Patterns of gabapentinoid use among long-term opioid users.

OBJECTIVE: Understanding the clinical and demographic profile of patients on gabapentinoids can highlight areas of prescribing disparities, inform clinical practice, and guide future research to optimize effectiveness and safety of gabapentinoids for pain management. We used a national sample of Medicare beneficiaries to examine trends, patterns, and patient-level predictors of gabapentinoid use among long-term opioid users. METHODS: Using a national Medicare sample between 2014 and 2020, we examined factors associated with gabapentinoid use among long-term opioid users. We included Medicare eligible long-term opioid users with no prior gabapentinoid use. The primary outcome was gabapentinoid use after the long-term opioid use episode. Logistic regression was used to test the association with gabapentinoid use for year, age, sex, race/ethnicity, region, Medicare entitlement, low-income status, frailty, pain locations, anxiety, depression, opioid use disorder, and opioid morphine milligrams equivalent. RESULTS: Gabapentinoid use among long-term opioid users increased from 12.6% in 2014 to 16.8% in 2019 (p < .0001). Factors associated with increased gabapentinoid use were Hispanic ethnicity, back pain, nerve pain, and moderate or high opioid usage. Factors associated with decreased gabapentinoid use were older age and Medicare entitlement due to old age. CONCLUSIONS: Variation of gabapentinoid use by socio-demographics and insurance status indicates opportunities to improve pain management and a need for shared therapeutic decision making informed by discussion between pain patients and providers regarding safety and effectiveness of pain therapies. Our findings underscore the need for future research into the comparative effectiveness and safety of gabapentinoids for non-cancer chronic pain in various subpopulations.

Pain-related hospitalization and emergency room visit following initial analgesic prescription after outpatient surgery.

PURPOSE: Our study examined the association between outpatient postsurgical analgesic prescription and risk of insufficiently managed pain characterized by pain-associated hospital admission and emergency room (ER) visit. METHODS: Eligible individuals were children 1-17 years of age who filled an incident analgesic following an outpatient surgery during 2013-2018. Pain-associated hospital admission or ER visit were measured within 30 days following the outpatient surgical procedure. A hierarchical multivariable logistic regression model with patients nested under prescribers was fitted to test the association between incident analgesic prescription and risk of having pain-associated hospital admission or ER visit. RESULTS: Of 14 277 children meeting the inclusion criteria, 6224 (43.6%) received an incident opioid and 8053 (56.4%) received an incident non-opioid analgesic prescription respectively. There were a total of 523 (3.7%) children undergoing surgical procedures that had pain-related hospital admissions or ER visits with 5.1% initiated on non-opioid analgesics and 1.8% on opioid analgesics. The multilevel model indicated that initial opioid analgesic recipients were 32% less likely of having a pain-associated hospital admission or ER visit [aOR: 0.68 (95% CI: 0.3-0.8)]. CONCLUSION: Majority of postsurgical patients do not require additional pain management strategies. In the 3.7% of patients requiring additional pain management strategies, those initiated on non-opioid analgesics are more likely to have a pain-associated hospital admission or ER visit compared with their opioid recipient counterparts.

Patients' willingness to pay for naloxone: A national cross-sectional survey of prescription opioid users with chronic pain in the United States.

