지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한외과학회 Annals of Surgical Treatment and Research Annals of Surgical Treatment and Research Vol.105 No.4
- 발행연도
- 2023.10
- 수록면
- 237 - 244 (8page)
이용수
초록· 키워드
오류제보하기
Purpose: Sepsis is one of the most common causes of death after surgery. Several conventional scoring systems have been developed to predict the outcome of sepsis; however, their predictive power is insufficient. The present study applies explainable machine-learning algorithms to improve the accuracy of predicting postoperative mortality in patients with sepsis caused by peritonitis.
Methods: We performed a retrospective analysis of data from demographic, clinical, and laboratory analyses, including the delta neutrophil index (DNI), WBC and neutrophil counts, and CRP level. Laboratory data were measured before surgery, 12-36 hours after surgery, and 60-84 hours after surgery. The primary study output was the probability of mortality. The areas under the receiver operating characteristic curves (AUCs) of several machine-learning algorithms using the Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score (SAPS) 3 models were compared. ‘SHapley Additive exPlanations’ values were used to indicate the direction of the relationship between a variable and mortality.
Results: The CatBoost model yielded the highest AUC (0.933) for mortality compared to SAPS3 and SOFA (0.860 and 0.867, respectively). Increased DNI on day 3, septic shock, use of norepinephrine therapy, and increased international normalized ratio on day 3 had the greatest impact on the model’s prediction of mortality.
Conclusion: Machine-learning algorithms increase the accuracy of predicting postoperative mortality in patients with sepsis caused by peritonitis.
Methods: We performed a retrospective analysis of data from demographic, clinical, and laboratory analyses, including the delta neutrophil index (DNI), WBC and neutrophil counts, and CRP level. Laboratory data were measured before surgery, 12-36 hours after surgery, and 60-84 hours after surgery. The primary study output was the probability of mortality. The areas under the receiver operating characteristic curves (AUCs) of several machine-learning algorithms using the Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score (SAPS) 3 models were compared. ‘SHapley Additive exPlanations’ values were used to indicate the direction of the relationship between a variable and mortality.
Results: The CatBoost model yielded the highest AUC (0.933) for mortality compared to SAPS3 and SOFA (0.860 and 0.867, respectively). Increased DNI on day 3, septic shock, use of norepinephrine therapy, and increased international normalized ratio on day 3 had the greatest impact on the model’s prediction of mortality.
Conclusion: Machine-learning algorithms increase the accuracy of predicting postoperative mortality in patients with sepsis caused by peritonitis.
목차
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Explainable Machine Learning Using Perioperative Serial Laboratory Results to Predict Postoperative Mortality in Patients with Peritonitis-Induced Sepsis
대한외과학회 학술대회 초록집
2023 .05
-
Mortality difference between early-identified sepsis and late-identified sepsis
Clinical and Experimental Emergency Medicine
2020 .01
-
Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study
Journal of Korean Medical Science
2024 .02
-
Application of Sepsis-3 Criteria to Korean Patients with Critical Illnesses
Acute and Critical Care
2019 .01
-
Biomarkers for Prediction of Mortality and Sepsis
대한외과학회 학술대회 초록집
2021 .11
-
Sepsis: Early Recognition and Optimized Treatment
Tuberculosis and Respiratory Diseases
2019 .01
-
Epidemiology of sepsis in Korea: a population-based study of incidence, mortality, cost and risk factors for death in sepsis
Clinical and Experimental Emergency Medicine
2019 .01
-
Clinical and Laboratory Evaluations of Polymicrobial Peritonitis of Experimental Canine Sepsis Model
한국실험동물학회 학술발표대회 논문집
2015 .08
-
Mortality of adult patients due to sepsis and septic shock in South Korea: a systematic review and metaanalysis
Clinical and Experimental Emergency Medicine
2023 .06
-
Sepsis-induced cardiomyopathy is associated with higher mortality rates in patients with sepsis
Acute and Critical Care
2021 .08
-
Pre-Sepsis Length of Hospital Stay and Mortality: A Nationwide Multicenter Cohort Study
Journal of Korean Medical Science
2024 .03
-
Increased Mortality in Young-Onset Parkinson’s Disease
Journal Of Movement Disorders
2021 .09
-
Serum procalcitonin and C-reactive protein as indices of early sepsis and mortality in North Indian pediatric burn injuries: a prospective evaluation and literature review
Acute and Critical Care
2024 .08
-
Improved MSS method in a CLP-induced mouse sepsis model
한국실험동물학회 학술발표대회 논문집
2021 .07
-
Effect of D-glucose feeding on mortality induced by sepsis
The Korean Journal of Physiology & Pharmacology
2016 .01
-
A comparison of scoring systems for predicting mortality and sepsis in the emergency department patients with a suspected infection
Clinical and Experimental Emergency Medicine
2021 .12
-
Public-health impact of outdoor air pollution for 2nd air pollution management policy in Seoul metropolitan area, Korea
대한직업환경의학회지
2015 .02
-
Quick Sepsis-related Organ Failure Assessment score is not sensitive enough to predict 28-day mortality in emergency department patients with sepsis: a retrospective review
Clinical and Experimental Emergency Medicine
2019 .01
-
Korean Registry for Improving Sepsis Survival (KISS): Protocol for a Multicenter Cohort of Adult Patients with Sepsis or Septic Shock
Infection and Chemotherapy
2020 .01
-
ChatGPT Predicts In-Hospital All-Cause Mortality for Sepsis: In-Context Learning with the Korean Sepsis Alliance Database
Healthcare Informatics Research
2024 .07
이 논문의 저자 정보
최근 본 자료
댓글(0)
0