Abstract Background Current guidelines regarding oxytocin stimulation are not tailored to individuals as they are based on randomised controlled trials. The objective of the study was to develop an artificial intelligence (AI) model for individual prediction of the risk of caesarean delivery (CD) in women with a cervical dilatation of 6 cm after oxytocin stimulation for induced labour. The model included not only variables known when labour induction was initiated but also variables describing the course of the labour induction. Methods Secondary analysis of data from the CONDISOX randomised controlled trial of discontinued vs. continued oxytocin infusion in the active phase of induced labour. Extreme gradient boosting (XGBoost) software was used to build the prediction model. To explain the impact of the predictors, we calculated Shapley additive explanation (SHAP) values and present a summary SHAP plot. A force plot was used to explain specifics about an individual’s predictors that result in a change of the individual’s risk output value from the population-based risk. Results Among 1060 included women, 160 (15.1%) were delivered by CD. The XGBoost model found women who delivered vaginally were more likely to be parous, taller, to have a lower estimated birth weight, and to be stimulated with a lower amount of oxytocin. In 108 women (10% of 1060) the model favoured either continuation or discontinuation of oxytocin. For the remaining 90% of the women, the model found that continuation or discontinuation of oxytocin stimulation affected the risk difference of CD by less than 5% points. Conclusion In women undergoing labour induction, this AI model based on a secondary analysis of data from the CONDISOX trial may help predict the risk of CD and assist the mother and clinician in individual tailored management of oxytocin stimulation after reaching 6 cm of cervical dilation.

Background:The increasing aging population and limited health care resources have placed new demands on the healthcare sector. Reducing the number of hospitalizations has become a political priority in many countries, and special focus has been directed at potentially preventable hospitalizations. Objectives:We aimed to develop an artificial intelligence (AI) prediction model for potentially preventable hospitalizations in the coming year, and to apply explainable AI to identify predictors of hospitalization and their interaction. Methods:We used the Danish CROSS-TRACKS cohort and included citizens in 2016-2017. We predicted potentially preventable hospitalizations within the following year using the citizens' sociodemographic characteristics, clinical characteristics, and health care utilization as predictors. Extreme gradient boosting was used to predict potentially preventable hospitalizations with Shapley additive explanations values serving to explain the impact of each predictor. We reported the area under the receiver operating characteristic curve, the area under the precision-recall curve, and 95% confidence intervals (CI) based on five-fold cross-validation. Results:The best performing prediction model showed an area under the receiver operating characteristic curve of 0.789 (CI: 0.782-0.795) and an area under the precision-recall curve of 0.232 (CI: 0.219-0.246). The predictors with the highest impact on the prediction model were age, prescription drugs for obstructive airway diseases, antibiotics, and use of municipality services. We found an interaction between age and use of municipality services, suggesting that citizens aged 75+ years receiving municipality services had a lower risk of potentially preventable hospitalization. Conclusion:AI is suitable for predicting potentially preventable hospitalizations. The municipality-based health services seem to have a preventive effect on potentially preventable hospitalizations.

Background The demand for healthcare is increasing due to an aging population, more people living with chronic diseases and medical comorbidities. To manage this demand, political institutions call for action to reduce the potentially avoidable hospitalizations. Quantitative and qualitative aspects should be considered to understand how and why interventions work, and for whom. The aim of this mixed methods systematic review was to identify and synthesize evidence on interventions targeting avoidable hospitalizations from the perspectives of the citizens and the healthcare professionals to improve the preventive healthcare services. Methods and Results A mixed methods systematic review was conducted following the JBI methodology using a convergent integrated approach to synthesis. The review protocol was registered in PROSPERO, reg. no. CRD42020134652. A systematic search was undertaken in six databases. In total, 45 articles matched the eligibility criteria, and 25 of these (five qualitative studies and 20 quantitative studies) were found to be of acceptable methodological quality. From the 25 articles, 99 meaning units were extracted. The combined evidence revealed four categories, which were synthesized into two integrated findings: (1) Addressing individual needs through care continuity and coordination prevent avoidable hospitalizations and (2) Recognizing preventive care as an integrated part of the healthcare work to prevent avoidable hospitalizations. Conclusions The syntheses highlight the importance of addressing individual needs through continuous and coordinated care practices to prevent avoidable hospitalizations. Engaging healthcare professionals in preventive care work and considering implications for patient safety may be given higher priority. Healthcare administers and policy-makers could support the delivery of preventive care through targeted educational material aimed at healthcare professionals and simple web-based IT platforms for information-sharing across healthcare settings. The findings are an important resource in the development and implementation of interventions to prevent avoidable hospitalizations, and may serve to improve patient safety and quality in preventive healthcare services. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=134652 , identifier: CRD42020134652.

