Dataset Information

Potentially inappropriate prescriptions for older patients in long-term care.

ABSTRACT:

Background

Inappropriate medication use is a major healthcare issue for the elderly population. This study explored the prevalence of potentially inappropriate prescriptions (PIPs) in long-term care in metropolitan Quebec.

Methods

A cross sectional chart review of 2,633 long-term care older patients of the Quebec City area was performed. An explicit criteria list for PIPs was developed based on the literature and validated by a modified Delphi method. Medication orders were reviewed to describe prescribing patterns and to determine the prevalence of PIPs. A multivariate analysis was performed to identify predictors of PIPs.

Results

Almost all residents (94.0%) were receiving one or more prescribed medication; on average patients had 4.8 prescribed medications. A majority (54.7%) of treated patients had a potentially inappropriate prescription (PIP). Most common PIPs were drug interactions (33.9% of treated patients), followed by potentially inappropriate duration (23.6%), potentially inappropriate medication (14.7%) and potentially inappropriate dosage (9.6%). PIPs were most frequent for medications of the central nervous system (10.8% of prescribed medication). The likelihood of PIP increased significantly as the number of drugs prescribed increased (odds ratio [OR]: 1.38, 95% confidence interval [CI]: 1.33-1.43) and with the length of stay (OR: 1.78, CI: 1.43-2.20). On the other hand, the risk of receiving a PIP decreased with age.

Conclusion

Potentially inappropriate prescribing is a serious problem in the highly medicated long-term care population in metropolitan Quebec. Use of explicit criteria lists may help identify the most critical issues and prioritize interventions to improve quality of care and patient safety.

SUBMITTER: Rancourt C

PROVIDER: S-EPMC529256 | biostudies-literature | 2004 Oct

REPOSITORIES: biostudies-literature

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Publications

Potentially inappropriate prescriptions for older patients in long-term care.

Rancourt Carol C Moisan Jocelyne J Baillargeon Lucie L Verreault René R Laurin Danielle D Grégoire Jean-Pierre JP

BMC geriatrics 20041015

<h4>Background</h4>Inappropriate medication use is a major healthcare issue for the elderly population. This study explored the prevalence of potentially inappropriate prescriptions (PIPs) in long-term care in metropolitan Quebec.<h4>Methods</h4>A cross sectional chart review of 2,633 long-term care older patients of the Quebec City area was performed. An explicit criteria list for PIPs was developed based on the literature and validated by a modified Delphi method. Medication orders were review ...[more]

PMID: 15488143

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Project description:IntroductionDeprescribing is associated with positive health outcomes for older adults in long-term care (LTC), however deprescribing is not universally implemented.ObjectiveThe primary aim of this study was to estimate the prevalence of potentially inappropriate medications (PIMs) prescribed to frail older adults in Irish long-term care facilities (LTCFs), as identified by the Screening Tool of Older Persons Prescriptions in Frail adults with limited life expectancy, version 2 (STOPPFrail v2).MethodsA retrospective chart review was conducted in two publicly funded LTCFs in Ireland. Eligible participants were those (1) ≥ 65 years of age; (2) resident in a LTCF; (3) eligible as per the STOPPFrail v2 criteria by the site's Medical Officer; and (4) receiving regular medication. Data collected included age, sex, drug, dose, frequency, regular/pro re nata prescribing and indication/relevant diagnoses. Rates of polypharmacy (taking five or more medications) and excessive polypharmacy (taking 10 or more medications) were calculated. STOPPFrail v2 was used to identify PIMs; however, clinical measurements were not taken. Descriptive and association statistics were calculated.ResultsOf the 103 residents, 89 were ≥ 65 years of age and categorised as frail and were therefore eligible for inclusion in the study. Of those eligible, 85 (95.5%) had polypharmacy and 57 (64%) experienced excessive polypharmacy. The mean number of regular medications was 10.8 (± 3.8), total medications 17.7 (± 5) and diagnoses 5.5 (± 2.5). The mean number of PIMs per resident was 4.8 (± 2.6). Of the eligible participants, 59.6% had at least one medicine without a documented indication, while 61.8%, 42.7% and 30.3% had at least one PIM from the vitamin D, antihypertensives and proton pump inhibitors drug classes, respectively.ConclusionMedication and PIM use was high among LTC residents, with inappropriate polypharmacy of concern. Lack of clear indication for prescribing medications appears to be an issue in LTC, potentially affecting healthcare professionals' engagement with deprescribing. The prevalence of PIMs may be overestimated in the antihypertensives/antidiabetic classes due to the lack of clinical measurements.

Project description:Potentially inappropriate prescribing (PIP), including potentially inappropriate medications (PIMs) and potential prescribing omissions (PPOs), is a major risk factor for adverse drug reactions (ADRs). Establishing a risk warning model for PIP to screen high-risk patients and implementing targeted interventions would significantly reduce the occurrence of PIP and adverse drug events. Elderly patients with cardiovascular disease hospitalized at the Sichuan Provincial People's Hospital were included in the study. Information about PIP, PIM, and PPO was obtained by reviewing patient prescriptions according to the STOPP/START criteria (2nd edition). Data were divided into a training set and test set at a ratio of 8:2. Five sampling methods, three feature screening methods, and eighteen machine learning algorithms were used to handle data and establish risk warning models. A 10-fold cross-validation method was employed for internal validation in the training set, and the bootstrap method was used for external validation in the test set. The performances were assessed by area under the receiver operating characteristic curve (AUC), and the risk warning platform was developed based on the best models. The contributions of features were interpreted using SHapley Additive ExPlanation (SHAP). A total of 404 patients were included in the study (318 [78.7%] with PIP; 112 [27.7%] with PIM; and 273 [67.6%] with PPO). After data sampling and feature selection, 15 datasets were obtained and 270 risk warning models were built based on them to predict PIP, PPO, and PIM, respectively. External validation showed that the AUCs of the best model for PIP, PPO, and PIM were 0.8341, 0.7007, and 0.7061, respectively. The results suggested that angina, number of medications, number of diseases, and age were the key factors in the PIP risk warning model. The risk warning platform was established to predict PIP, PIM, and PPO, which has acceptable accuracy, prediction performance, and potential clinical application perspective.

Dataset Information

Potentially inappropriate prescriptions for older patients in long-term care.

Background

Methods

Results

Conclusion

Publications

Potentially inappropriate prescriptions for older patients in long-term care.

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