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Background: Two randomized controlled trials compared the neonatal and infant outcomes after fetoscopic endoluminal tracheal occlusion with expectant prenatal management in fetuses with severe and moderate isolated congenital diaphragmatic hernia, respectively. Fetoscopic endoluminal tracheal occlusion was carried out at 27 +0 to 29 +6 weeks’ gestation (referred to as “early”) for severe and at 30 +0 to 31 +6 weeks (“late”) for moderate hypoplasia. The reported absolute increase in the survival to discharge was 13% (95% confidence interval, −1 to 28; P=.059) and 25% (95% confidence interval, 6–46; P=.0091) for moderate and severe hypoplasia. Objective: Data from the 2 trials were pooled to study the heterogeneity of the treatment effect by observed over expected lung-to-head ratio and explore the effect of gestational age at balloon insertion. Study Design: Individual participant data from the 2 trials were reanalyzed. Women were assessed between 2008 and 2020 at 14 experienced fetoscopic endoluminal tracheal occlusion centers and were randomized in a 1:1 ratio to either expectant management or fetoscopic endoluminal tracheal occlusion. All received standardized postnatal management. The combined data involved 287 patients (196 with moderate hypoplasia and 91 with severe hypoplasia). The primary endpoint was survival to discharge from the neonatal intensive care unit. The secondary endpoints were survival to 6 months of age, survival to 6 months without oxygen supplementation, and gestational age at live birth. Penalized regression was used with the following covariates: intervention (fetoscopic endoluminal tracheal occlusion vs expectant), early balloon insertion (yes vs no), observed over expected lung-to-head ratio, liver herniation (yes vs no), and trial (severe vs moderate). The interaction between intervention and the observed over expected lung-to-head ratio was evaluated to study treatment effect heterogeneity. Results: For survival to discharge, the adjusted odds ratio of fetoscopic endoluminal tracheal occlusion was 1.78 (95% confidence interval, 1.05–3.01; P=.031). The additional effect of early balloon insertion was highly uncertain (adjusted odds ratio, 1.53; 95% confidence interval, 0.60–3.91; P=.370). When combining these 2 effects, the adjusted odds ratio of fetoscopic endoluminal tracheal occlusion with early balloon insertion was 2.73 (95% confidence interval, 1.15–6.49). The results for survival to 6 months and survival to 6 months without oxygen dependence were comparable. The gestational age at delivery was on average 1.7 weeks earlier (95% confidence interval, 1.1–2.3) following fetoscopic endoluminal tracheal occlusion with late insertion and 3.2 weeks earlier (95% confidence interval, 2.3–4.1) following fetoscopic endoluminal tracheal occlusion with early insertion compared with expectant management. There was no evidence that the effect of fetoscopic endoluminal tracheal occlusion depended on the observed over expected lung-to-head ratio for any of the endpoints. Conclusion: This analysis suggests that fetoscopic endoluminal tracheal occlusion increases survival for both moderate and severe lung hypoplasia. The difference between the results for the Tracheal Occlusion To Accelerate Lung growth trials, when considered apart, may be because of the difference in the time point of balloon insertion. However, the effect of the time point of balloon insertion could not be robustly assessed because of a small sample size and the confounding effect of disease severity. Fetoscopic endoluminal tracheal occlusion with early balloon insertion in particular strongly increases the risk for preterm delivery.

INTRODUCTION: There is no global agreement on how to best determine pregnancy of unknown location viability and location using biomarkers. Measurements of progesterone and β human chorionic gonadotropin (βhCG) are still used in clinical practice to exclude the possibility of a viable intrauterine pregnancy (VIUP). We evaluate the predictive value of progesterone, βhCG, and βhCG ratio cut-off levels to exclude a VIUP in women with a pregnancy of unknown location. MATERIAL AND METHODS: This was a secondary analysis of prospective multicenter study data of consecutive women with a pregnancy of unknown location between January 2015 and 2017 collected from dedicated early pregnancy assessment units of eight hospitals. Single progesterone and serial βhCG measurements were taken. Women were followed up until final pregnancy outcome between 11 and 14 weeks of gestation was confirmed using transvaginal ultrasonography: (1) VIUP, (2) non-viable intrauterine pregnancy or failed pregnancy of unknown location, and (3) ectopic pregnancy or persisting pregnancy of unknown location. The predictive value of cut-off levels for ruling out VIUP were evaluated across a range of values likely to be encountered clinically for progesterone, βhCG, and βhCG ratio. RESULTS: Data from 2507 of 3272 (76.6%) women were suitable for analysis. All had data for βhCG levels, 2248 (89.7%) had progesterone levels, and 1809 (72.2%) had βhCG ratio. The likelihood of viability falls with the progesterone level. Although the median progesterone level associated with viability was 59 nmol/L, VIUP were identified with levels as low as 5 nmol/L. No single βhCG cut-off reliably ruled out the presence of viability with certainty, even when the level was more than 3000 IU/L, there were 39/358 (11%) women who had a VIUP. The probability of viability decreases with the βhCG ratio. Although the median βhCG ratio associated with viability was 2.26, VIUP were identified with ratios as low as 1.02. A progesterone level below 2 nmol/L and βhCG ratio below 0.87 were unlikely to be associated with viability but were not definitive when considering multiple imputation. CONCLUSIONS: Cut-off levels for βhCG, βhCG ratio, and progesterone are not safe to be used clinically to exclude viability in early pregnancy. Although βhCG ratio and progesterone have slightly better performance in comparison, single βhCG used in this manner is highly unreliable. ispartof: ACTA OBSTETRICIA ET GYNECOLOGICA SCANDINAVICA vol:101 issue:1 pages:46-55 ispartof: location:United States status: published

OBJECTIVE: Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING: We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD. RESULTS: Sixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n=12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]); and higher in development with validation studies (median: 49% [range: 33%-59%]). CONCLUSION: Reporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste. ispartof: JOURNAL OF CLINICAL EPIDEMIOLOGY vol:138 pages:60-72 ispartof: location:United States status: published

Covid-19 research made it painfully clear that the scandal of poor medical research, as denounced by Altman in 1994, persists today. The overall quality of medical research remains poor, despite longstanding criticisms. The problems are well known, but the research community fails to properly address them. We suggest that most problems stem from an underlying paradox: although methodology is undeniably the backbone of high-quality and responsible research, science consistently undervalues methodology. The focus remains more on the destination (research claims and metrics) than on the journey. Notwithstanding, research should serve society more than the reputation of those involved. While we notice that many initiatives are being established to improve components of the research cycle, these initiatives are too disjointed. The overall system is monolithic and slow to adapt. We assert that top-down action is needed from journals, universities, funders and governments to break the cycle and put methodology first. These actions should involve the widespread adoption of registered reports, balanced research funding between innovative, incremental and methodological research projects, full recognition and demystification of peer review, improved methodological review of reports, adherence to reporting guidelines, and investment in methodological education and research. Currently, the scientific enterprise is doing a major disservice to patients and society. ispartof: JOURNAL OF CLINICAL EPIDEMIOLOGY vol:138 pages:219-226 ispartof: location:United States status: published

Supplemental Digital Content is available in the text., Background: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated. Methods: A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data. Results: We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0–94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th–75th percentile [interquartile range (IQR)], 0.66–0.79), representing a median percent decrease in discrimination of −11.1% (IQR, −32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was −3.7% (IQR, −13.2 to 3.1) for closely related validations (n=123), −9.0 (IQR, −27.6 to 3.9) for related validations (n=862), and −17.2% (IQR, −42.3 to 0) for distantly related validations (n=717; P

Background Decision curve analysis is a method to evaluate prediction models and diagnostic tests that was introduced in a 2006 publication. Decision curves are now commonly reported in the literature, but there remains widespread misunderstanding of and confusion about what they mean. Summary of commentary In this paper, we present a didactic, step-by-step introduction to interpreting a decision curve analysis and answer some common questions about the method. We argue that many of the difficulties with interpreting decision curves can be solved by relabeling the y-axis as “benefit” and the x-axis as “preference.” A model or test can be recommended for clinical use if it has the highest level of benefit across a range of clinically reasonable preferences. Conclusion Decision curves are readily interpretable if readers and authors follow a few simple guidelines.

ispartof: LARYNGOSCOPE vol:129 issue:10 pages:E343-E343 ispartof: location:United States status: published

