|Year : 2017 | Volume
| Issue : 4 | Page : 221-228
Drug-drug interaction-related uncontrolled glycemia
Mohamed Anwar Hammad1, Balamurugan Tangiisuran1, Abeer Mohamed Kharshid1, Noorizan Abdul-Aziz2, Yahaya Hassan2, Nor Azizah Aziz3, Tarek Mohamed Elsayed4
1 Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia
2 Department of Pharmacy Practice, Universiti Teknologi Mara, Shah Alam, Malaysia
3 Department of Endocrinology Clinics, Penang General Hospital, Penang, Malaysia
4 Department of Pharmacy Practice, Kulliyyah of Pharmacy, International Islamic University Malaysia, Kuantan, Pehang, Malaysia
|Date of Web Publication||25-Jan-2018|
Mohamed Anwar Hammad
Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang Z.C.1800
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Context: The literature of drug-drug interaction (DDI)-related uncontrolled causality, and preventability of DDI-induced UCG (HbA1c >7%) in outpatients glycemia (UCG) among outpatients with Type 2 diabetes mellitus is still limited. Aims: The aim of this study is to identify the prevalence, mechanism, severity, with Type 2 diabetes. Settings and Design: A cross-sectional study was conducted in Penang General Hospital. Methods: A computerized system for DDI checking was used to assess the severity and mechanism of DDIs. Drug interaction probability scale was used to evaluate the likelihood of DDIs. Preventability of DDIs has been determined by the instrument of Hallas. The UCG prevalence related to DDIs was further assessed. Statistical Analysis Used: SPSS 21.00 was used in this study. Results: From 425 outpatients with HbA1c% test, their mean age was 58.7 ± 12.8 years. Only 225 (52.9%) cases had controlled glycemia while 200 (47.1%) cases with UCG. They had multiple comorbidities, with a mean number of 3.8 ± 2.2/patient and often prescribed with multiple medications, with a mean number of 6.33 ± 4.67/patient. It has been detected that 86 DDIs causing UCG in 46 patients (23%) with range of (1 – 4) DDIs per patient. Drugs with DDI-induced UCG were as follows: diuretics (79%), salbutamol (9.2%), cortisones (5.8%), and others (6%). The majority of these DDIs were categorized as possible (77.9%) and preventable (37%). Conclusion: Nearly one-quarter of UCG was induced by DDIs; most of these DDIs are possible, and more than one-third are preventable. It was concluded that thiazide diuretics have the highest prevalence of DDI-related UCG.
Keywords: Diabetes mellitus, drug interaction probability scale, HbA1c, hyperglycemia
|How to cite this article:|
Hammad MA, Tangiisuran B, Kharshid AM, Abdul-Aziz N, Hassan Y, Aziz NA, Elsayed TM. Drug-drug interaction-related uncontrolled glycemia. J Pharm Bioall Sci 2017;9:221-8
|How to cite this URL:|
Hammad MA, Tangiisuran B, Kharshid AM, Abdul-Aziz N, Hassan Y, Aziz NA, Elsayed TM. Drug-drug interaction-related uncontrolled glycemia. J Pharm Bioall Sci [serial online] 2017 [cited 2018 Dec 13];9:221-8. Available from: http://www.jpbsonline.org/text.asp?2017/9/4/221/223882
| Introduction|| |
There Is a category of drugs that interacts and interferes with the action of diabetes medications.,,, Hence, it is important to monitor patients’ glucose levels carefully., Complications of diabetes are the leading cause of morbidity and mortality in persons with diabetes., If Type 2 diabetes patients decrease their HbA1c level by 1%, there are a 19% reduction in cataract extractions, 16% decrease in heart failure, and 43% reduction in amputation or death due to peripheral vascular disease., Significant drug interactions and the patient harm that is associated with them are common concerns in clinical practice.,
Aim of the study
The current prospective observational study was conducted to investigate the prevalence, mechanism, severity, causality, and preventability of drug-drug interactions (DDIs) accounting for uncontrolled hyperglycemia development among outpatients with Type 2 diabetes at endocrine clinics and to identify the medications that are associated with DDI-related uncontrolled glycemia (UCG).
