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

ABSTRACT: 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 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. 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. PROSPERO REGISTRATION NUMBER:CRD42019130673.

SUBMITTER: Njim T

PROVIDER: S-EPMC6887018 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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

Njim Tsi T Tanyitiku Bayee Swiri BS

BMJ open 20191126 11

<h4>Objective</h4>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.<h4>Design</h4>A systematic review.<h4>Data sources</h4>Medline, Global health and CINAHL were searched up to 4 Sept ...[more]

PMID: 31772089

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Project description:BackgroundGestational diabetes (GDM) is increasingly common and has significant implications during pregnancy and for the long-term health of the mother and offspring. However, it is a heterogeneous condition with inter-related factors including ethnicity, body mass index and gestational weight gain significantly modifying the absolute risk of complications at an individual level. Predicting the risk of pregnancy complications for an individual woman with GDM presents a useful adjunct to therapeutic decision-making and patient education. Diagnostic prediction models for GDM are prevalent. In contrast, prediction models for risk of complications in those with GDM are relatively novel. This study will systematically review published prognostic prediction models for pregnancy complications in women with GDM, describe their characteristics, compare performance and assess methodological quality and applicability.MethodsStudies will be identified by searching MEDLINE and Embase electronic databases. Title and abstract screening, full-text review and data extraction will be completed independently by two reviewers. The included studies will be systematically assessed for risk of bias and applicability using appropriate tools designed for prediction modelling studies. Extracted data will be tabulated to facilitate qualitative comparison of published prediction models. Quantitative data on predictive performance of these models will be synthesised with meta-analyses if appropriate.DiscussionThis review will identify and summarise all published prognostic prediction models for pregnancy complications in women with GDM. We will compare model performance across different settings and populations with meta-analysis if appropriate. This work will guide subsequent phases in the prognosis research framework: further model development, external validation and model updating, and impact assessment. The ultimate model will estimate the absolute risk of pregnancy complications for women with GDM and will be implemented into routine care as an evidence-based GDM complication risk prediction model. It is anticipated to offer value to women and their clinicians with individualised risk assessment and may assist decision-making. Ultimately, this systematic review is an important step towards a personalised risk-stratified model-of-care for GDM to allow preventative and therapeutic interventions for the maximal benefit to women and their offspring, whilst sparing expense and harm for those at low risk.Systematic review registrationPROSPERO registration number CRD42019115223.

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

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

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