Association between trajectories of adherence to endocrine therapy and risk of treated breast cancer recurrence among US nonmetastatic breast cancer survivors.

BACKGROUND: Endocrine therapy is the mainstay treatment for breast cancer (BC) to reduce BC recurrence risk. During the first year of endocrine therapy use, nearly 30% of BC survivors are nonadherent, which may increase BC recurrence risk. This study is to examine the association between endocrine therapy adherence trajectories and BC recurrence risk in nonmetastatic BC survivors. METHODS: This retrospective cohort study included Medicare beneficiaries in the United States (US) with incident nonmetastatic BC followed by endocrine therapy initiation in 2010-2019 US Surveillance, Epidemiology, and End Results linked Medicare data. We calculated monthly fill-based proportion of days covered in the first year of endocrine therapy. We applied group-based trajectory models to identify distinct endocrine therapy adherence patterns. After the end of the first-year endocrine therapy trajectory measurement period, we estimated the risk of time to first treated BC recurrence within 4 years using Cox proportional hazards models. RESULTS: We identified 5 trajectories of adherence to endocrine therapy in BC Stages 0-I subgroup (n = 28,042) and in Stages II-III subgroup (n = 7781). A trajectory of discontinuation before 6 months accounted for 7.0% in Stages 0-I and 5.8% in Stages II-III subgroups, and this trajectory was associated with an increased treated BC recurrence risk compared to nearly perfect adherence (Stages 0-I: adjusted hazard [aHR] = 1.84, 95% CI = 1.46-2.33; Stages II-III: aHR = 1.38, 95% CI = 1.07-1.77). CONCLUSIONS: Nearly 7% of BC survivors who discontinued before completing 6 months of treatment was associated with an increased treated BC recurrence risk compared to those with nearly perfect adherence among Medicare nonmetastatic BC survivors.

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio. METHODS: We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups. RESULTS: We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups. CONCLUSIONS: Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.

Association between trajectories of prescription opioid use and risk of opioid use disorder and overdose among US nonmetastatic breast cancer survivors.

PURPOSE: To examine the association between prescription opioid use trajectories and risk of opioid use disorder (OUD) or overdose among nonmetastatic breast cancer survivors by treatment type. METHODS: This retrospective cohort study included female nonmetastatic breast cancer survivors with at least 1 opioid prescription fill in 2010-2019 Surveillance, Epidemiology and End Results linked Medicare data. Opioid mean daily morphine milligram equivalents (MME) calculated within 1.5 years after initiating active breast cancer therapy. Group-based trajectory models identified distinct opioid use trajectory patterns. Risk of time to first OUD/overdose event within 1 year after the trajectory period was calculated for distinct trajectory groups using Cox proportional hazards models. Analyses were stratified by treatment type. RESULTS: Four opioid use trajectories were identified for each treatment group. For 38,030 survivors with systemic endocrine therapy, 3 trajectories were associated with increased OUD/overdose risk compared with early discontinuation: minimal dose (< 5 MME; adjusted hazard ratio [aHR] = 1.73 [95% CI 1.43-2.09]), very low dose (5-25 MME; 2.67 [2.05-3.48]), and moderate dose (51-90 MME; 6.20 [4.69-8.19]). For 9477 survivors with adjuvant chemotherapy, low-dose opioid use was associated with higher OUD/overdose risk (aHR = 7.33 [95% CI 2.52-21.31]) compared with early discontinuation. For 3513 survivors with neoadjuvant chemotherapy, the differences in OUD/OD risks across the 4 trajectories were not significant. CONCLUSIONS: Among Medicare nonmetastatic breast cancer survivors receiving systemic endocrine therapy or adjuvant chemotherapy, compared with early discontinuation, low-dose or moderate-dose opioid use were associated with six- to sevenfold higher OUD/overdose risk. Breast cancer survivors at high-risk of OUD/overdose may benefit from targeted interventions (e.g., pain clinic referral).

L-DOPA induces iron accumulation in roots of Ipomoea aquatica and Arabidopsis thaliana in a pH-dependent manner.

BACKGROUND: Iron deficiency is the leading cause of anemia worldwide, particularly in countries with predominant plant-based diets. Plants constitute the main source of dietary iron. Increasing their iron concentration could reduce the occurrence of anemia. The water spinach Ipomoea aquatica is consumed as a vegetable throughout Asia and tolerates high iron concentrations making it an attractive candidate for iron biofortification. L-DOPA is an allelopathic molecule secreted by some legumes. L-DOPA can trigger the expression of Fe deficiency-inducible genes, and could potentially be used as a biostimulant to increase Fe concentration. RESULTS: L-DOPA significantly affected root growth of water spinach, and triggered a massive accumulation of Fe in roots. Both effects were exacerbated when L-DOPA was dissolved in KOH, which is surprising given that L-DOPA is less stable at high pH. To check whether a higher pH could indeed increase the bioactivity of L-DOPA, we used Arabidopsis thaliana, which grows at lower pH than water spinach, and subjected the plants to L-DOPA treatments at pH 5.5 and pH 6.0, which are both within the optimal range for Arabidopsis nutrition. At pH 6.0, the root growth of Arabidopsis was more strongly inhibited than at pH 5.5. We found that at higher pH, L-DOPA oxidizes to form a melanin precipitate. CONCLUSIONS: We concluded that the oxidation of L-DOPA that we observed upon solubilization in KOH, or in nutrient solutions at slightly higher pH produces melanin-related molecules that are more potent than L-DOPA itself to trigger the primary root growth inhibition, Fe uptake and root Fe accumulation in water spinach and Arabidopsis.

