Assessing medication use patterns in patients hospitalised with COVID-19: a retrospective study

Assessing medication use patterns by clinical outcomes severity among inpatients with COVID-19: A retrospective drug utilization study

PURPOSE

This study assessed medication patterns for inpatients at a central hospital in Portugal and explored their relationships with clinical outcomes in COVID-19 cases.

METHODS

A retrospective study analyzed inpatient medication data, coded using the Anatomical Therapeutic Chemical classification system, from electronic patient records. It investigated the association between medications and clinical severity outcomes such as ICU admissions, respiratory/circulatory support needs, and hospital discharge status, including mortality (identified by ICD-10-CM/PCS codes). Multivariate analyses incorporating demographic data and comorbidities were used to adjust for potential confounders and understand the impact of medication patterns on disease progression and outcomes.

RESULTS

The analysis of 2688 hospitalized COVID-19 patients (55.3% male, average age 62.8 years) revealed a significant correlation between medication types and intensity and disease severity. Cases requiring ICU admission or ECMO support often involved blood and blood-forming organ drugs. Increased use of nervous system and genitourinary hormones was observed in nonsurvivors. Corticosteroids, like dexamethasone, were common in critically ill patients, while tocilizumab was used in ECMO cases. Medications for the alimentary tract, metabolism, and cardiovascular system, although widely prescribed, were linked to more severe cases. Invasive mechanical ventilation correlated with higher usage of systemic anti-infectives and musculoskeletal medications. Trends in co-prescribing blood-forming drugs with those for acid-related disorders, analgesics, and antibacterials were associated with intensive interventions and worse outcomes.

CONCLUSIONS

The study highlights complex medication regimens in managing severe COVID-19, underscoring specific drug patterns associated with critical health outcomes. Further research is needed to explore these patterns.

Data Resource Profile: The Hospital Electronic Prescribing and Medicines Administration (HEPMA) National Data Collection in Scotland

Introduction

To support both electronic prescribing and documentation of medicines administration in secondary care, hospitals in Scotland are currently implementing the Hospital Electronic Prescribing and Medicines Administration (HEPMA) software. Driven by the COVID-19 pandemic, agreements have been put in place to centrally collate data stemming from the operational HEPMA system. The aim was to develop a national data resource based on records created in secondary care, in line with pre-existing collections of data from primary care.

Methods

HEPMA is a live clinical system and updated on a continuous basis. Data is automatically extracted from local systems at least weekly and, in most cases, on a nightly basis, and integrated into the national HEPMA dataset. Subsequently, the data are subject to quality checks including data consistency and completeness. Records contain a unique patient identified (Community Health Index number), enabling linkage to other routinely collected data including primary care prescriptions, hospital admission episodes, and death records.

Results

The HEPMA data resource captures and compiles information on all medicines prescribed within the ward/hospital covered by the system; this includes medicine name, formulation, strength, dose, route, and frequency of administration, and dates and times of prescribing. In addition, the HEPMA dataset also captures information on medicines administration, including dates and time of administration. Data is available from January 2019 onwards and held by Public Health Scotland.

Conclusion

The national HEPMA data resource supports cross-sectional/point-prevalence studies including drug utilisation studies, and also offers scope to conduct longitudinal studies, e.g., cohort and case-control studies. With the possibility to link to other relevant datasets, additional areas of interest may include health policy evaluations and health economics studies. Access to data is subject to approval; researchers need to contact the electronic Data Research and Innovation Service (eDRIS) in the first instance.