Purpose:Infertility may affect somatic and mental health later in life. Nevertheless, health status before diagnosed infertility is sparsely studied in women. We aimed to describe healthcare use in primary and secondary care before a first infertility diagnosis and compare use between cases and controls. Materials and Methods:The case-control study was based on register data and used incidence density sampling. From the CROSS-TRACKS Cohort, we included women residing in the Horsens area in Denmark in 2012-2018 (n = 54,175). Eligible women were aged 18-40 years, nulliparous, and living in heterosexual relationships. Cases were women with a first infertility diagnosis in the Danish National Patient Registry (index date). Five controls were matched on age, birth year, and calendar time. Through linkage to Danish national health registries, we identified general practitioner (GP) attendance, paraclinical examinations, hospital contacts, diagnoses, and redeemed prescriptions. Healthcare use from one year to five years before index date was compared with conditional logistic regression. Results:We identified 711 cases and 3555 controls. At one year before index date, cases consulted their GP (odds ratio (OR) = 5.2, 95% confidence interval (CI): 3.2, 8.3) and visited hospital (OR = 1.2, 95% CI: 1.0, 1.4) and redeemed prescriptions (OR = 2.3 95% CI: 1.9, 2.7) more often compared to controls. Cases more often had blood and hemoglobin tests performed, redeemed more drugs related to genitourinary and hormonal diseases, and were more often diagnosed with endocrine and genitourinary diseases in the year before a first infertility diagnosis compared to controls. Cases and controls had comparable healthcare use from five years to one year before a first infertility diagnosis. Conclusion:Cases and controls had similar healthcare use from five years to one year before a first infertility diagnosis. However, cases had a higher healthcare use in the year preceding a first infertility diagnosis compared to controls.

STUDY QUESTION:To what extent is dietary folate intake and total folate intake (dietary and supplemental intakes) associated with fecundability, the per cycle probability of conception?SUMMARY ANSWER:Preconception dietary folate intake was positively associated with fecundability in a monotonic pattern.WHAT IS KNOWN ALREADY:Supplemental folic acid has been associated with improved fertility, but little is known about the relation between dietary folate and fecundability.STUDY DESIGN, SIZE, DURATION:A prospective cohort study including 9559 women trying to conceive without fertility treatment and enrolled in the period 2013-2020.PARTICIPANTS/MATERIALS, SETTING, METHODS:We used data from two internet-based prospective cohort studies of pregnancy planners from Denmark, where folic acid fortification is not performed (SnartForældre.dk (SF); n = 3755) and North America, where the food supply is fortified with folic acid (Pregnancy Study Online (PRESTO); n = 5804). Women contributed menstrual cycles at risk until they reported conception or experienced a censoring event. We used proportional probabilities regression models to compute fecundability ratios (FRs) and 95% CI, adjusting for potential confounders.MAIN RESULTS AND THE ROLE OF CHANCE:Compared with a dietary folate intake ≥400 µg/day, the adjusted FRs for women in SF were 0.92 (95% CI: 0.85-0.99) for intake 250-399 µg/day, and 0.80 (95% CI: 0.68-0.94) for intake of <250 µg/day. The corresponding FRs in PRESTO were 0.95 (95% CI: 0.89-1.01) and 0.81 (95% CI: 0.65-1.00). Compared with the highest level of total folate intake (diet folate ≥400 µg/day plus folic acid supplementation), in both cohorts fecundability was lowest among women with the lowest dietary intake <250 µg/day dietary folate and no supplementation (FR: 0.76, 95% CI: 0.59-0.98 [SF] and 0.49, 95% CI: 0.31-0.77 [PRESTO]). Further, total intake dietary folate <250 µg/day plus supplementation was associated with reduced fecundability for SF participants (FR; 0.79, 95% CI: 0.65-0.98) and for PRESTO participants (FR; 0.92, 95% CI: 0.72-1.16).LIMITATIONS, REASONS FOR CAUTION:It is unknown whether dietary folate and folic acid intake affect fecundability on its own or if there is an interaction with other micronutrients provided in healthy diet. Thus, the observed associations may not reflect dietary folate intake alone, but overall healthy diet.WIDER IMPLICATIONS OF THE FINDINGS:Recommendations for preconception dietary folate intake and folic acid supplementation are of importance not only to prevent neural tube defects but also to enhance fecundability.STUDY FUNDING/COMPETING INTEREST(S):The study was supported by the National Institute of Child Health and Human Development (R01-HD086742). The authors report no competing interests.TRIAL REGISTRATION NUMBER:N/A.

