Proceedings of the 2023 Cannabis Clinical Outcomes Research Conference.

The Consortium for Medical Marijuana Clinical Outcomes Research, a multi-university collaboration established by the state of Florida in the USA, hosted its third annual Cannabis Clinical Outcomes Research Conference (CCORC) in May 2023. CCORC was held as a hybrid conference, with a scientific program consisting of in-person sessions, with some sessions livestreamed to virtual attendees. CCORC facilitated and promoted up-to-date research on the clinical effects of medical cannabis, fostering collaboration and active involvement among scientists, policymakers, industry professionals, clinicians, and other stakeholders. Three themes emerged from conference sessions and speaker presentations: (1) disentangling conflicting evidence for the effects of medical cannabis on public health, (2) seeking solutions to address barriers faced when conducting clinical cannabis research - especially with medical cannabis use in special populations such as those who are pregnant, and (3) unpacking the data behind cannabis use and mental health outcomes. The fourth annual CCORC is planned for the summer of 2024 in Florida, USA.

A Natural Language Processing Algorithm for Classifying Suicidal Behaviors in Alzheimer's Disease and Related Dementia Patients: Development and Validation Using Electronic Health Records Data.

This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) and Deep Learning (DL) techniques to identify and classify documentation of suicidal behaviors in patients with Alzheimer's disease and related dementia (ADRD). We utilized MIMIC-III and MIMIC-IV datasets and identified ADRD patients and subsequently those with suicide ideation using relevant International Classification of Diseases (ICD) codes. We used cosine similarity with ScAN (Suicide Attempt and Ideation Events Dataset) to calculate semantic similarity scores of ScAN with extracted notes from MIMIC for the clinical notes. The notes were sorted based on these scores, and manual review and categorization into eight suicidal behavior categories were performed. The data were further analyzed using conventional ML and DL models, with manual annotation as a reference. The tested classifiers achieved classification results close to human performance with up to 98% precision and 98% recall of suicidal ideation in the ADRD patient population. Our NLP model effectively reproduced human annotation of suicidal ideation within the MIMIC dataset. These results establish a foundation for identifying and categorizing documentation related to suicidal ideation within ADRD population, contributing to the advancement of NLP techniques in healthcare for extracting and classifying clinical concepts, particularly focusing on suicidal ideation among patients with ADRD. Our study showcased the capability of a robust NLP algorithm to accurately identify and classify documentation of suicidal behaviors in ADRD patients.

Protocol of a Combined Cohort and Cross-Sectional Study of Persons Receiving Medical Cannabis in Florida, USA: The Medical Marijuana and Me (M3) Study.

Significant knowledge gaps regarding the effectiveness and safety of medical cannabis (MC) create clinical challenges for MC physicians, making treatment recommendations and patients choosing treatment among the growing number of options offered in dispensaries. Additionally, data describing the characteristics of people who use MC and the products and doses they receive are lacking. The Medical Marijuana and Me (M3) Study was designed to collect patient-centered data from MC users. We aim to describe preferred MC use patterns that patients report as "most effective" for specific health conditions and symptoms, identify user characteristics associated with such use patterns, characterize adverse effects, including cannabis use disorder, identify products and patient characteristics associated with adverse effects, describe concurrent prescription medication use, and identify concomitant medication use with potential drug-MC interaction risk. Among MC initiators, we also aim to quantify MC use persistence and identify reasons for discontinuation, assess MC utilization pattern trajectories over time, describe outcome trajectories of primary reasons for MC use and determine factors associated with different trajectories, track changes in concomitant substance and medication use after MC initiation, and identify factors associated with such changes. M3 is a combined study comprised of: (1) a prospective cohort of MC initiators completing surveys at enrollment, 3 months, and 9 months after MC initiation and (2) a cross-sectional study of current MC users. A multidisciplinary committee including researchers, physicians, pharmacists, patients, and dispensary personnel designed and planned study protocols, established study measures, and created survey questionnaires. M3 will recruit 1,000-1,200 participants aged ≥18 years, with ∼50% new and ∼50% current MC patients from MC clinics across Florida, USA. Study enrollment started in May 2022 and will continue until the target number of patients is achieved. Survey domains include sociodemographic characteristics, physical and mental health, cannabis use history, reasons for MC use and discontinuation, MC products and use patterns, concurrent use of prescription medications and other substances, and side effects. Data collected in the M3 Study will be available for interested researchers affiliated with the Consortium for Medical Marijuana Clinical Outcomes Research. The M3 Study and Databank will be the largest cohort of current and new MC users in Florida, USA, which will provide data to support MC-related health research necessary to inform policy and clinical practice and ultimately improve patient outcomes.

