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A Comprehensive Way to Access Hospital Death Prediction Model for Acute Mesenteric Ischemia: A Combination of Traditional Statistics and Machine Learning

Authors Wu W, Zhou Z

Received 14 January 2021

Accepted for publication 4 February 2021

Published 25 February 2021 Volume 2021:14 Pages 591—602


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Scott Fraser

Wenhan Wu,1,2 Zongguang Zhou1,2

1Institute of Digestive Surgery of Sichuan University, Chengdu, 610041, Sichuan; 2Department of Gastrointestinal Surgery, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, 610041, Sichuan

Correspondence: Zongguang Zhou
Institute of digestive surgery of Sichuan University, and department of gastrointestinal surgery, West China hospital, west China school of medicine, Sichuan university, Chengdu, 610041, Sichuan
Email [email protected]

Purpose: This study aimed to use traditional statistics and machine learning to develop and validate prediction models for predicting hospital death in patients with AMI and compare these models’ performance.
Patients and Methods: Data were retrieved from the Medical Information Mart for Intensive Care (MIMIC III) electronic clinical database. A total of 338 eligible AMI patients were divided into a training cohort (n = 238) and a validation cohort (n = 100), and all patients were divided into survival groups and nonsurvival groups according to patients’ hospital outcomes. The performance of the traditional statistics prediction model and the optimal machine learning prediction model was evaluated and compared with respect to discrimination, calibration, and clinical utility in the validation cohort.
Results: Univariate and multivariate logistic regression analyses identified the following independent risk factors associated with hospital death for AMI in the training cohort, including diastolic blood pressure, blood lactate, blood creatinine, age, blood pH, and red blood cell distribution width. Both the nomogram (AUC = 77.0%, 67.9– 86.1%) and optimal machine learning model (AUC = 82.9%, 74.9– 91.0%) achieved good discrimination and calibration in the validation cohort. Decision curves analysis showed that the optimal machine learning model has a greater net benefit than that of nomogram in this study.
Conclusion: The nomogram achieved a concise and relatively accurate prediction of hospital death in patients with AMI, the machine learning model also has good discrimination and seems to have better clinical utility. Traditional statistics may help infer the relationship between risk factors and hospital death, while machine learning may contribute to a more accurate prediction. Traditional statistics and machine learning are complementary in developing the prediction model for hospital death of AMI. Therefore, a combination of nomogram–machine learning (Nomo-ML) predictive model may improve care and help clinicians make AMI management-related decisions.

Keywords: acute mesenteric ischemia, hospital mortality, prediction model, nomogram, machine learning

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