Prescriptions of opioid-containing drugs in patients with chronic cough.

BACKGROUND: Chronic cough (CC) affects about 10% of adults, but opioid use in CC is not well understood. OBJECTIVES: To determine the use of opioid-containing cough suppressant (OCCS) prescriptions in patients with CC using electronic health records. DESIGN: Retrospective cohort study. METHODS: Through retrospective analysis of Midwestern U.S. electronic health records, diagnoses, prescriptions, and natural language processing identified CC - at least three medical encounters with cough, with 56-120 days between first and last encounter - and a 'non-chronic cohort'. Student's t-test, Pearson's chi-square, and zero-inflated Poisson models were used. RESULTS: About 20% of 23,210 patients with CC were prescribed OCCS; odds of an OCCS prescription were twice as great in CC. In CC, OCCS drugs were ordered in 38% with Medicaid insurance and 15% with commercial insurance. CONCLUSION: Findings identify an important role for opioids in CC, and opportunity to learn more about the drugs' effectiveness.

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.

Cardiac psychology training in a rural health care setting: East Carolina Heart Institute.

The full partnership of clinical health psychology and cardiology optimizes patient-centered care to address the mental and behavioral needs of patients living with heart disease. This scenario is realized as the East Carolina Heart Institute (ECHI) at East Carolina University (ECU) in Greenville, North Caroline with co-location and co-training of psychologists and cardiologists. ECHI provides services to patients across the full disease continuum of cardiovascular disease (CVD), given the higher rates of heart disease in this rural area. The purpose of this article is to describe the cardiovascular behavioral medicine training model for clinical health psychology doctoral students and its adaptation during the coronavirus disease 2019 (COVID-19) era. Through illustrative case examples, we describe the training model before the COVID-19 pandemic, during the transition to telehealth following the onset of the COVID-19 pandemic, and then the entirely telehealth-based service model. We highlight notable strengths and challenges to delivering telehealth care to cardiac patients in a rural setting, while also discussing the health disparities that uniquely occur within this patient population in Eastern North Carolina. This innovative partnership fosters a responsive environment for training and clinical care, where the complex needs of patients with CVD are treated to increase their overall quality of life and well-being. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An Automated Line-of-Therapy Algorithm for Adults With Metastatic Non-Small Cell Lung Cancer: Validation Study Using Blinded Manual Chart Review.

BACKGROUND: Extraction of line-of-therapy (LOT) information from electronic health record and claims data is essential for determining longitudinal changes in systemic anticancer therapy in real-world clinical settings. OBJECTIVE: The aim of this retrospective cohort analysis is to validate and refine our previously described open-source LOT algorithm by comparing the output of the algorithm with results obtained through blinded manual chart review. METHODS: We used structured electronic health record data and clinical documents to identify 500 adult patients treated for metastatic non-small cell lung cancer with systemic anticancer therapy from 2011 to mid-2018; we assigned patients to training (n=350) and test (n=150) cohorts, randomly divided proportional to the overall ratio of simple:complex cases (n=254:246). Simple cases were patients who received one LOT and no maintenance therapy; complex cases were patients who received more than one LOT and/or maintenance therapy. Algorithmic changes were performed using the training cohort data, after which the refined algorithm was evaluated against the test cohort. RESULTS: For simple cases, 16 instances of discordance between the LOT algorithm and chart review prerefinement were reduced to 8 instances postrefinement; in the test cohort, there was no discordance between algorithm and chart review. For complex cases, algorithm refinement reduced the discordance from 68 to 62 instances, with 37 instances in the test cohort. The percentage agreement between LOT algorithm output and chart review for patients who received one LOT was 89% prerefinement, 93% postrefinement, and 93% for the test cohort, whereas the likelihood of precise matching between algorithm output and chart review decreased with an increasing number of unique regimens. Several areas of discordance that arose from differing definitions of LOTs and maintenance therapy could not be objectively resolved because of a lack of precise definitions in the medical literature. CONCLUSIONS: Our findings identify common sources of discordance between the LOT algorithm and clinician documentation, providing the possibility of targeted algorithm refinement.

Mental Disorders, Substance Use Disorders, and Psychotropic Medication Use Among Sudden-Death Victims.

OBJECTIVE: The authors sought to estimate the prevalence of mental and substance use disorders and psychotropic medication prescriptions among working-age sudden-death victims. METHODS: Using a written protocol, the authors screened for sudden deaths attended by emergency medical services (EMS) in a large metropolitan county in North Carolina from March 1, 2013, to February 28, 2015. Sudden-death cases were adjudicated by three cardiologists. Mental health and chronic disease diagnoses and treatments were abstracted from EMS, medical examiner, toxicology, and autopsy reports and from clinical records for the past 5 years before death. RESULTS: Sudden death was identified for 399 adults ages 18-64 years, 270 of whom had available medical records. Most sudden-death victims were White (63%) and male (65%), had a comorbid condition such as hypertension or respiratory disease, and had a mean±SD age of death of 53.6±8.8 years. Most victims (59%) had at least one mental health or substance use disorder documented in a recent medical record; 76%-78% of victims with a mental disorder had a documented psychotropic medication prescription. However, fewer than one-half (41%) had a documented referral to a mental health professional. The most common diagnostic categories were depressive, anxiety, and alcohol-related disorders. Almost one-half (46%) of the victims had a recent psychotropic prescription, most commonly antidepressants (29%) and benzodiazepines (19%). CONCLUSIONS: Mental illness, substance use disorders, and psychotropic medication prescriptions were prevalent among sudden-death victims. The health care needs of these individuals may be better addressed by collaborative care for general medical and mental disorders.

Identifying and Characterizing a Chronic Cough Cohort Through Electronic Health Records.

BACKGROUND: Chronic cough (CC) of 8 weeks or more affects about 10% of adults and may lead to expensive treatments and reduced quality of life. Incomplete diagnostic coding complicates identifying CC in electronic health records (EHRs). Natural language processing (NLP) of EHR text could improve detection. RESEARCH QUESTION: Can NLP be used to identify cough in EHRs, and to characterize adults and encounters with CC? STUDY DESIGN AND METHODS: A Midwestern EHR system identified patients aged 18 to 85 years during 2005 to 2015. NLP was used to evaluate text notes, except prescriptions and instructions, for mentions of cough. Two physicians and a biostatistician reviewed 12 sets of 50 encounters each, with iterative refinements, until the positive predictive value for cough encounters exceeded 90%. NLP, International Classification of Diseases, 10th revision, or medication was used to identify cough. Three encounters spanning 56 to 120 days defined CC. Descriptive statistics summarized patients and encounters, including referrals. RESULTS: Optimizing NLP required identifying and eliminating cough denials, instructions, and historical references. Of 235,457 cough encounters, 23% had a relevant diagnostic code or medication. Applying chronicity to cough encounters identified 23,371 patients (61% women) with CC. NLP alone identified 74% of these patients; diagnoses or medications alone identified 15%. The positive predictive value of NLP in the reviewed sample was 97%. Referrals for cough occurred for 3.0% of patients; pulmonary medicine was most common initially (64% of referrals). LIMITATIONS: Some patients with diagnosis codes for cough, encounters at intervals greater than 4 months, or multiple acute cough episodes may have been misclassified. INTERPRETATION: NLP successfully identified a large cohort with CC. Most patients were identified through NLP alone, rather than diagnoses or medications. NLP improved detection of patients nearly sevenfold, addressing the gap in ability to identify and characterize CC disease burden. Nearly all cases appeared to be managed in primary care. Identifying these patients is important for characterizing treatment and unmet needs.