Patient-reported experiences with refractory or unexplained chronic cough: a qualitative analysis.

BACKGROUND: Chronic cough, defined as a cough lasting 8 or more weeks, affects up to 10% of adults. Refractory chronic cough (RCC) is a cough that is uncontrolled despite comprehensive investigation and treatment of comorbid conditions while unexplained chronic cough (UCC) is a cough with no identifiable cause despite extensive evaluation of comorbid conditions. RCC and UCC are often poorly controlled. Understanding individuals' lived experience of the symptoms and impacts of these conditions may guide therapeutic strategies. OBJECTIVES: The primary objectives of this study were to assess respondents' perceptions of the key symptoms of RCC and UCC and the impacts of RCC and UCC and their symptoms on well-being, health-related quality of life, work productivity, and social relationships. DESIGN: Qualitative study. METHODS: This study enrolled 30 adults with physician-diagnosed RCC or UCC. Two trained qualitative researchers conducted individual, in-depth telephone interviews using a semi-structured interview guide. Interviews were audio-recorded, transcribed, coded, and systematically analyzed to identify content themes. RESULTS: A total of 15 respondents with RCC and 15 with UCC were included in the study. Many respondents had RCC or UCC for a long duration (median 9 years, range: 0-24). Half of the respondents reported having a coughing episode at least once daily. Only 40% of respondents reported that medication had improved their symptoms. In over half of the respondents, RCC or UCC hindered communication, caused embarrassment, frustration, and worry, and lowered quality of life. Perceptions of meaningful treatment benefits in RCC or UCC varied widely across respondents. CONCLUSION: RCC and UCC remained poorly managed in many individuals and were associated with a wide range of symptoms and cough triggers that hindered daily activities and reduced emotional well-being. Understanding individuals' lived experiences may inform the development of RCC and UCC therapeutic strategies.

Patient-reported experiences with refractory or unexplained chronic cough: a qualitative analysisChronic cough, particularly refractory and unexplained chronic cough, remain poorly managed in many individuals and are associated with a wide range of symptoms and cough triggers that hinder daily activities and reduce emotional well-being. Currently there are no US Food and Drug Administration-approved treatments for refractory or unexplained chronic cough. Understanding the experience and treatment preferences of individuals with these conditions may help inform the development of new therapies and clarify the potential impact of such therapies on the lives of individuals with chronic cough. Using in-depth interviews, the present study comprehensively evaluated individuals' experience with refractory or unexplained chronic cough and treatment priorities, a research area that has not been well-studied. This study detailed broad-ranging physical, behavioral, and emotional impacts of chronic cough, which hindered individuals' social well-being.

Systematic literature review of treatments used for refractory or unexplained chronic cough in adults.

BACKGROUND: Refractory or unexplained chronic cough (RCC or UCC) is difficult to manage and is usually treated by the off-label use of drugs approved for other indications. OBJECTIVE: The objectives of this systematic literature review (SLR) were to identify and characterize the current published body of evidence for the efficacy and safety of treatments for RCC or UCC. METHODS: The SLR was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The SLRs pre-defined population included patients ≥18 years of age who were diagnosed with chronic cough. The review was not restricted to any intervention type or study comparator, nor by timeframe. RESULTS: A total of 20 eligible publications from 19 unique trials were included. Seventeen of these trials were randomized controlled trials and most (14/17) were placebo-controlled. There was considerable variability between trials in the definition of RCC or UCC, participant exclusion and inclusion criteria, outcome measurement timepoints, and the safety and efficacy outcomes assessed. Several trials identified significant improvements in cough frequency, severity, or health-related quality of life measures while participants were on treatment, although these improvements did not persist in any of the studies that included a post-treatment follow-up timepoint. CONCLUSIONS: In the absence of an approved therapy, placebo remains the most common comparator in trials of potential RCC or UCC treatments. The between-study comparability of the published evidence is limited by heterogeneity of study design, study populations, and outcomes measures, as well as by concerns regarding study size and risk of bias.