Impact of Direct-acting Antivirals on Hepatocellular Carcinoma and Mortality Among Medicaid Beneficiaries With Hepatitis C.

OBJECTIVE: The effects of all-oral direct-acting antivirals (DAAs) on hepatocellular carcinoma (HCC) and liver-related and all-cause mortality were assessed among Medicaid beneficiaries with hepatitis C virus (HCV). SUBJECTS: This cohort study used 2013-2019 Arizona Medicaid data from beneficiaries with HCV aged 18-64 years. METHODS: Risks of HCC and liver-related and all-cause mortality were compared between patients with or without DAA treatment, stratified by liver disease severity, using inverse probability of treatment weighted multivariable Cox proportional hazards regression models. RESULTS: Of 29,289 patients, 13.3% received DAAs. Among patients with compensated cirrhosis (CC), DAA treatment was associated with a lower risk of HCC [adjusted hazard ratio (aHR), 0.57; 95% CI, 0.37-0.88] compared with untreated patients although this association was not statistically significant for patients without cirrhosis or with decompensated cirrhosis (DCC). Compared with untreated patients, DAA treatment was associated with decreased risk of liver-related mortality for patients without cirrhosis (aHR: 0.02; 95% CI: 0.004-0.11), with CC (aHR: 0.09; 95% CI: 0.06-0.13), or with DCC (aHR: 0.20; 95% CI: 0.14-0.27). Similarly, compared with untreated patients, DAA treatment was associated with lower all-cause mortality for patients without cirrhosis (aHR: 0.10; 95% CI: 0.08-0.14), with CC (aHR: 0.07; 95% CI: 0.05-0.10), or with DCC (aHR: 0.15; 95% CI: 0.11-0.20). CONCLUSIONS: Among Arizona Medicaid beneficiaries with HCV, DAA treatment was associated with decreased risk of HCC for patients with CC but not for patients without cirrhosis or with DCC. However, DAA treatment was associated with decreased risk of liver-related and all-cause mortality.

Trends, characteristics, race, and ethnicity associated with nonadherence to antidepressants among breast cancer survivors with depression.

