Background and Purpose:The purpose of this study was to assess factors causing delay in treatment of acute stroke in a tertiary care institute in South India.Methods:All clinically suspected cases of acute stroke presenting to the emergency department over a period of 1 year were prospectively followed up and data collected as per a preset pro forma. The various time intervals from stroke onset to definitive management and other pertinent data were collected. The time delays have been evaluated in the decision tree model: Chi-squared Automatic Interaction Detection. Significance was assessed at 5% level of significance (P < 0.05).Results:The mean prehospital time delay for all clinically suspected stroke (n = 361) in our institute was 716 min and the median time 190 min. The mean total in-hospital delay was 94.17 ± 54.5 min and median time being 82 min. The onset of symptoms to first medical contact was the main interval that influenced the prehospital delay. Computed tomographic (CT) diagnosis to stroke unit admission influenced the in-hospital delay the most.Conclusions:Lack of awareness regarding stroke leads to delayed seeking of treatment for the same. The factors that contribute to the in-hospital delay included patient admission procedure delay, lack of staff to transport the patient, and the distance between the stroke unit and CT room. Educating the community with regard to “stroke” and implementation of a better pre- and in-hospital stroke care system is a need of the hour in the country.
Covid-19 has once again brought into focus our limited preparedness to deal with epidemics. Most nations, across the globe, have responded with a resolve to come stronger out of this crisis and leaderships across the world have shown great commitment to protecting its people from Covid-19. Covid-19 has also taught us a few things for the future. One such learning has been that a strong shift in focus towards non-communicable diseases driving health infrastructure across the globe for the last few decades has come at neglect of communicable diseases. In that sense, therefore, the current pandemic has been a wake-up call. Organised Medicine Academic Guild (OMAG), an umbrella organization of professional associations gathered a group of health experts to develop a policy document on epidemic preparedness to limit the influence of epidemics like Covid-19.
Background and Objectives:Central venous catheter placement is a frequently performed procedure in emergency medicine as well as critical care unit. We aimed to compare real-time ultrasonography (USG)-guided and the traditional anatomical landmark (AL) technique for the insertion of internal jugular vein (IJV) catheters in an emergency department (ED) setting.Materials and Methods:Patients requiring IJV catheterization were prospectively recruited over a period of 1 year at a single center. Cannulation was done either by the AL or USG technique, according to ED physician's discretion. A preset pro forma was completed for each central line placed. Variables were compared using the independent t-test, Fisher's exact test, and the nonparametric Mann–Whitney U-test.Results and Discussion:Seventy patients were enrolled, of which 35 patients underwent IJV cannulation by USG-guided technique (USG group) and 35 patients by the AL technique (AL group). There were a 100% success rate (35/35) for cannulation in the USG group and a 91.4% success rate (32/35) in the AL group. The catheter was placed on the first attempt in 17 (48.6%) patients in the AL group and 32 (91.4%) patients in the USG group. In th AL group, there were three failed cannulation attempts in comparison to the USG group. The mean start to flash time for the AL technique was 16.59 s (±10.67) and 4.86 s (±2.18) in the USG group. The mean cannulation time was 305.88 s (±66.84) in the AL group and 293.03 s (±71.15) in the USG group. A total of seven acute complications were noted, of which 2 (5.7%) in the USG group and 5 (14.3%) in the AL group.Conclusion:The real-time USG guided technique significantly reduces the number of attempts to cannulate, has a higher first-pass success rate, a quicker flash time, and fewer complications when compared to the AL technique. In EDs equipped with USG, insertion of IJV catheters under real-time USG guidance should become the standard of care.
There have been multiple incidents where doctors have been assaulted by patient relatives and hospital facilities have been vandalized. This has led to mass agitations by Physicians across India. Violence and vandalism against health-care workers (HCWs) is one of the biggest public health and patient care challenge in India. The sheer intensity of emotional hijack and the stress levels in both practicing HCWs and patient relative's needs immediate and detail attention. The suffering of HCWs who are hurt, the damage to hospital facilities and the reactionary agitation which affects patients who need care are all together doing everything to damage the delivery of health care and relationship between a doctor and a patient. This is detrimental to India where illnesses and Injuries continue to be the biggest challenge to its growth curve. The expert group set by The Academic College of Emergency Experts and The Academy of Family Physicians of India makes an effort to study this Public Health and Patient Care Challenge and provide recommendations to solve it.
Introduction: Chest compression fraction (CCF) is the cumulative time spent providing chest compressions divided by the total time taken for the entire resuscitation. Targeting a CCF of at least 60% is intended to limit interruptions in compressions and maximize coronary perfusion during resuscitation. We aimed to identify the mean CCF and its relationship with various factors affecting it. Methods: Patients presenting to the emergency department in cardiac arrest at a single center were prospectively included in this study. Resuscitation was provided by trained health-care providers. The feedback device Cprmeter2™ was placed on the patient's sternum at the beginning of resuscitation. The total time taken for the entire resuscitation was noted by the device and CCF calculated. Results: The mean CCF was analyzed using descriptive statistics and was found to be 71.60% ± 7.52%. The total duration of resuscitation ( R = −0.55, P = < 0.001, min-max, 2.02–34.31, mean 12.25 ± 6.54), number of people giving chest compressions ( R = −0.48, P = < 0.001, min-max, 1–6, mean 4.04 ± 1.12), and total number of team members in resuscitation ( R = −0.50, P = < 0.001, min-max, 4–10, mean 6.65 ± 1.32) had negative correlation with CCF. Diurnal variation (day, n = 35; mean 69.20% ± 7% and night, n = 20; mean 75.80% ± 5.6%, P = 0.001) and patients receiving defibrillation (receiving n = 10 mean 67.00% ± 4.11% and not receiving n = 45 mean 72.62 ± 7.42%, P = 0.005) were found to significantly affect CCF. Conclusion: The mean CCF for cardiac arrest patients was well within the targets of guideline recommendation. CCF decreased when resuscitation lasted longer, during daytime when the defibrillator was used, the total team members increased, and also when the number of people giving chest compressions increased. CCF during resuscitation may improve if there is a focus on improving these factors and requires validation in multicentric settings.
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