Developing and Validating a Lung Cancer Risk Prediction Model: A Nationwide Population-Based Study.

Lung cancer can be challenging to diagnose in the early stages, where treatment options are optimal. We aimed to develop 1-year prediction models for the individual risk of incident lung cancer for all individuals aged 40 or above living in Denmark on 1 January 2017. The study was conducted using population-based registers on health and sociodemographics from 2007-2016. We applied backward selection on all variables by logistic regression to develop a risk model for lung cancer and applied the models to the validation cohort, calculated receiver-operating characteristic curves, and estimated the corresponding areas under the curve (AUC). In the populations without and with previously confirmed cancer, 4274/2,826,249 (0.15%) and 482/172,513 (0.3%) individuals received a lung cancer diagnosis in 2017, respectively. For both populations, older age was a relevant predictor, and the most complex models, containing variables related to diagnoses, medication, general practitioner, and specialist contacts, as well as baseline sociodemographic characteristics, had the highest AUC. These models achieved a positive predictive value (PPV) of 0.0127 (0.006) and a negative predictive value (NPV) of 0.989 (0.997) with a 1% cut-off in the population without (with) previous cancer. This corresponds to 1.2% of the screened population experiencing a positive prediction, of which 1.3% would be incident with lung cancer. We have developed and tested a prediction model with a reasonable potential to support clinicians and healthcare planners in identifying patients at risk of lung cancer.

Can National Registries Contribute to Predict the Risk of Cancer? The Cancer Risk Assessment Model (CRAM).

PURPOSE: To develop a predictive model based on Danish administrative registers to facilitate automated identification of individuals at risk of any type of cancer. METHODS: A nationwide register-based cohort study covering all individuals in Denmark aged +20 years. The outcome was all-type cancer during 2017 excluding nonmelanoma skin cancer. Diagnoses, medication, and contact with general practitioners in the exposure period (2007-2016) were considered for the predictive model. We applied backward selection to all variables by logistic regression to develop a risk model for cancer. We applied the models to the validation cohort, calculated the receiver operating characteristic curves, and estimated the corresponding areas under the curve (AUC). RESULTS: The study population consisted of 4.2 million persons; 32,447 (0.76%) were diagnosed with cancer in 2017. We identified 39 predictive risk factors in women and 42 in men, with age above 30 as the strongest predictor for cancer. Testing the model for cancer risk showed modest accuracy, with an AUC of 0.82 (95% CI 0.81-0.82) for men and 0.75 (95% CI 0.74-0.75) for women. CONCLUSION: We have developed and tested a model for identifying the individual risk of cancer through the use of administrative data. The models need to be further investigated before being applied to clinical practice.

SARS-CoV-2 infection in pregnancy in Denmark-characteristics and outcomes after confirmed infection in pregnancy: A nationwide, prospective, population-based cohort study.

INTRODUCTION: Assessing the risk factors for and consequences of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during pregnancy is essential to guide clinical care. Previous studies on SARS-CoV-2 infection in pregnancy have been among hospitalized patients, which may have exaggerated risk estimates of severe outcomes because all cases of SARS-CoV-2 infection in the pregnant population were not included. The objectives of this study were to identify risk factors for and outcomes after SARS-CoV-2 infection in pregnancy independent of severity of infection in a universally tested population, and to identify risk factors for and outcomes after severe infection requiring hospital admission. MATERIAL AND METHODS: This was a prospective population-based cohort study in Denmark using data from the Danish National Patient Register and Danish Microbiology Database and prospectively registered data from medical records. We included all pregnancies between March 1 and October 31, 2020 and compared women with a positive SARS-CoV-2 test during pregnancy to non-infected pregnant women. Cases of SARS-CoV-2 infection in pregnancy were both identified prospectively and through register linkage to ensure that all cases were identified and that cases were pregnant during infection. Main outcome measures were pregnancy, delivery, maternal, and neonatal outcomes. Severe infection was defined as hospital admission due to coronavirus disease 2019 (COVID-19) symptoms. RESULTS: Among 82 682 pregnancies, 418 women had SARS-CoV-2 infection during pregnancy, corresponding to an incidence of 5.1 per 1000 pregnancies, 23 (5.5%) of which required hospital admission due to COVID-19. Risk factors for infection were asthma (odds ratio [OR] 2.19, 95% CI 1.41-3.41) and being foreign born (OR 2.12, 95% CI 1.70-2.64). Risk factors for hospital admission due to COVID-19 included obesity (OR 2.74, 95% CI 1.00-7.51), smoking (OR 4.69, 95% CI 1.58-13.90), infection after gestational age (GA) 22 weeks (GA 22-27 weeks: OR 3.77, 95% CI 1.16-12.29; GA 28-36 weeks: OR 4.76, 95% CI 1.60-14.12), and having asthma (OR 4.53, 95% CI 1.39-14.79). We found no difference in any obstetrical or neonatal outcomes. CONCLUSIONS: Only 1 in 20 women with SARS-CoV-2 infection during pregnancy required admission to hospital due to COVID-19. Risk factors for admission comprised obesity, smoking, asthma, and infection after GA 22 weeks. Severe adverse outcomes of SARS-CoV-2 infection in pregnancy were rare.

Socioeconomic status in Danish women with polycystic ovary syndrome: A register-based cohort study.

INTRODUCTION: Low socioeconomic status (SES) may be associated with increased risk of polycystic ovary syndrome (PCOS) and vice versa. Possible associations between SES, obesity and ethnicity in PCOS are undetermined. MATERIAL AND METHODS: National register-based study including women with PCOS aged 25 years or above (PCOS Denmark and an embedded cohort; PCOS Odense University Hospital [OUH]) and one control population. PCOS Denmark (n = 13 891) included women with PCOS in the Danish National Patient Register. Women in PCOS OUH underwent clinical examination (n = 814). Three age-matched controls were included per patient (n = 41 584). The main outcome measure was SES (personal income, occupational status and education). RESULTS: The median (Q1; Q3) age of women in PCOS Denmark and controls was 33 (29; 39) years. Women with personal income in the lower tertile had a higher probability of a PCOS diagnosis than women in the high-income tertile (adjusted odds ratio [aOR] 1.5, 95% confidence interval [CI] 1.4-1.6). Women who were unemployed or on welfare payment (aOR 1.5, 95% CI 1.4-1.6), or who retired early (OR 1.8, 95% CI 1.7-2.0) had a higher probability of a PCOS diagnosis than women affiliated to the labor market. Women originating from the Middle East more often had PCOS (aOR 3.2, 95% CI 2.8-3.7) compared with women originating from Europe. In PCOS OUH, SES was lower in obese than in normal weight women. CONCLUSIONS: A diagnosis of PCOS was associated with lower SES. In PCOS, women of foreign origin and women with obesity more often had low SES.