BACKGROUND: Breast cancer is the most diagnosed cancer in the United States, and half of breast cancer survivors experience major depressive disorders (hereafter depression). Healthcare Effectiveness Data and Information Set (HEDIS) quality measures evaluating depression treatment practices recommend uninterrupted antidepressant treatment for 3 months in the acute phase and 3 months in the continuation phase for the general population. However, little is known about the extent of and trends in antidepressant nonadherence among breast cancer survivors with depression, which may impact adherence to breast cancer treatment, potentially leading to breast cancer recurrence and other adverse outcomes. OBJECTIVE: To examine the trends and characteristics associated with antidepressant nonadherence among breast cancer survivors with depression in the United States. METHODS: We conducted cross-sectional analyses of Surveillance, Epidemiology, and End Results linked with Medicare data (2010-2019) for women with breast cancer and depression who newly initiated antidepressant use. Using HEDIS measures of nonadherence (ie, antidepressant prescription coverage ≤84 days of the 114-day acute phase or ≤180 days of the 231-day continuation phase), we calculated the annual crude prevalence of antidepressant nonadherence and examined trends using unadjusted logistic regression. Multivariable logistic regression identified characteristics associated with antidepressant nonadherence. RESULTS: Among 9,452 eligible breast cancer survivors with depression (aged ≥65 years = 84% and White race = 82%), the crude prevalence of antidepressant nonadherence decreased from 2010 to 2019 for both the acute (49% to 40%; Ptrend<0.001) and continuation (67% to 57%; Ptrend<0.001) phases. Factors significantly associated with higher odds of antidepressant nonadherence in both the acute and continuation phases included Black race (odds ratios [ORs] [95% CI] for the acute/continuation phases: 2.0 [1.7-2.4]/2.0 [1.7-2.3]) and Hispanic ethnicity (1.5 [1.1-1.9]/2.2 [1.6-2.9]) compared with White race; receiving the first antidepressant from an oncologist vs a psychiatrist (1.4 [1.1-1.8]/1.6 [1.2-2.0]); and using antidepressants not recommended for older adults by the Beers criteria (2.2 [1.6-2.9]/2.0 [1.4-2.7]). Factors associated with lower odds of antidepressant nonadherence in both phases included receiving lymph node dissection (0.7 [0.5-0.9]/0.7 [0.5-0.9]), receiving endocrine therapy (0.9 [0.8-0.9]/0.8 [0.7-0.9]), having a higher National Cancer Institute comorbid index (0.8 [0.7-0.8]/0.9 [0.8-0.9]), having a follow-up visit with a psychiatrist (0.9 [0.8-0.9]/0.9 [0.8-0.9]), and switching to different antidepressants (0.7 [0.6-0.8]/0.7 [0.7-0.8]). CONCLUSIONS: Despite antidepressant nonadherence prevalence decreasing from 2010 to 2019, over half of breast cancer survivors with depression and Medicare were nonadherent in the continuation phase. Patients with identified nonadherence risk factors may benefit from close monitoring and targeted interventions. DISCLOSURES: Wei-Hsuan Lo-Ciganic reported grants from the National Institute on Drug Abuse (R01DA044985 and R01DA050676), the National Institute on Aging (R21AG060308), the National Institute of Mental Health (R01MH121907), Merck Sharp & Dohme, Bristol Myers Squibb, the Richard King Mellon Foundation at the University of Pittsburgh, the Clinical and Translational Science Institute of the University of Florida, the Pharmaceutical Research and Manufacturers of America (PhRMA) Foundation, and the US Department of Veterans Affairs outside the submitted work; in addition, Wei-Hsuan Lo-Ciganic has a patent pending for U1195.70174US00. Haesuk Park reported grants from Bristol Myers Squibb/Pfizer Alliance American Thrombosis Investigator Initiated Research Program (ARISTA-USA) outside the submitted work. Juan M. Hincapie-Castillo reported grants from Merck outside the submitted work. Debbie Wilson reported grants from the National Institute on Drug Abuse, the National Institute on Aging, Merck Sharp & Dohme, and Bristol Myers Squibb outside the submitted work; and serving as an editorial board member for the Journal of Pharmacy Technology. Ching-Yuan Chang's contributions to this manuscript were made while at the University of Florida College of Pharmacy. Ching-Yuan Chang is currently employed by Vertex Pharmaceuticals, Inc. Vertex did not fund or have any involvement in this study or publication. Vakaramoko Diaby is currently employed by Otsuka, Inc. Otsuka did not fund or have any involvement in this study or publication. No other disclosures were reported.

Disparities in Access to Hepatitis C Treatment Among Arizona Medicaid Beneficiaries With Chronic Hepatitis C.

BACKGROUND: High costs of direct-acting antivirals (DAAs) have led to their restricted access for patients with hepatitis C virus (HCV). OBJECTIVE: The aim was to assess how HCV treatment access and predictors of HCV treatment changed in the post-DAA period compared with pre-DAA period. METHODS: A retrospective cohort study using Arizona Medicaid data was conducted for patients with HCV to compare treatment initiation rates between pre-DAA (January 2008-October 2013) and post-DAA (November 2013-December 2018) periods. Multivariable logistic regression was used, controlling for demographic and clinical variables. RESULTS: Twenty-four thousand and ninety and 28,756 patients during the pre-DAA and post-DAA periods were identified. Overall, 12.6% were treated in the post-DAA period compared with 7.8% in the pre-DAA period ( P <0.001). The relative increase in the HCV treatment initiation rate from the pre-DAA to the post-DAA period was significant greater for Black beneficiaries compared with White beneficiaries ( P =0.002). Hispanic beneficiaries were less likely to be treated in the post-DAA period [adjusted odds ratios (aOR): 0.88; CI: 0.79-0.98] compared with White beneficiaries. Those with mental illness (aOR: 0.71; 95% CI: 0.63-0.80) and substance use disorders (aOR: 0.63; 95% CI: 0.58-0.68) were less likely to be treated in the post-DAA period. CONCLUSIONS: Although treatment initiation increased and disparities for Black beneficiaries compared with White beneficiaries attenuated in the post-DAA period, only 13% of Arizona Medicaid patients with HCV received DAA treatment. Disparities in DAA access remained among Hispanic patients and those with mental illness and substance use disorders.

Evaluation of Cough Medication Use Patterns in Ambulatory Care Settings in the United States: 2003-2018.

