Hospital performance in a large urban acute myocardial infarction emergency care system: Tokyo Cardiovascular Care Unit network

2021 ◽  
Author(s):  
Takeshi Yamamoto ◽  
Toshiaki Otsuka ◽  
Nobuko Yoshida ◽  
Yoshinori Kobayashi ◽  
Nobuyuki Komiyama ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Emergency Care ◽  
Hospital Performance ◽  
Emergency Care System ◽  
Cardiovascular Care ◽  
Unit Network ◽  
Care System

Apical Takotsubo syndrome versus anterior acute myocardial infarction: findings from the Tokyo Cardiovascular Care Unit network registry

2018 ◽  
Vol 8 (1) ◽  
pp. 86-95 ◽  
Author(s):  
Toshiaki Isogai ◽  
Tsutomu Yoshikawa ◽  
Tetsuro Ueda ◽  
Tetsuo Yamaguchi ◽  
Yoichi Imori ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Risk Difference ◽  
Takotsubo Syndrome ◽  
Cardiac Mortality ◽  
Lower Systolic Blood Pressure ◽  
Reactive Protein ◽  
St Elevation ◽  
Cardiovascular Care ◽  
Unit Network

Background: Although the typical apical form of Takotsubo syndrome and anterior acute myocardial infarction have similar electrocardiographic and echocardiographic presentations, data on the clinical differences between the two disorders are limited. Methods: Using the Tokyo Cardiovascular Care Unit network registry, we identified patients hospitalised with apical Takotsubo syndrome ( n=540; 2010–2014) or anterior acute myocardial infarction ( n=2,806; 2013–2014) and created 522 age and sex-matched pairs (mean age 74.1 years; women 78.5%). We compared the clinical characteristics and inhospital outcomes between the two groups. Results: On admission, patients with apical Takotsubo syndrome showed a lower body mass index, less frequent chest pain/tightness, lower systolic blood pressure, higher heart rate, lower creatine kinase, higher C-reactive protein and brain natriuretic peptide, and less frequent ST-elevation than patients with anterior acute myocardial infarction. Patients with apical Takotsubo syndrome received catecholamine (12.8% vs. 24.5%, P<0.001) and intra-aortic balloon pumping (5.9% vs. 15.1%, P<0.001) less frequently. Despite similar all-cause mortality (5.4% vs. 7.9%, P=0.134), patients with apical Takotsubo syndrome showed lower cardiac mortality (2.1% vs. 6.7%, P<0.001; risk difference −4.6% (95% confidence interval −7.1% to −2.1%)) but higher non-cardiac mortality (3.3% vs. 1.1%, P=0.033; 2.1% (0.3%–3.9%)). In subgroup comparisons, patients with physically triggered Takotsubo syndrome had higher non-cardiac mortality (7.0%) than those with non-physically triggered Takotsubo syndrome (1.2%, P=0.001) or anterior acute myocardial infarction (1.1%, P<0.001). Conclusions: This study found that cardiac and non-cardiac mortality risks differed significantly between apical Takotsubo syndrome and anterior acute myocardial infarction. Our findings underscore the importance of differentiating between the two disorders for appropriate management.

Trajectory of prehospital delay in patients with acute myocardial infarction in the Japanese health care system

2006 ◽  
Vol 107 (2) ◽  
pp. 188-193 ◽  
Author(s):  
Yoshimi Fukuoka ◽  
Kathleen Dracup ◽  
Fumio Kobayashi ◽  
Erika Sivarajan Froelicher ◽  
Sally H. Rankin ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Health Care ◽  
Acute Myocardial Infarction ◽  
Health Care System ◽  
Prehospital Delay ◽  
Care System

scholarly journals Mitigation Policies and Emergency Care Management in Europe's Ground Zero for COVID-19

2020 ◽  
Author(s):  
Gabriele Ciminelli ◽  
Sílvia Garcia-Mandicó
Keyword(s):  
Emergency Care ◽  
Service Sector ◽  
Emergency Services ◽  
Factory Workers ◽  
Hospital Emergency ◽  
Employment Share ◽  
Emergency Care System ◽  
Physical Interactions ◽  
Critical Patients ◽  
Care System

This paper draws from daily death registry data on 4,000 Italian municipalities to investigate two crucial policies that can dramatically affect the toll of COVID-19: the shutdown of non-essential businesses and the management of the emergency care system. Our results, which are robust to controlling for a host of co-factors, offer strong evidence that the closure of service activities is very effective in reducing COVID-19 mortality - this was about 15% lower in municipalities with a 10 percentage points higher employment share in shut down services. Shutting down factories, instead, is much less effective, plausibly because factory workers engage in more limited physical interactions relative to those in the consumer-facing service sector. Concerning the management of the health care system, we find that mortality strongly increases with distance from the intensive care unit (ICU). Municipalities at 10 km from the closest ICU experienced up to 50% higher mortality. This effect - which is largest within the epicenter and in days of abnormally high volumes of calls to the emergency line - underscores the importance of improving pre-hospital emergency services and building ambulance capacity to ensure timely transportation of critical patients to the ICU.

