Factors affecting the implementation of calcium supplementation strategies during pregnancy to prevent pre-eclampsia: a mixed-methods systematic review.

OBJECTIVES: Daily calcium supplements are recommended for pregnant women from 20 weeks' gestation to prevent pre-eclampsia in populations with low dietary calcium intake. We aimed to improve understanding of barriers and facilitators for calcium supplement intake during pregnancy to prevent pre-eclampsia. DESIGN: Mixed-method systematic review, with confidence assessed using the Grading of Recommendations, Assessment, Development and Evaluations-Confidence in the Evidence from Reviews of Qualitative research approach. DATA SOURCES: MEDLINE and EMBASE (via Ovid), CINAHL and Global Health (via EBSCO) and grey literature databases were searched up to 17 September 2022. ELIGIBILITY CRITERIA: We included primary qualitative, quantitative and mixed-methods studies reporting implementation or use of calcium supplements during pregnancy, excluding calcium fortification and non-primary studies. No restrictions were imposed on settings, language or publication date. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers extracted data and assessed risk of bias. We analysed the qualitative data using thematic synthesis, and quantitative findings were thematically mapped to qualitative findings. We then mapped the results to behavioural change frameworks to identify barriers and facilitators. RESULTS: Eighteen reports from nine studies were included in this review. Women reported barriers to consuming calcium supplements included limited knowledge about calcium supplements and pre-eclampsia, fears and experiences of side effects, varying preferences for tablets, dosing, working schedules, being away from home and taking other supplements. Receiving information regarding pre-eclampsia and safety of calcium supplement use from reliable sources, alternative dosing options, supplement reminders, early antenatal care, free supplements and support from families and communities were reported as facilitators. Healthcare providers felt that consistent messaging about benefits and risks of calcium, training, and ensuring adequate staffing and calcium supply is available would be able to help them in promoting calcium. CONCLUSION: Relevant stakeholders should consider the identified barriers and facilitators when formulating interventions and policies on calcium supplement use. These review findings can inform implementation to ensure effective and equitable provision and scale-up of calcium interventions. PROSPERO REGISTRATION NUMBER: CRD42021239143.

Predictors of outcome in sciatica patients following an epidural steroid injection: the POiSE prospective observational cohort study protocol.

INTRODUCTION: Sciatica can be very painful and, in most cases, is due to pressure on a spinal nerve root from a disc herniation with associated inflammation. For some patients, the pain persists, and one management option is a spinal epidural steroid injection (ESI). The aim of an ESI is to relieve leg pain, improve function and reduce the need for surgery. ESIs work well in some patients but not in others, but we cannot identify these patient subgroups currently. This study aims to identify factors, including patient characteristics, clinical examination and imaging findings, that help in predicting who does well and who does not after an ESI. The overall objective is to develop a prognostic model to support individualised patient and clinical decision-making regarding ESI. METHODS: POiSE is a prospective cohort study of 439 patients with sciatica referred by their clinician for an ESI. Participants will receive weekly text messages until 12 weeks following their ESIand then again at 24 weeks following their ESI to collect data on leg pain severity. Questionnaires will be sent to participants at baseline, 6, 12 and 24 weeks after their ESI to collect data on pain, disability, recovery and additional interventions. The prognosis for the cohort will be described. The primary outcome measure for the prognostic model is leg pain at 6 weeks. Prognostic models will also be developed for secondary outcomes of disability and recovery at 6 weeks and additional interventions at 24 weeks following ESI. Statistical analyses will include multivariable linear and logistic regression with mixed effects model. ETHICS AND DISSEMINATION: The POiSE study has received ethical approval (South Central Berkshire B Research Ethics Committee 21/SC/0257). Dissemination will be guided by our patient and public engagement group and will include scientific publications, conference presentations and social media.

Predicting the risk of acute kidney injury in primary care: derivation and validation of STRATIFY-AKI.

