Stroke program for India

Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms

Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers' role in prognosticating outcomes in ICH patients. We assessed 22 protein biomarkers using targeted proteomics in serum samples obtained from the ICH patient dataset (N = 150). We defined poor outcomes as modified Rankin scale score of 3-6. We incorporated clinical variables and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI). We used five-fold cross-validation and bootstrapping for internal validation of prediction models. We included 149 patients for 90-day and 144 patients with ICH for 180-day outcome analyses. In multivariable logistic regression, UCH-L1 (adjusted OR 9.23; 95%CI 2.41-35.33), alpha-2-macroglobulin (aOR 5.57; 95%CI 1.26-24.59), and Serpin-A11 (aOR 9.33; 95%CI 1.09-79.94) were independent predictors of 90-day poor outcome; MMP-2 (aOR 6.32; 95%CI 1.82-21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2.08; 95%CI 1.24-3.48) predicted 90-day and MMP-9 (aOR 1.98; 95%CI 1.19-3.32) predicted 180-day mortality. Machine learning identified additional predictors, including haptoglobin for poor outcomes and UCH-L1, APO-C1, and MMP-2 for mortality prediction. Overall, random forest models outperformed regression models for predicting 180-day poor outcomes (AUC 0.89), and 90-day (AUC 0.81) and 180-day mortality (AUC 0.81). Serum biomarkers independently predicted short-term poor outcomes and mortality after ICH. Further research utilizing a multi-omics platform and temporal profiling is needed to explore additional biomarkers and refine predictive models for ICH prognosis.

Insulin resistance and elevated C-reactive protein among first-degree relatives of ischemic stroke patients

BACKGROUND

Family history is one of the non-modifiable risk factors for ischemic stroke. Atherosclerosis and thrombosis are the two major mechanisms for cardiovascular disease and stroke. Screening of family members is an important method to identify individuals at risk. Therefore, this study was planned to assess the atherosclerotic risk factors in first-degree relatives of patients with ischemic stroke.

METHODOLOGY

Case group included 40 first-degree relatives of ischemic stroke patients between 30 and 50 years of age. Forty age and gender matched apparently healthy subjects without major risk factors were recruited as controls. Their blood samples were analysed for routine biochemical parameters, Fasting Insulin, high sensitivity C-reactive protein and Homocysteine.

RESULTS

First-degree relatives showed dyslipidemia (High total cholesterol, elevated Low Density Lipoprotein and elevated Non-High Density Lipoprotein), hyperinsulinemia and insulin resistance compared to controls. They had high levels of high sensitivity C-reactive protein (p=0.045). There was positive correlation between fasting insulin and Homeostasis Model Assessment of Insulin Resistance with high sensitivity C-reactive protein among first-degree relatives.

CONCLUSION

First-degree relatives of ischemic stroke patients exhibited hyperinsulinemia, Insulin resistance and dyslipidemia. Insulin resistance, a low grade inflammatory state that leads to increased C-reactive protein which triggers the development of atherosclerosis. So screening for insulin resistance and dyslipidemia in first-degree relatives of ischemic stroke patients may help in preventing adverse vascular events.

Epidemiology of Emergency Medical Services (EMS) Utilization in Four Indian Emergency Departments

Introduction Without a universal Emergency Medical Services (EMS) system in India, data on the epidemiology of patients who utilize EMS are limited. This retrospective chart review aimed to quantify and describe the burden of disease and patient demographics of patients who arrived by EMS to four Indian emergency departments (EDs) in order to inform a national EMS curriculum.

METHODS

A retrospective chart review was performed on patients transported by EMS over a three-month period in 2014 to four private EDs in India. A total of 17,541 patient records were sampled from the four sites over the study period. Of these records, 1,723 arrived by EMS and so were included for further review.

RESULTS

A range of 1.4%-19.4% of ED patients utilized EMS to get to the ED. The majority of EMS patients were male (59%-64%) and adult or geriatric (93%-99%). The most common chief complaints and ED diagnoses were neurological, pulmonary, cardiovascular, gastrointestinal, trauma, and infectious disease.

CONCLUSIONS

Neurological, pulmonary, cardiovascular, gastrointestinal, trauma, and infectious disease are the most common problems found in patients transported by EMS in India. Adult and geriatric male patients are the most common EMS utilizers. Emergency Medical Services curricula should emphasize these knowledge areas and skills. Wijesekera O , Reed A , Chastain PS , Biggs S , Clark EG , Kole T , Chakrapani AT , Ashish N , Rajhans P , Breaud AH , Jacquet GA . Epidemiology of Emergency Medical Services (EMS) utilization in four Indian emergency departments. Prehosp Disaster Med. 2016;31(6):675-679.

An analysis of the risk factors and the outcomes of cerebrovascular diseases in northern India

INTRODUCTION

Stroke is a growing epidemic in the developing countries. The risk factors of stroke are the same as those of atherosclerosis, but the relative incidences of the various risk factors keep changing with time and cultural and environmental factors. So, a periodic analysis should be done.

AIMS AND OBJECTIVE

The generation of the data which pertained to the clinical profile of stroke in our institution by using the WHO Steps Stroke Manual, version 2 and to make note of the risk factors and the correlation of the outcome with the Glasgow's Coma Scale (GCS) at admission and with the Modified Rankin Scale (MRS) on the 28th day.

MATERIAL AND METHODS

A prospective study was conducted on 157 cases of stroke patients who were admitted to the Medicine Department of a tertiary care institute during June 2011 through June 2012.

OBSERVATIONS

Amongst the 157 cases (92 males and 65 females), 86 % were above 40 years of age and this was the most prevalent age group in the age range of 51-60 years. The risk factor analysis showed hypertension as the most prevalent risk factor, followed by diabetes cum hypertension. Hemiplegia/hemi paresis was the most common presentation, followed by aphasia. Radiological studies showed that ischaemic strokes were the most frequent ones, followed by haemorrhagic strokes (71 vs. 29 %). Amongst the ischaemic infarcts, the anterior circulation infarct was the most frequent one. The outcome was poor with haemorrhagic strokes and in those who were presented with a GCS of less than 3. The overall mortality was 22.29 %. A significant residual deficit (MRS >3) was seen in 49.7 % of the patients.

CONCLUSIONS

There is a need for prospective representative population based surveys viz. the WHO Stroke Steps 2 and 3.Public health measures are essential to enhance the awareness about the importance of knowing the risk factors. Hence, the message which has to be percolated to the masses is - stroke is a 'brain attack' that is preventable.

Finite Element Modeling of scattered electromagnetic waves for stroke analysis

Stroke has become one of the leading causes of mortality worldwide and about 800 in every 100,000 people suffer from stroke each year. The occurrence of stroke is ranked third among the causes of acute death and first among the causes for neurological dysfunction. Currently, Neurological examinations followed by medical imaging with CT, MRI or Angiography are used to provide better identification of the location and the type of the stroke, however they are neither fast, cost-effective nor portable. Microwave technology has emerged to complement these modalities to diagnose stroke as it is sensitive to the differences between the distinct dielectric properties of the brain tissues and blood. This paper investigates the possibility of diagnosing the type of stroke using Finite Element Analysis (FEA). The object of interest is a simulated head phantom with stroke, created with its specifying material characteristics like electrical conductivity and relative permittivity. The phantom is then placed in an electromagnetic field generated by a dipole antenna radiating at 1 GHz. The FEM forward model solver computes the scattered electromagnetic field by finding the solution for the Maxwell's wave equation in the head volume. Subsequently the inverse scattering problem is solved using the Contrast Source Inversion (CSI) method to reconstruct the dielectric profile of the head phantom.