Risk Factors for Opioid Utilization in Patients with Intracerebral Hemorrhage.

BACKGROUND: Headache is a common presenting symptom of intracerebral hemorrhage (ICH) and often necessitates treatment with opioid medications. However, opioid prescribing patterns in patients with ICH are not well described. We aimed to characterize the prevalence and risk factors for short and longer-term opioid use in patients with ICH. METHODS: We conducted a retrospective cohort study using data from a single-center registry of patients with nontraumatic ICH. This registry included data on demographics, ICH-related characteristics, and premorbid, inpatient, and postdischarge medications. After excluding patients who died or received end-of-life care, we used multivariable regression models adjusted for premorbid opioid use to determine demographic and ICH-related risk factors for inpatient and postdischarge opioid use. RESULTS: Of 468 patients with ICH in our cohort, 15% (n = 70) had premorbid opioid use, 53% (n = 248) received opioids during hospitalization, and 12% (n = 53) were prescribed opioids at discharge. The most commonly used opioids during hospitalization were fentanyl (38%), oxycodone (30%), morphine (26%), and hydromorphone (7%). Patients who received opioids during hospitalization were younger (univariate: median [interquartile range] 64 [53.5-74] vs. 76 [67-83] years, p < 0.001; multivariable: odds ratio [OR] 0.96 per year, 95% confidence interval [CI] 0.94-0.98) and had larger ICH volumes (univariate: median [interquartile range] 10.1 [2.1-28.6] vs. 2.7 [0.8-9.9] cm3, p < 0.001; multivariable: OR 1.05 per cm3, 95% CI 1.03-1.08) than those who did not receive opioids. All patients who had external ventricular drain placement and craniotomy/craniectomy received inpatient opioids. Additional risk factors for increased inpatient opioid use included infratentorial ICH location (OR 4.8, 95% CI 2.3-10.0), presence of intraventricular hemorrhage (OR 3.9, 95% CI 2.2-7.0), underlying vascular lesions (OR 3.0, 95% CI 1.1-8.1), and other secondary ICH etiologies (OR 7.5, 95% CI 1.7-32.8). Vascular lesions (OR 4.0, 95% CI 1.3-12.5), malignancy (OR 5.0, 95% CI 1.5-16.4), vasculopathy (OR 10.0, 95% CI 1.8-54.2), and other secondary etiologies (OR 7.2, 95% CI 1.8-29.9) were also risk factors for increased opioid prescriptions at discharge. Among patients who received opioid prescriptions at discharge, 43% (23 of 53) continued to refill their prescriptions at 3 months post discharge. CONCLUSIONS: Inpatient opioid use in patients with ICH is common, with some risk factors that may be mechanistically connected to primary headache pathophysiology. However, the lower frequency of opioid prescriptions at discharge suggests that inpatient opioid use does not necessarily lead to a high rate of long-term opioid dependence in patients with ICH.

Gender Disparities in Stroke Code Activation in Patients with Intracerebral Hemorrhage.

OBJECTIVES: Routine implementation of protocol-driven stroke "codes" results in timelier and more effective acute stroke management. However, it is unclear if patient demographics contribute to disparities in stroke code activation. We aimed to explore these demographic factors in a retrospective cohort study of patients with intracerebral hemorrhage (ICH). MATERIALS AND METHODS: We identified consecutive patients with non-traumatic ICH who presented directly to our Comprehensive Stroke Center over 2 years and collected data on demographics, clinical features, and stroke code activation. We used multivariable logistic regression to examine differences in stroke code activation based on patient demographics while adjusting for initial clinical features (NIH Stroke Scale, FAST [facial drooping, arm weakness, speech difficulties] vs. non-FAST symptoms, time from last-known-well [LKW], and systolic blood pressure [SBP]). RESULTS: Among 265 patients, 68% (n=179) had a stroke code activation. Stroke codes occurred less frequently in women (62%) than men (72%) and in non-white (57%) vs. white patients (70%). Non-stroke code patients were less likely to have FAST symptoms (37% vs. 87%) and had lower initial SBP (mean±SD 159.3±34.2 vs. 176.0±31.9 mmHg) than stroke code patients. In our primary multivariable models, neither age nor race were associated with stroke code activation. However, women were significantly less likely to have stroke codes than men (OR 0.49 [95% CI 0.24-0.98]), as were non-FAST symptoms (OR 0.11 [95% CI 0.05-0.22]). CONCLUSIONS: Our data suggest gender disparities in emergency stroke care that should prompt further investigations into potential systemic biases. Increased awareness of atypical stroke symptoms is also warranted.

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.

BACKGROUND: This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose. METHODS: In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST). RESULTS: The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low. CONCLUSIONS: Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment.

Clinical researchers are using mobile-based sensors to obtain detailed and objective measures of the activity and health of research participants, but many investigators lack expertise in integrating wearables and sensor technologies effectively into their studies. Here, we describe the steps taken to design a study using sensors for disease monitoring in older adults and explore the benefits and drawbacks of our approach. In this study, the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare), we created an iOS app to collect data from the Apple Watch's gyroscope, accelerometer, and other sensors; results of cognitive and fitness tests; and participant-entered survey data. We created the study app using ResearchKit, an open-source framework developed by Apple for medical research that includes neuropsychological tests (e.g., of executive function and memory), gait speed, balance, and other health assessments. Data is transmitted via an Application Programming Interface (API) from the app to REDCap for researchers to monitor and analyze in real-time. Employing the lessons learned from GAPcare could help researchers create study-tailored research apps and access timely information about their research participants from wearables and smartphone devices for disease prevention, monitoring, and treatment.

Can Older Adult Emergency Department Patients Successfully Use the Apple Watch to Monitor Health?

OBJECTIVE: To determine usability of the Apple Watch in older adult emergency department (ED) patients after a fall. METHODS: We recruited older adults who fell and visited two urban EDs. They participated in an Apple Watch orientation and interviews on their experiences using the watch to complete varied tasks for 30 days. Interviews were recorded, transcribed, coded, and analyzed using framework analyses. RESULTS: Eight participants (mean age 77.6 years) enrolled from November 2019 to March 2020. Participants reported being able to apply and charge the watch but struggled with navigating screens, monitoring charging status, and responding with de novo text messages. Many cited difficulties with advanced tasks, such as the study's app-based movement and memory activities. Experience with smartphones and caregiver assistance enhanced users' ability to complete tasks. CONCLUSIONS: Older adults successfully performed basic Apple Watch functions. Family and community members may be necessary to assist with complex tasks.