Objective: To determine the balance of costs, risks, and benefits for different thromboprophylaxis strategies for medical patients during hospital admission.
Design: Decision analysis modelling study.
Setting: NHS hospitals in England.
Population: Eligible adult medical inpatients, excluding patients in critical care and pregnant women.
Interventions: Pharmacological thromboprophylaxis (low molecular weight heparin) for all medical inpatients, thromboprophylaxis for none, and thromboprophylaxis given to higher risk inpatients according to risk assessment models (Padua, Caprini, IMPROVE, Intermountain, Kucher, Geneva, and Rothberg) previously validated in medical cohorts.
Main outcome measures: Lifetime costs and quality adjusted life years (QALYs). Costs were assessed from the perspective of the NHS and Personal Social Services in England. Other outcomes assessed were incidence and treatment of venous thromboembolism, major bleeds including intracranial haemorrhage, chronic thromboembolic complications, and overall survival.
Results: Offering thromboprophylaxis to all medical inpatients had a high probability (>99%) of being the most cost effective strategy (at a threshold of £20 000 (€23 440; $25 270) per QALY) in the probabilistic sensitivity analysis, when applying performance data from the Padua risk assessment model, which was typical of that observed across several risk assessment models in a medical inpatient cohort. Thromboprophylaxis for all medical inpatients was estimated to result in 0.0552 additional QALYs (95% credible interval 0.0209 to 0.1111) while generating cost savings of £28.44 (-£47 to £105) compared with thromboprophylaxis for none. No other risk assessment model was more cost effective than thromboprophylaxis for all medical inpatients when assessed in deterministic analysis. Risk based thromboprophylaxis was found to have a high (76.6%) probability of being the most cost effective strategy only when assuming a risk assessment model with very high sensitivity is available (sensitivity 99.9% and specificity 23.7% v base case sensitivity 49.3% and specificity 73.0%).
Conclusions: Offering pharmacological thromboprophylaxis to all eligible medical inpatients appears to be the most cost effective strategy. To be cost effective, any risk assessment model would need to have a very high sensitivity resulting in widespread thromboprophylaxis in all patients except those at the very lowest risk, who could potentially avoid prophylactic anticoagulation during their hospital stay.
Objectives: To conduct a systematic review of studies externally validating the ADNEX (Assessment of Different Neoplasias in the adnexa) model for diagnosis of ovarian cancer and to present a meta-analysis of its performance.
Design: Systematic review and meta-analysis of external validation studies.
Data sources: Medline, Embase, Web of Science, Scopus, and Europe PMC, from 15 October 2014 to 15 May 2023.
Eligibility criteria for selecting studies: All external validation studies of the performance of ADNEX, with any study design and any study population of patients with an adnexal mass. Two independent reviewers extracted the data. Disagreements were resolved by discussion. Reporting quality of the studies was scored with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) reporting guideline, and methodological conduct and risk of bias with PROBAST (Prediction model Risk Of Bias Assessment Tool). Random effects meta-analysis of the area under the receiver operating characteristic curve (AUC), sensitivity and specificity at the 10% risk of malignancy threshold, and net benefit and relative utility at the 10% risk of malignancy threshold were performed.
Results: 47 studies (17 007 tumours) were included, with a median study sample size of 261 (range 24-4905). On average, 61% of TRIPOD items were reported. Handling of missing data, justification of sample size, and model calibration were rarely described. 91% of validations were at high risk of bias, mainly because of the unexplained exclusion of incomplete cases, small sample size, or no assessment of calibration. The summary AUC to distinguish benign from malignant tumours in patients who underwent surgery was 0.93 (95% confidence interval 0.92 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX with the serum biomarker, cancer antigen 125 (CA125), as a predictor (9202 tumours, 43 centres, 18 countries, and 21 studies) and 0.93 (95% confidence interval 0.91 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX without CA125 (6309 tumours, 31 centres, 13 countries, and 12 studies). The estimated probability that the model has use clinically in a new centre was 95% (with CA125) and 91% (without CA125). When restricting analysis to studies with a low risk of bias, summary AUC values were 0.93 (with CA125) and 0.91 (without CA125), and estimated probabilities that the model has use clinically were 89% (with CA125) and 87% (without CA125).
Conclusions: The results of the meta-analysis indicated that ADNEX performed well in distinguishing between benign and malignant tumours in populations from different countries and settings, regardless of whether the serum biomarker, CA125, was used as a predictor. A key limitation was that calibration was rarely assessed.
Systematic review registration:
Objective: To estimate the rate of breast cancer associated with use of vaginal oestradiol tablets according to duration and intensity of their use.
Design: Registry based, case-control study, nested in a nationwide cohort.