ObjectiveTo evaluate the performance of diagnostic prediction models for ovarian malignancy in all patients with an ovarian mass managed surgically or conservatively.DesignMulticentre cohort study.Setting36 oncology referral centres (tertiary centres with a specific gynaecological oncology unit) or other types of centre.ParticipantsConsecutive adult patients presenting with an adnexal mass between January 2012 and March 2015 and managed by surgery or follow-up.Main outcome measuresOverall and centre specific discrimination, calibration, and clinical utility of six prediction models for ovarian malignancy (risk of malignancy index (RMI), logistic regression model 2 (LR2), simple rules, simple rules risk model (SRRisk), assessment of different neoplasias in the adnexa (ADNEX) with or without CA125). ADNEX allows the risk of malignancy to be subdivided into risks of a borderline, stage I primary, stage II-IV primary, or secondary metastatic malignancy. The outcome was based on histology if patients underwent surgery, or on results of clinical and ultrasound follow-up at 12 (±2) months. Multiple imputation was used when outcome based on follow-up was uncertain.ResultsThe primary analysis included 17 centres that met strict quality criteria for surgical and follow-up data (5717 of all 8519 patients). 812 patients (14%) had a mass that was already in follow-up at study recruitment, therefore 4905 patients were included in the statistical analysis. The outcome was benign in 3441 (70%) patients and malignant in 978 (20%). Uncertain outcomes (486, 10%) were most often explained by limited follow-up information. The overall area under the receiver operating characteristic curve was highest for ADNEX with CA125 (0.94, 95% confidence interval 0.92 to 0.96), ADNEX without CA125 (0.94, 0.91 to 0.95) and SRRisk (0.94, 0.91 to 0.95), and lowest for RMI (0.89, 0.85 to 0.92). Calibration varied among centres for all models, however the ADNEX models and SRRisk were the best calibrated. Calibration of the estimated risks for the tumour subtypes was good for ADNEX irrespective of whether or not CA125 was included as a predictor. Overall clinical utility (net benefit) was highest for the ADNEX models and SRRisk, and lowest for RMI. For patients who received at least one follow-up scan (n=1958), overall area under the receiver operating characteristic curve ranged from 0.76 (95% confidence interval 0.66 to 0.84) for RMI to 0.89 (0.81 to 0.94) for ADNEX with CA125.ConclusionsOur study found the ADNEX models and SRRisk are the best models to distinguish between benign and malignant masses in all patients presenting with an adnexal mass, including those managed conservatively.Trial registrationClinicalTrials.gov NCT01698632.

Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models 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 or dying 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 17 February 2021, 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 126 978 titles were screened, and 412 studies describing 731 new prediction models or validations were included. Of these 731, 125 were diagnostic models (including 75 based on medical imaging) and the remaining 606 were prognostic models for either identifying those at risk of covid-19 in the general population (13 models) or predicting diverse outcomes in those individuals with confirmed covid-19 (593 models). Owing to the widespread availability of diagnostic testing capacity after the summer of 2020, this living review has now focused on the prognostic models. Of these, 29 had low risk of bias, 32 had unclear risk of bias, and 545 had high risk of bias. The most common causes for high risk of bias were inadequate sample sizes (n=408, 67%) and inappropriate or incomplete evaluation of model performance (n=338, 56%). 381 models were newly developed, and 225 were external validations of existing models. The reported C indexes varied between 0.77 and 0.93 in development studies with low risk of bias, and between 0.56 and 0.78 in external validations with low risk of bias. The Qcovid models, the PRIEST score, Carr’s model, the ISARIC4C Deterioration model, and the Xie model showed adequate predictive performance in studies at low risk of bias. Details on all reviewed models are publicly available at https://www.covprecise.org/.Conclusion Prediction models for covid-19 entered the academic literature to support medical decision making at unprecedented speed and in large numbers. Most published prediction model studies were poorly reported and at high risk of bias such that their reported predictive performances are probably optimistic. Models with low risk of bias should be validated before clinical implementation, preferably through collaborative efforts to also allow an investigation of the heterogeneity in their performance across various populations and settings. 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 modellers 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 the final version of a living systematic review that has been updated over the past two years to reflect emerging evidence. This version is update 4 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.

BACKGROUND: Early pregnancy losses are common, but their psychologic sequelae are often overlooked. Previous studies have established links between miscarriage and early symptoms of anxiety and depression. However, the incidence of posttraumatic stress symptoms and the psychologic response specifically to ectopic pregnancies have not been investigated. OBJECTIVE: The purpose of this study was to investigate levels of posttraumatic stress, depression, and anxiety in women in the 9 months after early pregnancy loss, with a focus on miscarriage and ectopic pregnancy. Morbidity at 1 month was compared with a control group in healthy pregnancy. STUDY DESIGN: This was a prospective cohort study. Consecutive women were recruited from the early pregnancy and antenatal clinics at 3 London hospitals and received emailed surveys that contained standardized psychologic assessments that included the Hospital Anxiety and Depression Scale and Posttraumatic stress Diagnostic Scale, at 1, 3, and 9 months after loss. Control subjects were assessed after a dating scan. We assessed the proportion of participants who met the screening criteria for posttraumatic stress and moderate/severe anxiety or depression. We used logistic regression to calculate adjusted odds ratios. RESULTS: Seven hundred thirty-seven of 1098 women (67%) with early pregnancy loss (including 537 miscarriages and 116 ectopic pregnancies) and 171 of 187 control subjects (91%) agreed to participate. Four hundred ninety-two of the women with losses (67%) completed the Hospital Anxiety and Depression Scale after 1 month; 426 women (58%) completed it after 3 months, and 338 women (46%) completed it after 9 months. Eighty-seven control subjects (51%) participated. Criteria for posttraumatic stress were met in 29% of women with early pregnancy loss after 1 month and in 18% after 9 months (odds ratio per month, 0.80; 95% confidence interval, 0.72-0.89). Moderate/severe anxiety was reported in 24% after 1 month and in 17% after 9 months (odds ratio per month, 0.69; 95% confidence interval, 0.50-0.94). Moderate/severe depression was reported in 11% of the women after 1 month and 6% of the women after 9 months (odds ratio per month, 0.87; 95% confidence interval, 0.53-1.44). After miscarriage, proportions after 9 months were 16% for posttraumatic stress, 17% for anxiety, and 5% for depression. Corresponding figures after ectopic pregnancy were 21%, 23%, and 11%, respectively. In contrast, among control women with viable pregnancies, 13% reported moderate-to-severe anxiety (odds ratio loss at 1 month vs controls: 2.14; 95% confidence interval, 1.14-4.36), and 2% reported moderate-to-severe depression (odds ratio loss at 1 month vs control subjects: 3.88; 95% confidence interval, 1.27-19.2). CONCLUSION: Women experience high levels of posttraumatic stress, anxiety, and depression after early pregnancy loss. Distress declines over time but remains at clinically important levels at 9 months. ispartof: AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY vol:222 issue:4 ispartof: location:United States status: published

ObjectiveTo review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population.DesignRapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection.Data sourcesPubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24thMarch 2020.Study selectionStudies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text.Data extractionData from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST. Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance.Results2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China. Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in infected patients included age, sex, features derived from CT, C-reactive protein, lactic dehydrogenase, and lymphocyte count.Estimated C-index estimates for the prediction models ranged from 0.73 to 0.81 in those for the general population (reported for all 3 general population models), from 0.81 to > 0.99 in those for diagnosis (reported for 13 of the 18 diagnostic models), and from 0.85 to 0.98 in those for prognosis (reported for 6 of the 10 prognostic models). All studies were rated at high 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, and poor statistical analysis, including high risk of model overfitting. Reporting quality varied substantially between studies. A description of the study population and intended use of the models was absent in almost all reports, and calibration of predictions was rarely assessed.ConclusionCOVID-19 related prediction models are quickly entering the academic literature, to support medical decision making at a time where this is urgently needed. Our review indicates proposed models are poorly reported and at high risk of bias. Thus, their reported performance is likely optimistic and using them to support medical decision making is not advised. We call for immediate sharing of the individual participant data from COVID-19 studies to support collaborative efforts in building more rigorously developed prediction models and validating (evaluating) existing models. The aforementioned predictors identified in multiple included studies could be considered as candidate predictors for new models. We also stress the need to follow methodological guidance when developing and validating prediction models, as unreliable predictions may cause more harm than benefit when used to guide clinical decisions. Finally, studies should adhere to the TRIPOD statement to facilitate validating, appraising, advocating and clinically using the reported models.Systematic review registration protocolosf.io/ehc47/,registration: osf.io/wy245Summary boxesWhat is already known on this topic-The sharp recent increase in COVID-19 infections has put a strain on healthcare systems worldwide, necessitating efficient early detection, diagnosis of patients suspected of the infection and prognostication of COVID-19 confirmed cases.-Viral nucleic acid testing and chest CT are standard methods for diagnosing COVID-19, but are time-consuming.-Earlier reports suggest that the elderly, patients with comorbidity (COPD, cardiovascular disease, hypertension), and patients presenting with dyspnoea are vulnerable to more severe morbidity and mortality after COVID-19 infection.What this study adds-We identified three models to predict hospital admission from pneumonia and other events (as a proxy for COVID-19 pneumonia) in the general population.-We identified 18 diagnostic models for COVID-19 detection in symptomatic patients.-13 of these were machine learning models based on CT images.-We identified ten prognostic models for COVID-19 infected patients, of which six aimed to predict mortality risk in confirmed or suspected COVID-19 patients, two aimed to predict progression to a severe or critical state, and two aimed to predict a hospital stay of more than 10 days from admission.-Included studies were poorly reported compromising their subsequent appraisal, and recommendation for use in daily practice. All studies were appraised at high risk of bias, raising concern that the models may be flawed and perform poorly when applied in practice, such that their predictions may be unreliable.