| Methods|| |
Study design and participants
The conducted prospective observational study involves outpatients with Type 2 diabetes at endocrine clinics in Penang General Hospital with confirmed UCG. According to the American Diabetes Association, UCG is defined (HbA1c >7% for patients <65 years and >8% for patients ≥65 years). The study presented in this paper was conducted from July 1, 2011, to February 1, 2012. Patients younger than 18-year-old, pregnant women, outpatients with Type 1 diabetes, and patients with cancer or HIV were excluded from the study. Furthermore, newly diagnosed outpatients with Type 2 diabetes (within 6 months) and outpatients with Type 2 diabetes under lifestyle modification only were not included in this study. [Figure 1] shows the flowchart of the conducted study.
|Figure 1: The flowchart of the study, AIDS: Acquired immune deficiency syndrome|
Click here to view
Patients’ sociodemographics, comorbidities, laboratory data, number and dose regimen of medications, and clinical characteristics were collected from patients’ records, the medical team in charge, and patients’interviews. Other confounding factors of UCG such as patients’ noncompliance, obesity under therapeutic doses, and monotherapy were assessed and ruled out.
Identification of drug-drug interaction-related uncontrolled glycemia
DDIs for each patient medication were investigated by the primary researcher, a clinical pharmacist. In this study, the definition of DDI by introduced Tatro (2010) was applied. Tatro’s description states that DDIs represents; the pharmacologic or clinical response to the administration of a medicine combination is different from that anticipated from the remarkable effects of the two drugs when given alone. Clinically significant drug interactions, which may induce potential harm to the patients, can be resulted from changes in pharmaceutical, pharmacokinetic, or pharmacodynamic properties.
A computerized system has been used in this study for DDI checking such as UpToDate, Lexicomp, drugs.com, and Micromedex and Medscape drug interaction checkers to assess and confirm the mechanism, severity, and significance of drug interactions. The DDI identification in this study relies mainly on Lexicomp and drugs.com, drug, interaction checkers which provide the references in addition to the evidence of literature review for each DDI. Thus, the DDI-related hyperglycemia was identified. Mechanism of DDIs is classified as pharmacodynamic or pharmacokinetic. The severity of DDIs was classified into major, moderate, and minor. Major represents a severely clinically significant.
DDI and that patient should not use the combinations if the risk of the interaction is more than the benefit. Moderate DDI is categorized under moderately clinically significant and that the combination can be given with certain conditions only; otherwise, co-administration should be bypassed. Minor DDI is classified as minimally clinically significant. Where instructions to the health-care providers in such case are to decrease harm; include estimating the potential risk and considering an alternative medicine, taking steps to avoid the interaction damage, and establishing a monitoring plan. The principal investigator of this study has determined and investigated the DDIs while other researchers have reviewed all the findings for agreement and correction.
Assessment of causality and preventability Of drug-drug interaction-related uncontrolled glycemia
Drug interaction probability scale (DIPS) proposed by Horn et al. was followed to evaluate the likelihood of DDI. UCG prevalence related to DDIs was then examined. The DIPS was modified from Naranjo et al. scale to meet the requirements of DIPS. It was developed to provide a guide for evaluating drug interaction causation in a particular patient. It is intended to be used to help practitioners in the evaluation of drug interaction-related adverse events. DIPS follows a series of questions related to the potential drug interaction to determine a probability score which is classified to highly probable, probable, possible and doubtful DDI.
The preventability of DDI-related UCG was assessed based on the method proposed by Hallas et al., where each DDIs was classified into definitely preventable, possibly preventable, or nonpreventable. In the analysis of this study, the final assessment of DDI-related UCG is reported as either preventable or nonpreventable. [Figure 2] shows the identification and classification of the DDIs.
