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
