Dataset Information

A model-based validation study of postoperative complications with considerations on operative timing.

ABSTRACT:

Background

Surgery is a highly technical procedure relying on high mental acuity and manual dexterity. The possibility that surgical outcomes and post-operative complications could be subject to influence by fatigue and/or circadian rhythms in surgeons has been investigated with inconsistent results.

Methods

We conducted a retrospective study to assess the significance of operative timing on classifying surgical complications using an interpretable machine learning approach. We trained various linear, generative as well as tree models on the surgical record data collected from a university-affiliated, tertiary teaching hospital in China by performing parameter tuning using grid search cross-validation for optimizing the F1 score.

Results

The results indicated that XGBoost was the best-performing model overall and its feature importance was shown to provide insight into possible timing-related associations with postoperative complications. We observed that the duration of surgery acted as the strongest indicator, and while surgery initiated at night (between 9 pm and 7 am) also ranked higher on the feature importance scale, it bore less significance than other factors such as the patient's age, gender, and type of surgery performed.

Conclusions

We showed that surgical records could be used to demonstrate that operative timing might affect the occurrence of postoperative complications, but only in a relatively mild way while potentially entangling with multiple factors.

SUBMITTER: Qian K

PROVIDER: S-EPMC8106084 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

A model-based validation study of postoperative complications with considerations on operative timing.

Qian Kun K Wu Simeng S Lee Weishan W Liu Shiwen S Li Ailun A Cang Jing J Fang Fang F

Annals of translational medicine 20210401 8

<h4>Background</h4>Surgery is a highly technical procedure relying on high mental acuity and manual dexterity. The possibility that surgical outcomes and post-operative complications could be subject to influence by fatigue and/or circadian rhythms in surgeons has been investigated with inconsistent results.<h4>Methods</h4>We conducted a retrospective study to assess the significance of operative timing on classifying surgical complications using an interpretable machine learning approach. We tr ...[more]

PMID: 33987406

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Project description:Background and purposeEmergency colectomies are associated with a higher risk of complications compared to elective ones. A critical assessment of complications occurring beyond post-operative day 30 (POD30) is lacking. This study aimed to assess the readmission rate and factors associated with readmission 6-months following emergency colectomy.MethodsA retrospective cohort study of adult patients who underwent emergency colectomy (2010-2018) was performed using the Nationwide Readmissions Database. The cohort was divided into two groups: (i) no readmission and (ii) emergency readmission(s) for complications related to colectomy (defined using ICD-9/10 codes). Readmissions were categorized as either "early" (POD0-30) or "late" (> POD30). Differences between groups were described and multivariable regression controlling for relevant covariates defined a priori were used to identify factors associated with timing of readmission and cost.ResultsOf 141,481 eligible cases, 13.22% (n = 18,699) were readmitted within 6-months of emergency colectomy for colectomy-related complications, 61.63% of which were "late" readmissions (> POD30). The most common reasons for "late" readmission were for bleeding, gastrointestinal, and infectious complications (20.80%, 25.30%, and 32.75%, respectively). On multiple logistic regression, female gender (OR 1.12; 95%CI 1.04-1.21), open procedures (OR 1.12, 95%CI 1.011-1.24), and sigmoidectomies (OR 1.51, 95%CI 1.39-1.65, relative to right hemicolectomies) were the strongest predictors of "late" readmission. On multiple linear regression, "late" readmissions were associated with a $1717.09 USD (95%CI $1717.05-$1717.12) increased cost compared to "early" readmissions.DiscussionThe majority of colectomy-related readmissions following emergency colectomy occur beyond POD30 and are associated with cases that are of overall higher morbidity, as well as open sigmoidectomies. Given the associated increased cost of care, mitigation of such readmissions by close follow-up prior to and beyond POD30 is advisable.

Project description:ObjectiveThis study aimed to investigate risk factors associated with severe postoperative complications following hip fracture surgery in elderly patients and to develop a nomogram-based risk prediction model for these complications.MethodsA total of 627 elderly patients with hip fractures treated at Yongchuan Hospital of Chongqing Medical University from January 2015 to April 2024 were collected. 439 patients were assigned to the training cohort for model development, and 188 to the validation cohort for model assessment. The training cohort was stratified based on the presence or absence of severe complications. We employed LASSO regression, as well as univariate and multivariate logistic regression analyses, to identify significant factors. A nomogram was constructed based on the outcomes of the multivariate regression. The model's discriminative ability was assessed using the area under the receiver operating characteristic curve (AUC), while calibration plots and decision curve analysis (DCA) evaluated its calibration and stability. Internal validation was performed using the validation cohort.ResultsOut of the 627 patients, 118 (18.82%) experienced severe postoperative complications. Both LASSO regression and multivariate logistic analysis identified the modified 5-item frailty index (mFI-5) and the preoperative C-reactive protein to albumin ratio (CAR) as significant predictors of severe complications. The nomogram model, derived from the multivariate analysis, exhibited strong discriminative ability, with an AUC of 0.963 (95% CI: 0.946-0.980) for the training cohort and 0.963 (95% CI: 0.938-0.988) for the validation cohort. Calibration plots demonstrated excellent agreement between the nomogram's predictions and actual outcomes. Decision curve analysis (DCA) indicated that the model provided clinical utility across all patient scenarios. These findings were consistent in the validation cohort.ConclusionsBoth the mFI-5 and CAR are predictive factors for severe postoperative complications in elderly patients undergoing hip fracture surgery.

Dataset Information

A model-based validation study of postoperative complications with considerations on operative timing.

Background

Methods

Results

Conclusions

Publications

A model-based validation study of postoperative complications with considerations on operative timing.

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