Cumulative Anticholinergic Burden and its Predictors among Older Adults with Alzheimer's Disease Initiating Cholinesterase Inhibitors.

BACKGROUND: Cumulative anticholinergic burden refers to the cumulative effect of multiple medications with anticholinergic properties. However, concomitant use of cholinesterase inhibitors (ChEIs) and anticholinergic burden can nullify the benefit of the treatment and worsen Alzheimer's disease (AD). A literature gap exists regarding the extent of the cumulative anticholinergic burden and associated risk factors in AD. Therefore, this study evaluated the prevalence and predictors of cumulative anticholinergic burden among patients with AD initiating ChEIs. METHODS: A retrospective longitudinal cohort study was conducted using the Medicare claims data involving parts A, B, and D from 2013 to 2017. The study sample included older adults (65 years and older) diagnosed with AD and initiating ChEIs (donepezil, rivastigmine, or galantamine). The cumulative anticholinergic burden was calculated based on the Anticholinergic Cognitive Burden scale and patient-specific dosing using the defined daily dose over the 1 year follow-up period after ChEI initiation. Incremental anticholinergic burden levels were dichotomized into moderate-high (sum of standardized daily anticholinergic exposure over a year (TSDD) score ≥ 90) versus low-no (score 0-89). The Andersen Behavioral Model was used as the conceptual framework for selecting the predictors under the predisposing, enabling, and need categories. A multivariable logistic regression model was used to evaluate the predictors of high-moderate versus low-no cumulative anticholinergic burden. A multinomial logistic regression model was also used to determine the factors associated with patients having moderate and high burdens compared to low/no burdens. RESULTS: The study included 222,064 older adults with AD with incident ChEI use (mean age 82.24 ± 7.29, 68.9% females, 83.6% White). Overall, 80.48% had some anticholinergic burden during the follow-up, with 36.26% patients with moderate (TSDD scores 90-499), followed by 24.76% high (TSDD score > 500), and 19.46% with low (TSDD score 1-89) burden categories. Predisposing factors such as age; African American, Asian, or Hispanic race; and need factors included comorbidities such as dyslipidemia, syncope, delirium, fracture, pneumonia, epilepsy, and claims-based frailty index were less likely to be associated with the moderate-high anticholinergic burden. The factors that increased the odds of moderate-high burden were predisposing factors such as female sex; enabling factors such as dual eligibility and diagnosis year; and need factors such as baseline burden, behavioral and psychological symptoms of dementia, depression, insomnia, urinary incontinence, irritable bowel syndrome, anxiety, muscle spasm, gastroesophageal reflux disease, heart failure, and dysrhythmia. Most of these findings remained consistent with multinomial logistic regression.  CONCLUSION: Four out of five older adults with AD had some level of anticholinergic burden, with over 60% having moderate-high anticholinergic burden. Several predisposing, enabling, and need factors were associated with the cumulative anticholinergic burden. The study findings suggest a critical need to minimize the cumulative anticholinergic burden to improve AD care.

Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis.

Objectives: Machine learning algorithms are being increasingly used for predicting hospital readmissions. This meta-analysis evaluated the performance of logistic regression (LR) and machine learning (ML) models for the prediction of 30-day hospital readmission among patients in the US. Methods: Electronic databases (i.e., Medline, PubMed, and Embase) were searched from January 2015 to December 2019. Only studies in the English language were included. Two reviewers performed studies screening, quality appraisal, and data collection. The quality of the studies was assessed using the Quality in Prognosis Studies (QUIPS) tool. Model performance was evaluated using the Area Under the Curve (AUC). A random-effects meta-analysis was performed using STATA 16. Results: Nine studies were included based on the selection criteria. The most common ML techniques were tree-based methods such as boosting and random forest. Most of the studies had a low risk of bias (8/9). The AUC was greater with ML to predict 30-day all-cause hospital readmission compared with LR [Mean Difference (MD): 0.03; 95% Confidence Interval (CI) 0.01-0.05]. Subgroup analyses found that deep-learning methods had a better performance compared with LR (MD 0.06; 95% CI, 0.04-0.09), followed by neural networks (MD: 0.03; 95% CI, 0.03-0.03), while the AUCs of the tree-based (MD: 0.02; 95% CI -0.00-0.04) and kernel-based (MD: 0.02; 95% CI 0.02 (-0.13-0.16) methods were no different compared to LR. More than half of the studies evaluated heart failure-related rehospitalization (N = 5). For the readmission prediction among heart failure patients, ML performed better compared with LR, with a mean difference in AUC of 0.04 (95% CI, 0.01-0.07). The leave-one-out sensitivity analysis confirmed the robustness of the findings. Conclusion: Multiple ML methods were used to predict 30-day all-cause hospital readmission. Performance varied across the ML methods, with deep-learning methods showing the best performance over the LR.

