Prediction models for in-hospital deaths of patients with COVID-19 using electronic healthcare data

Abstract

Objective

Many models for predicting various disease prognoses have achieved high performance without laboratory test results. However, whether laboratory test results can improve performance remains unclear. This study aimed to investigate whether laboratory test results improve the model performance for coronavirus disease 2019 (COVID-19).

Methods

Prediction models were developed using data from the electronic healthcare record database in Japan. Patients aged ≥18 years hospitalized for COVID-19 after February 11, 2020, were included. Their age, sex, comorbidities, laboratory test results, and number of days from February 11, 2020, were collected. We developed a logistic regression, XGBOOST, random forest, and neural network analysis and compared the performance with and without laboratory test results. The performance of predicting in-hospital death was evaluated using the area under the curve (AUC).

Results

Data from 8,288 hospitalized patients (females, 46.5%) were analyzed. The median patient age was 71 years. A total of 6,630 patients were included in the training dataset, and 312 (4.7%) died. In the logistic regression model, the area under the curve was 0.88 (95% confidence interval [CI] = 0.83–0.93) and 0.75 (95% CI = 0.68–0.81) with and without laboratory test results, respectively. The performance was not fundamentally different between the model types, and the laboratory test results improved the performance in all cases. The variables useful for prediction were blood urea nitrogen, albumin, and lactate dehydrogenase.

Conclusions

Laboratory test results, such as blood urea nitrogen, albumin, and lactate dehydrogenase levels, along with background information, helped estimate the prognosis of patients hospitalized for COVID-19.

Keywords:

• Database
• electronic health records
• machine learning
• model performance
• prognosis

Transparency

Declaration of funding

This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of financial/other relationship

Kenichi Hiraga is a paid consultant of Real World Data Co., Ltd. Masato Takeuchi declares no conflicts of interest. Takeshi Kimura is an employee of Real World Data Co., Ltd. Satomi Yoshida was employed by the Department of Digital Health and Epidemiology, an Industry-Academia Collaboration Course supported by Eisai Co., Ltd., Kyowa Kirin Co., Ltd., Real World Data Co., Ltd., and Mitsubishi Corporation. Satomi Yoshida has also received consulting fees from Real World Data Co., Ltd. Koji Kawakami has received research funds from Eisai Co., Ltd., Kyowa Kirin Co., Ltd., Mitsubishi Corporation, OMRON Corporation, Real World Data Co., Ltd., Sumitomo Pharma Co., Ltd., and Toppan Inc.; consulting fees from Advanced Medical Care Inc., JMDC Inc., LEBER Inc., and Shin Nippon Biomedical Laboratories Ltd.; executive compensation from Cancer Intelligence Care Systems, Inc.; honoraria from Chugai Pharmaceutical Co., Ltd., Kaken Pharmaceutical Co., Ltd., Mitsubishi Chemical Holdings Corporation, Mitsubishi Corporation, Pharma Business Academy, and Toppan Inc.; and held stock in Real World Data Co., Ltd. The data used in this study were provided free of charge by the Real World Data Co., Ltd. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

KH, MT, and TK contributed to the planning, conducting, and reporting of the work described in the article. SY and KK ensured that the reporting was standard and scientific. KH analyzed the data, and all authors interpreted the results. All authors collaborated in the drafting and critical revision of the manuscript. All authors approved the final version of the manuscript and vouched for the accuracy of the analyses and adherence to the protocol.

Acknowledgements

Authors would like to thank Editage (www.editage.com) for the English language editing. We thank the Health, Clinic, and Education Information Evaluation Institute for the database development and operation.

Data availability statement

Data distribution is not allowed by the data provider.