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BackgroundBiopsy remains the gold standard for determining fibrosis stage in patients with primary biliary cholangitis (PBC), but it is unavailable for most patients. We used data from the 11 US health systems in the FibrOtic Liver Disease Consortium to explore a combination of biochemical markers and electronic health record (EHR)-based diagnosis/procedure codes (DPCs) to identify the presence of cirrhosis in PBC patients.MethodsHistological fibrosis staging data were obtained from liver biopsies. Variables considered for the model included demographics (age, gender, race, ethnicity), total bilirubin, alkaline phosphatase, albumin, aspartate aminotransferase (AST) to platelet ratio index (APRI), Fibrosis 4 (FIB4) index, AST to alanine aminotransferase (ALT) ratio, and >100 DPCs associated with cirrhosis/decompensated cirrhosis, categorized into ten clusters. Using least absolute shrinkage and selection operator regression (LASSO), we derived and validated cutoffs for identifying cirrhosis.ResultsAmong 4328 PBC patients, 1350 (32%) had biopsy data; 121 (9%) were staged F4 (cirrhosis). DPC clusters (including codes related to cirrhosis and hepatocellular carcinoma diagnoses/procedures), Hispanic ethnicity, ALP, AST/ALT ratio, and total bilirubin were retained in the final model (AUROC=0.86 and 0.83 on learning and testing data, respectively); this model with two cutoffs divided patients into three categories (no cirrhosis, indeterminate, and cirrhosis) with specificities of 81.8% (for no cirrhosis) and 80.3% (for cirrhosis). A model excluding DPCs retained ALP, AST/ALT ratio, total bilirubin, Hispanic ethnicity, and gender (AUROC=0.81 and 0.78 on learning and testing data, respectively).ConclusionAn algorithm using laboratory results and DPCs can categorize a majority of PBC patients as cirrhotic or noncirrhotic with high accuracy (with a small remaining group of patients' cirrhosis status indeterminate). In the absence of biopsy data, this EHR-based model can be used to identify cirrhosis in cohorts of PBC patients for research and/or clinical follow-up.

Background: Cannabis use disorders (CUDs) have increased with more individuals using cannabis, yet few receive treatment. Health systems have adopted the Healthcare Effectiveness Data and Information Set (HEDIS) quality measures of initiation and engagement in alcohol and other drug (AOD) dependence treatment, but little is known about the performance of these among patients with CUDs. Methods: This cohort study utilized electronic health records and claims data from 7 health care systems to identify patients with documentation of a new index CUD diagnosis (no AOD diagnosis ≤60 days prior) from International Classification of Diseases, Ninth revision, codes (October 1, 2014, to August 31, 2015). The adjusted prevalence of each outcome (initiation, engagement, and a composite of both) was estimated from generalized linear regression models, across index identification settings (inpatient, emergency department, primary care, addiction treatment, and mental health/psychiatry), AOD comorbidity (patients with CUD only and CUD plus other AOD diagnoses), and patient characteristics. Results: Among 15,202 patients with an index CUD diagnosis, 30.0% (95% confidence interval [CI]: 29.2-30.7%) initiated, 6.9% (95% CI: 6.2-7.7%) engaged among initiated, and 2.1% (95% CI: 1.9-2.3%) overall both initiated and engaged in treatment. The adjusted prevalence of outcomes varied across index identification settings and was highest among patients diagnosed in addiction treatment, with 25.0% (95% CI: 22.5-27.6%) initiated, 40.9% (95% CI: 34.8-47.0%) engaged, and 12.5% (95% CI: 10.0-15.1%) initiated and engaged. The adjusted prevalence of each outcome was generally highest among patients with CUD plus other AOD diagnosis at index diagnosis compared with those with CUD only, overall and across index identification settings, and was lowest among uninsured and older patients. Conclusion: Among patients with a new CUD diagnosis, the proportion meeting HEDIS criteria for initiation and/or engagement in AOD treatment was low and demonstrated variation across index diagnosis settings, AOD comorbidity, and patient characteristics, pointing to opportunities for improvement.