BACKGROUND: Millions of U.S. people have been heavily affected by opioids. In March 2023, the Food and Drug Administration approved naloxone as an over-the-counter medication. This has allowed more access to patients at high risk of opioid overdose. However, the patient's willingness to pay for naloxone at the pharmacy counter has not been assessed. OBJECTIVES: This study aimed to characterize factors associated with the willingness to pay for naloxone among the patient group. METHODS: A cross-sectional Qualtrics online panel survey instrument was developed. This survey was distributed to patients in the United States, aged ≥ 18 years, with any chronic pain and taking opioids. The survey included demographics, and clinical characteristics (pain assessment, opioid use, and knowledge of naloxone). In addition, willingness to pay was assessed using a 7-point Likert scale ranging from strongly disagree to strongly agree. An ordinal logistic regression model was used to examine demographic and clinical characteristics. RESULTS: A total of 549 subjects completed the survey (women [53.01%], white or Caucasian (83.61%), age mean [SD] 44 [13]). Women were associated with less willingness to pay (adjusted odds ratio [aOR] 0.685 [95% CI 0.478-0.983], P = 0.0403). Compared with the high household income group (≥ $150,000), low household income ≤ $25,000 (aOR 0.326 [95% CI 0.160-0.662], P = 0.0020) or income between $25,000 and 74,999 (aOR 0.369 [95% CI 0.207-0.657], P = 0.0007) was associated with less likelihood of willing to pay. Patients with a previous diagnosis of obstructive sleep apnea were associated with a higher likelihood of willingness to pay (aOR 1.685 [95% CI 1.138-2.496], P = 0.0092). Each unit increase in pain was also associated with a higher likelihood of willingness to pay (aOR 1.247 [95% CI 1.139-1.365], P < 0.0001). CONCLUSIONS: Demographics and clinical factors were associated with willingness to pay for naloxone. This study's findings are useful in the development of interventions to address pharmacy-based naloxone distribution programs.

Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.

BACKGROUND: Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare. METHODS: PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC). RESULTS: Of 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6-0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%). CONCLUSIONS: The ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

How do we define high and low dose intensity of heart failure medications: a scoping review.

BACKGROUND: Older adults with heart failure often experience adverse drug events with high doses of heart failure medications. Recognizing whether a patient is on a high or low dose intensity heart failure medication can be helpful for daily practice, since it could potentially guide the physician on which symptoms to look for, whether from overdosing or underdosing. However, the current guideline does not provide sufficient information about the dose intensity below the target dose. Furthermore, the definition of high or low-intensity heart failure medication is unclear, and there is no consensus. METHODS: To close the knowledge gap, we conducted a scoping review of the current literature to identify the most frequently used definition of high versus low doses of heart failure medications. We searched Pubmed, Embase, CINAHL, and Cochrane Library using comprehensive search terms that can capture the intensity of heart failure medications. RESULTS: We reviewed 464 articles, including 144 articles that had information about beta-blockers (BB), 179 articles about angiotensin-converting enzyme inhibitors (ACEi), 75 articles about angiotensin receptor blockers (ARB), 80 articles about diuretics, 37 articles about mineralocorticoid receptor antagonists (MRA), and 33 articles about angiotensin receptor-neprilysin inhibitor (ARNI). For hydralazine with isosorbide dinitrate or ivabradine, we could not identify any eligible articles. We identified 40 medications with most frequently used definitions of dose intensity. Four medications (nadolol, pindolol, cilazapril, and torsemide) did not reach consensus in definitions. Most of the BBs, ACEis, or ARBs used the definition of low being < 50% of the target dose and high being ≥ 50% of the target dose from the guideline. However, for lisinopril and losartan, the most commonly used definitions of high or low were from pivotal clinical trials with a pre-defined definition of high or low. CONCLUSION: Our comprehensive scoping review studies identified the most frequently used definition of dose intensity for 40 medications but could not identify the definitions for 4 medications. The results of the current scoping review will be helpful for clinicians to have awareness whether the currently prescribed dose is considered high - requiring close monitoring of side effects, or low - requiring more aggressive up-titration.

Outpatient opioid prescribing by Alzheimer's diagnosis among older adults with pain in United States.

OBJECTIVE: To examine opioid prescribing practices for pain in older adults with and without Alzheimer's Disease and Related Dementias (ADRD). METHODS: This cross-sectional study used National Ambulatory Medical Care Survey data (2014-2016, and 2018). Adults aged ≥ 50 years with pain were analyzed. Prescribing of opioid and concomitant sedative prescriptions (including benzodiazepines, Z-drugs, and barbiturates) were identified by the Multum lexicon code. Multivariable logistic regression evaluated the risk of opioid prescribing or co-prescribing of opioid and sedative associated with ADRD in older adults with pain. RESULTS: There were 13,299 office visits in older adults with pain, representing 451.75 million visits. Opioid prescribing occurred in 27.19%; 30% involved co-prescribing of opioids and sedatives. ADRD was not associated with opioid prescribing or co-prescribing of opioid and sedative therapy. CONCLUSIONS: Opioid and sedatives are commonly prescribed in older adults with pain. Longitudinal studies need to understand the etiology and chronicity of opioid use in older patients, specifically with ADRD.