Using 2003−2018 National Ambulatory Medical Care Survey data for office-based visits and 2003−2018 National Hospital Ambulatory Medical Care Survey data for emergency department (ED) visits, we conducted cross-sectional analyses to examine cough medication (CM) use trends in the United States (US) ambulatory care settings. We included adult (≥18 years) patient visits with respiratory-infection-related or non-infection-related cough as reason-for-visit or diagnosis without malignant cancer or benign respiratory tumor diagnoses. Using multivariable logistic regressions, we examined opioid antitussive, benzonatate, dextromethorphan-containing antitussive, and gabapentinoid use trends. From 2003−2005 to 2015−2018, opioid antitussive use decreased in office-based visits (8.8% to 6.4%, Ptrend = 0.03) but remained stable in ED visits (6.3% to 5.9%, Ptrend = 0.99). In both settings, hydrocodone-containing antitussive use declined over 50%. Benzonatate use more than tripled (office-based:1.6% to 4.8%; ED:1.5% to 8.0%; both Ptrend < 0.001). Dextromethorphan-containing antitussive use increased in ED visits (1.8% to 2.6%, Ptrend = 0.003) but stayed unchanged in office-based visits (3.8% to 2.7%; Ptrend = 0.60). Gabapentinoid use doubled in office-based visits (1.1% in 2006−2008 to 2.4% in 2015−2018, Ptrend < 0.001) but was negligible in ED visits. In US office-based and ED ambulatory care settings, hydrocodone-containing antitussive use substantially declined from 2003 to 2018, while benzonatate use more than tripled, and dextromethorphan-containing antitussive and gabapentinoid use remained low (<3%).

Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study.

BACKGROUND: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state). METHODS: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013-16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017-18 Pennsylvania Medicaid data (n=318 585) and (2) 2015-17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use. FINDINGS: A total of 8641 (1·35%) 2013-16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017-18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015-17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013-16 Pennsylvania training dataset and validated on the 2013-16 Pennsylvania internal validation dataset, 2017-18 Pennsylvania external validation dataset, and 2015-17 Arizona external validation dataset were 0·841 (95% CI 0·835-0·847), 0·828 (0·822-0·834), and 0·817 (0·807-0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017-18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38-4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015-17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19-1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose. INTERPRETATION: A machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries. FUNDING: National Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

Trends in the Management of Headache Disorders in US Emergency Departments: Analysis of 2007-2018 National Hospital Ambulatory Medical Care Survey Data.

We examined trends in management of headache disorders in United States (US) emergency department (ED) visits. We conducted a cross-sectional study using 2007−2018 National Hospital Ambulatory Medical Care Survey data. We included adult patient visits (≥18 years) with a primary ED discharge diagnosis of headache. We classified headache medications by pharmacological group: opioids, butalbital, ergot alkaloids/triptans, acetaminophen/nonsteroidal anti-inflammatory drugs (NSAIDs), antiemetics, diphenhydramine, corticosteroids, and intravenous fluids. To obtain reliable estimates, we aggregated data into three time periods: 2007−2010, 2011−2014, and 2015−2018. Using multivariable logistic regression, we examined medication, neuroimaging, and outpatient referral trends, separately. Among headache-related ED visits, opioid use decreased from 54.1% in 2007−2010 to 28.3% in 2015−2018 (Ptrend < 0.001). There were statistically significant increasing trends in acetaminophen/NSAIDs, diphenhydramine, and corticosteroids use (all Ptrend < 0.001). Changes in butalbital (6.4%), ergot alkaloid/triptan (4.7%), antiemetic (59.2% in 2015−2018), and neuroimaging (37.3%) use over time were insignificant. Headache-related ED visits with outpatient referral for follow-up increased slightly from 73.3% in 2007−2010 to 79.7% in 2015−2018 (Ptrend = 0.02). Reflecting evidence-based guideline recommendations for headache management, opioid use substantially decreased from 2007 to 2018 among US headache-related ED visits. Future studies are warranted to identify strategies to promote evidence-based treatment for headaches (e.g., sumatriptan, dexamethasone) and appropriate outpatient referral and reduce unnecessary neuroimaging orders in EDs.

Dosing profiles of concurrent opioid and benzodiazepine use associated with overdose risk among US Medicare beneficiaries: group-based multi-trajectory models.

