External validation of prognostic models to predict stillbirth using the International Prediction of Pregnancy Complications (IPPIC) Network database: an 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. (c) 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.Peer reviewe

Comparative Bioavailability of two immediate release tablets of enalapril/hydrochlorothiazide in healthy volunteers

A bioequivalence study of two oral formulations of 20/12.5 mg tablets of enalaptil/hydrochlorothiazide was carried out in 20 healthy male volunteers according to a single dose, two-sequence, crossover randomized design. One washout period of nine days was observed between the two periods. Multiple samples were collected over 96 hours post-dosing. Bioavailability was evaluated on the basis of plasma concentrations of enalapril and its main active metabolite, enalaprilat and hydrochlorothiazide. Plasma samples were assayed for enalapril, enalaprilat and hydrochlorothiazide using a selective and sensitive high-performance liquid chromatography method with mass spectrometry detection (LC-MS). The pharmacokinetic parameter values of C-max and t(max) were obtained directly from plasma data, k(e) was estimated by log-linear regression, and AUC was calculated by trapezoidal rule. Different statistical tests were performed on the basis of untransformed and log-transformed data and the overall residual variance from ANOVA. Assuming the accepted tolerance intervals, a beta-error of 20% and 90% confidence intervals (alpha = 0.10), all the generally accepted tests (Schuirmann test and Wilcoxon-Tukey and Hauschke nonparametric tests) showed that the formulations can be considered as bioequivalent with respect to the extent of absorption, given by the AUC(0-infinity) and with respect to rate of absorption as assessed by C-max and t(max)