Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

Navlakha, Saket, Morjaria, Sejal, Perez-Johnston, Rocio, Zhang, Allen, Taur, Ying (May 2021) Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning. BMC Infectious Diseases, 21 (1). p. 391. ISSN 1471-2334

PDF 2021.Navlakha.Covid19CancerPatients.pdf Download (3MB)

Abstract

BACKGROUND: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how multiple variables interact to increase risk, remains unclear. METHODS: We used machine learning algorithms to predict COVID-19 severity in 348 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using only clinical variables collected on or before a patient's COVID-19 positive date (time zero), we sought to classify patients into one of three possible future outcomes: Severe-early (the patient required high levels of oxygen support within 3 days of being tested positive for COVID-19), Severe-late (the patient required high levels of oxygen after 3 days), and Non-severe (the patient never required oxygen support). RESULTS: Our algorithm classified patients into these classes with an area under the receiver operating characteristic curve (AUROC) ranging from 70 to 85%, significantly outperforming prior methods and univariate analyses. Critically, classification accuracy is highest when using a potpourri of clinical variables - including basic patient information, pre-existing diagnoses, laboratory and radiological work, and underlying cancer type - suggesting that COVID-19 in cancer patients comes with numerous, combinatorial risk factors. CONCLUSIONS: Overall, we provide a computational tool that can identify high-risk patients early in their disease progression, which could aid in clinical decision-making and selecting treatment options.

Item Type:	Paper
Subjects:	bioinformatics diseases & disorders > cancer diseases & disorders diseases & disorders > neoplasms diseases & disorders > viral diseases bioinformatics > computational biology > algorithms bioinformatics > computational biology diseases & disorders > viral diseases > coronavirus diseases & disorders > viral diseases > coronavirus > covid 19 bioinformatics > computational biology > algorithms > machine learning diseases & disorders > cancer > prognosis
CSHL Authors:	Navlakha, Saket
Communities:	CSHL labs > Navlakha lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	4 May 2021
Date Deposited:	07 May 2021 15:37
Last Modified:	25 Jan 2024 15:43
PMCID:	PMC8092998
URI:	https://repository.cshl.edu/id/eprint/40048

Actions (login required)

Administrator's edit/view item