Latent class models (LCMs) combine the results of multiple diagnostic tests through a statistical model to obtain estimates of disease prevalence and diagnostic test accuracy in situations where there is no single, accurate reference standard. We performed a systematic review of the methodology and reporting of LCMs in diagnostic accuracy studies. This review shows that the use of LCMs in such studies increased sharply in the past decade, notably in the domain of infectious diseases (overall contribution: 59%). The 64 reviewed studies used a range of differently specified parametric latent variable models, applying Bayesian and frequentist methods. The critical assumption underlying the majority of LCM applications (61%) is that the test observations must be independent within 2 classes. Because violations of this assumption can lead to biased estimates of accuracy and prevalence, performing and reporting checks of whether assumptions are met is essential. Unfortunately, our review shows that 28% of the included studies failed to report any information that enables verification of model assumptions or performance. Because of the lack of information on model fit and adequate evidence "external" to the LCMs, it is often difficult for readers to judge the validity of LCM-based inferences and conclusions reached.

ObjeCtiveTo perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy. DesignExternal validation of all published prognostic models in large scale, prospective, multicentre cohort study. setting 31 independent midwifery practices and six hospitals in the Netherlands. PartiCiPantsWomen recruited in their first trimester (<14 weeks) of pregnancy between December 2012 and January 2014, at their initial prenatal visit. Women with pre-existing diabetes mellitus of any type were excluded. Main OutCOMe MeasuresDiscrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots. results 3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated.The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit. COnClusiOnsIn this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact. IntroductionIn the field of obstetrics, the number of publications on prognostic models has more than tripled in the past decade, 1 which reflects an increasing interest in risk based medicine. Risk based medicine aims to provide the most appropriate care to each patient, often guided by outcome risk estimates based on individual patient characteristics, test results, or even genetic information. 2 As a result of the obesity pandemic, the incidence of gestational diabetes mellitus, notably occurring in the second or third trimester, is rising and is increasingly contributing to perinatal complications such as macrosomia, shoulder dystocia, caesarean section, and neonatal hypoglycaemia. 3 4 Moreover, long term sequelae of gestational diabetes mellitus are type 2 diabetes in mothers and obesity in their offspring. 5 6 Early diagnosis and treatment of gestational diabetes mellitus have been proven to improve pregnancy outcomes. 7 8 Some guidelines propose a population strategy for diagnosing the disorder [9][10][11][12] (that is, an oral glucose tolerance test) in all pregnant women, whereas others opt for a high ri...

Loes C. M. Bertens and colleagues survey the published diagnostic research literature for use of expert panels to define the reference standard, characterize components and missing information, and recommend elements that should be reported in diagnostic studies. Please see later in the article for the Editors' Summary

Purpose To investigate whether initial diagnostic laparoscopy can prevent futile primary cytoreductive surgery (PCS) by identifying patients with advanced-stage ovarian cancer in whom > 1 cm of residual disease will be left after PCS. Patients and Methods This multicenter, randomized controlled trial was undertaken within eight gynecologic cancer centers in the Netherlands. Patients with suspected advanced-stage ovarian cancer who qualified for PCS were eligible. Participating patients were randomly assigned to either laparoscopy or PCS. Laparoscopy was used to guide selection of primary treatment: either primary surgery or neoadjuvant chemotherapy followed by interval surgery. The primary outcome was futile laparotomy, defined as a PCS with residual disease of > 1 cm. Primary analyses were performed according to the intention-to-treat principle. Results Between May 2011 and February 2015, 201 participants were included, of whom 102 were assigned to diagnostic laparoscopy and 99 to primary surgery. In the laparoscopy group, 63 (62%) of 102 patients underwent PCS versus 93 (94%) of 99 patients in the primary surgery group. Futile laparotomy occurred in 10 (10%) of 102 patients in the laparoscopy group versus 39 (39%) of 99 patients in the primary surgery group (relative risk, 0.25; 95% CI, 0.13 to 0.47; P < .001). In the laparoscopy group, three (3%) of 102 patients underwent both primary and interval surgery compared with 28 (28%) of 99 patients in the primary surgery group ( P < .001). Conclusion Diagnostic laparoscopy reduced the number of futile laparotomies in patients with suspected advanced-stage ovarian cancer. In women with a plan for PCS, these data suggest that performance of diagnostic laparoscopy first is reasonable and that if cytoreduction to < 1 cm of residual disease seems feasible, to proceed with PCS.

In diabetic pregnancy, use of intermittent retrospective CGM did not reduce the risk of macrosomia. CGM provides detailed information concerning glycaemic fluctuations but, as a treatment strategy, does not translate into improved pregnancy outcome.

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ObjectiveTo assess barriers and facilitators to de-implementation.DesignA qualitative evidence synthesis with a framework analysis.Data sourcesMedline, Embase, Cochrane Library and Rx for Change databases until September 2018 were searched.Eligibility criteriaWe included studies that primarily focused on identifying factors influencing de-implementation or the continuation of low-value care, and studies describing influencing factors related to the effect of a de-implementation strategy.Data extraction and synthesisThe factors were classified on five levels: individual provider, individual patient, social context, organisational context, economic/political context.ResultsWe identified 333 factors in 81 articles. Factors related to the individual provider (n=131; 74% barriers, 17% facilitators, 9% both barrier/facilitator) were associated with their attitude (n=72; 55%), knowledge/skills (n=43; 33%), behaviour (n=11; 8%) and provider characteristics (n=5; 4%). Individual patient factors (n=58; 72% barriers, 9% facilitators, 19% both barrier/facilitator) were mainly related to knowledge (n=33; 56%) and attitude (n=13; 22%). Factors related to the social context (n=46; 41% barriers, 48% facilitators, 11% both barrier/facilitator) included mainly professional teams (n=23; 50%) and professional development (n=12; 26%). Frequent factors in the organisational context (n=67; 67% barriers, 25% facilitators, 8% both barrier/facilitator) were available resources (n=28; 41%) and organisational structures and work routines (n=24; 36%). Under the category of economic and political context (n=31; 71% barriers, 13% facilitators, 16% both barrier/facilitator), financial incentives were most common (n=27; 87%).ConclusionsThis study provides in-depth insight into the factors within the different (sub)categories that are important in reducing low-value care. This can be used to identify barriers and facilitators in low-value care practices or to stimulate development of strategies that need further refinement. We conclude that multifaceted de-implementation strategies are often necessary for effective reduction of low-value care. Situation-specific knowledge of impeding or facilitating factors across all levels is important for designing tailored de-implementation strategies.

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