Related Experiment Video
Updated: Nov 4, 2025
Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
Developing a Prognostic Model to Predict Mortality in Patients with Acute Bacterial Meningitis
Atiehsadat Mirkhani1, Arash Roshanpoor2, Omid Pournik3
1Biomedical Engineering faculty, Amirkabir University of Technology, Iran.
Abstract:
Bacterial meningitis is one of the harmful and deadly infectious diseases, and any delay in its treatment will lead to death. In this paper, a prognostic model was developed to predict the risk of death amongst probable cases of bacterial meningitis. Our prognostic model was developed using a decision tree algorithm on the national meningitis registry of the Iranian Center for Disease and Prevention (ICDCP) containing 3,923 records of meningitis suspected cases in 2018-2019. The most important features have been selected for the model construction. This model can predict the mortality risk for the meningitis probable cases with 78% accuracy, 84% sensitivity, and 73% specificity. The identified variables in prognosis the death included age and CSF protein level. CSF protein level (mg/dl) <= 65 versus > 65 provided the first branch of our decision tree. The highest mortality risk (85.8%) was seen in the patients >65 CSF protein level with 30 years < of age. For the patients <=30 year of age with CSF protein level >137 (mg/dl), the mortality risk was 60%. The prognostic factors identified in the present study draw the attention of clinicians to provide early specific measures, such as the admission of patients with a higher risk of death to intensive care units (ICU). It could also provide a helpful risk score tool in decision-making in the early phases of admission in pandemics, decrease mortality rate and improve public health operations efficiently in infectious diseases.
Insights
A new model predicts bacterial meningitis death risk using age and CSF protein levels. This tool aids clinicians in early interventions for high-risk patients, potentially reducing mortality.
Area of Science:
- Infectious Diseases
- Medical Informatics
- Public Health
Background:
- Bacterial meningitis is a severe, life-threatening infection where timely treatment is critical.
- Delayed treatment of bacterial meningitis significantly increases mortality risk.
- Predictive tools are needed to identify high-risk patients for prompt medical intervention.
Purpose of the Study:
- To develop and validate a prognostic model for predicting mortality risk in probable bacterial meningitis cases.
- To identify key clinical features associated with increased mortality in bacterial meningitis.
- To provide a decision-making tool for early management of bacterial meningitis patients.
Main Methods:
- A decision tree algorithm was employed to build the prognostic model.
- The model utilized data from 3,923 suspected bacterial meningitis cases from Iran's national registry (2018-2019).
- Key prognostic features, including age and cerebrospinal fluid (CSF) protein levels, were identified.
Main Results:
- The developed model achieved 78% accuracy, 84% sensitivity, and 73% specificity in predicting mortality.
- Cerebrospinal fluid (CSF) protein level and patient age were identified as significant prognostic factors.
- High mortality risk (85.8%) was observed in patients over 65 CSF protein level and under 30 years of age.
Conclusions:
- The prognostic model offers valuable insights for early risk stratification of bacterial meningitis patients.
- Identifying high-risk individuals allows for timely admission to intensive care units (ICUs).
- This tool can enhance public health operations during infectious disease outbreaks and reduce overall mortality.
More Related Videos
12:21A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness
Published on: September 28, 2022
04:05Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
Published on: June 30, 2023