Is your unconscious patient in cardiac arrest? A New protocol for telephonic diagnosis by emergency medical call-takers: A national study

Resuscitation ◽  
2020 ◽  
Vol 155 ◽  
pp. 199-206
Author(s):  
Desmond RenHao Mao ◽  
Alvin Zhan Quan Ee ◽  
Philip Weng Kee Leong ◽  
Benjamin Sieu-Hon Leong ◽  
Shalini Arulanandam ◽  
...  
Resuscitation ◽  
2013 ◽  
Vol 84 ◽  
pp. S34-S37
Author(s):  
Ewa Chemperek ◽  
Katarzyna Zielonka ◽  
Grzegorz Nowicki ◽  
Patryk Rzonca ◽  
Jakub Pelczar ◽  
...  

2007 ◽  
Vol 153 (5) ◽  
pp. 792-799 ◽  
Author(s):  
Heidi L. Estner ◽  
Christian Günzel ◽  
Gjin Ndrepepa ◽  
Frederic William ◽  
Dirk Blaumeiser ◽  
...  

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Ming-Ju Hsieh ◽  
Wen-Chu Chiang ◽  
Wei-Tien Chang ◽  
Chih-Wei Yang ◽  
Yu-Chun Chien ◽  
...  

Introduction: In-hospital early warning system scores for prediction of clinical deterioration have been well-developed. However, such prediction tools in prehospital setting remain unavailable. Hypothesis: To develop a model for predicting patients with emergency medical technicians witnessed out-of-hospital cardiac arrest (EMT-witnessed OHCA) . Methods: We used the fire-based emergency medical service (EMS) data from Taipei city to develop the prediction model. Patients included in this study were those initially alive, non-traumatic, and aged ≧20 years. Data were extracted from records of ambulance run sheets and OHCA registry in Taipei. The primary outcome (i.e. EMT-witnessed OHCA) was defined as cardiac arrest occurring during EMT services before arrival at the receiving hospital. The prediction model was developed through the standard cross-validation method (i.e. divided dataset for training group and validation group). Area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow (HL) test were used to test discrimination and calibration. The point value system with Youden’s J Index was used to find the best cut-off value for practical application. Results: From 2011 to 2015, a total of 252,771 patients were included. Of them, 660 (0.26%) were EMT-witnessed OHCA. The prediction model, including gender, respiratory rate, heart rate, systolic blood pressure, level of consciousness and oxygen saturation, showed excellent discrimination (AUC 0.94) and calibration ( p =0.42 for HL test). When applied to the validation dataset, it maintained good discriminatory ability (AUC 0.94) and calibration ( p =0.11). The optimal cut-off value (≧13) of the point value system of the tool showed high sensitivity (87.84%) and specificity (86.20%). Conclusions: The newly developed prediction model will help identify high-risk patients with EMT-witnessed OHCA and indicate potential prevention by situation awareness in EMS.


Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Linn Andelius ◽  
Carolina Malta Hansen ◽  
Freddy Lippert ◽  
Lena Karlsson ◽  
Christian Torp-Pedersen ◽  
...  

Introduction: Survival after out-of-hospital cardiac arrest (OHCA) is dependent on early defibrillation. To increase bystander defibrillation in OHCAs, a first-responder program dispatching lay rescuers (Heart Runners) through a smartphone application (Heart Runner-app) was implemented in the Capital Region of Denmark. We investigated the proportion of Heart Runners arriving prior to the Emergency Medical Services (EMS) and rates of bystander defibrillation. Methods: The Capital Region of Denmark comprises 1.8 mil. inhabitants and 19,048 Heart Runners were registered. In cases of suspected OHCA, the Heart Runner-app was activated by the Emergency Medical Dispatch Center. Up to 20 Heart Runners < 1.8 km from the OHCA were dispatched to either start cardiopulmonary resuscitation (CPR) or to retrieve and use a publicly accessible automated external defibrillator (AED). Through an electronic survey, Heart Runners reported if they arrived before EMS and if they applied an AED. OHCAs where at least one Heart Runner arrived before EMS were compared with OHCAs where EMS arrived first. All OHCAs from September 2017 to May 2018, where Heart Runners had been dispatched, were included. Results: Of 399 EMS treated OHCAs, 78% (n=313/399) had a matching survey. A Heart Runner arrived before EMS in 47% (n=147/313) of the cases, and applied an AED in 41% (n=61/147) of these cases. Rate of bystander defibrillation was 2.5-fold higher compared to cases where the EMS arrived first (Table 1). Conclusions: By activation of the Heart Runner-app, Heart Runners arrived prior to EMS in nearly half of all the OHCA cases. Bystander defibrillation rate was significantly higher when Heart Runners arrived prior to EMS.


2006 ◽  
Vol 21 (6) ◽  
pp. 445-450 ◽  
Author(s):  
Corita Grudzen

AbstractAmericans are living longer and are more likely to be chronically or terminally ill at the time of death. Although surveys indicate that most people prefer to die at home, the majority of people in the United States die in acute care hospitals. Each year, approximately 400,000 persons suffer sudden cardiac arrest in the US, the majority occurring in the out-of-hospital setting. Mortality rates are high and reach almost 100% when prehospital care has failed to restore spontaneous circulation. Nonetheless, patients who receive little benefit or may wish to forgo life-sustaining treatment often are resuscitated. Risk versus harm of resuscitation efforts can be differentiated by various factors, including cardiac rhythm. Emergency medical services policy regarding resuscitation should consider its utility in various clinical scenarios. Patients, family members, emergency medical providers, and physicians all are important stakeholders to consider in decisions about out-of-hospital cardiac arrest. Ideally, future policy will place greater emphasis on patient preferences and quality of life by including all of these viewpoints.


Author(s):  
Richard Chocron ◽  
Julia Jobe ◽  
Sally Guan ◽  
Madeleine Kim ◽  
Mia Shigemura ◽  
...  

Background Bystander cardiopulmonary resuscitation (CPR) is a critical intervention to improve survival following out‐of‐hospital cardiac arrest. We evaluated the quality of bystander CPR and whether performance varied according to the number of bystanders or provision of telecommunicator CPR (TCPR). Methods and Results We investigated non‐traumatic out‐of‐hospital cardiac arrest occurring in a large metropolitan emergency medical system during a 6‐month period. Information about bystander care was ascertained through review of the 9‐1‐1 recordings in addition to emergency medical system and hospital records to determine bystander CPR status (none versus TCPR versus unassisted), the number of bystanders on‐scene, and CPR performance metrics of compression fraction and compression rate. Of the 428 eligible out‐of‐hospital cardiac arrest, 76.4% received bystander CPR including 43.7% unassisted CPR and 56.3% TCPR; 35.2% had one bystander, 33.3% had 2 bystanders, and 31.5% had ≥3 bystanders. Overall compression fraction was 59% with a compression rate of 88 per minute. CPR differed according to TCPR status (fraction=52%, rate=87 per minute for TCPR versus fraction=69%, rate=102 for unassisted CPR, P <0.05 for each comparison) and the number of bystanders (fraction=55%, rate=87 per minute for 1 bystander, fraction=59%, rate=89 for 2 bystanders, fraction=65%, rate=97 for ≥3 bystanders, test for trend P <0.05 for each metric). Additional bystander actions were uncommon to include rotation of compressors (3.1%) or application of an automated external defibrillator (8.0%). Conclusions Bystander CPR quality as gauged by compression fraction and rate approached guideline goals though performance depended upon the type of CPR and number of bystanders.


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