Interhospital variability in Out-of-Hospital cardiac arrest survival in a large metropolitan area.

Background: Out-of-hospital cardiac arrest (OHCA) survival varies widely across the United States. The impact of hospital OHCA volume and ST-elevation myocardial infarction (STEMI) Receiving Center (SRC) designation on survival is not fully understood. Methods: This was a retrospective analysis of adult OHCA who survived to hospital admission reported to the Chicago Cardiac Arrest Registry to Enhance Survival (CARES) database from May 1, 2013 to December 31, 2019. Hierarchical logistic regression models were generated and adjusted by hospital characteristics. Survival to hospital discharge (SHD) and cerebral performance category (CPC) 1-2 at each hospital were calculated after adjusting for arrest characteristics. Hospitals were assigned quartiles (Q1-Q4) based on total arrest volume to allow for comparison of SHD and CPC 1-2 between quartiles. Results: 4,020 patients met inclusion criteria. 21 of the 33 Chicago hospitals included in this study were designated SRCs. Adjusted SHD and CPC 1-2 rates ranged from 27.3% to 37.0% and from 8.9% to 25.1%, respectively, by hospital. SRC designation did not significantly affect SHD (OR 0.96; 95% CI, 0.71-1.30) nor CPC 1-2 (OR 1.17; 95% CI, 0.74-1.84). OHCA volume quartiles did not significantly affect SHD (Q2: OR 0.94; 95% CI, 0.54-1.60; Q3: OR 1.30; 95% CI, 0.78-2.16; Q4: OR 1.25; 95% CI, 0.74-2.10) nor CPC 1-2 (Q2: OR 0.75; 95% CI, 0.36-1.54; Q3: OR 0.94; 95% CI, 0.48-1.87; Q4: OR 0.97; 95% CI, 0.48-1.97). Conclusion: Interhospital variability in both SHD and CPC 1-2 cannot be explained by hospital arrest volume nor SRC status. Further research is warranted to explore reasons for interhospital variability.

Prehospital Intranasal Glucagon for Hypoglycemia.

BACKGROUND: Prehospital hypoglycemia is usually treated with oral or intravenous (IV) dextrose in a variety of concentrations. In the absence of vascular access, intramuscular (IM) glucagon is commonly administered. Occupational needle-stick injury remains a significant risk while attempting to obtain vascular access or administer medications intramuscularly in the prehospital setting. We sought to determine if intranasal (IN) glucagon is effective in the prehospital treatment of hypoglycemia. METHODS: We performed a retrospective analysis of all consecutive cases where recombinant glucagon was administered IN by paramedics from January 1, 2015 through December 31, 2020. Excluded were cases without pre or post administration blood glucose documentation, and cases where another form of treatment for hypoglycemia was administered at any time during the EMS encounter. The primary outcome was clinical response to IN glucagon documented by paramedics; secondary outcomes included pre and post administration blood glucose values. RESULTS: Out of 44 cases that met study inclusion criteria, 14 patients (32%) had substantial improvement, 13 patients (30%) had slight improvement, and 17 patients (38%) had no improvement in mental status after administration of IN glucagon. In cases with substantial improvement (n = 14), the mean pre administration blood glucose was 33.8 mg/dl and the mean post administration blood glucose was 87.1 mg/dl (mean increase 53.3 mg/dl, 95% CI: 21.5 to 85.1). In cases with slight improvement (n = 13), the mean pre administration blood glucose was 23.9 mg/dl and the mean post administration blood glucose was 53.8 mg/dl (mean increase 29.9 mg/dl, 95% CI = 2.9 to 56.9). In case with no improvement (n = 17) the mean pre administration blood glucose was 30.1 mg/dl and the mean post administration glucose was 33.1 mg/dl (mean difference 3.1 mg/dl, 95% CI: -10.1 to 3.9). CONCLUSION: Intranasal administration of recombinant glucagon for hypoglycemia resulted in a clinically significant improvement in mental status and a corresponding increase in blood glucose levels in select cases in the prehospital setting.

Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning.

OBJECTIVES: To evaluate the impact of community level information on the predictability of out-of-hospital cardiac arrest (OHCA) survival. METHODS: We used the Cardiac Arrest Registry to Enhance Survival (CARES) to geocode 9,595 Chicago incidents from 2014 to 2019 into community areas. Community variables including crime, healthcare, and economic factors from public data were merged with CARES. The merged data were used to develop ML models for OHCA survival. Models were evaluated using Area Under the Receiver Operating Characteristic curve (AUROC) and features were analyzed using SHapley Additive exPansion (SHAP) values. RESULTS: Baseline results using CARES data achieved an AUROC of 84%. The final model utilizing community variables increased the AUROC to 88%. A SHAP analysis between high and low performing community area clusters showed the high performing cluster is positively impacted by good health related features and good community safety features positively impact the low performing cluster. CONCLUSION: Utilizing community variables helps predict neurologic outcomes with better performance than only CARES data. Future studies will use this model to perform simulations to identify interventions to improve OHCA survival.

A mixed methods analysis of caller-emergency medical dispatcher communication during 9-1-1 calls for out-of-hospital cardiac arrest.

OBJECTIVE: Recognition of out-of-hospital cardiac arrest (OHCA) during 9-1-1 calls is critically important, but little is known about how laypersons and emergency medical dispatchers (EMDs) communicate. We sought to describe 9-1-1 calls for OHCA. METHODS: We performed a mixed-methods, retrospective analysis of 9-1-1 calls for OHCA victims in a large urban emergency medical services (EMS) system using a random sampling of cases containing the term "cardiopulmonary resuscitation" (CPR) in the EMS electronic report. A constant comparison qualitative approach with four independent reviewers continued until thematic saturation was achieved. Quantitative analysis employed computational linguistics. Callers' emotional states were rated using the emotional content and cooperation score (ECCS). RESULTS: Thematic saturation was achieved after 46 calls. Three "OHCA recognition" themes emerged [ 1) disparate OHCA terms used, 2) OHCA mimics create challenges, 3) EMD questions influence recognition]. Three "CPR facilitation" themes emerged [ 1) directive language may facilitate CPR, 2) specific instructions assist CPR, 3) caller's emotions affect CPR initiation]. Callers were generally "anxious but cooperative." Callers saying "pulse" was associated with OHCA recognition. CONCLUSION: Communication characteristics appear to influence OHCA recognition and CPR facilitation. PRACTICE IMPLICATIONS: Dispatch protocols that acknowledge characteristics of callers' communication may improve OHCA recognition and CPR facilitation.

A machine learning approach for modeling decisions in the out of hospital cardiac arrest care workflow.

BACKGROUND: A growing body of research has shown that machine learning (ML) can be a useful tool to predict how different variable combinations affect out-of-hospital cardiac arrest (OHCA) survival outcomes. However, there remain significant research gaps on the utilization of ML models for decision-making and their impact on survival outcomes. The purpose of this study was to develop ML models that effectively predict hospital's practice to perform coronary angiography (CA) in adult patients after OHCA and subsequent neurologic outcomes. METHODS: We utilized all (N = 2398) patients treated by the Chicago Fire Department Emergency Medical Services included in the Cardiac Arrest Registry to Enhance Survival (CARES) between 2013 and 2018 who survived to hospital admission to develop, test, and analyze ML models for decisions after return of spontaneous circulation (ROSC) and patient survival. ML classification models, including the Embedded Fully Convolutional Network (EFCN) model, were compared based on their ability to predict post-ROSC decisions and survival. RESULTS: The EFCN classification model achieved the best results across tested ML algorithms. The area under the receiver operating characteristic curve (AUROC) for CA and Survival were 0.908 and 0.896 respectively. Through cohort analyses, our model predicts that 18.3% (CI 16.4-20.2) of patients should receive a CA that did not originally, and 30.1% (CI 28.5-31.7) of these would experience improved survival outcomes. CONCLUSION: ML modeling effectively predicted hospital decisions and neurologic outcomes. ML modeling may serve as a quality improvement tool to inform system level OHCA policies and treatment protocols.