scholarly journals Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Jamie Miles ◽  
Janette Turner ◽  
Richard Jacques ◽  
Julia Williams ◽  
Suzanne Mason
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Neural Networks ◽  
Logistic Regression ◽  
Emergency Care ◽  
Model Performance ◽  
Machine Learning Methods ◽  
C Statistic ◽  
Emergency Care System ◽  
Care System

Abstract Background The primary objective of this review is to assess the accuracy of machine learning methods in their application of triaging the acuity of patients presenting in the Emergency Care System (ECS). The population are patients that have contacted the ambulance service or turned up at the Emergency Department. The index test is a machine-learning algorithm that aims to stratify the acuity of incoming patients at initial triage. This is in comparison to either an existing decision support tool, clinical opinion or in the absence of these, no comparator. The outcome of this review is the calibration, discrimination and classification statistics. Methods Only derivation studies (with or without internal validation) were included. MEDLINE, CINAHL, PubMed and the grey literature were searched on the 14th December 2019. Risk of bias was assessed using the PROBAST tool and data was extracted using the CHARMS checklist. Discrimination (C-statistic) was a commonly reported model performance measure and therefore these statistics were represented as a range within each machine learning method. The majority of studies had poorly reported outcomes and thus a narrative synthesis of results was performed. Results There was a total of 92 models (from 25 studies) included in the review. There were two main triage outcomes: hospitalisation (56 models), and critical care need (25 models). For hospitalisation, neural networks and tree-based methods both had a median C-statistic of 0.81 (IQR 0.80-0.84, 0.79-0.82). Logistic regression had a median C-statistic of 0.80 (0.74-0.83). For critical care need, neural networks had a median C-statistic of 0.89 (0.86-0.91), tree based 0.85 (0.84-0.88), and logistic regression 0.83 (0.79-0.84). Conclusions Machine-learning methods appear accurate in triaging undifferentiated patients entering the Emergency Care System. There was no clear benefit of using one technique over another; however, models derived by logistic regression were more transparent in reporting model performance. Future studies should adhere to reporting guidelines and use these at the protocol design stage. Registration and funding This systematic review is registered on the International prospective register of systematic reviews (PROSPERO) and can be accessed online at the following URL: https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020168696 This study was funded by the NIHR as part of a Clinical Doctoral Research Fellowship.

scholarly journals Perception of the healthcare professionals towards the current trauma and emergency care system in Kabul, Afghanistan: a mixed method study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Umerdad Khudadad ◽  
Wafa Aftab ◽  
Asrar Ali ◽  
Nadeem Ullah Khan ◽  
Junaid Razzak ◽  
...  
Keyword(s):  
Emergency Care ◽  
Trauma Care ◽  
Mixed Method ◽  
Healthcare Providers ◽  
Trauma Management ◽  
Policy Makers ◽  
Hospital Managers ◽  
Care Services ◽  
Emergency Care System ◽  
Care System

Abstract Background Trauma and injury contribute to 11% of the all-cause mortality in Afghanistan. The study aimed to explore the perceptions of the healthcare providers (pre and in-hospital), hospital managers and policy makers of the public and private health sectors to identify the challenges in the provision of an effective trauma care in Kabul, Afghanistan. Methods A concurrent mixed method design was used, including key-informant interviews (healthcare providers, hospital managers and policy makers) of the trauma care system (N = 18) and simultaneous structured emergency care system assessment questionnaire (N = 35) from July 15 to September 25, 2019. Interviews were analyzed using content analysis approach and structured questionnaire data were descriptively analyzed. Results Four themes were identified that describe the challenges: 1) pre-hospital care, 2) cohesive trauma management system, 3) physical and human resources and 4) stewardship. Some key challenges were found related to scene and transportation care, in-hospital care and emergency preparedness within the wider trauma care system. Less than 25% of the population is covered by the pre-hospital ambulance system (n = 23, 65.7%) and there is no communication process between health care facilities to facilitate transfer (n = 28, 80%). Less than 25% of patients with an injury requiring emergent surgery have access to surgical care in a staffed operating theatre within 2 h of injury (n = 19, 54.2%) and there is no regular assessment of the ability of the emergency care system to mobilize resources (human and physical) to respond to disasters, and other large-scale emergencies (n = 28, 80%). Conclusion This study highlighted major challenges in the delivery of trauma care services across Kabul, Afghanistan. Systematic improvement in the workforce training, structural organization of the trauma care system and implementing externally validated clinical guidelines for trauma management could possibly enhance the functions of the existing trauma care services. However, an integrated state-run trauma care system will address the current burden of traumatic injury more effectively within the wider healthcare system of Afghanistan.

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