Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Epidemiology of potential drug-drug interactions in elderly population admitted to critical care units of Peshawar, Pakistan

BMC Pharmacology and Toxicology201819:85

https://doi.org/10.1186/s40360-018-0276-4

  • Received: 23 August 2018
  • Accepted: 26 November 2018
  • Published:
Open Peer Review reports

Abstract

Background

Aging population, is a reality in many countries because of improvement in the health care, patient safety and other supplemental factors. Pharmacotherapy in this population must be evaluated due to their higher susceptibility to adverse drug outcomes, like potential drug-drug interactions (PDDIs). Research in this regard is limited particularly in developing countries. The aim of the study was to evaluate the prevalence and associated factors in this population.

Methods

The multicentered study evaluated the prevalence of potential drug-drug interactions and associated factors in elderly population at critical care units in Peshawar, Pakistan. Potential drug-drug interactions were evaluated using Micromedex DrugReax, while statistical analysis was performed using SPSS.

Results

A total of 70.17% elderly patients were observed to have at least one PDDI. A significant association was observed between presence of PDDIs and number of prescribed drugs, duration of stay and age (p < 0.05). A total of 3019 PDDIs were observed, attributing to 225 drug pairs. Prevalent PDDIs were of moderate severity, good documentation and pharmacodynamic in nature. One-way ANOVA revealed a significant difference in the means of PDDIs between Northwest general hospital and the rest of the hospitals. Moreover, there was a significant difference in the means of PDDIs of CCU and SU with rest of the units.

Conclusion

The prevalence of PDDIs was observed to be high in elderly population which can be managed by avoiding or managing a limited number of drug combinations. Such studies are necessary to evaluate the risks of these PDDIs in a population which is already physiologically compromised.

Keywords

  • Geriatrics
  • Potential drug-drug interactions
  • Epidemiology

Background

An outcome of interrelated developmental achievements is an ageing population. Improvement in healthcare is a major factor for increasing life expectancy, along with supplemental factors like improved nutrition, education, income and sanitation [1]. Patient safety has gained much attention in recent years by health care providers, further incrementing the age of the population. Pharmacotherapy has aided in improving health of the patients, however, it has also led to a rise in adverse drug events. One such adverse event is drug-drug interactions [2].

Elderly population, known as geriatrics, face many health issues due to the natural process of ageing, beyond the control of humans. Treating multi-morbidities in geriatrics with drugs is complex and leads to the expression of adverse drug events. This along with the natural functional impairment tends to impart harm rather than benefit in geriatrics [3, 4].

Drug-drug interaction is the modification, increase or decrease in the effects of drugs when simultaneously administered with another drug. This leads to severe adverse effects which are totally preventable in most cases if suitably managed [5]. Geriatric population is at a higher risk to these interactions due to the natural functional impairment, and identification of the drug-drug interactions in this population becomes imperative [6, 7].

In Pakistan, monitoring of pharmacotherapy in geriatrics in neglected. Moreover, the prevalence of drug-drug interactions is also not known. Thus, understanding the mechanisms and factors involved in these potential drug-drug interactions (PDDIs) is important to aid the prevention of adverse effects of the interacting drug pairs.

Methods

The multicentered cross sectional study was conducted at the critical care units of four tertiary care hospitals in Peshawar, Pakistan; Lady Reading Hospital (LRH), Khyber Teaching Hospital (KTH), Hayatabad Medical Complex (HMC) and Northwest General Hospital and Research Center (NWGH & RC). The former 3 hospitals are government run, while the latter is a private hospital. The critical care units included were Medical Intensive Care Unit (MICU), Surgical Intensive Care Unit (SICU), Cardiac Care Unit (CCU), and Stroke Unit (SU). Patients from the Northwestern region of Pakistan and Afghanistan avail the medical facilities in these hospitals. Patients who fulfilled the inclusion criteria were randomly selected from these critical units. Inclusion criteria was set as patients of age of 60 years or above, prescribed 2 or more drugs, and admitted to the critical care unit for more than 24 h. Data of 2960 patients was collected over the period of 1 year (Dec 2013 – Dec 2014) of which 1044 met the inclusion criteria.

