Search

Healthcare disparities in the initial management of patients with acute coronary syndrome (ACS) exist. Yet, the complexity of interactions between demographic, social, economic, and geospatial determinants of health hinders incorporating such predictors in existing risk stratification models. We sought to explore a machine-learning-based approach to study the complex interactions between the geospatial and social determinants of health to explain disparities in ACS likelihood in an urban community. This study identified consecutive patients transported by Pittsburgh emergency medical service for a chief complaint of chest pain or ACS-equivalent symptoms. We extracted demographics, clinical data, and location coordinates from electronic health records. Median income was based on US census data by zip code. A random forest (RF) classifier and a regularized logistic regression model were used to identify the most important predictors of ACS likelihood. Our final sample included 2400 patients (age 59 ± 17 years, 47% Females, 41% Blacks, 15.8% adjudicated ACS). In our RF model (area under the receiver operating characteristic curve of 0.71 ± 0.03) age, prior revascularization, income, distance from hospital, and residential neighborhood were the most important predictors of ACS likelihood. In regularized regression (akaike information criterion = 1843, bayesian information criterion = 1912, χ2 = 193, df = 10, p

Introduction HEART score is widely used to stratify patients with chest pain in the emergency department but has never been validated for cocaine-associated chest pain (CACP). We sought to evaluate the performance of HEART score in risk stratifying patients with CACP compared to an age- and sex-matched cohort with non-CACP. Methods The parent study was an observational cohort study that enrolled consecutive patients with chest pain. We identified patients with CACP and age/sex matched them to patients with non-CACP in 1:2 fashion. HEART score was calculated retrospectively from charts. The primary outcome was major adverse cardiac events (MACE) within 30 days of indexed encounter. Results We included 156 patients with CACP and 312 age-and sex-matched patients with non-CACP (n = 468, mean age 51 ± 9, 22% females). There was no difference in rate of MACE between the groups (17.9% vs. 15.7%, p = 0.54). Compared to the non-CACP group, the HEART score had lower classification performance in those with CACP (AUC = 0.68 [0.56–0.80] vs. 0.84 [0.78–0.90], p = 0.022). In CACP group, Troponin score had the highest discriminatory value (AUC = 0.72 [0.60–0.85]) and Risk factors score had the lowest (AUC = 0.47 [0.34–0.59]). In patients deemed low-risk by the HEART score, those with CACP were more likely to experience MACE (14% vs. 4%, OR = 3.7 [1.3–10.7], p = 0.016). Conclusion In patients with CACP, HEART score performs poorly in stratifying risk and is not recommended as a rule out tool to identify those at low risk of MACE.

Emergency department (ED) nurses need to identify patients with potential acute coronary syndrome (ACS) rapidly because treatment delay could impact patient outcomes. Aims of this secondary analysis were to identify key patient factors that could be available at initial ED nurse triage that predict ACS. Consecutive patients with chest pain who called 9-1-1, received a 12-lead electrocardiogram in the prehospital setting, and were transported via emergency medical service were included in the study. A total of 750 patients were recruited. The sample had an average age of 59 years old, was 57% male, and 40% Black. One hundred and fifteen patients were diagnosed with ACS. Older age, non-Caucasian race, and faster respiratory rate were independent predictors of ACS. There was an interaction between heart rate by Type II diabetes receiving insulin in the context of ACS. Type II diabetics requiring insulin for better glycemic control manifested a faster heart rate. By identifying patient factors at ED nurse triage that could be predictive of ACS, accuracy rates of triage may improve, thus impacting patient outcomes.

