Predicting Out-of-Hospital Cardiac Arrest in the General Population Using Electronic Health Records.

BACKGROUND: The majority of out-of-hospital cardiac arrests (OHCAs) occur among individuals in the general population, for whom there is no established strategy to identify risk. In this study, we assess the use of electronic health record (EHR) data to identify OHCA in the general population and define salient factors contributing to OHCA risk. METHODS: The analytical cohort included 2366 individuals with OHCA and 23 660 age- and sex-matched controls receiving health care at the University of Washington. Comorbidities, electrocardiographic measures, vital signs, and medication prescription were abstracted from the EHR. The primary outcome was OHCA. Secondary outcomes included shockable and nonshockable OHCA. Model performance including area under the receiver operating characteristic curve and positive predictive value were assessed and adjusted for observed rate of OHCA across the health system. RESULTS: There were significant differences in demographic characteristics, vital signs, electrocardiographic measures, comorbidities, and medication distribution between individuals with OHCA and controls. In external validation, discrimination in machine learning models (area under the receiver operating characteristic curve 0.80-0.85) was superior to a baseline model with conventional cardiovascular risk factors (area under the receiver operating characteristic curve 0.66). At a specificity threshold of 99%, correcting for baseline OHCA incidence across the health system, positive predictive value was 2.5% to 3.1% in machine learning models compared with 0.8% for the baseline model. Longer corrected QT interval, substance abuse disorder, fluid and electrolyte disorder, alcohol abuse, and higher heart rate were identified as salient predictors of OHCA risk across all machine learning models. Established cardiovascular risk factors retained predictive importance for shockable OHCA, but demographic characteristics (minority race, single marital status) and noncardiovascular comorbidities (substance abuse disorder) also contributed to risk prediction. For nonshockable OHCA, a range of salient predictors, including comorbidities, habits, vital signs, demographic characteristics, and electrocardiographic measures, were identified. CONCLUSIONS: In a population-based case-control study, machine learning models incorporating readily available EHR data showed reasonable discrimination and risk enrichment for OHCA in the general population. Salient factors associated with OCHA risk were myriad across the cardiovascular and noncardiovascular spectrum. Public health and tailored strategies for OHCA prediction and prevention will require incorporation of this complexity.

Wolf Creek XVII part 3: Automated cardiac arrest diagnosis.

Introduction: Automated cardiac arrest diagnosis offers the possibility to significantly shorten the interval between onset of out-of-hospital cardiac arrest (OHCA) and notification of EMS, providing the opportunity for earlier resuscitation and possibly increased survival. Methods: Automated cardiac arrest diagnosis was one of six focus topics for the Wolf Creek XVII Conference held on June 14-17 2023 in Ann Arbor, Michigan, USA. Conference invitees included international thought leaders and scientists in the field of cardiac arrest resuscitation from academia and industry. Participants submitted via online survey knowledge gaps, barriers to translation and research priorities for each focus topic. Expert panels used the survey results and their own perspectives and insights to create and present a preliminary unranked list for each category that was debated, revised and ranked by all attendees to identify the top 5 for each category. Results: Top knowledge gaps include the accuracy of automated OHCA detection technologies and the feasibility and reliability of automated EMS activation. The main barriers to translation are the risk of false positives potentially overburdening EMS, development and application costs of technology and the challenge of integrating new technology in EMS IT systems. The top research priorities are large-scale evaluation studies to measure real world performance and user research regarding the willingness to adopt these technologies. Conclusion: Automated cardiac arrest diagnosis has the potential to significantly impact time to resuscitation and survival of OHCA because it could convert unwitnessed events into witnessed events. Validation and feasibility studies are needed. The specificity of the technology must be high not to overburden limited EMS resources. If adequate event classification is achieved, future research could shift toward event prediction, focusing on identifying potential digital biomarkers and signatures of imminent cardiac arrest. Implementation could be challenging due to high costs of development, regulatory considerations and instantiation logistics.

Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients.

