Developing and validating a preoperative risk score to predict 90-day mortality after liver resection

2019 ◽  
Vol 119 (4) ◽  
pp. 472-478 ◽  
Author(s):  
Bobby V. M. Dasari ◽  
James Hodson ◽  
Robert P. Sutcliffe ◽  
Ravi Marudanayagam ◽  
Keith J. Roberts ◽  
...  
Keyword(s):  
Liver Resection ◽  
Risk Score ◽  
Preoperative Risk

scholarly journals A novel preoperative risk score to optimize patient selection for performing concomitant liver resection with cytoreductive surgery/HIPEC

2020 ◽  
Vol 123 (1) ◽  
pp. 187-195
Author(s):  
Rachel M. Lee ◽  
Adriana C. Gamboa ◽  
Michael K. Turgeon ◽  
Mohammad Y. Zaidi ◽  
Charles Kimbrough ◽  
...  
Keyword(s):  
Liver Resection ◽  
Risk Score ◽  
Patient Selection ◽  
Cytoreductive Surgery ◽  
Preoperative Risk ◽  
Selection For

Developing and validating a preoperative risk score to predict 90‐day mortality after liver resection

2019 ◽  
Vol 119 (7) ◽  
pp. 1029-1030 ◽  
Author(s):  
Bobby V.M. Dasari ◽  
James Hodson ◽  
Robert P. Sutcliffe ◽  
Ravi Marudanayagam ◽  
Keith J. Roberts ◽  
...  
Keyword(s):  
Liver Resection ◽  
Risk Score ◽  
Preoperative Risk

scholarly journals Preoperative Risk Score Predicting 90-Day Mortality After Liver Resection in a Population-Based Study

Medicine ◽  
2014 ◽  
Vol 93 (12) ◽  
pp. e59 ◽  
Author(s):  
Chun-Ming Chang ◽  
Wen-Yao Yin ◽  
Yu-Chieh Su ◽  
Chang-Kao Wei ◽  
Cheng-Hung Lee ◽  
...  
Keyword(s):  
Liver Resection ◽  
Risk Score ◽  
Population Based ◽  
Preoperative Risk ◽  
Population Based Study

scholarly journals Development of a preoperative risk score for postoperative mortality after liver resection for presumed perihilar cholangiocarcinoma

HPB ◽  
2016 ◽  
Vol 18 ◽  
pp. e863-e864 ◽  
Author(s):  
J.K. Wiggers ◽  
B. Groot Koerkamp ◽  
K.P. Cieslak ◽  
A. Doussot ◽  
P.J. Allen ◽  
...  
Keyword(s):  
Liver Resection ◽  
Risk Score ◽  
Postoperative Mortality ◽  
Perihilar Cholangiocarcinoma ◽  
Preoperative Risk

The use of preoperative risk score to predict postoperative mortality following liver resection

2019 ◽  
Vol 119 (7) ◽  
pp. 1027-1028 ◽  
Author(s):  
Hai‐Jun Hou ◽  
Fu‐Shan Xue ◽  
Liu‐Jia‐Zhi Shao
Keyword(s):  
Liver Resection ◽  
Risk Score ◽  
Postoperative Mortality ◽  
Preoperative Risk

scholarly journals A New Preoperative Risk Score for Predicting Postoperative Complications in Elderly Patients Undergoing Hepatectomy

2021 ◽  
Author(s):  
Koichi Tomita ◽  
Itsuki Koganezawa ◽  
Masashi Nakagawa ◽  
Shigeto Ochiai ◽  
Takahiro Gunji ◽  
...  
Keyword(s):  
Risk Factors ◽  
Skeletal Muscle ◽  
Postoperative Complications ◽  
Elderly Patients ◽  
Risk Score ◽  
Surgical Procedure ◽  
Evaluation Method ◽  
Preoperative Risk ◽  
Preoperative Serum ◽  
Independent Risk Factors

