DyCRS: Dynamic Interpretable Postoperative Complication Risk Scoring

Objective.Studies have suggested higher rates of perioperative and postoperative complications in smokers compared to nonsmokers. The objective of this systematic review was to assess the association of smoking and postoperative outcomes following total hip arthroplasty (THA) or total knee arthroplasty (TKA).Methods.A search of 6 databases (The Cochrane Library, Scopus, Proquest Dissertation abstracts, CINAHL, Ovid Medline, and Embase) was performed by a Cochrane librarian. All titles and abstracts were screened by 2 independent reviewers with expertise in performing systematic reviews. Studies were included if they were fully published reports that included smoking and any perioperative or postoperative clinical outcome in patients with TKA or THA.Results.A total of 21 studies were included for the review, of which 6 provided multivariable-adjusted analyses, 14 univariate analyses, and one statistical modeling. For most outcomes, results from 1–2 studies could be pooled. Current smokers were significantly more likely to have any postoperative complication (risk ratio 1.24, 95% CI 1.01 to 1.54) and death (risk ratio 1.63, 95% CI 1.06 to 2.51) compared to nonsmokers. Former smokers were significantly more likely to have any post-operative complication (risk ratio 1.32, 95% CI 1.05 to 1.66) and death (risk ratio 1.69, 95% CI 1.08 to 2.64) compared to nonsmokers.Conclusion.This systematic review found that smoking is associated with significantly higher risk of postoperative complication and mortality following TKA or THA. Studies examining longterm consequences of smoking on implant survival and complications are needed. Smoking cessation may improve outcomes after THA or TKA.

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Machine learning approaches for the prediction of postoperative complication risk in liver resection patients

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01731-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Siyu Zeng ◽

Lele Li ◽

Yanjie Hu ◽

Li Luo ◽

Yuanchen Fang

Keyword(s):

Machine Learning ◽

Liver Resection ◽

Postoperative Complications ◽

Liver Cancer ◽

Prediction Model ◽

Postoperative Complication ◽

Cancer Patients ◽

Influencing Factors ◽

Machine Learning Algorithms ◽

Complication Risk

Abstract Background For liver cancer patients, the occurrence of postoperative complications increases the difficulty of perioperative nursing, prolongs the hospitalization time of patients, and leads to large increases in hospitalization costs. The ability to identify influencing factors and to predict the risk of complications in patients with liver cancer after surgery could assist doctors to make better clinical decisions. Objective The aim of the study was to develop a postoperative complication risk prediction model based on machine learning algorithms, which utilizes variables obtained before or during the liver cancer surgery, to predict when complications present with clinical symptoms and the ways of reducing the risk of complications. Methods The study subjects were liver cancer patients who had undergone liver resection. There were 175 individuals, and 13 variables were recorded. 70% of the data were used for the training set, and 30% for the test set. The performance of five machine learning models, logistic regression, decision trees-C5.0, decision trees-CART, support vector machines, and random forests, for predicting postoperative complication risk in liver resection patients were compared. The significant influencing factors were selected by combining results of multiple methods, based on which the prediction model of postoperative complications risk was created. The results were analyzed to give suggestions of how to reduce the risk of complications. Results Random Forest gave the best performance from the decision curves analysis. The decision tree-C5.0 algorithm had the best performance of the five machine learning algorithms if ACC and AUC were used as evaluation indicators, producing an area under the receiver operating characteristic curve value of 0.91 (95% CI 0.77–1), with an accuracy of 92.45% (95% CI 85–100%), the sensitivity of 87.5%, and specificity of 94.59%. The duration of operation, patient’s BMI, and length of incision were significant influencing factors of postoperative complication risk in liver resection patients. Conclusions To reduce the risk of complications, it appears to be important that the patient's BMI should be above 22.96 before the operation, and the duration of the operation should be minimized.

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