CONTEXT: Urologists regularly develop clinical risk prediction models to support clinical decisions. In contrast to traditional performance measures, decision curve analysis (DCA) can assess the utility of models for decision making. DCA plots net benefit (NB) at a range of clinically reasonable risk thresholds. OBJECTIVE: To provide recommendations on interpreting and reporting DCA when evaluating prediction models. EVIDENCE ACQUISITION: We informally reviewed the urological literature to determine investigators' understanding of DCA. To illustrate, we use data from 3616 patients to develop risk models for high-grade prostate cancer (n=313, 9%) to decide who should undergo a biopsy. The baseline model includes prostate-specific antigen and digital rectal examination; the extended model adds two predictors based on transrectal ultrasound (TRUS). EVIDENCE SYNTHESIS: We explain risk thresholds, NB, default strategies (treat all, treat no one), and test tradeoff. To use DCA, first determine whether a model is superior to all other strategies across the range of reasonable risk thresholds. If so, that model appears to improve decisions irrespective of threshold. Second, consider if there are important extra costs to using the model. If so, obtain the test tradeoff to check whether the increase in NB versus the best other strategy is worth the additional cost. In our case study, addition of TRUS improved NB by 0.0114, equivalent to 1.1 more detected high-grade prostate cancers per 100 patients. Hence, adding TRUS would be worthwhile if we accept subjecting 88 patients to TRUS to find one additional high-grade prostate cancer or, alternatively, subjecting 10 patients to TRUS to avoid one unnecessary biopsy. CONCLUSIONS: The proposed guidelines can help researchers understand DCA and improve application and reporting. PATIENT SUMMARY: Decision curve analysis can identify risk models that can help us make better clinical decisions. We illustrate appropriate reporting and interpretation of decision curve analysis. ispartof: EUROPEAN UROLOGY vol:74 issue:6 pages:796-804 ispartof: location:Switzerland status: published

© 2018 Elsevier B.V. Background: The extent of coronary artery disease (CAD) is relevant for the evaluation and the choice of treatment of patients and consists of the severity of stenoses and their distribution within the coronary tree. Diagnosis is not easy and severe CAD should not be missed. For low-risk patients one wants to avoid the invasive angiography. We aim to propose a diagnostic prediction model of CAD respecting the degree of disease severity. Methods: We included 4888 patients from the Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort. An ordinal regression model was applied to estimate the probabilities of five incrementally disease categories: no CAD, non-obstructive stenosis, and one-, two- and three-vessel disease. We included 11 predictors in the model: age, sex, chest pain, diabetes, hypertension, dyslipidaemia, smoking, HDL and LDL cholesterol, fibrinogen, and C-reactive protein. Bootstrapping was used to validate model performance (discrimination and calibration). Results: Age, sex, and three laboratory measures had a large predictive effect. The model poorly separated most adjacent disease categories, but performed well for categories far apart, with little optimism. The overall discrimination added up to a c statistic of 0.71 (95% CI 0.69 to 0.73). The model enables the estimation of individual patient probabilities of disease severity categories. Conclusions: The proposed ordinal diagnostic risk model, employing routinely obtainable variables, allows distinguishing the extent of CAD and can especially discriminate between non-obstructive stenosis and multi-vessel disease in our CARDIIGAN patients. This can help to decide on treatment strategy and thereby reduce the number of unnecessary angiographies. ispartof: INTERNATIONAL JOURNAL OF CARDIOLOGY vol:267 pages:8-12 ispartof: location:Netherlands status: published

BACKGROUND: Awareness is growing that cancer can be treated during pregnancy, but the effect of this change on maternal and neonatal outcomes is unknown. The International Network on Cancer, Infertility and Pregnancy (INCIP) registers the incidence and maternal, obstetric, oncological, and neonatal outcomes of cancer occurring during pregnancy. We aimed to describe the oncological management and obstetric and neonatal outcomes of patients registered in INCIP and treated in the past 20 years, and assess associations between cancer type or treatment modality and obstetric and neonatal outcomes. METHODS: This descriptive cohort study included pregnant patients with cancer registered from all 37 centres (from 16 countries) participating in the INCIP registry. Oncological, obstetric, and neonatal outcome data of consecutive patients diagnosed with primary invasive cancer during pregnancy between Jan 1, 1996, and Nov 1, 2016, were retrospectively and prospectively collected. We analysed changes over time in categorical patient characteristics, outcomes, and treatment methods with log-binomial regression. We used multiple logistic regression to analyse preterm, prelabour rupture of membranes (PPROM) or preterm contractions, small for gestational age, and admission to the neonatal intensive care unit (NICU). The INCIP registry study is registered with ClinicalTrials.gov, number NCT00330447, and is ongoing. FINDINGS: 1170 patients were included in the analysis and 779 (67%) received treatment during pregnancy. Breast cancer was the most common malignant disease (462 [39%]). Every 5 years, the likelihood of receiving treatment during pregnancy increased (relative risk [RR] 1·10, 95% CI 1·05-1·15), mainly related to an increase of chemotherapeutic treatment (1·31, 1·20-1·43). Overall, 955 (88%) of 1089 singleton pregnancies ended in a livebirth, of which 430 (48%) of 887 pregnancies ended preterm. Each 5 years, we observed more livebirths (RR 1·04, 95% CI 1·01-1·06) and fewer iatrogenic preterm deliveries (0·91, 0·84-0·98). Our data suggest a relationship between platinum-based chemotherapy and small for gestational age (odds ratio [OR] 3·12, 95% CI 1·45-6·70), and between taxane chemotherapy and NICU admission (OR 2·37, 95% CI 1·31-4·28). NICU admission seemed to depend on cancer type, with gastrointestinal cancers having highest risk (OR 7·13, 95% CI 2·86-17·7) and thyroid cancers having lowest risk (0·14, 0·02-0·90) when compared with breast cancer. Unexpectedly, the data suggested that abdominal or cervical surgery was associated with a reduced likelihood of NICU admission (OR 0·30, 95% CI 0·17-0·55). Other associations between treatment or cancer type and outcomes were less clear. INTERPRETATION: Over the years, the proportion of patients with cancer during pregnancy who received antenatal treatment increased, especially treatment with chemotherapy. Our data indicate that babies exposed to antenatal chemotherapy might be more likely to develop complications, specifically small for gestational age and NICU admission, than babies not exposed. We therefore recommend involving hospitals with obstetric high-care units in the management of these patients. FUNDING: Research Foundation-Flanders, European Research Council, Charles University, Ministry of Health of the Czech Republic. ispartof: The Lancet Oncology vol:19 issue:3 pages:337-346 ispartof: location:England status: published

Polytomous discrimination index is a novel and important diagnostic accuracy measure for multi-category classification. After reconstructing its probabilistic definition, we propose a nonparametric approach to the estimation of polytomous discrimination index based on an empirical sample of biomarker values. In this paper, we provide the finite-sample and asymptotic properties of the proposed estimators and such analytic results may facilitate the statistical inference. Simulation studies are performed to examine the performance of the nonparametric estimators. Two real data examples are analysed to illustrate our methodology.

ispartof: JOURNAL OF ULTRASOUND IN MEDICINE vol:40 issue:8 pages:1731-1732 ispartof: location:England status: published

OBJECTIVE: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. STUDY DESIGN AND SETTING: We performed logistic (LR), lasso, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n=11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks, and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with GCS, Data used in preparation of this manuscript were obtained in the context of CENTER-TBI, a large collaborative project with the support of the European Union 7th Framework program (EC grant 602150). Additional funding was obtained from the Hannelore Kohl Stiftung (Germany), the OneMind (USA) and the Integra LifeSciences Corporation (USA). The funder had no role in the study design, enrollment, collection of data, writing, or publication decisions.