Dose-dependent relation between metformin and the risk of hormone receptor-positive, her2-negative breast cancer among postmenopausal women with type-2 diabetes.

PURPOSE: Metformin has demonstrated a chemoprotective effect in breast cancer but there is limited evidence on the effect of cumulative exposure to metformin and the risk of hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR + /HER2-) breast cancer. This study assessed this risk with dose and intensity of metformin in postmenopausal women with type-2 diabetes mellitus (T2DM). METHODS: This nested case-control study used the Surveillance, Epidemiology, and End Results-Medicare data (2008-2015). Cohort entry was the date of incident T2DM diagnosis. Cases were those diagnosed with HR + /HER2- breast cancer (event date) as their first/only cancer. Non-cancer T2DM controls were matched using variable-ratio-matching. Cumulative dose and average intensity of metformin were measured during the 1-year lookback period. Dose(mg) was categorized as: (1)0, (2)0-30,000, (3)30,001-136,000, (4)136,001-293,000, and (5) > 293,000, and intensity(mg/day) as: 0, 1-500, and > 500. Covariates were conceptualized using the Andersen Behavioral Model. Conditional logistic regression was used to assess the risk of HR + /HER2- breast cancer with metformin-use. RESULTS: There were 690 cases and 2747 controls. The median duration of T2DM was 1178 days in controls and 1180 days in cases. Higher cumulative dose categories: 4 (adjusted odds ratio(aOR) = 0.72, 95% CI 0.55-0.95,p = 0.02), and 5 (OR = 0.60, 95% CI 0.42-0.85,p < 0.01) had significantly lower odds of HR + /HER2- breast cancer compared to category 0. The highest intensity category of metformin had 39% lower odds of HR + /HER2- breast cancer (OR = 0.61, 95% CI 0.46-0.82,p < 0.01) compared to the 0 mg/day group. CONCLUSIONS: Higher metformin exposure was associated with reduced risk of HR + /HER2- breast cancer, adding to the evidence supporting metformin's chemoprotective effect.

Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database.

BACKGROUND: Hospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance. METHODS: This population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance. RESULTS: Of the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance. CONCLUSION: The performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.

Impact of geographic access to primary care providers on pediatric behavioral health screening.

Behavioral Health (BH) screening is critical for early diagnosis and treatment of pediatric mental disorders. The objective of this study was to assess the impact of geographic access to primary care providers (PCP) on pediatric BH screening in children with different race/ethnicity. A retrospective cohort study was conducted using the 2013-2016 administrative claims data from a large pediatric Medicaid Managed Care Plan that have been linked to 2010 US Census data and the 2017 National Provider Identifier (NPI) Registry. Geographic access was defined as the actual travel distance to nearest PCP and the PCP density within 10-mile travel radius from each individual's residence. Stratified multivariate logistic regression was conducted to examine the association between the geographic access to PCP and the likelihood of receiving screening for behavioral disorders within each racial/ethnic group. BH screening rate was 12.6% among 402,655 children and adolescents who met the inclusion criteria. Multivariable analysis stratified by individual race/ethnicity revealed that Hispanic and Black children were more vulnerable to the geographic access barriers than their non-Hispanic White counterparts. The increase in travel distance to the nearest PCP was negatively associated with screening uptake only among Hispanics (10-20 miles vs. 0-10 miles: OR = 0.78, 95% CI [0.71-0.86]; 20-30 miles vs. 0-10 miles: OR = 0.35, 95% CI [0.23-0.54]). In a subgroup that had access to at least one PCP within 10 miles of travel distance, the variation in PCP density had a greater impact on the screening uptake among Hispanics and Blacks than that in non-Hispanic Whites.

Application of machine learning in predicting hospital readmissions: a scoping review of the literature.

