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

Abstract

ObjectiveMalaria 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.DesignA systematic review.Data sourcesMedline, Global health and CINAHL were searched up to 4 September 2019.Eligibility criteria for selecting studiesPublished 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 synthesisTwo independent reviewers extracted the data and assessed risk of bias using the Prediction model Risk Of Bias Assessment Tool.ResultsA 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 real-life 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.ConclusionEvidence 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.PROSPERO registration numberCRD42019130673.

Factors Associated with Prolonged Hospital Length of Stay in Adults with Imported Falciparum Malaria—An Observational Study from a Tertiary Care University Hospital in Berlin, Germany

“…All nine models available so far use neurological dysfunction as a predictor, six use respiratory failure, and five use metabolic acidosis. Renal failure and shock are also common variables in these prognostic models [ 36 ].…”

The healthier healthcare management models for COVID-19

“…Development of effective public health management policy with best medical countermeasures (e.g., vaccines, diagnostics, therapeutics) is helpful for the control of disease incidence. Models predicting severity and mortality of malaria infection lacking on the generalizability as most of these model’s due lack of external validation [ 16 ]. Overall, malaria and COVID-19 disease research together can give hope for better model of control [ [14] , [15] , [16] ].…”

How to conduct a systematic review and meta-analysis of prognostic model studies

“…For many diseases, target populations and outcomes, multiple prognostic models have already been developed. For example, there are more than 400 prediction models for prognosis of chronic obstructive pulmonary disease ( 18 ), 363 models for predicting cardiovascular disease occurrence in the general population ( 19 ), 232 models for diagnosis and prognosis of covid-19 ( 20 ), 37 models for predicting pulmonary tuberculosis treatment outcomes ( 21 ), and 27 models for the clinical management of malaria ( 22 ). Systematic reviews of prognostic models provide an overview of the existing models, their quality (risks of bias) and their predictive performance.…”