Introduction The risk of asbestosis, malignant mesothelioma and lung cancer among motor vehicle mechanics is of concern because of potential exposure to chrysotile asbestos during brake, clutch and gasket repair and maintenance. Asbestos has also been used in insulation and exhaust systems. Methods We examined the long-term risk of incident mesothelioma, lung cancer, asbestosis and other lung diseases and mortality due to mesothelioma, lung cancer, asbestosis and other lung diseases in a nationwide cohort of all men registered as motor vehicle mechanics since 1970 in Denmark. This was compared with the corresponding risk in a cohort of male workers matched 10:1 by age and calendar year, with similar socioeconomic status (instrument makers, dairymen, upholsterers, glaziers, butchers, bakers, drivers, farmers and workers in the food industry, trade or public services). Results Our study included 138 559 motor vehicle mechanics (median age 24 years; median follow-up 20 years (maximum 45 years)) and 1 385 590 comparison workers (median age 25 years; median follow-up 19 years (maximum 45 years)). Compared with other workers, vehicle mechanics had a lower risk of morbidity due to mesothelioma/pleural cancer (n=47 cases) (age-adjusted and calendar-year-adjusted HR=0.74 (95% CI 0.55 to 0.99)), a slightly increased risk of lung cancer (HR=1.09 (95% CI 1.03 to 1.14)), increased risk of asbestosis (HR=1.50 (95% CI 1.10 to 2.03)) and a chronic obstructive pulmonary disease risk close to unity (HR=1.02 (95% CI 0.99 to 1.05)). Corresponding HRs for mortality were 0.86 (95% CI 0.64 to 1.15) for mesothelioma/pleural cancer, 1.06 (95% CI 1.01 to 1.12) for lung cancer, 1.79 (95% CI 1.10 to 2.92) for asbestosis, 1.06 (95% CI 0.86 to 1.30) for other lung diseases caused by external agents and 1.00 (95% CI 0.98 to 1.01) for death due to all causes. Conclusions We found that the risk of asbestosis was increased among vehicle mechanics. The risk of malignant mesothelioma/pleural cancers was not increased among vehicle mechanics.

To evaluate the effect of a transitional care intervention among older medical inpatients. The follow-home transitional care intervention did not have an impact on readmission and mortality rates. In a real-world context, several aspects make it difficult to measure the true impact of a multicomponent intervention. To evaluate the effect of a transitional care intervention (TCI) on readmission among older medical inpatients. This non-randomised quasi-experimental study was conducted at Horsens Regional Hospital in Denmark from 1 February 2017 to 31 December 2018. Inclusion criteria were patients ≥ 75 years old admitted for at least 48 h. First, patients were screened for eligibility. Then, the allocation to the intervention or control group was performed according to the municipality of residence. Patients living in three municipalities were offered the hospital-based intervention, and patients living in a fourth municipality were allocated to the control group. The intervention components were (1) discharge transportation with a home visit, (2) a post-discharge cross-sectorial video conference and (3) seven-day telephone consultation. The primary outcome was 30-day unplanned readmission. Secondary outcomes were 30- and 90-day mortality and days alive and out of hospital (DAOH). The study included 1205 patients (intervention: n = 615; usual care: n = 590). In the intervention group, the median age was 84.3 years and 53.7

