Dexamethasone for the treatment of established postoperative nausea and vomiting: A randomised dose finding trial.

Dexamethasone is widely used for the prevention of postoperative nausea and vomiting (PONV) but little is known about its efficacy for the treatment of established PONV. To test the antiemetic efficacy of intravenous dexamethasone for the treatment of established PONV in adults undergoing surgery under general anaesthesia and to determine whether there is dose-responsiveness. The DexPonv trial is a multicentre, placebo-controlled, randomised, double-blind, dose-finding study. Inclusion of patients was between September 2012 and November 2017. Follow-up for PONV symptoms was for 24 h. Thirty days postoperatively, patients were contacted by study nurses for any information on postoperative bleeding and infection. Four public hospitals in Switzerland. A total of 803 adults scheduled for elective surgery without any antiemetic prophylaxis signed the consent form; 714 were included. Among those, 319 had PONV and 281 patients were eventually randomised (intention to treat population and safety set). The per protocol set consisted of 260 patients. Patients with PONV symptoms (including retching) were randomised to a single intravenous dose of dexamethasone 3, 6 or 12 mg or matching placebo. The primary endpoint was the absence of further nausea or vomiting (including retching), within 24 h after administration of the study drug. Dexamethasone was ineffective during the first 24 h, whatever the dosage, compared to placebo, even when the model was adjusted for known risk factors (P = 0.170). There were no differences in the time to treatment failure or the quality of sleep during the first night. There was a positive correlation between the dose of dexamethasone and blood glucose concentrations (P < 0.001), but not with bleeding risk, wound infections or other adverse effects. This randomised trial failed to show anti-emetic efficacy of any of the tested intravenous regimens of dexamethasone for the treatment of established PONV in adults undergoing surgery under general anaesthesia. clinicaltrials.gov (NCT01975727)

Effect of pregnancy prolongation in early-onset pre-eclampsia on postpartum maternal cardiovascular, renal and metabolic function in primiparous women: an observational study.

OBJECTIVE: To evaluate the association between deferred delivery in early-onset pre-eclampsia and offspring outcome and maternal cardiovascular, renal and metabolic function in the postpartum period. DESIGN: Observational study. SETTING: Tertiary referral hospital. POPULATION: Nulliparous women diagnosed with pre-eclampsia before 34 weeks' gestation who participated in a routine postpartum cardiovascular risk assessment programme. Women with hypertension, diabetes mellitus or renal disease prior to pregnancy were excluded. METHODS: Regression analyses were performed to assess the association between pregnancy prolongation and outcome measures. MAIN OUTCOME MEASURES: Offspring outcome and prevalence of deviant maternal cardiovascular, renal and metabolic function. RESULTS: The study population included 564 women with a median pregnancy prolongation of 10 days (interquartile range [IQR] 4-18) who were assessed at on average 8 months (IQR 6-12) postpartum. Pregnancy prolongation after diagnosis resulted in a decrease in infant mortality (adjusted odd ratio [aOR] 0.907, 95% CI 0.852-0.965 per day prolongation). This improvement in offspring outcome was associated with an elevated risk of moderately increased albuminuria (aOR 1.025, 95% CI 1.006-1.045 per day prolongation), but not with aberrant cardiac geometry, cardiac systolic or diastolic dysfunction, persistent hypertension or metabolic syndrome. CONCLUSION: Pregnancy prolongation in early-onset pre-eclampsia is associated with improved offspring outcome and survival. These effects do not appear to be deleterious to short-term maternal cardiovascular and metabolic function but are associated with a modest increase in risk of residual albuminuria. TWEETABLE ABSTRACT: Pregnancy prolongation in pre-eclampsia has only a limited effect on postpartum maternal cardiovascular function

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

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE: To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN: Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES: PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION: Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION: At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS: 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION: Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION: Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity