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ASSOCIATION BETWEEN PSYCHOTROPIC DRUG USE AND HEART FAILURE THERAPY IN ELDERLY LONG‐TERM CARE RESIDENTS

2006· letter· en· W1552973625 on OpenAlexaffabout
George Heckman, Brian Misiaszek, Fatima Merali, Irene Turpie, Christopher J. Patterson, Norman Flett, Robert S. McKelvie

Bibliographic record

VenueJournal of the American Geriatrics Society · 2006
Typeletter
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsWilliam Osler Health SystemMcMaster University
Fundersnot available
KeywordsMedicinePsychotropic drugDrugLong-term careTerm (time)PharmacotherapyHeart failureIntensive care medicinePsychiatryGerontologyFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

To the Editor: Heart failure (HF) is common in residents of long-term care (LTC) facilities, with prevalence estimates ranging from 15% to 30%.1 Elderly persons with HF are also at risk of depression, with both conditions sharing similar clinical features such as weight change, sleep disturbances, fatigue, poor energy, and cognitive disturbances.2 Anxiety disorders are also common.3 The prescription of sedative-hypnotics for insomnia in older patients with HF has been associated with underuse of angiotensin-converting enzyme (ACE) inhibitors.4 In the context of a cross-sectional survey of elderly LTC residents with HF in Hamilton, Ontario, Canada, whether the use of antidepressant and anxiolytic agents was associated with HF drug use, specifically ACE inhibitors and digoxin, was explored. The methods are described in greater detail elsewhere.1 Briefly, this study was a cross-sectional chart review of elderly LTC residents previously diagnosed with HF. Ethical approval was obtained from the governing boards of participating facilities. Data abstracted from the chart included demographic information, hospitalizations within the previous year, comorbidities, functional status, weight, hemoglobin, potassium, creatinine, thyroid stimulating hormone, serum digoxin concentration, electrocardiogram, and left ventricular function were recorded. All prescribed medications and dosing schedules were recorded. ACE inhibitor dosing was considered adequate if at least half the optimal dose, adjusted for calculated creatinine clearance, was achieved.5 Clinical characteristics are presented with descriptive statistics. Differences in categorical data were determined using chi-square or Fisher exact tests, and differences in continuous data were determined using the Student t test or one-way analysis of variance. Logistic regression was used to identify correlates of antidepressant and anxiolytic use. All analyses were performed using SPSS version 10.0 (SPSS Inc., Chicago, IL). A total of 1,223 charts were screened, and 245 (20%) residents with HF were identified. The mean age±standard deviation of residents with HF was 85.9±7.5, and 77% were women. Antidepressants were prescribed to 101 of these residents, 63% of whom received selective serotonin reuptake inhibitors, 17% trazodone, and 16% tricyclic antidepressants. Residents with HF and a history of depression and who were receiving appropriate ACE inhibitor doses were less likely to be prescribed an antidepressant (odds ratio=0.11, 95% confidence interval=0.02–0.76). Antidepressant prescribing was not associated with usage of beta-blockers or other HF therapies. Sedative hypnotics, 90% of which were short- and intermediate-acting benzodiazepines, were prescribed to 82 residents with a history of HF, 80% of whom received these medications on a regular schedule. Serum digoxin concentrations, available for 57 residents with HF who were prescribed digoxin, were higher in those receiving sedative hypnotics than those who were not (1.74±0.05 mmol/L vs 1.21±0.04 mmol/L, P=.02). Sedative-hypnotic use was not associated with prescription of ACE inhibitors or other HF therapies. Clinical trial data suggest that optimal HF outcomes are achieved when ACE inhibitor doses are maximized.6 The findings from the current study suggest that appropriate ACE inhibitor doses are associated with a lower likelihood of antidepressants being prescribed to patients with HF previously diagnosed with depression. One possible explanation is that residents with depression who are unable to tolerate higher doses of ACE inhibitors are frailer. Alternatively, vigorous management of HF may alleviate symptoms such as sleep disturbances, fatigue, and poor energy, which may have been misattributed to depression.2 Indeed, clinical presentations of common medical conditions are often atypical in frail older people, further compounding the difficulties in achieving an accurate diagnosis of HF in primary care settings.7,8 The possibility that ACE inhibitors directly improve mood symptoms in patients with HF, possibly through improved cerebral perfusion, cannot be discounted.2 The association between sedative-hypnotic use and higher serum digoxin concentrations may reflect higher doses of digoxin used for more-severe HF symptoms but also raises the question of digoxin-induced neuropsychiatric side effects, which are more likely to occur in elderly persons and at concentrations normally considered therapeutic.9 Limitations of this study include the cross-sectional chart review design, precluding a formal prospective confirmation of psychiatric diagnoses, and the small numbers of participants. However, the preliminary findings suggest that psychiatric symptoms may be associated with symptomatic or undertreated HF in frail elderly persons, consistent with concerns that inappropriate psychotropic drug prescribing in LTC residents may mask symptoms resulting from underlying conditions.10 Prospective studies are required to evaluate the relationship between psychiatric syndromes with HF and whether appropriate HF therapy can alleviate these symptoms and reduce inappropriate prescribing of psychotropic agents. Financial Disclosure: Dr. Heckman has received unrestricted financial support for research from the Canadian Institutes of Health Research and Novartis. He has received consultant and unrestricted speaker fees from Pfizer, Janssen-Ortho, and Novartis. He has received unrestricted travel allowances from Bristol-Myers-Squibb and Astra-Zeneca. Dr. Misiaszek has received research funding from Novartis. Dr. Merali has received speakers fees from Merck Frosst. Dr. Turpie has received research funding from Sanofi-Synthelabo Canada and Pharmacia UpJohn and consultant fees from Pfizer and Janssen-Ortho. Dr. Patterson has received research funds from Pfizer and Janssen-Ortho, speakers fees from Pfizer, and consultant fees from Hoechst. Dr. Flett has no disclosures to make at this time. Dr. McKelvie has received research support from AstraZeneca, Bristol Myers Squibb, Sanofi/Aventis, and Scios and speaker fees from AstraZeneca, Bristol Myers Squibb, Sanofi/Aventis, and Merck Frosst. At the time this study was undertaken, Dr. Heckman was supported by a Junior Personnel Support Grant from the Heart and Stroke Foundation of Canada under the supervision of Drs. McKelvie and Turpie. Author Contributions: Dr. Heckman was the primary author and lead developer of the study concept, protocol development, data collection, and analysis. Dr. Misiaszek assisted in protocol development and data collection and analysis, and reviewed and contributed to manuscript. Dr. Merali assisted in data collection and reviewed and contributed to manuscript. Dr. Turpie assisted in development of study concept and protocol and reviewed and contributed to manuscript. Dr. Patterson and Dr. Flett assisted in development of study concept and reviewed and contributed to the manuscript. Dr. McKelvie assisted in development of study concept, protocol, and data collection forms and reviewed and contributed to the manuscript. Sponsor's Role: The Foundation played no role in the design, methods, subject recruitment, data collection, analysis, or preparation of the letter.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.272
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations12
Published2006
Admission routes2
Has abstractyes

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