A Diagnostic Nomogram for Predicting Hypercapnic Respiratory Failure in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Abstract

Purpose

To develop and validate a nomogram for assessing the risk of developing hypercapnic respiratory failure (HRF) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).

Patients and Methods

From January 2019 to August 2023, a total of 334 AECOPD patients were enrolled in this research. We employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression to determine independent predictors and develop a nomogram. This nomogram was appraised by the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer–Lemeshow goodness-of-fit test (HL test), decision curve analysis (DCA), and clinical impact curve (CIC). The enhanced bootstrap method was used for internal validation.

Results

Sex, prognostic nutritional index (PNI), hematocrit (HCT), and activities of daily living (ADL) were independent predictors of HRF in AECOPD patients. The developed nomogram based on the above predictors showed good performance. The AUCs for the training, internal, and external validation cohorts were 0.841, 0.884, and 0.852, respectively. The calibration curves and HL test showed excellent concordance. The DCA and CIC showed excellent clinical usefulness. Finally, a dynamic nomogram was developed (https://a18895635453.shinyapps.io/dynnomapp/).

Conclusion

This nomogram based on sex, PNI, HCT, and ADL demonstrated high accuracy and clinical value in predicting HRF. It is a less expensive and more accessible approach to assess the risk of developing HRF in AECOPD patients, which is more suitable for primary hospitals, especially in developing countries with high COPD-related morbidity and mortality.

Keywords:

• acute exacerbation of chronic obstructive pulmonary disease
• hypercapnic respiratory failure
• nomogram
• prediction model

Abbreviations

COPD, chronic obstructive pulmonary disease; AECOPD, acute exacerbation of chronic obstructive pulmonary disease; HRF, hypercapnic respiratory failure; OSAHA, obstructive sleep apnea hypopnea syndrome; PaO₂, partial pressure of oxygen in arterial blood; PaCO₂, partial pressure of carbon dioxide in arterial blood; LASSO, Least Absolute Shrinkage and Selection Operator; AUC, area under the receiver operating characteristics curve; DCA, decision curve analysis; CIC, clinical impact curve; SI, sarcopenia index; VET, venous thromboembolism; Va-Q, ventilation-perfusion ratio; BMI, body mass index; WBC, white blood cell count; NEU, neutrophil count; LYM, lymphocyte count; E, eosinophil; PLT, platelet count; HCT, hematocrit; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; ALB, albumin; BUN, blood urea nitrogen; PNI, prognostic nutritional indicators; ADL, activity of daily living; OR, Odds Ratio; CI, Confidence Interval; PPV, positive predictive value; NPV, positive predictive value; ICU, intensive care unit.

Data Sharing Statement

The de-characterized data included in this study can be obtained from the corresponding author upon reasonable request.

Ethics Approval and Informed Consent

Ethical approval was gained from The Second People’s Hospital of Hefei (No. 2023-keyan-111) and conformed to the Declaration of Helsinki.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

There is no funding to report.