External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis.

OBJECTIVE: Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. METHODS: MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. RESULTS: Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. CONCLUSIONS: The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Prediction of pre-eclampsia: review of reviews.

OBJECTIVE: Primary studies and systematic reviews provide estimates of varying accuracy for different factors in the prediction of pre-eclampsia. The aim of this study was to review published systematic reviews to collate evidence on the ability of available tests to predict pre-eclampsia, to identify high-value avenues for future research and to minimize future research waste in this field. METHODS: MEDLINE, EMBASE and The Cochrane Library including DARE (Database of Abstracts of Reviews of Effects) databases, from database inception to March 2017, and bibliographies of relevant articles were searched, without language restrictions, for systematic reviews and meta-analyses on the prediction of pre-eclampsia. The quality of the included reviews was assessed using the AMSTAR tool and a modified version of the QUIPS tool. We evaluated the comprehensiveness of search, sample size, tests and outcomes evaluated, data synthesis methods, predictive ability estimates, risk of bias related to the population studied, measurement of predictors and outcomes, study attrition and adjustment for confounding. RESULTS: From 2444 citations identified, 126 reviews were included, reporting on over 90 predictors and 52 prediction models for pre-eclampsia. Around a third (n = 37 (29.4%)) of all reviews investigated solely biochemical markers for predicting pre-eclampsia, 31 (24.6%) investigated genetic associations with pre-eclampsia, 46 (36.5%) reported on clinical characteristics, four (3.2%) evaluated only ultrasound markers and six (4.8%) studied a combination of tests; two (1.6%) additional reviews evaluated primary studies investigating any screening test for pre-eclampsia. Reviews included between two and 265 primary studies, including up to 25 356 688 women in the largest review. Only approximately half (n = 67 (53.2%)) of the reviews assessed the quality of the included studies. There was a high risk of bias in many of the included reviews, particularly in relation to population representativeness and study attrition. Over 80% (n = 106 (84.1%)) summarized the findings using meta-analysis. Thirty-two (25.4%) studies lacked a formal statement on funding. The predictors with the best test performance were body mass index (BMI) > 35 kg/m2 , with a specificity of 92% (95% CI, 89-95%) and a sensitivity of 21% (95% CI, 12-31%); BMI > 25 kg/m2 , with a specificity of 73% (95% CI, 64-83%) and a sensitivity of 47% (95% CI, 33-61%); first-trimester uterine artery pulsatility index or resistance index > 90th centile (specificity 93% (95% CI, 90-96%) and sensitivity 26% (95% CI, 23-31%)); placental growth factor (specificity 89% (95% CI, 89-89%) and sensitivity 65% (95% CI, 63-67%)); and placental protein 13 (specificity 88% (95% CI, 87-89%) and sensitivity 37% (95% CI, 33-41%)). No single marker had a test performance suitable for routine clinical use. Models combining markers showed promise, but none had undergone external validation. CONCLUSIONS: This review of reviews calls into question the need for further aggregate meta-analysis in this area given the large number of published reviews subject to the common limitations of primary predictive studies. Prospective, well-designed studies of predictive markers, preferably randomized intervention studies, and combined through individual-patient data meta-analysis are needed to develop and validate new prediction models to facilitate the prediction of pre-eclampsia and minimize further research waste in this field. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Predicción de la preeclampsia: revisión de revisiones OBJETIVO: Los estudios primarios y las revisiones sistemáticas proporcionan estimaciones de precisión variable para diferentes factores en la predicción de la preeclampsia. El objetivo de este estudio fue revisar las revisiones sistemáticas publicadas para recopilar evidencia sobre la capacidad de las pruebas disponibles para predecir la preeclampsia, identificar avenidas de investigación futura valiosas y minimizar el desperdicio futuro de investigación en este campo. MÉTODOS: Se realizaron búsquedas de artículos relevantes en bibliografías sobre el tema y en las bases de datos MEDLINE, EMBASE y The Cochrane Library, incluida DARE (Database of Abstracts of Reviews of Effects), desde el inicio de cada base de datos hasta marzo de 2017, sin restricciones de idioma, para obtener revisiones sistemáticas y metaanálisis sobre la predicción de la preeclampsia. La calidad de las revisiones incluidas se evaluó utilizando la herramienta AMSTAR y una versión modificada de la herramienta QUIPS. Se evaluó la amplitud de la búsqueda, el tamaño de la muestra, las pruebas y los resultados evaluados, los métodos de síntesis de datos, las estimaciones de la capacidad de predicción, el riesgo de sesgo relacionado con la población estudiada, la medición de los predictores y los resultados, la deserción del estudio y el ajuste por confusión. RESULTADOS: De las 2444 citas identificadas, se incluyeron 126 revisiones, que informaron sobre más de 90 predictores y 52 modelos de predicción para la preeclampsia. Alrededor de un tercio (n=37 (29,4%)) de todas las revisiones investigaron únicamente marcadores bioquímicos para predecir la preeclampsia, 31 (24,6%) investigaron asociaciones genéticas con la preeclampsia, 46 (36,5%) informaron sobre las características clínicas, cuatro (3,2%) evaluaron sólo marcadores ecográficos y seis (4,8%) estudiaron una combinación de pruebas; dos (1,6%) revisiones adicionales evaluaron los estudios primarios que investigaron cualquier prueba de diagnóstico de la preeclampsia. Las revisiones incluyeron entre dos y 265 estudios primarios, que incluyeron hasta 25 356 688 mujeres en la revisión más grande. Sólo aproximadamente la mitad (n=67 (53,2%)) de las revisiones evaluaron la calidad de los estudios incluidos. En muchas de las revisiones incluidas hubo un alto riesgo de sesgo, particularmente en relación con la representatividad de la población y la deserción de los estudios. Más del 80% (n=106 (84,1%)) resumió los hallazgos utilizando el metaanálisis. Treinta y dos (25,4%) estudios carecían de una declaración formal sobre la financiación. Los predictores con el mejor rendimiento de la prueba fueron el índice de masa corporal (IMC) >35 kg.m-2 , con una especificidad del 92% (IC 95%, 89-95%) y una sensibilidad del 21% (IC 95%, 12-31%); IMC >25 kg.m-2 , con una especificidad del 73% (IC 95%: 64-83%) y una sensibilidad del 47% (IC 95%: 33-61%); índice de pulsatilidad de la arteria uterina en el primer trimestre o índice de resistencia >90° percentil (especificidad del 93% (IC 95%: 90-96%) y sensibilidad del 26% (IC 95%: 23-31%)); factor de crecimiento placentario (especificidad 89% (IC 95%, 89-89%) y sensibilidad 65% (IC 95%, 63-67%)); y proteína placentaria 13 (especificidad 88% (IC 95%, 87-89%) y sensibilidad 37% (IC 95%, 33-41%)). Ningún marcador por sí solo tuvo un rendimiento de la prueba adecuado para el uso clínico rutinario. Los modelos que combinan marcadores son prometedores, pero ninguno fue sometido a una validación externa. CONCLUSIONES: Esta revisión de revisiones ha puesto en duda la necesidad de un metaanálisis agregado adicional en esta área, dado el gran número de revisiones publicadas sujetas a las limitaciones comunes de los estudios predictivos primarios. Se necesitan estudios prospectivos bien diseñados de marcadores predictivos, preferiblemente en estudios de intervención aleatorios, y combinados mediante el metaanálisis de datos de pacientes individuales, para desarrollar y validar nuevos modelos predictivos que faciliten la predicción de la preeclampsia y minimicen el desperdicio de investigación adicional en este campo.

