Comparing Cannabis Use for Pain to Use for Other Reasons in Primary Care Patients.

BACKGROUND: Medical cannabis is commonly used for chronic pain, but little is known about differences in characteristics, cannabis use patterns, and perceived helpfulness among primary care patients who use cannabis for pain versus nonpain reasons. METHODS: Among 1688 patients who completed a 2019 cannabis survey administered in a health system in Washington state, where recreational use is legal, participants who used cannabis for pain (n = 375) were compared with those who used cannabis for other reasons (n = 558) using survey and electronic health record data. We described group differences in participant characteristics, use patterns, and perceptions and applied adjusted multinomial logistic and modified Poisson regression. RESULTS: Participants who used cannabis for pain were significantly more likely to report using applied (50.7% vs 10.6%) and beverage cannabis products (19.2% vs 11.6%), more frequent use (47.1% vs 33.1% for use ≥2 times per day; 81.6% vs 69.7% for use 4 to 7 days per week), and smoking tobacco cigarettes (19.2% vs 12.2%) than those who used cannabis for other reasons. They were also significantly more likely to perceive cannabis as very/extremely helpful (80.5% vs 72.7%), and significantly less likely to use cannabis for nonmedical reasons (4.8% vs 58.8%) or report cannabis use disorder symptoms (51.7% vs 61.1%). DISCUSSION: Primary care patients who use cannabis for pain use it more frequently, often in applied and ingested forms, and have more co-use of tobacco, which may differentially impact safety and effectiveness. These findings suggest the need for different approaches to counseling in clinical care.

Pre-Operative Predictors for Discharge to Post-Acute Care Facilities After Total Knee Arthroplasty.

BACKGROUND: Joint replacement surgery is in increasing demand and is the most common inpatient surgery for Medicare beneficiaries. The venue for post-operative rehabilitation, including early outpatient therapy after surgery, influences recovery and quality of life. As part of a comprehensive total joint program at Kaiser Permanente Colorado, we developed and validated a predictive model to anticipate and plan the disposition for rehabilitation of our patients after total knee arthroplasty (TKA). METHODS: We analyzed data for TKA patients who completed a pre-operative Total Knee Risk Assessment in 2017 (the model development cohort) or during the first 6 months of 2018 (the model validation cohort). The Total Knee Risk Assessment, which is used to guide disposition for rehabilitation, included questions in mobility, social, and environment domains. Multivariable logistic regression was used to predict discharge to post-acute care facilities (PACFs) (ie, skilled nursing facilities or acute rehabilitation centers). RESULTS: Data for a total of 1481 and 631 patients who underwent TKA were analyzed in the development and validation cohorts, respectively. Ninety-three patients (6.3%) in the development cohort and 22 patients (3.5%) in the validation cohort were discharged to PACFs. Eight risk factors for discharge to PACFs were included in the final multivariable model. Patients with a diagnosis of neurological disorder and with a mobility/balance issue had the greatest chance of discharge to PACFs. CONCLUSION: This validated predictive model for discharge disposition following TKA may be used as a tool in shared decision-making and discharge planning for patients undergoing TKA.

Development and Validation of a Model for Predicting Rehabilitation Care Location Among Patients Discharged Home After Total Knee Arthroplasty.

BACKGROUND: Demand for joint replacement is increasing, with many patients receiving postsurgical physical therapy (PT) in non-inpatient settings. Clinicians need a reliable tool to guide decisions about the appropriate PT setting for patients discharged home after surgery. We developed and validated a model to predict PT location for patients in our health system discharged home after total knee arthroplasty. METHODS: We analyzed data for patients who completed a preoperative total knee risk assessment in 2017 (model development cohort) or during the first 6 months of 2018 (model validation cohort). The initial total knee risk assessment, to guide rehabilitation disposition, included 28 variables in mobility, social, and environment domains, and on patient demographics and comorbidities. Multivariable logistic regression was used to identify factors that best predict discharge to home health service (HHS) vs home with outpatient PT. Model performance was assessed by standard criteria. RESULTS: The development cohort included 259 patients (19%) discharged to HHS and 1129 patients (81%) discharged to home with outpatient PT. The validation cohort included 609 patients, with 91 (15%) discharged to HHS. The final model included age, gender, motivation for outpatient PT, and reliable transportation. Patients without motivation for outpatient PT had the highest probability of discharge to HHS, followed by those without reliable transportation. Model performance was excellent in the development and validation cohort, with c-statistics of 0.91 and 0.86, respectively. CONCLUSION: We developed and validated a predictive model for total knee arthroplasty PT discharge location. This model includes 4 variables with accurate prediction to guide patient-clinician preoperative decision making.