BACKGROUND: Antihypertensives reduce the risk of cardiovascular disease but are also associated with harms including acute kidney injury (AKI). Few data exist to guide clinical decision making regarding these risks. AIM: To develop a prediction model estimating the risk of AKI in people potentially indicated for antihypertensive treatment. DESIGN AND SETTING: Observational cohort study using routine primary care data from the Clinical Practice Research Datalink (CPRD) in England. METHOD: People aged ≥40 years, with at least one blood pressure measurement between 130 mmHg and 179 mmHg were included. Outcomes were admission to hospital or death with AKI within 1, 5, and 10 years. The model was derived with data from CPRD GOLD (n = 1 772 618), using a Fine-Gray competing risks approach, with subsequent recalibration using pseudo-values. External validation used data from CPRD Aurum (n = 3 805 322). RESULTS: The mean age of participants was 59.4 years and 52% were female. The final model consisted of 27 predictors and showed good discrimination at 1, 5, and 10 years (C-statistic for 10-year risk 0.821, 95% confidence interval [CI] = 0.818 to 0.823). There was some overprediction at the highest predicted probabilities (ratio of observed to expected event probability for 10-year risk 0.633, 95% CI = 0.621 to 0.645), affecting patients with the highest risk. Most patients (>95%) had a low 1- to 5-year risk of AKI, and at 10 years only 0.1% of the population had a high AKI and low CVD risk. CONCLUSION: This clinical prediction model enables GPs to accurately identify patients at high risk of AKI, which will aid treatment decisions. As the vast majority of patients were at low risk, such a model may provide useful reassurance that most antihypertensive treatment is safe and appropriate while flagging the few for whom this is not the case.

Menopausal status, ultrasound and biomarker tests in combination for the diagnosis of ovarian cancer in symptomatic women.

BACKGROUND: Ovarian cancer (OC) has the highest case fatality rate of all gynaecological cancers. Diagnostic delays are caused by non-specific symptoms. Existing systematic reviews have not comprehensively covered tests in current practice, not estimated accuracy separately in pre- and postmenopausal women, or used inappropriate meta-analytic methods. OBJECTIVES: To establish the accuracy of combinations of menopausal status, ultrasound scan (USS) and biomarkers for the diagnosis of ovarian cancer in pre- and postmenopausal women and compare the accuracy of different test combinations. SEARCH METHODS: We searched CENTRAL, MEDLINE (Ovid), Embase (Ovid), five other databases and three trial registries from 1991 to 2015 and MEDLINE (Ovid) and Embase (Ovid) form June 2015 to June 2019. We also searched conference proceedings from the European Society of Gynaecological Oncology, International Gynecologic Cancer Society, American Society of Clinical Oncology and Society of Gynecologic Oncology, ZETOC and Conference Proceedings Citation Index (Web of Knowledge). We searched reference lists of included studies and published systematic reviews. SELECTION CRITERIA: We included cross-sectional diagnostic test accuracy studies evaluating single tests or comparing two or more tests, randomised trials comparing two or more tests, and studies validating multivariable models for the diagnosis of OC investigating test combinations, compared with a reference standard of histological confirmation or clinical follow-up in women with a pelvic mass (detected clinically or through USS) suspicious for OC. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data and assessed quality using QUADAS-2. We used the bivariate hierarchical model to indirectly compare tests at commonly reported thresholds in pre- and postmenopausal women separately. We indirectly compared tests across all thresholds and estimated sensitivity at fixed specificities of 80% and 90% by fitting hierarchical summary receiver operating characteristic (HSROC) models in pre- and postmenopausal women separately. MAIN RESULTS: We included 59 studies (32,059 women, 9545 cases of OC). Two tests evaluated the accuracy of a combination of menopausal status and USS findings (IOTA Logistic Regression Model 2 (LR2) and the Assessment of Different NEoplasias in the adneXa model (ADNEX)); one test evaluated the accuracy of a combination of menopausal status, USS findings and serum biomarker CA125 (Risk of Malignancy Index (RMI)); and one test evaluated the accuracy of a combination of menopausal status and two serum biomarkers (CA125 and HE4) (Risk of Ovarian Malignancy Algorithm (ROMA)). Most studies were at high or unclear risk of bias in participant, reference standard, and flow and timing domains. All studies were in hospital settings. Prevalence was 16% (RMI, ROMA), 22% (LR2) and 27% (ADNEX) in premenopausal women and 38% (RMI), 45% (ROMA), 52% (LR2) and 55% (ADNEX) in postmenopausal women. The prevalence of OC in the studies was considerably higher than would be expected in symptomatic women presenting in community-based settings, or in women referred from the community to hospital with a suspicion of OC. Studies were at high or unclear applicability because presenting features were not reported, or USS was performed by experienced ultrasonographers for RMI, LR2 and ADNEX. The higher sensitivity and lower specificity observed in postmenopausal compared to premenopausal women across all index tests and at all thresholds may reflect highly selected patient cohorts in the included studies. In premenopausal women, ROMA at a threshold of 13.1 (± 2), LR2 at a threshold to achieve a post-test probability of OC of 10% and ADNEX (post-test probability 10%) demonstrated a higher sensitivity (ROMA: 77.4%, 95% CI 72.7% to 81.5%; LR2: 83.3%, 95% CI 74.7% to 89.5%; ADNEX: 95.5%, 95% CI 91.0% to 97.8%) compared to RMI (57.2%, 95% CI 50.3% to 63.8%). The specificity of ROMA and ADNEX were lower in premenopausal women (ROMA: 84.3%, 95% CI 81.2% to 87.0%; ADNEX: 77.8%, 95% CI 67.4% to 85.5%) compared to RMI 92.5% (95% CI 90.3% to 94.2%). The specificity of LR2 was comparable to RMI (90.4%, 95% CI 84.6% to 94.1%). In postmenopausal women, ROMA at a threshold of 27.7 (± 2), LR2 (post-test probability 10%) and ADNEX (post-test probability 10%) demonstrated a higher sensitivity (ROMA: 90.3%, 95% CI 87.5% to 92.6%; LR2: 94.8%, 95% CI 92.3% to 96.6%; ADNEX: 97.6%, 95% CI 95.6% to 98.7%) compared to RMI (78.4%, 95% CI 74.6% to 81.7%). Specificity of ROMA at a threshold of 27.7 (± 2) (81.5, 95% CI 76.5% to 85.5%) was comparable to RMI (85.4%, 95% CI 82.0% to 88.2%), whereas for LR2 (post-test probability 10%) and ADNEX (post-test probability 10%) specificity was lower (LR2: 60.6%, 95% CI 50.5% to 69.9%; ADNEX: 55.0%, 95% CI 42.8% to 66.6%). AUTHORS' CONCLUSIONS: In specialist healthcare settings in both premenopausal and postmenopausal women, RMI has poor sensitivity. In premenopausal women, ROMA, LR2 and ADNEX offer better sensitivity (fewer missed cancers), but for ROMA and ADNEX this is off-set by a decrease in specificity and increase in false positives. In postmenopausal women, ROMA demonstrates a higher sensitivity and comparable specificity to RMI. ADNEX has the highest sensitivity in postmenopausal women, but reduced specificity. The prevalence of OC in included studies is representative of a highly selected referred population, rather than a population in whom referral is being considered. The comparative accuracy of tests observed here may not be transferable to non-specialist settings. Ultimately health systems need to balance accuracy and resource implications to identify the most suitable test.