Prehospital Comprehensive Stroke Center vs Primary Stroke Center Triage in Patients With Suspected Large Vessel Occlusion Stroke.

Importance: Endovascular therapy (EVT) improves functional outcomes in acute ischemic stroke (AIS) with large vessel occlusion (LVO). Whether implementation of a regional prehospital transport policy for comprehensive stroke center triage increases use of EVT is uncertain. Objective: To evaluate the association of a regional prehospital transport policy that directly triages patients with suspected LVO stroke to the nearest comprehensive stroke center with rates of EVT. Design, Setting, and Participants: This retrospective, multicenter preimplementation-postimplementation study used an interrupted time series analysis to compare treatment rates before and after implementation in patients with AIS arriving at 15 primary stroke centers and 8 comprehensive stroke centers in Chicago, Illinois, via emergency medical services (EMS) transport from December 1, 2017, to May 31, 2019 (9 months before and after implementation in September 2018). Data were analyzed from December 1, 2017, to May 31, 2019. Interventions: Prehospital EMS transport policy to triage patients with suspected LVO stroke, using a 3-item stroke scale, to comprehensive stroke centers. Main Outcomes and Measures: Rates of EVT before and after implementation among EMS-transported patients within 6 hours of AIS onset. Results: Among 7709 patients with stroke, 663 (mean [SD] age, 68.5 [14.9] years; 342 women [51.6%] and 321 men [48.4%]; and 348 Black individuals [52.5%]) with AIS arrived within 6 hours of stroke onset by EMS transport: 310 of 2603 (11.9%) in the preimplementation period and 353 of 2637 (13.4%) in the postimplementation period. The EVT rate increased overall among all patients with AIS (preimplementation, 4.9% [95% CI, 4.1%-5.8%]; postimplementation, 7.4% [95% CI, 7.5%-8.5%]; P < .001) and among EMS-transported patients with AIS within 6 hours of onset (preimplementation, 4.8% [95% CI, 3.0%-7.8%]; postimplementation, 13.6% [95% CI, 10.4%-17.6%]; P < .001). On interrupted time series analysis among EMS-transported patients, the level change within 1 month of implementation was 7.15% (P = .04) with no slope change before (0.16%; P = .71) or after (0.08%; P = .89), which indicates a step rather than gradual change. No change in time to thrombolysis or rate of thrombolysis was observed (step change, 1.42%; P = .82). There were no differences in EVT rates in patients not arriving by EMS in the 6- to 24-hour window or by interhospital transfer or walk-in, irrespective of time window. Conclusions and Relevance: Implementation of a prehospital transport policy for comprehensive stroke center triage in Chicago was associated with a significant, rapid, and sustained increase in EVT rate for patients with AIS without deleterious associations with thrombolysis rates or times.

Retrospective Study of Midazolam Protocol for Prehospital Behavioral Emergencies.

INTRODUCTION: Agitated patients in the prehospital setting pose challenges for both patient care and emergency medical services (EMS) provider safety. Midazolam is frequently used to control agitation in the emergency department setting; however, limited data exist in the prehospital setting. We describe our experience treating patients with midazolam for behavioral emergencies in a large urban EMS system. We hypothesized that using midazolam for acute agitation leads to improved clinical conditions without causing significant clinical deterioration. METHODS: We performed a retrospective review of EMS patient care reports following implementation of a behavioral emergencies protocol in a large urban EMS system from February 2014-June 2016. For acute agitation, paramedics administered midazolam 1 milligram (mg) intravenous (IV), 5 mg intramuscular (IM), or 5 mg intranasal (IN). Results were analyzed using descriptive statistics, Levene's test for assessing variance among study groups, and t-test to evaluate effectiveness based on route. RESULTS: In total, midazolam was administered 294 times to 257 patients. Median age was 30 (interquartile range 24-42) years, and 66.5% were male. Doses administered were 1 mg (7.1%) and 5 mg (92.9%). Routes were IM (52.0%), IN (40.8%), and IV (7.1%). A second dose was administered to 37 patients. In the majority of administrations, midazolam improved the patient's condition (73.5%) with infrequent adverse events (3.4%). There was no significant difference between the effectiveness of IM and IN midazolam (71.0% vs 75.4%; p = 0.24). CONCLUSION: A midazolam protocol for prehospital agitation was associated with reduced agitation and a low rate of adverse events.