Machine learning to identify chronic cough from administrative claims data.

Accurate identification of patient populations is an essential component of clinical research, especially for medical conditions such as chronic cough that are inconsistently defined and diagnosed. We aimed to develop and compare machine learning models to identify chronic cough from medical and pharmacy claims data. In this retrospective observational study, we compared 3 machine learning algorithms based on XG Boost, logistic regression, and neural network approaches using a large claims and electronic health record database. Of the 327,423 patients who met the study criteria, 4,818 had chronic cough based on linked claims-electronic health record data. The XG Boost model showed the best performance, achieving a Receiver-Operator Characteristic Area Under the Curve (ROC-AUC) of 0.916. We selected a cutoff that favors a high positive predictive value (PPV) to minimize false positives, resulting in a sensitivity, specificity, PPV, and negative predictive value of 18.0%, 99.6%, 38.7%, and 98.8%, respectively on the held-out testing set (n = 82,262). Logistic regression and neural network models achieved slightly lower ROC-AUCs of 0.907 and 0.838, respectively. The XG Boost and logistic regression models maintained their robust performance in subgroups of individuals with higher rates of chronic cough. Machine learning algorithms are one way of identifying conditions that are not coded in medical records, and can help identify individuals with chronic cough from claims data with a high degree of classification value.

Chronic cough: more than just a persistent cough: a systematic literature review to understand the impact of chronic cough on quality of life.

PURPOSE: Chronic cough (CC), defined as a cough persisting ≥ 8 weeks, can have a substantial negative impact on health-related quality of life (HRQoL). This is exacerbated by challenges with timely diagnosis and a lack of approved therapies. A systematic literature review (SLR) was conducted to identify evidence on HRQoL and health state utility values associated with refractory CC or unexplained CC. METHODS: Electronic database searches were supplemented with searches of conference proceedings and health technology assessment body websites. Two independent reviewers assessed all citations for inclusion based on predefined inclusion/exclusion criteria. Key inclusion criteria were patient populations with CC and reporting of patient-reported outcomes or utilities using generic or disease-specific measures. RESULTS: Following screening, 65 studies were identified for inclusion in the SLR. Of these, 23 studies assessed HRQoL among patients with CC who were not treated or treated with unspecified interventions, and 42 studies in patients who were treated with specified interventions. The studies indicated a substantial decrement to HRQoL as a result of CC, characterized by generic and disease-specific patient-reported outcome measures. HRQoL was impacted across multiple domains, including physical, psychological, and social functioning. The studies also demonstrated the potential for treatments to have a significant positive impact on HRQoL. CONCLUSIONS: CC can substantially affect HRQoL in patients, across physical, psychological, and social domains. Although treatments can improve HRQoL in these patients, the available evidence is limited. There remains an unmet need for approved pharmacological treatments to alleviate CC and improve HRQoL for these patients.

Understanding the economic burden of chronic cough: a systematic literature review.

Chronic cough (CC) is associated with high healthcare resource utilization (HCRU) due to challenges in diagnosis and treatment and is anticipated to have a substantial economic impact. This systematic literature review (SLR) sought to identify evidence on the cost-effectiveness of treatments and the economic burden associated with CC. Electronic database searches were supplemented with searches of conference proceedings and health technology assessment body websites. Two independent reviewers assessed all citations for inclusion based on predefined inclusion/exclusion criteria. Key inclusion criteria were patient population with CC, and outcomes related to cost-effectiveness and HCRU and costs. After screening, one cost-effectiveness analysis was identified, alongside eight studies reporting HCRU and costs related to CC. Though evidence was limited, studies suggest that patients with CC incur higher costs and use more resources than those with acute cough. Types of resource use reported included healthcare contacts and prescriptions, diagnostic tests, referrals and specialist evaluations, and treatment use. There is a paucity of literature on HCRU and costs in CC, and very limited cost-effectiveness analyses. The economic burden appears higher in these patients however, without direct comparison to the general population it is difficult to determine the total impact. The increased burden is expected to be a result of the challenges with diagnosis and lack of approved treatments. However, limited conclusions can be drawn in the absence of further data. Future studies should endeavor to quantify the HCRU and cost attributable to patients with CC.

