Catherine H Olson1, Sanjoy Dey2, Vipin Kumar3, Karen A Monsen4, Bonnie L Westra5. 1. Health Informatics, University of Minnesota, 330 Diehl Hall, 505 Essex Street SE Minneapolis, MN 55455, United States. Electronic address: hart0110@umn.edu. 2. Research Assistant, Computer Science and Engineering University of Minnesota Minneapolis, MN, United States. Electronic address: sanjoy@cs.umn.edu. 3. Department Head, Computer Science and Engineering University of Minnesota Minneapolis, MN, United States. Electronic address: kumar@cs.umn.edu. 4. School of Nursing University of Minnesota Minneapolis, MN, United States. Electronic address: mons0122@umn.edu. 5. School of Nursing University of Minnesota Minneapolis, MN, United States. Electronic address: westr006@umn.edu.
Abstract
INTRODUCTION: High Risk Medication Regimen (HRMR) scores are weakly predictive of hospital readmissions for elderly home health care patients. HRMR is composed of three elements related to drug risks: polypharmacy (number of medications); Potentially Inappropriate Medications (PIM) known to be harmful to the elderly; and the Medication Regimen Complexity Index (MRCI) that weighs drugs by the complexity of their dosing and instructions. In this paper, we hypothesized that HRMR scores are more predictive for demographic subgroups of elderly patients. The study used Outcome and Assessment Information Set (OASIS) variables to identify subgroups of patients for whom the HRMR measures appeared more predictive for hospital readmissions. METHODS: OASIS and medication data were reused from a study of 911 patients (355 males, 556 females; mean age 78.9) from 15 Medicare-certified home health care agencies that established the relationship between HRMR and hospital readmissions. Hierarchical agglomerative clustering using the Jaccard distance measure and average-link method identified patient subgroups based on the OASIS data. Receiver operating curve (ROC) analyses evaluated the predictive strength of the HRMR variables for each subgroup. Additional False Discovery Rate (FDR) analyses assessed whether the clustered relationships were chance. RESULTS: Clustering of OASIS data for 911 patients identified six subgroups: patients with Good Functional Status (n=382); Females with Moderate to Severe Pain (n=354); patients with poor prognosis needing functional status assistance (n=419); patients with Poor Functional Status (n=287); Males with Adult Children as Caregiver (n=198); adults living alone with spouses as primary caregiver (n=127). ROC results relating these subgroups to HRMR risks were strongest for Males with Adult Children as Caregivers (AUC: polypharmacy, 0.73; PIM, 0.64; MRCI, 0.77). The findings for this subgroup also met the FDR analysis threshold (<=0.20). CONCLUSIONS: A risk of medication-related readmissions in elderly men with adult children as caregivers is consistent with research showing problems in medication adherence when seniors are supported by informal caregivers. The results from clustering analysis present a hypothesis for research on HRMR and on the relationship between adult caregivers and their fathers.
INTRODUCTION: High Risk Medication Regimen (HRMR) scores are weakly predictive of hospital readmissions for elderly home health care patients. HRMR is composed of three elements related to drug risks: polypharmacy (number of medications); Potentially Inappropriate Medications (PIM) known to be harmful to the elderly; and the Medication Regimen Complexity Index (MRCI) that weighs drugs by the complexity of their dosing and instructions. In this paper, we hypothesized that HRMR scores are more predictive for demographic subgroups of elderly patients. The study used Outcome and Assessment Information Set (OASIS) variables to identify subgroups of patients for whom the HRMR measures appeared more predictive for hospital readmissions. METHODS: OASIS and medication data were reused from a study of 911 patients (355 males, 556 females; mean age 78.9) from 15 Medicare-certified home health care agencies that established the relationship between HRMR and hospital readmissions. Hierarchical agglomerative clustering using the Jaccard distance measure and average-link method identified patient subgroups based on the OASIS data. Receiver operating curve (ROC) analyses evaluated the predictive strength of the HRMR variables for each subgroup. Additional False Discovery Rate (FDR) analyses assessed whether the clustered relationships were chance. RESULTS: Clustering of OASIS data for 911 patients identified six subgroups: patients with Good Functional Status (n=382); Females with Moderate to Severe Pain (n=354); patients with poor prognosis needing functional status assistance (n=419); patients with Poor Functional Status (n=287); Males with Adult Children as Caregiver (n=198); adults living alone with spouses as primary caregiver (n=127). ROC results relating these subgroups to HRMR risks were strongest for Males with Adult Children as Caregivers (AUC: polypharmacy, 0.73; PIM, 0.64; MRCI, 0.77). The findings for this subgroup also met the FDR analysis threshold (<=0.20). CONCLUSIONS: A risk of medication-related readmissions in elderly men with adult children as caregivers is consistent with research showing problems in medication adherence when seniors are supported by informal caregivers. The results from clustering analysis present a hypothesis for research on HRMR and on the relationship between adult caregivers and their fathers.
Authors: A E M J H Linkens; V Milosevic; P H M van der Kuy; V H Damen-Hendriks; C Mestres Gonzalvo; K P G M Hurkens Journal: Int J Clin Pharm Date: 2020-05-30
Authors: Nadia Al Mazrouei; Rana M Ibrahim; Ahmad Z Al Meslamani; Derar H Abdel-Qader; Osama Mohamed Ibrahim Journal: J Am Pharm Assoc (2003) Date: 2021-02-12
Authors: Carl Willers; Anne-Marie Boström; Lennart Carlsson; Anton Lager; Rikard Lindqvist; Elisabeth Rydwik Journal: PLoS One Date: 2021-03-22 Impact factor: 3.240