Diabetes Care · 2023 · 11 citations · 24 references
MedicineHealth SciencesClinical EpidemiologyDiabetologyEpidemiologyDiabetes MellitusDisease Risk AssessmentMorbidity AnalysisMortality AnalysisMortality ModelingRisk PredictionAge-specific Risk AssessmentAge-specific Risk EstimationAge-specific Risk IdentificationDiabetes MortalityHospitalization Risk PredictionIntensive Care Unit MortalityIntensive Care UseMachine Learning Prediction Models
OBJECTIVE: Type 2 diabetes is an established risk factor for hospitalization and death in COVID-19 infection, while findings with respect to type 1 diabetes have been diverging. RESEARCH DESIGN AND METHODS: Using nationwide health registries, we identified all patients aged ≥18 years with type 1 and type 2 diabetes in Sweden. Odds ratios (ORs) describe the general and age-specific risk of being hospitalized, need for intensive care, or dying, adjusted for age, socioeconomic factors, and coexisting conditions, compared with individuals without diabetes. Machine learning models were used to find predictors of outcomes among individuals with diabetes positive for COVID-19. RESULTS: Until 30 June 2021, we identified 365 (0.71%) and 11,684 (2.31%) hospitalizations in 51,402 and 504,337 patients with type 1 and 2 diabetes, respectively, with 67 (0.13%) and 2,848 (0.56%) requiring intensive care unit (ICU) care and 68 (0.13%) and 4,020 (0.80%) dying (vs 7,824,181 individuals without diabetes [41,810 hospitalizations (0.53%), 8,753 (0.11%) needing ICU care, and 10,160 (0.13%) deaths). Although those with type 1 diabetes had moderately raised odds of being hospitalized (multiple-adjusted OR 1.38 [95% CI 1.24-1.53]), there was no independent effect on ICU care or death (OR of 1.21 [95% CI 0.94-1.52] and 1.13 [95% CI 0.88-1.48], respectively). Age and socioeconomic factors were the dominating features for predicting hospitalization and death in both types of diabetes. CONCLUSIONS: Type 2 diabetes was associated with increased odds for all outcomes, whereas patients with type 1 diabetes had moderately increased odds of hospitalization but not ICU care and death.
24
Leo Breiman · Machine Learning · 2001 · 126.2K citations · Full text
A working guide to boosted regression trees
Jane Elith, John R. Leathwick, Trevor Hastie · Journal of Animal Ecology · 2008 · 6.5K citations · Full text
Computing, Biostatistics, Ecology +14
External review and validation of the Swedish national inpatient register
Jonas F. Ludvigsson, Eva Andersson, Anders Ekbom et al. · BMC Public Health · 2011 · 5.1K citations · Full text
The Swedish personal identity number: possibilities and pitfalls in healthcare and medical research
Jonas F. Ludvigsson, P Otterblad-Olausson, Birgitta U. Pettersson et al. · European Journal of Epidemiology · 2009 · 2.4K citations · Full text
Health Professions, Health Systems, Electronic Health Records +11