Optimization of Decision Support Tool using Medication Regimens to Assess Rehospitalization Risks

Olson, Catherine H.; Dierich, Mary; Adam, Terrence J.; Westra, Bonnie L.

doi:10.4338/aci-2014-04-ra-0040

Cited by 17 publications

(5 citation statements)

References 35 publications

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“…In a recent study that included people with a mean MRCI of 26.8, those with an MRCI of 22 or above at hospital discharge were at increased risk for unplanned hospital readmission within 30 days ( 36 ). Another study that investigated the value of a decision support tool for recipients of home care who had a mean MRCI of 35.4 concluded that an MRCI cutoff of 33 was optimal for predicting medication-related readmission risks ( 37 ). These results suggest that cutoffs are specific to the population in which the MRCI is applied.…”

Section: Discussionmentioning

confidence: 99%

Medication Regimen Complexity and Number of Medications as Factors Associated With Unplanned Hospitalizations in Older People: A Population-based Cohort Study

Wimmer

Bell

Fastbom

et al. 2015

GERONA

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Background:Adverse drug events are a leading cause of hospitalization among older people. Up to half of all medication-related hospitalizations are potentially preventable. The objective of this study was to investigate and compare the association between medication regimen complexity and number of medications with unplanned hospitalizations over a 3-year period.Methods:Data were analyzed for 3,348 participants aged 60 years or older in Sweden. Regimen complexity was assessed using the 65-item Medication Regimen Complexity Index (MRCI) and number of medications was assessed as a continuous variable. Cox proportional hazard models were used to compute unadjusted and adjusted hazard ratios with 95% confidence intervals (CIs) for associations between regimen complexity and number of medications with unplanned hospitalizations over a 3-year period. Receiver operating characteristics curves with corresponding areas under the curve were calculated for regimen complexity and number of medications in relation to unplanned hospitalizations. The population attributable fraction of unplanned hospitalizations was calculated for MRCI and number of medications.Results:In total, 1,125 participants (33.6%) had one or more unplanned hospitalizations. Regimen complexity (hazard ratio 1.22; 95% CI 1.14–1.34) and number of medications (hazard ratio 1.07; 95% CI 1.04–1.09) were both associated with unplanned hospitalizations and had similar sensitivity and specificity (area under the curve 0.641 for regimen complexity and area under the curve 0.644 for number of medications). The population attributable fraction was 14.08% (95% CI 9.62–18.33) for MRCI and 17.61% (95% CI 12.59–22.35) for number of medications.Conclusions:There was no evidence that using a complex tool to assess regimen complexity was better at predicting unplanned hospitalization than number of medications.

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Section: Discussionmentioning

confidence: 99%

Medication Regimen Complexity and Number of Medications as Factors Associated With Unplanned Hospitalizations in Older People: A Population-based Cohort Study

Wimmer

Bell

Fastbom

et al. 2015

GERONA

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“…Other cut-off point proposals have been presented. Olson et al (2014) 34 suggested a score cut-off point of 33, which included an analysis of elderly patients in nursing homes from cut-off points that were previously selected based on clinical experience and the review of the research literature that distinguished patients at high and low risk of rehospitalization. This study showed that High Risk Medication Regimen calculations were optimized by increasing the MRCI cut-off point that distinguishes patients by their medication-related risks for hospital readmissions.…”

Section: Discussionmentioning

confidence: 99%

“…This study showed that High Risk Medication Regimen calculations were optimized by increasing the MRCI cut-off point that distinguishes patients by their medication-related risks for hospital readmissions. 34 Dierich et al . (2011) 35 analyzed older patients in a home care setting and found a mean MRCI of 35.4 (75% of then had scores >20), a relationship between readmission and higher polypharmacy, potentially inappropriate medications, and high complexity scores.…”

Section: Discussionmentioning

confidence: 99%

Medication regimen complexity in adults and the elderly in a primary healthcare setting: determination of high and low complexities

Ferreira¹,

Galato²,

Melo³

2015

Pharm Pract (Granada)

