Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality
- 주제(키워드) American Society of Anesthesiologists physical status , surgery , surgical Apgar score , Machine learning , Observational study
- 주제(DDC) 570
- 발행기관 아주대학교
- 지도교수 박래웅
- 발행년도 2022
- 학위수여년월 2022. 2
- 학위명 박사
- 학과 및 전공 일반대학원 의생명과학과
- 실제URI http://www.dcollection.net/handler/ajou/000000031820
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
The development of new surgical instruments and techniques for surgical treatment has facilitated an increase in the number of patients undergoing surgery. As the proportion of elderly patients and patients with delayed recovery also increases, reducing the additional burden of clinical outcomes after surgery is a new challenge for perioperative management. While numerous models have been created to predict mortality and to stratify risks in the preoperative period, few studies evaluated postoperative risk factors that significantly affects survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality. The databases of two tertiary hospitals were used in the current study: one to develop model and the other for external validation of the final models. LASSO logistic regression, random forest, deep neural network, and XGBoost algorhythms were utilized to create model. Following creation of the models based on the laboratory values of postoperative blood tests, their efficiency were demonstrated through comparison using SASA scoring system. There was a total of 3,817 patients with postoperative blood test values. All the developed models outperformed the SASA model. Furthermore, the best AUROC of 0.82 and AUPRC of 0.13 was obtained by the random forest model, and the phosphorus level was the most contributing factor to the random forest model. The machine learning models that were trained on postoperative laboratory values outperformed the previous approaches in regards to predicting postoperative 30-day mortality, which indicates that the current model may be a useful tool to identify patients who are at increased risk of postoperative mortality.
more목차
I. Introduction 1
A. Background 1
B. Purpose of study 8
II. Materials and Methods 9
A. Data Sources 9
B. Study Design 11
C. Use of the SASA Scoring System 20
D. ML-Based Model Development 21
E. Statistical Analysis 23
F. Development of prediction model utilization tools 25
III. Results 26
A. Characteristics of the Target population 26
B. Postoperative Laboratory Values 29
C. ML Approach for Predicting Postoperative Mortality 33
D. Importance of Model Feature 36
E. Utilization of the mortality risk calculation tool 59
IV. Discussion 60
A. Main findings 60
B. Limitations 63
V. Conclusion 65
References 66