Setting: Based in Denmark using the civil registration system, the national registry of medicinal product statistics, the Danish cancer registry, the Danish birth registry, and statistics Denmark.
Participants: Women aged 50-60 years in year 2000 or turning 50 years during the study period of 1 January 2000 to 31 December 2018 were included. Exclusions were a history of cancer, mastectomy, use of systemic hormone treatment, use of the levonorgestrel releasing intrauterine system, or use of vaginal oestrogen treatments other than oestradiol tablets. To each woman who developed breast cancer during follow-up (18 997), five women in the control group (94 985) were incidence density matched by birth year.
Main outcome measure: The main outcome was pathology confirmed breast cancer diagnosis.
Results: 2782 (14.6%) women with breast cancer (cases) and 14 999 (15.8%) women with no breast cancer diagnosis (controls) had been exposed to vaginal oestradiol tablets with 234 cases and 1232 controls having been in treatment for at least four years at a high intensity (>50 micrograms per week). Increasing durations and intensities of use (cumulative dose/cumulative duration) of vaginal oestradiol tablets was not associated with increasing rates of breast cancer. Compared with never-use, cumulative use of vaginal oestradiol for more than nine years was associated with an adjusted hazard ratio of 0.87 (95% confidence interval 0.69 to 1.11). Results were similar in women who had long term use (≥four years) and with high intensity of use (>50-70 micrograms per week) with an adjusted hazard ratio 0.93 (95% confidence interval 0.81 to 1.08).
Conclusions: Use of vaginal oestradiol tablets was not associated with increased breast cancer rate compared with never-use. Increasing duration and intensity of use was not associated with increased rates of breast cancer.
Objective: To determine the extent to which the choice of timeframe used to define a long term condition affects the prevalence of multimorbidity and whether this varies with sociodemographic factors.
Design: Retrospective study of disease code frequency in primary care electronic health records.
Data sources: Routinely collected, general practice, electronic health record data from the Clinical Practice Research Datalink Aurum were used.
Main outcome measures: Adults (≥18 years) in England who were registered in the database on 1 January 2020 were included. Multimorbidity was defined as the presence of two or more conditions from a set of 212 long term conditions. Multimorbidity prevalence was compared using five definitions. Any disease code recorded in the electronic health records for 212 conditions was used as the reference definition. Additionally, alternative definitions for 41 conditions requiring multiple codes (where a single disease code could indicate an acute condition) or a single code for the remaining 171 conditions were as follows: two codes at least three months apart; two codes at least 12 months apart; three codes within any 12 month period; and any code in the past 12 months. Mixed effects regression was used to calculate the expected change in multimorbidity status and number of long term conditions according to each definition and associations with patient age, gender, ethnic group, and socioeconomic deprivation.
Results: 9 718 573 people were included in the study, of whom 7 183 662 (73.9%) met the definition of multimorbidity where a single code was sufficient to define a long term condition. Variation was substantial in the prevalence according to timeframe used, ranging from 41.4% (n=4 023 023) for three codes in any 12 month period, to 55.2% (n=5 366 285) for two codes at least three months apart. Younger people (eg, 50-75% probability for 18-29 years v 1-10% for ≥80 years), people of some minority ethnic groups (eg, people in the Other ethnic group had higher probability than the South Asian ethnic group), and people living in areas of lower socioeconomic deprivation were more likely to be re-classified as not multimorbid when using definitions requiring multiple codes.
Conclusions: Choice of timeframe to define long term conditions has a substantial effect on the prevalence of multimorbidity in this nationally representative sample. Different timeframes affect prevalence for some people more than others, highlighting the need to consider the impact of bias in the choice of method when defining multimorbidity.
Objective: To better understand the state of research on the effects of climate change on human health, including exposures, health conditions, populations, areas of the world studied, funding sources, and publication characteristics, with a focus on topics that are relevant for populations at risk.
Design: Cross sectional study.
Data sources: The National Institute of Environmental Health Sciences climate change and human health literature portal, a curated bibliographical database of global peer reviewed research and grey literature was searched. The database combines searches of multiple search engines including PubMed, Web of Science, and Google Scholar, and includes added-value expert tagging of climate change exposures and health impacts.
Eligibility criteria: Inclusion criteria were peer reviewed, original research articles that investigated the health effects of climate change and were published in English from 2012 to 2021. After identification, a 10% random sample was selected to manually perform a detailed characterisation of research topics and publication information.
Results: 10 325 original research articles were published between 2012 and 2021, and the number of articles increased by 23% annually. In a random sample of 1014 articles, several gaps were found in research topics that are particularly relevant to populations at risk, such as those in the global south (134 countries established through the United Nations Office for South-South Cooperation) (n=444; 43.8%), adults aged 65 years or older (n=195; 19.2%), and on topics related to human conflict and migration (n=25; 2.5%) and food and water quality and security (n=148; 14.6%). Additionally, fewer first authors were from the global south (n=349; 34.4%), which may partly explain why research focusing on these countries is disproportionally less.
