Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

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Abstract

Background

The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes.

Methods

Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients ( N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die.

Results

SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power ( Q ² = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors.

Conclusions

An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

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Most cited references 31

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Wei-jie Guan, Zheng-yi Ni, Yu Hu … (2020)

Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)

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Giacomo Grasselli, Alberto Zangrillo, Alberto Zanella … (2020)

In December 2019, a novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) emerged in China and has spread globally, creating a pandemic. Information about the clinical characteristics of infected patients who require intensive care is limited.

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Covid-19 in Critically Ill Patients in the Seattle Region — Case Series

Pavan K. Bhatraju, Bijan J Ghassemieh, Michelle Nichols … (2020)

Abstract Background Community transmission of coronavirus 2019 (Covid-19) was detected in the state of Washington in February 2020. Methods We identified patients from nine Seattle-area hospitals who were admitted to the intensive care unit (ICU) with confirmed infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Clinical data were obtained through review of medical records. The data reported here are those available through March 23, 2020. Each patient had at least 14 days of follow-up. Results We identified 24 patients with confirmed Covid-19. The mean (±SD) age of the patients was 64±18 years, 63% were men, and symptoms began 7±4 days before admission. The most common symptoms were cough and shortness of breath; 50% of patients had fever on admission, and 58% had diabetes mellitus. All the patients were admitted for hypoxemic respiratory failure; 75% (18 patients) needed mechanical ventilation. Most of the patients (17) also had hypotension and needed vasopressors. No patient tested positive for influenza A, influenza B, or other respiratory viruses. Half the patients (12) died between ICU day 1 and day 18, including 4 patients who had a do-not-resuscitate order on admission. Of the 12 surviving patients, 5 were discharged home, 4 were discharged from the ICU but remained in the hospital, and 3 continued to receive mechanical ventilation in the ICU. Conclusions During the first 3 weeks of the Covid-19 outbreak in the Seattle area, the most common reasons for admission to the ICU were hypoxemic respiratory failure leading to mechanical ventilation, hypotension requiring vasopressor treatment, or both. Mortality among these critically ill patients was high. (Funded by the National Institutes of Health.)

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Author and article information

Contributors

Mohammad M. Banoei: mmbanoei@ucalgary.ca

Mehdi Mirsaeidi:

ORCID: http://orcid.org/0000-0001-5298-8442

msm249@med.miami.edu

Journal

Journal ID (nlm-ta): Crit Care

Title: Critical Care

Publisher: BioMed Central (London )

ISSN (Print): 1364-8535

ISSN (Electronic): 1466-609X

Publication date (Electronic): 8 September 2021

Publication date PMC-release: 8 September 2021

Publication date Collection: 2021

Volume: 25

Electronic Location Identifier: 328

Affiliations

[1 ]GRID grid.22072.35, ISNI 0000 0004 1936 7697, Department of Critical Care Medicine, , University of Calgary, ; Alberta, Canada

[2 ]GRID grid.22072.35, ISNI 0000 0004 1936 7697, Department of Biological Science, , University of Calgary, ; Alberta, Canada

[3 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Division of Pulmonary and Critical Care Medicine, , Johns Hopkins University, ; Baltimore, MD 21218 USA

[4 ]GRID grid.484420.e, Division of Pulmonary and Critical Care, , Miami VA Medical Center, ; Miami, FL USA

[5 ]GRID grid.26790.3a, ISNI 0000 0004 1936 8606, Division of Pulmonary and Critical Care, Department of Medicine, , University of Miami, ; Miami, FL USA

Author information

Mehdi Mirsaeidi http://orcid.org/0000-0001-5298-8442

Article

Publisher ID: 3749

DOI: 10.1186/s13054-021-03749-5

PMC ID: 8424411

PubMed ID: 34496940

SO-VID: ed932983-65f1-4514-b160-870b751e4276

License:

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