Prior to collection of data, ethical approvals from the respective hospitals were obtained beforehand vide letter number 8075–79/HMC, 488/pharm (KTH), 010 (LRH), and NWGH/Research/01. A predesigned proforma was used to collect the patient demographic data (age, gender, hospital, unit, date of admission and discharge) and treatment profile (diagnosis, drugs administered, dose and frequency, duration of drug administration); patient identification and other personal data were not disclosed. ICH guidelines for good clinical practice were followed [8]. Written informed consent was not necessary because no personal patient data has been included in the manuscript and data was collected from the medication charts of the patients, for which the hospital ethical committee provided approval.

Evaluation of drug-drug interactions were carried out through Micromedex DrugReax [9] which provides details on the severity, documentation, onset and mechanism of the PDDIs. Severity is classified by Micromedex as major, moderate and minor, while documentation is classified as excellent, good and fair. Micromedex also elaborates the mechanism of the interacting drug pairs. Drugs administered simultaneously during treatment were evaluated for PDDIs.

Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 20 (Armonk, NY: IBM Corp.) [10]. Various statistical tools were used for analysis of descriptive data, along with logistic regression to evaluate the association of PDDIs with predictive factors. One-way ANOVA was also employed to observe the difference in means of PDDIs among the four hospitals.

Results

Of the total 1044 patients included in the study, 877 (84%) aged ≤75 years while 167 (16%) patients aged > 75 years. Male patients were predominant (60.3%) as compared to female patients (39.7%). The mean stay of the patients in critical care units was 4.56 ± (3.12) days, while the mean number of prescribed drugs was 5.99 ± (1.88) drugs. CCU saw the highest flow of inpatients as compared to other units. Similarly, patients with a primary diagnosis of myocardial infarction were predominant, as shown in Table 1.
Table 1

Demographics and general characteristics of study population (N = 1044)

Variables

Mean ± SD

Frequency (%)

Range

Gender

 

 Male

630 (60.3%)

 Female

414 (39.7%)

Age (years)

68.53 (± 7.81)

60–100

Drugs prescribed per patient

5.99 ± (1.88)

2–13

Stay in ICU (days)

4.56 ± (3.12)

1–38

Critical Unit

1044 (100%)

 Surgical ICU

151 (14.5%)

 Medical ICU

264 (25.3%)

 Cardiac ICU

499 (47.7%)

 Stroke Unit

130 (12.5%)

Hospital

1044 (100%)

 NWGH

420 (40.2%)

 LRH

174 (16.7%)

 KTH

223 (21.4%)

 HMC

227 (21.7%)

Diagnosis

1044 (100%)

 Myocardial Infarction

261 (25.00%)

 Cerebrovascular Accident

96 (9.20%)

 Acute Coronary Syndrome

82 (7.85%)

 Heart Failure

59 (5.65%)

 Chronic Obstructive Pulmonary Disorder

34 (3.26%)

 Miscellaneous

512 (49.04%)

ICU Intensive care unit, NWGH Northwest General Hospital, LRH Lady Reading Hospital, KTH Khyber Teaching Hospital, HMC Hayatabad Medical Complex, SD Standard Deviation

Prevalence of potential drug-drug interactions (PDDIs) was 71.07% of all the patients. A total of 137 (13.1%) patients were observed to have at least one PDDI, 110 (10.5%) had 5 PDDIs, while 2 (0.2%) had 19 PDDIs. PDDIs were most prevalent in patients with myocardial infarction, as shown in Table 2.
Table 2

Characteristics of potential drug-drug interactions

Variables

No. of Patients (%)

Potential drug-drug interactions

 Present

742 (71.07%)

