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PREVALENCE AND PREDICTORS OF POTENTIALLY INAPPROPRIATE MEDICATION USE IN ELDERLY PATIENTS IN TWO INDIAN TEACHING HOSPITALS PARTHASARATHI G, HARUGERI A,

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Presentation on theme: "PREVALENCE AND PREDICTORS OF POTENTIALLY INAPPROPRIATE MEDICATION USE IN ELDERLY PATIENTS IN TWO INDIAN TEACHING HOSPITALS PARTHASARATHI G, HARUGERI A,"— Presentation transcript:

1 PREVALENCE AND PREDICTORS OF POTENTIALLY INAPPROPRIATE MEDICATION USE IN ELDERLY PATIENTS IN TWO INDIAN TEACHING HOSPITALS PARTHASARATHI G, HARUGERI A, JOSEPH J, RAMESH M, GUIDO S. Presenting Author G Parthasarathi JSS University, INDIA 1

2 Introduction Several factors contribute to greater propensity of ADRs in the elderly, including potentially inappropriate medication use About one-fourth of the adverse outcomes in the elderly are estimated to be due to PIM use Indian elderly -12.8% of worldwide elderly (2007)– expected to reach 100 million by 2013 There is a need for prospective studies that evaluate PIM use and burden of adverse events due to PIM use in India 2

3 Objectives General  To determine the nature and frequency of PIM use in hospitalized Indian elderly Specific  To determine the prevalence of PIM use in hospitalized elderly  Identify the predictors of PIM use  Assess the relationship between PIM use and ADRs 3

4 Methods The prospective observational study was conducted in medicine wards of two teaching hospitals Patients aged > 60 years admitted to medicine wards receiving at least one medication were randomly included. Patients were reviewed from the day of admission to discharge for PIM use and ADRs. Beers Criteria 2003 were used to assess PIM use 4

5 Methods Causality of the ADRs was assessed using Naranjo’s algorithm Polypharmacy (5–9 medications) and High-level Polypharmacy (≥10 medications) Association between ADRs and PIM use and Predictors associated with PIM use was assessed Bivariate analysis and multivariate logistic regression 5

6 CharacteristicDistribution (%) (n=814) No. of patients with PIM Use [% Prevalence] S ex Male493 (60.6)122 [24.7] Female321 (39.4)69 [21.5] Age 60 – 64 yrs234 (28.7)60 [25.6] 65 – 69 yrs254 (31.2)59 [23.2] 70 – 74 yrs144 (17.7)24 [16.7] 75 – 79 yrs111 (13.6)27 [24.3] > 80 yrs71 (8.7)21 [29.6] No. of diseases 1155 (19)44 [28.4] 2265 (32.6)59 [22.3] 3212 (26)44 [20.7] > 4182 (22.4)44 [24.2] No. of medications at admission 1 – 4452 (55.5)15 [3.3] 5 – 8309 (38)5 [1.6] 9 – 1253 (6.5)0 No. of medications during hospital stay 1 – 478 (9.6)17 [21.8] 5 – 8298 (36.6)42 [14.1] 9 – 12269 (33)60 [22.3] 13 – 16120 (14.7)41 [34.2] >1749 (6)20 [40.8] Length of hospital stay (days) < 4177 (21.7)34 [19.2] 5 – 9473 (58.1)105 [24] 10 – 14131 (16.1)39 [29.8] >1533 (4)13 [39.4] Patient Characteristics and Prevalence of PIM Use 6

7 Results Three most frequent diagnoses in the study population – Essential hypertension (41.5%) – Non-insulin-dependent diabetes mellitus (34%) – Chronic obstructive pulmonary disease (18.5%). PIMs were received by 191 (23.5%) patients PIM use was observed both at admission and during hospital stay One, two and three PIMs were received by 134, 46 and 11 patients respectively 7

