Family Help With Medication Management: A Predictive Marker for Early Readmission

Moulder, Magali P. Disdier; LaRock, Jenna; Garofoli, Adrian C.; Foley, David A.

doi:10.1016/j.mayocpiqo.2017.10.004

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Many medication management activities are carried out by families of older patients. These activities include: assisting with administering medications, recognising therapeutic benefits and adverse effects of medications, and clarifying information for patients [5, 6]. To date, there has been no review of the role of families in older people’s medication management at transitions of care.…”

Section: Introductionmentioning

confidence: 99%

Family involvement in managing medications of older patients across transitions of care: a systematic review

et al. 2019

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Background As older patients’ health care needs become more complex, they often experience challenges with managing medications across transitions of care. Families play a major role in older patients’ lives. To date, there has been no review of the role of families in older people’s medication management at transitions of care. This systematic review aimed to examine family involvement in managing older patients’ medications across transitions of care. Methods Five databases were searched for quantitative, qualitative and mixed methods empirical studies involving families of patients aged 65 years and older: Cumulative Index to Nursing and Allied Health Literature Complete, Medline, the Cochrane Central Register of Controlled Trials, PsycINFO, and EMBASE. All authors participated independently in conducting data selection, extraction and quality assessment using the Mixed Methods Appraisal Tool. A descriptive synthesis and thematic analysis were undertaken of included papers. Results Twenty-three papers were included, comprising 17 qualitative studies, 5 quantitative studies and one mixed methods study. Families participated in information giving and receiving, decision making, managing medication complexity, and supportive interventions in regard to managing medications for older patients across transitions of care. However, health professionals tended not to acknowledge the medication activities performed by families. While families actively engaged with older patients in strategies to ensure safe medication management, communication about medication plans of care across transitions tended to be haphazard and disorganised, and there was a lack of shared decision making between families and health professionals. In managing medication complexity across transitions of care, family members perceived a lack of tailoring of medication plans for patients’ needs, and believed they had to display perseverance to have their views heard by health professionals. Conclusions Greater efforts are needed by health professionals in strengthening involvement of families in medication management at transitions of care, through designated family meetings, clinical bedside handovers, ward rounds, and admission and discharge consultations. Future work is needed on evaluating targeted strategies relating to family members’ contribution to managing medications at transitions of care, with outcomes directed on family understanding of medication changes and their input in preventing and identifying medication-related problems. Electronic supplementary material The online version of this article (10.1186/s12877-019-1102-6) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Family involvement in managing medications of older patients across transitions of care: a systematic review

et al. 2019

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“…Some scholars recommend 30 days as the readmission time window for HF patients [ 4 , 24 ] ; some scholars use 90 days as the time window, considering that 90 days is the vulnerable period for HF [ 25 , 26 ] ; a few scholars have also set 180 days as the time window for readmission. [ 27 , 28 ] In contrast, this study included 28-day, 90-day, and 180-day readmission time windows to explore the relationship between CCI and readmission in patients with HF, and the results were more comprehensive. In our study, the 28-day readmission rate was 6.9%, the 3-month readmission rate was 24.8%, and the 6-month readmission rate was as high as 37.8% in patients with HF.…”

Section: Discussionmentioning

confidence: 99%

The Charlson comorbidity index and short-term readmission in patients with heart failure: A retrospective cohort study

et al. 2023

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The relationship between the Charlson comorbidity index (CCI) and short-term readmission is as yet unknown. Therefore, we aimed to investigate whether the CCI was independently related to short-term readmission in patients with heart failure (HF) after adjusting for other covariates. From December 2016 to June 2019, 2008 patients who underwent HF were enrolled in the study to determine the relationship between CCI and short-term readmission. Patients with HF were divided into 2 categories based on the predefined CCI (low < 3 and high > =3). The relationships between CCI and short-term readmission were analyzed in multivariable logistic regression models and a 2-piece linear regression model. In the high CCI group, the risk of short-term readmission was higher than that in the low CCI group. A curvilinear association was found between CCI and short-term readmission, with a saturation effect predicted at 2.97. In patients with HF who had CCI scores above 2.97, the risk of short-term readmission increased significantly (OR, 2.66; 95% confidence interval, 1.566–4.537). A high CCI was associated with increased short-term readmission in patients with HF, indicating that the CCI could be useful in estimating the readmission rate and has significant predictive value for clinical outcomes in patients with HF.

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Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis

et al. 2021

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ObjectiveTo describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.DesignSystematic review and meta-analysis.Data sourceMedline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.Eligibility criteria for selecting studiesStudies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months.Primary and secondary outcome measuresModel discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.ResultsSixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled.ConclusionSome promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.PROSPERO registration numberCRD42020159839.

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Family Help With Medication Management: A Predictive Marker for Early Readmission

Cited by 3 publications

References 12 publications

Family involvement in managing medications of older patients across transitions of care: a systematic review

Family involvement in managing medications of older patients across transitions of care: a systematic review

The Charlson comorbidity index and short-term readmission in patients with heart failure: A retrospective cohort study

Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis

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