Observational studySurgical endoscopy2019
Complications after discharge predict readmission after colorectal surgery.
Observational study in Surgical endoscopy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Bias in Prediction Models to Identify Patients With Colorectal Cancer at High Risk for Readmission After Resection.JCO clinical cancer informatics · 2024Article
- Development and Validation of Machine Learning Models to Predict Readmission After Colorectal Surgery.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2022Article
- Use of a standardized discharge checklist with daily post-operative C-reactive protein monitoring does not impact readmission rates after colon and rectal surgery.International journal of colorectal disease · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundHealth care providers, hospitals, and pay-for-performance programs are focused on strategies identifying patients at highest risk for re-admission after colorectal surgery. The study objective was to determine characteristics most associated with re-admission after elective colorectal surgery using a conceptual framework approach.
methodsThis is an observational study of Michigan Surgical Quality Collaborative clinical registry data for 8962 colorectal surgery cases between July-2012 and April-2015. Separate mixed models were fit using known re-admission risk factors aligned in categories that may impact re-admissions by different mechanisms. Overall model discrimination was evaluated using Area Under the Curve estimated on a hold-out data set and examining differences in predicted versus observed re-admission across risk quintiles.
resultsThe overall 30-day re-admission rate was 10.5%. From Model 1 to Model 6, discrimination of re-admission was poor until Model 6 (AUC, 0.56, 0.61, 0.65, 0.63, 0.72, 0.81). Differences for observed re-admission rates comparing 'very low' versus 'very high' risk strata from Model 1 to Model 6 were 6%, 11%, 15%, 14%, 20%, and 30% respectively, and all comparisons were significant (p < 0.01). Though there were significant predictors in the first five models, most were no longer significant when additional predictors were included in subsequent models. Complications identified after discharge significantly increased the likelihood of re-admission and were the strongest predictors.
conclusionStatistical models that include complications identified after discharge predict re-admission. Strategies to reduce re-admission after colorectal surgery should emphasize prevention of complications and more effective interventions to manage and ameliorate evolving complications identified after discharge.
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Registered trials
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