Large urban center improves out-of-hospital cardiac arrest survival.

BACKGROUND: Large cities pose unique challenges that limit the effectiveness of system improvement interventions. Successful implementation of integrated cardiac resuscitation systems of care can serve as a model for other urban centers. METHODS: This was a retrospective analysis of prospectively collected data of adult cases of non-traumatic cardiac arrest who received treatment by Chicago Fire Department EMS from September 1, 2013 through December 31, 2016. We measured temporal OHCA outcomes during implementation of system-wide initiatives including telephone-assisted and community CPR training programs; high performance CPR and team based simulation training; new post resuscitation care and destination protocols; and case review for EMS providers. Outcomes measured included bystander CPR rates, return of spontaneous circulation (ROSC), hospital admission and survival, and favorable neurologic outcomes (CPC 1-2). Relative risk was determined by logistic regression model where observed group-specific outcomes are expressed as odds ratios (OR). RESULTS: We included 6103 adult OHCA cases occurring outside of health care facilities from September 1, 2013 through December 31, 2016. Significantly improved outcomes (p < 0.05) were observed between 2013 and 2016 for bystander CPR (11.6% vs 19.4%), ROSC (28.6% vs 36.9%), hospital admission (22.5% vs 29.4%), survival (7.3% vs 9.9%), and CPC 1-2 (4.3% vs 6.4%). Utstein survival increased from 16.3%-35.4% and CPC 1-2 survival from 11.6%-29.1% (p < 0.05). After adjustment for OHCA characteristics, survival with CPC 1-2 increased over time (OR 1.15, p = 0.0277). CONCLUSIONS: Densely populated cities with low survival rates can overcome systematic challenges and improve OHCA survival.

Cincinnati Prehospital Stroke Scale Can Identify Large Vessel Occlusion Stroke.

OBJECTIVE: Accurate prehospital identification of patients with acute ischemic stroke (AIS) from large vessel occlusion (LVO) facilitates direct transport to hospitals that perform endovascular thrombectomy. We hypothesize that a cut-off score of the Cincinnati Prehospital Stroke Scale (CPSS), a simple assessment tool currently used by emergency medical services (EMS) providers, can be used to identify LVO. METHODS: Consecutively enrolled, confirmed AIS patients arriving via EMS between August 2012 and April 2014 at a high-volume stroke center in a large city with a single municipal EMS provider agency were identified in a prospective, single-center registry. Head and neck vessel imaging confirmed LVO. CPSS scores were abstracted from prehospital EMS records. Spearman's rank correlation, Wilcoxon rank-sum test, and Student's t-test were performed. Cohen's kappa was calculated between CPSS abstractors. The Youden index identified the optimal CPSS cut-off. Multivariate logistic regression controlling for age, sex, and race determined the odds ratio (OR) for LVO. RESULTS: Of 144 eligible patients, 138 (95.8%) had CPSS scores in the EMS record and were included for analysis. The median age was 69 (IQR 58-81) years. Vessel imaging was performed in 97.9% of patients at a median of 5.9 (IQR 3.6-10.2) hours from hospital arrival, and 43.7% had an LVO. Intravenous tissue plasminogen activator was administered to 29 patients, in whom 12 had no LVO on subsequent vessel imaging. The optimal CPSS cut-off predicting LVO was 3, with a Youden index of 0.29, sensitivity of 0.41, and specificity of 0.88. The adjusted OR for LVO with CPSS = 3 was 5.7 (95% CI 2.3-14.1). Among patients with CPSS = 3, 72.7% had an LVO, compared with 34.3% of patients with CPSS ≤ 2 (p < 0.0001). CONCLUSIONS: A CPSS score of 3 reliably identifies LVO in AIS patients. EMS providers may be able to use the CPSS, a simple, widely adopted prehospital stroke assessment tool, with a cut-off score to screen for patients with suspected LVO.

