RESUMO
BACKGROUND: Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence. METHODS: We analyzed clinical and socioeconomic data from a prospectively collected public health care-based data set of 41â 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014-2020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and >365 days, respectively. C statistics and area under the receiver operating characteristic curve were used to assess the accuracy of the models. RESULTS: Overall, 16.21% (5932 of 36â 114) of patients had stroke recurrence during a median follow-up of 2.69 years. The most powerful predictors of stroke recurrence were time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex, which were used to create a simplified model with similar performance, together with modifiable vascular risk factors (glycemia, body mass index, high blood pressure, cholesterol, tobacco dependence, and alcohol abuse). The areas under the receiver operating characteristic curve were 0.76 (95% CI, 0.74-0.77), 0.60 (95% CI, 0.58-0.61), and 0.71 (95% CI, 0.69-0.72) for early, late, and long-term recurrence risk, respectively. The areas under the receiver operating characteristic curve of the Cox risk class probability were 0.73 (95% CI, 0.72-0.75), 0.59 (95% CI, 0.57-0.61), and 0.67 (95% CI, 0.66-0.70); machine learning approaches (random forest and AdaBoost) showed statistically significant improvement (P<0.05) over the Cox model for the 3 recurrence time periods. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors. CONCLUSIONS: PRERISK is a novel approach that provides a personalized and fairly accurate risk prediction of stroke recurrence over time. The model has the potential to incorporate dynamic control of risk factors.
RESUMO
Treatment with calcitriol, the hormonal form of vitamin D, has shown beneficial effects in experimental models of acute lung injury. In this study, we aimed to analyze the associations between calcitriol supplementation and the risk of SARS-CoV2 infection or COVID-19 mortality. Individuals ≥18 years old living in Catalonia and supplemented with calcitriol from April 2019 to February 2020 were compared with propensity score matched controls. Outcome variables were SARS-CoV2 infection, severe COVID-19 and COVID-19 mortality. Associations between calcitriol supplementation and outcome variables were analyzed using multivariable Cox proportional regression. A total of 8076 patients were identified as being on calcitriol treatment. Advanced chronic kidney disease and hypoparathyroidism were the most frequent reasons for calcitriol supplementation in our population. Calcitriol use was associated with reduced risk of SARS-CoV2 infection (HR 0.78 [CI 95% 0.64-0.94], p = 0.010), reduced risk of severe COVID-19 and reduced COVID-19 mortality (HR 0.57 (CI 95% 0.41-0.80), p = 0.001) in patients with advanced chronic kidney disease. In addition, an inverse association between mean daily calcitriol dose and COVID-19 severity or mortality was observed in treated patients, independently of renal function. Our findings point out that patients with advanced chronic kidney disease could benefit from calcitriol supplementation during the COVID-19 pandemic.