Background: Only 10% of patients with alcohol and other drug (AOD) disorders receive treatment. The AOD Initiation and Engagement in Treatment (AOD-IET) measure was added to the national Healthcare Effectiveness Data and Information Set (HEDIS) to improve access to care. This study identifies factors related to improving AOD-IET rates. Methods: We include data from 7 health systems with differing geographic, patient demographic, and organizational characteristics; all used a common Virtual Data Warehouse containing electronic health records and insurance claims data. Multilevel logistic regression models examined AOD-IET among adults (18+). Results: A total of 86,565 patients had an AOD diagnosis qualifying for the HEDIS denominator. The overall initiation rate was 27.9% with wide variation; the overall engagement rate was 11.5% and varied from 4.5% to 17.9%. Women versus men (odds ratio [OR] = 0.81, 95% confidence interval [CI] = 0.76-0.86); Hispanics (OR = 0.85, 95% CI = 0.79-0.91), black/African Americans (OR = 0.82, 95% CI = 0.75-0.90), and Asian Americans (OR = 0.83, 95% CI = 0.72-0.95) versus whites; and patients aged 65+ versus 18-29 (OR = 0.82, 95% CI = 0.74-0.90) had lower odds of initiation. Patients aged 30-49 versus 18-29 (OR = 1.11, 95% CI = 1.04-1.19) and those with prior psychiatric (OR = 1.26, 95% CI = 1.18-1.35) and medical (OR = 1.18, 95% CI = 1.10-1.26) conditions had higher odds of initiation. Identification in primary care versus other departments was related to lower odds of initiation (emergency department [ED]: OR = 1.55, 95% CI = 1.45-1.66; psychiatry/AOD treatment: OR = 3.58, 95% CI = 3.33-3.84; other outpatient: OR = 1.19, 95% CI = 1.06-1.32). Patients aged 30-49 versus 18-29 had higher odds of engagement (OR = 1.26, 95% CI = 1.10-1.43). Patients aged 65+ versus 18-29 (OR = 0.51, 95% CI = 0.43-0.62) and black/African Americans versus whites (OR = 0.64, 95% CI = 0.53-0.77) had lower odds. Those initiating treatment in psychiatry/AOD treatment versus primary care (OR = 7.02, 95% CI = 5.93-8.31) had higher odds of engagement; those in inpatient (OR = 0.40, 95% CI = 0.32-0.50) or other outpatient (OR = 0.73, 95% CI = 0.59-0.91) settings had lower odds. Discussion: Rates of initiation and engagement varied but were low. Findings identified age, race/ethnicity, co-occurring conditions, and department of identification as key factors associated with AOD-IET. Focusing on these could help programs develop interventions that facilitate AOD-IET for those less likely to receive care.

Background: Medical comorbidity may influence treatment initiation and engagement for alcohol and other drug (AOD) use disorders. We examined the association between medical comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) treatment initiation and engagement measures.Methods: We used electronic health record and insurance claims data from 7 US health care systems to identify patients with AOD use disorders between October 1, 2014, and August 15, 2015 (N = 86,565). Among patients identified with AOD use disorders in outpatient and emergency department (ED) settings, we examined how Charlson/Deyo comorbidity index scores and medical complications of AOD use were associated with treatment initiation. Among those who initiated treatment in inpatient and outpatient/ED settings, we also examined how comorbidity and AOD use-related medical complications were associated with treatment engagement. Analyses were conducted using generalized estimating equation logistic regression modeling.Results: Among patients identified as having an AOD diagnosis in outpatient and ED settings (n = 69,965), Charlson/Deyo comorbidity index scores of 2 or more were independently associated with reduced likelihood of initiation (risk ratio [RR] = 0.80, 95% confidence interval [CI] = 0.74, 0.86; reference score = 0), whereas prior-year diagnoses of cirrhosis (RR = 1.25, 95% CI = 1.12, 1.35) and pancreatic disease (RR = 1.34, 95% CI = 1.15, 1.56) were associated with greater likelihood of initiation. Among those who were identified in outpatient/ED settings and initiated, higher comorbidity scores were associated with lower likelihood of engagement (score 1: RR = 0.85, 95% CI = 0.76, 0.94; score 2+: RR = 0.61, 95% CI = 0.53, 0.71).Conclusion: Medical comorbidity was associated with lower likelihood of initiating or engaging in AOD treatment, but cirrhosis and pancreatic disease were associated with greater likelihood of initiation. Interventions to improve AOD treatment initiation and engagement for patients with comorbidities are needed, such as integrating medical and AOD treatment.