 Absent

302 (28.93%)

Most severe PDDI seen in each patient

 Contraindicated

6 (0.81%)

 Major

618 (83.29%)

 Moderate

113 (15.23%)

 Minor

5 (0.67%)

Diseases with highest prevalence of PDDIs

 Myocardial Infarction

257 (34.64%)

 Cerebrovascular Accident

82 (11.05%)

 Acute Coronary Syndrome

79 (10.65%)

 Heart Failure

44 (5.93%)

A total of 3019 PDDIs were observed in 1044 patients, of which 1398 (46.3%) were of major severity, 1533 (50.8%) were of moderate severity, 82 (2.7%) were of minor severity while 6 (0.2%) PDDIs were contraindicated. In terms of documentation, 372 (12.3%) PDDIs were of excellent, 1485 (49.2%) PDDIs were of good, and 1162 (38.5%) PDDIs were of fair documentation. The onset of 1758 (58.2%) PDDIs was unknown, while 529 (17.5%) and 732 (24.2%) PDDIs were of rapid and delayed onset respectively. Pharmacodynamic nature PDDIs (66.5%) were common, while synergistic mechanism (44%) was predominantly involved in the PDDIs.

Multivariate logistic regression was applied to associate multiple predictors with the presence of PDDIs. whereas, the individual effect of predictors was also evaluated by applying univariate logistic regression.

Univariate logistic regression revealed a positive, statistically significant association between the presence of PDDIs with the following independent variables: > 6 prescribed drugs, > 3 days stay in the critical care unit and diagnosis of myocardial infarction, cerebrovascular accident and acute coronary syndrome.

Multivariate logistic regression analysis revealed that the presence of PDDIs was 2.8 times more likely in patients prescribed > 6 drugs, and 0.5 times more likely in patients of age > 75 years. The results of univariate and multivariate logistic regression are shown in Table 3.
Table 3

Factors associated with drug-drug interaction using logistic regression (n = 1044)

Variable

Univariate Regression

Multivariate Regression

OR (95%CI)

p-value

OR (95%CI)

p-value

Prescribed drugs

 ≤ 6

Reference

 

Reference

 

 > 6

2.172 (1.590–2.968)

< 0.001

2.870 (2.011–4.095)

< 0.001

Age

 ≤ 75

Reference

0.106

Reference

 

 > 75

0.748 (0.526–1.064)

 

0.588 (0.386–0.897)

< 0.05

Duration of stay

 ≤ 3

Reference

< 0.001

Reference

 

 > 3

0.622 (0.472–0.820)

 

0.868 (0.626–1.204)

0.397

Gender

 Male

Reference

0.250

Reference

 

 Female

0.853 (0.650–1.119)

 

0.820 (0.595–1.129)

0.224

Chronic illness

 Myocardial infarction

0.018 (0.005–0.058)

< 0.001

0.012 (0.003–0.040)

< 0.001

 Cerebrovascular accident

0.192 (0.080–0.463)

< 0.001

0.147 (0.059–0.367)

< 0.001

 Acute coronary syndrome

0.043 (0.011–0.162)

< 0.001

0.037 (0.009–0.142)

< 0.001

 Heart failure

0.384 (0.157–0.936)

< 0.05

0.265 (0.105–0.673)

< 0.05

CI Confidence interval

A total of 225 drug combinations were involved in all the PDDIs, of which 70.2% attributed to 16 pairs. The common interacting pairs along with their potential outcomes and management are shown in Table 4.
Table 4

