Study design and setting.
This study was a cross-sectional retrospective evaluation of ADRs among hospitalized elderly, using a medical chart review at the internal medicine wards of a public University teaching hospital, in Southwest, Nigeria. The hospital which is 205-bedded serves as a referral centre for many healthcare facilities in the region and had a number of geriatricians consulting in the medical wards at the time of this study.
Study population/Eligibility Criteria
Patients aged ≥60 years that were hospitalized in the internal medicine wards of the hospital between 1st January and 31st December 2016 were eligible for inclusion in the study. In Nigeria, adults aged ≥60 years are recognized as elderly in line with the United Nations cut-off and this was consistent with studies in many low and middle-income countries [4,17,19,24]. Eligible participants with incomplete socio-demographic information, discharged or transferred to another level of care, died within 24 hours of hospitalization, readmitted within the study period or on chemotherapeutic agents were excluded from this study. The data collection was carried out between 13th April and 30th July 2017.
Selection of participants
Figure 1 depicts the selection process of the participants. The study participants were identified from the hospital admission records. The eligible participants’ medical record numbers were listed, and the included records selected using a simple non-blinded randomization technique with the aid of computer-generated random numbers. Included records with missing data were deleted before analysis.
Sample size calculation
The sample size was calculated based on the population of 846 elderly patients that were hospitalized at the internal medicine wards within the study period using the hospital admission records. Using this total population with a margin error of 5%, a power of 95% and 50% response distribution yielding a minimum sample size of 265, with additional 10% being added for attrition resulting in 292 participants for the maximum sample size as computed using a formula previously described [25].
Data collection
With the help of a trained research assistant, the selected records were retrieved and information including patients’ socio-demographics, medical and medication histories, medicine utilization during hospitalization and duration of hospital stay were extracted using the researchers’ designed checklist. The complexity of the patients’ health conditions was determined using the Charlson’s comorbidity index [26]. The patients’ specific laboratory data including serum creatinine (mg/dL) or estimated glomerular filtration rate (eGFR) mL/min) calculated using the Cockcroft–Gault formula were documented [27]. Platelet counts (cells/mm3), level of potassium and sodium electrolytes (mmol/L) at hospitalization and during hospital stay (where available) were also extracted. Physicians’ clinical judgment of ADR occurrence documented in the charts and the clinical decisions that were taken to mitigate the ADRs including the substitution of medication and reduction in doses were extracted from the review sheet.
Study procedure
Measurement of ADRs
A multifaceted approach including a detailed review of nursing and physicians’ charts, laboratory data, and other clinical parameters was conducted. Attention was paid to patients’ verbalized complaints documented in the charts. Radio-imaging data, such as ultrasound, echocardiography, and electrocardiogram parameters were however not considered for review because they were rarely ordered by the physicians. Potential ADRs were identified using the Institute for Healthcare Improvement (IHI) global trigger tool which has been found useful in retrospective evaluations of ADRs among inpatients in many healthcare settings [28].
Two clinical pharmacists independently assessed the ADRs using the Naranjo algorithm which was preferred to other tools for its high specificity and ease of use [29]. A further analysis was performed for only “definite”, “probable” and “possible” ADR categories. The assessors also agreed on the causality of the ADRs. Where there were disagreements between the assessors, it was referred to a physician and consensus were reached. This study evaluated the PIMs for both predictable and unpredictable ADRs but determined the association for the predictable only.
Assessment of PIMs
The appropriateness of medication use during hospitalization was assessed using the 2015 AGS-Beers Criteria. The PIMs of “independent of diagnosis”, to be avoided due to drug-disease or drug-syndrome interactions to non-anti-infective DDIs and those to be avoided or have their doses reduced due to the level of kidney functions in elderly were evaluated. The strong anticholinergics listed in the Criteria were evaluated. The prevalence of medications that increase falls risks in the elderly according to the AGS clinical practice guidelines on fall prevention was also assessed [30].
Data management and analysis
The data were fed into the Statistical Packages for the Social Sciences software (SPSS version 25) and were primarily analyzed using descriptive statistics. Participants with missing data were deleted before the analysis. Student’s independent t-test was used to compare normally distributed continuous variables and the results were presented in means and standard deviations. Bivariate analyses and subsequently, a binary logistic regression, were carried out to determine the associations between categorical variables and ADRs. Independent variables reported being associated with ADRs in previous studies including gender, age, comorbidity index, PIMs, diagnoses of heart failure, chronic kidney diseases and musculoskeletal disorders, use of anticholinergics and Falls associated medications and duration of hospitalization [2,4-7] were included in the model. In line with previous studies, age ≥ 80 years, comorbidity index ≥4 and duration of hospital stay ≥12 days were used as references in the categorization of the variables (1,4,31). P-values < 0.05 were considered significant.
Ethical considerations
This study was exempted from requiring patients’ consent to participate by the Health Research Committees of the Department of Health, Province of Kwazulu-Natal under the reference HRKM090/17 KZ_2017RP 16_591 and the Olabisi Onabanjo University Teaching Hospital Health Research Ethics Committee under the reference OOUTH/HREC/97/2016. Permission to access the data set was obtained from the management of the study sites and the Heads of the Medical Departments in the hospitals. The University of KwaZulu-Natal Biomedical Research Ethics Committee approved the study under the reference (BE 591/16). Unique numbers were used to code the identity of the patients and the data were secured through a password. Access to the data was restricted to the authorized personnel only.