Descriptive analyses were performed, and categorical variables were described by frequencies and percentages. Continuous variables were documented by means and standard deviations or range. Chi-square test was performed for the categorical data, and Student’s t-test was used to compare the continuous data. The variables for multicollinearity were assessed determining the variance inflation factors and value of standard errors to prevent any strong correlation between the variables. Independent predictors of DDI-related UCG were described using a logistic regression analysis, which was modified for gender, age, and any other variables that were significant. In this study, variables were selected according to their clinical and statistical significance (described thoroughly in Steyerberg et al.’s study), and overfitting was prevented by assigning the maximum number of variables entered in multivariate regression as one variable for every eight DDI events (which was also followed in Vittinghoff and McCulloch). The statistical software packages followed were SPSS 21.00 (SPSS Inc., Chicago, IL, USA). A two-sided P < 0.05 was considered to be statically significant.
This study was approved by National Institute of Health in Malaysia, Clinical Research Centre at Penang General Hospital, and Malaysian Ministry of Health and Ethics Committee. The approved unique National Medical Research Register (NMRR) registration ID is NMRR-11-407-8807.
| Results|| |
It was observed that among 720 outpatients with Type 2 diabetes who were scanned for HbA1c% test, only 425 (59%) of them had HbA1c% test in the past 3 months while the rest 295 (41%) outpatients had not. Among 425 with HbA1c% test, only 225 (52.9%) outpatients had controlled glycemia.
The remaining 200 patients with UCG were included in the study. Their mean age was 58.7 ± 12.8 years. Approximately half of the patients were male, 101 (50.5%). From 200 UCG patients, 88 (44%) were Chinese, 56 (28%) were Malay, 52 (26%) were Indian, and only 4 (2%) patients were from other ethnicities. UCG patients in this study had multiple comorbidities, with a mean number of 3.8 ± 2.2/patient. Patients in this study were often prescribed with multiple medications, with mean number of 6.33 ± 4.67/patient.
We detected 86 DDIs with UCG in 46 patients with a mean number of 1.9/patient (range 1–4 drug interactions). Investigators identified at least one-drug interactions in 46 patients (23%). One drug interaction has been detected for every 14 drug exposures. The major category was diuretics 79% (hydrochlorothiazide 58.1%, furosemide 13.9%, spironolactone 4.6%, and chlorothiazide 2.40%), followed by salbutamol (9.2%) and cortisones (5.8%) as presented in [Table 1] and [Table 2] that show the details of the 86 DDIs with UCG and their medications, respectively.
|Table 1: Details of drug-drug interactions with uncontrolled glycemia (n=86)|
Click here to view
|Table 2: Medications with uncontrolled glycemia secondary to drugs interactions (n=86)|
Click here to view
Using DIPS, it was found that 77.9% of DDIs were possible DDIs (DIPS score: 2-4) and 22.1% were probable DDIs (DIPS score: 5–8). Most of the DDIs mechanism. Most of the DDIs-related UCG cases have a moderate significance of 98.8%, and only 1.2% has a minor significance. Furthermore, 37.2% from these DDIs are preventable, and 62.8% are nonpreventable as shown in [Table 3] that presents the drug interaction probability, mechanisms, and severity of the mentioned 86 drug interactions.
|Table 3: Drug interaction probability, mechanisms, and severity of 86 drug interactions|
Click here to view
According to multivariate logistic regression, there is no association relation between the number of medications and DDI-related UCG incidence (P value 0.112). Furthermore, there is no association relationship of DDIs incidence with the comorbidity number (Charlson et al. index) (P = 0.131) as shown in [Table 4] that presents the of DDIs’ predicators.
