BACKGROUND: Understanding the incidence, causes, and trends of sudden cardiac death (SCD) among young competitive athletes is critical to inform preventive policies. METHODS: This study included National Collegiate Athletic Association athlete deaths during a 20-year time frame (July 1, 2002, through June 30, 2022). Athlete deaths were identified through 4 separate independent databases and search strategies (National Collegiate Athletic Association resolutions list, Parent Heart Watch database and media reports, National Center for Catastrophic Sports Injury Research database, and insurance claims). Autopsy reports and medical history were reviewed by an expert panel to adjudicate causes of SCD. RESULTS: A total of 143 SCD cases in National Collegiate Athletic Association athletes were identified from 1102 total deaths. The National Collegiate Athletic Association resolutions list identified 117 of 143 (82%), the Parent Heart Watch database or media reports identified 89 of 143 (62%), the National Center for Catastrophic Sports Injury Research database identified 63 of 143 (44%), and insurance claims identified 27 of 143 (19%) SCD cases. The overall incidence of SCD was 1:63 682 athlete-years (95% CI, 1:54 065–1:75 010). Incidence was higher in male athletes than in female athletes (1:43 348 [95% CI, 1:36 228–1:51 867] versus 1:164 504 [95% CI, 1:110 552–1:244 787] athlete-years, respectively) and Black athletes compared with White athletes (1:26 704 [1:20 417–1:34 925] versus 1:74 581 [1:60 247–1:92 326] athlete-years, respectively). The highest incidence of SCD was among Division I male basketball players (1:8188 [White, 1:5848; Black, 1:7696 athlete-years]). The incidence rate for SCD decreased over the study period (5-year incidence rate ratio, 0.71 [95% CI, 0.61–0.82]), whereas the rate of noncardiovascular deaths remained stable (5-year incidence rate ratio, 0.98 [95% CI, 0.94–1.04]). Autopsy-negative sudden unexplained death (19.5%) was the most common postmortem examination finding, followed by idiopathic left ventricular hypertrophy or possible cardiomyopathy (16.9%) and hypertrophic cardiomyopathy (12.7%), in cases with enough information for adjudication (118 of 143). Eight cases of death were attributable to myocarditis over the study period (1 case from January 1, 2020, through June 30, 2022), with none attributed to COVID-19 infection. SCD events were exertional in 50% of cases. Exertional SCD was more common among those with coronary artery anomalies (100%) and arrhythmogenic cardiomyopathy (83%). CONCLUSIONS: The incidence of SCD in college athletes has decreased. Male sex, Black race, and basketball are associated with a higher incidence of SCD.

Introduction Influenza epidemics and pandemics cause significant morbidity and mortality. An effective response to a potential pandemic requires the infrastructure to rapidly detect, characterise, and potentially contain new and emerging influenza strains at both an individual and population level. The objective of this study is to use data gathered simultaneously from community and hospital sites to develop a model of how influenza enters and spreads in a population. Methods and analysis Starting in the 2018–2019 season, we have been enrolling individuals with acute respiratory illness from community sites throughout the Seattle metropolitan area, including clinics, childcare facilities, Seattle-Tacoma International Airport, workplaces, college campuses and homeless shelters. At these sites, we collect clinical data and mid-nasal swabs from individuals with at least two acute respiratory symptoms. Additionally, we collect residual nasal swabs and data from individuals who seek care for respiratory symptoms at four regional hospitals. Samples are tested using a multiplex molecular assay, and influenza whole genome sequencing is performed for samples with influenza detected. Geospatial mapping and computational modelling platforms are in development to characterise the regional spread of influenza and other respiratory pathogens. Ethics and dissemination The study was approved by the University of Washington’s Institutional Review Board (STUDY00006181). Results will be disseminated through talks at conferences, peer-reviewed publications and on the study website (www.seattleflu.org).