There is increasing awareness that the methodology and findings of research should be transparent. This includes studies using artificial intelligence to develop predictive algorithms that make individualized diagnostic or prognostic risk predictions. We argue that it is paramount to make the algorithm behind any prediction publicly available. This allows independent external validation, assessment of performance heterogeneity across settings and over time, and algorithm refinement or updating. Online calculators and apps may aid uptake if accompanied with sufficient information. For algorithms based on "black box" machine learning methods, software for algorithm implementation is a must. Hiding algorithms for commercial exploitation is unethical, because there is no possibility to assess whether algorithms work as advertised or to monitor when and how algorithms are updated. Journals and funders should demand maximal transparency for publications on predictive algorithms, and clinical guidelines should only recommend publicly available algorithms. ispartof: JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION vol:26 issue:12 pages:1651-1654 ispartof: location:England status: published

ObjectivesTo determine the prevalence of burnout in doctors practising obstetrics and gynaecology, and assess the association with defensive medical practice and self-reported well-being.DesignNationwide online cross-sectional survey study; December 2017–March 2018.SettingHospitals in the UK.Participants5661 practising obstetrics and gynaecology consultants, specialty and associate specialist doctors and trainees registered with the Royal College of Obstetricians and Gynaecologists.Primary and secondary outcome measuresPrevalence of burnout using the Maslach Burnout Inventory and defensive medical practice (avoiding cases or procedures, overprescribing, over-referral) using a 12-item questionnaire. The odds ratios (OR) of burnout with defensive medical practice and self-reported well-being.Results3102/5661 doctors (55%) completed the survey. 3073/3102 (99%) met the inclusion criteria (1462 consultants, 1357 trainees and 254 specialty and associate specialist doctors). 1116/3073 (36%) doctors met the burnout criteria, with levels highest amongst trainees (580/1357 (43%)). 258/1116 (23%) doctors with burnout reported increased defensive practice compared with 142/1957 (7%) without (adjusted OR 4.35, 95% CI 3.46 to 5.49). ORs of burnout with well-being items varied between 1.38 and 6.37, and were highest for anxiety (3.59, 95% CI 3.07 to 4.21), depression (4.05, 95% CI 3.26 to 5.04) and suicidal thoughts (6.37, 95% CI 95% CI 3.95 to 10.7). In multivariable logistic regression, being of younger age, white or ‘other’ ethnicity, and graduating with a medical degree from the UK or Ireland had the strongest associations with burnout.ConclusionsHigh levels of burnout were observed in obstetricians and gynaecologists and particularly among trainees. Burnout was associated with both increased defensive medical practice and worse doctor well-being. These findings have implications for the well-being and retention of doctors as well as the quality of patient care, and may help to inform the content of future interventions aimed at preventing burnout and improving patient safety.

Background Clinical prediction models are useful in estimating a patient’s risk of having a certain disease or experiencing an event in the future based on their current characteristics. Defining an appropriate risk threshold to recommend intervention is a key challenge in bringing a risk prediction model to clinical application; such risk thresholds are often defined in an ad hoc way. This is problematic because tacitly assumed costs of false positive and false negative classifications may not be clinically sensible. For example, when choosing the risk threshold that maximizes the proportion of patients correctly classified, false positives and false negatives are assumed equally costly. Furthermore, small to moderate sample sizes may lead to unstable optimal thresholds, which requires a particularly cautious interpretation of results. Main text We discuss how three common myths about risk thresholds often lead to inappropriate risk stratification of patients. First, we point out the contexts of counseling and shared decision-making in which a continuous risk estimate is more useful than risk stratification. Second, we argue that threshold selection should reflect the consequences of the decisions made following risk stratification. Third, we emphasize that there is usually no universally optimal threshold but rather that a plausible risk threshold depends on the clinical context. Consequently, we recommend to present results for multiple risk thresholds when developing or validating a prediction model. Conclusion Bearing in mind these three considerations can avoid inappropriate allocation (and non-allocation) of interventions. Using discriminating and well-calibrated models will generate better clinical outcomes if context-dependent thresholds are used.

IntroductionInfertility and in vitro fertilisation (IVF; with or without intracytoplasmic sperm injection) result in considerable emotional and financial burden. Increasing evidence suggests that lifestyle factors, including diet, physical activity and personal well-being, are associated with IVF-success rates. Currently, IVF is not routinely combined with a lifestyle programme. The preconception lifestyle (PreLiFe) randomised controlled trial (RCT) assesses the effects of a new mobile PreLiFe programme in couples undergoing IVF.Methods and analysisA multicentre RCT including 460 heterosexual couples starting IVF in Belgian fertility clinics. IVF couples are randomised between an attention control group or the PreLiFe programme for a period of 12 months or until an ongoing pregnancy is confirmed by ultrasound. The attention control programme includes a mobile application with treatment information (ie, appointments and medication instructions) in addition to standard care. The PreLiFe programme includes a mobile application with the same treatment information in combination with a lifestyle programme. This new lifestyle programme includes tailored advice and skills training on diet, physical activity and mindfulness in combination with text messages and telephone interaction with a healthcare professional trained in motivational interviewing. The primary outcome of this RCT is the cumulative ongoing pregnancy rate within 12 months after randomisation. Secondary outcomes include changes in diet, physical activity, emotional distress, body mass index, waist circumference, quality of life and other reproductive outcomes including IVF discontinuation, clinical pregnancy rate and time to pregnancy. Additionally, partner support and the feasibility (use and acceptability) of the PreLiFe programme will be evaluated in the intervention group. Analysis will be according to intention to treat.Ethics and disseminationThis study has been approved by the Medical Ethical Committee of the Leuven University Hospital (Belgium) and the other recruiting clinics. The findings of this RCT will be disseminated through presentations at international scientific meetings and peer-reviewed publications.Trial registration numberNCT03790449; Pre-results.

OBJECTIVES: The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature. STUDY DESIGN AND SETTING: We conducted a Medline literature search (1/2016 to 8/2017) and extracted comparisons between LR and ML models for binary outcomes. RESULTS: We included 71 of 927 studies. The median sample size was 1,250 (range 72-3,994,872), with 19 predictors considered (range 5-563) and eight events per predictor (range 0.3-6,697). The most common ML methods were classification trees, random forests, artificial neural networks, and support vector machines. In 48 (68%) studies, we observed potential bias in the validation procedures. Sixty-four (90%) studies used the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was not addressed in 56 (79%) studies. We identified 282 comparisons between an LR and ML model (AUC range, 0.52-0.99). For 145 comparisons at low risk of bias, the difference in logit(AUC) between LR and ML was 0.00 (95% confidence interval, -0.18 to 0.18). For 137 comparisons at high risk of bias, logit(AUC) was 0.34 (0.20-0.47) higher for ML. CONCLUSION: We found no evidence of superior performance of ML over LR. Improvements in methodology and reporting are needed for studies that compare modeling algorithms. ispartof: JOURNAL OF CLINICAL EPIDEMIOLOGY vol:110 pages:12-22 ispartof: location:United States status: published

BACKGROUND: Angiopoietin 1 and 2 regulate angiogenesis and vascular remodelling by interacting with the tyrosine kinase receptor Tie2, and inhibition of angiogenesis has shown promise in the treatment of ovarian cancer. We aimed to assess whether trebananib, a peptibody that inhibits binding of angiopoietin 1 and 2 to Tie2, improved progression-free survival when added to carboplatin and paclitaxel as first-line therapy in advanced epithelial ovarian, primary fallopian tube, or peritoneal cancer in a phase 3 clinical trial. METHODS: TRINOVA-3, a multicentre, multinational, phase 3, double-blind study, was done at 206 investigational sites (hospitals and cancer centres) in 14 countries. Eligible patients were aged 18 years or older with biopsy-confirmed International Federation of Gynecology and Obstetrics (FIGO) stage III to IV epithelial ovarian, primary peritoneal, or fallopian tube cancers, and an ECOG performance status of 0 or 1. Eligible patients were randomly assigned (2:1) using a permuted block method (block size of six patients) to receive six cycles of paclitaxel (175 mg/m2) and carboplatin (area under the serum concentration-time curve 5 or 6) every 3 weeks, plus weekly intravenous trebananib 15 mg/kg or placebo. Maintenance therapy with trebananib or placebo continued for up to 18 additional months. The primary endpoint was progression-free survival, as assessed by the investigators, in the intention-to-treat population. Safety analyses included patients who received at least one dose of study treatment. This trial is registered with ClinicalTrials.gov, number NCT01493505, and is complete. FINDINGS: Between Jan 30, 2012, and Feb 25, 2014, 1164 patients were screened and 1015 eligible patients were randomly allocated to treatment (678 to trebananib and 337 to placebo). After a median follow-up of 27·4 months (IQR 17·7-34·2), 626 patients had progression-free survival events (405 [60%] of 678 in the trebananib group and 221 [66%] of 337 in the placebo group). Median progression-free survival did not differ between the trebananib group (15·9 months [15·0-17·6]) and the placebo group (15·0 months [12·6-16·1]) groups (hazard ratio 0·93 [95% CI 0·79-1·09]; p=0·36). 512 (76%) of 675 patients in the trebananib group and 237 (71%) of 336 in the placebo group had grade 3 or worse treatment-emergent adverse events; of which the most common events were neutropenia (trebananib 238 [35%] vs placebo 126 [38%]) anaemia (76 [11%] vs 40 [12%]), and leucopenia (81 [12%] vs 35 [10%]). 269 (40%) patients in the trebananib group and 104 (31%) in the placebo group had serious adverse events. Two fatal adverse events in the trebananib group were considered related to trebananib, paclitaxel, and carboplatin (lung infection and neutropenic colitis); two were considered to be related to paclitaxel and carboplatin (general physical health deterioration and platelet count decreased). No treatment-related fatal adverse events occurred in the placebo group. INTERPRETATION: Trebananib plus carboplatin and paclitaxel did not improve progression-free survival as first-line treatment for advanced ovarian cancer. The combination of trebananib plus carboplatin and paclitaxel did not produce new safety signals. These results show that trebananib in combination with carboplatin and paclitaxel is minimally effective in this patient population. FUNDING: Amgen. ispartof: LANCET ONCOLOGY vol:20 issue:6 pages:862-876 ispartof: location:England status: published

ispartof: JAMA Internal Medicine vol:179 issue:5 pages:731-731 ispartof: location:United States status: published