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Background:The complexity of a medication regimen is related to the multiple characteristics of the prescribed regimen and can negatively influence the health outcomes of patients.Objective:To propose cut-off points in the complexity of pharmacotherapy to distinguish between patients with low and high complexities seen in a primary health care (PHC) setting to enable prioritization of patient management.Methods:This is a cross-sectional study, which included 517 adult and elderly patients, analyzing different cut-off points to define the strata of low and high pharmacotherapy complexities based on percentiles of the population evaluated. Data collection began with the solicitation of prescriptions, followed by a questionnaire that was administered by an interviewer. The complexity of a medication regimen was estimated from the Medication Regimen Complexity Index (MRCI). High complexity pharmacotherapy scores were analyzed from patient profiles, the use of health services, and pharmacotherapy. The criteria for subject inclusion in the sample population were as follows: inhabitant of the area covered by the municipality, 18 years or older, and being prescribed at least one drug during the collection period. Exclusion criteria at the time of collection were the use of any medication whose prescription was not available. All medications were accessed through the Primary Healthcare Service (PHS).Results:The median total pharmacotherapy complexity score was 8.5. High MRCI scores were correlated with age, medications taken with in the Brazilian PHS, having at least one potential drug-related problem, receiving up to eight years of schooling, number of medications and polypharmacy (five or more medicines), number of medical conditions, number of medical appointments, and number of cardiovascular diseases and endocrine metabolic diseases. We suggest different complexity tracks according to age (e.g., adult or elderly) that consider the pharmacotherapy and population coverage characteristics as high complexity limits. For the elderly patients, the tracks were as follows: MRCI≥25.4, MRCI≥20.9, MRCI≥17.5, MRCI≥15.7, MRCI≥14.0, and MRCI≥13.0. For adult patients, the limits of high complexity were MRCI≥25.1; MRCI ≥ 23.8; MRCI≥21.0; MRCI≥17.0; MRCI≥16.5; and MRCI≥15.5.Conclusion:The medication regimen complexity is associated with the patient’s illness profile and problems with the use of drugs; therefore, the proposed scores can be useful in prioritizing patients for clinical care by pharmacists and other health professionals.

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“…7 This high incidence of DRP negatively affects the quality of life of the patient and increases the economic and social burden of illnesses. 5 Many of the admissions to emergency departments, 10 11 many causes of extended hospital stays 12 or of patient re-admissions, 13 and even of deaths, are due to a DRP. It has been reported that these negative outcomes are proportional to the complexity of the drug use process, 14 with some of the described risk factors being polypharmacy, hepatopathies, nephropathies and the use of high-risk medicines.…”

Section: Introductionmentioning

confidence: 99%

Incidence, types and acceptability of pharmaceutical interventions about drug related problems in a general hospital: an open prospective cohort

et al. 2020

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ObjectivesTo evaluate the incidence and types of drug-related problems (DRP) in a general teaching hospital and to evaluate the acceptability of pharmaceutical interventions by the medical team.DesignProspective cohort study during 2 years.SettingConducted in a Brazilian University Hospital.ParticipantsThe patient cohort consisted of 9303 patients with a total of 12 286 hospitalisation episodes.Primary outcome measuresDRP detected by pharmacists’ review of 100% medication orders using Pharmaceutical Care Network Europe 6.2 classification.ResultsPatients with a mean age of 52.6±17.7 years and 50.9% females. A total of 3373 DRP in 1903 hospital episodes were identified, corresponding to a cumulative incidence of 15.5%. ‘Treatment ineffectiveness’ (11.5%) and ‘Treatment costs’ (5.90%) were the most common DRP and ‘Drug use process’ (18.4%) and ‘Treatment duration’ (31.0%) the main causes of DRP. The medicines involved most often involved in DRP were anti-infectives (36.0%), mainly cephalosporins (20.2%), antiulcer (38.6%), analgesics/antipyretics (61.2%), propulsives (51.2%), opioids (38.5%) and antiemetics (57.4%). From 1939 pharmaceutical interventions, at least, 21.4% were not approved by the medical team.ConclusionDRP detected by 100% medication order review by hospital pharmacists occur in a significant proportion of hospital episodes, the most frequent being related to treatment effectiveness and treatment costs. The medications mostly involved were cephalosporins, penicillins, antidyspeptics, analgesics, antipyretics, opioids and antiemetics. Pharmaceutical interventions had low acceptability by the medical staff.

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Optimization of Decision Support Tool using Medication Regimens to Assess Rehospitalization Risks

Cited by 17 publications

References 35 publications

Medication Regimen Complexity and Number of Medications as Factors Associated With Unplanned Hospitalizations in Older People: A Population-based Cohort Study

Medication Regimen Complexity and Number of Medications as Factors Associated With Unplanned Hospitalizations in Older People: A Population-based Cohort Study

Medication regimen complexity in adults and the elderly in a primary healthcare setting: determination of high and low complexities

Incidence, types and acceptability of pharmaceutical interventions about drug related problems in a general hospital: an open prospective cohort

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