title: Preoperative risk score predicting 90-day mortality after liver resection in a population-based study.
abstract: The impact of important preexisting comorbidities, such as liver and renal disease, on the outcome of liver resection remains unclear. Identification of patients at risk of mortality will aid in improving preoperative preparations. The purpose of this study is to develop and validate a population-based score based on available preoperative and predictable parameters predicting 90-day mortality after liver resection using data from a hepatitis endemic country.We identified 13,159 patients who underwent liver resection between 2002 and 2006 in the Taiwan National Health Insurance Research Database. In a randomly selected half of the total patients, multivariate logistic regression analysis was used to develop a prediction score for estimating the risk of 90-day mortality by patient demographics, preoperative liver disease and comorbidities, indication for surgery, and procedure type. The score was validated with the remaining half of the patients.Overall 90-day mortality was 3.9%. Predictive characteristics included in the model were age, preexisting cirrhosis-related complications, ischemic heart disease, heart failure, cerebrovascular disease, renal disease, malignancy, and procedure type. Four risk groups were stratified by mortality scores of 1.1%, 2.2%, 7.7%, and 15%. Preexisting renal disease and cirrhosis-related complications were the strongest predictors. The score discriminated well in both the derivation and validation sets with c-statistics of 0.75 and 0.75, respectively.This population-based score could identify patients at risk of 90-day mortality before liver resection. Preexisting renal disease and cirrhosis-related complications had the strongest influence on mortality. This score enables preoperative risk stratification, decision-making, quality assessment, and counseling for individual patients.
authors.affiliation: Department of Surgery (C-MC, W-YY, C-KW, C-HL, J-CC); Department of Otolaryngology (C-CL); Center for Clinical Epidemiology and Biostatistics (S-YJ, C-CL); Division of Hematology-Oncology, Department of Internal Medicine (Y-CS); Cancer Center (Y-CS, C-CL), Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi; School of Medicine, Tzu Chi University, Hualian (C-MC, W-YY, C-KW, C-HL, J-CC, C-CL); and Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City (Y-TC), Taiwan.
authors.forename: Chun-Ming
authors.initials: CM
authors.lastname: Chang
authors.forename: Wen-Yao
authors.initials: WY
authors.lastname: Yin
authors.forename: Yu-Chieh
authors.initials: YC
authors.lastname: Su
authors.forename: Chang-Kao
authors.initials: CK
authors.lastname: Wei
authors.forename: Cheng-Hung
authors.initials: CH
authors.lastname: Lee
authors.forename: Shiun-Yang
authors.initials: SY
authors.lastname: Juang
authors.forename: Yi-Ting
authors.initials: YT
authors.lastname: Chen
authors.forename: Jin-Cherng
authors.initials: JC
authors.lastname: Chen
authors.forename: Ching-Chih
authors.initials: CC
authors.lastname: Lee
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D002318:Cardiovascular Diseases / Q000150:complications*; D015897:Comorbidity; D005260:Female; D005500:Follow-Up Studies; D006498:Hepatectomy / Q000401:mortality*; D006801:Humans; D007674:Kidney Diseases / Q000150:complications*; D008103:Liver Cirrhosis / Q000150:complications*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000150:complications*; D011237:Predictive Value of Tests; D057234:Preoperative Period; D012189:Retrospective Studies; D018570:Risk Assessment; D015996:Survival Rate; D013624:Taiwan; D013997:Time Factors