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. 2024 Jan 5;103(1):e36840.
doi: 10.1097/MD.0000000000036840.

Construction and validation of nomogram prediction model for risk of acute heart failure in patients with acute exacerbation of chronic obstructive pulmonary disease

Li-Na Yan  1 , Min Chen  2 , Hui Wei  3 , Hao-Ran Ma  4
Affiliations

Construction and validation of nomogram prediction model for risk of acute heart failure in patients with acute exacerbation of chronic obstructive pulmonary disease

Li-Na Yan et al. Medicine (Baltimore). .

Abstract

To investigate the influencing factors of in-hospital acute heart failure (AHF) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and to construct and validate a risk prediction nomogram model. Three Hundred Thirty patients with AECOPD admitted to our hospital from June 2020 to June 2023 were retrospectively analyzed as a training set for the construction of the model. Three Hundred Twenty-five AECOPD patients admitted to the Second People's Hospital of Hefei from 2006 to June 2023 were also collected as the validation set for the validation of the model. A nomogram model was constructed to predict the risk of nosocomial AHF in patients with AECOPD, and C-index and receiver operating characteristic curve were drawn to assess the predictive predictive efficacy of the model. Model fit was evaluated by Hosmer-Lemeshow test, calibration curve was drawn to evaluate the calibration of the model; decision curve was drawn to analyze the net benefit rate of this nomogram model. Multivariate logistic regression analysis indicated that body mass index, mmRC grade, neutrophils, lymphocytes, hemoglobin, creatinine, PO2, PCO2, and Homocysteine were independent risk factors for in-hospital AHF in patients with AECOPD. To construct a nomogram model for risk prediction of in-hospital AHF in patients with AECOPD. The C-index of the training set was 0.949 (95% CI: 0.91-0.961); the C-index of the validation set was 0.936 (95% CI: 0.911-0.961) suggesting good model discrimination. The receiver operating characteristic curve calculated area under curve for the training set was 0.949 (95% CI: 0.928-0.97); area under curve for the validation set was 0.936 (95% CI: 0.91-0.961) suggesting good model accuracy. The results of Hosmer-Lemeshoe goodness-of-fit test and calibration curve analysis showed that the calibration curve of this nomogram model was close to the ideal curve. The clinical decision curve also showed good clinical net benefit of the nomogram model. Body mass index, mmRC grade, neutrophils, lymphocytes, hemoglobin, creatinine, PO2, PCO2, and Homocysteine are risk factors for in-hospital AHF in AECOPD patients, and nomogram models constructed based on the above factors have some predictive value for in-hospital AHF in AECOPD patients. It is also vital for nursing staff to strengthen nursing care.

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Conflict of interest statement

The authors have no funding and conflicts of interest to disclose.

Figures

Figure 1.
Figure 1.
A nomogram of acute in-hospital heart failure in patients with AECOPD. AECOPD = acute exacerbation of chronic obstructive pulmonary disease.
Figure 2.
Figure 2.
ROC curve was used to assess the predictive power of the model to predict the risk of in-hospital AHF in patients with AECOPD. AECOPD = acute exacerbation of chronic obstructive pulmonary disease, AHF = acute heart failure, ROC = receiver operating characteristic.
Figure 3.
Figure 3.
Calibration curve; (A) training set calibration curve.
Figure 4.
Figure 4.
Clinical decision curve analysis for nomogram models.
Figure 5.
Figure 5.
Clinical impact curve analysis for nomogram models.
See this image and copyright information in PMC

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References

    1. Brennan M, McDonnell MJ, Walsh SM, et al. . Review of the prevalence, pathogenesis and management of OSA-COPD overlap. Sleep Breath. 2022;26:1551–60. - PubMed
    1. Feng Z, Zhang L, Yu H, et al. . High-flow nasal cannula oxygen therapy versus non-invasive ventilation for AECOPD patients after extubation: a systematic review and meta-analysis of randomized controlled trials. Int J Chron Obstruct Pulmon Dis. 2022;17:1987–99. - PMC - PubMed
    1. Alparslan Bekir S, Tuncay E, Gungor S, et al. . Can red blood cell distribution width (RDW) level predict the severity of acute exacerbation of chronic obstructive pulmonary disease (AECOPD)? Int J Clin Pract. 2021;75:e14730. - PubMed
    1. Vogelmeier CF, Criner GJ, Martinez FJ, et al. . Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report GOLD executive summary. Am J Respir Crit Care Med. 2017;195:557–82. - PubMed
    1. Yao C, Liu X, Tang Z. Prognostic role of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio for hospital mortality in patients with AECOPD. Int J Chron Obstruct Pulmon Dis. 2017;12:2285–90. - PMC - PubMed