Objectives: To evaluate the consequences of the framing of machine learning risk prediction models. We evaluate how framing affects model performance and model learning in four different approaches previously applied in published artificial-intelligence (AI) models. Setting and participants: We analysed structured secondary healthcare data from 221,283 citizens from four Danish municipalities who were 18 years of age or older. Results: The four models had similar population level performance (a mean area under the receiver operating characteristic curve of 0.73 to 0.82), in contrast to the mean average precision, which varied greatly from 0.007 to 0.385. Correspondingly, the percentage of missing values also varied between framing approaches. The on-clinical-demand framing, which involved samples for each time the clinicians made an early warning score assessment, showed the lowest percentage of missing values among the vital sign parameters, and this model was also able to learn more temporal dependencies than the others. The Shapley additive explanations demonstrated opposing interpretations of SpO2 in the prediction of sepsis as a consequence of differentially framed models. Conclusions: The profound consequences of framing mandate attention from clinicians and AI developers, as the understanding and reporting of framing are pivotal to the successful development and clinical implementation of future AI technology. Model framing must reflect the expected clinical environment. The importance of proper problem framing is by no means exclusive to sepsis prediction and applies to most clinical risk prediction models.

OBJECTIVES:The aim of this work was to evaluate the prognostic impact of statin therapy in symptomatic patients without obstructive CAD. BACKGROUND:Information on the prognostic impact of post-coronary computed tomographic angiography (CTA) statin use in patients with no or nonobstructive coronary artery disease (CAD) is sparse. METHODS:Patients undergoing CTA with suspected CAD in western Denmark from 2008 to 2017 with <50% coronary stenoses were identified. Information on post-CTA use of statin therapy and cardiovascular events were obtained from national registries. RESULTS:The study included 33,552 patients, median aged 56 years, 58% female, with no (n = 19,669) or nonobstructive (n = 13,883) CAD and a median follow-up of 3.5 years. The absolute risk of the combined end point of myocardial infarction (MI) or all-cause mortality was directly associated with the CAD burden with an event rate/1,000 patient-years of 4.13 (95% CI: 3.69-4.61) in no, 7.74 (95% CI: 6.88-8.71) in mild (coronary artery calcium score [CACS] 0-99), 13.72 (95% CI: 11.61-16.23) in moderate (CACS 100-399), and 32.47 (95% CI: 26.25-40.16) in severe (CACS ≥400) nonobstructive CAD. Statin therapy was associated with a multivariable adjusted HR for MI and death of 0.52 (95% CI: 0.36-0.75) in no, 0.44 (95% CI: 0.32-0.62) in mild, 0.51 (95% CI: 0.34-0.75) in moderate, and 0.52 (95% CI: 0.32-0.86) in severe nonobstructive CAD. The estimated numbers needed to treat to prevent the primary end point were 92 (95% CI: 61-182) in no, 36 (95% CI: 26-58) in mild, 24 (95% CI: 15-61) in moderate, and 13 (95% CI: 7-86) in severe nonobstructive CAD. Residual confounding may persist, but not to an extent explaining all of the observed risk reduction associated with statin treatment. CONCLUSIONS:The risk of MI and all-cause mortality in patients without obstructive CAD is directly associated with the CAD burden. Statin therapy is associated with a reduction of MI and all-cause death across the spectrum of CAD, however, the absolute benefit of treatment is directionally proportional with the CAD burden.

Problem framing is critical to developing risk prediction models because all subsequent development work and evaluation takes place within the context of how a problem has been framed and explicit documentation of framing choices makes it easier to compare evaluation metrics between published studies. In this work, we introduce the basic concepts of framing, including prediction windows, observation windows, window shifts and event-triggers for a prediction that strongly affects the risk of clinician fatigue caused by false positives. Building on this, we apply four different framing structures to the same generic dataset, using a sepsis risk prediction model as an example, and evaluate how framing affects model performance and learning. Our results show that an apparently good model with strong evaluation results in both discrimination and calibration is not necessarily clinically usable. Therefore, it is important to assess the results of objective evaluations within the context of more subjective evaluations of how a model is framed.

