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Sökning: WFRF:(Holzhauer Björn)

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1.
  • Lisspers, Karin, Docent, 1954-, et al. (författare)
  • Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients' data using machine learning - Based on the ARCTIC study
  • 2021
  • Ingår i: Respiratory Medicine. - : Elsevier BV. - 0954-6111 .- 1532-3064. ; 185
  • Tidskriftsartikel (refereegranskat)abstract
    • Objective: The ability to predict impending asthma exacerbations may allow better utilization of healthcare resources, prevention of hospitalization and improve patient outcomes. We aimed to develop models using machine learning to predict risk of exacerbations. Methods: Data from 29,396 asthma patients was collected from electronic medical records and national registers covering clinical and epidemiological factors (e.g. comorbidities, health care contacts), between 2000 and 2013. Machine-learning classifiers were used to create models to predict exacerbations within the next 15 days. Model selection was done using the mean cross validation score of area under precision-recall curve (AUPRC). Results: The most important predictors of exacerbation were comorbidity burden and previous exacerbations. Model validation on test data yielded an AUPRC = 0.007 (95% CI: +/- 0.0002), indicating that historic clinical information alone may not be sufficient to predict a near future risk of asthma exacerbation. Conclusions: Supplementation with additional data on environmental triggers, (e.g. weather, pollen count, air quality) and from wearables, might be necessary to improve performance of the short-term predictive model to develop a more clinically useful tool.
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2.
  • McMurray, John J, et al. (författare)
  • Effect of valsartan on the incidence of diabetes and cardiovascular events
  • 2010
  • Ingår i: New England Journal of Medicine. - 0028-4793 .- 1533-4406. ; 362:16, s. 1477-1490
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND: It is not known whether drugs that block the renin-angiotensin system reduce the risk of diabetes and cardiovascular events in patients with impaired glucose tolerance. METHODS: In this double-blind, randomized clinical trial with a 2-by-2 factorial design, we assigned 9306 patients with impaired glucose tolerance and established cardiovascular disease or cardiovascular risk factors to receive valsartan (up to 160 mg daily) or placebo (and nateglinide or placebo) in addition to lifestyle modification. We then followed the patients for a median of 5.0 years for the development of diabetes (6.5 years for vital status). We studied the effects of valsartan on the occurrence of three coprimary outcomes: the development of diabetes; an extended composite outcome of death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, hospitalization for heart failure, arterial revascularization, or hospitalization for unstable angina; and a core composite outcome that excluded unstable angina and revascularization. RESULTS: The cumulative incidence of diabetes was 33.1% in the valsartan group, as compared with 36.8% in the placebo group (hazard ratio in the valsartan group, 0.86; 95% confidence interval [CI], 0.80 to 0.92; P<0.001). Valsartan, as compared with placebo, did not significantly reduce the incidence of either the extended cardiovascular outcome (14.5% vs. 14.8%; hazard ratio, 0.96; 95% CI, 0.86 to 1.07; P=0.43) or the core cardiovascular outcome (8.1% vs. 8.1%; hazard ratio, 0.99; 95% CI, 0.86 to 1.14; P=0.85). CONCLUSIONS: Among patients with impaired glucose tolerance and cardiovascular disease or risk factors, the use of valsartan for 5 years, along with lifestyle modification, led to a relative reduction of 14% in the incidence of diabetes but did not reduce the rate of cardiovascular events. (ClinicalTrials.gov number, NCT00097786.)
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3.
  • Ställberg, Björn, Docent, et al. (författare)
  • Predicting Hospitalization Due to COPD Exacerbations in Swedish Primary Care Patients Using Machine Learning - Based on the ARCTIC Study
  • 2021
  • Ingår i: The International Journal of Chronic Obstructive Pulmonary Disease. - : Informa UK Limited. - 1176-9106 .- 1178-2005. ; 16, s. 677-688
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose: Chronic obstructive pulmonary disease (COPD) exacerbations can negatively impact disease severity, progression, mortality and lead to hospitalizations. We aimed to develop a model that predicts a patient's risk of hospitalization due to severe exacerbations (defined as COPD-related hospitalizations) of COPD, using Swedish patient level data. Patients and Methods: Patient level data for 7823 Swedish patients with COPD was collected from electronic medical records (EMRs) and national registries covering healthcare contacts, diagnoses, prescriptions, lab tests, hospitalizations and socioeconomic factors between 2000 and 2013. Models were created using machine-learning methods to predict risk of imminent exacerbation causing patient hospitalization due to COPD within the next 10 days. Exacerbations occurring within this period were considered as one event. Model performance was assessed using the Area under the Precision-Recall Curve (AUPRC). To compare performance with previous similar studies, the Area Under Receiver Operating Curve (AUROC) was also reported. The model with the highest mean cross validation AUPRC was selected as the final model and was in a final step trained on the entire training dataset. Results: The most important factors for predicting severe exacerbations were exacerbations in the previous six months and in whole history, number of COPD-related healthcare contacts and comorbidity burden. Validation on test data yielded an AUROC of 0.86 and AUPRC of 0.08, which was high in comparison to previously published attempts to predict COPD exacerbation. Conclusion: Our work suggests that clinically available information on patient history collected via automated retrieval from EMRs and national registries or directly during patient consultation can form the basis for future clinical tools to predict risk of severe COPD exacerbations.
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