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

1 
 
© Ken Bisabu Kelu et al., 2017 
 
                        January 25, 2017 
Clinical Prediction Models for Pediatric In-Hospital Death Risk in Congolese Severe malaria 
Children Using Machine Learning Based-Algorithms 
 
Clinical Prediction Models for Pediatric In-Hospital Death Risk in Congolese Severe 
malaria Children Using Machine Learning Based-Algorithms 
 
I. FOCUS OF THE MANUSCRIPT 
 
Severe malaria (SM) is associated with a high risk of in-hospital death in resource-
constrained countries. Improved risk-stratification methods can assist in referral 
decision-making and resource allocation. Investigators aim to 1) develop a prediction 
model for in-hospital death risk among children with severe malaria, compare its 
predictive performance to the current models, 2) validate previous models in our severe 
malaria population, and 3) assess the plasmodium-induced changes in clinical and 
biological parameters. 
 
II. STUDY DESIGN 
 
• Type of study: Retrospective study of data collected prospectively during a 
period from January 30, 2017 to August 01, 2025. 
• Study population: Children with severe malaria, admitted to the paediatric 
intensive care unit (PICU) of the Monkole Hospital Center (MHC) and the 
Kimbondo Pediatric Center (KPC), all in Kinshasa, DR.  
• Inclusion criteria: Severe malaria children aged 2-9 years 
 
III. STATISTICAL ANALYSIS PLAN  
 
III.1. Descriptive statistics of the study population (Overall, Survivor and deceased 
children): 
 
Socio-demographic and anthropometric Data: 
• Age 
• Sex 
• Geographical origin (district) 

2 
 
© Ken Bisabu Kelu et al., 2017 
 
                        January 25, 2017 
Clinical Prediction Models for Pediatric In-Hospital Death Risk in Congolese Severe malaria 
Children Using Machine Learning Based-Algorithms 
 
 
Data from Clinical Examination: 
• Febrile episode 
• Temperature 
• Coma 
• Blantyre coma scale  
• Convulsions 
• Pallor 
• Jaundice 
• Heart rate 
• Respiratory rate 
• Ketoacidosis respiration 
• Hepatomegaly 
• Splenomegaly 
• Length of stay at PICU 
 
Final Diagnoses and Antimalarial treatment 
• Cerebral malaria 
• Severe malaria Anaemia 
• Severe respiratory malaria 
• Antimarial treatment using Artesunate 
• Antimarial treatment using Quinine 
 
Laboratory Assessments 
• PaCO2 
• PaO2 
• pH 
• SBE 
• Bicarbonate 
• L-Lactate 
• Glucose 
• Potassium 
• Sodium 
• Calcium 
• Magnesium 
• Chlore 
• Phosphore 
• Blood urea nitrogen 

3 
 
© Ken Bisabu Kelu et al., 2017 
 
                        January 25, 2017 
Clinical Prediction Models for Pediatric In-Hospital Death Risk in Congolese Severe malaria 
Children Using Machine Learning Based-Algorithms 
 
• Creatinine 
• Albumin 
• Phosphate 
• Hematocrit 
• Total Bilirubin 
• Parasitemia 
 
III.2. Approaches by objectives  
 
Objective 1. To develop a prediction model for in-hospital death risk among children 
with severe malaria   
• Study population and inclusion criteria: Severe malaria children aged 2-9 years  
• Outcome (dichotomous): Death during hospitalization (Survivors = 0, Deceased = 
1). The end of hospitalization is defined as death (patients who died) or recovery 
(patients who survived), assessed up to 1-week post-admission. 
• Predictors: Data from socio-demographic, anthropometric, clinical examination 
and laboratory Assessments. 
 
Objective 2. To validate previous models in our severe malaria population 
• Study population and inclusion criteria: Severe malaria children aged 2-9 years 
• Primary Outcome: Death during hospitalization (Survivors = 0, Deceased = 1) 
• Secondary Outcome: Time-to-risk event (risk event = death = 1 and censored 
event = Survival = 0). The end of hospitalization is defined as death (patients who 
died) or recovery (patients who survived), assessed up to 1-week post-
admission. 
• Predictors: Data from socio-demographic, anthropometric, clinical examination 
and laboratory Assessments. 
 
Objective 3. To assess the plasmodium-induced changes in clinical and biological 
parameters. 
• Study population and inclusion criteria: Severe malaria children aged 2-9 years  
• Primary Outcome: Death during hospitalization (Survivors = 0, Deceased = 1) 
• Secondary Outcome: Multiple organ dysfunction (MOD ≥ 2 organ systems fail)  
• Predictor: Parasitemia