Mortality and implant revision rates of hip arthroplasty in patients with osteoarthritis: registry based cohort study.

OBJECTIVES: To examine mortality and revision rates among patients with osteoarthritis undergoing hip arthroplasty and to compare these rates between patients undergoing cemented or uncemented procedures and to compare outcomes between men undergoing stemmed total hip replacements and Birmingham hip resurfacing. DESIGN: Cohort study. SETTING: National Joint Registry. POPULATION: About 275,000 patient records. MAIN OUTCOME MEASURES: Hip arthroplasty procedures were linked to the time to any subsequent mortality or revision (implant failure). Flexible parametric survival analysis methods were used to analyse time to mortality and also time to revision. Comparisons between procedure groups were adjusted for age, sex, American Society of Anesthesiologists (ASA) grade, and complexity. RESULTS: As there were large baseline differences in the characteristics of patients receiving cemented, uncemented, or resurfacing procedures, unadjusted comparisons are inappropriate. Multivariable survival analyses identified a higher mortality rate for patients undergoing cemented compared with uncemented total hip replacement (adjusted hazard ratio 1.11, 95% confidence interval 1.07 to 1.16); conversely, there was a lower revision rate with cemented procedures (0.53, 0.50 to 0.57). These translate to small predicted differences in population averaged absolute survival probability at all time points. For example, compared with the uncemented group, at eight years after surgery the predicted probability of death in the cemented group was 0.013 higher (0.007 to 0.019) and the predicted probability of revision was 0.015 lower (0.012 to 0.017). In multivariable analyses restricted to men, there was a higher mortality rate in the cemented group and the uncemented group compared with the Birmingham hip resurfacing group. In terms of revision, the Birmingham hip resurfacings had a similar revision rate to uncemented total hip replacements. Both uncemented total hip replacements and Birmingham hip resurfacings had a higher revision rate than cemented total hip replacements. CONCLUSIONS: There is a small but significant increased risk of revision with uncemented rather than cemented total hip replacement, and a small but significant increased risk of death with cemented procedures. It is not known whether these are causal relations or caused by residual confounding. Compared with uncemented and cemented total hip replacements, Birmingham hip resurfacing has a significantly lower risk of death in men of all ages. Previously, only adjusted analyses of hip implant revision rates have been used to recommend and justify use of cheaper cemented total hip implants. Our investigations additionally consider mortality rates and suggest a potentially higher mortality rate with cemented total hip replacements, which merits further investigation.