BACKGROUND AND AIMS: One-third of opioid (OPI) overdose deaths involve concurrent benzodiazepine (BZD) use. Little is known about concurrent opioid and benzodiazepine use (OPI-BZD) most associated with overdose risk. We aimed to examine associations between OPI-BZD dose and duration trajectories, and subsequent OPI or BZD overdose in US Medicare. DESIGN: Retrospective cohort study. SETTING: US Medicare. PARTICIPANTS: Using a 5% national Medicare data sample (2013-16) of fee-for-service beneficiaries without cancer initiating OPI prescriptions, we identified 37 879 beneficiaries (age ≥ 65 = 59.3%, female = 71.9%, white = 87.6%, having OPI overdose = 0.3%). MEASUREMENTS: During the 6 months following OPI initiation (i.e. trajectory period), we identified OPI-BZD dose and duration patterns using group-based multi-trajectory models, based on average daily morphine milligram equivalents (MME) for OPIs and diazepam milligram equivalents (DME) for BZDs. To label dose levels in each trajectory, we defined OPI use as very low (< 25 MME), low (25-50 MME), moderate (51-90 MME), high (91-150 MME) and very high (>150 MME) dose. Similarly, we defined BZD use as very low (< 10 DME), low (10-20 DME), moderate (21-40 DME), high (41-60 DME) and very high (> 60 DME) dose. Our primary analysis was to estimate the risk of time to first hospital or emergency department visit for OPI overdose within 6 months following the trajectory period using inverse probability of treatment-weighted Cox proportional hazards models. FINDINGS: We identified nine distinct OPI-BZD trajectories: group A: very low OPI (early discontinuation)-very low declining BZD (n = 10 598; 28.0% of the cohort); B: very low OPI (early discontinuation)-very low stable BZD (n = 4923; 13.0%); C: very low OPI (early discontinuation)-medium BZD (n = 4997; 13.2%); D: low OPI-low BZD (n = 5083; 13.4%); E: low OPI-high BZD (n = 3906; 10.3%); F: medium OPI-low BZD (n = 3948; 10.4%); G: very high OPI-high BZD (n = 1371; 3.6%); H: very high OPI-very high BZD (n = 957; 2.5%); and I: very high OPI-low BZD (n = 2096; 5.5%). Compared with group A, five trajectories (32.3% of the study cohort) were associated with increased 6-month OPI overdose risks: E: low OPI-high BZD [hazard ratio (HR) = 3.27, 95% confidence interval (CI) = 1.61-6.63]; F: medium OPI-low BZD (HR = 4.04, 95% CI = 2.06-7.95); G: very high OPI-high BZD (HR = 6.98, 95% CI = 3.11-15.64); H: very high OPI-very high BZD (HR = 4.41, 95% CI = 1.51-12.85); and I: very high OPI-low BZD (HR = 6.50, 95% CI = 3.15-13.42). CONCLUSIONS: Patterns of concurrent opioid and benzodiazepine use most associated with overdose risk among fee-for-service US Medicare beneficiaries initiating opioid prescriptions include very high-dose opioid use (MME > 150), high-dose benzodiazepine use (DME > 40) or medium-dose opioid with low-dose benzodiazepine use.

Industry Payments to Physicians and Prescribing Branded Memantine and Donepezil Combination.

BACKGROUND: Once-daily extended-released memantine with donepezil (hereafter memantine/donepezil) may improve medication adherence but has a 60-fold higher cost compared with combined generic components. Little is known about factors associated with prescribing memantine/donepezil. We examined the association between pharmaceutical industry payments to physicians and prescribing memantine/donepezil in Medicare. METHODS: A cross-sectional study was conducted. Using 2015-2016 Centers for Medicare and Medicaid Services Open Payments and Part D prescription databases, we identified unique physicians who prescribed ≥11 memantine/donepezil prescriptions from 2015 to 2016. Outcome variable was the number of memantine/donepezil prescriptions written per physician per year. The key independent variable was physician receipt of industry payments defined in 2 models: (1) number of payments and (2) amount of payment ($100 units) for memantine/donepezil received per physician per year. Multivariable Poisson regression was used, adjusting for potential confounders. RESULTS: Among 4,895 unique eligible physicians in 2015-2016, the median number of memantine/donepezil prescriptions per physician per year was 19.5 (25th percentile 13, 75th percentile 32). Physicians received between 0 and 75 payments per year (median 1, 25th percentile 0, 75th percentile 2.5) for memantine/donepezil, totaling an average of $92 per year (median $10.5, 25th percentile $0, 75th percentile $33.20). Every 1 additional payment received was associated with a 2% increase in new memantine/donepezil prescriptions prescribed per physician per year (rate ratio [RR] 1.02, 95% confidence interval [CI] 1.02-1.02). Every $100 increase in payment for memantine/donepezil was associated with a 0.3% increase in new memantine/donepezil prescriptions prescribed per physician per pear (RR 1.003, 95% CI 1.002-1.004). CONCLUSIONS: Receipt of industry payments for memantine/donepezil was independently associated with increased likelihood of physician prescribing memantine/donepezil in Medicare.

Using Baseplating and a Miniscope Preanchored with an Objective Lens for Calcium Transient Research in Mice.

Neuroscientists use miniature microscopes (miniscopes) to observe neuronal activity in freely behaving animals. The University of California, Los Angeles (UCLA) Miniscope team provides open resources for researchers to build miniscopes themselves. The V3 UCLA Miniscope is one of the most popular open-source miniscopes currently in use. It permits imaging of the fluorescence transients emitted from genetically modified neurons through an objective lens implanted on the superficial cortex (a one-lens system), or in deep brain areas through a combination of a relay lens implanted in the deep brain and an objective lens that is preanchored in the miniscope to observe the relayed image (a two-lens system). Even under optimal conditions (when neurons express fluorescence indicators and the relay lens has been properly implanted), a volume change of the dental cement between the baseplate and its attachment to the skull upon cement curing can cause misalignment with an altered distance between the objective and relay lenses, resulting in the poor image quality. A baseplate is a plate that helps mount the miniscope onto the skull and fixes the working distance between the objective and relay lenses. Thus, changes in the volume of the dental cement around the baseplate alter the distance between the lenses. The present protocol aims to minimize the misalignment problem caused by volume changes in the dental cement. The protocol reduces the misalignment by building an initial foundation of dental cement during relay lens implantation. The convalescence time after implantation is sufficient for the foundation of dental cement to cure the baseplate completely, so the baseplate can be cemented on this scaffold using as little new cement as possible. In the present article, we describe strategies for baseplating in mice to enable imaging of neuronal activity with an objective lens anchored in the miniscope.

