# Construction and validation of a nomogram model to predict the poor prognosis in patients with pulmonary cryptococcosis

## TL;DR

This study builds a predictive model to identify patients with pulmonary cryptococcosis who are at risk of poor outcomes, using clinical and lab data to improve prognosis and treatment strategies.

## Contribution

A novel nomogram model is developed and validated for predicting poor prognosis in pulmonary cryptococcosis patients.

## Key findings

- Psychological symptoms, lab indicators, and treatment factors were identified as independent predictors of poor prognosis.
- The model achieved high accuracy with AUCs of 0.851 in the training set and 0.949 in the validation set.
- Calibration and decision curve analyses confirmed the model's strong predictive performance and clinical utility.

## Abstract

Patients with poor prognosis of pulmonary cryptococcosis (PC) are prone to other complications such as meningeal infection, recurrence or even death. Therefore, this study aims to analyze the influencing factors in the poor prognosis of patients with PC, so as to build a predictive nomograph model of poor prognosis of PC, and verify the predictive performance of the model.

This retrospective study included 410 patients (78.1%) with improved prognosis of PC and 115 patients (21.9%) with poor prognosis of PC. The 525 patients with PC were randomly divided into the training set and validation set according to the ratio of 7:3. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to screen the demographic information, including clinical characteristics, laboratory test indicators, comorbidity and treatment methods of patients, and other independent factors that affect the prognosis of PC. These factors were included in the multivariable logistic regression model to build a predictive nomograph. The receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to verify the accuracy and application value of the model.

It was finally confirmed that psychological symptoms, cytotoxic drugs, white blood cell count, hematocrit, platelet count, CRP, PCT, albumin, and CD4/CD8 were independent predictors of poor prognosis of PC patients. The area under the curve (AUC) of the predictive model for poor prognosis in the training set and validation set were 0.851 (95% CI: 0.818-0.881) and 0.949, respectively. At the same time, calibration curve and DCA results confirmed the excellent performance of the nomogram in predicting poor prognosis of PC.

The nomograph model for predicting the poor prognosis of PC constructed in this study has good prediction ability, which is helpful for improving the prognosis of PC and further optimizing the clinical management strategy.