BackgroundTreatment of cervical cancer during pregnancy is often complex and challenging. This study aimed to analyze current patterns of practice in the management of pregnant patients diagnosed with cervical cancer.MethodsThis was a matched cohort study comprising patients managed for cervical cancer during pregnancy from six European centers. Patient information was retrieved from the dataset of the International Network for Cancer, Infertility and Pregnancy from 1990 to 2012. Each center matched its patients with two non-pregnant controls for age (±5 years) and International Federation of Gynecology and Obstetrics (FIGO) 2009 stage. Information on age, histological type, grade, lymphovascular space invasion, stage, tumor size, method of diagnosis, site of recurrence, delivery, date of recurrence, and date of death was recorded. Progression-free survival was compared using multivariable Cox proportional hazards regression.ResultsA total of 132 pregnant patients and 256 controls were analyzed. The pregnant patients (median age 34 years, range 21–43) were diagnosed at a median gestational age of 18.4 weeks of pregnancy (range 7–39). Stage distribution during pregnancy was 14.4% for stage IA, 47.0% for IB1, 18.9% for IB2, and 19.7% for II-IV. For treatment during pregnancy, 17.4% of the patients underwent surgery, 16.7% received neoadjuvant chemotherapy, 26.5% delayed their treatment, 12.9% had a premature delivery, and 26.5% had their pregnancy terminated. Median follow-up was 84 months (67 months for pregnant and 95 months for non-pregnant patients). The unadjusted hazard ratio of pregnancy for progression-free survival was 1.18 (95% confidence interval 0.74 to 1.88).ConclusionSurgery and chemotherapy is increasingly used in the management of pregnant patients with cervical cancer and prognosis is similar to that of non-pregnant patients.

Background In estrogen receptor-positive (ER+), human epidermal growth factor receptor 2 (HER-2) negative breast cancers, the progesterone receptor (PR) is an independent prognostic marker. Little is known about the prognostic value of PR by tumor grade. We assessed this in two independent datasets. Patients and Methods Women with primary operable, invasive ER+ HER-2 negative breast cancer diagnosed between 2000 and 2012, treated at University Hospitals Leuven, were included. We assessed the association of PR status and subtype (grade 1–2 vs. grade 3) with distant recurrence-free interval (DRFI) and breast cancer-specific survival. The interaction between PR status and subtype was investigated, and associations of PR status by subtype were calculated. The BIG 1-98 data set was used for validation. Results In total, 4,228 patients from Leuven and 5,419 from BIG 1-98 were analyzed. In the Leuven cohort, the adjusted hazard ratio (HR) of PR-positive versus PR-negative tumors for DRFI was 0.66 (95% confidence interval [CI], 0.50–0.89). For the interaction with subtype (p = .34), the HR of PR status was 0.79 (95% CI, 0.61–1.01) in luminal A-like and 0.59 (95% CI, 0.46–0.76) in luminal B-like tumors. In luminal A-like tumors, observed 5-year cumulative incidences of distant recurrence were 4.1% for PR-negative and 2.8% for PR-positive tumors, and in luminal B-like 18.7% and 9.2%, respectively. In the BIG 1-98 cohort, similar results were observed; for the interaction with subtype (p = .12), the adjusted HR of PR status for DRFI was 0.88 (95% CI, 0.57–1.35) in luminal A-like and 0.58 (95% CI, 0.43–0.77) in luminal B-like tumors. Observed 5-year cumulative incidences were similar. Conclusion PR positivity may be more protective against metastatic relapse in luminal B-like versus luminal A-like breast cancer, but no strong conclusions can be made. In absolute risk, results suggest an absent PR is clinically more important in high compared with low proliferative ER+ HER-2 negative tumors. Implications for Practice An absent progesterone receptor (PR) predicts a worse outcome in women treated for an estrogen receptor-positive, human epidermal growth factor receptor 2 negative breast cancer. As low proliferative tumors lacking PR are now also classified high risk, the prognostic value of PR across risk groups was studied. Despite a negative test for interaction of the prognostic value of PR by tumor grade, the magnitude of an absent PR on breast cancer relapse is much larger in high than in low proliferative breast cancers.

Study Question Do sonographic characteristics of ovarian endometriomas vary with age in premenopausal women? Summary Answer With increasing age, multilocular cysts and cysts with papillations and other solid components become more common whereas ground glass echogenicity of cyst fluid becomes less common. What is Known Already Expectant or medical management of women with endometriomas is now accepted. Therefore, the accuracy of non-invasive diagnosis of these cysts is pivotal. A clinically relevant question is whether the sonographic characteristics of ovarian endometriomas are the same irrespective of the age of the woman. Study Design, Size, Duration This is a secondary analysis of cross-sectional data in the International Ovarian Tumor Analysis (IOTA) database. The database contains clinical and ultrasound information collected pre-operatively between 1999 and 2012 from 5914 patients with adnexal masses in 24 ultrasound centres in 10 countries. Participants/Materials, Setting, Methods There were 1005 histologically confirmed endometriomas in adult premenopausal patients found in the database and these were used in our analysis. The following ultrasound variables (defined using IOTA terminology) were used to describe the ultrasound appearance of the endometriomas: tender mass at ultrasound, largest diameter of lesion, tumour type (unilocular, unilocular-solid, multilocular, multilocular-solid, solid), echogenicity of cyst content, presence of papillations, number of papillations, height (mm) of largest papillation, presence and proportion of solid tissue and number of cyst locules, as well as vascularity in papillations and colour content of the tumour scan (colour score) on colour or power Doppler ultrasounds. Results are reported as median difference or odds ratio (OR) per 10 years increase in age. Main Results and the Role of Chance Maximal lesion diameter did not vary substantially with age (+1.3 mm difference per 10 years increase in age, 95% confidence interval (CI)-1.4 to 4.0). Tender mass at scan was less common in the older the woman (OR 0.75, 95% CI 0.63-0.89), as were unilocular cysts relative to multilocular cysts (OR 0.70, 95% CI 0.57-0.85) and to lesions with solid components (OR 0.61, 95% CI 0.48-0.77), and ground glass echogenicity relative to homogeneous low-level echogenicity (OR 0.74, 95% CI 0.58-0.94) and other types of echogenicity of cyst contents (OR 0.64, 95% CI 0.50-0.81). Papillations were more common the older the woman (OR 1.65, 95% CI 1.24-2.21), but their height and vascularization showed no clear relation to age. LIMITATIONS, REASONS FOR CAUTION It is a limitation that we have little clinical information on the women included, e.g. previous surgery or medical treatment for endometriosis. It is important to emphasize that we do not know the age of the endometrioma itself and that our study is not longitudinal and so does not describe changes in endometriomas over time. The differences in the ultrasound appearance of endometriomas between women of different ages might be explained by previous surgery or medical treatment and might not be an effect of age per se. Wider Implications of the Findings Awareness of physicians that the ultrasound appearance of endometriomas differs between women of different ages may facilitate a correct diagnosis of endometrioma. STUDY FUNDING/COMPETING INTEREST(S) This study was supported in part by the Regione Autonoma della Sardegna (project code CPR-24750). B.V.C., A.C. and D.T. are supported by the Fund for Scientific Research Flanders, Belgium (FWO). The authors declare that there is no conflict of interest.