Adherence to Statins Use and Risk of Dementia among Patients with Diabetes and Comorbid Hyperlipidemia.

The results from previous observational studies and clinical trials about the neuroprotective benefits of statins use for the prevention of dementia are contradictory. It is unclear whether the neuroprotective benefits are experienced in a specific group with a higher risk of dementia, such as patients with concurrent diabetes and hyperlipidemia. We aimed to examine the association between adherence to statins and the risk of dementia among patients with diabetes and comorbid hyperlipidemia. This was a retrospective study with a new user design. We used data from the Taiwan National Health Insurance Research Database to identify patients with diabetes and comorbid hyperlipidemia. The occurrence of dementia was the study outcome. The adherence to statins was the exposure, which was measured by the proportion of days covered (PDC) of statins. The good adherence included patients with ≥80% PDC of statins. Cox proportional hazards regression models were used to evaluate the association between adherence to statins and dementia. Among 18,125 included individuals with diabetes and comorbid hyperlipidemia, 33.5% had good adherence to statins. Compared to poor adherence to statins, good adherence to statins was not significantly associated with a reduced risk of dementia (hazard ratio = 0.94; 95%confidence interval = 0.70-1.24) among patients with diabetes and comorbid hyperlipidemia. Good adherence to statins was not found to be associated with the risk of dementia among patients with diabetes and comorbid hyperlipidemia in Taiwan. Future studies with a more diverse study population are needed to evaluate the neuroprotective effects of statins use on dementia prevention.

Survey of the triple-mentoring program for students at a religious medical school.

BACKGROUND: Tzu Chi University in Taiwan offers a unique mentoring program. This program differs from others as it comprises triple mentorship, namely, faculty mentors, Tzu Cheng/Yi De (TC/YD; senior volunteers), and school counselors. This study aimed to survey the role functions of the mentors from the perspective of medical students. METHODS: The Role Functions of the Mentoring Program Scale (RFMPS) was developed on the basis of literature reviews and focus groups and it underwent exploratory factor analysis for internal consistency and reliability. RFMPS comprises four role functions, namely, mental, educational, career, and humanistic/moral guidance counseling. The survey was distributed to 171 medical students via an online network with two-month intervals and was analyzed using multivariate analysis of variance. RESULTS: The overall response rate was 64% (116/171). The mean scores of the four role functions in descending order belonged to faculty mentors, TC/YD, and school counselors. For humanistic/moral guidance, students had an equal preference for the faculty mentors and TC/YD over school counselors. As for educational, career, and mental guidance counseling, students preferred faculty mentors over TC/YD and school counselors. Faculty mentors provided students with the required guidance counseling for all the four role functions, especially educational guidance; TC/YD in particular offered prominent humanistic/moral guidance and career counseling; school counselors were less preferred but guided students in need. CONCLUSIONS: Medical students value different role functions provided by faculty mentors, TC/YD, and school counselors. A diversified focus could be provided by the faculty mentors, particularly in educational, career, mental, and humanistic/moral counseling; TC/YD specialized in humanistic/moral guidance; and the school counselors carried out their role function only when needed. Humanistic/moral guidance is equally preferred to other types of guidance, which can be equally valuable in future mentoring programs.

Dual-trajectories of opioid and gabapentinoid use and risk of subsequent drug overdose among Medicare beneficiaries in the United States: a retrospective cohort study.