Prediction of persistent chronic cough in patients with chronic cough using machine learning.

Introduction: The aim of this study was to develop and validate prediction models for risk of persistent chronic cough (PCC) in patients with chronic cough (CC). This was a retrospective cohort study. Methods: Two retrospective cohorts of patients 18-85 years of age were identified for years 2011-2016: a specialist cohort which included CC patients diagnosed by specialists, and an event cohort which comprised CC patients identified by at least three cough events. A cough event could be a cough diagnosis, dispensing of cough medication or any indication of cough in clinical notes. Model training and validation were conducted using two machine-learning approaches and 400+ features. Sensitivity analyses were also conducted. PCC was defined as a CC diagnosis or any two (specialist cohort) or three (event cohort) cough events in year 2 and again in year 3 after the index date. Results: 8581 and 52 010 patients met the eligibility criteria for the specialist and event cohorts (mean age 60.0 and 55.5 years), respectively. 38.2% and 12.4% of patients in the specialist and event cohorts, respectively, developed PCC. The utilisation-based models were mainly based on baseline healthcare utilisations associated with CC or respiratory diseases, while the diagnosis-based models incorporated traditional parameters including age, asthma, pulmonary fibrosis, obstructive pulmonary disease, gastro-oesophageal reflux, hypertension and bronchiectasis. All final models were parsimonious (five to seven predictors) and moderately accurate (area under the curve: 0.74-0.76 for utilisation-based models and 0.71 for diagnosis-based models). Conclusions: The application of our risk prediction models may be used to identify high-risk PCC patients at any stage of the clinical testing/evaluation to facilitate decision making.

Health-related experiences of adults with chronic cough: Empirical research mixed methods.

AIM: To describe adults' health-related experiences with chronic cough. DESIGN: Survey and interviews. METHODS: Participants completed questionnaires and interviews, to explore chronic cough's impact and management. DATA SOURCES: Patients aged 18-85 years with at least three cough-related encounters within 56-120 days. RESULTS: Forty-one patients were surveyed. Mean cough severity was 4.5 (scale 0-9). Chronic cough-related problems included embarrassment (66%), fatigue (56%), and anxiety or depression (49%). Testing was judged insufficient by 44%. Only 28% were satisfied with treatment; 20% reported abandoning treatment due to ineffectiveness. Interview themes (N = 30) included frustration with diagnostic uncertainty, and feelings of therapeutic futility. Some reported psychological distress. Work and socializing were commonly disrupted. CONCLUSION: Diagnostic uncertainty, perceived limitations of testing, and treatment failures suggest needs for better approaches to evaluating and treating chronic cough. Special attention to identifying and addressing mental health issues appears warranted.

Development of a natural language processing algorithm to detect chronic cough in electronic health records.

