Evidence map›Paper›PMID 41084626›Full record

ArticlePeerJ2025

Development and external validation of a multivariate model for predicting pneumonia in patients receiving maintenance hemodialysis: a retrospective study.

Xiao Hua Yang, Ju Zhang, Xi Sheng Xie, Wen Wu Tang

Abstract readValidation Study
In one paragraph

Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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4 authors.

Xiao Hua YangDepartment of Nephrology, Guangyuan Central Hospital, Guangyuan City, China.
Ju ZhangDepartment of Nephrology, Guangyuan Central Hospital, Guangyuan City, China.
Xi Sheng XieDepartment of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong City, China.
Wen Wu TangDepartment of Nephrology, Guangyuan Central Hospital, Guangyuan City, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patients receiving maintenance hemodialysis (MHD) who develop pneumonia experience substantially elevated risks of hospitalization and mortality, while also incurring significantly heightened healthcare-related financial burdens. Our goal is to establish a forecasting model to assess the individual risk of pneumonia in patients undergoing MHD. Materials and Methods: A retrospective analysis was carried out between January 2018 and November 2024, involving 405 MHD patients from two medical centers. The variables underwent adjustment through multivariate Cox regression analysis, and the forecasting model was created and verified. Results: The median follow-up time of the external validation set was 35 months (interquartile range: 20-43), and the median follow-up time of the modeling set was 22 months (12-24). The event happened in 101 (34.83%) out of 290 patients in the modeling set and 45 (39.13%) out of 115 patients in the external validation set. The model predictors included history of diabetes and coronary heart disease; serous effusion; white blood cell; albumin-globulin ratio; left ventricular mass index, and age. The C-index was 0.753 (0.684, 0.822) for the external validation set and 0.772 (95% CI [0.724-0.821]) for the modeling set. The model showed excellent calibration ability throughout the risk spectrum, and decision curve analysis showed that it could maximize the prognosis of patients. Conclusion: The created predictive model provided a precise, individualized evaluation of pneumonia risk in patients with MHD. It can be used to identify individuals at high risk of pulmonary infection in patients undergoing MHD and guide their treatment and prognosis follow-up.

Indexed as

PneumoniaRenal DialysisAgedFemaleHumansMaleMiddle AgedMultivariate AnalysisRetrospective StudiesRisk AssessmentRisk FactorsExternal validationPatients on MHDPneumoniaPredictive modelRetrospective study

Identifiers

PMID41084626
PMCPMC12515429

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