8 Results Polypharmacy was observed in 44.5% and 90.4% of patients at admission and during hospital stay. High severity PIM use showed a higher prevalence compared to low severity (26.8% Vs. 5.5%). A total of 360 ADRs were observed in 292 patients. Of these, 11 (3%) ADRs were due to medications listed in BC. Of these 11 ADRs, 3 were due to clonidine, 2 each were due to amiodarone, diazepam, hydroxyzine and digoxin. Among the ADRs due to medications not listed in BC (349), insulin (14%, 49), furosemide (6.3%, 22) and prednisolone (5.1%, 18) were the most frequently implicated in ADRs. Medications not listed in BC resulted in more number of ADRs than medications listed in BC [χ 2 =98.4, p<0.001 (df=1)] [Odds ratio (OR): 13.51 {95% confidence interval (CI): 7.19-25}; p<0.001]. 8

9 Prevalence of PIM Use Generic medication nameNo. of patients (% Prevalence) (n=814) At admission to medicine ward High severity Alprazolam1 (0.1) Amiodarone1 (0.1) Digoxin4 (0.5) Nifedipine4 (0.5) Low severity Clonidine2 (0.2) Diazepam2 (0.2) Ferrous sulfate1 (0.1) During the stay in medicine ward High severity Amiodarone9 (1.1) Amitriptyline19 (2.3) Bisacodyl6 (0.7) Chlordiazepoxide1 (0.1) Chlorpheniramine1 (0.1) Chlorzoxazone2 (0.2) Diazepam8 (1) Diphenhydramine3 (0.4) Hydroxyzine7 (0.9) Hyoscine8 (1) Ketorolac1 (0.1) Lorazepam1 (0.1) Meperidine3 (0.4) Methyldopa3 (0.4) Mineral oil52 (6.4) Nifedipine5 (0.6) Nitrofurantoin3 (0.4) Low severity Clonidine7 (0.9) Digoxin12 (1.5) Propoxyphene20 (2.5) 9

10 Prevalence of PIM Use Disease / conditionGeneric medication name No. of patients (% Prevalence) (n=814) At admission to medicine ward High severity Gastric ulcerDiclofenac2 (0.3) Ibuprofen1 (0.1) Parkinson’s diseaseChlorpromazine1 (0.1) Low severity HyponatremiaSertraline1 (0.1) During the stay in medicine ward High severity Blood clotting disorder or anticoagulant therapy Aspirin42 (5.2) Clopidogrel25 (3.1) Diclofenac1 (0.1) Gastric ulcerDiclofenac1 (0.1) Stress incontinanceIpratropium bromide1 (0.1) SeizureChlorpromazine1 (0.1) DepressionMethyldopa1 (0.1) Predictors of PIM Use: Multivariate Regression Analysis Predictor Regression coefficient (B) [Standard Error] Odds Ratio [CI]P Value* Constant-0.63 [0.17] 1.87 <0.001 Number of medications received during hospital stay (>9) -0.64 [0.18]1.9 [1.34-2.69] <0.001 Duration of hospital stay (>10 days) -0.36 [0.2]1.43 [0.97-2.12] 0.071 10

11 Summary The largest prospective study of PIM usage evaluation in hospitalized elderly in India. The main findings of the study are:  23.5% of study patients received at least one PIM (at admission or during the hospital stay).  More than one third of these patients were prescribed with aspirin/non-steroidal anti inflammatory drug (NSAID) in the presence of bleeding disorder or anticoagulant.  Medications not listed in BC were associated with increased occurrence of ADRs compared to medications listed in BC (χ 2 =98.4, p<0.001).  Multivariative analysis showed that patients receiving >9 medications during the hospital stay was the influential predictor of PIM use. 11

12 Policy Implications and Conclusion PIM use was found to be common among the hospitalized elderly of medicine ward Campaigns promoting rational drug use in elderly in India should focus on decreasing the occurrence of PIM use PIM use was associated with patients’ age, number of concurrent medications used and length of hospital stay Measures to reduce the risk of ADRs in the elderly should target medications commonly implicated in ADRs rather than focusing only on medications listed in Beers Criteria Interventions aimed at rational medication use in elderly Indians should focus on the predictors of PIM use There is a great scope for conducting intense research to determine inappropriate medication use and its health-related adverse consequences in the increasing Indian elderly population 12

13 13

14 THANK YOU 14


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