Identifying Key Words in 9-1-1 Calls for Stroke: A Mixed Methods Approach.

OBJECTIVES: Identifying stroke during a 9-1-1 call is critical to timely prehospital care. However, emergency medical dispatchers (EMDs) recognize stroke in less than half of 9-1-1 calls, potentially due to the words used by callers to communicate stroke signs and symptoms. We hypothesized that callers do not typically use words and phrases considered to be classical descriptors of stroke, such as focal neurologic deficits, but that a mixed-methods approach can identify words and phrases commonly used by 9-1-1 callers to describe acute stroke victims. METHODS: We performed a mixed-method, retrospective study of 9-1-1 call audio recordings for adult patients with confirmed stroke who were transported by ambulance in a large urban city. Content analysis, a qualitative methodology, and computational linguistics, a quantitative methodology, were used to identify key words and phrases used by 9-1-1 callers to describe acute stroke victims. Because a caller's level of emotional distress contributes to the communication during a 9-1-1 call, the Emotional Content and Cooperation Score was scored by a multidisciplinary team. RESULTS: A total of 110 9-1-1 calls, received between June and September 2013, were analyzed. EMDs recognized stroke in 48% of calls, and the emotional state of most callers (95%) was calm. In 77% of calls in which EMDs recognized stroke, callers specifically used the word "stroke"; however, the word "stroke" was used in only 38% of calls. Vague, non-specific words and phrases were used to describe stroke victims' symptoms in 55% of calls, and 45% of callers used distractor words and phrases suggestive of non-stroke emergencies. Focal neurologic symptoms were described in 39% of calls. Computational linguistics identified 9 key words that were more commonly used in calls where the EMD identified stroke. These words were concordant with terms identified through qualitative content analysis. CONCLUSIONS: Most 9-1-1 callers used vague, non-specific, or distractor words and phrases and infrequently provide classic stroke descriptions during 9-1-1 calls for stroke. Both qualitative and quantitative methodologies identified similar key words and phrases associated with accurate EMD stroke recognition. This study suggests that tools incorporating commonly used words and phrases could potentially improve EMD stroke recognition.

Increasing bystander CPR: potential of a one question telecommunicator identification algorithm.

OBJECTIVES: Telecommunicators use a two-question algorithm to identify cardiac arrest: Is the individual conscious? Is the individual breathing normally? Although this approach increases arrest identification and consequently bystander CPR, the strategy does not identify all arrests and requires time to complete. We evaluated the implications of a one-question strategy that inquired only about consciousness. METHODS: We undertook a 3-month observational study of consecutive cases identified as unconscious by the telecommunicator prior to EMS arrival who were not receiving bystander CPR. We evaluated the extent that a one-question strategy could increase arrest identification and reduce the identification interval; and the trade-off whereby additional persons without arrest could potentially receive CPR. RESULTS: Among 679 eligible cases, 20% (n = 135) were in arrest and 80% (n = 544) were not in arrest. The two-question algorithm identified 90% (121/135) as true arrest. Of the 135 in arrest, 70% (n = 95) received compressions. The median interval from call to arrest identification was 72 seconds, with a median of 14 seconds for the breathing normally question. Using the two-question algorithm, telecommunicators incorrectly classified 30% (n = 164/544) of non-arrests as arrest. Bystanders proceeded to compressions in 16% (n = 85/544) of persons not in arrest. A one-question approach that inquired only about consciousness could potentially increase the arrest identification by 10% (14/135) and reduce the interval to compressions by a median of 14 seconds; however the strategy would potentially triple the number of non-arrest cases (544 versus 164) eligible for CPR instructions. CONCLUSION: A single-question arrest identification algorithm may not achieve a favorable balance of risk and benefit.