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Stroke screening tools should have good diagnostic performance for early diagnosis and a proper therapeutic plan. This paper describes and compares various diagnostic tools used to identify stroke in emergency departments and prehospital setting.The meta-analysis was conducted according to the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. The PubMed and Scopus databases were searched until December 31, 2021, for studies published on stroke screening tools. These tools' diagnostic performance (sensitivity and specificity) was pooled using a bivariate random-effects model whenever appropriate.Eleven screening tools for stroke were identified in 29 different studies. The various tools had a wide range of sensitivity and specificity in different studies. In the meta-analysis, the Cincinnati Pre-hospital Stroke Scale, Face Arm Speech Test, and Recognition of Stroke in the Emergency Room (ROSIER) had sensitivity (between 83 and 91%) but poor specificity (all below 64%). When comparing all the tools, ROSIER had the highest sensitivity 90.5%. Los Angeles Pre-hospital Stroke Screen performed best in terms of specificity 88.7% but had low sensitivity (73.9%). Melbourne Ambulance Stroke Screen had a balanced performance in terms of sensitivity (86%) and specificity (76%). Sensitivity analysis consisting of only prospective studies showed a similar range of sensitivity and specificity.All the stroke screening tools included in the review were comparable, but no clear superior screening tool could be identified. Simple screening tools like Cincinnati prehospital stroke scale (CPSS) have similar performance compared to more complex tools.

COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 has been occurring in waves due to emergence of different strains. During second wave of COVID pandemic in Nepal, largely caused by Delta variant, due to rapid rise in cases, existing hospitals and health facilities were overwhelmed. As a result, telemedicine was expanded to help reduce strain on the healthcare system and meet unusually high demands. A team of physicians with the support from the local government provided hospital level care for patients at home in rural district of Dang in Nepal. This research analyzes implementation of this model of care and outcomes based on review of patient records and treatment guidelines. Within a month, from May 5 to June 13, 2021, a total of 102 patients received care at home. While most patients had mild to moderate disease, 12.8% of patients had severe disease. Telemedicine usage was highest among the 40-59 years age group, with overall recovery rate of 85.3%. The patient satisfaction survey revealed that majority, 86.5% were happy with the medical care they received. This home hospital care model has shown excellent clinical outcomes and high patient satisfaction even in resource limited setting.

Introduction: The rise of ischemic stroke among young adults has stressed the need to understand their risk profiles and outcomes better. This study aimed to examine the five-year ischemic stroke recurrence and survival probability among young patients in rural Pennsylvania. Methods: This retrospective cohort study included first-time ischemic stroke patients from the Geisinger Health System between September 2003 and May 2014. The outcomes included all-cause mortality and ischemic stroke recurrence at five years. Kaplan-Meier estimator, cumulative incidence function, Cox proportional hazards model, and Cause-specific hazard model were used to examine the association of independent variables with the outcomes. Results: A total of 4459 first-time ischemic stroke patients were included in the study, with 664 (14.9%) patients in the 18–55 age group and 3795 (85.1%) patients in the >55 age group. In the 18–55 age group, the five-year survival probability was 87.2%, and the cumulative incidence of recurrence was 8%. Patients in the 18–55 age group had significantly lower hazard for all-cause mortality (HR = 0.37, 95% CI 0.29–0.46, p < 0.001), and non-significant hazard for five-year recurrence (HR = 0.81, 95% CI 0.58–1.12, p = 0.193) compared to the >55 age group. Chronic kidney disease was found to be associated with increased mortality in the 18–55 age group. Conclusion: In our rural population, younger ischemic stroke patients were at the same risk of long-term ischemic stroke recurrence as the older ischemic stroke patients. Identifying the factors and optimizing adequate long-term secondary prevention may reduce the risk of poor outcomes among younger ischemic stroke patients.

With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based risk factors and genetic risk factors(rs2227306/IL8). Our pipeline includes 1)leveraging phenotyping algorithm to minimize temporal bias, 2)performing simulation studies to determine the predictive power in samples without genetic information, 3)propensity score matching to control for the confoundings, 4)selecting machine learning algorithms to capture complex feature interactions, 5)performing oversampling to address data imbalance, and 6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performace of including common clinical risk factors in prediction of CDI and the benefit of including genetic feature(s) into the prediction models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and the value of genetic feature should be thoroughly evaluated in general population and subgroups.

