ORCID Profile
0000-0002-8755-8546
Current Organisations
University of Southampton
,
Aston University
,
Mirzyme Therapeutics Limited
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Publisher: National Institute for Health and Care Research
Date: 04-2017
DOI: 10.3310/HTA21180
Abstract: The prognosis of early-onset pre-ecl sia (before 34 weeks’ gestation) is variable. Accurate prediction of complications is required to plan appropriate management in high-risk women. To develop and validate prediction models for outcomes in early-onset pre-ecl sia. Prospective cohort for model development, with validation in two external data sets. Model development: 53 obstetric units in the UK. Model transportability: PIERS (Pre-ecl sia Integrated Estimate of RiSk for mothers) and PETRA (Pre-Ecl sia TRial Amsterdam) studies. Pregnant women with early-onset pre-ecl sia. Nine hundred and forty-six women in the model development data set and 850 women (634 in PIERS, 216 in PETRA) in the transportability (external validation) data sets. The predictors were identified from systematic reviews of tests to predict complications in pre-ecl sia and were prioritised by Delphi survey. The primary outcome was the composite of adverse maternal outcomes established using Delphi surveys. The secondary outcome was the composite of fetal and neonatal complications. We developed two prediction models: a logistic regression model (PREP-L) to assess the overall risk of any maternal outcome until postnatal discharge and a survival analysis model (PREP-S) to obtain in idual risk estimates at daily intervals from diagnosis until 34 weeks. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) and external validation (of the reduced models in the transportability data), we computed the ability of the models to discriminate between those with and without poor outcomes ( c -statistic), and the agreement between predicted and observed risk (calibration slope). The PREP-L model included maternal age, gestational age at diagnosis, medical history, systolic blood pressure, urine protein-to-creatinine ratio, platelet count, serum urea concentration, oxygen saturation, baseline treatment with antihypertensive drugs and administration of magnesium sulphate. The PREP-S model additionally included exaggerated tendon reflexes and serum alanine aminotransaminase and creatinine concentration. Both models showed good discrimination for maternal complications, with anoptimism-adjusted c -statistic of 0.82 [95% confidence interval (CI) 0.80 to 0.84] for PREP-L and 0.75 (95% CI 0.73 to 0.78) for the PREP-S model in the internal validation. External validation of the reduced PREP-L model showed good performance with a c -statistic of 0.81 (95% CI 0.77 to 0.85) in PIERS and 0.75 (95% CI 0.64 to 0.86) in PETRA cohorts for maternal complications, and calibrated well with slopes of 0.93 (95% CI 0.72 to 1.10) and 0.90 (95% CI 0.48 to 1.32), respectively. In the PIERS data set, the reduced PREP-S model had a c -statistic of 0.71 (95% CI 0.67 to 0.75) and a calibration slope of 0.67 (95% CI 0.56 to 0.79). Low gestational age at diagnosis, high urine protein-to-creatinine ratio, increased serum urea concentration, treatment with antihypertensive drugs, magnesium sulphate, abnormal uterine artery Doppler scan findings and estimated fetal weight below the 10th centile were associated with fetal complications. The PREP-L model provided in idualised risk estimates in early-onset pre-ecl sia to plan management of high- or low-risk in iduals. The PREP-S model has the potential to be used as a triage tool for risk assessment. The impacts of the model use on outcomes need further evaluation. Current Controlled Trials ISRCTN40384046. The National Institute for Health Research Health Technology Assessment programme.
Publisher: Springer Science and Business Media LLC
Date: 06-2019
Publisher: Wiley
Date: 07-2019
DOI: 10.1002/UOG.20117
Abstract: Primary studies and systematic reviews provide estimates of varying accuracy for different factors in the prediction of pre-ecl sia. The aim of this study was to review published systematic reviews to collate evidence on the ability of available tests to predict pre-ecl sia, to identify high-value avenues for future research and to minimize future research waste in this field. 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-ecl sia. 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, s le 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. From 2444 citations identified, 126 reviews were included, reporting on over 90 predictors and 52 prediction models for pre-ecl sia. Around a third (n = 37 (29.4%)) of all reviews investigated solely biochemical markers for predicting pre-ecl sia, 31 (24.6%) investigated genetic associations with pre-ecl sia, 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-ecl sia. 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/m 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 in idual-patient data meta-analysis are needed to develop and validate new prediction models to facilitate the prediction of pre-ecl sia and minimize further research waste in this field. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.
Publisher: Wiley
Date: 06-07-2021
DOI: 10.1111/BPH.15582
Abstract: Emerging data show that pregnant women with COVID‐19 are at significantly higher risk of severe outcomes compared with non‐pregnant women of similar age. This review discusses the invaluable insight revealed from vaccine clinical trials in women who were vaccinated and inadvertently became pregnant during the trial period. It further explores a number of clinical avenues in their management and proposes a drug development strategy in line with clinical trials for vaccines and drug treatments for the drug development community. Little is known of the long‐term effects of COVID‐19 on the mother and the baby. Our hypothesis that COVID‐19 predisposes pregnant women to pre‐ecl sia or hypertensive disorders during pregnancy is supported by a clinical study, and this may also adversely impact a woman's cardiovascular disease risk later in life. It may also increase a woman's risk of pre‐ecl sia in subsequent pregnancy. This is an ever‐evolving landscape, and early knowledge for healthcare providers and drug innovators is offered to ensure benefits outweigh the risks. COVID‐19 mRNA vaccines appear to generate robust humoral immunity in pregnant and lactating women. This novel approach to vaccination also offers new ways to therapeutically tackle disorders of many unmet medical needs. This article is part of a themed issue on The second wave: are we any closer to efficacious pharmacotherapy for COVID 19? (BJP 75th Anniversary). To view the other articles in this section visit oi/10.1111/bph.v179.10/issuetoc
Publisher: National Institute for Health and Care Research
Date: 12-2020
DOI: 10.3310/HTA24720
Abstract: Pre-ecl sia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. To assess the performance of existing pre-ecl sia prediction models and to develop and validate models for pre-ecl sia using in idual participant data meta-analysis. We also estimated the prognostic value of in idual markers. This was an in idual participant data meta-analysis of cohort studies. Source data from secondary and tertiary care. We identified predictors from systematic reviews, and prioritised for importance in an international survey. Early-onset (delivery at 34 weeks’ gestation), late-onset (delivery at ≥ 34 weeks’ gestation) and any-onset pre-ecl sia. We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C -statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I 2 and τ 2 . A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of in idual predictors for pre-ecl sia as odds ratios with 95% confidence and prediction intervals. The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-ecl sia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C -statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-ecl sia, and lowest for the first-trimester clinical characteristics models to predict any pre-ecl sia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-ecl sia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-ecl sia. Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the in idual participant data. For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings. Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate. This study is registered as PROSPERO CRD42015029349. This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment Vol. 24, No. 72. See the NIHR Journals Library website for further project information.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Asif Ahmed.