Dataset Information

Population-based cohort study comparing 30- and 90-day institutional mortality rates after colorectal surgery.

ABSTRACT:

Background

Surgical mortality results are increasingly being reported and published in the public domain as indicators of surgical quality. This study examined how mortality outlier status at 90 days after colorectal surgery compares with mortality at 30 days and subsequent intervals in the first year after surgery.

Methods

All adults undergoing elective and emergency colorectal resection between April 2001 and February 2007 in English National Health Service (NHS) Trusts were identified from administrative data. Funnel plots of postoperative case mix-adjusted institutional mortality rate against caseload were created for 30, 90, 180 and 365 days. High- or low-mortality unit status of individual Trusts was defined as breaching upper or lower third standard deviation confidence limits on the funnel plot for 90-day mortality.

Results

A total of 171 688 patients from 153 NHS Trusts were included. Some 14 537 (8·5 per cent) died within 30 days of surgery, 19 466 (11·3 per cent) within 90 days, 23 942 (13·9 per cent) within 180 days and 31 782 (18·5 per cent) within 365 days. Eight institutions were identified as high-mortality units, including all four units with high outlying status at 30 days. Twelve units were low-mortality units, of which six were also low outliers at 30 days. Ninety-day mortality correlated strongly with later mortality results (rs ?=?0·957, P?ConclusionExtending mortality reporting to 90 days identifies a greater number of mortality outliers when compared with the 30-day death rate. Ninety-day mortality is proposed as the preferred indicator of perioperative outcome for local analysis and public reporting.

SUBMITTER: Byrne BE

PROVIDER: S-EPMC4065361 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Publications

Population-based cohort study comparing 30- and 90-day institutional mortality rates after colorectal surgery.

Byrne B E BE Mamidanna R R Vincent C A CA Faiz O O

The British journal of surgery 20131201 13

<h4>Background</h4>Surgical mortality results are increasingly being reported and published in the public domain as indicators of surgical quality. This study examined how mortality outlier status at 90 days after colorectal surgery compares with mortality at 30 days and subsequent intervals in the first year after surgery.<h4>Methods</h4>All adults undergoing elective and emergency colorectal resection between April 2001 and February 2007 in English National Health Service (NHS) Trusts were ide ...[more]

PMID: 24227369

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Project description:BackgroundPostoperative mortality after colorectal cancer surgery varies across hospitals and countries. The aim of this study was to test the Association of Coloproctologists of Great Britain and Ireland (ACPGBI) models as predictors of 30-day mortality in an Australian cohort.MethodsData from patients who underwent surgery in six hospitals between 1996 and 2015 (CRC data set) were reviewed to test ACPGBI models, and patients from 79 hospitals in the Bi-National Colorectal Cancer Audit between 2007 and 2016 (BCCA data set) were analysed to validate model performance. Recalibrated models based on ACPGBI risk models were developed, tested and validated on a data set of Australasian patients.ResultsOf 18 752 patients observed during the study, 6727 (CRC data set) and 3814 (BCCA data set) were analysed. The 30-day mortality rate was 1·1 and 3·5 per cent in the CRC and BCCA data sets respectively. Both the original and revised ACPGBI models overestimated 30-day mortality for the CRC data set (observed to expected (O/E) ratio 0·17 and 0·21 respectively). Their ability to correctly predict mortality risk was poor (P < 0·001, Hosmer-Lemeshow test); however, the area under the curve for both models was 0·88 (95 per cent c.i. 0·85 to 0·92) showing good discriminatory power to classify 30-day mortality. The recalibrated original model performed well for calibration and discrimination, whereas the recalibrated revised model performed well for discrimination but not for calibration. Risk prediction was good for both recalibrated models. On external validation using the BCCA data set, the recalibrated models underestimated mortality risk (O/E ratio 3·06 and 2·98 respectively), whereas both original and revised ACPGBI models overestimated the risk (O/E ratio 0·48 and 0·69). All models showed similar good discrimination.ConclusionThe original and revised ACPGBI models overpredicted risk of 30-day mortality. The new Australasian calibrated ACPGBI model needs to be tested further in clinical practice.

Project description:ImportanceQuality improvement programs for colorectal cancer surgery have been introduced with benchmarking based on quality indicators, such as mortality. Detailed (pre)operative characteristics may offer relevant information for proper case-mix correction.ObjectiveTo investigate the added value of machine learning to predict quality indicators for colorectal cancer surgery and identify previously unrecognized predictors of 30-day mortality based on a large, nationwide colorectal cancer registry that collected extensive data on comorbidities.Design, setting, and participantsAll patients who underwent resection for primary colorectal cancer registered in the Dutch ColoRectal Audit between January 1, 2011, and December 31, 2016, were included. Multiple machine learning models (multivariable logistic regression, elastic net regression, support vector machine, random forest, and gradient boosting) were made to predict quality indicators. Model performance was compared with conventionally used scores. Risk factors were identified by logistic regression analyses and Shapley additive explanations (ie, SHAP values). Statistical analysis was performed between March 1 and September 30, 2020.Main outcomes and measuresThe primary outcome of this cohort study was 30-day mortality. Prediction models were trained on a training set by performing 5-fold cross-validation, and outcomes were measured by the area under the receiver operating characteristic curve on the test set. Machine learning was further used to identify risk factors, measured by odds ratios and SHAP values.ResultsThis cohort study included 62 501 records, most patients were male (35 116 [56.2%]), were aged 61 to 80 years (41 560 [66.5%]), and had an American Society of Anesthesiology score of II (35 679 [57.1%]). A 30-day mortality rate of 2.7% (n = 1693) was found. The area under the curve of the best machine learning model for 30-day mortality (0.82; 95% CI, 0.79-0.85) was significantly higher than the American Society of Anesthesiology score (0.74; 95% CI, 0.71-0.77; P < .001), Charlson Comorbidity Index (0.66; 95% CI, 0.63-0.70; P < .001), and preoperative score to predict postoperative mortality (0.73; 95% CI, 0.70-0.77; P < .001). Hypertension, myocardial infarction, chronic obstructive pulmonary disease, and asthma were comorbidities with a high risk for increased mortality. Machine learning identified specific risk factors for a complicated course, intensive care unit admission, prolonged hospital stay, and readmission. Laparoscopic surgery was associated with a decreased risk for all adverse outcomes.Conclusions and relevanceThis study found that machine learning methods outperformed conventional scores to predict 30-day mortality after colorectal cancer surgery, identified specific patient groups at risk for adverse outcomes, and provided directions to optimize benchmarking in clinical audits.

Dataset Information

Population-based cohort study comparing 30- and 90-day institutional mortality rates after colorectal surgery.

Background

Methods

Results

Publications

Population-based cohort study comparing 30- and 90-day institutional mortality rates after colorectal surgery.

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