BACKGROUND: Advances in machine learning (ML) provide great opportunities in the prediction of hospital readmission. This review synthesizes the literature on ML methods and their performance for predicting hospital readmission in the US. METHODS: This review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) Statement. The extraction of items was also guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Electronic databases PUBMED, MEDLINE, and EMBASE were systematically searched from January 1, 2015, through December 10, 2019. The articles were imported into COVIDENCE online software for title/abstract screening and full-text eligibility. Observational studies using ML techniques for hospital readmissions among US patients were eligible for inclusion. Articles without a full text available in the English language were excluded. A qualitative synthesis included study characteristics, ML algorithms utilized, and model validation, and quantitative analysis assessed model performance. Model performances in terms of Area Under the Curve (AUC) were analyzed using R software. Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of the reviewed studies. RESULTS: Of 522 citations reviewed, 43 studies met the inclusion criteria. A majority of the studies used electronic health records (24, 56%), followed by population-based data sources (15, 35%) and administrative claims data (4, 9%). The most common algorithms were tree-based methods (23, 53%), neural network (NN) (14, 33%), regularized logistic regression (12, 28%), and support vector machine (SVM) (10, 23%). Most of these studies (37, 85%) were of high quality. A majority of these studies (28, 65%) reported ML algorithms with an AUC above 0.70. There was a range of variability within AUC reported by these studies with a median of 0.68 (IQR: 0.64-0.76; range: 0.50-0.90). CONCLUSIONS: The ML algorithms involving tree-based methods, NN, regularized logistic regression, and SVM are commonly used to predict hospital readmission in the US. Further research is needed to compare the performance of ML algorithms for hospital readmission prediction.

Comparative risk of adverse outcomes associated with nonselective and selective antimuscarinic medications in older adults with dementia and overactive bladder.

OBJECTIVE: The differential muscarinic receptor selectivity could cause selective antimuscarinics to offer advantages over nonselective agents with respect to adverse effects. The objective was to examine the comparative risk of falls/fractures and all-cause hospitalizations among older adults with dementia and overactive bladder (OAB) using nonselective and selective antimuscarinics METHODS/DESIGN: A retrospective cohort study design was conducted among older patients with dementia and OAB using incident antimuscarinics. The primary exposure was classified as nonselective (oxybutynin, tolterodine, trospium, and fesoterodine) and selective (solifenacin and darifenacin). Cox proportional-hazards regression using inverse probability of treatment weighting (IPTW) evaluated the risk of falls/fractures and all-cause hospitalizations within 6 months of nonselective and selective antimuscarinic use. RESULTS: The study cohort consisted of 13,896 (76.9%) nonselective and 4,179 (23.1%) selective antimuscarinic incident users. The unadjusted falls/fractures rate was 27.14% (3,772) for nonselective and 24.55% (1,026) for selective users (p-value< 0.01). The unadjusted all-cause hospitalizations rate was 24.14% (3,354) for nonselective and 21.58% (902) for selective users (p-value <0.01). The IPTW models did not find a significant difference in the risk of falls/fractures (Hazard Ratio [HR] 1.03; 95% Confidence Interval [CI] 0.99-1.07) and risk of all-cause hospitalizations (HR 1.04; 95% CI 0.99-1.08) between nonselective and selective antimuscarinics. Several sensitivity analyses corroborated the main findings. CONCLUSIONS: The study did not find a differential risk of falls/fractures and all-cause hospitalizations in older adults with dementia and OAB using nonselective and selective antimuscarinics. More research is needed to understand the role of pharmacodynamics and pharmacokinetics in the safety profile of antimuscarinics in dementia.

Opioid Prescribing Among Outpatients with Rheumatoid Arthritis.

