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.