| Discussion|| |
This study evaluates the hyperglycemic effect of DDIs which lead to UCG in outpatients with Type 2 diabetes. Most of the previous studies have discussed the hypoglycemic effect of drug interactions of hypoglycemic agents.,,,
In a study by McDonnell and Jacobs, which is conducted over 1 year, 158 adverse drug reactions (ADRs) were directly related to hospital admission, which supports findings of the study was presented in this paper. The relationship of these admissions to drug use was determined to be probable or highly probable in 154 (97.4%) of these cases. From this category, 96 (62.3%) of the events were considered potentially preventable, with 23 (24%) were considered as severe to life-threatening. Characteristics accompanied with these ADRs include documentation of a toxic drug concentration or abnormal laboratory value of (80%); the inadequate monitoring of a patient’s drug therapy is (67%). While it was revealed that inappropriate is a dose (51%), patient noncompliance is (33%), DDIs is (26%) which tallies with the findings of the current study (23%), contraindication to treatment is (3%), and reported allergy is (1%). These ADRs lead to 595 hospital days, with a mean length of stay of 6.1 days.,
In a more recent study conducted by Strandell and Wahlin, the characteristics of problematic drug combinations that result in adverse reactions were analyzed. Drug combinations that are most frequently suspected as interacting in VigBase were determined for their interaction mechanism(s). In this group of the database, there was a prominent (40%) portion of drug combinations with pharmacodynamic mechanisms being additive pharmacological effects. While 25% of the drug interactions had a pharmacokinetic mechanism which is mainly the inhibition of hepatic cytochrome P450 (CYP) enzymes, 19% the mechanism were not yet illustrated, and 16% had both pharmacodynamic and pharmacokinetic mechanisms. In the study was presented in this paper, the mechanism of interaction was mainly pharmacodynamic mechanism.
The finding of the study was presented in this article agrees with another review by Strandell et al. where it was stated that spontaneous reporting systems are still the cornerstone of the early identification of previously unknown ADRs. However, a significant proportion of ADRs are known and preventable, and they are often due to the co-administration of medications known to interact. In a meta-analysis study by Hakkarainen et al., data were analyzed from 16 original studies on outpatients with 48797 emergency visits. Among adult outpatients, 2.0% (95% confidence interval [CI]: 1.2%–3.2%) had preventable ADRs and 52% (95% CI: 42%–62%) of these identified adverse drug events were preventable. This meta-analysis has confirmed that preventable ADRs are a significant burden to health care among adult outpatients. Among outpatients approximately half of ADRs are preventable, reporting that further evidence on prevention strategies is required.,,
Furosemide and thiazide ↔ acarbose, insulin, and sulfonylureas drug interactions
These are possible, moderate, pharmacodynamic, and preventable drugs interactions. Certain drugs, including thiazides and other diuretics, may diminish the efficacy of oral hypoglycemic agents and insulin. These drugs can cause hyperglycemia, glucose intolerance, new-onset diabetes mellitus, and exacerbation of preexisting diabetes as they may interfere with blood glucose control., Close monitoring of glycemic control is required if the patient co-administered these drugs with antidiabetic medications. Likewise, patients should be monitored for hypoglycemia when the physician stops or patient withdraws these medicines from their therapeutic regimen. Dose modification of the hypoglycemic agent may be required. These DDIs are preventable in the case of furosemide by substituting object drug by metformin and decreasing the dose of furosemide. However, the interactions of thiazides with sulfonylureas or insulin are nonpreventable when patients cannot use metformin due to renal impairment or that metformin was contraindicated.,
Thiazide and spironolactone ↔ metformin drug interactions
These are probable with moderate significance, pharmacodynamic, and preventable drugs interactions. Diuretic-induced renal impairment and dehydration may increase the risk of lactic acidosis in patients who are concomitantly taking metformin. Furthermore, thiazides and other diuretics may interfere with glucose control by causing hyperglycemia, glucose intolerance, new-onset diabetes mellitus, and exacerbation of preexisting diabetes. A close clinical monitoring is needed if diuretics are co-administered with antidiabetic drugs. The patients should be advised to monitor their blood glucose and to promptly communicate their doctor when experiencing possible signs and symptoms of lactic acidosis (such as abdominal upset, hyperventilation, irregular heartbeat, malaise, respiratory distress, and somnolence) or loss of glycemic control. Furthermore, dosage regimen correction of metformin might be needed. Likewise, if the patient stops diuretics from the therapeutic regimen, the patient should be observed for hypoglycemia. These interactions are preventable by substituting the participate drug (thiazide by furosemide) or substituting metformin by sulfonylureas in the case of spironolactone drug interactions with metformin.,
Corticosteroids, levothyroxine, risperidone, and salbutamol interactions with hypoglycemic agents
All these drug interactions with hypoglycemic Drugs were probable of moderate significance pharmacodynamic and nonpreventable. Drugs that may diminish the effectiveness of the oral hypoglycemic agents and insulin are including corticosteroids, thyroid hormones, atypical antipsychotics, sympathomimetic amines, and salbutamol. These medicines may interact with blood glucose control and result in hyperglycemia, glucose intolerance, exacerbation of preexisting diabetes mellitus, and new-onset diabetes mellitus. When these drugs are co-administered with antidiabetic drugs, a close clinical monitoring of glycemic control is recommended. In the same context, patients should be monitored for hypoglycemia when they stop these medicines, or the physician withdraws it from their treatment regimen. Dosing adjustment of the hypoglycemic agent may be required.