STUDY OBJECTIVES: To examine the outpatient opioid prescribing practices and the factors associated with opioid prescriptions in patient visits with rheumatoid arthritis (RA). DESIGN: This cross-sectional study used the 2011-2016 National Ambulatory Medical Care Survey. Descriptive weighted analyses were used to examine the trends in opioid prescribing practices for RA. Multivariable logistic regression was used to examine the factors associated with opioid prescriptions among RA visits. SUBJECTS: Adult patients (>18 years of age) with a primary diagnosis of RA based on the International Classification of Diseases. RESULTS: According to the national surveys, an average of 4.45 (95% confidence interval [CI], 2.30-6.60) million office visits were made annually for RA. Approximately 24.28% of these visits involved opioid prescriptions. The RA visits involving opioid prescriptions increased from 1.43 million in 2011-2012 to 3.69 million in 2015-2016 (P < .0001). Being in the age group of 50-64 years (odds ratio [OR] = 3.40; 95% CI, 1.29-9.00), being Hispanic or Latino (OR = 2.92, 95% CI, 1.10-7.74), visiting primary physician (OR = 4.67; 95% CI, 1.86-11.75), prescribing of muscle relaxants (OR = 64.32; 95% CI, 9.71-426.09), acetaminophen (OR = 93.40; 95% CI, 26.19-333.04), antidepressants (OR = 6.10; 95% CI, 2.63-14.14), and glucocorticoids (OR = 3.20; 95% CI, 1.61-6.38), were associated with an increased likelihood of receiving opioid prescriptions in RA. CONCLUSIONS: One in four adult RA visits resulted in opioid prescriptions, and the opioid visits more than doubled during the study period. Several patient and provider factors were associated with the opioid prescribing among RA visits. Understanding these prescribing practices can help to devise strategies for safe opioid prescribing practices in RA.

Risk of Mortality Associated with Non-selective Antimuscarinic medications in Older Adults with Dementia: a Retrospective Study.

BACKGROUND: Selective antimuscarinics may offer a favorable safety profile over non-selective antimuscarinics for the management of overactive bladder (OAB) in patients with dementia. OBJECTIVE: To test the hypothesis that non-selective antimuscarinics are associated with increased risk of mortality compared to selective antimuscarinics in older adults with dementia and OAB. DESIGN: Propensity score-matched retrospective new-user cohort design among Medicare beneficiaries in community settings. PATIENTS: Older adults with dementia and OAB with incident antimuscarinic use. MAIN MEASURES: The primary exposure was antimuscarinic medications classified as non-selective (oxybutynin, tolterodine, trospium, fesoterodine) and selective (solifenacin, darifenacin) agents. All-cause mortality within 180 days of incident antimuscarinic use formed the outcome measure. New users of non-selective and selective antimuscarinics were matched on propensity scores using the Greedy 5 → 1 matching technique. Cox proportional-hazards model stratified on matched pairs was used to evaluate the risk of mortality associated with the use of non-selective versus selective antimuscarinics in the sample. KEY RESULTS: The study identified 16,955 (77.6%) non-selective antimuscarinic users and 4893 (22.4%) selective antimuscarinic users. Propensity score matching yielded 4862 patients in each group. The unadjusted mortality rate at 180 days was 2.6% (126) for non-selective and 1.6% (78) for selective antimuscarinic users in the matched cohort (p value < 0.01). The Cox model stratified on matched pairs found 50% higher risk of 180-day mortality with non-selective antimuscarinics as compared to selective ones (hazard ratio (HR) 1.50; 95% confidence interval (CI) 1.04-2.16). The study findings remained consistent across multiple sensitivity analyses. CONCLUSIONS: Use of non-selective antimuscarinics was associated with a 50% increase in mortality risk among older adults with dementia and OAB. Given the safety concerns regarding non-selective antimuscarinic agents, there is a significant need to optimize their use in the management of OAB for older patients with dementia.

Risk of Mortality Associated With Atypical Antipsychotic use: A National Cohort Study of Older Adults With Depression and Parkinson's Disease.

