a) Background: Polypharmacy is common in the elderly, as this age group often suffers from multimorbidity, such as cardiovascular disease and diabetes that require multiple medications for treatment and prophylaxis
(1). Exposure to multiple medications is associated with increased risk of serious adverse events, including falls, cognitive impairment, functional decline, hospitalization, increased length of hospital stays and death (2). Also, a developing country like India, most of the patients who visit the public tertiary health care centres are from lower socioeconomic strata and/or rural areas, and not literate enough to comprehend the instructions written by the doctor in the prescription or given verbally in the short consultation due to time constraints. Pharmacies also usually are unable to fill this information gap. In addition, some of prescribed drugs may be potentially inappropriate medications (PIM), defined as a medication that should not be prescribed because the risk of adverse events outweighs the clinical benefit, especially when more effective alternatives are available (3). It is therefore necessary to periodically review medication intake of the elderly and discontinue or reduce the dose of the ones that may lead to adverse effects, drug interactions or are no longer required for the patient. This is termed as deprescribing. Deprescribing is defined as a process of medication withdrawal, supervised by a health care professional, with the goal of managing polypharmacy and improving outcomes (4). In addition to avoiding unwanted adverse effects and interactions, deprescribing can lead to reduced cost of therapy, which is important since most medication costs are met out of pocket in India.
There are many tools for deprescribing, including implicit and explicit tools.(5) But none of these tools has been developed for the Indian context, which differs in terms of a high prevalence of diabetes with or without hypertension requiring multiple drugs, widespread use of fixed dose combinations (FDCs), use of medicines from different systems of medicines, and high out of pocket expenditure on medicines.
We therefore propose to use the available deprescribing tools to develop a deprescribing tool for the Indian elderly population and evaluate its impact on prescription quality and risk minimization by averting potential adverse drug reactions and interactions.
b) Aim/objective
c) Methods:
Study Design:
Objective 1: (Delphi Process)
Objective 2: (Cross-sectional validation study)
Objective 3: (Qualitative Study)
Inclusion & Exclusion criteria
Objective 1:
Inclusion Criteria:
Exclusion criteria:
Objective 2:
Inclusion criteria:
Exclusion criteria
Objective 3
Inclusion criteria:
Exclusion criteria
Study setting:
Duration of the study: 3 Years
Study Participants:
We will include all consenting patients who are 60 years and above. Patients will be recruited from all from OPDs of general medicine OPD and Rural health centre. In the rural center we will recruit patients through the associated health services of community medicine department.
Sampling Frame: All eligible patients from out-patient departments of general medicine and associated out-reach centre will be included. We will invite all the eligible elderly patients to participate in the data-collection. In the OPDs, the physicians will identify the patients and send them to the study team seated in the common waiting area.
Data collection: Study will be conducted from out-patients department of General Medicine and Rural Health Centre. In first step for control study and in second phase for intervention study.
Data Handling: The data will be recorded on a structured case report form (CRF). These will be then transferred on RedCap software.
Ethical Considerations, Patient Confidentiality, and Consent: This study will be conducted in compliance with the protocol, principles laid down in the Declaration of Helsinki. Before study initiation, the Investigator will obtain approval from the Institutional Ethics Committee (IRB/IEC) for the protocol and consent and assent forms.
Ethics approvals from the St. John’s Medical College IEC will be obtained. We will obtain written informed consent from patients in this study. All participants’ data will be maintained with the utmost confidentiality and only authorized personnel will have access to study documents. All documents will contain de-identified data from the participants.
Data management: Data will be collected on paper case record forms. We will set up a database at St. John’s Medical College and Research Institute. The data will be stored on a secure server with adequate backup. All patient information will be stored on a password secured computer system and kept strictly confidential. Subject confidentiality will be further ensured by utilizing subject identification code numbers.
e) Sample size:
In the Systematic Review and Meta-Analysis of Prevalence of Polypharmacy,
Hyper-polypharmacy and Potentially Inappropriate Medication Use in Older Adults in India in 2021 Bhagavathula et al, reported a pooled prevalence of 28% prescriptions with at least one PIM prescribed to the Indian elderly. Hence to compare the rate of potentially inappropriate medication (PIM) in older patients measured with and without using deprescribing tool, assuming the PIM rate to be 8% lesser when deprescribing tool is applied compared to PIM rate when not applied and assuming ICC of 0.05, which accounts for the clustering at prescriber level, cluster size of 55, with 80% power of the study and 5% level of significance (two sided); the required sample size is 1641 patients in 30 clusters.
Hence, we will include 15 clusters i.e. prescribers each in control and intervention arm and each prescriber will prescribe 55 elderly patients. The total sample size will be 1650 patients, 825 each in intervention and control arm.
The sample size was calculated using nMaster 2.0 software.
Calculations:
| Cluster Design - Two groups - Unmatched studies - Comparison of proportions (Design Effect) | |
| Proportion of outcome in the experimental group (With Tool) | 0.20 |
| Proportion of outcome in the control group (Without Tool) | 0.28 |
| Size of the cluster | 55 |
| Intra cluster correlation coefficient (ICC) | 0.05 |
| Design effect | 3.7 |
| Power (1- beta) % | 80 |
| Alpha Error (%) | 5 |
| 1 or 2 sided | 2 |
| No. of clusters required | 30 |
| Required sample size | 1641 |
Where,
p1 : Success rate in the experimental group (0.20)
p2 : Success rate in the control group (0.28)
m : Size of the cluster (55)
ρ : Intra cluster correlation coefficient (0.05)
α : Significance level (5%)
1- β : Power (80%)
Thus, 1650 will be the final sample size for the quantitative study, 825 from each study site
(AIIMS Bhopal and St Johns). Out of 825 prescriptions, 550 will be recruited from the tertiary care hospital and 275 from primary health center (PHC).
An average of approximately 420-440 patients visit the General Medicine OPD of each study site daily, of which approximately 14% (i.e. 58-60) are ≥ 60 years. Considering that not all patients will meet inclusion criteria and/or consent, we aim to include 5 patients each on 5 working days every week over 32 weeks or 8 months.
f) Study update:
Data collection started from: 2nd April 2025 for pilot study and 19th May 2025 for Main study
Baseline data collection*: 300 prescriptions collected for baseline prescribing information. (Pilot study with the tool: 2nd April 2025 *
Prescriptions without the tool : 7 + 7
Prescriptions with the tool : 7 +7 Ongoing
Control Group Recruitment:
Data collection started: 19th May 2025
30 participants have been enrolled out of the targeted total of
330. (No. of eligible patients visited OPD=90 in this period)
Recruitment status: Ongoing
Updated July 2025