Development and temporal external validation of a prediction model for unplanned readmission in hospitalized patients with cancer and coronary heart disease

Abstract Background Unplanned readmission is a substantial burden in patients with cancer, particularly when coronary heart disease (CHD) coexists, yet no risk tool has been developed for this population. We developed and temporally validated a prediction model for unplanned readmission based on routine data available at admission. Methods We conducted a single-center retrospective cohort study of adults with cancer and CHD discharged between January 2022 and June 2024 (438 admissions; 407 unique patients). The outcome was unplanned readmission (33 events, 7.5%). Thirtyfour candidate predictors with < 5% missing data were screened by univariable analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold × 5-repeat cross-validation (1-SE rule) and refitted in an unpenalized multivariable logistic model. Discrimination was assessed with the c-index and 1000-bootstrap optimism correction; calibration with the Hosmer-Lemeshow test, calibration slope, and intercept; and clinical utility with decision curve analysis. The model was applied without refitting to a temporally non-overlapping cohort discharged from October 2023 to June 2024 (n = 118; 6 events). Results The final model comprised serum sodium, diastolic blood pressure, hemoglobin, and digestive cancer (overall likelihood-ratio test χ2 = 16.55, P = 0.002). The apparent and optimism-corrected c-indices were 0.709 and 0.682 (95% confidence interval [CI] 0.599–0.766), and the temporal validation c-index was 0.841 (bootstrap 95% CI 0.643–0.985). Calibration was adequate in both cohorts (Hosmer-Lemeshow P = 0.932 and 0.606). At the Youden-optimal threshold (predicted probability 0.089), sensitivity and specificity were 0.61 and 0.75. The model outperformed the Padua score (c-index 0.538) and NRS2002 (0.502). Conclusion A four-variable model using routine admission data provides moderate discrimination with adequate calibration for unplanned readmission in patients with cancer and CHD and remained

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Publication Details

Journal
Research Square
Published
2026-11-05
DOI
https://doi.org/10.21203/rs.3.rs-11046775/v1
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
Type
preprint
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preprint

Development and temporal external validation of a prediction model for unplanned readmission in hospitalized patients with cancer and coronary heart disease

Wenyan Yang, Xinlian Zhang, Nuoyi Wu, WENLI YU et al.
Research Square
Inflammatory Biomarkers in Disease Prognosis
preprint

Development and temporal external validation of a prediction model for unplanned readmission in hospitalized patients with cancer and coronary heart disease

Wenyan Yang, Xinlian Zhang, Nuoyi Wu, WENLI YU, Qian Wen, Yuyi Yang, Mei Yang, Juan Liu
preprint en

Abstract

Abstract Background Unplanned readmission is a substantial burden in patients with cancer, particularly when coronary heart disease (CHD) coexists, yet no risk tool has been developed for this population. We developed and temporally validated a prediction model for unplanned readmission based on routine data available at admission. Methods We conducted a single-center retrospective cohort study of adults with cancer and CHD discharged between January 2022 and June 2024 (438 admissions; 407 unique patients). The outcome was unplanned readmission (33 events, 7.5%). Thirtyfour candidate predictors with < 5% missing data were screened by univariable analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold × 5-repeat cross-validation (1-SE rule) and refitted in an unpenalized multivariable logistic model. Discrimination was assessed with the c-index and 1000-bootstrap optimism correction; calibration with the Hosmer-Lemeshow test, calibration slope, and intercept; and clinical utility with decision curve analysis. The model was applied without refitting to a temporally non-overlapping cohort discharged from October 2023 to June 2024 (n = 118; 6 events). Results The final model comprised serum sodium, diastolic blood pressure, hemoglobin, and digestive cancer (overall likelihood-ratio test χ2 = 16.55, P = 0.002). The apparent and optimism-corrected c-indices were 0.709 and 0.682 (95% confidence interval [CI] 0.599–0.766), and the temporal validation c-index was 0.841 (bootstrap 95% CI 0.643–0.985). Calibration was adequate in both cohorts (Hosmer-Lemeshow P = 0.932 and 0.606). At the Youden-optimal threshold (predicted probability 0.089), sensitivity and specificity were 0.61 and 0.75. The model outperformed the Padua score (c-index 0.538) and NRS2002 (0.502). Conclusion A four-variable model using routine admission data provides moderate discrimination with adequate calibration for unplanned readmission in patients with cancer and CHD and remained

Research Square
Army Medical University (CN)
Inflammatory Biomarkers in Disease Prognosis
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