Developing and validating a natural language processing algorithm to extract preoperative cannabis use status documentation from unstructured narrative clinical notes.

OBJECTIVE: This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) techniques to identify and classify documentation of preoperative cannabis use status. MATERIALS AND METHODS: We developed and applied a keyword search strategy to identify documentation of preoperative cannabis use status in clinical documentation within 60 days of surgery. We manually reviewed matching notes to classify each documentation into 8 different categories based on context, time, and certainty of cannabis use documentation. We applied 2 conventional ML and 3 deep learning models against manual annotation. We externally validated our model using the MIMIC-III dataset. RESULTS: The tested classifiers achieved classification results close to human performance with up to 93% and 94% precision and 95% recall of preoperative cannabis use status documentation. External validation showed consistent results with up to 94% precision and recall. DISCUSSION: Our NLP model successfully replicated human annotation of preoperative cannabis use documentation, providing a baseline framework for identifying and classifying documentation of cannabis use. We add to NLP methods applied in healthcare for clinical concept extraction and classification, mainly concerning social determinants of health and substance use. Our systematically developed lexicon provides a comprehensive knowledge-based resource covering a wide range of cannabis-related concepts for future NLP applications. CONCLUSION: We demonstrated that documentation of preoperative cannabis use status could be accurately identified using an NLP algorithm. This approach can be employed to identify comparison groups based on cannabis exposure for growing research efforts aiming to guide cannabis-related clinical practices and policies.

Proceedings of the 2022 Cannabis Clinical Outcomes Research Conference.

The Consortium for Medical Marijuana Clinical Outcomes Research, a multi-university collaboration established by the state of Florida in the USA, hosted its second annual Cannabis Clinical Outcomes Research Conference (CCORC) in May 2022. CCORC was held as a hybrid conference, with a scientific program consisting of in-person and virtual sessions. CCORC fostered and disseminated current research on clinical outcomes of medical marijuana while stimulating collaboration and engagement between the scientific community, policymakers, industry representatives, clinicians, and other interested stakeholders. Three themes emerged from conference sessions and speakers: (1) disentangling research findings comparing use and outcomes of medical and nonmedical cannabis, (2) addressing barriers and promoting facilitators for clinical cannabis research, and (3) resolving uncertainties around cannabis dosing. The third annual CCORC is planned for the summer of 2023 in Florida, USA.

What's Up Your Sleeve? A Scoping Review of White Coat Contamination and Horizontal Microbial Transmission.

OBJECTIVES: White coats have been suggested to serve as fomites carrying and transmitting pathogenic organisms and potentially increasing the risk of healthcare-associated infections (HAIs). We aimed to examine the current evidence regarding white coat contamination and its role in horizontal transmission and HAIs risk. We also examined handling practices and policies associated with white coat contamination in the reviewed literature. METHODS: We conducted a literature search through PubMed and Web of Science Core Collection/Cited Reference Search, and manually searched the bibliographies of the articles identified in electronic searches. Studies published up to March 3, 2021 that were accessible in English-language full-text format were included. RESULTS: Among 18 included studies, 15 (83%) had ≥100 participants, 16 (89%) were cross-sectional studies, and 13 (72%) originated outside of the United States. All of the studies showed evidence of microbial colonization. Colonization with Staphylococcus aureus and Escherichia coli was reported in 100% and 44% of the studies, respectively. Antibacterial-resistant strains, including methicillin-resistant Staphylococcus aureus and multidrug-resistant organisms were reported in 8 (44%) studies. There was a lack of studies assessing the link between white coat contamination and HAIs. The data regarding white coat handling and laundering practices showed inconsistencies between healthcare facilities and a lack of clear policies. CONCLUSIONS: There is robust evidence that white coats serve as fomites, carrying dangerous pathogens, including multidrug-resistant organisms. A knowledge gap exists, however, regarding the role of contaminated white coats in HAI risk that warrants further research to generate the evidence necessary to guide the current attire policies for healthcare workers.