BACKGROUND: Chronic cough (CC) is difficult to identify in electronic health records (EHRs) due to the lack of specific diagnostic codes. We developed a natural language processing (NLP) model to identify cough in free-text provider notes in EHRs from multiple health care providers with the objective of using the model in a rules-based CC algorithm to identify individuals with CC from EHRs and to describe the demographic and clinical characteristics of individuals with CC. METHODS: This was a retrospective observational study of enrollees in Optum's Integrated Clinical + Claims Database. Participants were 18-85 years of age with medical and pharmacy health insurance coverage between January 2016 and March 2017. A labeled reference standard data set was constructed by manually annotating 1000 randomly selected provider notes from the EHRs of enrollees with ≥ 1 cough mention. An NLP model was developed to extract positive or negated cough contexts. NLP, cough diagnosis and medications identified cough encounters. Patients with ≥ 3 encounters spanning at least 56 days within 120 days were defined as having CC. RESULTS: The positive predictive value and sensitivity of the NLP algorithm were 0.96 and 0.68, respectively, for positive cough contexts, and 0.96 and 0.84, respectively, for negated cough contexts. Among the 4818 individuals identified as having CC, 37% were identified using NLP-identified cough mentions in provider notes alone, 16% by diagnosis codes and/or written medication orders, and 47% through a combination of provider notes and diagnosis codes/medications. Chronic cough patients were, on average, 61.0 years and 67.0% were female. The most prevalent comorbidities were respiratory infections (75%) and other lower respiratory disease (82%). CONCLUSIONS: Our EHR-based algorithm integrating NLP methodology with structured fields was able to identify a CC population. Machine learning based approaches can therefore aid in patient selection for future CC research studies.

Application of unsupervised deep learning algorithms for identification of specific clusters of chronic cough patients from EMR data.

BACKGROUND: Chronic cough affects approximately 10% of adults. The lack of ICD codes for chronic cough makes it challenging to apply supervised learning methods to predict the characteristics of chronic cough patients, thereby requiring the identification of chronic cough patients by other mechanisms. We developed a deep clustering algorithm with auto-encoder embedding (DCAE) to identify clusters of chronic cough patients based on data from a large cohort of 264,146 patients from the Electronic Medical Records (EMR) system. We constructed features using the diagnosis within the EMR, then built a clustering-oriented loss function directly on embedded features of the deep autoencoder to jointly perform feature refinement and cluster assignment. Lastly, we performed statistical analysis on the identified clusters to characterize the chronic cough patients compared to the non-chronic cough patients. RESULTS: The experimental results show that the DCAE model generated three chronic cough clusters and one non-chronic cough patient cluster. We found various diagnoses, medications, and lab tests highly associated with chronic cough patients by comparing the chronic cough cluster with the non-chronic cough cluster. Comparison of chronic cough clusters demonstrated that certain combinations of medications and diagnoses characterize some chronic cough clusters. CONCLUSIONS: To the best of our knowledge, this study is the first to test the potential of unsupervised deep learning methods for chronic cough investigation, which also shows a great advantage over existing algorithms for patient data clustering.

Risk Factors for Persistent Chronic Cough During Consecutive Years: A Retrospective Database Analysis.

BACKGROUND: The identification of patients at high risk for diseases provides clinicians essential information to better manage such patients. Persistent chronic cough (PCC) is a condition with high clinical burden and limited knowledge of the risk factors that drive the persistent symptoms. OBJECTIVE: To understand the risk factors of PCC in patients with CC diagnosed by specialists. METHODS: In this retrospective study, adults aged 18 to 85 years diagnosed with CC by a pulmonologist, allergist, otolaryngologist, or gastroenterologist in the period 2011 to 2016 were identified. PCC was defined by another CC code or at least 2 cough events at least 8 weeks but no more than 4 months apart in each of the 2 consecutive years beginning 1 year after the original CC diagnosis. Unadjusted and adjusted risk ratios with 95% CI for patient characteristics at baseline in relationship to PCC were estimated by Poisson regression models with robust error variance. RESULTS: Of the adults with CC, 3270 (27.4%) had PCC and 5302 (44.5%) did not have CC during follow-up; 3341 (28.1%) had CC in only 1 follow-up year and were excluded from the analysis. Compared with patients without PCC, patients with PCC were noted to have significantly increased adjusted risk ratios for the following baseline features: (1) demographic characteristics (elderly, females, and less educated); (2) comorbidities (chronic obstructive pulmonary disease, chronic sinusitis, bronchiectasis, pulmonary fibrosis, hypertension, depression, and cough complications); (3) medication dispensed (inhaled corticosteroids/long-acting beta-agonists, leukotriene modifiers, nasal corticosteroids, nasal short-acting muscarinic antagonists, proton pump inhibitors, antitussives with narcotics, and neuromodulators); and (4) specialist care, particularly with pulmonologists. CONCLUSIONS: Knowledge of the independent risk factors associated with PCC should aid clinicians in identifying such patients and improve their management.

