Development and validation of risk prediction models for adverse maternal and neonatal outcomes in severe preeclampsia in a low-resource setting, Mpilo Central Hospital, Bulawayo, Zimbabwe

Ngwenya, Solwayo; Jones, Brian Christopher; Mwembe, Desmond; Nare, Hausitoe; Heazell, Alexander

doi:10.1016/j.preghy.2020.10.011

Cited by 10 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The outcome of severely ill children is based on more than just in-hospital mortality as admission, readmission, and post-discharge mortality are intricately intertwined in impacting child survival. Other models were limited to predefined populations such as infants born to mothers with severe preeclampsia or premature/low birth weight infants, making these models less generalizable [ 37 , 38 ]. One of the models developed at a neonatal intensive care unit in Tanzania [ 36 ] identified birthweight, temperature, and respiratory rate to be predictive of in-hospital mortality and we also identified these factors to be important predictors for our composite endpoint.…”

Section: Discussionmentioning

confidence: 99%

Geographical validation of the Smart Triage Model by age group

Zhang,

Wiens,

Dunsmuir

et al. 2024

PLOS Digit Health

View full text Add to dashboard Cite

Infectious diseases in neonates account for half of the under-five mortality in low- and middle-income countries. Data-driven algorithms such as clinical prediction models can be used to efficiently detect critically ill children in order to optimize care and reduce mortality. Thus far, only a handful of prediction models have been externally validated and are limited to neonatal in-hospital mortality. The aim of this study is to externally validate a previously derived clinical prediction model (Smart Triage) using a combined prospective baseline cohort from Uganda and Kenya with a composite endpoint of hospital admission, mortality, and readmission. We evaluated model discrimination using area under the receiver-operator curve (AUROC) and visualized calibration plots with age subsets (< 30 days, ≤ 2 months, ≤ 6 months, and < 5 years). Due to reduced performance in neonates (< 1 month), we re-estimated the intercept and coefficients and selected new thresholds to maximize sensitivity and specificity. 11595 participants under the age of five (under-5) were included in the analysis. The proportion with an endpoint ranged from 8.9% in all children under-5 (including neonates) to 26% in the neonatal subset alone. The model achieved good discrimination for children under-5 with AUROC of 0.81 (95% CI: 0.79–0.82) but poor discrimination for neonates with AUROC of 0.62 (95% CI: 0.55–0.70). Sensitivity at the low-risk thresholds (CI) were 85% (83%–87%) and 68% (58%–76%) for children under-5 and neonates, respectively. After model revision for neonates, we achieved an AUROC of 0.83 (95% CI: 0.79–0.87) with 13% and 41% as the low- and high-risk thresholds, respectively. The updated Smart Triage performs well in its predictive ability across different age groups and can be incorporated into current triage guidelines at local healthcare facilities. Additional validation of the model is indicated, especially for the neonatal model.

show abstract

Section: Discussionmentioning

confidence: 99%

Geographical validation of the Smart Triage Model by age group

Zhang,

Wiens,

Dunsmuir

et al. 2024

PLOS Digit Health

View full text Add to dashboard Cite

show abstract

“…Most neonates born to a mother with HELLP syndrome or sPE require extended hospitalization in neonatal intensive care units [3]. Ngwenya et al [4] emphasized the reduced applicability and accuracy of predictive models developed using data from high-income countries towards populations in LMICs due to the large discrepancy of resources between these settings. To address this issue, the mini Preeclampsia Integrated Estimate of RiSk (miniPIERS) model was developed for LMICs juxtaposing the fullPIERS model, which was developed to assess adverse maternal and neonatal outcomes in high-income countries.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Neonatal Outcomes Using Gestational Age or Diagnosis of Severe Preeclampsia vs. HELLP Syndrome: A Case-Only Analysis

HAUPTMAN,

GILL,

LIM

et al. 2024

Preprint

View full text Add to dashboard Cite

Purpose:Hypertensive disorders of pregnancy cause significant neonatal complications. Disease severity is often used to predict neonatal outcomes, however gestational age (GA) at delivery may be a better predictor. We aimed to assess whether disease severity or GA was more predictive of adverse neonatal outcomes. Methods:We included 165 participants with confirmed HELLP syndrome or severe preeclampsia (sPE). Two predictive models were constructed to assess the ability of disease severity compared to GA to predict a composite adverse neonatal outcome. The composite outcome included low birth weight, SGA, IUGR, Apgar score, and neonatal death. Results:Using severity as a predictor of binary neonatal outcome had an AUC of 0.73 (0.65-0.81), with a sensitivity (SE) of 70.3% and a specificity (SP) of 64.4%. For GA, we observed an AUC of 0.82 (0.75-0.89), with a SE of 75.7% and a SP of 76.7%. Conclusion: For the composite neonatal outcome, GA was a better predictor than ACOG diagnosis (severity). This observation underscores the need for further research to validate these findings in larger cohorts and to determine their applicability to maternal outcomes.

show abstract

“…Esta condición afecta alrededor del 2 al 8% de los embarazos en el mundo 2 . En Latinoamérica y el caribe, la preeclampsia es responsable del 26% de las muertes maternas, lo que representa una alta mortalidad y morbilidad no solo materna, sino también neonatal de unos 500 recién nacidos por año alrededor de todo el mundo, más cuando se asocia a restricción de crecimiento fetal, proteinuria y obesidad materna [3][4][5][6] llevando a un alto costo en los sistemas de salud 3 .…”

Section: Introductionunclassified

Relación del volumen de agua corporal total, el extracelular, y preeclampsia. Revisión narrativa de la literatura

Herrera¹,

Bedoya²

2021

Rev Medicas UIS

View full text Add to dashboard Cite

La preeclampsia es una importante causa de mortalidad materna mundial, identificar de manera temprana las gestantes con riesgo de desarrollarla, es una medida preventiva de gran impacto. La medición del agua corporal se ha documentado desde 1994, y desde 2015 se demostró su relación con el riesgo de desarrollar preeclampsia. Por lo anterior se realizó una revisión de la relación del agua corporal y la preeclampsia hasta junio de 2019, con 17 artículos seleccionados. Producto de la revisión se concluyó que la medición del agua corporal en gestantes podría detectar la aparición de preeclampsia para establecer un seguimiento estricto temprano a las mujeres con mayor riesgo de presentarla. Estas mediciones se realizan con métodos sencillos, no invasivos y de bajo costo, como la impedancia eléctrica por análisis espectral. Sin embargo, se requieren estudios con mayor rigor metodológico para el estudio de una prueba diagnóstica como la que se propone.

show abstract

Development and validation of risk prediction models for adverse maternal and neonatal outcomes in severe preeclampsia in a low-resource setting, Mpilo Central Hospital, Bulawayo, Zimbabwe

Cited by 10 publications

References 29 publications

Geographical validation of the Smart Triage Model by age group

Geographical validation of the Smart Triage Model by age group

Prediction of Neonatal Outcomes Using Gestational Age or Diagnosis of Severe Preeclampsia vs. HELLP Syndrome: A Case-Only Analysis

Relación del volumen de agua corporal total, el extracelular, y preeclampsia. Revisión narrativa de la literatura

Contact Info

Product

Resources

About