Background: Psychiatric comorbidity is common among patients with alcohol and other drug (AOD) use disorders. To better understand how psychiatric comorbidity influences AOD treatment access in health care systems, the present study examined treatment initiation and engagement among a large, diverse sample of patients with comorbid psychiatric and AOD use disorders. Methods: This study utilized data from a multisite observational study examining Healthcare Effectiveness Data and Information Set (HEDIS) measures of initiation and engagement in treatment (IET) among patients with AOD use disorders from 7 health care systems. Participants were aged 18 or older with at least 1 AOD index diagnosis between October 1, 2014, and August 15, 2015. Data elements extracted from electronic health records and insurance claims data included patient demographic characteristics, ICD-9 (International Classification of Diseases, Ninth Revision) diagnostic codes, and procedure codes. Descriptive analyses and multivariate logistic regression models were used to examine the relationship between patient-level factors and IET measures. Results: Across health care systems, out of a total of 86,565 patients who had at least 1 AOD index diagnosis during the study period, 66.2% (n = 57,335) patients also had a comorbid psychiatric disorder. Among patients with a comorbid psychiatric disorder, 34.9% (n = 19,998) initiated AOD treatment, and of those, 10.3% (n = 2,060) engaged in treatment. After adjusting for age, sex, and race/ethnicity, patients with comorbid psychiatric disorders were more likely to initiate (odds ratio [OR] = 3.20, 95% confidence interval [CI] = 3.08, 3.32) but no more likely to engage (OR = 0.56, 95% CI = 0.51, 0.61) in AOD treatment, compared with those without a comorbid psychiatric disorder. Conclusions: Findings suggest that identification of comorbid psychiatric disorders may increase initiation in AOD treatment. However, innovative efforts are needed to enhance treatment engagement both generally and especially for individuals without diagnosed psychiatric conditions.

Background: Problematic use of alcohol and other drugs (AOD) is highly prevalent among people living with the human immunodeficiency virus (PLWH), and untreated AOD use disorders have particularly detrimental effects on human immunodeficiency virus (HIV) outcomes. The Healthcare Effectiveness Data and Information Set (HEDIS) measures of treatment initiation and engagement are important benchmarks for access to AOD use disorder treatment. To inform improved patient care, we compared HEDIS measures of AOD use disorder treatment initiation and engagement and health care utilization among PLWH and patients without an HIV diagnosis. Methods: Patients with a new AOD use disorder diagnosis documented between October 1, 2014, and August 15, 2015, were identified using electronic health records (EHR) and insurance claims data from 7 health care systems in the United States. Demographic characteristics, clinical diagnoses, and health care utilization data were also obtained. AOD use disorder treatment initiation and engagement rates were calculated using HEDIS measure criteria. Factors associated with treatment initiation and engagement were examined using multivariable logistic regression models. Results: There were 469 PLWH (93% male) and 86,096 patients without an HIV diagnosis (60% male) in the study cohort. AOD use disorder treatment initiation was similar in PLWH and patients without an HIV diagnosis (10% vs. 11%, respectively). Among those who initiated treatment, few engaged in treatment in both groups (9% PLWH vs. 12% patients without an HIV diagnosis). In multivariable analysis, HIV status was not significantly associated with either AOD use disorder treatment initiation or engagement. Conclusions: AOD use disorder treatment initiation and engagement rates were low in both PLWH and patients without an HIV diagnosis. Future studies need to focus on developing strategies to efficiently integrate AOD use disorder treatment with medical care for HIV.