Applying interpretable deep learning models to identify chronic cough patients using EHR data.

BACKGROUND AND OBJECTIVE: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD code specific for chronic cough makes it challenging to identify such patients from electronic health records (EHRs). For clinical and research purposes, computational methods using EHR data are urgently needed to identify chronic cough cases. This research aims to investigate the data representations and deep learning algorithms for chronic cough prediction. METHODS: Utilizing real-world EHR data from a large academic healthcare system from October 2005 to September 2015, we investigated Natural Language Representation of the EHR data and systematically evaluated deep learning and traditional machine learning models to predict chronic cough patients. We built these machine learning models using structured data (medication and diagnosis) and unstructured data (clinical notes). RESULTS: The sensitivity and specificity of a transformer-based deep learning algorithm, specifically BERT with attention model, was 0.856 and 0.866, respectively, using structured data (medication and diagnosis). Sensitivity and specificity improved to 0.952 and 0.930 when we combined structured data with symptoms extracted from clinical notes. We further found that the attention mechanism of deep learning models can be used to extract important features that drive the prediction decisions. Compared with our previously published rule-based algorithm, the deep learning algorithm can identify more chronic cough patients with structured data. CONCLUSIONS: By applying deep learning models, chronic cough patients can be reliably identified for prospective or retrospective research through medication and diagnosis data, widely available in EHR and electronic claims data, thus improving the generalizability of the patient identification algorithm. Deep learning models can identify chronic cough patients with even higher sensitivity and specificity when structured and unstructured EHR data are utilized. We anticipate language-based data representation and deep learning models developed in this research could also be productively used for other disease prediction and case identification.

Patient-Reported Burden of Chronic Cough in a Managed Care Organization.

BACKGROUND: The burden of chronic cough (CC) requires better understanding. OBJECTIVE: To determine the severity, health status, and health care resource utilization among patients with CC identified by electronic health records on 2 visits separated by ≥1 year. METHODS: Information on cough-related burden was collected through survey from patients with CC, including validated questionnaires (the cough health status Leicester Cough Questionnaire [LCQ], the cough hypersensitivity Hull Airway Reflux Questionnaire [HARQ], and the Cough Quality of Life Questionnaire [CQLQ]), CC-associated respiratory and gastrointestinal comorbidities, and treatment responses. Spearman correlation coefficients were reported to examine the associations among the LCQ, HARQ, and CQLQ. Patient demographics and patient-reported CC features were compared between males and females, and among ethnic groups using Robust Poisson regression models. RESULTS: The survey was completed by 565 patients who were 64.8 ± 12.6 years, 75.8% female, and 60.4% white. CC duration was 8.6 ± 10.5 years with an average weekly severity of 5.3 ± 2.3 (maximum 10). The LCQ score was 11.3 ± 3.9 (maximum 21). The HARQ score was 33.3 ± 13.6 (normal ≤13). The CQLQ score was 56.9 ± 17.5 (maximum 112, worse with higher scores). The Spearman rank correlations were high between the LCQ and HARQ (-0.65), the LCQ and CQLQ (-0.80), and the HARQ and CQLQ (0.69). Patients with CC-associated respiratory and gastrointestinal comorbidities generally showed similar results regarding the above questionnaires. Treatment responses were suboptimal. Women compared with men and non-whites compared with whites reported significantly worse cough severity and poorer LCQ, HARQ, and CQLQ scores. CONCLUSIONS: CC is self-reported as a burdensome condition, particularly in women and non-white minorities, which markedly affects daily living with inadequate response to treatments.

Burden of Specialist-Diagnosed Chronic Cough in Adults.