Purpose This paper describes the open cohort CROSS-TRACKS, which comprises population-based data from primary care, secondary care and national registries to study patient pathways and transitions across sectors while adjusting for sociodemographic characteristics.Participants A total of 221 283 individuals resided in the four Danish municipalities that constituted the catchment area of Horsens Regional Hospital in 2012–2018. A total of 96% of the population used primary care, 35% received at least one transfer payment and 66% was in contact with a hospital at least once in the period. Additional clinical information is available for hospital contacts (eg, alcohol intake, smoking status, body mass index and blood pressure). A total of 23% (n=8191) of individuals aged ≥65 years had at least one potentially preventable hospital admission, and 73% (n=5941) of these individuals had more than one.Findings to date The cohort is currently used for research projects in epidemiology and artificial intelligence. These projects comprise a prediction model for potentially preventable hospital admissions, a clinical decision support system based on artificial intelligence, prevention of medication errors in the transition between sectors, health behaviour and sociodemographic characteristics of men and women prior to fertility treatment, and a recently published study applying machine learning methods for early detection of sepsis.Future plans The CROSS-TRACKS cohort will be expanded to comprise the entire Central Denmark Region consisting of 1.3 million residents. The cohort can provide new knowledge on how to best organise interventions across healthcare sectors and prevent potentially preventable hospital admissions. Such knowledge would benefit both the individual citizen and society as a whole.

Purpose: To examine the occurrence and severity of coronary artery disease (CAD) in patients with rheumatoid arthritis (RA) compared to non-RA patients in a population referred for coronary computed tomography angiography (CTA) due to chest pain. Patients and Methods: In this cross-sectional study, 46,210 patients from a national CTA database were included. Patients with RA were stratified on serology, treatment with conventional synthetic or biological disease-modifying antirheumatic drugs (DMARDs), and the need for relapse or flare treatment with intraarticular or -muscular glucocorticoid injections (GCIs). Primary outcomes were coronary artery calcium score (CACS) >0 and CACS >= 400, and secondary outcome was obstructive CAD. Associations between RA and outcomes were examined using logistic regression and results were adjusted for age, sex, cardiovascular risk factors and comorbidities. Results: A total of 395 (0.9%) RA patients were identified. In overall RA, crude odds ratio (OR) for having CACS >0 was 1.48 (1.21-1.82) and 1.52 (1.15-2.01) for CACS >= 400, whereas adjusted ORs were 1.08 (0.86-1.36) and 1.21 (0.89-1.65), respectively. Seropositive RA patients had adjusted OR of 1.16 (0.89-1.50) for CACS >0 and 1.37 (0.98-1.90) for CACS >= 400. Patients who had received >= 1 GCI in the period of 3 years prior to the CTA had an adjusted OR of 1.37 (0.94-2.00) for having CACS >0 and 1.46 (0.92-2.31) for CACS >= 400. Conclusion: This is the first large-scale, CTA-based study examining the occurrence and severity of CAD in RA patients with symptoms suggestive of cardiovascular disease. A higher prevalence of coronary artery calcification was found in RA patients. After adjusting for age, sex, cardiovascular risk factors and comorbidities, the tendency was less pronounced. We found a trend for increased coronary calcification in RA patients being seropositive or needing treatment with GCI for a relapse or flare.

