kruser, Richard G. Wunderink, Alexander J. Hodakowski, Juan Carlos Q. Velez, Eboni G. Price-Haywood, Luis A. Matute-Trochez, Anna E. Hasty, Muner MB Mohamed, Rupali S. Avasare, David Zonies, David E. Leaf, Shruti Gupta, Rebecca M. Baron, Meghan E. Sise, Erik T. Newman, Samah Abu Omar, Kapil K. Pokharel, Shreyak Sharma, Harkarandeep Singh, Simon Correa Gaviria, Tanveer Shaukat, Omer Kamal, Wei Wang, Heather Yang, Jeffery O. Boateng, Meghan Lee, Ian A. Strohbehn, Jiahua Li, Saif A. Muhsin, Ernest I. Mandel, Ariel L. Mueller, Nicholas S. Cairl, Farah Madhani-Lovely, Chris Rowan, Vasil Peev, Jochen Reiser, John J. Byun, Andrew Vissing, Esha M. Kapania, Zoe Post, Nilam P. Patel, Joy-Marie Hermes, Anne K. Sutherland, Amee Patrawalla, Diana G. Finkel, Barbara A. Danek, Sowminya Arikapudi, Jeffrey M. Paer, Jared Radbel, Sonika Puri, Jag Sunderram, Matthew T. Scharf, Ayesha Ahmed, Ilya Berim, Jayanth Vatson, Shuchi Anand, Joseph E. Levitt, Pablo Garcia, Suzanne M. Boyle, Rui Song, Zhang Jingjing, Moh’d A. Sharshir, Vadym V. Rusnak, Anip Bansal, Amber S. Podoll, Michel Chonchol, Sunita Sharma, Ellen L. Burnham, Arash Rashidi, Rana Hejal, Eric Judd, Laura Latta, Ashita Tolwani, Timothy E. Albertson, Jason Y. Adams, Steven Y. Chang, Rebecca M. Beutler, Carl E. Schulze, Etienne Macedo, Harin Rhee, Kathleen D Liu, Vasantha K. Jotwani, Jay L. Koyner, Chintan V. Shah, Vishal Jaikaransingh, Stephanie M. Toth-Manikowski, Min J. Joo, James P. Lash, Javier A. Neyra, Nourhan Chaaban, Alfredo Iardino, Elizabeth H. Au, Jill H. Sharma, Marie Anne Sosa, Sabrina Taldone, Gabriel Contreras, David De La Zerda, Hayley B. Gershengorn, Salim S. Hayek, Pennelope Blakely, Hanna Berlin, Tariq U. Azam, Husam Shadid, Michael Pan, Patrick O’ Hayer, Chelsea Meloche, Rafey Feroze, Kishan J. Padalia, Jeff Leya, John P. Donnelly, Andrew J. Admon, Jennifer E. Flythe, Matthew J. Tugman, Brent R. Brown, Amanda K. Leonberg-Yoo, Ryan C. Spiardi, Todd A. Miano, Meaghan S. Roche, Charles R. Vasquez, Amar D. Bansal, Natalie C. Ernecoff, Csaba P. Kovesdy, Miklos Z. Molnar, S. Susan Hedayati, Mridula V. Nadamuni, Sadaf S. Khan, Duwayne L. Willett, Samuel A.P. Short, Amanda D. Renaghan, Pavan Bhatraju, A. Bilal Malik, Matthew W. Semler, Anitha Vijayan, Christina Mariyam Joy, Tingting Li, Seth Goldberg, Patricia F. Kao, Greg L. Schumaker, Nitender Goyal, Anthony J. Faugno, Greg L. Schumaker, Caroline M. Hsu, Asma Tariq, Leah Meyer, Marta Christov, Francis P. Wilson, Tanima Arora, Ugochukwu Ugwuowo

Abstract

Objectives: Critically ill patients with coronavirus disease 2019 have variable mortality. Risk scores could improve care and be used for prognostic enrichment in trials. We aimed to compare machine learning algorithms and develop a simple tool for predicting 28-day mortality in ICU patients with coronavirus disease 2019.

