Prognostic models for the clinical management of malaria and its complications: a systematic review

Objective Malaria infection could result in severe

disease with high mortality. Prognostic models and scores

predicting severity of infection, complications and mortality

could help clinicians prioritise patients. We conducted a

systematic review to assess the various models that have

been produced to predict disease severity and mortality in

patients infected with malaria.

Design A systematic review.

Data sources Medline, Global health and CINAHL were

searched up to 4 September 2019.

Eligibility criteria for selecting studies Published

articles on models which used at least two points (or

variables) of patient data to predict disease severity;

potential development of complications (including coma

or cerebral malaria; shock; acidosis; severe anaemia;

acute kidney injury; hypoglycaemia; respiratory failure

and sepsis) and mortality in patients with malaria

infection.

Data extraction and synthesis Two independent

reviewers extracted the data and assessed risk of bias

using the Prediction model Risk Of Bias Assessment Tool.

Results A total of 564 articles were screened and 24

articles were retained which described 27 models/

scores of interests. Two of the articles described models

predicting complications of malaria (severe anaemia in

children and development of sepsis); 15 articles described

original models predicting mortality in severe malaria; 3

articles described models predicting mortality in different

contexts but adapted and validated to predict mortality

in malaria; and 4 articles described models predicting

severity of the disease. For the models predicting mortality,

all the models had neurological dysfunction as a predictor;

in children, half of the models contained hypoglycaemia

and respiratory failure as a predictor meanwhile, six out

of the nine models in adults had respiratory failure as a

clinical predictor. Acidosis, renal failure and shock were

also common predictors of mortality. Eighteen of the

articles described models that could be applicable in reallife settings and all the articles had a high risk of bias due

to lack of use of consistent and up-to-date methods of

internal validation.

Conclusion Evidence is lacking on the generalisability

of most of these models due lack of external validation.

Emphasis should be placed on external validation of

existing models and publication of the findings of their

use in clinical settings to guide clinicians on management

options depending on the priorities of their patients.