Differences in Perceived Threat and Efficacy in Managing Opioid Use Disorder versus Alcohol Use Disorder.

Background: Efforts to increase the availability of Medication Assisted Treatment for alcohol use disorder (AUD) and opioid use disorder (OUD) may be futile if patients lack motivation for recovery and are unwilling to seek treatment. Objectives: In this cross-sectional, online survey, we used the Extended Parallel Process Model (EPPM) to assess how participants at risk of AUD or OUD react to their perceived threat and assess their response to pharmacotherapy as a potential treatment. EPPM constructs were assessed using the Risk Based Diagnosis Scale. Descriptive statistics measure the proportion of treated vs untreated participants. Untreated participants were sorted into one of three groups categorizing perceived threat - low threat appraisal, and danger or fear control. Results: Of 411 total responses, most (n = 293[71.29%]) sorted into the AUD cohort and 118(28.71%) into the OUD cohort. Overall, 104(25.30%) had received treatment and 307(74.70%) didn't. Within the OUD cohort, there were 67 untreated participants - 16(23.88%) exhibited low threat appraisal, 13(19.40%) were likely to undergo fear control, and 38(56.72%) were likely to undergo danger control. Within the AUD cohort, there were 240 untreated participants - 75(31.25%) exhibited low threat appraisal, 100(41.67%) were likely to experience fear control, and 65(27.08%) were likely to experience danger control. Participants in the OUD cohort were more likely to undergo danger control than those in the AUD cohort (χ2 = 19.26, p < 0.05). Conclusions: This study identified perceived threat and efficacy when an individual was at risk of a SUD, but more insight into potential early interventions is needed - particularly in those individuals with polysubstance use disorder.

Clinical efficacy and safety of cannabidiol for pediatric refractory epilepsy indications: A systematic review and meta-analysis.

Antiseizure medications (ASMs) are the mainstay for the treatment of seizure disorders. However, about one-third of people with epilepsy remain refractory to current ASMs. Cannabidiol (CBD) has recently been approved as ASM for three refractory epilepsy syndrome indications in children and adults. In this study, we evaluated the overall clinical potential of an oral CBD to treat refractory epilepsy in patients with Dravet syndrome (DS), Lennox-Gastaut syndrome (LGS), and tuberous sclerosis complex (TSC) through a systematic review and meta-analysis. A comprehensive search of databases was conducted, including randomized controlled trials (RCTs) assessing the effect of CBD in epilepsy patients. The review was conducted as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review focused on RCTs involving patients receiving highly purified oral CBD (Epidiolex, 10 to 50 mg/kg/day) for up to 16 weeks. A subgroup analysis by syndrome and CBD with or without concomitant clobazam was conducted. The key outcomes were reduction in seizure frequency, differences in 50% responder rates, adverse events, and interactions with clobazam as co-therapy. Odds ratio (OR) with 95% confidence interval (CI) were estimated. Of 1183 articles screened, we included 6 RCTs meeting our eligibility criteria. All studies were considered to have a low risk of bias. In the pooled analysis, CBD treatment was found to be more efficacious compared to placebo (OR = 2.45, 95% CI =1.81-3.32, p < 0.01). Subgroup analysis by syndrome demonstrated the odds of ≥50% reduction in seizures with CBD treatment in patients with DS (OR = 2.26, 95% CI:1.38-3.70), LGS (OR = 2.98, 95% CI:1.83-4.85) and TSC (OR = 1.99, 95% CI = 1.06-3.76). Compared with placebo, CBD was associated with increased adverse events (OR = 1.81, 95% CI = 1.33-2.46) such as diarrhea, somnolence, and sedation, and any serious adverse events (OR = 2.86, 95% CI = 1.63-5.05). Other factors, including dosage and clobazam co-therapy, were significantly associated with a greater effect on seizure control and side effects of CBD. In conclusion, the study shows that CBD is highly efficacious both as standalone and adjunct therapy with clobazam for controlling seizures in DS, LGS, and TSC conditions while limiting side effects. Further pharmacodynamic investigation of CBD actions, drug interaction assessments, and therapeutic management guidelines are warranted.