Background/Purpose: Venous thromboembolism (VTE) is a common cardiovascular complication in middle-aged adults. There is a likelihood a patient may experience VTE when admitted to a hospital. Prophylactic medications such as low-dose unfractionated heparin and enoxaparin are started to prevent VTE. A pharmacist-led health system-wide Grand Rounds promoted prophylactic enoxaparin 40 mg twice daily instead of once daily in patients with a body mass index (BMI) 40 kg/m2 or greater. Methodology: This case-control study was conducted at a Essentia Health – Fargo, in the Upper Midwest. Data of acute care patients were extracted from electronic health records 2 years before and after the pharmacist-led education. Patients in the study were 18 years old or older, hospitalized with a need for prophylactic anticoagulation receiving enoxaparin, and had a BMI 40 kg/m2 or greater. Patients with a diagnosis of COVID-19 and recent bleeding were excluded. Groups were compared to determine the effect of pharmacist-led education. The outcome was the number of patients who received enoxaparin 40 mg twice daily compared to once daily. Results: In the control group, 9 out of 15 hospitalizations received enoxaparin 40 mg subcutaneous twice daily and in the case group 34 out of 70 hospitalizations received the twice daily dosing. The odds ratio of receiving enoxaparin 40 mg twice daily after the pharmacist-led education compared to before the education was OR =.99, 95% CI =.96, 1.02. Conclusions: There was no difference in enoxaparin 40 mg once daily and twice daily dosing after the pharmacist-led education. [ABSTRACT FROM AUTHOR]

Purpose: A pharmacist-led COVID-19 vaccination screening and administration tool was implemented at a tertiary care hospital in the Upper Midwest, allowing pharmacists to determine if an inpatient is a candidate for the COVID-19 vaccination. The purpose of this observational study was to examine the feasibility of a pharmacist-led, COVID-19 vaccination administration workflow to the inpatient population in a tertiary care hospital. Methods: This observational study was conducted at an Upper-Midwest facility and includes a study population of adult patients admitted to the hospital between 10/1/2021 - 1/1/2022. The primary outcome of this study was the number of COVID-19 vaccinations administered. Secondary outcomes included the number of attending physician approvals, pharmacist time dedicated to the vaccination program, patient demographics, and patient acceptance of vaccination. Group differences were evaluated using odds ratios, 95% confidence intervals, Pearson's chi-square test, the Mann-Whitney U test, and multivariate logistic regression. Results: There were 715 patients eligible to receive COVID-19 vaccination. Of those, 23 (3.2%) patients received a COVID-19 vaccine while inpatient. After adjusting for sex at birth, age at admission, and length of hospital stay, compared to previously unvaccinated patients, those with their second dose due were 7.3 times more likely to have been given a vaccination (95% CI 1.3-42.1, P = 0.026), while patients with their booster due were 4 times more likely to have been given a vaccination (95% CI 1.3-12.3, P = 0.014). Conclusions: Although unvaccinated patients were successfully identified, only 23 were able to be vaccinated. Additional barriers need to be addressed to make this program successful. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity and functional limitation are associated with poor outcomes in heart failure (HF). However, the individual and combined effect of these on health‐related quality of life in patients with HF is not well understood. Methods: Patients aged ≥30 years with two or more HF diagnostic codes and one or more HF‐related prescription drugs from four U.S. institutions were mailed a survey to measure patient‐centric factors including functional status (activities of daily living [ADLs]) and health‐related quality of life (PROMIS‐29 Health Profile). Patients with HF from January 1, 2013 to February 1, 2018 were included. Multimorbidity was defined as ≥2 non‐cardiovascular comorbidities; functional limitation as any limitation in at least one of eight ADLs. Patients were categorized into four groups by multimorbidity (Yes/No) and functional limitation (Yes/No). We dichotomized the PROMIS‐29 sub‐scale scores at the median and calculated odd ratios for the four multimorbidity/functional limitation groups. Results: A total of 3330 patients with HF returned the survey (response rate 31%); 3020 completed the questions of interest and were retained. Among these patients (45% female; mean age 73 [standard deviation: 12] years), 29% had neither multimorbidity nor functional limitation, 24% had multimorbidity only, 22% had functional limitation only, and 25% had both. After adjustment, having functional limitation only was associated with higher anxiety (odds ratio [OR]: 3.44, 95% confidence interval [CI]: 2.66–4.45), depression (OR: 3.11, 95% CI: 2.39–4.06), and fatigue (OR: 4.19, 95% CI: 3.25–5.40); worse sleep (OR: 2.14, 95% CI: 1.69–2.72) and pain (OR: 6.73, 95% CI: 5.15–8.78); and greater difficulty with social activities (OR: 9.40, 95% CI: 7.19–12.28) compared with having neither. Results were similar for having both multimorbidity and functional limitation. Conclusion: Patients with only functional limitation have similar poor health‐related quality of life scores as those with both multimorbidity and functional limitation, underscoring the important role that physical functioning plays in the well‐being of patients with HF. [ABSTRACT FROM AUTHOR]