ObjectiveRheumatoid arthritis (RA) is a known risk factor for developing coronary artery disease (CAD). The influence of RA on the prognosis after initial CAD diagnosis and treatment is however largely unknown. We examined the risk of major cardiovascular events among RA and non-RA patients with chest pain referred to cardiac CT.MethodsThis was a follow-up study, using data from the Western Denmark Heart Registry, containing data on CT angiography examinations (Cardiac CT). Information on RA diagnosis and covariates were identified through nationwide administrative registers. The primary outcome was a combined outcome including, myocardial infarction, ischaemic or unspecified stroke, coronary artery bypass grafting, percutaneous coronary intervention, and all-cause mortality. Median time until events or censoring was 3.5 years (min/max: 0.0: 9.2). Cox proportional hazard models were used to examine the association between RA/non-RA patients and outcomes.ResultsAmong 42 257 patients, referred between 2008 and 2016, we identified 358 (0.8%) with RA. An increased risk was seen in RA compared with non-RA (adjusted HR 1.35, 95% CI 0.93 to 1.96). Among patients who had received flare treatment more than once prior to cardiac CT the adjusted HR 1.80 (95% CI 1.08 to 3.00), and among patients with seropositive RA the adjusted HR 1.42 (95% CI 0.93 to 2.16).ConclusionIn patients referred to cardiac CT due to chest pain, we found a trend of an association between RA and the combined primary outcome, supporting that RA per se, but in particular seropositive and active RA, may increase the risk of CAD even after initial CAD diagnosis and treatment.

Background: Among men and women diagnosed with colorectal cancer (CRC), 20-50% will develop a cancer recurrence. Cancer recurrences are not routinely captured by most population-based registries; however, linkage across Danish registries allows for the development of predictive models to detect recurrence. Successful application of such models in population-based settings requires validation against a gold standard to ensure the accuracy of recurrence identification. Objective: We apply a recently developed validation study design for prospectively collected validation data to validate predicted CRC recurrences against gold standard diagnoses from medical records in an actively followed cohort of CRC patients in Denmark. Methods: We use a Bayesian monitoring framework, traditionally used in clinical trials, to iteratively update classification parameters (positive and negative predictive values, and sensitivity and specificity) in an adaptive validation substudy design. This design allows determination of the sample size necessary to estimate the corresponding parameters and to identify when validation efforts can cease based on predefined criteria for parameter values and levels of precision. Results: Among 355 men and women diagnosed with CRC in Denmark and actively followed semi-annually, there were 63 recurrences diagnosed by active follow-up and 70 recurrences identified by a predictive algorithm. The adaptive validation design met stopping criteria for the classification parameters after 120 patients had their recurrence information validated. This stopping point yielded parameter estimates for the classification parameters similar to those obtained when the entire cohort was validated, with 66% less patients needed for the validation study. Conclusion: In this proof of concept application of the adaptive validation study design for outcome misclassification, we demonstrated the ability of the method to accurately determine when sufficient validation data have been collected. This method serves as a novel validation substudy design for prospectively collected data with simultaneous implementation of a validation study.

BACKGROUND:Biologic and epidemiologic evidence suggests that tumor cells depend on reprogrammed lipid metabolic function for survival and growth. Lipids may promote tumor recurrence by providing energy needed for proliferation. Studies have found associations of serum lipids with cancer incidence, mortality, and disease-free mortality, though they have yet to evaluate the prognostic potential of serum lipids for colorectal cancer (CRC) recurrence. METHODS:341 Danish CRC patients who underwent surgical resection were actively followed between 2003-2011 from date of surgery until December 31, 2012, or death. Serum lipids including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG), were collected at regular intervals. Lipids were assigned as time-varying exposures evaluated with a one-year lag. Cox proportional hazards models were used to assess recurrence rate, adjusting for clinically relevant covariates. A restricted analysis was performed in a group of non-statin users (n = 236). RESULTS:Among 341 CRC patients, increased HDL-C appeared to have a beneficial impact on recurrence-free survival (RFS) for CRC patients, especially among statin users (hazard ratio [HR] for 0.1 mmol/L increase = 0.58; 95 % confidence interval [CI]: 0.43, 0.78). Increased LDL-C and TG were not associated with RFS. Increased lipids showed a near-null effect on CRC recurrence [e.g. HR (95 % CI) for 0.1 mmol/L increase LDL = 1.01 (0.97, 1.19)] among non-statin users. CONCLUSION:Serum lipid levels of LDL-C and TG do not appear to be associated with CRC recurrence. Further investigation of the role of HDL-C in CRC recurrence may be of interest based on the suggestive inverse association observed here.