Design: This was an observational study of adult patients with coronavirus disease 2019. The primary outcome was 28-day inhospital mortality. Machine learning models and a simple tool were derived using variables from the first 48 hours of ICU admission and validated externally in independent sites and temporally with more recent admissions. Models were compared with a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 using the area under the receiver operating characteristic curve and calibration.

Setting: Sixty-eight U.S. ICUs.

Patients: Adults with coronavirus disease 2019 admitted to 68 ICUs in the United States between March 4, 2020, and June 29, 2020.

Interventions: None.

Measurements and main results: The study included 5,075 patients, 1,846 (36.4%) of whom died by day 28. eXtreme Gradient Boosting had the highest area under the receiver operating characteristic curve in external validation (0.81) and was well-calibrated, while k-nearest neighbors were the lowest performing machine learning algorithm (area under the receiver operating characteristic curve 0.69). Findings were similar with temporal validation. The simple tool, which was created using the most important features from the eXtreme Gradient Boosting model, had a significantly higher area under the receiver operating characteristic curve in external validation (0.78) than the Sequential Organ Failure Assessment score (0.69), National Early Warning Score (0.60), and CURB-65 (0.65; p < 0.05 for all comparisons). Age, number of ICU beds, creatinine, lactate, arterial pH, and Pao2/Fio2 ratio were the most important predictors in the eXtreme Gradient Boosting model.

Conclusions: eXtreme Gradient Boosting had the highest discrimination overall, and our simple tool had higher discrimination than a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 on external validation. These models could be used to improve triage decisions and clinical trial enrichment.

Keywords: artificial intelligence; coronavirus disease 2019; intensive care unit; machine learning.

Conflict of interest statement

Dr. Churpek is supported by an R01 from National Institute of General Medical Sciences (NIGMS) (R01 GM123193), has a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients, and has received research support from EarlySense (Tel Aviv, Israel). Dr. Gupta is a scientific coordinator for the A Study of Cardiovascular Events in Diabetes trial (GlaxoSmithKline). Dr. Shaefi is supported by a K08 from NIGMS (K08GM134220) and an R03 from National Institute of Aging (R03AG060179). Dr. Leaf is supported by an R01 from National Heart, Lung, and Blood Institute (R01HL144566). The remaining authors have disclosed that they do not have any potential conflicts of interest.

Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.

Figures

Figure 1.
Figure 1.
Comparison of model discrimination between the different models in both the external and temporal validation cohorts. As shown, with point estimates and 95% CIs for the area under the receiver operating characteristic curve (AUC), the eXtreme Gradient Boosting (XGBoost) model had the highest discrimination in both validation datasets. KNN = K-nearest neighbors, NEWS = National Early Warning Score, SCMI = Study of the Treatment and Outcomes in Critically Ill Patients With Coronavirus Disease 2019 Mortality Index, SOFA = Sequential Organ Failure Assessment, SVM = support vector machine.
Figure 2.
Figure 2.
Variable importance for the eXtreme Gradient Boosting (XGBoost) model. Permutation variable importance for the most accurate model (XGBoost) scaled to a maximum of 100, which shows that age, number of ICU beds, creatinine, and lactate were the most important variables for predicting 28-d mortality when using data from the first 2 d of ICU admission. ALT = alanine transaminase, AST = aspartate aminotransferase, BMI = body mass index, CPK = creatine phosphokinase, CRP= c-reactive protein, P/F ratio = Pao2/Fio2 ratio.
Figure 3.
Figure 3.
Partial dependence plots for eXtreme Gradient Boosting (XGBoost) illustrating the relationship between 28-d mortality and values of the six most important predictor variables. As shown, the risk of mortality increases with age greater than 40 yr, fewer than 100 (and especially o2/Fio2 ratio (P/F ratio) less than 150 mm Hg.
Figure 4.
Figure 4.
Calibration results for Study of the Treatment and Outcomes in Critically Ill Patients With Coronavirus Disease 2019 Mortality Index (SCMI) in the external and temporal validation cohorts. Bar chart showing the percentage of patients who died across values of the SCMI scoring system in both the external (A) and temporal (B) validation cohorts.

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