Background: Stroke misdiagnosis is estimated to occur in 9% of all stroke patients and is associated with poor outcomes. We hypothesized that machine learning (ML) could be used to detect ischemic stroke at the point of care in emergency departments (ED). Methods: Clinical data from 13 hospitals of a large health system in Pennsylvania, United States, from September 2003 to May 2019 were used for model development. Data from June 2019 to December 2020 were prospectively collected and divided into pre- and post-COVID cohorts for validation. We simulated three clinical settings by enrollment with different inclusion/exclusion criteria and created two ML pipelines: ML applied to pre-event clinical data, and natural language processing (NLP) with ML applied to clinical notes during triaging. Misdiagnosed stroke in ED was a case study to show the discriminative power. Results: We included a total of 49,155 patient encounters (8,900 consecutive ischemic stroke patients and 40,255 controls). The best model, based on the patient’s pre-event information, was XGBoost (AUROC=0.91). Using NLP+ML pipeline, we collected 2,070 notes during the triage process and 9,607 notes (non-stroke) for modeling. For independent validation, we identified 2,989 notes for stroke and 4,303 notes for non-stroke. We identified nine models that reached a balanced performance in terms of AUROC, sensitivity, and specificity. Model performance, in terms of AUROC, ranged from 0.876 to 0.985. Model sensitivity and specificity reached 0.993 and 0.975, respectively. There was no performance difference between models tested on pre- and post-COVID. Three out of four models correctly identified a separate case series of five misdiagnosed strokes. Conclusion: Available clinical information can be used to reduce stroke misdiagnosis in real-time. In addition, this study serves as a foundation for prospective trials to establish how pre-event clinical data and triage information can be used to improve care.

Long-term mortality in ischemic stroke patients with concomitant COPD has been largely unexplored. This study aimed to compare long-term all-cause mortality in ischemic stroke patients with and without COPD.This was a retrospective cohort study of ischemic stroke patients with and without COPD in the Geisinger Neuroscience Ischemic Stroke database to examine all-cause mortality up to 3 years using Kaplan-Meier estimator and Cox proportional hazards model.Of the 6,589 ischemic stroke patients included in this study, 5,525 (83.9%) did not have COPD (group A). Group B (n=1,006) consisted of patients with COPD diagnosis by ICD-9/10-CM codes. COPD patients in Group C (n=233) were diagnosed by spirometry, and in Group D (n=175) by both ICD-9/10-CM codes and spirometry confirmation. The survival probabilities at three years in Group B, C, and D were significantly lower than in Group A. Group B (HR=1.262, 95% CI 1.122-1.42, p0.001) and group C (HR=1.251, 95% CI 1.01-1.55, p=0.041) had significantly lower hazard of mortality compared to group A. There was no significant difference in survival between COPD subtypes of chronic bronchitis and emphysema. Patients in Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2 stage had an increased mortality hazard compared to the GOLD 1 stage.While ischemic stroke patients with preexisting COPD have worse long-term survival than those without COPD, the results largely depended on the definition of COPD used. These results suggest that ischemic stroke patients with COPD need more personalized medical care to decrease long-term mortality.

Introduction: The cut-point for defining the age of young ischemic stroke (IS) is clinically and epidemiologically important, yet it is arbitrary and differs across studies. In this study, we leveraged electronic health records (EHRs) and data science techniques to estimate an optimal cut-point for defining the age of young IS. Methods: Patient-level EHRs were extracted from 13 hospitals in Pennsylvania, and used in two parallel approaches. The first approach included ICD9/10, from IS patients to group comorbidities, and computed similarity scores between every patient pair. We determined the optimal age of young IS by analyzing the trend of patient similarity with respect to their clinical profile for different ages of index IS. The second approach used the IS cohort and control (without IS), and built three sets of machine-learning models—generalized linear regression (GLM), random forest (RF), and XGBoost (XGB)—to classify patients for seventeen age groups. After extracting feature importance from the models, we determined the optimal age of young IS by analyzing the pattern of comorbidity with respect to the age of index IS. Both approaches were completed separately for male and female patients. Results: The stroke cohort contained 7555 ISs, and the control included 31,067 patients. In the first approach, the optimal age of young stroke was 53.7 and 51.0 years in female and male patients, respectively. In the second approach, we created 102 models, based on three algorithms, 17 age brackets, and two sexes. The optimal age was 53 (GLM), 52 (RF), and 54 (XGB) for female, and 52 (GLM and RF) and 53 (RF) for male patients. Different age and sex groups exhibited different comorbidity patterns. Discussion: Using a data-driven approach, we determined the age of young stroke to be 54 years for women and 52 years for men in our mainly rural population, in central Pennsylvania. Future validation studies should include more diverse populations.