Characteristics of common drug-drug interactions

Drug combination

Frequency

Severity

Documentation

Potential outcome

Mechanism

Management

Aspirin & clopidogrel

420

Major

Fair

Increased risk of bleeding

Synergism

Monitor blood count

Clopidogrel & Enoxaparin

277

Major

Fair

Increased risk of bleeding

Synergism

Monitor blood count

Aspirin & Ramipril

186

Moderate

Fair

Decreased Ramipril effectiveness

Inhibition of prostaglandin synthesis

Replace Ramipril with suitable drug

Aspirin & Diuretics

239

Major

Good

Reduced diuretic effectiveness and risk of nephrotoxicity

Decreased renal prostaglandin synthesis

Monitor for any changes

Aspirin & Betablockers

251

Moderate

Good

Antihypertensive effect of beta blockers may be reduced

Decreased renal prostaglandin synthesis

Monitor for any changes

Aspirin & Nitroglycerin

156

Moderate

Good

Increased nitroglycerin plasma concentration

Synergism

Monitor for any changes

Clopidogrel & Atorvastatin

97

Moderate

Excellent

Decreased formation of clopidogrel active metabolite

Competition with CYP3A4

Replace with rosuvastatin or pravastatin

Ramipril & Furosemide

69

Moderate

Good

Postural hypotension

Vasodilation

Monitor for any changes

Aspirin & Ranitidine

59

Minor

Excellent

Decreased plasma levels of aspirin

Reduced absorption of aspirin

Monitor for any changes

Clopidogrel & Omeprazole

37

Major

Excellent

Decreased plasma concentration of clopidogrel

Inhibition of CYP2C19 mediated clopidogrel metabolism.

Use pantoprazole instead of omeprazole

Dexamethasone & Nimodipine

33

Major

Fair

Decreased plasma concentration of nimodipine

Induction of CYP3A4 mediated metabolism of nimodipine

Replace with suitable drug

One-way ANOVA revealed that there was a significant difference (p < 0.001) in the means of PDDIs among all the hospitals. Post hoc test showed that there was a significant difference (p < 0.05) in the means of PDDIs between NWGH and the rest of the hospitals. Moreover, there was a significant difference (p < 0.001) in the means of PDDIs among all the units. Post hoc test showed that there was a significant difference (p < 0.05) in the means of PDDIs between CCU and the rest of the units and between SU and the rest of the units.

Discussion

Previously neglected, the prevalence of PDDIs and their potential outcomes in geriatrics were studied for the first time in Pakistan. The present study revealed a high prevalence of PDDIs in the elderly population. This coincides with the results of other studies conducted in geriatrics and patients admitted to other critical care units [3, 11]. The prevalence of PDDIs was higher in NWGH, which is a private hospital as compared to the other three hospitals. This variation in prevalence may be due to the higher number of prescribed drugs per patient in NWGH. PDDIs is of great concern in elderly population, due to the physiological changes that occur with aging, which may lead to an increased risk of adverse effects due to drug-drug interactions.

The risk of cardiovascular diseases increases with age [12], and the current study also reported cardiovascular disorders to be the most prevalent in the study population. Cardiovascular disorders were also significantly associated with the presence of PDDIs. This emphasizes for greater care when dealing with geriatrics with a cardiovascular disorder.

PDDIs of major and moderate severity were prevalent. A limited number of other studies have observed the categories of PDDIs in elderly. Studies conducted in elderly population at tertiary hospitals reported PDDIs of moderate severity to be the most prevalent [3, 13]. Another study conducted in geriatrics in outpatient settings reported that most of the patients had PDDIs of major severity [14]. Similarly, studies conducted in ICU’s also reported moderate and major severity PDDIs to be among the most prevalent [15, 16]. PDDIs of pharmacodynamic nature were prevalent in the present study due to the involvement of cardiovascular drugs, the mechanism of interaction of most of them was synergistic or antagonistic.

Furthermore, a significant relationship was observed between PDDIs with polypharmacy and age. A cross-sectional study conducted in Brazil also reported a similar significant association between PDDIs with polypharmacy and age [17]. Another research observed a strong association between polypharmacy and negative clinical consequences in elderly population [18]. A Swedish study also reported a similar relationship between drug-drug interactions and increasing number of prescribed drugs [19]. A US study also found a significant association between PDDIs and polypharmacy [20].

