Key result
Pulmonary infections linked to ~280% higher 30-day readmission risk in elderly HF and T2D patients.
Why the study?
Elderly T2DM patients complicated with HF have a high rate of 30-day readmission, and predictive models to identify high-risk patients are needed.
Does a risk prediction model incorporating pulmonary infections, anti-hypertensive drug use, and NPAR accurately predict 30-day readmission in elderly patients with T2DM and HF?
Cohort (n=1,899)
Yes
Does a risk prediction model incorporating pulmonary infections, anti-hypertensive drug use, and NPAR accurately predict 30-day readmission in elderly patients with T2DM and HF?
Odds Ratio: 3.816 (95% CI 2.377–6.128)
p-value: p=<0.001
A novel risk prediction model using three easily obtainable clinical variables (pulmonary infections, anti-hypertensive drug use, and NPAR) can effectively identify elderly patients with T2DM and HF at high risk for 30-day readmission.
May support infection prevention in elderly T2DM-HF patients; leaves open whether targeting pulmonary infections reduces readmissions.
Background Elderly type 2 diabetes mellitus (T2DM) patients complicated with heart failure (HF) exhibit a high rate of 30-day readmission. Predictive models have been suggested as tools for identifying high-risk patients. Thus, we aimed to develop and validate a predictive model using multicenter electronic medical records (EMRs) data to estimate the risk of 30-day readmission in elderly T2DM patients complicated with HF. Methods EMRs data of elderly T2DM patients complicated with HF from five tertiary hospitals, spanning 2012 to 2023, were utilized to develop and validate the 30-day readmission model. The model were evaluated using holdout data with the area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). Results A total of 1899 patients were included, with 955, 409, and 535 in the derivation, internal validation, and external validation cohorts, respectively. Pulmonary infections (odds ratio [OR]: 3.816, 95% confidence interval [CI]: 2.377-6.128, P < 0.001), anti-hypertensive drug use (OR: 5.536, 95% CI: 1.658-18.486, P = 0.005), and neutrophil percentage-to-albumin ratio (NPAR) (OR: 1.144, 95% CI: 1.093-1.197, P < 0.001) were independent predictors of 30-day readmission risk. AUROC in the derivation, internal validation, and external validation cohorts were 0.782 (95% CI: 0.737-0.826), 0.746 (95% CI: 0.654-0.838), and 0.753 (95% CI: 0.684-0.813), respectively. The calibration curve, DCA results, and CIC results indicated that the model also possessed good predictive power. Additionally, an operation interface on a web page ( https://cqykdxtjt.shinyapps.io/readmission/ ) was created for clinical practitioners to apply. Conclusion A 30-day readmission risk prediction model was developed and externally validated. This model facilitates the targeting of interventions for elderly T2DM patients complicated with HF who are at high risk of an early readmission.
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He et al. (2025) conducted a cohort in Type 2 diabetes mellitus complicated with heart failure (n=1,899). Pulmonary infections vs. No pulmonary infections was evaluated on 30-day readmission (OR 3.816, 95% CI 2.377-6.128, p=<0.001). Pulmonary infections were identified as a strong independent predictor of 30-day readmission in elderly patients with type 2 diabetes and heart failure (OR 3.816).
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