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. 2021 Dec 1;11(12):1271.
doi: 10.3390/jpm11121271.

Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality

Jaehyeong Cho  1 , Jimyung Park  1 , Eugene Jeong  2 , Jihye Shin  3 , Sangjeong Ahn  4 , Min Geun Park  5 , Rae Woong Park  1   6 , Yongkeun Park  5
Affiliations

Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality

Jaehyeong Cho et al. J Pers Med. .

Abstract

Background: Several prediction models have been proposed for preoperative risk stratification for mortality. However, few studies have investigated postoperative risk factors, which have a significant influence on survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality.

Methods: Two tertiary hospital databases were used in this research: one for model development and another for external validation of the resulting models. The following algorithms were utilized for model development: LASSO logistic regression, random forest, deep neural network, and XGBoost. We built the models on the lab values from immediate postoperative blood tests and compared them with the SASA scoring system to demonstrate their efficacy.

Results: There were 3817 patients who had immediate postoperative blood test values. All models trained on immediate postoperative lab values outperformed the SASA model. Furthermore, the developed random forest model had the best AUROC of 0.82 and AUPRC of 0.13, and the phosphorus level contributed the most to the random forest model.

Conclusions: Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death.

Keywords: American Society of Anesthesiologists physical status; surgery; surgical Apgar score.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
SHAP bee swarm plots and feature importance: (A) in the honey bee swarm plot, each point corresponds to a laboratory value observed in an individual person; (B) mean absolute SHAP values suggest a rank order for feature importance in the 30-day mortality.
Figure 2
Figure 2
SHAP feature dependence plots for four variables: (A,B) higher values of albumin and total protein are associated with higher risk of 30-day postoperative mortality; (C,D) higher C-reactive protein and total bilirubin are associated with lower risk of 30-day postoperative mortality.
See this image and copyright information in PMC

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