Sphingosine-1-phosphate (S1P) receptor modulators are a new class of oral disease-modifying therapies used for Multiple Sclerosis (MS). These are immunomodulatory drugs and can thus increase the risk of certain infections in these patients. This paper summarizes the existing data on the most common opportunistic infections associated with the drugs in this class: Varicella Zoster Virus (VZV), Herpes Simplex Virus (HSV), and Cryptococcus neoformans. A literature review and descriptive analysis of reported cases and clinical phase III study findings on the incidences of these infections were conducted using PubMed and Google Scholar. Results regarding fingolimod, siponimod, ozanimod, and ponesimod were obtained. Overall, the incidence of these infections was found to be extremely low in MS patients treated with S1P receptor modulators. Among the four drugs in this class, the incidence rates of VZV, HSV, and cryptococcal infections were either similar or slightly higher than placebo, with some infections not reported in cases of ozanimod and ponesimod. Most of these resulted in favorable outcomes, with very few disabilities or fatalities. However, this paper highlights the increasing relevance of assessing infectious risk factors to promote the early identification of serious complications related to these drugs. Opportunistic infections should be considered in the differential diagnosis of an MS relapse in the setting of disease-modifying treatment.

Background. The imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation techniques tend to disregard this key distinction. Consequently, the development of an adaptive imputation strategy designed specifically for EHR is an important step in improving the data imbalance and enhancing the predictive power of modeling tools for healthcare applications. Method. We analyzed the laboratory measures derived from Geisinger&rsquo, s EHR on patients in three distinct cohorts&mdash, patients tested for Clostridioides difficile (Cdiff) infection, patients with a diagnosis of inflammatory bowel disease (IBD), and patients with a diagnosis of hip or knee osteoarthritis (OA). We extracted Logical Observation Identifiers Names and Codes (LOINC) from which we excluded those with 75% or more missingness. The comorbidities, primary or secondary diagnosis, as well as active problem lists, were also extracted. The adaptive imputation strategy was designed based on a hybrid approach. The comorbidity patterns of patients were transformed into latent patterns and then clustered. Imputation was performed on a cluster of patients for each cohort independently to show the generalizability of the method. The results were compared with imputation applied to the complete dataset without incorporating the information from comorbidity patterns. Results. We analyzed a total of 67,445 patients (11,230 IBD patients, 10,000 OA patients, and 46,215 patients tested for C. difficile infection). We extracted 495 LOINC and 11,230 diagnosis codes for the IBD cohort, 8160 diagnosis codes for the Cdiff cohort, and 2042 diagnosis codes for the OA cohort based on the primary/secondary diagnosis and active problem list in the EHR. Overall, the most improvement from this strategy was observed when the laboratory measures had a higher level of missingness. The best root mean square error (RMSE) difference for each dataset was recorded as &minus, 35.5 for the Cdiff, &minus, 8.3 for the IBD, and &minus, 11.3 for the OA dataset. Conclusions. An adaptive imputation strategy designed specifically for EHR that uses complementary information from the clinical profile of the patient can be used to improve the imputation of missing laboratory values, especially when laboratory codes with high levels of missingness are included in the analysis.

(1) Background: Stroke incidence and outcomes are influenced by socioeconomic status. There is a paucity of reported population-level studies regarding these determinants. The goal of this ecological analysis was to determine the county-level associations of social determinants of stroke hospitalization and death rates in the United States. (2) Methods: Publicly available data as of 9 April 2021, for the socioeconomic factors and outcomes, was extracted from the Centers for Disease Control and Prevention. The outcomes of interest were “all stroke hospitalization rates per 1000 Medicare beneficiaries” (SHR) and “all stroke death rates per 100,000 population” (SDR). We used a multivariate binomial generalized linear mixed model after converting the outcomes to binary based on their median values. (3) Results: A total of 3226 counties/county-equivalents of the states and territories in the US were analyzed. Heart disease prevalence (odds ratio, OR = 2.03, p < 0.001), blood pressure medication nonadherence (OR = 2.02, p < 0.001), age-adjusted obesity (OR = 1.24, p = 0.006), presence of hospitals with neurological services (OR = 1.9, p < 0.001), and female head of household (OR = 1.32, p = 0.021) were associated with high SHR while cost of care per capita for Medicare patients with heart disease (OR = 0.5, p < 0.01) and presence of hospitals (OR = 0.69, p < 0.025) were associated with low SHR. Median household income (OR = 0.6, p < 0.001) and park access (OR = 0.84, p = 0.016) were associated with low SDR while no college degree (OR = 1.21, p = 0.049) was associated with high SDR. (4) Conclusions: Several socioeconomic factors (e.g., education, income, female head of household) were found to be associated with stroke outcomes. Additional research is needed to investigate intermediate and potentially modifiable factors that can serve as targeted interventions.