Abstract Background Postoperative complications are not rare in the elderly population after hepatectomy. However, predicting postoperative risk in elderly patients undergoing hepatectomy is not easy. We aimed to develop a new preoperative evaluation method to predict postoperative complications in patients above 65 years of age using biological impedance analysis (BIA). Methods Clinical data of 59 consecutive patients (aged 65 years or older) who underwent hepatectomy at our institution between 2017 and 2020 were retrospectively analyzed. Risk factors for postoperative complications (Clavien-Dindo ≥ III) were evaluated using multivariate regression analysis. Additionally, a new preoperative risk score was developed for predicting postoperative complications. Results Fifteen patients (25.4%) had postoperative complications, with biliary fistula being the most common complication. Abnormal skeletal muscle mass index from BIA and type of surgical procedure were found to be independent risk factors in the multivariate analysis. These two variables and preoperative serum albumin levels were used for developing the risk score. The postoperative complication rate was 0.0% with a risk score of ≤ 1 and 57.1% with a risk score of ≥ 4. The area under the receiver operating characteristic curve of the risk score was 0.810 (p = 0.001), which was better than that of other known surgical risk indexes. Conclusion Decreased skeletal muscle and the type of surgical procedure for hepatectomy were independent risk factors for postoperative complications after elective hepatectomy in elderly patients. The new preoperative risk score is simple, easy to perform, and will help in the detection of high-risk elderly patients undergoing elective hepatectomy.

scholarly journals Preoperative predictions of in-hospital mortality using electronic medical record data

2018 ◽  
Author(s):  
Brian Hill ◽  
Robert Brown ◽  
Eilon Gabel ◽  
Christine Lee ◽  
Maxime Cannesson ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Hospital Mortality ◽  
Risk Score ◽  
Surgical Mortality ◽  
Physical Status ◽  
Preoperative Risk ◽  
Physical Status Classification ◽  
Postoperative Risk ◽  
Status Classification

AbstractBackgroundPredicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting surgical outcomes, obtaining this classification requires a clinician to review the patient’s medical records. Our goal here is to create an improved risk score using electronic medical records and demonstrate its utility in predicting in-hospital mortality without requiring clinician-derived ASA scores.MethodsData from 49,513 surgical patients were used to train logistic regression, random forest, and gradient boosted tree classifiers for predicting in-hospital mortality. The features used are readily available before surgery from EMR databases. A gradient boosted tree regression model was trained to impute the ASA Physical Status Classification, and this new, imputed score was included as an additional feature to preoperatively predict in-hospital post-surgical mortality. The preoperative risk prediction was then used as an input feature to a deep neural network (DNN), along with intraoperative features, to predict postoperative in-hospital mortality risk. Performance was measured using the area under the receiver operating characteristic (ROC) curve (AUC).ResultsWe found that the random forest classifier (AUC 0.921, 95%CI 0.908-0.934) outperforms logistic regression (AUC 0.871, 95%CI 0.841-0.900) and gradient boosted trees (AUC 0.897, 95%CI 0.881-0.912) in predicting in-hospital post-surgical mortality. Using logistic regression, the ASA Physical Status Classification score alone had an AUC of 0.865 (95%CI 0.848-0.882). Adding preoperative features to the ASA Physical Status Classification improved the random forest AUC to 0.929 (95%CI 0.915-0.943). Using only automatically obtained preoperative features with no clinician intervention, we found that the random forest model achieved an AUC of 0.921 (95%CI 0.908-0.934). Integrating the preoperative risk prediction into the DNN for postoperative risk prediction results in an AUC of 0.924 (95%CI 0.905-0.941), and with both a preoperative and postoperative risk score for each patient, we were able to show that the mortality risk changes over time.ConclusionsFeatures easily extracted from EMR data can be used to preoperatively predict the risk of in-hospital post-surgical mortality in a fully automated fashion, with accuracy comparable to models trained on features that require clinical expertise. This preoperative risk score can then be compared to the postoperative risk score to show that the risk changes, and therefore should be monitored longitudinally over time.Author summaryRapid, preoperative identification of those patients at highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level, or utilize the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. In this manuscript we report on using machine-learning algorithms, specifically random forest, to create a fully automated score that predicts preoperative in-hospital mortality based solely on structured data available at the time of surgery. This score has a higher AUC than both the ASA physical status score and the Charlson comorbidity score. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.

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