Automation of a high risk medication regime algorithm in a home health care population

Drug-related problems among transfusion-dependent thalassemia patients: A real-world evidence study

Introduction: Thalassemia is among the most common genetic disorders globally and many patients suffer from iron overload (IOL) complications that mainly affect the heart, liver and endocrine system. These events may be further complicated by drug-related problems (DRP), an inherent issue among patients with chronic diseases. Objective: The study aimed to evaluate the burden, associated factors and impacts of DRP in transfusion-dependent thalassemia (TDT) patients. Method: Eligible TDT patients under follow-up in a tertiary hospital between 01 March 2020 to 30 April 2021 were interviewed and their medical records were reviewed retrospectively to identify any DRP. DRPs were classified using the Pharmaceutical Care Network Europe (PCNE) classification version 9.1. The incidence and preventability of DRP were assessed and the associated risk factors were estimated by univariate and multivariate logistic regression. Results: A total of 200 patients were enrolled with a median (interquartile range: IQR) age of 28 years at enrolment. Approximately 1 in 2 patients were observed to suffer from thalassemia-related complications. Throughout the study period, 308 DRPs were identified among 150 (75%) participants, with a median DRP per participant of 2.0 (IQR 1.0-3.0). Of the three DRP dimensions, treatment effectiveness was the most common DRP (55.8%) followed by treatment safety (39.6%) and other DRP (4.6%). The median serum ferritin level was statistically higher in patients with DRP compared with patients without DRP (3833.02 vs. 1104.98 μg/L, p < 0.001). Three risk factors were found to be significantly associated with the presence of DRP. Patients with frequent blood transfusion, moderate to high Medication Complexity Index (MRCI) and of Malay ethnicity were associated with higher odds of having a DRP (AOR 4.09, 95% CI: 1.83, 9.15; AOR 4.50, 95% CI: 1.89, 10.75; and AOR 3.26, 95% CI: 1.43, 7.43, respectively). Conclusion: The prevalence of DRP was relatively high amongst TDT patients. Increased medication complexity, more severe form of the disease and Malay patients were more likely to experience DRP. Hence, more viable interventions targeted to these groups of patients should be undertaken to mitigate the risk of DRP and achieve better treatment outcomes.

Are Clinical Outcomes Associated With Medication Regimen Complexity? A Systematic Review and Meta-analysis

Background: Current evidence of the influence of the medication regimen complexity (MRC) on the patients’ clinical outcomes are not conclusive. Objective: To systematically and analytically assess the association between MRC measured by the Medication Regimen Complexity Index (MRCI) and clinical outcomes. Methods: A search was carried out in the databases Cochrane Library, LILACS, PubMed, Scopus, EMBASE, Open Thesis, and Web of Science to identify studies evaluating the association between MRC and clinical outcomes that were published from January 1, 2004, to April 2, 2018. The search terms included outcome assessment, drug therapy, and medication regimen complexity index and their synonyms in different combinations for case-control and cohort studies that used the MRCI to measure MRC and related the MRCI with clinical outcomes. Odds ratios (ORs), hazard ratios (HRs), and mean differences (WMDs) were calculated, and heterogeneity was assessed using the I2 test. Results: A total of 12 studies met the eligibility criteria. The meta-analysis showed that MRC is associated with the following clinical outcomes: hospitalization (HR = 1.20; 95% CI = 1.14 to 1.27; I2 = 0%) in cohort studies, hospital readmissions (WMD = 7.72; 95% CI = 1.19 to 14.25; I2 = 84%) in case-control studies, and medication nonadherence (adjusted OR = 1.05; 95% CI = 1.02 to 1.07; I2 = 0%) in cohort studies. Conclusion and Relevance: This systematic review and meta-analysis gathered relevant scientific evidence and quantified the combined estimates to show the association of MRC with clinical outcomes: hospitalization, hospital readmission, and medication adherence.

Medication Regimen Complexity Measured by MRCI: A Systematic Review to Identify Health Outcomes