Lindsay J Collin 1,2 Anders H Riis Richard F MacLehose Thomas P Ahern Rune Erichsen 2,5 Ole Thorlacius-Ussing Timothy L Lash 1Department of Epidemiology, Emory University, Atlanta, GA, USA; 2Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; 3Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA; 4Department of Surgery, The Robert Larner, M.D. College of Medicine at the University of Vermont, Burlington, VT, USA; 5Department of Surgery, Randers Regional Hospital, Randers, Denmark; 6Department of Gastrointestinal Surgery, Aalborg University Hospital, Aalborg, Denmark Background: Among men and women diagnosed with colorectal cancer (CRC), 20–50% will develop a cancer recurrence. Cancer recurrences are not routinely captured by most population-based registries; however, linkage across Danish registries allows for the development of predictive models to detect recurrence. Successful application of such models in population-based settings requires validation against a gold standard to ensure the accuracy of recurrence identification. Objective: We apply a recently developed validation study design for prospectively collected validation data to validate predicted CRC recurrences against gold standard diagnoses from medical records in an actively followed cohort of CRC patients in Denmark. Methods: We use a Bayesian monitoring framework, traditionally used in clinical trials, to iteratively update classification parameters (positive and negative predictive values, and sensitivity and specificity) in an adaptive validation substudy design. This design allows determination of the sample size necessary to estimate the corresponding parameters and to identify when validation efforts can cease based on predefined criteria for parameter values and levels of precision. Results: Among 355 men and women diagnosed with CRC in Denmark and actively followed semi-annually, there were 63 recurrences diagnosed by active follow-up and 70 recurrences identified by a predictive algorithm. The adaptive validation design met stopping criteria for the classification parameters after 120 patients had their recurrence information validated. This stopping point yielded parameter estimates for the classification parameters similar to those obtained when the entire cohort was validated, with 66% less patients needed for the validation study. Conclusion: In this proof of concept application of the adaptive validation study design for outcome misclassification, we demonstrated the ability of the method to accurately determine when sufficient validation data have been collected. This method serves as a novel validation substudy design for prospectively collected data with simultaneous implementation of a validation study.

STUDY QUESTION: To what extent does fecundability vary across seasons? SUMMARY ANSWER: After accounting for seasonal patterns in pregnancy planning, we observed higher fecundability in the fall and lower fecundability in the spring, particularly at lower latitudes. WHAT IS KNOWN ALREADY: In human populations, there are strong seasonal patterns of births that vary across geographic regions and time periods. However, previous studies of seasonality and fecundity are limited because they examine season of birth rather than season of conception and therefore neglect to account for seasonal variation in initiating attempts to conceive or pregnancy loss or differences in gestational length. STUDY DESIGN, SIZE, DURATION: We conducted a preconception cohort study of 14 331 women residing in North America (June 20 13-May 2018: n = 5827) and Denmark ( June 2007-May 2018: n = 8504). Participants were attempting to conceive without fertility treatment and had been attempting pregnancy for <6 menstrual cycles at enrolment. PARTICIPANTS/MATERIAL, SETTING, METHODS: We collected information on season of each pregnancy attempt using last menstrual period dates over the study period. Pregnancy was reported on female bi-monthly follow-up questionnaires. We fit log-binomial models with trigonometric regression to examine periodic variation in fecundability. We accounted for seasonal variation in initiation of pregnancy attempts by including indicator variables for menstrual cycle of attempt in the regression models. MAIN RESULTS AND THE ROLE OF CHANCE: Initiation of pregnancy attempts peaked in September, with stronger seasonality in North America than in Denmark (48 vs. 16% higher probability initiating attempts in September compared with March). After accounting for seasonal variation in initiation of pregnancy attempts, we observed modest seasonal variation in fecundability, with a peak in the late fall and early winter in both cohorts, but stronger peak/low ratios in North America (1.16; 95% confidence interval [CI]: 1.05, 1.28) than in Denmark (1.08; 95% CI: 1.00, 1.16). When we stratified the North American data by latitude, we observed the strongest seasonal variation in the southern USA (peak/low ratio of 1.45 [95% CI: I.14, 1.84]), with peak fecundability in late November. LIMITATIONS, REASONS FOR CAUTION: We estimated menstrual cycle dates between follow-up questionnaires, which may have introduced exposure misclassification, particularly when women skipped follow-up questionnaires. We were unable to measure seasonally varying factors that may have influenced fecundability, including ambient temperature, vitamin D levels or infectious disease. WIDER IMPLICATIONS OF THE FINDINGS: An understanding of how fecundability varies across seasons could help identify factors that can impair reproductive function. Neglecting to account for seasonal variation in initiation of pregnancy attempts could bias estimates of seasonal patterns in fecundability. This is the first preconception cohort study to examine seasonal variation in fecundability after accounting for seasonality in initiation of pregnancy attempts. Fecundability was highest in the fall and lowest in the spring, with stronger effects in southern latitudes of North America, suggesting that seasonal exposures may affect fecundity.