OBJECTIVES: This study examined the risk of all-cause-mortality in patients with Parkinson's Disease (PD) and comorbid depression using inappropriate atypical antipsychotics (AAPs), based on the 2015 American Geriatrics Society Beers criteria. METHODS: A retrospective analysis of 2007-2010 Minimum Data Set linked Medicare data was conducted using a propensity-matched approach. The cohort included PD patients aged 65 years or older without schizophrenia or bipolar disorder who started AAPs. All patients had a diagnosis of comorbid depression. Risk of 6-month all-cause-mortality was compared across appropriate AAPs (aripiprazole, clozapine, or quetiapine) and inappropriate AAPs (olanzapine, asenapine, brexpiprazole, iloperidone, lurasidone, paliperidone, risperidone, or ziprasidone) using robust Cox regression models involving the matched cohort. RESULTS: All-cause mortality rate was 15.65% in appropriate AAP group (n = 6,038) and 16.91% in inappropriate AAP group (n = 6,038) over 6-month follow-up in the matched cohort. The robust Cox proportional hazards models revealed increased risk of all-cause mortality (hazard ratio [HR] 1.13 [95% confidence interval {CI}: 1.01-1.28)] for patients who used inappropriate compared to appropriate AAPs. Risk of death was also higher for risperidone compared to quetiapine (HR: 1.20 [95% CI: 1.03-1.40]) in sensitivity analysis. However, there was a significant relationship between pneumonia and death in all analyses. The impact of inappropriate AAP use on mortality was not significant when pneumonia was modeled as a mediator. CONCLUSIONS: Inappropriate AAP use is associated with a higher risk of all-cause-mortality in older patients with PD which is mainly mediated by pneumonia. Therefore, inappropriate AAP use should be avoided to improve quality of care in PD.

Documentation of asthma control and severity in pediatrics: analysis of national office-based visits.

Objective: To evaluate the extent of documentation of asthma control and severity and associated characteristics among pediatric asthma patients in office-based settings. Methods: This cross-sectional study utilized data from the 2012-2015 National Ambulatory Medical Care Survey (NAMCS). Patients aged 6-17 years with a diagnosis of asthma were included. Weighted descriptive analysis examined the extent of documentation and uncontrolled asthma; while logistic regression evaluated associated characteristics. Results: Overall, there were 2.47 million (95% confidence interval, 95% CI: 2.04-2.90) average annual visits with asthma as a primary diagnosis. Asthma control and severity was documented in only 36.1% and 33.8% of the visits, respectively. An established patient (odds ratio, OR = 3.81), Hispanic ethnicity (OR = 2.10), chronic sinusitis (OR = 5.59), and visits in the Northeast (OR = 2.12) and Midwest (OR = 2.25) regions had higher odds of documented asthma control status, whereas undocumented asthma severity (OR = 0.02), and visits in spring (OR = 0.34), had lower odds. Osteopathic doctors (OR = 0.18), visits in the Northeast region (OR = 0.23), chronic sinusitis (OR = 0.08), and undocumented asthma control status (OR = 0.03) had lower odds of documented asthma severity, whereas visits in spring (OR = 3.88) and autumn (OR = 3.32) had higher odds. Moderate/severe persistent asthma (OR = 15.35) had higher odds of uncontrolled asthma (as compared to intermittent asthma), while visits in the summer (OR = 0.14) had lower odds. Conclusion: The findings of this study suggest a critical need to increase the documentation of asthma severity and control to improve quality of asthma care in children.

Racial/ethnic differences in treatment quality among youth with primary care provider-initiated versus mental health specialist-initiated care for major depressive disorders.