BACKGROUND: The burden of chronic cough (CC) in patients seeking specialist care is infrequently researched. OBJECTIVE: To describe patient characteristics and disease burden associated with specialist-diagnosed CC. METHODS: Using administrative pharmacy and medical data, we identified patients aged 18 to 85 years with CC diagnosed by pulmonologists, allergists, otolaryngologists, or gastroenterologists. Patients were stratified into 4 subgroups on the basis of the presence or absence of common respiratory diseases or gastroesophageal reflux disease (GERD). Clinical features and health care resource utilization (HCRU) in the baseline and outcome years were compared between the CC subgroups. The baseline factors associated with persistence of CC and a comparison of the CC cohort to a matched noncough cohort were also determined. RESULTS: The 11,290 patients with specialist-diagnosed CC were aged about 61 years and 66.7% were females. The CC cohort experienced frequent GERD (44.1%), asthma (31.2%), obesity (24.3%), upper airway cough syndrome (20.4%), common cough complications (19.4%), and hospitalizations (9.8%). The patients with CC with both respiratory disease and GERD exhibited at baseline and follow-up the most common cough comorbidities, higher HCRU, specialist care, and dispensed respiratory and nonrespiratory medications including proton pump inhibitors, antitussives, psychotherapeutics, oral corticosteroids, and antibiotics compared with the other subgroups. A 40.6% persistence of CC occurred similarly between CC subgroups. In addition, patients with CC in the matched analysis experienced significantly more comorbidities, laboratory evaluations, HCRU, and antitussives than patients without cough. CONCLUSIONS: Specialist-diagnosed CC was associated with considerable disease burden, particularly among those with both respiratory disease and GERD. In addition, CC burden was more pronounced than in matched patients without cough.

Anticholinergic burden and risk of cognitive impairment in elderly nursing home residents with depression.

BACKGROUND: Although the adverse cognitive effects of anticholinergic medications in the elderly are well-documented, little is known regarding the cognitive impact of anticholinergics among nursing home residents with depression. OBJECTIVE: This study examined the risk of mild-to-moderate cognitive impairment due to anticholinergic burden among elderly nursing home residents with depression. METHODS: A population-based nested case-control study was conducted using Minimum Data Set (MDS)-linked Medicare data where the base cohort included patients ≥ 65 years with depression who had intact cognition (MDS Cognition score of 0 or 1) and no dementia. Cases were identified as those who had mild-to-moderate cognition (MDS Cognition score of 2-4). Each case was matched on age and sex to one control using incidence density sampling. The study evaluated cumulative anticholinergic burden (defined as score of 3 or more) within 30, 60 and 90 days preceding the event date based on the Anticholinergic Drug Scale (ADS). Conditional logistic regression model stratified on matched case-control sets was performed to evalaute cognitive impairment due to cumulative anticholinergic burden after controlling for other risk factors. RESULTS: The study sample included 3707 cases with mild-to-moderate cognition and 3707 matched controls with intact cognition. Bivariate analysis showed significant association between cumulative anticholinergic exposure and cognitive impairment (Odds Ratio [OR], 1.15; 95% Confidence Interval [CI],1.04-1.30); after controlling for potential risk factors, cumulative anticholinergic exposure 30 days preceding the event was no longer associated with cognitive impairment, (aOR, 1.07; 95% CI, 0.95-1.21). However, the odds of cognitive impairment increased with cumulative anticholinergic exposure 60 days (aOR 1.16; 1.04-1.30) and 90 days (aOR 1.28; 1.14-1.44) before the event date. CONCLUSION: Cumulative anticholinergic use for prolonged exposure periods was associated with modestly increased risk of cognitive impairment in elderly residents with depression who had intact cognition. The findings suggest the need to be cautious when prescribing multiple anticholinergic drugs in residents, including those with intact cognition.

Prevalence and Characteristics of Chronic Cough in Adults Identified by Administrative Data.