Background: Medicare beneficiaries incur 27%-30% of lifetime charges in the last year of life; most charges occur in the last quarter. Factors associated with high end-of-life Medicare charges include less advanced age, non-white race, absence of advance directive, and urban residence. Methods: We analyzed Medicare hospital charges in the last year of life for nursing home residents with severe cognitive impairment, focusing on rural-urban differences. The study population consisted of 3,703 nursing home residents (1,882 rural, 1,821 urban) in Minnesota and Texas who died in 2000-2001. Data on Medicare hospital charges were obtained from 1998-2001 Centers for Medicare and Medicaid Services MedPAR files. Results: During the last year of life, unadjusted charges averaged $12,448 for rural subjects; $31,780 for urban. The charges were distributed across the last 4 quarters similarly for the 2 populations, with 15%-20% of charges incurred in each of the first 3 quarters, and 47% (rural) and 52% (urban) in the last quarter. At the individual level, a higher percentage of hospital charges were incurred in the last 90 days by urban than by rural residents (P [less than] 0.001). A larger proportion of urban (43%) than rural (37%) residents were hospitalized in the final quarter. The charges for hospitalized residents (N = 1,994) were distributed similarly to those of the entire study population. Discussion: Medicare hospital charges during the last year of life were lower for rural nursing home residents with cognitive impairment than for their urban counterparts. Charges tend to be more concentrated in the last 90 days of life for urban residents.

Objective: Obesity is stigmatized and people with obesity report experiencing stigmatizing situations when seeking health care. The implications of these experiences are not well understood. This study tests an indirect effects model of negative care experiences as an intermediate variable between obesity and care avoidance/utilization and switching primary care doctors. Methods: A survey was completed by 2380 primary care patients in the Learning Health Systems Network (LHSNet) Clinical Data Research Network with a BMI >25 kg/m2. Measures included scales assessing stigmatizing situations, perceived patient‐centered communication, perceived respect, having delayed needed care, and having looked for a new primary doctor in the past 12 months. Sequential and serial indirect effects of care experiences and respect in the association between BMI and care utilization outcomes was modeled. Results: The hypothesized model was supported by findings. The associations between BMI and delaying needed care (OR = 1.06, p < 0.001) and attempting to switch primary doctors (OR = 1.02, p = 0.04) was mediated by both stigmatizing situations experienced in a health care context and lower patient‐centered communication. Lower perceived respect mediated the association between care experiences and utilization outcomes. Conclusions: People with higher BMIs may avoid care or switch doctors as a result of stigmatizing experiences and poor communication with doctors. These outcomes may contribute to morbidity in people with obesity if they delay or avoid care for health concerns when symptoms first present. [ABSTRACT FROM AUTHOR]

A study was conducted to determine the physiological significance of the expression of the obese gene in animals carrying one obese gene (+/ob) and those with the homozygous wild type gene (+/+). Gender, body weight, fasting blood glucose, and body core temperature were examined and compared. Results suggest that +/ob mice are physiologically different to +/+ mice. The latter have extreme sensitivity to stress which is caused by their homozygous wild type obese genes.