Performance of International Classification of Disease-10 codes in detecting emergency department patients with opioid misuse.

BACKGROUND AND AIMS: Accurate case discovery is critical for disease surveillance, resource allocation and research. International Classification of Disease (ICD) diagnosis codes are commonly used for this purpose. We aimed to determine the sensitivity, specificity and positive predictive value (PPV) of ICD-10 codes for opioid misuse case discovery in the emergency department (ED) setting. DESIGN AND SETTING: Retrospective cohort study of ED encounters from January 2018 to December 2020 at an urban academic hospital in the United States. A sample of ED encounters enriched for opioid misuse was developed by oversampling ED encounters with positive urine opiate screens or pre-existing opioid-related diagnosis codes in addition to other opioid misuse risk factors. CASES: A total of 1200 randomly selected encounters were annotated by research staff for the presence of opioid misuse within health record documentation using a 5-point scale for likelihood of opioid misuse and dichotomized into cohorts of opioid misuse and no opioid misuse. MEASUREMENTS: Using manual annotation as ground truth, the sensitivity and specificity of ICD-10 codes entered during the encounter were determined with PPV adjusted for oversampled data. Metrics were also determined by disposition subgroup: discharged home or admitted. FINDINGS: There were 541 encounters annotated as opioid misuse and 617 with no opioid misuse. The majority were males (54.4%), average age was 47 years and 68.5% were discharged directly from the ED. The sensitivity of ICD-10 codes was 0.56 (95% confidence interval [CI], 0.51-0.60), specificity 0.99 (95% CI, 0.97-0.99) and adjusted PPV 0.78 (95% CI, 0.65-0.92). The sensitivity was higher for patients discharged from the ED (0.65; 95% CI, 0.60-0.69) than those admitted (0.31; 95% CI, 0.24-0.39). CONCLUSIONS: International Classification of Disease-10 codes appear to have low sensitivity but high specificity and positive predictive value in detecting opioid misuse among emergency department patients in the United States.

Geolocation Patterns, Wi-Fi Connectivity Rates, and Psychiatric Symptoms Among Urban Homeless Youth: Mixed Methods Study Using Self-report and Smartphone Data.

BACKGROUND: Despite significant research done on youth experiencing homelessness, few studies have examined movement patterns and digital habits in this population. Examining these digital behaviors may provide useful data to design new digital health intervention models for youth experiencing homelessness. Specifically, passive data collection (data collected without extra steps for a user) may provide insights into lived experience and user needs without putting an additional burden on youth experiencing homelessness to inform digital health intervention design. OBJECTIVE: The objective of this study was to explore patterns of mobile phone Wi-Fi usage and GPS location movement among youth experiencing homelessness. Additionally, we further examined the relationship between usage and location as correlated with depression and posttraumatic stress disorder (PTSD) symptoms. METHODS: A total of 35 adolescent and young adult participants were recruited from the general community of youth experiencing homelessness for a mobile intervention study that included installing a sensor data acquisition app (Purple Robot) for up to 6 months. Of these participants, 19 had sufficient passive data to conduct analyses. At baseline, participants completed self-reported measures for depression (Patient Health Questionnaire-9 [PHQ-9]) and PTSD (PTSD Checklist for DSM-5 [PCL-5]). Behavioral features were developed and extracted from phone location and usage data. RESULTS: Almost all participants (18/19, 95%) used private networks for most of their noncellular connectivity. Greater Wi-Fi usage was associated with a higher PCL-5 score (P=.006). Greater location entropy, representing the amount of variability in time spent across identified clusters, was also associated with higher severity in both PCL-5 (P=.007) and PHQ-9 (P=.045) scores. CONCLUSIONS: Location and Wi-Fi usage both demonstrated associations with PTSD symptoms, while only location was associated with depression symptom severity. While further research needs to be conducted to establish the consistency of these findings, they suggest that the digital patterns of youth experiencing homelessness offer insights that could be used to tailor digital interventions.

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study.

BACKGROUND: The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. OBJECTIVE: We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity. METHODS: This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI. RESULTS: Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier's predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009). CONCLUSIONS: Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

Implementation of Gender Identity and Assigned Sex at Birth Data Collection in Electronic Health Records: Where Are We Now?

In 2015, the United States Department of Health and Human Services instantiated rules mandating the inclusion of sexual orientation and gender identity (SO/GI) data fields for systems certified under Stage 3 of the Meaningful Use of Electronic Health Records (EHR) program. To date, no published assessments have benchmarked implementation penetration and data quality. To establish a benchmark for a U.S. health system collection of gender identity and sex assigned at birth, we analyzed one urban academic health center's EHR data; specifically, the records of patients with unplanned hospital admissions during 2020 (N = 49,314). Approximately one-quarter of patient records included gender identity data, and one percent of them indicated a transgender or nonbinary (TGNB) status. Data quality checks suggested limited provider literacy around gender identity as well as limited provider and patient comfort levels with gender identity disclosures. Improvements are needed in both provider and patient literacy and comfort around gender identity in clinical settings. To include TGNB populations in informatics-based research, additional novel approaches, such as natural language processing, may be needed for more comprehensive and representative TGNB cohort discovery. Community and stakeholder engagement around gender identity data collection and health research will likely improve these implementation efforts.

Screening for Frailty in Thoracic Surgical Patients.

BACKGROUND: The presence of frailty or prefrailty in older adults is a risk factor for postsurgical complications. The frailty phenotype can be improved through long-term resistance and aerobic training. It is unknown whether short-term preoperative interventions targeting frailty will help to mitigate surgical risk. The purpose of this study was to determine the proportion of frail and prefrail patients presenting to a thoracic surgical clinic who could benefit from a frailty reduction intervention. METHODS: A prospective cohort study was performed at a single-site thoracic surgical clinic. Starting October 1, 2014, surgical candidates 60 years of age or older who consented to be screened were included. Patients were screened using an adapted version of Fried's phenotypic frailty criteria: weakness (grip strength), slow gait (15-foot walk), unintentional weight loss, self-reported exhaustion, and low self-reported physical activity (Physical Activity Scale for the Elderly). Prefrailty was identified when participants demonstrated one to two frailty characteristics; frailty was identified when participants demonstrated three to five frailty characteristics. RESULTS: Of 180 eligible patients, 126 consented, and 125 completed screening. Thirty-nine participants (31%) were not frail, 71 (57%) were prefrail, and 15 (12%) were frail. Exhaustion was the most common frailty symptom (34%). Frailty prevalence did not significantly differ among men and women (men: 10%, women: 14%; p = 0.75). CONCLUSIONS: We found a high proportion of prefrail and frail patients among patients deemed candidates for thoracic surgical procedures. This finding indicates that frailty may be underrecognized. Substantial numbers of patients may be considered for a presurgical frailty reduction intervention.