Background: Patients with acute illness who receive intravenous (IV) fluids prior to hospital arrival may have a lower in-hospital mortality. To better understand whether this is a direct treatment effect or epiphenomenon of downstream care, we tested the association between a prehospital fluid bolus and the change in inflammatory cytokines measured at prehospital and emergency department timepoints in a sample of non-trauma, non-cardiac arrest patients at risk for critical illness. Methods: In a prospective cohort study, we screened 4,013 non-trauma, non-cardiac arrest encounters transported by City of Pittsburgh Emergency Medical Services (EMS) to 2 hospitals from August 2013 to February 2014. In 345 patients, we measured prehospital biomarkers (IL-6, IL-10, and TNF) at 2 time points: the time of prehospital IV access placement by EMS and at ED arrival. We determined the relative change for marker X as: ([XED – XEMS]/XEMS). We determined the risk-adjusted association between prehospital IV fluid bolus and relative change for each marker using multivariable linear regression. Results: Among 345 patients, 88 (26%) received a prehospital IV fluid bolus and 257 (74%) did not. Compared to patients who did not receive prehospital fluids, median prehospital IL-6 was greater initially in subjects receiving a prehospital IV fluid bolus (22.3 [IQR 6.4–113] vs. 11.5 [IQR 5.5–47.6]). Prehospital IL-10 and TNF were similar in both groups (IL-10: 3.5 [IQR 2.2–25.6] vs. 3.0 [IQR 1.9–9.0]; TNF: 7.5 [IQR 6.4–10.4] vs. 6.9 [IQR 6.0–8.3]). After adjustment for demographics, illness severity, and prehospital transport time, we observed a relative decrease in IL-6 at hospital arrival in those receiving a prehospital fluid bolus (adjusted β = −10.0, 95% CI: −19.4, −0.6, p = 0.04), but we did not detect a significant change in IL-10 (p = 0.34) or TNF (p = 0.53). Conclusions: Among non-trauma, non-cardiac arrest patients at risk for critical illness, a prehospital IV fluid bolus was associated with a relative decrease in IL-6, but not IL-10 or TNF.

Background The COVID-19 pandemic's impact on stroke care is two-fold direct impact of the infection and indirect impact on non-COVID-19 diseases. Anecdotal evidence and clinical observation suggest that there is a decrease in the number of patients presenting with stroke during the pandemic. We aim to understand the impact of the COVID-19 pandemic on the utilization of stroke emergency services on a single comprehensive stroke center (CSC). Methods We performed a retrospective analysis of a prospectively maintained database and compared all emergency department (ED) encounters, acute stroke admissions (including TIA), and thrombectomy cases admitted in March 2017-2019 to patients admitted in March 2020 at a comprehensive stroke center. Results Number of total ED encounters (22%, p=0.005), acute ischemic strokes (40%, p=0.001), and TIAs (60%, p=0.163) decreased between March of 2017–2019 compared to March of 2020. The number of patients undergoing EVT in March 2020 was comparable to March 2017–2019 (p=0.430). Conclusion A pandemic-related stay-at-home policy reduces the utilization of stroke emergency services at a CSC. This effect appears to be more prominent for ED encounters, all stroke admissions and TIAs, and less impactful for severe strokes. Given the relatively low prevalence of COVID-19 cases in our region, this decrement is likely related to healthcare seeking behavior rather than capacity saturation.

Multiple national organizations have recommended and supported a national investment to increase the scientific evidence available to guide patient care delivered by Emergency Medical Services (EMS) and incorporate that evidence directly into EMS systems. Ongoing efforts seek to develop, implement, and evaluate prehospital evidence-based guidelines (EBGs) using the National Model Process created by a multidisciplinary panel of experts convened by the Federal Interagency Committee on EMS (FICEMS) and the National EMS Advisory Council (NEMSAC). Yet, these and other EBG efforts have occurred in relative isolation, with limited direct collaboration between national projects, and have experienced challenges in implementation of individual guidelines. There is a need to develop sustainable relationships among stakeholders that facilitate a common vision that facilitates EBG efforts. Herein, we summarize a National Strategy on EBGs developed by the National Association of EMS Physicians (NAEMSP) with involvement of 57 stakeholder organizations, and with the financial support of the National Highway Traffic Safety Administration (NHTSA) and the EMS for Children program. The Strategy proposes seven action items that support collaborative efforts in advancing prehospital EBGs. The first proposed action is creation of a Prehospital Guidelines Consortium (PGC) representing national medical and EMS organizations that have an interest in prehospital EBGs and their benefits to patient outcomes. Other action items include promoting research that supports creation and evaluates the impact of EBGs, promoting the development of new EBGs through improved stakeholder collaboration, and improving education on evidence-based medicine for all prehospital providers. The Strategy intends to facilitate implementation of EBGs by improving guideline dissemination and incorporation into protocols, and seeks to establish standardized evaluation methods for prehospital EBGs. Finally, the Strategy proposes that key stakeholder organizations financially support the Prehospital Guidelines Consortium as a means of implementing the Strategy, while together promoting additional funding for continued EBG efforts.