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.

Pain management practices for outpatients with breast cancer.

Objectives: Pain is a significant problem in patients with breast cancer. Limited data exist regarding the nature and extent of pain management in women with breast cancer visiting outpatient settings. This study examined the pain management practices and the factors associated with prescribing pain medications among breast cancer patients. Methods: This cross-sectional study used the National Ambulatory Medical Care Survey (NAMCS) 2011-2016, nationally representative outpatient survey data. Women (age ≥18 years) with breast cancer as the primary diagnosis were included. Weighted descriptive analyses examined national-level pain management practices, while multivariable logistic regression evaluated the factors associated with the prescribing of pain medications and opioids. Results: There were 23.95 million (95% confidence interval [CI], 19.29-28.60) outpatient visits for breast cancer during the study period. Pain medications were prescribed in 27.12% of these visits, with non-opioids prescribed in 17.13% and opioids in 15.16% of visits. Logistic regression analyses revealed that patients on Medicaid/other state-based insurance (odds ratio [OR] =2.38, 95% CI:1.15-4.93), those visiting general/family practice physicians (OR = 3.18, 95% CI:1.22-8.29) and patients receiving adjuvant pain medications (OR = 4.74, 95% CI: 3.10-7.24) were associated with a greater odds of receiving pain medications; while patients who were white (OR = 0.50, 95% CI:0.3-0.85), those residing in the northeast region (OR = 0.31, 95% CI: 0.10-0.99), and non-primary care provider visits (OR = 0.37, 95% CI:0.15-0.94) were associated with lower odds of receiving pain medications. Regional variations were observed among those receiving pain medications: women in the Northeast (OR = 0.06, 95% CI:0.01-0.29), Midwest (OR = 0.15, 95% CI:0.04-0.62), and South (OR = 0.24, 95% CI:0.06-0.92) regions were less likely to receive opioids. However, patients visiting general and family practice specialties (OR = 6.76, 95% CI:1.71-26.70) were more likely to prescribe opioids than non-opioids. Conclusions: The national survey data revealed one in four women visits and one in seven office visits for breast cancer received pain medication prescriptions and opioid medications, respectively. Both patient and provider characteristics contribute to variations in pain management in breast cancer patients. Further research is needed to evaluate the long-term consequences of these variations in breast cancer.

Prescribing of disease modifying agents in older adults with multiple sclerosis.

BACKGROUND: The use of disease-modifying agents (DMAs) to treat Multiple Sclerosis (MS) in older adults is debated as the disease activity decreases with aging. However, limited data exist regarding prescribing patterns of DMAs among older adults with MS. OBJECTIVE: To examine prescribing patterns of DMAs and the factors associated with DMA prescribing practices among older adults with MS using electronic medical records (EMR) data. METHODS: A retrospective longitudinal cohort study was conducted using the TriNetX, a federated EMR network from the US, data from 2016 to 2019. The study included older adults (≥60 years) with MS diagnosis and at least one prescription record during the study period. Patients with DMA prescriptions were identified and further classified into injectable, oral, or infusion users based on their last DMA prescription. A multivariable logistic regression model was used to evaluate the factors associated with prescribing of DMAs. A multinomial logistic regression model was also used to determine the factors associated with prescribing a particular dosage form of DMA. RESULTS: The study cohort consisted of 12,922 older adults with MS, with 2,455 (18.99%) receiving DMA prescriptions. The commonly prescribed DMAs were injectables (10.46%), followed by orals (6.06%) and infusions (2.40%). Multivariable logistic regression revealed that older adults between 60- to 64 years (Adjusted Odds Ratio [aOR]= 2.38) and 65-69 years (aOR=1.60) had higher odds of receiving DMA compared to older adults of 70 years and above. African Americans (aOR=1.71) had higher odds of receiving DMA prescriptions compared to Caucasians. The presence of symptoms (pain, fatigue, speech, walking difficulty) and use of symptomatic medication (anti-fatigue medication, bladder dysfunction medication, antispasmodics, antidepressants, and relapse medication) increased the odds of being prescribed DMAs. Multinomial logistic regression found that patients 60-64 years of age had higher odds of being prescribed infusion (aOR, 95% Confidence Interval [CI] =2.06, 1.35-3.15) and oral (65-69 years: aOR=1.60, 1.24-2.07) over injectable DMAs compared to the older adults aged 70 years and above.Older males (aOR=1.68, 95% CI: 1.23-2.30) were associated with increased odds of being prescribed infusion DMA over injectable DMA compared to females. The presence of comorbidities such as coagulopathy and peripheral vascular disorders decreased the odds of being prescribed oral DMA over injectable DMA. Patients with cerebellar symptoms had an increased likelihood of being prescribed with an infusion DMA over injectable DMA. Patients using drugs for treating relapses had higher odds of being prescribed an infusion DMA over an injectable DMA. In terms of healthcare utilization, older adults with outpatient visits had higher odds of being prescribed an infusion DMA over an injectable DMA, while older adults with inpatient visits had lower odds of being prescribed an infusion DMA over an injectable DMA. CONCLUSION: Nearly one in five older adults with MS are prescribed DMAs, with a majority receiving injectable DMAs. Several demographic and clinical factors were associated with DMA prescribing . This study fills the data gap regarding the utilization of DMAs in older adults with MS.

