Clinical Prediction Models for Pediatric In-Hospital Death Risk in Congolese Severe Malaria Children Using Machine Learning Based-Algorithms

Background:

Severe malaria has associated with a high risk of paediatric hospital mortality in resource-constrained countries, which remains deplorable. Improved methods of risk-stratification can assist in referral decision making and resource allocation. Investigators aim to i) create prediction model for in-hospital mortality risk among children presenting with severe malaria and compare its predictive performance to the current models, ii) validate the latter, and iii) assess the plasmodium-induced changes in clinical and biological parameters.

Methods:

This is a retrospective study of data collected prospectively during a period from January 30, 2017 to August 01, 2025, from children with severe malaria, admitted to the PICU of the Monkole Hospital Center (MHC) and the Kimbondo Pediatric Center (KPC), all in Kinshasa, DR. Congo. Baseline clinical and laboratory variables were collected on enrolled children. The primary outcome is death up to 1 week post-admission, and the second outcome, the length of stay in pediatric intensive care following admission for severe malaria. Machine learning algorithms will be employed to accomplish the three specific research objectives.

Expected Results:

In line with research objectives, the following results are expected:

• The prevalence of Multiple Organ Dysfunction Syndrome (MODS) and metabolic acidosis in children presenting with severe malaria will be determined.
• A novel model for predicting associated mortality risk of severe malaria will be developed:

• This novel model will be based on predictors of disease severity and will measure:

• The degree of severity of MODS and metabolic acidosis.
• The length of stay for severe malaria in the PICU
• The risk of death following hospitalization for severe malaria
• The influence of parasitemia on disease severity
• The performance of the proposed novel model will be measured
• The predictive nomogram and scoring system will be associated with it.
• Investigators validate and compare the performance of existing models for predicting severe malaria severity against the proposed novel model.

Together, research data will provide proof of principle supporting early interventions and treatment choices in children presenting with severe malaria.

I) Inclusion Criteria:

-Admission to the pediatric intensive care unit (PICU) for severe malaria, as defined by the World Health Organization (WHO) criteria.

Definitions:

• Severe malaria was defined by the presence of at least one major clinical manifestation, including:

• Coma
• Repeated seizures (≥ 2 episodes within 24 hours)
• Neurological disorders
• Respiratory distress
• Liver failure
• Dark ("Coca-Cola") urine
• Jaundice
• Renal failure (anuria)
• Severe anaemia (Hb ≤ 5 g/dL)
• Bleeding abnormalities
• Circulatory collapse or systolic blood pressure < 50 mmHg
• Data collection periods varied by health zone and clinical unit. However, within each zone, all consecutively admitted patients during the study period were included.

II) Exclusion Criteria:

• Another medical condition (non-parasitic infection or other) capable of causing anemia or similar abnormalities.
• Comorbidities that could interfere with the clinical presentation or outcomes of severe malaria.