BACKGROUND AND AIMS: Little is known about opioid and gabapentinoid (OPI-GABA) use duration and dose patterns' associations with adverse outcome risks. We examined associations between OPI-GABA dose and duration trajectories and subsequent drug overdose. DESIGN: Retrospective cohort study. SETTING: US Medicare. PARTICIPANTS: Using a 5% sample (2011-16), we identified 71 005 fee-for-service Medicare beneficiaries with fibromyalgia, low back pain, neuropathy and/or osteoarthritis initiating OPIs and/or GABAs [mean age ± standard deviation (SD) = 65.5 ± 14.5 years, female = 68.1%, white = 76.8%]. MEASUREMENTS: Group-based multi-trajectory models identified distinct OPI-GABA use patterns during the year of OPI and/or GABA initiation, based on weekly average standardized daily dose (i.e. OPIs = morphine milligram equivalent, GABAs = minimum effective daily dose). We estimated models with three to 12 trajectories and selected the best model based on Bayesian information criterion (BIC) and Nagin's criteria. We estimated risk of time to first drug overdose diagnosis within 12 months following the index year, adjusting for socio-demographic and health factors using inverse probability of treatment weighted multivariable Cox proportional hazards models. FINDINGS: We identified 10 distinct trajectories (BIC = -1 176 954; OPI-only = 3, GABA-only = 3, OPI-GABA = 4). Compared with OPI-only early discontinuers (40.6% of the cohort), 1-year drug overdose risk varied by trajectory group: consistent low-dose OPI-only users [16.6%; hazard ratio (HR) = 1.47, 95% confidence interval (CI) = 1.19-1.82], consistent high-dose OPI-only users (1.8%; HR = 4.57, 95% CI = 2.99-6.98), GABA-only early discontinuers (12.5%; HR = 1.39, 95% CI = 1.09-1.77), consistent low-dose GABA-only users (11.0%; HR = 1.44, 95% CI = 1.12-1.85), consistent high-dose GABA-only users (3.1%; HR = 1.43, 95% CI = 0.94-2.17), early discontinuation of OPIs and consistent low-dose GABA users (6.9%; HR = 1.24, 95% CI = 0.90-1.69), consistent low-dose OPI-GABA users (3.4%; HR = 2.49, 95% CI = 1.76-3.52), consistent low-dose OPI and high-dose GABA users (3.2%; HR = 2.46, 95% CI = 1.71-3.53) and consistent high-dose OPI and moderate-dose GABA users (0.9%; HR = 7.22, 95% CI = 4.46-11.69). CONCLUSIONS: Risk of drug overdose varied substantially among US Medicare beneficiaries on different use trajectories of opioids and gabapentinoids. High-dose opioid-only users and all consistent opioid and gabapentinoid users (regardless of doses) had more than double the risk of subsequent drug overdose compared with opioid-only early discontinuers.

Association between race/ethnicity and the risk of amputation of lower extremities among medicare beneficiaries with diabetic foot ulcers and diabetic foot infections.

INTRODUCTION: This study aimed to examine the association of race and ethnicity on the risk of lower extremity amputations among Medicare beneficiaries with diabetic foot ulcers (DFUs) and diabetic foot infections (DFIs). RESEARCH DESIGN AND METHODS: A retrospective study included 2011-2015 data of a 5% sample of fee-for-service Medicare beneficiaries with a newly diagnosed DFU and/or DFI. The primary outcome was the time to the first major amputation episode after a DFU and/or DFI were identified using the diagnosis and procedure codes. We used multivariable Cox proportional hazards models to estimate the risk of time to the first major amputation across races, adjusting for sociodemographic and health status factors. Adjusted hazard ratios (aHRs) with a 95% CI were reported. RESULTS: Among 92 929 Medicare beneficiaries newly diagnosed with DFUs and/or DFIs, 77% were whites, 14.3% African Americans (AAs), 3.3% Hispanics, 0.7% Native Americans (NAs), and 4.0% were other races. The incidence rates of major amputation were 0.02 person-years for NAs, 0.02 person-years for AAs, 0.01 person-years for Hispanics, 0.01 person-years for other races, and 0.01 person-years for whites (p<0.05). Multivariable analysis showed that AAs (aHR=1.9, 95% CI 1.7 to 2.2, p<0.0001) and NAs (aHR=1.8, 95% CI 1.3 to 2.6, p=0.001) were associated with an increased risk of major amputation compared with whites. Beneficiaries with DFUs and/or DFIs diagnosed by a podiatrist or primary care physician (aHR=0.7, 95% CI 0.6 to 0.8, p<0.0001, specialists as reference) or at an outpatient visit (aHR=0.3, 95% CI 0.3 to 0.3, p<0.0001, inpatient stay as reference) were associated with a decreased risk of major amputation. CONCLUSIONS: Racial and ethnic disparities in the risk of lower extremity amputations appear to exist among fee-for-service Medicare beneficiaries with diabetic foot problems. AAs and NAs with DFUs and/or DFIs were associated with an increased risk of major amputations compared with white Medicare beneficiaries.

Immune Checkpoint Inhibitors and Immune-Related Adverse Events in Patients With Advanced Melanoma: A Systematic Review and Network Meta-analysis.