OBJECTIVES: To evaluate factors predictive of clinical progression among coronavirus disease 2019 patients following admission, and whether continuous, automated assessments of patient status may contribute to optimal monitoring and management. DESIGN: Retrospective cohort for algorithm training, testing, and validation. SETTING: Eight hospitals across two geographically distinct regions. PATIENTS: Two-thousand fifteen hospitalized coronavirus disease 2019-positive patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Anticipating Respiratory failure in Coronavirus disease (ARC), a clinically interpretable, continuously monitoring prognostic model of acute respiratory failure in hospitalized coronavirus disease 2019 patients, was developed and validated. An analysis of the most important clinical predictors aligns with key risk factors identified by other investigators but contributes new insights regarding the time at which key factors first begin to exhibit aberrency and distinguishes features predictive of acute respiratory failure in coronavirus disease 2019 versus pneumonia caused by other types of infection. Departing from prior work, ARC was designed to update continuously over time as new observations (vitals and laboratory test results) are recorded in the electronic health record. Validation against data from two geographically distinct health systems showed that the proposed model achieved 75% specificity and 77% sensitivity and predicted acute respiratory failure at a median time of 32 hours prior to onset. Over 80% of true-positive alerts occurred in non-ICU settings. CONCLUSIONS: Patients admitted to non-ICU environments with coronavirus disease 2019 are at ongoing risk of clinical progression to severe disease, yet it is challenging to anticipate which patients will develop acute respiratory failure. A continuously monitoring prognostic model has potential to facilitate anticipatory rather than reactive approaches to escalation of care (e.g., earlier initiation of treatments for severe disease or structured monitoring and therapeutic interventions for high-risk patients).

Occurrence and Timing of Subsequent Severe Acute Respiratory Syndrome Coronavirus 2 Reverse-transcription Polymerase Chain Reaction Positivity Among Initially Negative Patients.

Using data for 20 912 patients from 2 large academic health systems, we analyzed the frequency of severe acute respiratory syndrome coronavirus 2 reverse-transcription polymerase chain reaction test discordance among individuals initially testing negative by nasopharyngeal swab who were retested on clinical grounds within 7 days. The frequency of subsequent positivity within this window was 3.5% and was similar across institutions.

Impact of COVID-19 response on global surgical volumes: an ongoing observational study.

OBJECTIVE: To determine whether location-linked anaesthesiology calculator mobile application (app) data can serve as a qualitative proxy for global surgical case volumes and therefore monitor the impact of the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We collected data provided by users of the mobile app "Anesthesiologist" during 1 October 2018-30 June 2020. We analysed these using RStudio and generated 7-day moving-average app use plots. We calculated country-level reductions in app use as a percentage of baseline. We obtained data on COVID-19 case counts from the European Centre for Disease Prevention and Control. We plotted changing app use and COVID-19 case counts for several countries and regions. FINDINGS: A total of 100 099 app users within 214 countries and territories provided data. We observed that app use was reduced during holidays, weekends and at night, correlating with expected fluctuations in surgical volume. We observed that the onset of the pandemic prompted substantial reductions in app use. We noted strong cross-correlation between COVID-19 case count and reductions in app use in low- and middle-income countries, but not in high-income countries. Of the 112 countries and territories with non-zero app use during baseline and during the pandemic, we calculated a median reduction in app use to 73.6% of baseline. CONCLUSION: App data provide a proxy for surgical case volumes, and can therefore be used as a real-time monitor of the impact of COVID-19 on surgical capacity. We have created a dashboard for ongoing visualization of these data, allowing policy-makers to direct resources to areas of greatest need.

Multi-Channel Facial Photoplethysmography Sensing.

Motivated by the need for continuous cardiovascular monitoring, we present a system for performing photoplethysmography sensing at multiple facial locations. As a proof-of-concept, our system incorporates an optical sensor array into a wearable face mask form factor for application in a surgical hemodynamic monitoring use case. Here we demonstrate that our design can accurately detect pulse timing by validating estimated heart rate against ground truth electrocardiogram recordings. In an experiment across 10 experimental subjects, our system achieves an error standard deviation of 2.84 beats per minute. This system shows promise for performing non-invasive, continuous pulse waveform recording from multiple locations on the face.

Incidence of Health Care-Associated COVID-19 During Universal Testing of Medical and Surgical Admissions in a Large US Health System.