Aspirin was involved in most of the prevalent PDDIs. This drug is one of the most common drug used in cardiovascular disorders, however due to its potential for an interaction with other drugs, its use must be continuously monitored for any adverse effects. The frequent use of aspirin and its potential for involvement in PDDIs has also been reported by Tushar et al. in geriatric outpatients [14].

Limitations

The current study design could not measure the actual adverse clinical outcomes of the PDDIs, for which further studies have to be conducted. Furthermore, these results are specific for geriatrics so must be generalized with caution in pediatric and adult population.

Conclusion

A higher prevalence of PDDIs in geriatrics poses a great health concern, due to the weak physiological condition of the aging population. Thus, avoidance of PDDIs and managing them appropriately becomes vital. Monitoring systems should be placed in developing countries to monitor not only PDDIs but also other drug related problems to provide quality health care to patients. Moreover, replacing the drugs involved in PDDIs with appropriate drugs having a lesser potential for PDDI can be implemented to further reduce the risk of PDDIs. Education and training regarding this must be provided to the health care professionals.

Abbreviations

CCU: 

Cardiac care unit

HMC: 

Hayatabad medical complex

KTH: 

Khyber teaching hospital

LRH: 

Lady reading hospital

MICU: 

Medical intensive care unit

NWGH & RC: 

Northwest general hospital and research center

PDDIs: 

Potential drug-drug interactions

SICU: 

Surgical intensive care unit

SU: 

Stroke Unit

Declarations

Acknowledgments

The authors are grateful for the support and co-operation provided by the staff of all the hospitals involved in this study.

Funding

No external funding was provided to the investigators, and was completely self-funded.

Availability of data and materials

The dataset generated and analyzed during the study is included as Additional file 1.

Authors’ contributions

FS was involved in the concept and design of the study, data collection, analysis and drafting of the article. MA as involved in data collection and analysis. AFK was involved in the drafting and review of the article. TNK was involved in the drafting of the article. SK was involved in the concept and analysis. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Written informed consent was not necessary because no personal patient data has been included in the manuscript and data was collected from the medication charts of the patients, for which the hospital ethical committee provided approval.

Ethical approvals to collect data from medication charts of the patients were obtained from the respective hospitals, namely ethical committee of Hayatabad medical complex, medical superintendent Khyber teaching hospital, institutional research and ethical board, post graduate medical institute lady reading hospital and ethical committee of northwest general hospital and research center vide letter numbers 8075-79/HMC, 488/pharm (KTH), 010 (LRH), and NWGH/Research/01 respectively.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Pharmacy, Sarhad University of Science and IT, Peshawar, Pakistan
(2)
University of Vermont Medical Centre, Burlington, VT, USA
(3)
Punjabi Community Health Services, Calgary, Alberta, Canada
(4)
Department of Pharmacy, University of Malakand, Lower Dir District, Pakistan

