Mortality risk prediction model in AIDS patients with pneumocystis pneumonia in China

Pneumocystis pneumonia (PCP) is a common and serious complication of HIV/AIDS, with a higher prevalence in patients not receiving antiretroviral therapy. Due to the high mortality rate of PCP, accurate prediction of its case fatality rate is very important for clinical treatment. We aimed to develop a risk model for the near-term prognosis of people with HIV/AIDS and PCP and verify its effectiveness.

This single-center, retrospective observational study was conducted at Beijing Youan Hospital from January 2012 to October 2022. 972 AIDS patients with Pneumocystis pneumonia met our criteria were recruited. The patients were divided into death group and survival group according to clinical outcome during hospitalization. Data of the two groups were collected including general information and laboratory test results. 53 medical characteristics of the two groups were collected. Prediction variables were screened with Multivariate logistic regression analysis and Lasso regression model. We used ROC curve to identify the discrimination of training and testing data sets. The Shapley Additive exPlanation (SHAP) method was applied to explain the final model and the weights of features.

The overall mortality rate among hospitalized patients was 17.8%. We found that the best prediction effect can be obtained when ALB, PO₂, TBIL, LDH, CD4⁺ T lymphocyte counts are incorporated into the PCP risk prediction model. The model had a perfect discrimination with AUC of 0.994 and 0.947 in training and validation cohorts. The prognosis risk grade was divided into three grades: low-risk group (0-25 points with mortality of 5.9%), moderate-risk group (25-50 points with mortality of 45.1%) and high-risk group (above 50 points with mortality of 80%). There is a statistically significant difference in mortality among these three grades (χ² = 419.271, P<0.001).

We developed and validated a model of the prognostic risk level of PCP in patients of AIDS with the results of blood tests reviewed by patients at routine visits. The model is more convenient to use, allowing clinicians to obtain a determined probability value of PCP mortality with simple calculations within the first 72 hours of the patient's admission.