Concerns about severe acute respiratory syndrome coronavirus 2 exposure in health care settings may cause patients to delay care. Among 2992 patients testing negative on admission to an academic, 3-hospital system, 8 tested positive during hospitalization or within 14 days postdischarge. Following adjudication of each instance, health care-associated infection incidence ranged from 0.8 to 5.0 cases per 10 000 patient-days.

Real-time assessment of COVID-19 impact on global surgical case volumes.

Importance: The COVID-19 pandemic has disrupted global surgical capacity. The impact of the pandemic in low and middle income countries has the potential to worsen already strained access to surgical care. Timely assessment of surgical volumes in these countries remains challenging. Objective: To determine whether usage data from a globally used anesthesiology calculator mobile application can serve as a proxy for global surgical case volume and contribute to monitoring of the impact of the COVID-19 pandemic, particularly in World Bank low income countries where official data collection is not currently practical. Design: Subset of data from an ongoing observational cohort study of users of the application collected from October 1, 2018 to April 18, 2020. Setting: The mobile application is available from public sources; users download and use the application per their own clinical needs on personal mobile devices. Participants: No user data was excluded from the study. Exposures: Events with impacts on surgical case volumes, including weekends, holidays, and the COVID-19 pandemic. Main Outcomes and Measures: It was previously noted that application usage was decreased on weekends and during winter holidays. We subsequently hypothesized that more detailed analysis would reveal impacts of country-specific or region-specific holidays on the volume of app use. Results: 4,300,975 data points from 92,878 unique users were analyzed. Physicians and other anesthesia providers comprised 85.8% of the study population. Application use was reduced on holidays and weekends and correlated with fluctuations in surgical volume. The COVID-19 pandemic was associated with substantial reductions in app use globally and regionally. There was strong cross correlation between COVID-19 case count and reductions in app use. By country, there was a median global reduction in app use to 58% of baseline (interquartile range, 46%-75%). Application use in low-income continues to decline but in high-income countries has stabilized. Conclusions and Relevance: Application usage metadata provides a real-time indicator of surgical volume. This data may be used to identify impacted regions where disruptions to surgical care are disproportionate or prolonged. A dashboard for continuous visualization of these data has been deployed.

Contactless cardiac arrest detection using smart devices.

Out-of-hospital cardiac arrest is a leading cause of death worldwide. Rapid diagnosis and initiation of cardiopulmonary resuscitation (CPR) is the cornerstone of therapy for victims of cardiac arrest. Yet a significant fraction of cardiac arrest victims have no chance of survival because they experience an unwitnessed event, often in the privacy of their own homes. An under-appreciated diagnostic element of cardiac arrest is the presence of agonal breathing, an audible biomarker and brainstem reflex that arises in the setting of severe hypoxia. Here, we demonstrate that a support vector machine (SVM) can classify agonal breathing instances in real-time within a bedroom environment. Using real-world labeled 9-1-1 audio of cardiac arrests, we train the SVM to accurately classify agonal breathing instances. We obtain an area under the curve (AUC) of 0.9993 ± 0.0003 and an operating point with an overall sensitivity and specificity of 97.24% (95% CI: 96.86-97.61%) and 99.51% (95% CI: 99.35-99.67%). We achieve a false positive rate between 0 and 0.14% over 82 h (117,985 audio segments) of polysomnographic sleep lab data that includes snoring, hypopnea, central, and obstructive sleep apnea events. We also evaluate our classifier in home sleep environments: the false positive rate was 0-0.22% over 164 h (236,666 audio segments) of sleep data collected across 35 different bedroom environments. We prototype our proof-of-concept contactless system using commodity smart devices (Amazon Echo and Apple iPhone) and demonstrate its effectiveness in identifying cardiac arrest-associated agonal breathing instances played over the air.

Association of Adverse Effects of Medical Treatment With Mortality in the United States: A Secondary Analysis of the Global Burden of Diseases, Injuries, and Risk Factors Study.

