Pulmonary cryptococcosis: A rare but emerging disease

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

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Pulmonary cryptococcosis: A review of pathobiology and clinical aspects

Pulmonary cryptococcosis is an important opportunistic invasive mycosis in immunocompromised patients, but it is also increasingly seen in immunocompetent patients. The main human pathogens are Cryptococcus neoformans and C. gattii, which have a worldwide distribution. In contrast to cryptococcal meningitis, pulmonary cryptococcosis is still underdiagnosed because of limitations in diagnostic tools. It can mimic lung cancer, pulmonary tuberculosis, bacterial pneumonia, and other pulmonary mycoses both clinically and radiologically. Pulmonary nodules are the most common radiological feature, but these are not specific to pulmonary cryptococcosis. The sensitivity of culture of respiratory samples for Cryptococcus is poor and a positive result may also reflect colonisation. Cryptococcal antigen (CrAg) with lateral flow device is a fast and sensitive test and widely used on serum and cerebrospinal fluid, but sera from patients with pulmonary cryptococcosis are rarely positive in the absence of disseminated disease. Detection of CrAg from respiratory specimens might assist the diagnosis of pulmonary cryptococcosis but there are very few data. Molecular detection techniques such as multiplex reverse transcription polymerase chain reaction (RT-PCR) could also provide better sensitivity but these still require validation for respiratory specimens. The first line of treatment for pulmonary cryptococcosis is fluconazole, or amphotericin B and flucytosine for those with central nervous system involvement. Pulmonary cryptococcosis worsens the prognosis of cryptococcal meningitis. In this review, we summarize the biological aspects of Cryptococcus and provide an update on the diagnosis and management of pulmonary cryptococcosis.