Prediction model for future OHCAs based on geospatial and demographic data: An observational study.

This study used demographic data in a novel prediction model to identify areas with high risk of out-of-hospital cardiac arrest (OHCA) in order to target prehospital preparedness. We combined data from the nationwide Danish Cardiac Arrest Registry with geographical- and demographic data on a hectare level. Hectares were classified in a hierarchy according to characteristics and pooled to square kilometers (km2). Historical OHCA incidence of each hectare group was supplemented with a predicted annual risk of at least 1 OHCA to ensure future applicability. We recorded 19,090 valid OHCAs during 2016 to 2019. The mean annual OHCA rate was highest in residential areas with no point of public interest and 100 to 1000 residents per hectare (9.7/year/km2) followed by pedestrian streets with multiple shops (5.8/year/km2), areas with no point of public interest and 50 to 100 residents (5.5/year/km2), and malls with a mean annual incidence per km2 of 4.6. Other high incidence areas were public transport stations, schools and areas without a point of public interest and 10 to 50 residents. These areas combined constitute 1496 km2 annually corresponding to 3.4% of the total area of Denmark and account for 65% of the OHCA incidence. Our prediction model confirms these areas to be of high risk and outperforms simple previous incidence in identifying future risk-sites. Two thirds of out-of-hospital cardiac arrests were identified in only 3.4% of the area of Denmark. This area was easily identified as having multiple residents or having airports, malls, pedestrian shopping streets or schools. This result has important implications for targeted intervention such as automatic defibrillators available to the public. Further, demographic information should be considered when implementing such interventions.

Predicting the risks of kidney failure and death in adults with moderate to severe chronic kidney disease: multinational, longitudinal, population based, cohort study.

OBJECTIVE: To train and test a super learner strategy for risk prediction of kidney failure and mortality in people with incident moderate to severe chronic kidney disease (stage G3b to G4). DESIGN: Multinational, longitudinal, population based, cohort study. SETTINGS: Linked population health data from Canada (training and temporal testing), and Denmark and Scotland (geographical testing). PARTICIPANTS: People with newly recorded chronic kidney disease at stage G3b-G4, estimated glomerular filtration rate (eGFR) 15-44 mL/min/1.73 m2. MODELLING: The super learner algorithm selected the best performing regression models or machine learning algorithms (learners) based on their ability to predict kidney failure and mortality with minimised cross-validated prediction error (Brier score, the lower the better). Prespecified learners included age, sex, eGFR, albuminuria, with or without diabetes, and cardiovascular disease. The index of prediction accuracy, a measure of calibration and discrimination calculated from the Brier score (the higher the better) was used to compare KDpredict with the benchmark, kidney failure risk equation, which does not account for the competing risk of death, and to evaluate the performance of KDpredict mortality models. RESULTS: 67 942 Canadians, 17 528 Danish, and 7740 Scottish residents with chronic kidney disease at stage G3b to G4 were included (median age 77-80 years; median eGFR 39 mL/min/1.73 m2). Median follow-up times were five to six years in all cohorts. Rates were 0.8-1.1 per 100 person years for kidney failure and 10-12 per 100 person years for death. KDpredict was more accurate than kidney failure risk equation in prediction of kidney failure risk: five year index of prediction accuracy 27.8% (95% confidence interval 25.2% to 30.6%) versus 18.1% (15.7% to 20.4%) in Denmark and 30.5% (27.8% to 33.5%) versus 14.2% (12.0% to 16.5%) in Scotland. Predictions from kidney failure risk equation and KDpredict differed substantially, potentially leading to diverging treatment decisions. An 80-year-old man with an eGFR of 30 mL/min/1.73 m2 and an albumin-to-creatinine ratio of 100 mg/g (11 mg/mmol) would receive a five year kidney failure risk prediction of 10% from kidney failure risk equation (above the current nephrology referral threshold of 5%). The same man would receive five year risk predictions of 2% for kidney failure and 57% for mortality from KDpredict. Individual risk predictions from KDpredict with four or six variables were accurate for both outcomes. The KDpredict models retrained using older data provided accurate predictions when tested in temporally distinct, more recent data. CONCLUSIONS: KDpredict could be incorporated into electronic medical records or accessed online to accurately predict the risks of kidney failure and death in people with moderate to severe CKD. The KDpredict learning strategy is designed to be adapted to local needs and regularly revised over time to account for changes in the underlying health system and care processes.

Overweight in childhood and consumer purchases in a Danish cohort.

BACKGROUND: Prevention and management of childhood overweight involves the entire family. We aimed to investigate purchase patterns in households with at least one member with overweight in childhood by describing expenditure on different food groups. METHODS: This Danish register-based cohort study included households where at least one member donated receipts concerning consumers purchases in 2019-2021 and at least one member had their Body mass index (BMI) measured in childhood within ten years prior to first purchase. A probability index model was used to evaluate differences in proportion expenditure spent on specific food groups. RESULTS: We identified 737 households that included a member who had a BMI measurement in childhood, 220 with overweight and 517 with underweight or normal weight (reference households). Adjusting for education, income, family type, and urbanization, households with a member who had a BMI classified as overweight in childhood had statistically significant higher probability of spending a larger proportion of expenditure on ready meals 56.29% (95% CI: 51.70;60.78) and sugary drinks 55.98% (95% CI: 51.63;60.23). Conversely, they had a statistically significant lower probability of spending a larger proportion expenditure on vegetables 38.44% (95% CI: 34.09;42.99), compared to the reference households. CONCLUSION: Households with a member with BMI classified as overweight in childhood spent more on unhealthy foods and less on vegetables, compared to the reference households. This study highlights the need for household/family-oriented nutrition education and intervention.