ArticleIISE transactions on healthcare systems engineering2023
Predicting Colorectal Surgery Readmission Risk: a Surgery-Specific Predictive Model.
Article in IISE transactions on healthcare systems engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Ambulatory Surgery Ensemble: Predicting Adult and Pediatric Same-Day Surgery Cases Across Specialties.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
- Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians.Current oncology (Toronto, Ont.) · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Most current predictive models for risk of readmission were primarily designed from non-surgical patients and often utilize administrative data alone. Models built upon comprehensive data sources specific to colorectal surgery may be key to implementing interventions aimed at reducing readmissions. This study aimed to develop a predictive model for risk of 30-day readmission specific to colorectal surgery patients including administrative, clinical, laboratory, and socioeconomic status (SES) data. Patients admitted to the colorectal surgery service who underwent surgery and were discharged from an academic tertiary hospital between 2017 and 2019 were included. A total of 1549 patients met eligibility criteria for this retrospective split-sample cohort study. The 30-day readmission rate of the cohort was 19.62%. A multivariable logistic regression was developed (C=0.70, 95% CI 0.61-0.73), which outperformed two internationally used readmission risk prediction indices (C=0.58, 95% CI 0.52-0.65) and (C=0.60, 95% CI 0.53-0.66). Tailored surgery-specific readmission models with comprehensive data sources outperform the most used readmission indices in predicting 30-day readmission in colorectal surgery patients. Model performance is improved by using more comprehensive datasets that include administrative and socioeconomic details about a patient, as well as clinical information used for decision-making around the time of discharge.
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Registered trials
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