BACKGROUND: Ovarian tumours are usually surgically removed because of the presumed risk of complications. Few large prospective studies on long-term follow-up of adnexal masses exist. We aimed to estimate the cumulative incidence of cyst complications and malignancy during the first 2 years of follow-up after adnexal masses have been classified as benign by use of ultrasonography. METHODS: In the international, prospective, cohort International Ovarian Tumor Analysis Phase 5 (IOTA5) study, patients aged 18 years or older with at least one adnexal mass who had been selected for surgery or conservative management after ultrasound assessment were recruited consecutively from 36 cancer and non-cancer centres in 14 countries. Follow-up of patients managed conservatively is ongoing at present. In this 2-year interim analysis, we analysed patients who were selected for conservative management of an adnexal mass judged to be benign on ultrasound on the basis of subjective assessment of ultrasound images. Conservative management included ultrasound and clinical follow-up at intervals of 3 months and 6 months, and then every 12 months thereafter. The main outcomes of this 2-year interim analysis were cumulative incidence of spontaneous resolution of the mass, torsion or cyst rupture, or borderline or invasive malignancy confirmed surgically in patients with a newly diagnosed adnexal mass. IOTA5 is registered with ClinicalTrials.gov, number NCT01698632, and the central Ethics Committee and the Belgian Federal Agency for Medicines and Health Products, number S51375/B32220095331, and is ongoing. FINDINGS: Between Jan 1, 2012, and March 1, 2015, 8519 patients were recruited to IOTA5. 3144 (37%) patients selected for conservative management were eligible for inclusion in our analysis, of whom 221 (7%) had no follow-up data and 336 (11%) were operated on before a planned follow-up scan was done. Of 2587 (82%) patients with follow-up data, 668 (26%) had a mass that was already in follow-up at recruitment, and 1919 (74%) presented with a new mass at recruitment (ie, not already in follow-up in the centre before recruitment). Median follow-up of patients with new masses was 27 months (IQR 14-38). The cumulative incidence of spontaneous resolution within 2 years of follow-up among those with a new mass at recruitment (n=1919) was 20·2% (95% CI 18·4-22·1), and of finding invasive malignancy at surgery was 0·4% (95% CI 0·1-0·6), 0·3% (

AIM: We have assessed the potential predictive ability of the biomarkers activin B and fibronectin (FN1) alone and when added to established markers for triaging patients as being at low or high risk of ectopic pregnancy (EP). We also assessed their use as predictors of viability at 12 weeks gestation. METHODS: Exploratory secondary analysis of a prospective study including all women classified as a pregnancy of known location (PUL) based on transvaginal ultrasonography between January and December 2007 at the early pregnancy unit of St Georges’ Hospital (London). We used multinomial logistic regression to assess the diagnostic potential of the biomarkers to triage PUL at high risk of complications (EP or persistent PUL), and standard binary logistic regression to predict first trimester viability at 12 weeks. RESULTS: For discriminating high‐risk (n = 16) from low‐risk PUL (n = 93), the area under the receiver operating characteristic curve (AUC) was 0.75 (95% confidence interval 0.60–0.85) for activin B and 0.55 (0.41–0.68) for FN1. Adding activin B to a multinomial logistic regression model incorporating β‐hCG ratio and initial progesterone yielded odds ratios of 0.16 (0.05–0.55) for failing vs high‐risk PUL and 0.29 (0.07–1.19) for intrauterine vs high‐risk PUL and increased the model's AUC from 0.84 to 0.89. At a risk threshold of 5% for high‐risk PUL, sensitivity increased from 84% to 87% and specificity from 48% to 64%. For discriminating viable (n = 28) from non‐viable (n = 81) pregnancies at 12 weeks, both markers had an AUC of 0.54. CONCLUSIONS: Our results suggested that activin B may be a promising marker to improve PUL triage in addition to established markers.

PURPOSE: Controversy remains around acceptable surgical delay of acute hip fractures with current guidelines ranging from 24 to 48 h. Increasing healthcare costs force us to consider the economic burden as well. We aimed to evaluate the adjusted effect of surgical delay for hip fracture surgery on early mortality, healthcare costs and readmission rate. We hypothesized that shorter delays resulted in lower early mortality and costs. METHODS: In this retrospective cohort study 2573 consecutive patients aged ≥50 years were included, who underwent surgery for acute hip fractures between 2009 and 2017. Main endpoints were thirty- and ninety-day mortality, total cost, and readmission rate. Multivariable regression included sex, age and ASA score as covariates. RESULTS: Thirty-day mortality was 5% (n = 133), ninety-day mortality 12% (n = 304). Average total cost was €11960, dominated by hospitalization (59%) and honoraria (23%). Per 24 h delay, the adjusted odds ratio was 1.07 (95% CI 0.98-1.18) for thirty-day mortality, 1.12 (95% CI 1.04-1.19) for ninety-day mortality, and 0.99 (95% CI = 0.88-1.12) for readmission. Per 24 h delay, costs increased with 7% (95% CI 6-8%). For mortality, delay was a weaker predictor than sex, age, and ASA score. For costs, delay was the strongest predictor. We did not find clear cut-points for surgical delay after which mortality or costs increased abruptly. CONCLUSIONS: Despite only modest associations with mortality, we observed a steady increase in healthcare costs when delaying surgery. Hence, a more pragmatic approach with surgery as soon as medically and organizationally possible seems justifiable over rigorous implementation of the current guidelines. ispartof: INJURY-INTERNATIONAL JOURNAL OF THE CARE OF THE INJURED vol:49 issue:6 pages:1169-1175 ispartof: location:Netherlands status: published

Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion. The objective of this study was to challenge these notions and show via a case study concerning risk-based screening for prostate cancer how calculators can be dynamically and locally tailored to improve on-site patient accuracy. Yearly data from five international prostate biopsy cohorts (3 in the US, 1 in Austria, 1 in England) were used to compare 6 methods for annual risk prediction: static use of the online US-developed Prostate Cancer Prevention Trial Risk Calculator (PCPTRC); recalibration of the PCPTRC; revision of the PCPTRC; building a new model each year using logistic regression, Bayesian prior-to-posterior updating, or random forests. All methods performed similarly with respect to discrimination, except for random forests, which were worse. All methods except for random forests greatly improved calibration over the static PCPTRC in all cohorts except for Austria, where the PCPTRC had the best calibration followed closely by recalibration. The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand. publisher: Elsevier articletitle: Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators journaltitle: Journal of Biomedical Informatics articlelink: http://dx.doi.org/10.1016/j.jbi.2015.05.001 content_type: article copyright: Copyright © 2015 Elsevier Inc. All rights reserved. ispartof: Journal of Biomedical Informatics vol:56 pages:87-93 ispartof: location:United States status: published

Key content Introduction to preoperative classification of ovarian and tubal neoplasms. A discussion of existing guidelines by professional societies. Updated evidence and new strategies for preoperative diagnosis of ovarian cancer. Evidence-based recommendations for the diagnosis and characterisation of adnexal masses. Areas of future interest in the field of adnexal tumour diagnosis. Learning objectives To understand the importance of accurate preoperative discrimination of adnexal tumours to ensure optimal patient management. To critically appraise current guidelines by professional societies. To discuss findings from a recent systematic review and meta-analysis, and two diagnostic accuracy studies comparing different types of prediction models for ovarian cancer diagnosis. To understand the advantages and disadvantages of different International Ovarian Tumour Analysis (IOTA) prediction models. Ethical issues Consequences of misclassification of ovarian and tubal masses. Inappropriate use of IOTA prediction models.

Background: The aim of this study was to assess and compare the performance of different ultrasound-based International Ovarian Tumor Analysis (IOTA) strategies and subjective assessment for the diagnosis of early stage ovarian malignancy. Methods: This is a secondary analysis of a prospective multicenter cross-sectional diagnostic accuracy study that included 1653 patients recruited at 18 centers from 2009 to 2012. All patients underwent standardized transvaginal ultrasonography by experienced ultrasound investigators. We assessed test performance of the IOTA Simple Rules (SRs), Simple Rules Risk (SRR), the Assessment of Different NEoplasias in the adneXa (ADNEX) model and subjective assessment to discriminate between stage I-II ovarian cancer and benign disease. Reference standard was histology after surgery. Results: 230 (13.9%) patients proved to have stage I–II primary invasive ovarian malignancy, and 1423 (86.1%) had benign disease. Sensitivity and specificity with respect to malignancy (95% confidence intervals) of the original SRs (classifying all inconclusive cases as malignant) were 94.3% (90.6% to 96.7%) and 73.4% (71.0% to 75.6%). Subjective assessment had a sensitivity and specificity of 90.0% (85.4% to 93.2%) and 86.7% (84.9% to 88.4%), respectively. The areas under the receiver operator characteristic curves of SRR and ADNEX were 0.917 (0.902 to 0.933) and 0.905 (0.920 to 0.934), respectively. At a 1% risk cut-off, sensitivity and specificity for SRR were 100% (98.4% to 100%) and 38.0% (35.5% to 40.6%), and for ADNEX were 100% (98.4% to 100%) and 19.4% (17.4% to 21.5%). At a 30% risk cut-off, sensitivity and specificity for SRR were 88.3% (83.5% to 91.8%) and 81.1% (79% to 83%), and for ADNEX were 84.5% (80.5% to 89.6%) and 84.5% (82.6% to 86.3%). Conclusion: This study shows that all three IOTA strategies have good ability to discriminate between stage I-II ovarian malignancy and benign disease.

