Analysis of factors associated with severe disease and construction of a monogram for elderly patients with community-acquired pneumonia complicated by heart failure.

Authors

Yanli Sun, Simiao Yu, Qianli Zhan, Jianlong Li

Published in

Pakistan journal of medical sciences. Volume 42. Issue 8. Pages 1999-2005.

Abstract

To identify independent influencing factors of severe disease among old community-acquired pneumonia(CAP) patients complicated by heart failure(HF), and to establish the nomogram model for identifying severe illness early in clinical settings.
This was a retrospective study. Altogether 140 elderly patients with concurrent CAP and HF admitted into Baoding No.1 Central Hospital between January 2022 to November 2025. According to the CURB-65 (confusion, blood urea nitrogen, respiratory rate, systolic blood pressure, age ≥65 years) score, patients were classified as the non-high-risk(n =100) or high-risk(n =40) group. Later, demographic and baseline data, infection and inflammatory markers, cardiac and other organ function parameters, coagulation parameters, systemic parameters, and clinical manifestations were compared between these two groups.
After preliminary screening through univariate analysis, multivariate logistic regression identified procalcitonin(PCT), B-type natriuretic peptide(BNP), and impaired consciousness as independent predictors of severe disease(P< 0.05, respectively). These significant predictors were incorporated into a nomogram model to estimate individual risk. The ROC curve analysis showed that the combined model yielded an area under the curve of 0.891(95% confidence interval: 0.818-0.963). Moreover, our calibration curve closely aligned with the ideal curve, indicating that our predicted risk was highly consistent with the actual risk. As for clinical utility, the DCA revealed substantial net clinical benefits of our constructed model among a broad threshold probability spectrum(0.05-1.0).
The nomogram model incorporating PCT, BNP, and impaired consciousness demonstrates greater predictive power than those using any single parameter alone. With good calibration and favorable clinical utility.

PMID:
42668929
Bibliographic data and abstract were imported from PubMed on 30 Aug 2026.

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