Objective: This study aimed to develop and validate a prognostic model for the 1-year risk of late poststroke epilepsy (PSE). Materials and methods: We included patients initially diagnosed with ischemic stroke between 2003 and 2014 in a National Health Insurance claims-based cohort in Taiwan. Patients were further divided into development and validation cohorts based on their year of stroke diagnosis. Multivariable Cox regression with backward elimination was used to analyze the association between 1-year PSE and risk factors before and on stroke admission. Results: In total, 1,684 (1.93%) and 725 (1.87%) ischemic stroke patients comprising the development and validation cohorts, respectively, experienced late PSE within 1 year after stroke. Seven clinical variables were examined to be independently associated with 1-year risk of PSE. We developed a risk score called “PSEiCARe” ranging from 0 to 16 points, comprising the following factors: prolonged hospital stay (>2 weeks, 1 point), seizure on admission (6 points), elderly patients (age ≥80 years, 1 point), intensive care unit stay on admission (3 points), cognitive impairment (dementia, 2 points), atrial fibrillation (2 points), and respiratory tract infection (pneumonia) on admission (1 point). Patients were further classified into low-, medium-, high-, and very-high-risk groups. The incidence (per 100 person-years) was 0.64 (95% CI: 0.56–0.71) for the low-risk, 2.62 (95% CI: 2.43–2.82) for the medium-risk, 10.3 (95% CI: 9.48–11.3) for the high-risk, and 28.2 (95% CI: 24.0–33.0) for the very-high-risk groups. Discrimination and calibration were satisfactory, with a Harrell’s C of 0.762 in the development model and 0.792 in the validation model. Conclusion: PSEiCARe is an easy-to-use prognostic score that integrates patient characteristics and clinical factors on stroke admission to predict 1-year PSE risk; it has the potential to assist individualized patient management and improve clinical practice, thereby preventing the occurrence of late PSE.

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

Objective: This study aimed to investigate the efficacy of high-intensity functional exercise among older adults with dementia. Methods: In this systematic review and meta-analysis of randomized controlled trials, we collected articles published before August 2020 from PubMed, Embase, and the Cochrane Library to evaluate the effect of high-intensity functional exercise on older adults with dementia. Primary outcomes included improvements in balance function and gait performance (speed, cadence, and stride length). The secondary outcomes included lower limb strength, activities of daily living, psychiatric well-being, depression, and cognition. Furthermore, we performed subgroup analysis with two high-intensity functional exercise programs: the Umeå program and Hauer’s program. Results: We identified 15 articles describing six trials including older adults with dementia undergoing high-intensity functional exercise or control activity. The meta-analysis indicated that high-intensity functional exercise, both in Hauer’s program and in the Umeå program, significantly improved balance function (pooled standardized mean difference 0.57, 95% confidence interval 0.31–0.83). Hauer’s program significantly improved gait speed, cadence, stride length, and lower limb strength. Beneficial effects on speed, cadence, and lower limb strength were retained for several months. The Umeå program facilitated activities of daily living and psychiatric well-being, with effects on activities of daily living lasting several months. In the only eligible trial, no effects on cognition were observed. Adverse effects of high-intensity functional exercise were minimal to none. Conclusions: High-intensity functional exercise is generally safe and is recommended for older individuals with mild or moderate dementia to provide benefits in motor performance and daily functioning.