CONTEXT: International Classification of Diseases-9/10 codes for chronic cough (CC) do not exist, limiting investigation. OBJECTIVE: To develop a computerized algorithm to determine CC prevalence and its characteristics. DESIGN: This observational study using administrative data identified hierarchically patients aged 18 to 85 years with CC from 2013 to 2016. First, a specialist-diagnosed CC group was identified using an internal CC encounter code during an outpatient visit to a pulmonologist, allergist, otolaryngologist, or gastroenterologist. Subsequently, an event-diagnosed CC group was identified based on clinical notes through natural language processing, ICD-9/ICD-10 cough codes, and dispensed antitussives. MAIN OUTCOME MEASURES: Prevalence of CC and comparison of clinical characteristics between specialist-diagnosed and event-diagnosed CC subgroups. RESULTS: A total of 50,163 patients with CC of more than 8 weeks were identified. Of these, 11,290 (22.5%) were specialist diagnosed, and 38,873 (77.5%) were event diagnosed. The CC cohort was 57.4 ± 16.5 years of age; 67.6% were female. The overall prevalence was 1.04% (95% confidence interval = 1.03-1.06) in 2016. Prevalence in 2016 was higher in female patients (1.21%) than in male patients (0.81%), higher in patients aged 65 to 85 years (2.2%) than in patients aged 18 to 44 years (0.43%), and higher in Blacks (1.38%) than in Whites (1.21%). Compared with patients with event-diagnosed CC, patients with specialist-diagnosed CC exhibited significantly higher frequencies of laboratory tests and respiratory and nonrespiratory comorbidities and dispensed medication and lower frequency of pneumonia, all-cause and respiratory-cause emergency department visits and hospitalizations, and dispensed antitussives. CONCLUSIONS: We identified a CC cohort using electronic data in a managed care organization. Prevalences varied by sex, age, and ethnicity. Clinical characteristics varied between specialist-diagnosed and event-diagnosed CC.

Cardiovascular Safety of Concomitant Use of Atypical Antipsychotics and Long-Acting Stimulants in Children and Adolescents With ADHD.

OBJECTIVE: This study examined cardiovascular safety of concomitant use of long-acting stimulants (LAS) and atypical antipsychotics (AAP) in children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD). METHOD: The study used 2004-2007 IMS LifeLink™ claims data involving 6- to 16-year-old children with ADHD and at least one LAS prescription from July 2004 to December 2006. Time-dependent Cox regression analysis was performed to evaluate the risk of cardiovascular disease (CVD) events due to concomitant use of LAS and AAP. RESULTS: The analytical cohort consisted of 37,903 children: 538 (1.9%) used LAS and AAP concurrently and the rest used LAS monotherapy. Time-dependent Cox regression analysis revealed no difference in CVD risk among concomitant users of LAS and AAP (hazard ratio = 1.19; 95% confidence interval = [0.60, 2.53]) when compared with users of LAS monotherapy. CONCLUSION: Concomitant use of LAS and AAP was not associated with risk of CVD events in ADHD patients when compared with LAS monotherapy.

Identification of Undiagnosed Hyperlipidemia: Do Work Site Screening Programs Work?

PURPOSE: We evaluated the rate of hyperlipidemia identified during workplace screening in previously undiagnosed individuals, the association between workplace hyperlipidemia screening and use of medical care during follow-up, and changes in lipid profile among individuals with hyperlipidemia at screening. DESIGN: Nonexperimental longitudinal study. SETTING: Employees who participated in a workplace health screening. PARTICIPANTS: A total of 18 993 individuals from 39 self-insured employers in the United States. MEASURES: Total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides were measured during screening. A claims-based algorithm was used to identify hyperlipidemia cases. ANALYSIS: Discrete-time survival analysis was used to estimate monthly rates of new hyperlipidemia diagnoses or prescriptions. Paired t tests were used to evaluate 1-year changes in lipid profile. RESULTS: A total of 1872 (9.9%) individuals had hyperlipidemia at screening. Among all individuals, a significantly greater rate of new hyperlipidemia diagnoses was observed during the first month after screening, compared to the 3 months before screening (odds ratio [95% CI]: 2.99 [2.66-3.36]). Among the 987 individuals who were followed up 1 year later, significant improvements were observed in total cholesterol (-8.5% ± 13.6%) and LDL levels (-10.2% ± 19.3%). CONCLUSION: Workplace health screenings in an insured population were associated with a subsequent increase in physician visits and prescriptions for hyperlipidemia. After 1 year, significant improvements in total cholesterol and LDL levels were observed among individuals who screened positive for hyperlipidemia.

