Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.
BMC Infect Dis
; 21(1): 391, 2021 May 04.
Article
in English
| MEDLINE | ID: covidwho-1215099
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
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 futureoutcomes:
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Risk Factors
/
Decision Support Systems, Clinical
/
Machine Learning
/
COVID-19
/
Neoplasms
Type of study:
Observational study
/
Prognostic study
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Country/Region as subject:
North America
Language:
English
Journal:
BMC Infect Dis
Journal subject:
Communicable Diseases
Year:
2021
Document Type:
Article
Affiliation country:
S12879-021-06038-2
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