Phenytoin and hypoglycemic agents drug interactions
These are probable of moderate significance, pharmacokinetic, and nonpreventable DDIs. The co-administration with phenytoin may decrease the hypoglycemic effect of sulfonylureas. It is well known that phenytoin causes hyperglycemia, hypoinsulinemia, and glucose intolerance; thus, it may interfere with blood glucose control. Conversely, some sulfonylureas may increase the plasma concentrations and pharmacologic effect of phenytoin and reduce its metabolism by competitive inhibition of CYP450 2C9 and 2C19 isoenzymes. Toxicity of phenytoin has been documented particularly during co-administration with tolbutamide.
Attention should be given more carefully checking up blood glucose and phenytoin levels during co-administration with sulfonylureas. Patients should be educated to inform their doctors when they experience symptoms of possible phenytoin toxicity such as drowsiness, visual disturbances, mental status change, seizures, nausea, and ataxia or the loss of glycemic control.
Acarbose and metformin drug interactions
It is a probable with minor significance, pharmacokinetic, and preventable drug interaction. Metformin, when co-administered with acarbose, may have a delayed onset of action and decreased bioavailability. Peak serum concentration and AUC are reduced significantly by 35%. The mechanism is due to slow intestinal absorption of metformin. The clinician may need to monitor more closely for reduced metformin effect. However, no changes in therapy are suggested. The interaction can be prevented by stopping the use of the precipitant drug (acarbose) or adjusting the dose of metformin.
Relationship between number of drug interactions and the numbers of comorbidities and medications
From the current prospective observational study, it was found that there is no significant relation between the numbers of drug interactions inducing UCG and comorbidity numbers and/or number of medications for each patient. However, it was noticed that the number of drug interactions depends on the type of comorbidity and/or medication.
To explain these findings, some examples in the context of the tacts, recommendations, and justifications presented in the online database are provided in the following. Outpatients with Type 2 diabetes had hypertension and treated with metformin, acarbose, and hydrochlorothiazide. While another outpatient with Type 2 diabetes had hypertension, dyslipidemia, ischemic heart diseases, and treated with metformin, atorvastatin, perindopril, aspirin, insulin, metoprolol, isosorbide dinitrate, and ranitidine. From previous examples, the first model patient has had only two diseases and had used only three medications and had three drug interactions that led to UCG. While, in the second example, the patient has had four conditions and had used eight drugs and had no drug interactions that can result in UCG.,
The prospective observational study was presented in this paper covers only the outpatients at endocrine clinics and does not account for DDI-related UCG of outpatients with Type 2 diabetes in the other clinics, and time is limited for the period of study.
| Conclusion|| |
Patients with Type 2 diabetes mellitus are at high risk of developing DDI-related UCG. Nearly one-quarter of UCG is induced by drug interactions. The majority of these drug interactions are possible and more than one-third is preventable. Thiazides have the highest prevalence of DDI-related UCG. The prevalence of DDI-related UCG depends on the type of medications and comorbidities, and on the other hand, it does not rely on the number of drugs or comorbidities.
