Analysis and Validation of a Diagnostic Nomogram for Predicting the Risk of Acute Respiratory Failure for Non-HIV Related Pneumocystis Jirovecii Pneumonia Patients.

OBJECTIVE

Pneumocystis Pneumonia (PCP), primarily affecting individuals with weakened immune systems, is a severe respiratory infection caused by pneumocystis jirovecii and can lead to acute respiratory failure (ARF). In this article, we explore the risk factors of ARF and propose a prognostic model of ARF for PCP patients.

METHODS

In this multi-center, retrospective study in 6 secondary or tertiary academic hospitals in China, 120 PCP patients were screened from the Dryad database for the development of a predictive model. A total of 49 patients from Peking University People's Hospital were collected for external validation. Crucial clinical features of these patients are selected applying univariate and multivariate logistic regression analysis. We established an intuitive nomogram. Receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) were plotted to evaluate the model's performance.

RESULTS

A cohort of 120 patients formed the training cohort for the development of the model, with 49 patients constituting the test cohort. Univariate and multivariate logistic regression analysis identified five risk factors associated with ARF, which are age, fever, dyspnea, high neutrophil count and use of antibiotics. A nomogram was then proposed based on these factors. The area under the ROC curve (AUROC) in the development group has reached 0.8576, while the validation group has an AUROC of 0.7372, indicating commendable ability for predicting ARF. In addition, results for Hosmer-Lemeshow test indicate the effectiveness of our model. Furthermore, DCA and CIC curves demonstrate excellent clinical benefit.

CONCLUSION

We present a nomogram for predicting ARF in non-HIV related PCP patients. The prognostic model may provide references in clinical medicine, promote timely treatment and improve therapeutic outcomes of PCP patients.