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.

Anticholinergic medications and risk of dementia in older adults: Where are we now?

INTRODUCTION: Anticholinergic medications are effective for a wide variety of indications, but are associated with significant central adverse effects, especially cognitive decline and dementia in older adults. AREAS COVERED: We conducted a review of relevant literature in the past decade to address anticholinergic scales and evidence of anticholinergic-related dementia/cognitive decline in older adults. We discussed various anticholinergic scales used to classify anticholinergic medications. The review focused on the evidence from previous reviews and individual studies evaluating the anticholinergic-related risk of developing cognitive decline/dementia. This review also discussed clinical and methodological issues of studies along with recommendations for practice and research. EXPERT OPINION: The review demonstrates moderate to strong risk of dementia with anticholinergic use in multiple studies involving older adults, irrespective of the study design, analytical approach, anticholinergic exposure and outcome definition. This risk is particularly significant with the cumulative burden and high-level anticholinergics. There also exists a dose-response relationship between anticholinergic use and increased risk for dementia. Therefore, anticholinergic agents can be considered as a modifiable risk factor for dementia and cognitive decline in older adults. Based on the current evidence, regular assessment and optimization of anticholinergic burden prior to prescribing these medications can minimize anticholinergic-related morbidity in older adults.

Fondaparinux Sodium Compared With Low-Molecular-Weight Heparins for Perioperative Surgical Thromboprophylaxis: A Systematic Review and Meta-analysis.

Background Fondaparinux sodium has been compared with low-molecular-weight heparins ( LMWH ) in randomized controlled trials for perioperative surgical thromboprophylaxis. However, the results from these studies are inconsistent in terms of efficacy and safety to reach a clinical decision. The objective of this study was to systematically review the randomized controlled trials comparing the efficacy and safety of fondaparinux and LMWH for perioperative surgical thromboprophylaxis. Methods and Results Systematic search in various databases was done to identify randomized controlled trials comparing fondaparinux and LMWH published during the years 2000 to 2017. Outcomes of interest in this study included venous thromboembolism up to day 15, all-cause mortality up to day 90, major bleeding, and minor bleeding during the treatment period. Analyses were performed with the relative odds based on a random-effects model using Mantel-Haenszel statistics. Results were presented as odds ratios with their 95% CIs. The assessment of study quality was performed as per Cochrane collaboration. After screening 10 644 articles, 12 randomized controlled trials including 14 906 patients were included in the final analyses. Pooled analyses showed the odds of venous thromboembolism in the fondaparinux group were 0.49 times the odds in LMWH group ( OR =0.49 [0.38-0.64]). However, the odds of major bleeding in the fondaparinux group were 1.48 times the odds in the LMWH group ( OR =1.48 [1.15-1.90]). Conclusions Fondaparinux was associated with a superior efficacy in terms of reduction of venous thromboembolism in this meta-analysis. However, it was also associated with increased odds of major bleeding.