Objective: To perform a systematic review to identify health outcomes related to medication regimen complexity as measured by the Medication Regimen Complexity Index (MRCI) instrument. Data Sources: Cochrane Library, LILACS, PubMed, Scopus, EMBASE, Open Thesis, and Web of Science were searched from January 1, 2004, until April 02, 2018, using the following search terms: outcome assessment, drug therapy, and Medication Regimen Complexity Index and their synonyms in different combinations. Study Selection and Data Extraction: Studies that used the MRCI instrument to measure medication regimen complexity and related it to clinical, humanistic, and/or economic outcomes were evaluated. Two reviewers independently carried out the analysis of the titles, abstracts, and complete texts according to the eligibility criteria, performed data extraction, and evaluated study quality. Data Synthesis: A total of 23 studies met the inclusion criteria; 18 health outcomes related to medication regimen complexity were found. The health outcomes most influenced by medication regimen complexity were hospital readmission, medication adherence, hospitalization, adverse drug events, and emergency sector visit. Only one study related medication regimen complexity with humanistic outcomes, and no study related medication regimen complexity to economic outcomes. Most of the studies were of good methodological quality. Relevance to Patient Care and Clinical Practice: Health care professionals should pay attention to medication regimen complexity of the patients because this may influence health outcomes. Conclusion: This study identified some health outcomes that may be influenced by medication regimen complexity: hospitalization, hospital readmission, and medication adherence were more prevalent, showing a significant association between MRCI increase and these health outcomes.

Hospitalization Risk and Potentially Inappropriate Medications among Medicare Home Health Nursing Patients

BACKGROUND

Hospitalizations and potentially inappropriate medication (PIM) use are significant and costly issues among older home health patients, yet little is known about the prevalence of PIM use in home health or the relationship between PIM use and hospitalization risk in this population.

OBJECTIVE

To describe the prevalence of PIM use and association with hospitalization among Medicare home health patients.

DESIGN

Cross-sectional analysis using data from 132 home health agencies in the US.

SUBJECTS

Medicare beneficiaries starting home health nursing services between 2013 and 2014 (n = 87,780).

MAIN MEASURES

Prevalence of individual and aggregate PIM use at start of care, measured using the 2012 Beers criteria. Relative risk (RR) of 30-day hospitalization or re-hospitalization associated with individual and aggregate PIM use, compared to no PIM use.

KEY RESULTS

In total, 30,168 (34.4%) patients were using at least one PIM, with 5969 (6.8%) taking at least two PIMs according to the Beers list. The most common types of PIMs were those affecting the brain or spinal cord, analgesics, and medications with anticholinergic properties. With the exception of nonsteroidal anti-inflammatory drugs (NSAIDs), PIM use across all classes was associated with elevated risk (10-33%) of hospitalization compared to non-use. Adjusting for demographic and clinical characteristics, patients using at least one PIM (excluding NSAIDs) had a 13% greater risk (RR = 1.13, 95% CI: 1.09, 1.17) of being hospitalized than patients using no PIMs, while patients using at least two PIMs had 21% greater risk (RR = 1.21, 95% CI: 1.12, 1.30). Similar associations were found between PIMs and re-hospitalization risk among patients referred to home health from a hospital.

CONCLUSIONS

Given the high prevalence of PIM use and the association between PIMs and hospitalization risk, home health episodes represent opportunities to substantially reduce PIM use among older adults and prevent adverse outcomes. Efforts to address medication use during home health episodes, hospitalizations, and care transitions are justified.

Big data science: A literature review of nursing research exemplars

BACKGROUND

Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge.

PURPOSE

The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals.

METHODS

A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice.

DISCUSSION

Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods.

CONCLUSION

There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science.

Optimization of decision support tool using medication regimens to assess rehospitalization risks

BACKGROUND

Unnecessary hospital readmissions are costly for the U.S. health care system. An automated algorithm was developed to target this problem and proven to predict elderly patients at greater risk of rehospitalization based on their medication regimens.

OBJECTIVE

Improve the algorithm for predicting elderly patients' risks for readmission by optimizing the sensitivity of its medication criteria.

METHODS

Outcome and Assessment Information Set (OASIS) and medication data were reused from a study that defined and tested an algorithm for assessing rehospitalization risks of 911 patients from 15 Medicare-certified home health care agencies. Odds Ratio analyses, literature reviews and clinical judgments were used to adjust the scoring of patients' High Risk Medication Regimens (HRMRs). Receiver Operating Characteristic (ROC) analysis evaluated whether these adjustments improved the predictive strength of the algorithm's components.

RESULTS

HRMR scores are composed of polypharmacy (number of drugs), potentially inappropriate medications (PIM) (drugs risky to the elderly), and Medication Regimen Complexity Index (MRCI) (complex dose forms, dose frequency, instructions or administration). Strongest ROC results for the HRMR components were Areas Under the Curve (AUC) of .68 for polypharmacy when excluding supplements; and .60 for PIM and .69 for MRCI using the original HRMR criteria. The "cut point" identifying MRCI scores as indicative of medication-related readmission risk was increased from 20 to 33.

CONCLUSION

The automated algorithm can predict elderly patients at risk of hospital readmissions and its underlying criteria is improved by a modification to its polypharmacy definition and MRCI cut point.