We sought to test reliability of two approaches to classify adverse events (AEs) associated with helicopter EMS (HEMS) transport.The first approach for AE classification involved flight nurses and paramedics (RN/Medics) and mid-career emergency physicians (MC-EMPs) independently reviewing 50 randomly selected HEMS medical records. The second approach involved RN/Medics and MC-EMPs meeting as a group to openly discuss 20 additional medical records and reach consensus-based AE decision. We compared all AE decisions to a reference criterion based on the decision of three senior emergency physicians (Sr-EMPs). We designed a study to detect an improvement in agreement (reliability) from fair (kappa = 0.2) to moderate (kappa = 0.5). We calculated sensitivity, specificity, percent agreement, and positive and negative predictive values (PPV/NPV).For the independent reviews, the Sr-EMP group identified 26 AEs while individual clinician reviewers identified between 19 and 50 AEs. Agreement on the presence/absence of an AE between Sr-EMPs and three MC-EMPs ranged from κ = 0.20 to κ = 0.25. Agreement between Sr-EMPs and three RN/Medics ranged from κ = 0.11 to κ = 0.19. For the consensus/open-discussion approach, the Sr-EMPs identified 13 AEs, the MC-EMP group identified 18 AEs, and RN/medic group identified 36 AEs. Agreement between Sr-EMPs and MC-EMP group was (κ = 0.30 95%CI -0.12, 0.72), whereas agreement between Sr-EMPs and RN/medic group was (κ = 0.40 95%CI 0.01, 0.79). Agreement between all three groups was fair (κ = 0.33, 95%CI 0.06, 0.66). Percent agreement (58-68%) and NPV (63-76%) was moderately dissimilar between clinicians, while sensitivity (25-80%), specificity (43-97%), and PPV (48-83%) varied.We identified a higher level of agreement/reliability in AE decisions utilizing a consensus-based approach for review rather than independent reviews.

Serum lactate elevations are associated with morbidity and mortality in trauma patients, but their value in prehospital medical patients prior to resuscitation is unknown. We sought to assess the distribution of blood lactate concentrations prior to intravenous (i.v.) resuscitation and examine the association of elevation on in-hospital death.A convenience sample of adult patients over 14 months who received an i.v. line by eight EMS agencies in Western Pennsylvania had lactate measurement prior to any i.v. treatment. We assessed the lactate values and any relationship between these and hospital mortality (our primary outcome) and admission to the intensive care unit (ICU). We also compared the ability of lactate to discriminate outcomes with a prehospital critical illness score using age, Glasgow Coma Score, and initial vital signs.We included 673 patients, among whom 71 (11%) were admitted to the ICU and 21 (3.1%) died in-hospital. Elevated lactate (≥2 mmol/L) occurred in 307 (46%) patients and was strongly associated with hospital death after adjustment for known covariates (odds ratio = 3.57, 95% confidence interval [CI]: 1.10, 11.6). Lactate ≥2 mmol/L had a modest sensitivity (76%) and specificity (55%), and discrimination for hospital death (area under the curve [AUC] = 0.66, 95%CI: 0.56, 0.75). Compared to the prehospital critical illness score alone (AUC = 0.69, 95% CI: 0.59, 0.80), adding lactate to the score offered modest improvement (net reclassification improvement = 0.63, 95%CI: 0.23, 1.01, p0.05).Initial lactate concentration in our prehospital medical patient population was associated with hospital mortality. However, it is a modest predictor of outcome, offering similar discrimination to a prehospital critical illness score.