OBJECTIVES: To compare the racial/ethnic differences in treatment quality among youth with primary care provider-initiated versus mental health specialist-initiated care for major depressive disorders (MDD). METHODS: A retrospective cohort study was conducted using the 2005-2007 Medicaid claims data from Texas. Youth aged 10-20 during the study period were identified if they had two consecutive MDD diagnoses and received either medications for MDD or psychotherapy. Patients who received ≥84 days of medications and/or ≥4 sessions of psychotherapy for MDD treatment during 4 months of follow-up were considered meeting the minimum adequacy of treatment. RESULTS: The generalized linear multilevel model (MLM) analysis revealed that both Hispanics and Blacks were approximately 30% less likely to receive adequate treatment (Hispanics - OR: 0.67; 95% CI: 0.6-0.8) (Blacks - OR: 0.66; 95% CI: 0.6-0.8) and Hispanic children were 50% more likely to undergo MH-related hospitalization (OR: 1.53; 95% CI: 1.1-2.2) compared to their White counterparts. The odds of meeting the minimum MDD treatment adequacy were comparable between pediatric MDD cases first identified by primary care providers (PCP-I) and psychiatrists (PSY-I) (PCP-I vs. PSY-I: OR: 0.97; 95% CI: 0.8-1.2), and slightly lower in those first identified by social workers/psychologists (SWP-I) as compared to PSY-I (SWP-I vs. PSY-I: OR: 0.81; 95% CI: 0.7-0.9). In all models, the interaction between race/ethnicity and type of provider who initiated MDD care was not statistically significant. CONCLUSIONS: Minority youths received less adequate MDD treatment compared to Whites. Hispanic children had the highest risk of having mental health-related hospitalization. The specialty of provider who initiated MDD care had limited impact on treatment quality and was not associated with the racial/ethnic variations in treatment completion and mental health-related hospitalizations.

The concomitant use of tyrosine kinase inhibitors and proton pump inhibitors: Prevalence, predictors, and impact on survival and discontinuation of therapy in older adults with cancer.

BACKGROUND: The concomitant use of tyrosine kinase inhibitors (TKIs) and proton pump inhibitors (PPIs) is a significant concern because of potential drug-drug interaction that reduces TKI absorption, thus potentially reducing the effectiveness of TKIs. The objective of this study was to evaluate the prevalence and predictors of concomitant TKI-PPI receipt and its impact on survival and therapy discontinuation in older adults with cancer. METHODS: This retrospective study used linked Surveillance, Epidemiology, and End Results-Medicare data for the years 2007 through 2012. In total, 12,538 patients with lung cancer, renal cell cancer, chronic myelogenous leukemia, liver cancer, or pancreatic cancer were included. The primary exposure variable was concomitant receipt of TKI-PPI, defined as at least 30 days of PPI use in the first 90 days from the start of the TKI (exposure period). The outcomes measured were overall survival and discontinuation of therapy in 90 days and 1 year after the end of the exposure period. Cox proportional-hazards regression with inverse probability of treatment weighting was used to evaluate the association between exposure and outcome. RESULTS: The overall prevalence of TKI-PPI receipt was 22.7%. Predictors that were associated with increased use included polypharmacy and prior PPI receipt. TKI-PPI use decreased survival in 90 days (hazard ratio, 1.16; 95% confidence interval, 1.05-1.28) and in 1 year (hazard ratio, 1.10; 95% confidence interval, 1.04-1.18) but was not associated with discontinuation. CONCLUSIONS: Nearly 1 in 4 older adults with cancer who receive TKIs also receive PPIs concomitantly, and concomitant use is associated with an increased risk of death. Concerted efforts to manage medications are needed to identify and reduce the receipt of PPIs when TKIs are initiated.

Outcomes associated with Clostridium difficile infection in patients with chronic liver disease.

Patients with chronic liver disease (CLD) have frequent exposure to Clostridium difficile infection (CDI) risk factors but the incidence and aetiology of CDI on this population is poorly understood. The aim of this study was to assess the incidence, disease presentation and outcomes of CDI in patients with underlying CLD. The Health Care and Utilization Project National Inpatient Sample (HCUP-NIS) 2009 dataset was used to identify patients with CLD who developed CDI along with matched non-CLD patients with CDI. Using the NIS dataset, the incidence rate of CDI was 189.4/10 000 discharges in CLD patients vs. 83.7/10 000 discharges in the non-CLD matched cohort (P < 0.001). Compared with non-CLD, comorbidity-matched controls with CDI, CLD patients with CDI had higher likelihood of in-hospital mortality (8.8% vs. 18.6%, P < 0.001), increased length of stay by 1.19 days (P < 0.001) and increased total costs by $8632 (P < 0.001). In separate analyses using a tertiary case database of hospitalised patients in Houston, Texas (2006-2016) with CLD and CDI (n = 41) compared with patients with CDI but not CLD (n = 111), CLD patients had significantly higher Charlson comorbidity index (P < 0.0001) but similar risk factors for CDI and CDI-related disease presentation compared with non-CLD patients. In conclusion, CDI-related risk factors were almost universally present in the CLD population. CDI resulted in worse outcomes in this population.