Purpose: To evaluate the utility of preoperative diagnostic models for ovarian cancer based on ultrasound and/or biomarkers for referring patients to specialized oncology care. The investigated models were RMI, ROMA, and 3 models from the International Ovarian Tumor Analysis (IOTA) group [LR2, ADNEX, and the Simple Rules risk score (SRRisk)]. Experimental Design: A secondary analysis of prospectively collected data from 2 cross-sectional cohort studies was performed to externally validate diagnostic models. A total of 2,763 patients (2,403 in dataset 1 and 360 in dataset 2) from 18 centers (11 oncology centers and 7 nononcology hospitals) in 6 countries participated. Excised tissue was histologically classified as benign or malignant. The clinical utility of the preoperative diagnostic models was assessed with net benefit (NB) at a range of risk thresholds (5%–50% risk of malignancy) to refer patients to specialized oncology care. We visualized results with decision curves and generated bootstrap confidence intervals. Results: The prevalence of malignancy was 41% in dataset 1 and 40% in dataset 2. For thresholds up to 10% to 15%, RMI and ROMA had a lower NB than referring all patients. SRRisks and ADNEX demonstrated the highest NB. At a threshold of 20%, the NBs of ADNEX, SRrisks, and RMI were 0.348, 0.350, and 0.270, respectively. Results by menopausal status and type of center (oncology vs. nononcology) were similar. Conclusions: All tested IOTA methods, especially ADNEX and SRRisks, are clinically more useful than RMI and ROMA to select patients with adnexal masses for specialized oncology care. Clin Cancer Res; 23(17); 5082–90. ©2017 AACR.

The principal aim of the IOTA project has been to develop approaches to the evaluation of adnexal pathology using ultrasound that can be transferred to all examiners. Creating models that use simple, easily reproducible ultrasound characteristics is one approach.

Introduction In head and neck squamous cell carcinoma (HNSCC) the ability to anticipate an individual patient's outcome is very valuable. With this study we wanted to assess the prognostic value of pretreatment apparent diffusion coefficient (ADC) in a large patient population and integrate it into a multivariable prognostic model. Methods From 2004 to 2010 175 patients with pathology proven HNSCC were included in this study. All patients underwent a pretreatment MRI with diffusion weighted imaging (DWI) using six b -values. For each tumor, three ADC values were calculated using different b -value combinations: ADC low ( b 0–50–100s/mm 2 ), ADC high ( b 500–750–1000s/mm 2 ) and ADC avg (all b -values). The clinical and radiological variables included: tumor and nodal volume, tumor location and age. Disease recurrence was analyzed using competing risk regression. A prognostic model for disease recurrence was developed, and internal validation was performed using bootstrapping and by dividing patients in three equal sized groups based on prognosis. Results One hundred and sixty-one patients were eligible for analysis. Median follow-up was 50months (range 4–86). A total of 67 patients experienced disease recurrence during follow-up (42%). ADC high was a prognostic factor for disease recurrence (adjusted hazard ratio: 1.14 per 10 –4 mm 2 /s, 95% CI 1.04–1.25). Harrell's c-index of the multivariable prognostic model was 0.62 (95% CI 0.56–0.70) after internal validation. The validated 3-year disease recurrence rates for the groups with worst, intermediate, and best prognosis were 56%, 33% and 31% respectively. Conclusion Pretreatment ADC value derived from high b -values is an independent prognostic factor in HNSCC and increases the performance of a multivariable prognostic model in addition to known clinical and radiological variables. Integration of other biomarkers and external validation is necessary to ensure its clinical applicability.

This is a discussion of issues in risk prediction based on the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gerard Biau, Michael Kohler, Inke R. Konig, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. Konig, and Andreas Ziegler.

Background Estrogen receptor (ER) positive, HER2 negative breast cancer (BC) can be classified into luminal A and luminal B-like tumors according to tumor grade. Several evidences point to the fact that IHC expression of the progesterone receptor (PR) has prognostic value. In this study, we assess to what extent a negative PR in luminal A BCs increases the risk of distant metastasis and whether or not luminal B BC might have a good prognosis if PR is positive. Patients and methods Women with primary operable ER positive, HER2 negative BC treated at University Hospitals Leuven between 2000 and 2009 were retrieved from our database. So called luminal A tumors were defined as grade 1-2 BC, whereas so called luminal B BC were defined as grade 3 BC. Distant metastasis free interval (DMFI) and breast cancer specific survival (BCSS) were investigated by their PR status. PR was considered negative if the semi-quantitative Allred score was 0-2. Before 2003 the semi-quantitative H-score was used and a score Results In total, 3294 patients from Leuven were analyzed. From this cohort, 285 patients experienced metastases (8.7%) and 172 patients died of BC (5.2%). Details are shown in Table 1. The median age at diagnosis was 58 years with ages ranging from 22 to 95 years, 2358 patients (71.6%) were aged above 50 at diagnosis. The median follow-up period was 8.1 years. Table 1: Number of patients that metastasized and died of BC by luminal subgroup and PR status. MetastasesBC-related deathluminal APR positive110/2103 (5%)61/2103 (3%) PR negative16/267 (6%)9/267 (3%)luminal BPR positive120/786 (15%)75/786 (10%) PR negative39/138 (28%)27/138 (20%)BC: breast cancer, PR: progesterone receptor In Leuven, the reduction in risk of metastasis in patients with PR positive luminal A and luminal B BC was respectively 14% (Hazard ratio (HR): 0.86, 95% confidence interval (CI) 0.52-1.51) and 47% (HR: 0.53, 95% CI 0.37-0.78) compared with PR negative tumors. PR positive luminal A and luminal B BC patients had a 16% (HR: 0.84, 95 % CI 0.42-1.69) and 53% (HR: 0.47, 95% CI 0.30-0.75) reduction in the risk of BC-related death compared with PR negative tumors respectively. The same analysis was also carried out for postmenopausal patients (older than 50 years) only. In this subcohort, PR positivity was associated with a 9% (HR 0.91, 95% CI 0.50 to 1.83) and 59% (HR 0.41, 95% CI 0.28 to 0.63) reduction in the risk of metastatic events in luminal A and luminal B lesions, respectively. For BCSS, a 31% (HR 0.69, 95% CI 0.30 to 1.57) and 66% (HR 0.34, 95% CI 0.20 to 0.57) reduction in the risk of BC-related death for respectively PR positive luminal A and luminal B BC patients was found. Conclusion These results suggest that the prognostic effect of PR in primary operable BC depends on the tumor grade. Compared with luminal PR negative BC, PR positivity improves outcome more in luminal B than in luminal A lesions. Citation Format: Kathleen Van Asten, Ben Van Calster, Anneleen Lintermans, Olivier Brouckaert, Giuseppe Floris, Hans Wildiers, Patrick Neven. Prognostic value of the progesterone receptor by proliferation rate in patients with luminal HER2 negative breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-08-05.

Objective: •To evaluate the impact of baseline serum C-reactive protein (CRP) level on outcome in patients with metastatic renal cell carcinoma (mRCC) treated with sunitinib. Patients and Methods: •We reviewed the charts of patients with mRCC who started sunitinib as a first targeted treatment between 2005 and 2012 in three hospitals in Belgium and France. •Collected data included known prognostic factors for mRCC, anatomical location of metastatic sites, response rate (RR), progression-free survival (PFS) and overall survival (OS). Results: •A total of 200 eligible patients were identified by retrospective chart review. The median PFS and OS were 12 and 20 months, respectively. •We observed a clear impact of baseline CRP levels on outcome: the median PFS was 25 months in the group with baseline CRP ≤5 mg/L and 8 months in the group with baseline CRP >5 mg/L (hazard ratio [HR] 2.48, 95% CI 1.74–3.59). The median OS in each group was 50 vs 12 months, respectively (HR 3.17, 2.20–4.68). In the group with baseline CRP ≤5 mg/L, 61% of patients experienced a partial response compared with 32% of patients in the group with baseline CRP >5 mg/L (difference = 29%, 95% CI 15–42). •When adding baseline CRP (with a log transformation) to the six variables of the International Metastatic RCC Database Consortium (IMDC) model in a multivariable Cox regression model, baseline CRP was independently associated with poor PFS (HR for each doubling in CRP level: 1.14, 95% CI 1.03–1.26; P = 0.01) and OS (HR: 1.29, 95% CI 1.16–1.43; P < 0.001). •Adding baseline CRP to the model increased the c-statistic of PFS at 5 years from 0.63 (0.59–0.68) to 0.69 (0.65–0.73), and the c-statistic of OS at 5 years from 0.65 (0.60–0.69) to 0.70 (0.66–0.74). •Patients with elevated baseline CRP levels had a poor prognosis independent of the IMDC risk group, whereas patients with a low baseline CRP in the IMDC favourable risk group had a very good outcome. Conclusion: •Baseline serum CRP level is a strong independent variable linked with RR, PFS and OS in patients with mRCC treated with sunitinib.