We introduce our special thanks to all staff at the clinics of endocrinology, department of pharmacy and laboratory team in Penang General Hospital, for their kind support and help in facilitating this study.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Nathan DM, Davidson MB, DeFronzo RA, Heine RJ, Henry RR, Pratley R, et al
. Impaired fasting glucose and impaired glucose tolerance: Implications for care. Diabetes Care 2007;30:753-9.
Rehman A, Setter SM, Vue MA. Drug-induced glucose alterations Part 2: Drug-induced hyperglycemia. Diabetes Spectr 2011;24:234-8.
Hasnain H, Ali H, Zafar F, Akbar AS, Hameed K, Shareef H, et al
. Drug-drug interaction; facts and comparisons with national and international bentch marks; a threat more than a challenge for patient safety in clinical and economic scenario. Prof Med J 2017;24:357-65.
Martins IJ. Drug-drug interactions with relevance to drug induced mitochondrial toxicity and accelerated global chronic diseases. ECPT 2017;3:18-21.
Clement S, Braithwaite SS, Magee MF, Ahmann A, Smith EP, Schafer RG, et al
. Management of diabetes and hyperglycemia in hospitals. Diabetes Care 2004;27:553-91.
Tatro DS. Drug Interaction Facts 2004. 1st ed. New York; Facts & Comparisons; 2003.
Cade WT. Diabetes-related microvascular and macrovascular diseases in the physical therapy setting. Phys Ther 2008;88:1322-35.
Hammad MA, Mohamed Noor DA, Syed Sulaiman SA, Aziz NA, Elsobky Y. A prospective study of prevalence of uncontrolled glycaemia in type 2 diabetes mellitus outpatients, 2016 ACCP Virtual Poster Symposium. Pharmacotherapy 2016; 36:e83-138.
Bykov K, Gagne JJ. Generating evidence of clinical outcomes of drug-drug interactions. Drug Saf 2017;40:101-3.
Hammad MA, Tangiisuran B, El Aziz NA, Hassan Y. A prospective study of uncontrolled glycaemia secondary to drug-drug interactions in type 2 diabetes mellitus patients at Penang General Hospital in Malaysia. Pharmacotherapy 2013:33:e50-80.
Dechanont S, Maphanta S, Butthum B, Kongkaew C. Hospital admissions/visits associated with drug-drug interactions: A systematic review and meta-analysis. Pharmacoepidemiol Drug Saf 2014;23:489-97.
American Diabetes Association. Standards of Medical Care in Diabetes-2016. Diabetes Care 2016;39:S39-46.
Tatro D, editor. Drug Interaction Facts: The Authority on Drug Interactions. St. Louis, Mo: Wolters Kluwer Health Facts & Comparisons; 2010.
Qorraj-Bytyqi H, Hoxha R, Krasniqi S, Bahtiri E, Kransiqi V. The incidence and clinical relevance of drug interactions in pediatrics. J Pharmacol Pharmacother 2012;3:304-7.
] [Full text]
Horn JR, Hansten PD, Chan LN. Proposal for a new tool to evaluate drug interaction cases. Ann Pharmacother 2007;41:674-80.
Naranjo CA, Busto U, Sellers EM, Sandor P, Ruiz I, Roberts EA, et al
. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther 1981;30:239-45.
Hallas J, Harvald B, Gram LF, Grodum E, Brøsen K, Haghfelt T, et al
. Drug related hospital admissions: The role of definitions and intensity of data collection, and the possibility of prevention. J Intern Med 1990;228:83-90.
Saheb Sharif-Askari N, Syed Sulaiman SA, Saheb Sharif-Askari F, Hussain AA. Adverse drug reaction-related hospitalisations among patients with heart failure at two hospitals in the United Arab Emirates. Int J Clin Pharm 2015;37:105-12.
