Pediatric In-Hospital Mortality Risk in Congolese Children With Severe Malaria: Derivation of Clinical Prediction Models Using Machine Learning Based-Algorithms

The goal of this observational study is to optimize the management of severe childhood malaria, based on understanding and controlling the severity factors of the disease in Congolese children aged 2 to 9 years (the age group at risk of developing various severe forms of malaria), admitted to the paediatric intensive care units (PICU).

The main question it aims to answer is whether the scores or models used to predict the severity of severe malaria and the associated risk of mortality accurate enough to warrant early interventions, including treatments, on their own?

Thus, investigators aim to fill three knowledge gaps associated with the following hypotheses:

Hypothesis-1: Children with severe malaria show signs of disease severity based on their severity scores on admission. Higher severity scores on admission are associated with a higher risk of mortality.

Hypothesis-2: Validation of the predictive power and transferability of severe malaria severity scores to additional independent populations is needed to support their clinical utility.

Hypothesis-3: The severity of the clinical and biological changes induced by plasmodium depends not only on the ability of the parasite to invade and grow in the host organism, but also and above all on the number of parasites present in the host (parasitemia).

For any child admitted to the PICU and meeting the inclusion criteria, as part of clinical care, investigators proceeded before any treatment:

1. An arterial blood sample of 3 ml by puncture of the radial artery for instant arterial blood gaz as well as for venous biochemistry, including albumin, phosphate, chlorine, magnesium, urea, creatinine and total bilirubin dosages, and,
2. A one-drop finger pulp blood test for parasitemia measurement and the rapid diagnosis test for plasmodium falciparum.

Then, the diagnostic parameters of acid-base disorders will be calculated, including AG (anion gap), AGCAP (AG corrected for albumin and phosphate plasmatic concentrations), SIG (Strong ion gap), SBE (Standard base excess) and SBDCAP (Standard base deficit corrected for albumin and phosphate plasmatic concentrations).