Fairness in the prediction of acute postoperative pain using machine learning models.

Introduction: Overall performance of machine learning-based prediction models is promising; however, their generalizability and fairness must be vigorously investigated to ensure they perform sufficiently well for all patients. Objective: This study aimed to evaluate prediction bias in machine learning models used for predicting acute postoperative pain. Method: We conducted a retrospective review of electronic health records for patients undergoing orthopedic surgery from June 1, 2011, to June 30, 2019, at the University of Florida Health system/Shands Hospital. CatBoost machine learning models were trained for predicting the binary outcome of low (≤4) and high pain (>4). Model biases were assessed against seven protected attributes of age, sex, race, area deprivation index (ADI), speaking language, health literacy, and insurance type. Reweighing of protected attributes was investigated for reducing model bias compared with base models. Fairness metrics of equal opportunity, predictive parity, predictive equality, statistical parity, and overall accuracy equality were examined. Results: The final dataset included 14,263 patients [age: 60.72 (16.03) years, 53.87% female, 39.13% low acute postoperative pain]. The machine learning model (area under the curve, 0.71) was biased in terms of age, race, ADI, and insurance type, but not in terms of sex, language, and health literacy. Despite promising overall performance in predicting acute postoperative pain, machine learning-based prediction models may be biased with respect to protected attributes. Conclusion: These findings show the need to evaluate fairness in machine learning models involved in perioperative pain before they are implemented as clinical decision support tools.

A Mapping Literature Review of Medical Cannabis Clinical Outcomes and Quality of Evidence in Approved Conditions in the USA from 2016 to 2019.

In 2017, a National Academies of Sciences, Engineering, and Medicine (NASEM) report comprehensively evaluated the body of evidence regarding cannabis health effects through the year 2016. The objectives of this study are to identify and map the most recently (2016-2019) published literature across approved conditions for medical cannabis and to evaluate the quality of identified recent systematic reviews, published following the NASEM report. Following the literature search from 5 databases and consultation with experts, 11 conditions were identified for evidence compilation and evaluation: amyotrophic lateral sclerosis, autism, cancer, chronic noncancer pain, Crohn's disease, epilepsy, glaucoma, human immunodeficiency virus/AIDS, multiple sclerosis (MS), Parkinson's disease, and posttraumatic stress disorder. A total of 198 studies were included after screening for condition-specific relevance and after imposing the following exclusion criteria: preclinical focus, non-English language, abstracts only, editorials/commentary, case studies/series, and non-U.S. study setting. Data extracted from studies included: study design type, outcome definition, intervention definition, sample size, study setting, and reported effect size. Few completed randomized controlled trials (RCTs) were identified. Studies classified as systematic reviews were graded using the Assessing the Methodological Quality of Systematic Reviews-2 tool to evaluate the quality of evidence. Few high-quality systematic reviews were available for most conditions, with the exceptions of MS (9 of 9 graded moderate/high quality; evidence for 2/9 indicating cannabis improved outcomes; evidence for 7/9 indicating cannabis inconclusive), epilepsy (3 of 4 graded moderate/high quality; 3 indicating cannabis improved outcomes; 1 indicating cannabis inconclusive), and chronic noncancer pain (12 of 13 graded moderate/high quality; evidence for 7/13 indicating cannabis improved outcomes; evidence from 6/7 indicating cannabis inconclusive). Among RCTs, we identified few studies of substantial rigor and quality to contribute to the evidence base. However, there are some conditions for which significant evidence suggests that select dosage forms and routes of administration likely have favorable risk-benefit ratios (i.e., epilepsy and chronic noncancer pain). The body of evidence for medical cannabis requires more rigorous evaluation before consideration as a treatment option for many conditions, and evidence necessary to inform policy and treatment guidelines is currently insufficient for many conditions.

Cannabis use assessment and documentation in healthcare: Priorities for closing the gap.