BACKGROUND Characterizing ovarian pathology is fundamental to optimizing management in both pre- and post-menopausal women. Inappropriate referral to oncology services can lead to unnecessary surgery or overly radical interventions compromising fertility in young women, whilst the consequences of failing to recognize cancer significantly impact on prognosis. By reflecting on recent developments of new diagnostic tests for preoperative identification of malignant disease in women with adnexal masses, we aimed to update a previous systematic review and meta-analysis. METHODS An extended search was performed in MEDLINE (PubMed) and EMBASE (OvidSp) from March 2008 to October 2013. Eligible studies provided information on diagnostic test performance of models, designed to predict ovarian cancer in a preoperative setting, that contained at least two variables. Study selection and extraction of study characteristics, types of bias, and test performance was performed independently by two reviewers. Quality was assessed using a modified version of the QUADAS assessment tool. A bivariate hierarchical random effects model was used to produce summary estimates of sensitivity and specificity with 95% confidence intervals or plot summary ROC curves for all models considered. RESULTS Our extended search identified a total of 1542 new primary articles. In total, 195 studies were eligible for qualitative data synthesis, and 96 validation studies reporting on 19 different prediction models met the predefined criteria for quantitative data synthesis. These models were tested on 26 438 adnexal masses, including 7199 (27%) malignant and 19 239 (73%) benign masses. The Risk of Malignancy Index (RMI) was the most frequently validated model. The logistic regression model LR2 with a risk cut-off of 10% and Simple Rules (SR), both developed by the International Ovarian Tumor Analysis (IOTA) study, performed better than all other included models with a pooled sensitivity and specificity, respectively, of 0.92 [95% CI 0.88-0.95] and 0.83 [95% CI 0.77-0.88] for LR2 and 0.93 [95% CI 0.89-0.95] and 0.81 [95% CI 0.76-0.85] for SR. A meta-analysis of centre-specific results stratified for menopausal status of two multicentre cohorts comparing LR2, SR and RMI-1 (using a cut-off of 200) showed a pooled sensitivity and specificity in premenopausal women for LR2 of 0.85 [95% CI 0.75-0.91] and 0.91 [95% CI 0.83-0.96] compared with 0.93 [95% CI 0.84-0.97] and 0.83 [95% CI 0.73-0.90] for SR and 0.44 [95% CI 0.28-0.62] and 0.95 [95% CI 0.90-0.97] for RMI-1. In post-menopausal women, sensitivity and specificity of LR2, SR and RMI-1 were 0.94 [95% CI 0.89-0.97] and 0.70 [95% CI 0.62-0.77], 0.93 [95% CI 0.88-0.96] and 0.76 [95% CI 0.69-0.82], and 0.79 [95% CI 0.72-0.85] and 0.90 [95% CI 0.84-0.94], respectively. CONCLUSIONS An evidence-based approach to the preoperative characterization of any adnexal mass should incorporate the use of IOTA Simple Rules or the LR2 model, particularly for women of reproductive age.

Introduction With this pictorial essay, we aimed to provide gestational age specific reference ranges of relevant fetal structures using micro-ultrasound, as well as its correlation with postmortem MRI and whole body sections. Material and Methods Time-mated pregnant rabbits (n = 24) were assessed once at various gestational ages in the second half of pregnancy (15, 17, 21 to 23, 25 to 28, and 30 to 31 days; term = 31 days). We obtained biometric data, together with Doppler flow patterns in the ductus venosus, umbilical artery and umbilical vein. After euthanasia, micro-ultrasound images were compared with images obtained by 9.4 Tesla MRI and whole body paraffin sections at 15, 23, 26, and 28 days. Results We constructed biometric normative curves, which showed a significant correlation with gestational age. The pulsatility index (PI) in the umbilical artery decreased with gestation (PI = 5.746–0.2969(GA) + 0.004931(GA)2; R2 = 0.30), whereas pulsatility index for veins (PIV) in the ductus venosus remained constant (median PIV = 0.82 (0.60–1.12)). In this report, we provide an anatomical atlas of fetal thoracic development using both micro-ultrasound and MRI. Conclusion We describe anatomical fetal leporine development as can be visualized by micro-ultrasound and MR imaging. The reported reference ranges may be useful for researchers using the fetal rabbit model. © 2013 John Wiley & Sons, Ltd.

Purpose To investigate the clinical behavior of triple negative breast cancer (TNC), including age distribution, occurrence of LN (lymph node) invasion and prognosis in different histological subtypes. Methods For this cohort study we used data on 476 patients with newly diagnosed TNC at the University Hospitals Leuven (Belgium) between 1999 and 2009. Of these, 395 received upfront surgery, 68 neoadjuvant chemotherapy and 21 had metastases at diagnosis. Results Apocrine and invasive lobular TNC occur more often in older patients compared to IDC-NOS. Of the primarily operated patients with TNC, 35.1% has pathological LN involvement. There were no significant differences in nodal invasion between different histological subtypes, but most subtypes contained few patients. In contrast to previous reports, 6/14 of apocrine TNC had LN involvement. Disease free survival (DFS) was different in different histological subtypes, but group sizes were insufficient to be able to draw firm conclusions. Within the histologically ‘homogeneous’ IDC-NOS group with primary surgery and outcome data (n = 300), DFS with 3.5 year median follow-up decreased with increasing age, but chemotherapy and radiotherapy were much less frequently given with increasing age. In multivariable analysis, lower age, presence of LN involvement, lack of administration of chemotherapy and radiotherapy were significant predictors of relapse. Conclusion TNC is not a uniform disease. Different histological subtypes have different age distribution and behavior. The prognosis of the most common histological subgroup, IDC-NOS, is better in older patients, but this is counterbalanced by significantly decreased use of chemotherapy and radiotherapy.

Objective The identification of novel biomarkers led to the development of the ROMA algorithm incorporating both HE4 and CA125 to predict malignancy in women with a pelvic mass. An ultrasound based prediction model (LR2) developed by the International Ovarian Tumor Analysis (IOTA) study offers better diagnostic performance than CA125 alone. In this study we compared the diagnostic accuracy between LR2 and ROMA. Methods This study included women with a pelvic mass scheduled for surgery and enrolled in a previous prospective diagnostic accuracy study. Experienced ultrasound examiners, general gynecologists and trainees supervised by one of the experts performed the preoperative transvaginal ultrasound examinations. Serum biomarkers were taken prior to surgery. Accuracy of LR2 and ROMA was estimated at completion of this study and did not form part of the decision making process. Final outcome was histology of removed tissues and surgical stage if relevant. Results In total 360 women were evaluated. 216 women had benign disease and 144 a malignancy. Overall test performance of LR2 (AUC 0.952) with 94% sensitivity and 82% specificity was significantly better than ROMA (AUC 0.893) with 84% sensitivity and 80% specificity. Difference in AUC was 0.059 (95% CI: 0.026–0.091; P-value 0.0004). Similar results were obtained when stratified for menopausal status. Conclusion LR2 shows a better diagnostic performance than ROMA for the characterization of a pelvic mass in both pre- and postmenopausal women. These findings suggest that HE4 and CA125 may not play an important role in the diagnosis of ovarian cancer if good quality ultrasonography is available.

Purpose: The combination of two tumour markers, CA125 and HE4, in the risk of ovarian malignancy assay (ROMA) has been shown to be successful in classifying patients into those who have a high or low risk of epithelial ovarian cancer. In the present study, the diagnostic accuracy of ROMA was assessed and compared to the diagnostic accuracy of the two most widely used ultrasound methods, namely the risk of malignancy index (RMI) and subjective assessment by ultrasound. Methods: From August, 2005 to March, 2009, 432 women with a pelvic mass who were scheduled to have surgery were enrolled in a single-centre prospective cohort study. A preoperative ultrasound was performed and preoperative CA125 and HE4 serum levels were measured. Once the final surgical pathology reports were obtained, the diagnostic accuracy and performance indices of ROMA, RMI and subjective assessment were calculated. Results: Of the 432 eligible patients, 374 could be analysed. Subjective assessment had the highest area under the receiver operator characteristic curve (AUC) (0.968, 95% CI: 0.945-0.984), followed by the RMI(0.931, 95% CI: 0.901-0.955). The subjective assessment and RMI both had significantly higher AUCs than the ROMA (0.893, 95% CI: 0.857-0.922; P