Objective To examine the association between history of miscarriage and fecundability (the cycle-specific probability of conception). Design Nationwide prospective cohort study using web-based questionnaires. Setting Denmark, 2007–2012. Participants 977 women attempting to conceive, not using fertility treatment, and with a reproductive history of only miscarriage or only live birth. Exposure and outcome measures Information on previous pregnancy outcomes, including miscarriage, came from self-report or from relevant registries. Participants were followed for up to 12 months or until they reported a pregnancy, stopped trying to conceive or started fertility treatment, whichever came first. We used Kaplan-Meier methods to estimate cumulative probabilities of conception for women whose reproductive history included only miscarriage or only live birth. Using proportional probabilities regression modelling, we computed fecundability ratios (FR) with 95% CI comparing women with a history of only miscarriage with women with a history of only live birth. Results After adjustment for potential confounders, the cumulative probabilities of conception within 12 cycles of follow-up were 85% (95% CI 81% to 89%) for women with a history of 1 miscarriage, 85% (95% CI 73% to 92%) for women with a history of ≥2 miscarriages and 88% (95% CI 87% to 89%) for women whose reproductive history included only live birth. Adjusted FRs were 0.87 (95% CI 0.71 to 1.07) and 0.65 (95% CI 0.36 to 1.17) for women with a history of 1 and ≥2 miscarriages, respectively. Conclusions Our results indicate that women with a history of miscarriage may have slightly reduced fecundability compared with women with a history of only live birth. The reduction in fecundability was greater for women with repeated miscarriages, although the estimates were imprecise. Despite a potential delay in conception, women with previous miscarriage may have similar probability of pregnancy by 12 cycles of attempts to women with proven fertility.

AIMS We examined whether severity of coronary artery disease (CAD) measured by coronary computed tomography angiography can be used to predict rates of myocardial infarction (MI) and death in patients with and without diabetes. METHODS AND RESULTS A cohort study of consecutive patients (n = 48 731) registered in the Western Denmark Cardiac Computed Tomography Registry from 2008 to 2016. Patients were stratified by diabetes status and CAD severity (no, non-obstructive, or obstructive). Endpoints were MI and death. Event rates per 1000 person-years, unadjusted and adjusted incidence rate ratios were computed. Median follow-up was 3.6 years. Among non-diabetes patients, MI event rates per 1000 person-years were 1.4 for no CAD, 4.1 for non-obstructive CAD, and 9.1 for obstructive CAD. Among diabetes patients, the corresponding rates were 2.1 for no CAD, 4.8 for non-obstructive CAD, and 12.6 for obstructive CAD. Non-diabetes and diabetes patients without CAD had similar low rates of MI [adjusted incidence rate ratio 1.40, 95% confidence interval (CI): 0.71-2.78]. Among diabetes patients, the adjusted risk of MI increased with severity of CAD (no CAD: reference; non-obstructive CAD: adjusted incidence rate ratio 1.71, 95% CI: 0.79-3.68; obstructive CAD: adjusted incidence rate ratio 4.42, 95% CI: 2.14-9.17). Diabetes patients had higher death rates than non-diabetes patients, irrespective of CAD severity. CONCLUSION In patients without CAD, diabetes patients have a low risk of MI similar to non-diabetes patients. Further, MI rates increase with CAD severity in both diabetes and non-diabetes patients; with diabetes patients with obstructive CAD having the highest risk of MI.