Several factors, including the lack of a systematic cannabis use assessment within healthcare systems, have led to significant under-documentation of cannabis use and its correlates in medical records, the unpreparedness of clinicians, and poor quality of cannabis-related electronic health record data, limiting its utilization in research. Multiple steps are required to overcome the existing knowledge gaps and accommodate the health needs implied by the increasing cannabis use prevalence. These steps include (1) enhancing clinician and patient education on the importance of cannabis use assessment and documentation, (2) implementing a standardized approach for comprehensive cannabis use assessment within and across healthcare systems, (3) improving documentation of cannabis use and its correlates in medical records and electronic health records by building in prompts, (4) developing and validating reliable computable phenotypes of cannabis use, (5) conducting research utilizing electronic health data to study a wide array of related health outcomes, (6) and establishing evidence-based guidelines to inform clinical practices and policies. Integrating comprehensive cannabis use assessment and documentation within healthcare systems is necessary to enhance patient care and improve the quality of electronic health databases. Employing electronic health record data in cannabis-related research is crucial to accelerate research in light of the existing knowledge gaps on a wide array of health outcomes. Thus, improving and modernizing cannabis use assessment and documentation in healthcare is an integral step on which research conduct and evidence generation primarily rely.

Practice Patterns and Training Needs Among Physicians Certifying Patients for Medical Marijuana in Florida.

BACKGROUND: Little is known about the clinical training or practice experiences among physicians who certify patients for medical marijuana. The objective of this study was to determine information sources, factors influencing recommendations, clinical practices in patient assessment, communications, and recommendations, and priority areas for additional training among physicians who certify patients for medical marijuana. METHODS: A cross-sectional state-wide anonymous survey of registered medical marijuana physicians in Florida between June and October 2020 was administered. Numerical responses were quantified using counts and percentages. The frequencies for "often" and "always" responses were aggregated when appropriate. RESULTS: Among 116 respondents, the mean (standard deviation) age was 57 (12) years old, and 70% were male. The most frequently used information sources were research articles (n = 102, 95%), followed by online sources (n = 99, 93%), and discussions with other providers and dispensary staff (n = 84, 90%). Safety concerns were most influential in patient recommendations (n = 39, 39%), followed by specific conditions (n = 30, 30%) and patient preferences (n = 26, 30%). Ninety-three physicians (92%) reported they "often" or "always" perform a patient physical exam. Eighty-four (77%) physicians provided specific administration route recommendations. Half (n = 56) "often" or "always" provided specific recommendations for Δ-9-tetrahydrocannabinol: cannabidiol ratios, while 69 (62%) "often" or "always" provided specific dose recommendations. Online learning/training modules were the most preferred future training mode, with 88 (84%) physicians "likely" or "very likely" to participate. The top 3 desired topics for future training were marijuana-drug interactions (n = 84, 72%), management of specific medical conditions or symptoms (n = 83, 72%), and strategies to reduce opioids or other drugs use (n = 78, 67%). CONCLUSIONS: This survey of over 100 medical marijuana physicians indicates that their clinical practices rely on a blend of research and anecdotal information sources. While physicians report clinical factors as influential during patient recommendation, patient assessment practices and treatment regimen recommendations vary substantially and rely on experimental approaches. More research is needed to inform evidence-based practice and training, especially considering details on drug interactions, risk-benefit of treatment for specific clinical conditions, and strategies to reduce opioid use.

Change in marijuana use and its associated factors among persons living with HIV (PLWH) during the COVID-19 pandemic: Findings from a prospective cohort.

BACKGROUND: Emerging literature shows increased drug use during the COVID-19 pandemic. However, limited research has examined the change in marijuana use among persons living with HIV (PLWH). This study aimed to investigate how marijuana use changed in a cohort of PLWH during the first year of the pandemic and identify factors associated with the change. METHOD: 222 PLWH (mean age = 50.2 ± 11.2, 50.9 % female, 14.5 % Hispanic, 64.7 % Black, 15.8 % White, 5 % other, 80.2 % persons using marijuana [at least weekly use], 19.8 % persons not using marijuana) completed a baseline survey on demographics and behavioral/health characteristics between 2018 and 2020 and a brief phone survey between May and October 2020 that assessed changes in marijuana use and overall/mental health, and perceived risks/benefits of marijuana use during the COVID-19 pandemic. RESULTS: During the pandemic, 64/222(28.8 %) of the whole sample reported increased marijuana use, 36(16.2 %) reported decreased use, and 122(55 %) reported no change. Multinomial logistic regression results indicated that: Compared to those reporting no change, increased marijuana use during the pandemic was associated with more frequent marijuana use and PTSD symptoms at baseline, worsened mental health during the pandemic, and not perceiving marijuana use as a risk factor for COVID-19 infection. More frequent marijuana use at baseline was the only factor significantly associated with decreased marijuana use during the pandemic. CONCLUSION: The COVID-19 pandemic has resulted in changes in marijuana use among a considerable proportion (45 %) of PLWH. Future research is needed to understand the temporality of the increases in marijuana use with worsening mental health.