Steyerberg EW, Eijkemans MJ, Harrell FE Jr. Habbema JD. Prognostic modelling with logistic regression analysis: A comparison of selection and estimation methods in small data sets. Stat Med 2000;19:1059-79.
Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and cox regression. Am J Epidemiol 2007;165:710-8.
National Medical Research Register. National Institute of Health NIH Guideline. Online Resources. Available from: https://www.nmrr.gov.my/fwbLoginPage.jsp
. [Last updated on 2016 Jun 29; Last accessed on 2016 Oct 28].
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 1987;40:373-83.
Scheen AJ, Lefèbvre PJ. Antihyperglycaemic agents. Drug interactions of clinical importance. Drug Saf 1995;12:32-45.
Scheen AJ. Drug interactions of clinical importance with antihyperglycaemic agents: An update. Drug Saf 2005;28:601-31.
Drzewoski J, Kopff B. Clinically important effects of oral antidiabetic drug interactions. Pol Merkur Lekarski 2000;9:605-7.
Hansten PD, Horn JR. The Top 100 Drug Interactions: A Guide to Patient Management. Edmonds, WA: H & H Publications; 2004. p. 157-69.
McDonnell PJ, Jacobs MR. Hospital admissions resulting from preventable adverse drug reactions. Ann Pharmacother 2002;36:1331-6.
Hanlon JT, Perera S, Newman AB, Thorpe JM, Donohue JM, Simonsick EM, et al
. Potential drug-drug and drug-disease interactions in well-functioning community-dwelling older adults. J Clin Pharm Ther 2017;42:228-33.
Strandell J, Wahlin S. Pharmacodynamic and pharmacokinetic drug interactions reported to vigiBase, the WHO global individual case safety report database. Eur J Clin Pharmacol 2011;67:633-41.
Strandell J, Bate A, Lindquist M, Edwards IR. Swedish, Finnish, Interaction X-referencing Drug-drug Interaction Database (SFINX Group). Drug-drug interactions-A preventable patient safety issue? Br J Clin Pharmacol 2008;65:144-6.
Hakkarainen KM, Hedna K, Petzold M, Hägg S. Percentage of patients with preventable adverse drug reactions and preventability of adverse drug reactions – A meta-analysis. PLoS One 2012;7:e33236.
Sánchez-Fidalgo S, Guzmán-Ramos MI, Galván-Banqueri M, Bernabeu-Wittel M, Santos-Ramos B. Prevalence of drug interactions in elderly patients with multimorbidity in primary care. Int J Clin Pharm 2017;39:343-53.
Qureshi A, Ghoto M, Dayo A, Arain M, Parveen R, Mangi A. Drug-drug interactions (DDIs); prevalence of various levels in prescriptions at public sector teaching hospital of Hyderabad, Pakistan. Prof Med J 2017;24:239-43.
Merel SE, Paauw DS. Common drug side effects and drug-drug interactions in elderly adults in primary care. J Am Geriatr Soc 2017;65:1578-85.
Jones GC, Macklin JP, Alexander WD. Contraindications to the use of metformin. BMJ 2003;326:4-5.
Eurich DT, McAlister FA, Blackburn DF, Majumdar SR, Tsuyuki RT, Janice Varney J, et al
. Benefits and harms of antidiabetic agents in patients with diabetes and heart failure: Systematic review. BMJ 2007;335:497.
Hammad MA, Khamis AA, Al-Akhali KM, Elsayed T, Alasmri AM, Al-Ahmari EM, et al
. Evaluation of drug dosing in renal failure. IOSR J Pharm Biol Sci 2016;11:39-50.
Jain S, Jain P, Sharma K, Saraswat P. A prospective analysis of drug interactions in patients of intensive cardiac care unit. J Clin Diagn Res 2017;11:FC01-FC04.
Romagnoli KM, Nelson SD, Hines L, Empey P, Boyce RD, Hochheiser H, et al
. Information needs for making clinical recommendations about potential drug-drug interactions: A synthesis of literature review and interviews. BMC Med Inform Decis Mak 2017;17:21.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4]