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Derivation and validation of clinical prediction models for Cryptosporidium-attributed acute diarrhea in African
Léna Mazza1, Ben J Brintz2, M Jahangir Hossain3
1Division of Infectious Diseases, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
Insights
New clinical prediction models can identify Cryptosporidium-attributed diarrhea in young children. Incorporating weather data, like rainfall and temperature, improves accuracy for these parasitic infections in low- and middle-income countries (LMICs).
Area of Science:
- Infectious Diseases
- Epidemiology
- Clinical Prediction Modeling
Background:
- Clinical decision-support tools are needed for parasitic infections like Cryptosporidium, a major cause of childhood illness in LMICs.
- Existing tools for identifying Cryptosporidium-attributed acute diarrhea in children are lacking.
- This study addresses the need for diagnostic and screening tools for Cryptosporidium in vulnerable populations.
Purpose of the Study:
- To develop and validate clinical prediction models for identifying Cryptosporidium-attributed acute diarrhea in young children.
- To assess the utility of demographic and environmental factors in predicting Cryptosporidium infections.
- To provide a foundation for improved diagnostic and public health strategies for diarrheal diseases.
Main Methods:
- Clinical and demographic data from the Global Enteric Multicenter Study (GEMS) were used for model derivation.
- Site-specific weather data from NOAA were integrated with clinical data.
- Random forest (RF) and logistic regression (LR) models were trained and validated using cross-validation and external datasets (VIDA).
Main Results:
- Prediction models achieved high accuracy, with an Area Under the Curve (AUC) of 0.77 for RF and 0.75 for LR using top predictors.
- Key predictors identified included mean rainfall, mean temperature in the preceding 30 days, and patient age.
- External validation showed LR models offered better calibration and net benefit on the VIDA dataset.
Conclusions:
- Clinical prediction models for Cryptosporidium infections in children with acute diarrhea were successfully derived and validated.
- Incorporating weather variables significantly enhances the predictive value for diarrheal diseases.
- These models show potential for real-time decision support, especially considering climate change impacts.
Background:
Clinical decision-support tools have the potential to guide empiric treatment and prioritize diagnostic testing at the individual-level, and screening strategies for vaccine and therapeutic trials at the population-level. Such tools for the parasitic pathogen Cryptosporidium, a leading cause of morbidity and mortality among children in low- and middle-income countries (LMICs), are lacking. We aimed to develop clinical prediction models to identify Cryptosporidium-attributed acute diarrhea in young children.
Methods:
For model derivation, we used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) and integrated site-specific weather data from the NOAA database. Random forest (RF) and logistic regression (LR) models were trained and evaluated using 5-fold cross-validation.
Results:
Using only the top three predictors, prediction models achieved a mean area under the curve (AUC) of 0.77 for RF and 0.75 for LR models. RF classification identified mean rainfall and mean temperature in the 30 days preceding enrollment, along with patient age, as the most important predictors of Cryptosporidium-attributed diarrhea. For external validation, models were trained on the full GEMS dataset and tested on the Vaccine Impact on Diarrhea in Africa (VIDA) dataset, with the LR models having better calibration and net benefit.
Conclusion:
In conclusion, we derived and externally validated clinical prediction models for identifying Cryptosporidium infections among LMIC children with acute diarrhea. Our findings highlight the value of incorporating weather variables into clinical prediction models for diarrheal diseases, and their potential use for real-time decision support in the context of climate change and extreme weather events.
