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  11. Construction And Validation Of A Nomogram Model To Predict The Poor Prognosis In Patients With Pulmonary Cryptococcosis

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

Xiaoli Tan1, Yingqing Zhang1, Jianying Zhou2

  • 1Department of Respiratory, The Affiliated Hospital of Jiaxing University, Jiaxing, China.

Peerj
|March 15, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study identifies key factors predicting poor outcomes in pulmonary cryptococcosis (PC). A predictive model was developed to aid in clinical management and improve patient prognosis.

Area of Science:

  • Infectious Diseases
  • Medical Informatics
  • Clinical Prediction Models

Background:

  • Pulmonary cryptococcosis (PC) poses significant risks, including meningeal infection, recurrence, and mortality.
  • Identifying patients with poor prognosis is crucial for timely intervention and improved outcomes.

Purpose of the Study:

  • To analyze factors influencing poor prognosis in PC patients.
  • To develop and validate a predictive nomogram model for PC prognosis.

Main Methods:

  • Retrospective analysis of 525 PC patients, divided into training (70%) and validation (30%) sets.
  • Utilized Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection.
  • Multivariable logistic regression and nomogram construction for prediction.
Keywords:
NomogramPrediction modelPrognosisPrognostic factor

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Main Results:

  • Psychological symptoms, cytotoxic drugs, WBC, hematocrit, platelets, CRP, PCT, albumin, and CD4/CD8 ratio identified as independent predictors.
  • The nomogram achieved an AUC of 0.851 (training) and 0.949 (validation).
  • Calibration curves and decision curve analysis confirmed the model's accuracy and clinical utility.

Conclusions:

  • The developed nomogram demonstrates strong predictive performance for poor PC prognosis.
  • This model can assist in optimizing clinical management strategies for PC patients.
  • Aiding clinicians in identifying at-risk patients for improved therapeutic approaches.
Pulmonary cryptococcosis