Deep‐learning artificial intelligence analysis of clinical variables predicts mortality in COVID‐19 patients

Abstract

Study objective The large number of clinical variables associated with coronavirus disease 2019 (COVID‐19) infection makes it challenging for frontline physicians to effectively triage COVID‐19 patients during the pandemic. This study aimed to develop an efficient deep‐learning artificial intelligence algorithm to identify top clinical variable predictors and derive a risk stratification score system to help clinicians triage COVID‐19 patients. Methods This retrospective study consisted of 181 hospitalized patients with confirmed COVID‐19 infection from January 29, 2020 to March 21, 2020 from a major hospital in Wuhan, China. The primary outcome was mortality. Demographics, comorbidities, vital signs, symptoms, and laboratory tests were collected at initial presentation, totaling 78 clinical variables. A deep‐learning algorithm and a risk stratification score system were developed to predict mortality. Data were split into 85% training and 15% testing. Prediction performance were compared with those using COVID‐19 severity score, CURB‐65 score and pneumonia severity index (PSI). Results Of the 181 COVID‐19 patients, 39 expired and 142 survived. Five top predictors of mortality were D‐dimer, O 2 Index, neutrophil:lymphocyte ratio, C‐reactive protein, and lactate dehydrogenase. The top 5 predictors and the resultant risk score yielded, respectively, an area under curve (AUC) of 0.968 ([95% CI:0.87‐1.0]) and 0.954 ([95% CI:0.80‐0.99]) for the testing dataset. Our models outperformed COVID‐19 severity score (AUC = 0.756), CURB‐65 score (AUC = 0.671), and PSI (AUC = 0.838). The mortality rates for our risk stratification scores (0‐5) were 0, 0, 6.7, 18.2, 67.7, and 83.3%, respectively. Conclusions and relevance Deep‐learning prediction model and the resultant risk stratification score may prove useful in clinical decision‐making under time‐sensitive and resource‐constrained environment. This article is protected by copyright. All rights reserved

Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs

“…The spatiotemporal characteristics on pCXR of COVID-19 infection and its relation to clinical outcomes are unknown. Future endeavors could include developing AI algorithms to stage severity, and predict progression, treatment response, recurrence, and survival, to inform and advise risk management and resource allocation associated with the COVID-19 pandemic, with inclusion of clinical variables in predictive models ( Lam et al, 2020 ; Zhao et al, 2020 ; Zhu et al, 2020b ).…”

Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network

“…We wanted to do a complete and thorough analysis of the Deep-Neo-V algorithm, so we compared it to other deep-learning models out there on COVID-19 mortality prediction currently available in medical literature (34). The Deep-Neo-V model has some limitations in terms of the available dataset, retrospective nature of the dataset and data form a single hospital, analyzed at admission and day-one data, other observational study confounders may exist and are unaccounted for.…”

Peer Review #1 of "Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs (v0.1)"

“…Manuscript to be reviewed developing AI algorithms to stage severity, and predict progression, treatment response, recurrence, and survival, to inform and advise risk management and resource allocation associated with the COVID-19 pandemic, with inclusion of clinical variables in predictive models (Lam et al 2020;Zhao et al 2020;Zhu et al 2020b).…”