The Impact of Pharmacotherapy on Substance Use in Adolescents With Attention-Deficit/Hyperactivity Disorder: Variations Across Subtypes.

OBJECTIVE: The primary purpose of this study was to investigate the impact of attention-deficit/hyperactivity disorder (ADHD) pharmacotherapy on the risk of substance use within each ADHD subtype. METHODS: The study used data from the National Comorbidity Survey-Adolescent supplement, a nationally representative sample of US adolescents (ages 13-18) collected from 6,483 adolescent-parent interviews conducted between 2001 and 2004. ADHD was categorized into three subtypes: ADHD-predominantly hyperactive-impulsive type (ADHD-H); ADHD-predominantly inattentive type (ADHD-I); and ADHD-combined type (ADHD-C) using Diagnostic and Statistical Manual of Mental Disorders-IV criteria. Substance use information was obtained from the adolescents' interview. The impact of ADHD-pharmacotherapy on substance use was examined using multivariable logistic regression analysis. RESULTS: Among the adolescents with ADHD, ADHD pharmacotherapy significantly associated with reduced risk of substance use (OR = 0.53, 95%CI [0.31-0.90]); with regards to ADHD subtypes, ADHD pharmacotherapy is negatively associated with substance use in adolescents with ADHD-C (OR = 0.53, 95%CI [0.24-0.97]) and those with ADHD-H (OR = 0.23, 95% CI [0.07-0.78]), but it did not have statistically significant effect on risk of substance use in those with ADHD-I subtype (OR = 0.49, 95%CI [0.17-1.39]). Among the group who never received ADHD-pharmacotherapy before the interview, individuals with ADHD-H and ADHD-C had a similar risk of substance use compared to adolescents with ADHD-I (ADHD-C: OR = 1.5, 95%CI [0.77-2.95] and ADHD-H: OR = 2.10, 95%CI [0.87-4.95]). CONCLUSIONS: Adolescents with ADHD were equally susceptible to future substance use disregard their ADHD subtypes. Receipt of pharmacotherapy could decrease risk of substance use in adolescents with ADHD-H and ADHD-C, but it may not affect risk of substance use among individuals with ADHD-I.

Anticholinergic Medication Use and Risk of Dementia Among Elderly Nursing Home Residents with Depression.

OBJECTIVE: To examine the risk of dementia with anticholinergic use among elderly nursing home residents with depression. DESIGN: Population-based nested case-control study. SETTING: Population-based study involving 2007-2010 Minimum Data Set-linked Medicare data from all 50 states. PARTICIPANTS: Medicare beneficiaries aged 65 years and older, diagnosed with depression, and no history of dementia as of 2007 (baseline period). Cases were identified as patients with incident dementia following the baseline period. For each case, four age- and sex-matched control subjects were selected using incidence density sampling. MEASUREMENTS: Anticholinergic exposure was defined using Anticholinergic Drug Scale. Prescription of clinically significant anticholinergic medications (levels 2 and 3) 30 days preceding the event date formed the primary exposure. The primary outcome was dementia diagnosis, between January 1, 2008, and December 31, 2010. A conditional logistic regression model stratified on matched case-control sets was performed to assess dementia risk, after controlling for other risk factors. RESULTS: The study sample included 28,388 cases diagnosed with dementia and 113,352 matched control subjects. After adjusting for other risk factors, clinically significant anticholinergic use was associated with significant risk of dementia (OR: 1.26; 95% CI: 1.22-1.29) compared with non-use. The findings remained consistent across levels of anticholinergic potency (level 2, OR: 1.37, 95% CI: 1.31-1.44; level 3, OR: 1.15, 95% CI: 1.10-1.19). CONCLUSION: Use of clinically significant anticholinergic medications was associated with a 26% increase in risk of dementia among elderly nursing home residents with depression. With increasing safety concerns, there is a significant need to optimize anticholinergic use, especially for those who are at risk for dementia.