ArticleInternational journal of colorectal disease2020
A risk model for prediction of 30-day readmission rates after surgical treatment for colon cancer.
Article in International journal of colorectal disease, 2020. 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.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Data analytics and artificial intelligence in predicting length of stay, readmission, and mortality: a population-based study of surgical management of colorectal cancer.Discover oncology · 2022Article
- Development of a Clinical Decision Support System to Predict Unplanned Cancer Readmissions.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study was to develop a risk model for the prediction of 30-day unplanned readmission rate after surgery for colon cancer.
methodThis study was a cross-sectional analysis of data from Nationwide Readmissions Database, collected during 2010-2014. Patients ≥ 18 years of age who underwent surgery for colon cancer were included in the study. The primary outcome of the study was 30-day unplanned readmission rate.
resultsThere were 141,231 index hospitalizations for surgical treatment of colon cancers and 16,551 had unplanned readmissions. Age, sex, primary payer, Elixhauser comorbidity index, node positive or metastatic disease, length of stay, hospital bedsize, teaching status, hospital ownership, presence of stoma, surgery types, surgery procedures, infectious complications, surgical complications, mechanical wounds, pulmonary complications, and gastrointestinal complications were selected for the risk analysis during backward regression model. Based on the estimated coefficients of selected variables, risk scores were developed and stratified as low risk (≤ 1.08), moderate risk (> 1.08 to ≤ 1.5), and high risk (> 1.5) for unplanned readmission. Validation analysis (n = 42,269) showed that 7.1% of low-risk individuals, 11.1% of moderate-risk individuals, and 17.1% of high-risk individuals experienced unplanned readmissions (P < 0.001). Pairwise comparisons also showed statistically significant differences between low-risk and moderate-risk participants (P < 0.001), between moderate-risk and high-risk participants (P < 0.001), and between low-risk and high-risk participants (P < 0.001). The area under the ROC curve was 0.622.
conclusionsOur risk model could be helpful for risk-stratifying patients for readmission after surgical treatment for colon cancer. This model needs further validation by incorporating all possible clinical variables.
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