Importance: Since 2011, immune checkpoint inhibitors (ICIs) have been effective treatment options for advanced melanoma. Little is known about how risks of immune-related adverse events (irAEs) vary by ICIs. Objective: To compare the risk of irAEs across different treatment regimens for advanced melanoma using network meta-analysis. Data Sources: PubMed/MEDLINE, Embase, Web of Science, and Scopus were searched for all randomized clinical trial (RCT) articles published from January 1, 2010, through June 30, 2019. Study Selection: Studies included phases 2 and 3 RCTs in the treatment of advanced melanoma that compared ICIs (ipilimumab, nivolumab, and pembrolizumab) with chemotherapy drugs (eg, dacarbazine, carboplatin, and paclitaxel) or different ICI regimens. Data Extraction and Synthesis: Different treatment regimens were compared using bayesian network meta-analysis with Markov chain Monte Carlo simulation with noninformative prior distribution and random-effects generalized linear models. Main Outcomes and Measures: Primary outcomes were the cumulative incidence of any irAEs (regardless of severity) and severe irAEs (grades 3-5). Based on the pooled odds ratios (ORs) and 95% credible intervals (95% CrI), the probability of being associated with the lowest irAE risks was estimated for each treatment regimen. Results: Nine RCTs with 8 different treatment regimens for advanced melanoma and involving a total of 5051 patients were included. Overall, the 3 ICI treatment regimens associated with the lowest risk of any or severe irAEs were pembrolizumab, 2 mg/kg, every 3 weeks; nivolumab, 3 mg/kg, every 2 weeks; and pembrolizumab, 10 mg/kg, every 3 weeks. Compared with ipilimumab, 10 mg/kg, every 3 weeks, only nivolumab, 3 mg/kg, every 2 weeks, was associated with a decreased risk for any irAEs (OR, 0.34; 95% CrI, 0.13-0.94). A decreased risk for severe irAEs was observed for ipilimumab, 3 mg/kg, every 3 weeks (OR, 0.35; 95% CrI, 0.14-0.74); pembrolizumab, 10 mg/kg, every 2 weeks (OR, 0.22; 95% CrI, 0.05-0.95) and 10 mg/kg every 3 weeks (OR, 0.20; 95% CrI, 0.06-0.68); and nivolumab, 3 mg/kg, every 2 weeks (OR, 0.20; 95% CrI, 0.07-0.48) compared with ipilimumab, 10 mg/kg, every 3 weeks. An increased risk for severe irAEs was associated with nivolumab, 1 mg/kg, every 3 weeks combined with ipilimumab, 3 mg/kg, every 3 weeks compared with other ICI regimens (ORs ranging from 4.09 [95% CrI, 1.73-10.99] to 7.40 [95% CrI, 1.12-49.29]) except ipilimumab, 10 mg/kg, every 3 weeks. Conclusions and Relevance: These findings suggest that for patients with advanced melanoma at high risk of irAEs, pembrolizumab, 2 mg/kg, every 3 weeks, nivolumab, 3 mg/kg, every 2 weeks, and pembrolizumab, 10 mg/kg, every 3 weeks may be the preferred treatment regimens (with respect to irAE risks) among the ICI regimens reported, whereas ipilimumab, 10 mg/kg, every 3 weeks alone and nivolumab, 1 mg/kg, every 3 weeks combined with ipilimumab, 3 mg/kg, every 3 weeks should be used with caution. A network analysis may be valuable for clinical decision-making when evidence from head-to-head comparisons is lacking.

In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology.

This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms after the image calibration and the benchmark of a laser displacement sensor (LDS). The embedded program of machine vision has used zero-mean normalized correlation (ZNCC) and peak finding (PF) for tracking the registered characteristics on the object surface. The calibrated VMS provides time-displacement curves related to both horizontal and vertical directions, promising remote inspections of selected points without attaching additional markers or sensors. The experimental setup of the VMS is cost-effective and uncomplicated, supporting universal combinations between the imaging system and computational devices. The procedures of the proposed scheme are (1) setting up a digital camera, (2) calibrating the imaging system, (3) retrieving the data of image streaming, (4) executing the ZNCC criteria, and providing the time-displacement results of selected points. The experiment setup of the proposed VMS is straightforward and can cooperate with surveillances in industrial environments. The embedded program upgrades the functionality of the camera system from the events monitoring to remote measurement without the additional cost of attaching sensors on motors or targets. Edge nodes equipped with the image-tracking program serve as the physical layer and upload the extracted features to a cloud server via the wireless sensor network (WSN). The VMS can provide customized services under the architecture of the cyber-physical system (CPS), and this research offers an early warning alarm of the mechanical system before unexpected downtime. Based on the smart sensing technology, the in situ diagnosis of industrial motors given from the VMS enables preventative maintenance and contributes to the precision measurement of intelligent automation.

Real-Time Evaluation of the Mechanical Performance and Residual Life of a Notching Mold using Embedded PVDF Sensors and SVM Criteria.

The geometric tolerance of notching machines used in the fabrication of components for induction motor stators and rotators is less than 50 µm. The blunt edges of worn molds can cause the edge of the sheet metal to form a burr, which can seriously impede assembly and reduce the efficiency of the resulting motor. The overuse of molds without sufficient maintenance leads to wasted sheet material, whereas excessive maintenance shortens the life of the punch/die plate. Diagnosing the mechanical performance of die molds requires extensive experience and fine-grained sensor data. In this study, we embedded polyvinylidene fluoride (PVDF) films within the mechanical mold of a notching machine to obtain direct measurements of the reaction forces imposed by the punch. We also developed an automated diagnosis program based on a support vector machine (SVM) to characterize the performance of the mechanical mold. The proposed cyber-physical system (CPS) facilitated the real-time monitoring of machinery for preventative maintenance as well as the implementation of early warning alarms. The cloud server used to gather mold-related data also generated data logs for managers. The hyperplane of the CPS-PVDF was calibrated using a variety of parameters pertaining to the edge characteristics of punches. Stereo-microscopy analysis of the punched workpiece verified that the accuracy of the fault classification was 97.6%.