A Dynamic Model for Predicting Survival up to 1 Year After Ischemic Stroke

Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke

Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures.

Time-Varying Risk Factors Associated With the Progress of Functional Recovery and Psychological Distress in First-Ever Stroke Patients

ABSTRACT

BACKGROUND: Evaluation of stroke recovery outcome is crucial and a major goal of clinical practice. A recovery trajectory model serves as a prognostic tool that enables development of effective intervention and long-term management to improve poststroke recovery outcomes. This study explored time-varying risk factors associated with the progression of functional recovery and psychological distress poststroke. METHODS: Participants were patients with first-ever stroke who underwent assessment for activities of daily living, psychological distress, and social support at the onset (within 72 hours) and at 1, 3, and 6 months. A generalized estimation equation was used to account for the correlation between the repeated measurements. RESULTS: Of the 101 patients, 60.4% were men, and the mean (SD) age was 63.06 (13.12) years. Over time, the physical functions of patients after stroke significantly increased, and anxiety and depression significantly decreased. Approximately 50% of patients achieved full functional recovery after 6 months. The time-varying risk factors for National Institutes of Health Stroke Scale scores and depression levels affected the trajectory of functional recovery during follow-up. Factors associated with patient anxiety levels were National Institutes of Health Stroke Scale scores and depression levels. Factors associated with patient depression levels included education, anxiety, and social support levels. CONCLUSION: This study demonstrates the progression of time-varying risk factors for functional recovery and psychological distress in patients with first-ever stroke. We recommend that nurses work with patients and their families in the early poststroke stages to identify comprehensive goals based on individual needs and related factors at different stages and that they educate patients on what is required for them to regain independence.

Key concepts and opportunities

Neuropalliative care is an emerging field dedicated to applying palliative care approaches to meet the needs of persons living with neurologic illness and their families. The development of this field acknowledges the unique needs of this population, including in terms of neuropsychiatric symptoms, the impact of neurologic illness on personhood, and the logistics of managing neurologic disability. In defining the goals of this field, it is important to distinguish between neuropalliative care as an approach to care, as a skillset, as a medical subspecialty, and as a public health goal as each of these constructs offers their own contributions and opportunities. As a newly emerging field, there are nearly unlimited opportunities to improve care through research, clinical care, education, and advocacy.