Risk of Mortality Associated with Anticholinergic Use in Elderly Nursing Home Residents with Depression.

BACKGROUND: Few studies have evaluated the association between anticholinergic use and mortality in elderly nursing home residents. OBJECTIVE: The aim of this study was to examine the risk of mortality associated with anticholinergic use among elderly nursing home residents with depression. METHODS: The study employed a population-based nested case-control design using 2007-2010 Minimum Data Set (MDS)-linked Medicare data from all states. The base cohort included Medicare beneficiaries aged ≥65 years, diagnosed with depression as of 2007, and with any MDS assessment in 2007. Cases were identified as patients who died anytime between January 1, 2008 and December 31, 2010. For each case, four age- and sex-matched controls were selected using incidence density sampling. Anticholinergic exposure was defined using the Anticholinergic Drug Scale (ADS). Prescription of clinically significant anticholinergic medications (ADS level 2/3) 60 days preceding the event date formed the primary exposure. Conditional logistic regression model stratified on matched case-control sets was performed to assess mortality risk, after controlling for other risk factors. RESULTS: The study sample included 44,948 cases who died and 179,792 matched controls. After adjusting for other risk factors, clinically significant anticholinergic use was associated with significant risk of death (odds ratio [OR] 1.31; 95% CI 1.28-1.34) compared with non-use. Level-specific analysis indicated high mortality risk with only markedly anticholinergic (ADS level 3) medication use (OR 1.46; 95% CI 1.42-1.51). CONCLUSIONS: Use of clinically significant anticholinergic medications was associated with a 31% increase in risk of mortality among elderly nursing home residents with depression. With increasing safety concerns, there is a significant need to optimize anticholinergic use in the vulnerable population.

Risk of Mortality in Elderly Nursing Home Patients with Depression Using Paroxetine.

OBJECTIVE: Among selective serotonin reuptake inhibitors (SSRIs), paroxetine is strongly anticholinergic and might lead to a higher risk of adverse outcomes such as mortality. This study examined the risk of mortality in depressed elderly nursing home patients using paroxetine and other SSRIs. METHODS: This study used 2007-2010 Minimum Data Set-linked Medicare data and a propensity score (PS)-matched retrospective cohort study design to achieve the study objective. New users of paroxetine and other SSRIs were followed until they reached the end of the follow-up period (1 year), switched to a different antidepressant class, used psychotherapy, or had a gap of more than 15 days in the use of index antidepressant class, whichever occurred earlier. A robust Cox proportional hazard (PH) model was used to evaluate the risk of mortality associated with the use of paroxetine and other SSRIs in depressed elderly nursing home residents. RESULTS: The PS matching yielded 4620 patients each in the two treatment groups. The unadjusted incidence of mortality was 269 (2.9%) for paroxetine and 288 (3.1%) for other SSRIs users in the matched cohort. The robust Cox PH model did not find any significant difference in the risk mortality between the two groups (hazard ratio 1.01; 95% confidence interval 0.86-1.19). CONCLUSIONS: This study did not find any significant difference in the risk of mortality between users of paroxetine and other SSRIs among elderly nursing home patients with depression. There is a need for further evaluation of other adverse effects of paroxetine due to its anticholinergic effects in the geriatric population.