Objective Lung cancer is the most common cause of cancer-related death in the USA. While most patients are diagnosed following symptomatic presentation, no studies have compared symptoms and physical examination signs at or prior to diagnosis from electronic health records (EHRs) in the USA. We aimed to identify symptoms and signs in patients prior to diagnosis in EHR data. Design Case–control study. Setting Ambulatory care clinics at a large tertiary care academic health centre in the USA. Participants, outcomes We studied 698 primary lung cancer cases in adults diagnosed between 1 January 2012 and 31 December 2019, and 6841 controls matched by age, sex, smoking status and type of clinic. Coded and free-text data from the EHR were extracted from 2 years prior to diagnosis date for cases and index date for controls. Univariate and multivariable conditional logistic regression were used to identify symptoms and signs associated with lung cancer at time of diagnosis, and 1, 3, 6 and 12 months before the diagnosis/index dates. Results Eleven symptoms and signs recorded during the study period were associated with a significantly higher chance of being a lung cancer case in multivariable analyses. Of these, seven were significantly associated with lung cancer 6 months prior to diagnosis: haemoptysis (OR 3.2, 95% CI 1.9 to 5.3), cough (OR 3.1, 95% CI 2.4 to 4.0), chest crackles or wheeze (OR 3.1, 95% CI 2.3 to 4.1), bone pain (OR 2.7, 95% CI 2.1 to 3.6), back pain (OR 2.5, 95% CI 1.9 to 3.2), weight loss (OR 2.1, 95% CI 1.5 to 2.8) and fatigue (OR 1.6, 95% CI 1.3 to 2.1). Conclusions Patients diagnosed with lung cancer appear to have symptoms and signs recorded in the EHR that distinguish them from similar matched patients in ambulatory care, often 6 months or more before diagnosis. These findings suggest opportunities to improve the diagnostic process for lung cancer.

The diagnosis of lung cancer in ambulatory settings is often challenging due to non-specific clinical presentation, but there are currently no clinical quality measures (CQMs) in the United States used to identify areas for practice improvement in diagnosis. We describe the pre-diagnostic time intervals among a retrospective cohort of 711 patients identified with primary lung cancer from 2012–2019 from ambulatory care clinics in Seattle, Washington USA. Electronic health record data were extracted for two years prior to diagnosis, and Natural Language Processing (NLP) applied to identify symptoms/signs from free text clinical fields. Time points were defined for initial symptomatic presentation, chest imaging, specialist consultation, diagnostic confirmation, and treatment initiation. Median and interquartile ranges (IQR) were calculated for intervals spanning these time points. The mean age of the cohort was 67.3 years, 54.1% had Stage III or IV disease and the majority were diagnosed after clinical presentation (94.5%) rather than screening (5.5%). Median intervals from first recorded symptoms/signs to diagnosis was 570 days (IQR 273–691), from chest CT or chest X-ray imaging to diagnosis 43 days (IQR 11–240), specialist consultation to diagnosis 72 days (IQR 13–456), and from diagnosis to treatment initiation 7 days (IQR 0–36). Symptoms/signs associated with lung cancer can be identified over a year prior to diagnosis using NLP, highlighting the need for CQMs to improve timeliness of diagnosis.

Background Most patients with lung cancer are diagnosed at advanced stages. However, the advent of oral targeted therapies has improved the prognosis of many patients with lung cancer. Purpose We aimed to understand the diagnostic experiences of patients with advanced lung cancer with oncogenic mutations. Methods Qualitative interviews were conducted with patients with advanced or metastatic non-small cell lung cancer with oncogenic alterations. Patients were recruited from online support groups within the USA. Interviews were conducted remotely or in person. Analysis used an iterative inductive and deductive process. Themes were mapped to the Model for Pathways to Treatment. Results 40 patients (12 male and 28 female) with a median age of 48 were included. We identified nine distinct themes. During the ‘patient interval’, individuals became concerned about symptoms, but often attributed them to other causes. Prolonged or more severe symptoms prompted care-seeking. During the ‘primary care interval’, doctors initially treated for illnesses other than cancer. Discovery of an imaging abnormality was a turning point in diagnostic pathways. Occasionally, severity of symptoms prompted patients to seek emergency care. During the ‘secondary care interval’, obtaining tissue samples was pivotal in confirming diagnosis. Delays in accessing oncology care sometimes led to patient distress. Obtaining genetic testing was crucial in directing patients to receive targeted treatments. Conclusions Patients experienced multiple different routes to their diagnosis. Some patients perceived delays, inefficiencies and lack of coordination, which could be distressing. Shifting the stage of diagnosis of lung cancer to optimise the impact of targeted therapies will require concerted efforts in early detection.