From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning.

As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FNrate and 4.59% FPrate. Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients' NIHSS progression from the time of admission until their discharge.

Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning.

Despite therapeutic advancements, stroke remains a leading cause of death and long-term disability. The quality of current stroke prognostic models varies considerably, whereas prediction models of post-stroke disability and mortality are restricted by the sample size, the range of clinical and risk factors and the clinical applicability in general. Accurate prognostication can ease post-stroke discharge planning and help healthcare practitioners individualize aggressive treatment or palliative care, based on projected life expectancy and clinical course. In this study, we aimed to develop an explainable machine learning methodology to predict functional outcomes of stroke patients at discharge, using the Modified Rankin Scale (mRS) as a binary classification problem. We identified 35 parameters from the admission, the first 72 h, as well as the medical history of stroke patients, and used them to train the model. We divided the patients into two classes in two approaches: "Independent" vs. "Non-Independent" and "Non-Disability" vs. "Disability". Using various classifiers, we found that the best models in both approaches had an upward trend, with respect to the selected biomarkers, and achieved a maximum accuracy of 88.57% and 89.29%, respectively. The common features in both approaches included: age, hemispheric stroke localization, stroke localization based on blood supply, development of respiratory infection, National Institutes of Health Stroke Scale (NIHSS) upon admission and systolic blood pressure levels upon admission. Intubation and C-reactive protein (CRP) levels upon admission are additional features for the first approach and Erythrocyte Sedimentation Rate (ESR) levels upon admission for the second. Our results suggest that the said factors may be important predictors of functional outcomes in stroke patients.

Blink reflex habituation in migraine and chronic tension-type headache.

ΑBSTRACT: R1 and R2 blink reflex latencies were investigated blind in 55 patients with chronic tension-type headache, 55 patients with migraine, and 55 headache-free controls. Standard electrical stimulation of the supraorbital nerve was applied and the response was recorded from the ipsilateral orbicularis oculi muscles. There were no R1 or R2 latency differences between the three groups. During migraine attacks we observed a statistically significant reduction of R2 amplitude and area. The main finding of our study was the elicitation of the late R2" response at different interstimulus intervals in migraine patients compared to the tension-type headache and control groups. This could be considered an indication of habituation mechanism hyperexcitability, although further investigation is needed to confirm these findings and establish the neurophysiologic basis. This study suggests that blink reflex studies can be used routinely as a non-evasive and inexpensive method for the evaluation of headache patients.

Melatonin 4 mg as prophylactic therapy for primary headaches: a pilot study.

There is growing evidence that headaches are connected to melatonin secretion. Our aim was to assess the potential effectiveness of melatonin for primary headache prevention. Forty-nine patients (37 with migraine and 12 with chronic tension-type headache, TTH) were prescribed oral melatonin, 4 mg, 30 minutes before bedtime for six months. Forty-one (83.6%) of the 49 patients completed the study, while eight dropped out for personal reasons. A statistically significant reduction in headache frequency was found between baseline and final follow-up after six months of treatment (p=0.033 for TTH patients and p<0.001 for migraineurs). The Headache Impact Test score was significantly reduced in both groups of headache patients (p=0.002 and p<0.001, respectively). At baseline, melatonin levels, measured both during a headache attack and a pain-free period, did not differ between patients with TTH and migraineurs (p=0.539 and p=0.693, respectively), and no statistically significant differences in Hamilton Depression Rating Scale scores were found between the two groups. This pilot study shows promising results, in terms of headache frequency reduction and daily quality of life improvement, in both groups.

Focal dystonia, tremor and myokymic discharges secondary to electrical injury.

We describe the case of a male patient who developed electromyographically confirmed myokymia, dystonia and tremor and clinically confirmed focal dystonia and tremor, secondary to electrical injury. Dystonia is a rare complication of electrical injury. Myokymic discharges secondary to electrical injury are previously unreported. Dystonia and tremor EMG findings were present not only at the clinically affected muscles of the lower limb but also at the clinically unaffected upper limb muscles. This is the first case report to link myokymia as a secondary complication of an electrical injury.