Materials & Methods
A prospective observational cross-sectional study was conducted in a
tertiary care teaching hospital of Punjab in north India after obtaining
Institutional Ethics Committee (IEC) clearance in accordance with the
Indian Council for Medical Research Bioethics guidelines
(ERB/UCER/2018/9/3). Patients having age ≥ 65 years with a history of
cardiovascular disease admitted to the Cardiology/Medicine department
willing to participate included in the study after written informed
consent.
Data to find out PIM predictors in relation to age, sex, education
qualification, clinical features, number of comorbidities, Laboratory
and radiological investigations (Serum creatinine value), and
drug-related characteristics (number of medications during hospital
stay) were noted. All of the patient treatment charts were reviewed
daily, and the PIM were identified according to the American Geriatric
Society (AGS) updated Beers criteria 2019 applicable to the general
population aged over 65 years regardless of the level of frailty or
place of residence. The Creatinine clearance (CrCl) value was calculated
based on serum creatinine of the patient-reported at the time of
admission with the help of the Cockcroft- Gault
equation.[12]
Statistical analysis was carried out by using Stata 16 (Stata Corp) and
Statistical Package for the Social Science (SPSS) free version
24.[13] Numerical data was expressed as mean and
standard deviation or median and interquartile range depended on the
data’s normality distribution. Frequency and percentage were used to
express categorical data. The prevalence of PIMs was calculated based on
the patient-level as follows.
Prevalence of PIMs= total number of patients with at least one PIMs use/
the total population of the older adult patients hospitalized with
cardiovascular disease.
The risk factors related to PIM prescription, including
socio-demographic variables like age, gender, number of medications,
length of hospital stay, and creatinine clearance of the older adult
patients, were assessed using binary logistic regression. The Odds Ratio
(OR) with a confidence interval of 95% (CI) was used for the
identification of predictors for prescribing PIMs. P-value
<0.05 was considered statistically significant.