Prognostic models for predicting relapse or recurrence of major depressive disorder in adults.

BACKGROUND: Relapse (the re-emergence of depressive symptoms after some level of improvement but preceding recovery) and recurrence (onset of a new depressive episode after recovery) are common in depression, lead to worse outcomes and quality of life for patients and exert a high economic cost on society. Outcomes can be predicted by using multivariable prognostic models, which use information about several predictors to produce an individualised risk estimate. The ability to accurately predict relapse or recurrence while patients are well (in remission) would allow the identification of high-risk individuals and may improve overall treatment outcomes for patients by enabling more efficient allocation of interventions to prevent relapse and recurrence. OBJECTIVES: To summarise the predictive performance of prognostic models developed to predict the risk of relapse, recurrence, sustained remission or recovery in adults with major depressive disorder who meet criteria for remission or recovery. SEARCH METHODS: We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2020. We also searched sources of grey literature, screened the reference lists of included studies and performed a forward citation search. There were no restrictions applied to the searches by date, language or publication status . SELECTION CRITERIA: We included development and external validation (testing model performance in data separate from the development data) studies of any multivariable prognostic models (including two or more predictors) to predict relapse, recurrence, sustained remission, or recovery in adults (aged 18 years and over) with remitted depression, in any clinical setting. We included all study designs and accepted all definitions of relapse, recurrence and other related outcomes. We did not specify a comparator prognostic model. DATA COLLECTION AND ANALYSIS: Two review authors independently screened references; extracted data (using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS)); and assessed risks of bias of included studies (using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)). We referred any disagreements to a third independent review author. Where we found sufficient (10 or more) external validation studies of an individual model, we planned to perform a meta-analysis of its predictive performance, specifically with respect to its calibration (how well the predicted probabilities match the observed proportions of individuals that experience the outcome) and discrimination (the ability of the model to differentiate between those with and without the outcome). Recommendations could not be qualified using the GRADE system, as guidance is not yet available for prognostic model reviews. MAIN RESULTS: We identified 11 eligible prognostic model studies (10 unique prognostic models). Seven were model development studies; three were model development and external validation studies; and one was an external validation-only study. Multiple estimates of performance measures were not available for any of the models and, meta-analysis was therefore not possible. Ten out of the 11 included studies were assessed as being at high overall risk of bias. Common weaknesses included insufficient sample size, inappropriate handling of missing data and lack of information about discrimination and calibration. One paper (Klein 2018) was at low overall risk of bias and presented a prognostic model including the following predictors: number of previous depressive episodes, residual depressive symptoms and severity of the last depressive episode. The external predictive performance of this model was poor (C-statistic 0.59; calibration slope 0.56; confidence intervals not reported). None of the identified studies examined the clinical utility (net benefit) of the developed model. AUTHORS' CONCLUSIONS: Of the 10 prognostic models identified (across 11 studies), only four underwent external validation. Most of the studies (n = 10) were assessed as being at high overall risk of bias, and the one study that was at low risk of bias presented a model with poor predictive performance. There is a need for improved prognostic research in this clinical area, with future studies conforming to current best practice recommendations for prognostic model development/validation and reporting findings in line with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis.

BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. OBJECTIVES: To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. DESIGN: This was an individual participant data meta-analysis of cohort studies. SETTING: Source data from secondary and tertiary care. PREDICTORS: We identified predictors from systematic reviews, and prioritised for importance in an international survey. PRIMARY OUTCOMES: Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at ≥ 34 weeks' gestation) and any-onset pre-eclampsia. ANALYSIS: We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I2 and τ2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. RESULTS: The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia. LIMITATIONS: Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data. CONCLUSION: For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings. FUTURE WORK: Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate. STUDY REGISTRATION: This study is registered as PROSPERO CRD42015029349. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.

WHAT IS THE PROBLEM?: Pre-eclampsia, a condition in pregnancy that results in raised blood pressure and protein in the urine, is a major cause of complications for the mother and baby. WHAT IS NEEDED?: A way of accurately identifying women at high risk of pre-eclampsia to allow clinicians to start preventative interventions such as administering aspirin or frequently monitoring women during pregnancy. WHERE ARE THE RESEARCH GAPS?: Although over 100 tools (models) have been reported worldwide to predict pre-eclampsia, to date their performance in women managed in the UK NHS is unknown. WHAT DID WE PLAN TO DO?: We planned to comprehensively identify all published models that predict the risk of pre-eclampsia occurring at any time during pregnancy and to assess if this prediction is accurate in the UK population. If the existing models did not perform satisfactorily, we aimed to develop new prediction models. WHAT DID WE FIND?: We formed the International Prediction of Pregnancy Complications network, which provided data from a large number of studies (78 studies, 25 countries, 125 researchers, 3,570,993 singleton pregnancies). We were able to assess the performance of 24 out of the 131 models published to predict pre-eclampsia in 11 UK data sets. The models did not accurately predict the risk of pre-eclampsia across all UK data sets, and their performance varied within individual data sets. We developed new prediction models that showed promising performance on average across all data sets, but their ability to correctly identify women who develop pre-eclampsia varied between populations. The models were more clinically useful when used in the care of first-time mothers pregnant with one child, compared to a strategy of treating them all as if they were at high-risk of pre-eclampsia. WHAT DOES THIS MEAN?: Before using the International Prediction of Pregnancy Complications models in various populations, they need to be adjusted for characteristics of the particular population and the setting of application.

Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes.

When designing a study to develop a new prediction model with binary or time-to-event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9, (ii) small absolute difference of ≤ 0.05 in the model's apparent and adjusted Nagelkerke's R2 , and (iii) precise estimation of the overall risk in the population. Criteria (i) and (ii) aim to reduce overfitting conditional on a chosen p, and require prespecification of the model's anticipated Cox-Snell R2 , which we show can be obtained from previous studies. The values of n and E that meet all three criteria provides the minimum sample size required for model development. Upon application of our approach, a new diagnostic model for Chagas disease requires an EPP of at least 4.8 and a new prognostic model for recurrent venous thromboembolism requires an EPP of at least 23. This reinforces why rules of thumb (eg, 10 EPP) should be avoided. Researchers might additionally ensure the sample size gives precise estimates of key predictor effects; this is especially important when key categorical predictors have few events in some categories, as this may substantially increase the numbers required.

Heavy + light chain analysis to assign myeloma response is analogous to the IMWG response criteria.

Automated serum heavy + light chain (HLC) immunoassays can measure the intact immunoglobulins of each light chain type separately. We though to compare HLC assays with electrophoretic techniques in determining International Myeloma Working Group (IMWG) response criteria. 114 myeloma patients from 2 trials were included. HLC measurements were made utilizing archived sera and response assessments compared with those based on electrophoretic analysis at the time of the trials. Assessments at ∼90 days and maximal response were compared as was the power of the 2 techniques for predicting later responses, overall survival, and progression. The kappa statistic indicated good agreement between the 2 methods for determining IMWG response criteria, although HLC measurements might give better predictions of subsequent responses and frequently gave an earlier indication of change. HLC measurements could represent an alternative to electrophoretic techniques in determining IMWG response. Validation with a greater range of patient responses is needed for confirmation.

Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?

If individual participant data are available from multiple studies or clusters, then a prediction model can be externally validated multiple times. This allows the model's discrimination and calibration performance to be examined across different settings. Random-effects meta-analysis can then be used to quantify overall (average) performance and heterogeneity in performance. This typically assumes a normal distribution of 'true' performance across studies. We conducted a simulation study to examine this normality assumption for various performance measures relating to a logistic regression prediction model. We simulated data across multiple studies with varying degrees of variability in baseline risk or predictor effects and then evaluated the shape of the between-study distribution in the C-statistic, calibration slope, calibration-in-the-large, and E/O statistic, and possible transformations thereof. We found that a normal between-study distribution was usually reasonable for the calibration slope and calibration-in-the-large; however, the distributions of the C-statistic and E/O were often skewed across studies, particularly in settings with large variability in the predictor effects. Normality was vastly improved when using the logit transformation for the C-statistic and the log transformation for E/O, and therefore we recommend these scales to be used for meta-analysis. An illustrated example is given using a random-effects meta-analysis of the performance of QRISK2 across 25 general practices.

Prediction of risk of recurrence of venous thromboembolism following treatment for a first unprovoked venous thromboembolism: systematic review, prognostic model and clinical decision rule, and economic evaluation.

BACKGROUND: Unprovoked first venous thromboembolism (VTE) is defined as VTE in the absence of a temporary provoking factor such as surgery, immobility and other temporary factors. Recurrent VTE in unprovoked patients is highly prevalent, but easily preventable with oral anticoagulant (OAC) therapy. The unprovoked population is highly heterogeneous in terms of risk of recurrent VTE. OBJECTIVES: The first aim of the project is to review existing prognostic models which stratify individuals by their recurrence risk, therefore potentially allowing tailored treatment strategies. The second aim is to enhance the existing research in this field, by developing and externally validating a new prognostic model for individual risk prediction, using a pooled database containing individual patient data (IPD) from several studies. The final aim is to assess the economic cost-effectiveness of the proposed prognostic model if it is used as a decision rule for resuming OAC therapy, compared with current standard treatment strategies. METHODS: Standard systematic review methodology was used to identify relevant prognostic model development, validation and cost-effectiveness studies. Bibliographic databases (including MEDLINE, EMBASE and The Cochrane Library) were searched using terms relating to the clinical area and prognosis. Reviewing was undertaken by two reviewers independently using pre-defined criteria. Included full-text articles were data extracted and quality assessed. Critical appraisal of included full texts was undertaken and comparisons made of model performance. A prognostic model was developed using IPD from the pooled database of seven trials. A novel internal-external cross-validation (IECV) approach was used to develop and validate a prognostic model, with external validation undertaken in each of the trials iteratively. Given good performance in the IECV approach, a final model was developed using all trials data. A Markov patient-level simulation was used to consider the economic cost-effectiveness of using a decision rule (based on the prognostic model) to decide on resumption of OAC therapy (or not). RESULTS: Three full-text articles were identified by the systematic review. Critical appraisal identified methodological and applicability issues; in particular, all three existing models did not have external validation. To address this, new prognostic models were sought with external validation. Two potential models were considered: one for use at cessation of therapy (pre D-dimer), and one for use after cessation of therapy (post D-dimer). Model performance measured in the external validation trials showed strong calibration performance for both models. The post D-dimer model performed substantially better in terms of discrimination (c = 0.69), better separating high- and low-risk patients. The economic evaluation identified that a decision rule based on the final post D-dimer model may be cost-effective for patients with predicted risk of recurrence of over 8% annually; this suggests continued therapy for patients with predicted risks ≥ 8% and cessation of therapy otherwise. CONCLUSIONS: The post D-dimer model performed strongly and could be useful to predict individuals' risk of recurrence at any time up to 2-3 years, thereby aiding patient counselling and treatment decisions. A decision rule using this model may be cost-effective for informing clinical judgement and patient opinion in treatment decisions. Further research may investigate new predictors to enhance model performance and aim to further externally validate to confirm performance in new, non-trial populations. Finally, it is essential that further research is conducted to develop a model predicting bleeding risk on therapy, to manage the balance between the risks of recurrence and bleeding. STUDY REGISTRATION: This study is registered as PROSPERO CRD42013003494. FUNDING: The National Institute for Health Research Health Technology Assessment programme.