Importance: More than 20 years have passed since the first publication of estimates of the extent of medical harm occurring in hospitals in the United States. Since then, considerable resources have been allocated to improve patient safety, yet policymakers lack a clear gauge of the progress made. Objectives: To quantify the cause-specific mortality associated with adverse effects of medical treatment (AEMT) in the United States from 1990 to 2016 by age group, sex, and state of residence and to describe trends in types of harm and associations with other diseases and injuries. Design, Setting, and Participants: Cohort study using 1990-2016 data on mortality due to AEMT from the Global Burden of Diseases, Injuries, and Risk Factors (GBD) 2016 study, which assessed death certificates of US decedents. Exposures: Death with International Classification of Diseases (ICD)-coded registration. Main Outcomes and Measures: Mortality associated with AEMT. Secondary analyses were performed on all ICD codes in the death certificate's causal chain to describe associations between AEMT and other diseases and injuries. Results: From 1990 to 2016, there were an estimated 123 603 deaths (95% uncertainty interval [UI], 100 856-163 814 deaths) with AEMT as the underlying cause. Despite an overall increase in the number of deaths due to AEMT over time, the national age-standardized mortality rate due to AEMT decreased by 21.4% (95% UI, 1.3%-32.2%) from 1.46 (95% UI, 1.09-1.76) deaths per 100 000 population in 1990 to 1.15 (95% UI, 1.00-1.60) deaths per 100 000 population in 2016. Men and women had similar rates of AEMT mortality, and those 70 years or older had mortality rates nearly 20-fold greater compared with those aged 15 to 49 years (mortality rate in 2016 for both sexes, 7.93 [95% UI, 7.23-11.45] per 100 000 population for those aged ≥70 years vs 0.38 [95% UI, 0.34-0.43] per 100 000 population for those aged 15-49 years). Per 100 000 population, California had the lowest age-standardized AEMT mortality rate at 0.84 deaths (95% UI, 0.57-1.47 deaths), whereas Mississippi had the highest mortality rate at 1.67 deaths (95% UI, 1.19-2.03 deaths). Surgical and perioperative events were the most common subtype of AEMT, accounting for 63.6% of all deaths for which an AEMT was identified as the underlying cause. Conclusions and Relevance: This study's findings suggest a modest reduction in the mortality rate associated with AEMT in the United States from 1990 to 2016 while also observing increased mortality associated with advancing age and noted geographic variability. The annual GBD releases may allow for tracking of the burden of AEMT in the United States.

Opioid overdose detection using smartphones.

Early detection and rapid intervention can prevent death from opioid overdose. At high doses, opioids (particularly fentanyl) can cause rapid cessation of breathing (apnea), hypoxemic/hypercarbic respiratory failure, and death, the physiologic sequence by which people commonly succumb from unintentional opioid overdose. We present algorithms that run on smartphones and unobtrusively detect opioid overdose events and their precursors. Our proof-of- concept contactless system converts the phone into a short-range active sonar using frequency shifts to identify respiratory depression, apnea, and gross motor movements associated with acute opioid toxicity. We develop algorithms and perform testing in two environments: (i) an approved supervised injection facility (SIF), where people self-inject illicit opioids, and (ii) the operating room (OR), where we simulate rapid, opioid-induced overdose events using routine induction of general anesthesia. In the SIF (n = 209), our system identified postinjection, opioid-induced central apnea with 96% sensitivity and 98% specificity and identified respiratory depression with 87% sensitivity and 89% specificity. These two key events commonly precede fatal opioid overdose. In the OR, our algorithm identified 19 of 20 simulated overdose events. Given the reliable reversibility of acute opioid toxicity, smartphone-enabled overdose detection coupled with the ability to alert naloxone-equipped friends and family or emergency medical services (EMS) could hold potential as a low-barrier, harm reduction intervention.

Frequency of Operative Anesthesia Care After Traumatic Injury.

