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Enregistrement W1552973625 · doi:10.1111/j.1532-5415.2006.00995.x

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

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

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2006
Typeletter
Langueen
DomaineMedicine
ThématiqueHeart Failure Treatment and Management
Établissements canadiensWilliam Osler Health SystemMcMaster University
Organismes subventionnairesnon disponible
Mots-clésMedicinePsychotropic drugDrugLong-term careTerm (time)PharmacotherapyHeart failureIntensive care medicinePsychiatryGerontologyFamily medicineInternal medicine

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,272
Écart entre enseignants0,260 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations12
Publié2006
Routes d'admission2
Résumé présentoui

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