Reasons for Marijuana Use and Its Perceived Effectiveness in Therapeutic and Recreational Marijuana Users Among People Living with HIV in Florida.

Therapeutic and recreational marijuana use are common among people living with HIV (PLWH). However, the distinction between perceived "therapeutic" and "recreational" use is blurred, with little information about the specific reasons for use and perceived marijuana effectiveness in adults with chronic conditions. We aimed to compare reasons for use and reason-specific perceived marijuana effectiveness between therapeutic and recreational users among PLWH. In 2018-2019, 213 PLWH currently using marijuana (mean age 48 years, 59% male, 69% African American) completed a questionnaire assessing their specific reasons for using marijuana, including the "main reason." Participants were categorized into one of three motivation groups: therapeutic, recreational, or both equally. For each specific reason, participants rated marijuana effectiveness as 0-10, with 10 being the most effective. The mean effectiveness scores were compared across the three motivation groups via ANOVA, with p <0.05 considered statistically significant. The most frequent main reasons for marijuana use in the therapeutic (n=63, 37%), recreational (n=48, 28%), and both equally (n=59, 35%) categories were "Pain" (21%), "To get high" (32%), and "To relax" (20%), respectively. Compared to recreational users, therapeutic and both equally users provided significantly higher mean effectiveness scores for "Pain," and "To reduce anger." The "Both equally" group also provided significantly higher mean effectiveness scores for "To feel better in general," "To get high," and "To relax" compared to the other two categories. There is a significant overlap in self-reported reasons for marijuana use in primarily therapeutic or recreational users. Perceived marijuana effectiveness was lowest among recreational users.

Secondary Analysis of a Randomized Clinical Trial of Naltrexone Among Women Living With HIV: Correlations Between Reductions in Self-Reported Alcohol Use and Changes in Phosphatidylethanol.

BACKGROUND: Direct biomarkers such as phosphatidylethanol (PEth) have the capability to detect heavy alcohol use, but it is unclear how strongly self-reported reduction in alcohol use correlates with reduction in PEth. We sought to explore the strength of correlation between reductions in self-reported alcohol use and change in PEth among a sample of women living with HIV (WLWH) who participated in a clinical trial to reduce heavy alcohol use. We also sought to determine whether this correlation was stronger in women with lower body mass index (BMI) and women without an alcohol use disorder (AUD). METHODS: 81 WLWH (mean age = 48.7, 80% Black) engaging in a randomized trial of naltrexone versus placebo with a positive baseline PEth (≥8 ng/ml), and alcohol use data at baseline, 2, and 7 months were included in this analysis. Spearman correlation coefficients were compared to measure the correlation between baseline PEth and number of drinks per week by demographic, biological, and alcohol use factors. Mini-International Neuropsychiatric Interview was used to screen for AUD. Further analyses were stratified by BMI and AUD. Spearman correlation coefficients were calculated for the change in PEth and the change in number of drinks per week over 7 months, including 3 time-points: baseline, 2, and 7 months. RESULTS: At baseline, the correlation between baseline PEth and the number of drinks per week was significantly stronger for those with a BMI ≤25 compared to those with a BMI > 25 (r = 0.66; r = 0.26, respectively). Similarly, the correlation between baseline PEth and number of drinks was stronger for those who did not screen positive for AUD compared with those who did (r = 0.66; r = 0.25, respectively). When stratifying by BMI, a low-to-moderate correlation (r = 0.32, p = 0.02) was present for persons with a BMI > 25; when stratifying by AUD, a moderate correlation (r = 0.50, p < 0.01) was present for persons without an AUD between 0 and 2 months only. CONCLUSIONS: In this sample of WLWH, BMI and AUD affected the strength of correlation between PEth and drinks per week. Future work examining changes in PEth over time in broader populations is needed, particularly to understand the sex differences in PEth levels.