BACKGROUND: Virtually all anesthesiologists care for patients who sustain traumatic injuries; however, the frequency with which operative anesthesia care is provided to this specific patient population is unclear. We sought to better understand the degree to which anesthesia providers participate in operative trauma care and how this differs by trauma center designation (levels I-V), using data from a comprehensive, regional database-the Washington State Trauma Registry (WSTR). We also sought to specifically assess operative anesthesia care frequency vis a vis the American College of Surgeons guidelines for continuous anesthesiology coverage for Level II trauma center accreditation. METHODS: We conducted a retrospective analysis measuring the frequency of operative anesthesia care among patients enrolled in the WSTR. Univariate comparisons were made between trauma patients who had surgery during their admission and those who did not (medical management only). In addition, clinical factors associated with surgical intervention were measured. We also measured the average times from hospital admission to surgery and compared these times across trauma centers, grouped level I, II, and III-V. RESULTS: From 2004 to 2014, there were approximately 176,000 encounters meeting WSTR inclusion criteria. Approximately 60% of these trauma encounters included exposure to operative anesthesia during the admission. Among all surgical procedures during the trauma admission, approximately 33% occurred within a level I trauma center, 23% occurred within a level II trauma center, and 44% occurred in a trauma center with a III, IV, or V designation. The predominant procedure category during a trauma admission was orthopedic. The presence of hypotension on admission (P < .01), increasing injury severity score (P < .01) and higher emergency department Glasgow Coma Score (P < .01) were all associated with surgical intervention during the trauma hospitalization, after adjustment for potential confounders. In level I trauma centers, for general surgical procedures, the median time to surgery was 2.5 hours; in level II trauma centers, the median time was 1.7 hours. CONCLUSIONS: This study highlights the frequent role anesthesiologists play in caring for patients who sustain traumatic injuries, in trauma centers levels I-V. In level II trauma centers, in-house anesthesiology coverage might have benefit for those patients requiring surgery within 1 hour, whereas the former American College of Surgeons requirement of 30-minute response time for out-of-hospital anesthesiology coverage is likely sufficient to provide satisfactory care to patients requiring surgery within 3 hours. Whether the increased cost of such in-house anesthesiology coverage at level II trauma centers is justified by its clinical benefit remains an unanswered question.

Alcohol-impaired driving in US counties, 2002-2012.

BACKGROUND: Excessive alcohol consumption and alcohol-impaired driving remain significant public health problems, leading to considerable morbidity and mortality, particularly among younger populations. METHODS: Using data from the Behavioral Risk Factor Surveillance System (BRFSS), we employed a small areas modeling strategy to estimate the county-level annual prevalence of alcohol-impaired driving in every United States county for the years 2002 through 2012, the latest year in which county identifiers were publicly available. RESULTS: Alcohol-impaired driving episodes declined from 157.0 million in 2002 (prevalence 3.8%: 95% uncertainty interval [UI], 3.7%-4.0%) to 129.7 million in 2012 (prevalence 3.7%: 95% UI, 3.5%-3.8%), a 17.4% decline. There is considerable variation in the prevalence of alcohol-impaired driving at the county level, ranging from 2.0% in the Sitka City Borough of Alaska to 9.3% in Nance County, Nebraska. Clusters of increased alcohol-impaired driving were observed in Northern Wisconsin (Marinette, Florence, Forest, Vilas, Oneida, Iron counties), North Dakota (Cavalier, Pembina, Walsh, Ramsey, Nelson, Benson, Eddy counties) and Montana (Sheridan, Daniels, Roosevelt, Valley, Phillips, Petroleum, Garfield counties). CONCLUSIONS: This study showed guarded progress with respect to the occurrence of alcohol-impaired driving episodes in the US from 2002 to 2012. Because these data rely on self-report, this likely represents an underestimate of the true prevalence of alcohol-impaired driving in the US. As the US continues to have several million episodes of alcohol-impaired driving each month, renewed efforts are needed to mitigate this high-risk health behavior.

Methylprednisolone Therapy in Acute Traumatic Spinal Cord Injury: Analysis of a Regional Spinal Cord Model Systems Database.