Conclusions: Although the body of research on the health effects of climate change has grown substantially over the past decade, including those with a focus on the global south, a disproportionate focus continues to be on countries in the global north and less at risk populations. Governments are the largest source of funding for such research, and governments, particularly in the global north, need to re-orient their climate and health research funding to support researchers in the global south and to be more inclusive of issues that are relevant to the global south.
Objective: To test the effect of a complex, interdisciplinary, lifestyle and psychosocial intervention on metabolic and mental health outcomes in women with gestational diabetes mellitus during pregnancy and in the post partum.
Design: Single centred, single blinded, randomised, controlled trial (the MySweetheart trial).
Setting: Lausanne University Hospital, Switzerland, from 2 September 2016 to 25 October 2021.
Participants: 211 women aged at least 18 years with a diagnosis of gestational diabetes mellitus at 24-32 gestational weeks were randomly assigned (1:1) to the intervention (n=105) or to usual care (n=106).
Interventions: In addition to a comparator based on active guidelines for prepartum and postpartum usual care, the intervention consisted of four individual lifestyle visits during pregnancy and four interdisciplinary visits in the postpartum group, a peer support group workshop in pregnancy and post partum, and a bimonthly lifestyle coach support through telemedicine. The intervention focused on tailored behavioural and psychosocial strategies to improve diet, physical activity, mental health, social support, and adherence to gestational weight gain during pregnancy and weight retention recommendations.
Main outcome measures: Primary outcomes were between-group differences in the decrease in maternal weight and depression symptom scores between baseline and one year post partum. Secondary outcomes included changes in total and central body fat, anxiety, wellbeing, glycaemic parameters (homeostatic model assessment for insulin resistance (known as HOMA-IR) and Matsuda indices), aerobic fitness (maximal oxygen uptake), gestational weight gain, and weight retention. Assessors were blinded to primary and secondary outcomes.
Results: 84 (80%) of 105 women in the intervention and 95 (90%) of 106 in the usual care completed the study. There was not enough evidence of a difference in the decrease in weight (mean difference -0.38 kg (95% confidence interval -2.08 to 1.30)) or depression scores (-0.67 (-1.84 to 0.49)). The intervention led to an increase in fat-free mass (0.02 kg (0.01 to 0.03)). The intervention also decreased gestational weight gain since the first gestational diabetes mellitus visit (-1.20 kg (-2.14 to -0.26)) and weekly weight gain throughout the entire pregnancy (-0.14 kg (-0.25 to -0.03)), and led to a higher proportion of women without weight retention at one year post partum (34.1% (28/82) v 20.8% (20/96), P=0.034).
Conclusions: Compared with active usual care based on guidelines, there was not enough evidence to conclude that the intervention led to decrease in weight or depression symptoms. However, the intervention decreased gestational weight gain and increased the proportion of women without weight retention.
Trial
Objective: To explore how design emulation and population differences relate to variation in results between randomised controlled trials (RCT) and non-randomised real world evidence (RWE) studies, based on the RCT-DUPLICATE initiative (Randomised, Controlled Trials Duplicated Using Prospective Longitudinal Insurance Claims: Applying Techniques of Epidemiology).
Design: Meta-analysis of RCT-DUPLICATE data.
Data sources: Trials included in RCT-DUPLICATE, a demonstration project that emulated 32 randomised controlled trials using three real world data sources: Optum Clinformatics Data Mart, 2004-19; IBM MarketScan, 2003-17; and subsets of Medicare parts A, B, and D, 2009-17.
Eligibility criteria for selecting studies: Trials where the primary analysis resulted in a hazard ratio; 29 RCT-RWE study pairs from RCT-DUPLICATE.
Results: Differences and variation in effect sizes between the results from randomised controlled trials and real world evidence studies were investigated. Most of the heterogeneity in effect estimates between the RCT-RWE study pairs in this sample could be explained by three emulation differences in the meta-regression model: treatment started in hospital (which does not appear in health insurance claims data), discontinuation of some baseline treatments at randomisation (which would have been an unusual care decision in clinical practice), and delayed onset of drug effects (which would be under-reported in real world clinical practice because of the relatively short persistence of the treatment). Adding the three emulation differences to the meta-regression reduced heterogeneity from 1.9 to almost 1 (absence of heterogeneity).
Conclusions: This analysis suggests that a substantial proportion of the observed variation between results from randomised controlled trials and real world evidence studies can be attributed to differences in design emulation.
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