Demystifying COVID-19 mortality causes with interpretable data mining.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

COVID-19*/mortality ; Data Mining*; Humans ; Aged ; Male ; Female ; Retrospective Studies ; Middle Aged ; Risk Factors ; SARS-CoV-2/isolation & purification ; Aged, 80 and over ; Algorithms

While COVID-19 becomes periodical, old individuals remain vulnerable to severe disease with high mortality. Although there have been some studies on revealing different risk factors affecting the death of COVID-19 patients, researchers rarely provide a comprehensive analysis to reveal the relationships and interactive effects of the risk factors of COVID-19 mortality, especially in the elderly. Through retrospectively including 1917 COVID-19 patients (102 were dead) admitted to Xiangya Hospital from December 2022 to March 2023, we used the association rule mining method to identify the risk factors leading causes of death among the elderly. Firstly, we used the Affinity Propagation clustering to extract key features from the dataset. Then, we applied the Apriori Algorithm to obtain 6 groups of abnormal feature combinations with significant increments in mortality rate. The results showed a relationship between the number of abnormal feature combinations and mortality rates within different groups. Patients with "C-reactive protein > 8 mg/L", "neutrophils percentage > 75.0 %", "lymphocytes percentage < 20%", and "albumin < 40 g/L" have a 2 × mortality rate than the basic one. When the characteristics of "D-dimer > 0.5 mg/L" and "WBC > 9.5 × 10 9 /L" are continuously included in this foundation, the mortality rate can be increased to 3 × or 4 × . In addition, we also found that liver and kidney diseases significantly affect patient mortality, and the mortality rate can be as high as 100%. These findings can support auxiliary diagnosis and treatment to facilitate early intervention in patients, thereby reducing patient mortality.
(© 2024. The Author(s).)

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2022YFC2009800 National Key Research and Development Program of China

Date Created: 20240502 Date Completed: 20240502 Latest Revision: 20240509

20240509

PMC11066015

10.1038/s41598-024-60841-w

38698064