BACKGROUND: The objective of this study was to assess the relationship between exposure to methylprednisolone (MP) and improvements in motor function among patients with acute traumatic spinal cord injury (TSCI). MP therapy for patients with TSCI is controversial because of the current conflicting evidence documenting its benefits and risks. METHODS: We conducted a retrospective cohort study from September 2007 to November 2014 of 311 patients with acute TSCI who were enrolled into a model systems database of a regional, level I trauma center. We linked outcomes and covariate data from the model systems database with MP exposure data from the electronic medical record. The primary outcomes were rehabilitation discharge in American Spinal Injury Association (ASIA) motor scores (sum of 10 key muscles bilaterally as per International Standards for Neurological Classification of Spinal Cord Injury, range, 0-100) and Functional Independence Measure (FIM) motor scores (range, 13-91). Secondary outcomes measured infection risk and gastrointestinal (GI) complications among MP recipients. For the primary outcomes, multivariable linear regression was used. RESULTS: There were 160 MP recipients and 151 nonrecipients. Adjusting for age, sex, weight, race, respective baseline motor score, surgical intervention, injury level, ASIA Impairment Scale (AIS) grade, education, and insurance status, there was no association with improvement in discharge ASIA motor function or FIM motor score among MP recipients: -0.34 (95% CI, -2.8, 2.1) and 0.75 (95% CI, -2.8, 4.3), respectively. Adjusting for age, sex, race, weight, injury level, and receipt of surgery, no association with increased risk of infection or GI complications was observed. CONCLUSIONS: This retrospective cohort study involving patients with acute TSCI observed no short-term improvements in motor function among MP recipients compared with nonrecipients. Our findings support current recommendations that MP use in this population should be limited.

Etomidate, adrenal function, and mortality in critically ill patients.

BACKGROUND: In critically ill patients, induction with etomidate is hypothesized to be associated with an increased risk of mortality. Previous randomized studies suggest a modest trend toward an increased risk of death among etomidate recipients; however, this relationship has not been measured with great statistical precision. We aimed to test whether etomidate is associated with risk of hospital mortality and other clinical outcomes in critically ill patients. METHODS: We conducted a retrospective cohort study from January 1, 2001, to December 31, 2005, of 824 subjects requiring mechanical ventilation, who underwent adrenal function testing in the ICUs of 2 academic medical centers. The primary outcome was in-hospital mortality, comparing subjects given etomidate (n = 452) to those given an alternative induction agent (n = 372). The secondary outcome was diagnosis of critical illness-related corticosteroid insufficiency following etomidate exposure. RESULTS: Overall mortality was 34.3%. After adjustment for age, sex, and baseline illness severity, the relative risk of death among the etomidate recipients was higher than that of subjects given an alternative agent (relative risk 1.20, 95% CI 0.99-1.45). Among subjects whose adrenal function was assessed within the 48 hours following intubation, the adjusted risk of meeting the criteria for critical illness-related corticosteroid insufficiency was 1.37 (95% CI 1.12-1.66), comparing etomidate recipients to subjects given another induction agent. CONCLUSIONS: In this study of critically ill patients requiring endotracheal intubation, etomidate administration was associated with a trend toward a relative increase in mortality, similar to the collective results of smaller randomized trials conducted to date. If a small relative increased risk is truly present, though previous trials have been underpowered to detect it, in absolute terms the number of deaths associated with etomidate in this high-risk population would be considerable. Large, prospective controlled trials are needed to finalize the role of etomidate in critically ill patients.

Barriers to training family medicine residents in community health centers.

BACKGROUND AND OBJECTIVES: Training partnerships between family medicine residencies (FMRs) and community health centers (CHCs) are a potential solution to the chronic problem of health workforce shortages in CHCs. We conducted a national survey to identify the barriers to training family medicine residents in CHCs. METHODS: We asked US family medicine residency directors to identify barriers to training residents in CHCs. Using grounded theory, three coders grouped responses by theme. We examined differences in barriers between residency programs that currently train in CHCs with programs that do not currently train in CHCs. RESULTS: A total of 51% (226/439) of residency program directors responded. Of these, 29% cited governance as a barrier to affiliation, 26% cited administrative complexity, 24% cited financial considerations, 21% cited leadership, and 18% cited access. Programs that trained in CHCs were more likely to cite financial considerations and administrative complexity than programs that did not train in CHCs. CONCLUSIONS: Governance and administrative complexity are the most commonly cited barriers to effective CHC-FMR partnerships. Financial consideration and leadership issues are also common barriers.