Methodological rigor of prognostic models for predicting in-hospital paediatric mortality in low- and middle-income countries: a systematic review protocol

Ogero, Morris; Sarguta, Rachel; Malla, Lucas; Aluvaala, Jalemba; Agweyu, Ambrose; Akech, Samuel

doi:10.12688/wellcomeopenres.15955.1

Cited by 3 publications

(11 citation statements)

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“…2 External validation of published prognostic models in populations/settings comparable to the model's derivation is recommended for establishing model transportability and generalisability. [3][4][5] Most clinical prediction models may not perform well in external validation and end up being rejected because of poor predictive performance. This is partly because clinical environments continuously evolve in various ways, including shifts in clinical practice, even though clinical practice guidelines tend to standardise this.…”

Section: Backg Rou N Dmentioning

confidence: 99%

“…The RISC-Malawi 11 model and 3 models by Lowlaavar et al (2016) 12 were identified in an earlier review 3,4…”

Section: Details Of the Models To Be Recalibratedmentioning

confidence: 99%

“…Developing new models without investigating the performance of existing models wastes potentially important historical data and research efforts 2 . External validation of published prognostic models in populations/settings comparable to the model's derivation is recommended for establishing model transportability and generalisability 3‐5 …”

Section: Introductionmentioning

confidence: 99%

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Recalibrating prognostic models to improve predictions of in‐hospital child mortality in resource‐limited settings

Ogero

Ndiritu

Sarguta

et al. 2023

Paediatric Perinatal Epid

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Section: Backg Rou N Dmentioning

confidence: 99%

“…The RISC-Malawi 11 model and 3 models by Lowlaavar et al (2016) 12 were identified in an earlier review 3,4…”

Section: Details Of the Models To Be Recalibratedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recalibrating prognostic models to improve predictions of in‐hospital child mortality in resource‐limited settings

Ogero

Ndiritu

Sarguta

et al. 2023

Paediatric Perinatal Epid

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“…As recommended, a research protocol for this review was published in a peer-reviewed journal. 11 This study is reported as per guidelines by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. 12…”

Section: Protocol and Registrationmentioning

confidence: 99%

Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review

et al. 2020

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ObjectivesTo identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).DesignSystematic review of peer-reviewed journals.Data sourcesMEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.Eligibility criteriaWe included model development studies predicting in-hospital paediatric mortality in LMIC.Data extraction and synthesisThis systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. The risk of bias assessment was conducted using Prediction model Risk of Bias Assessment Tool (PROBAST). No quantitative summary was conducted due to substantial heterogeneity that was observed after assessing the studies included.ResultsOur search strategy identified a total of 4054 unique articles. Among these, 3545 articles were excluded after review of titles and abstracts as they covered non-relevant topics. Full texts of 509 articles were screened for eligibility, of which 15 studies reporting 21 models met the eligibility criteria. Based on the PROBAST tool, risk of bias was assessed in four domains; participant, predictors, outcome and analyses. The domain of statistical analyses was the main area of concern where none of the included models was judged to be of low risk of bias.ConclusionThis review identified 21 models predicting in-hospital paediatric mortality in LMIC. However, most reports characterising these models are of poor quality when judged against recent reporting standards due to a high risk of bias. Future studies should adhere to standardised methodological criteria and progress from identifying new risk scores to validating or adapting existing scores.PROSPERO registration numberCRD42018088599.

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“…In this study, we conduct external validation of four prognostic models predicting in‐hospital mortality for pediatric patients in LMICs using routine hospital data collected by the Clinical Information Network (CIN) in Kenya. These models were identified in the recent systematic review of pediatric prognostic models 8,9 …”

Section: Introductionmentioning

confidence: 99%

Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study

Ogero,

Ndiritu,

Sarguta

et al. 2023

Health Science Reports

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Background and AimsPrognostic models provide evidence‐based predictions and estimates of future outcomes, facilitating decision‐making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)‐Malawi model and three other models by Lowlavaar et al.MethodsThe study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in‐hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in‐hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept.ResultsThe RISC‐Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case‐fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77−0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 −1.06), and calibration intercept was 0.81 (95% CI: 0.77−0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients were included, with an in‐hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72−0.77), the calibration slope was 0.78 (95% CI: 0.71−0.84), and the calibration intercept was 0.37 (95% CI: 0.28−0.46). All models markedly underestimated the risk of mortality.ConclusionAll externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability.

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Methodological rigor of prognostic models for predicting in-hospital paediatric mortality in low- and middle-income countries: a systematic review protocol

Cited by 3 publications

References 12 publications

Recalibrating prognostic models to improve predictions of in‐hospital child mortality in resource‐limited settings

Recalibrating prognostic models to improve predictions of in‐hospital child mortality in resource‐limited settings

Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review

Pediatric prognostic models predicting inhospital child mortality in resource‐limited settings: An external validation study

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