| 1. |
- Bjerregaard, Bine Kjoller, et al.
(författare)
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Tobacco smoke and bladder cancer-in the European prospective investigation into cancer and nutrition
- 2006
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Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136. ; 119:10, s. 2412-2416
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Tidskriftsartikel (refereegranskat)abstract
- The purpose of the present study was to investigate the association between smoking and the development of bladder cancer. The study population consisted of 429,906 persons participating in the European Prospective Investigation into Cancer and Nutrition (EPIC), 633 of whom developed bladder cancer during the follow-up period. An increased risk of bladder cancer was found for both current- (incidence rate ratio 3.96, 95% confidence interval: 3.07-5.09) and ex- (2.25, 1.74-2.91) smokers, compared to never-smokers. A positive association with intensity (per 5 cigarettes) was found among current-smokers (1.18, 1.09-1.28). Associations (per 5 years) were observed for duration (1.14, 1.08-1.21), later age at start (0.75, 0.66-0.85) and longer time since quitting (0.92, 0.86-0.98). Exposure to environmental tobacco smoke (ETS) during childhood increased the risk of bladder cancer (1.38, 1.00-1.90), whereas for ETS exposure as adult no effect was detected. The present study confirms the strong association between smoking and bladder cancer. The indication of a higher risk of bladder cancer for those who start smoking at a young age and for those exposed to ETS during childhood adds to the body of evidence suggesting that children are more sensitive to carcinogens than adults. (c) 2006 Wiley-Liss, Inc.
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| 2. |
- Lisspers, Karin, Docent, 1954-, et al.
(författare)
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Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients' data using machine learning - Based on the ARCTIC study
- 2021
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Ingår i: Respiratory Medicine. - : Elsevier. - 0954-6111 .- 1532-3064. ; 185
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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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| 3. |
- Ställberg, Björn, Docent, et al.
(författare)
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Predicting Hospitalization Due to COPD Exacerbations in Swedish Primary Care Patients Using Machine Learning - Based on the ARCTIC Study
- 2021
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Ingår i: The International Journal of Chronic Obstructive Pulmonary Disease. - : Taylor & Francis. - 1176-9106 .- 1178-2005. ; 16, s. 677-688
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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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