Objectives Estimates of prehospital transport times are an important part of emergency care system research and planning; however, the accuracy of these estimates is unknown. The authors examined the accuracy of three estimation methods against observed transport times in a large cohort of prehospital patient transports. Methods This was a validation study using prehospital records in King County, Washington, and southwestern Pennsylvania from 2002 to 2006 and 2005 to 2011, respectively. Transport time estimates were generated using three methods: linear arc distance, Google Maps, and ArcGIS Network Analyst. Estimation error, defined as the absolute difference between observed and estimated transport time, was assessed, as well as the proportion of estimated times that were within specified error thresholds. Based on the primary results, a regression estimate was used that incorporated population density, time of day, and season to assess improved accuracy. Finally, hospital catchment areas were compared using each method with a fixed drive time. Results The authors analyzed 29,935 prehospital transports to 44 hospitals. The mean (± standard deviation [±SD]) absolute error was 4.8 (±7.3) minutes using linear arc, 3.5 (±5.4) minutes using Google Maps, and 4.4 (±5.7) minutes using ArcGIS. All pairwise comparisons were statistically significant (p

To determine the association between poor sleep quality, fatigue, and self-reported safety outcomes among emergency medical services (EMS) workers.We used convenience sampling of EMS agencies and a cross-sectional survey design. We administered the 19-item Pittsburgh Sleep Quality Index (PSQI), 11-item Chalder Fatigue Questionnaire (CFQ), and 44-item EMS Safety Inventory (EMS-SI) to measure sleep quality, fatigue, and safety outcomes, respectively. We used a consensus process to develop the EMS-SI, which was designed to capture three composite measurements of EMS worker injury, medical errors and adverse events (AEs), and safety-compromising behaviors. We used hierarchical logistic regression to test the association between poor sleep quality, fatigue, and three composite measures of EMS worker safety outcomes.We received 547 surveys from 30 EMS agencies (a 35.6% mean agency response rate). The mean PSQI score exceeded the benchmark for poor sleep (6.9, 95% confidence interval [CI] 6.6, 7.2). More than half of the respondents were classified as fatigued (55%, 95% CI 50.7, 59.3). Eighteen percent of the respondents reported an injury (17.8%, 95% CI 13.5, 22.1), 41% reported a medical error or AE (41.1%, 95% CI 36.8, 45.4), and 90% reported a safety-compromising behavior (89.6%, 95% CI 87, 92). After controlling for confounding, we identified 1.9 greater odds of injury (95% CI 1.1, 3.3), 2.2 greater odds of medical error or AE (95% CI 1.4, 3.3), and 3.6 greater odds of safety-compromising behavior (95% CI 1.5, 8.3) among fatigued respondents versus nonfatigued respondents.In this sample of EMS workers, poor sleep quality and fatigue are common. We provide preliminary evidence of an association between sleep quality, fatigue, and safety outcomes.

Introduction Prompt treatment of severe sepsis in the Emergency Department reduces deaths, but the role of prehospital fluid resuscitation is unknown. We sought to determine the risk-adjusted association between prehospital fluid administration and hospital mortality among emergency medical services (EMS) patients admitted with severe sepsis. Methods We performed a prospective, observational study of patients hospitalized with severe sepsis on admission among 45,394 adult EMS encounters taken to 15 hospitals from 11/2009 to 12/2010 by a two-tier EMS system in King County, Washington. The region mandated recording of prehospital intravenous catheter and fluid administration in prehospital records, along with detailed demographic, incident, physiologic, and hospital adjustment variables. We determined the effect of prehospital intravenous catheter or fluid versus no catheter or fluid on all-cause mortality using multivariable logistic regression. Results Of all encounters, 1,350 met criteria for severe sepsis on admission, of whom 205 (15%) died by hospital discharge, 312 (23%) received prehospital intravenous fluid, 90 (7%) received a prehospital catheter alone and 948 (70%) did not receive catheter or fluid. EMS administered a median prehospital fluid volume of 500 mL (interquartile range (IQR): 200, 1000 mL). In adjusted models, the administration of any prehospital fluid was associated with reduced hospital mortality (Odds ratio =0.46; 95% Confidence interval: 0.23, 0.88; P =0.02) compared to no prehospital fluid. The odds of hospital mortality were also lower among severe sepsis patients treated with prehospital intravenous catheter alone (Odds ratio =0.3; 95% Confidence interval: 0.17 to 0.57; P