References

  1. de Figueiredo TP, de Souza Groia RC, Barroso SCC, do Nascimento MMG, Reis AMM. Factors associated with adverse drug reactions in older inpatients in teaching hospital. Int J Clin Pharm. 2017;39(4):679–85.View ArticlePubMedGoogle Scholar
  2. Pantuzza LL, Ceccato MGB, Silveira MR, Junqueira LMR, Reis AMM. Association between medication regimen complexity and pharmacotherapy adherence: a systematic review. Eur J Clin Pharmacol. 2017;73(11):1475–89.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Salwe KJ, Kalyansundaram D, Bahurupi Y. A study on polypharmacy and potential drug-drug interactions among elderly patients admitted in Department of Medicine of a tertiary care hospital in Puducherry. J Clin Diagn Res. 2016;10(2):FC06–10.PubMedPubMed CentralGoogle Scholar
  4. Hines LE, Murphy JE. Potentially harmful drug–drug interactions in the elderly: a review. Am J Geriatr Pharmacother. 2011;9(6):364–77.View ArticlePubMedGoogle Scholar
  5. Dookeeram D, Bidaisee S, Paul JF, Nunes P, Robertson P, Maharaj VR, et al. Polypharmacy and potential drug–drug interactions in emergency department patients in the Caribbean. Int J Clin Pharm. 2017;39(5):1119–27.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Lin C-F, Wang C-Y, Bai C-H. Polypharmacy, aging and potential drug-drug interactions in outpatients in Taiwan. Drugs Aging. 2011;28(3):219–25.View ArticlePubMedGoogle Scholar
  7. Hohl CM, Dankoff J, Colacone A, Afilalo M. Polypharmacy, adverse drug-related events, and potential adverse drug interactions in elderly patients presenting to an emergency department. Ann Emerg Med. 2001;38(6):666–71.View ArticlePubMedGoogle Scholar
  8. Bhuiyan P, Rege N. ICH Harmonised tripartite guideline: guideline for good clinical practice; 2001.Google Scholar
  9. Micromedex T. Drug-Reax® system. www.micromedexsolutions.com.
  10. SPSS I. IBM SPSS statistics for windows, version 20.0. New York: IBM Corp; 2011.Google Scholar
  11. Uijtendaal EV, Harssel LL, Hugenholtz GW, Kuck EM, Zwart-van Rijkom JE, Cremer OL, et al. Analysis of potential drug-drug interactions in medical intensive care unit patients. Pharmacotherapy. 2014;34(3):213–9.View ArticlePubMedGoogle Scholar
  12. Conroy R, Pyörälä K, Ae F, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003.View ArticlePubMedGoogle Scholar
  13. Nobili A, Pasina L, Tettamanti M, Lucca U, Riva E, Marzona I, Monesi L, Cucchiani R, Bortolotti A, Fortino I, Merlino L, Walter Locatelli G, Giuliani G. Potentially severe drug interactions in elderly outpatients: results of an observational study of an administrative prescription database. J Clin Pharm Ther. 2009;34(4):377–386.Google Scholar
  14. Nishandar TB, Kale AS, Pise HN. Study of potential drug interactions between prescribed drugs in geriatric patients attending outpatient department in a government tertiary care hospital in Maharashtra. Int J Basic Clin Pharmacol. 2016;5(4):1569–73.View ArticleGoogle Scholar
  15. Shakeel F, Khan JA, Aamir M, Asim SM, Ullah I. A multicentered pharmacoepidemiological approach to evaluate clinically significant potential drug–drug interactions in medical intensive care settings in Pakistan. Hong Kong J Emerg Med. 2018;25(4):190–5.View ArticleGoogle Scholar
  16. Shakeel F, Khan JA, Aamir M. Relationship of factors affecting clinically important drug interactions and their significance in surgical intensive care units in Pakistan. Lat Am J Pharm. 2018;37(4):643–50.Google Scholar
  17. Novaes PH, da Cruz DT, Lucchetti ALG, Leite ICG, Lucchetti G. The “iatrogenic triad”: polypharmacy, drug–drug interactions, and potentially inappropriate medications in older adults. Int J Clin Pharm. 2017;39(4):818–25.View ArticlePubMedGoogle Scholar
  18. Maher RL, Hanlon J, Hajjar ER. Clinical consequences of polypharmacy in elderly. Expert Opin Drug Saf. 2014;13(1):57–65.View ArticlePubMedGoogle Scholar
  19. Johnell K, Klarin I. The relationship between number of drugs and potential drug-drug interactions in the elderly. Drug Saf. 2007;30(10):911–8.View ArticlePubMedGoogle Scholar
  20. Greene M, Steinman MA, McNicholl IR, Valcour V. Polypharmacy, drug–drug interactions, and potentially inappropriate medications in older adults with human immunodeficiency virus infection. J Am Geriatr Soc. 2014;62(3):447–53.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2018

Advertisement