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Evaluation of rational nonsteroidal anti-inflammatory drugs and gastro-protective agents use; association rule data mining using outpatient prescription patterns

BMC Medical Informatics and Decision MakingBMC series – open, inclusive and trusted201717:96

DOI: 10.1186/s12911-017-0496-3

Received: 1 February 2017

Accepted: 28 June 2017

Published: 4 July 2017

Abstract

Background

Nonsteroidal anti-inflammatory drugs (NSAIDs) and gastro-protective agents should be co-prescribed following a standard clinical practice guideline; however, adherence to this guideline in routine practice is unknown. This study applied an association rule model (ARM) to estimate rational NSAIDs and gastro-protective agents use in an outpatient prescriptions dataset.

Methods

A database of hospital outpatients from October 1st, 2013 to September 30th, 2015 was searched for any of following drugs: oral antacids (A02A), peptic ulcer and gastro-oesophageal reflux disease drugs (GORD, A02B), and anti-inflammatory and anti-rheumatic products, non-steroids or NSAIDs (M01A). Data including patient demographics, diagnoses, and drug utilization were also retrieved. An association rule model was used to analyze co-prescription of the same drug class (i.e., prescriptions within A02A-A02B, M01A) and between drug classes (A02A-A02B & M01A) using the Apriori algorithm in R. The lift value, was calculated by a ratio of confidence to expected confidence, which gave information about the association between drugs in the prescription.

Results

We identified a total of 404,273 patients with 2,575,331 outpatient visits in 2 fiscal years. Mean age was 48 years and 34% were male. Among A02A, A02B and M01A drug classes, 12 rules of associations were discovered with support and confidence thresholds of 1% and 50%. The highest lift was between Omeprazole and Ranitidine (340 visits); about one-third of these visits (118) were prescriptions to non-GORD patients, contrary to guidelines. Another finding was the concomitant use of COX-2 inhibitors (Etoricoxib or Celecoxib) and PPIs. 35.6% of these were for patients aged less than 60 years with no GI complication and no Aspirin, inconsistent with guidelines.

Conclusions

Around one-third of occasions where these medications were co-prescribed were inconsistent with guidelines. With the rapid growth of health datasets, data mining methods may help assess quality of care and concordance with guidelines and best evidence.

Keywords

Data mining Association rule Apriori algorithm Prescription patterns Rational drug use Hospital Data warehouse Nonsteroidal anti-inflammatory drugs Gastro-protective agents

Background

Nonsteroidal anti-inflammatory drugs (NSAIDs) are used to relieve pain and inflammation. However, conventional NSAIDs (e.g., Diclofenac, Meloxicam, Ibuprofen) can induce gastrointestinal (GI) upset and adverse events, especially peptic ulceration [1]. To reduce this risk, gastro-protective agents are commonly co-prescribed with NSAIDs; alternatively, cyclooxygenase (COX)-2 inhibitors (e.g., Etoricoxib, Celecoxib) are used, a new generation of NSAIDs claimed to cause fewer gastrointestinal adverse events [24]. Co-prescription of COX-2 inhibitors with gastro-protective agents are recommended only in patients at high risk of GI disease, such as elderly patients (aged ≥ 60 years), those using antiplatelet agents (e.g., Aspirin), or patients with a history of GI events [2, 5].

Commonly used gastro-protective agents are histamine H2-receptor antagonists (H2RAs, e.g., Ranitidine) and proton pump inhibitors (PPIs, e.g., Omeprazole, Pantoprazole, Esomeprazole, Lansoprazole). The H2RAs competitively antagonize the histamine effects at H2-receptors in the stomach to reduce the amount and concentration of gastric acid. PPIs suppress stomach acid secretion by specific inhibition of the H+/K± ATPase system found at the secretory surface of gastric parietal cells [69]. Concomitant use of H2RAs and PPIs are recommended only in the treatment of gastro-oesophageal reflux disease (GORD) [10, 11].

In the past, identification of poor quality drug use in the hospital was not easily done, because of the volume and complexity of prescription data. In our institution (Ramathibodi Hospital, Bangkok, Thailand) data warehouses have been available since 2014, and there has been interest in using these to drive quality improvement in health care practice and service delivery. These data include drug prescriptions, demographic data, diagnoses, laboratory tests, imaging, etc., and are routinely extracted from hospital information systems (HIS).

Currently, a wide variety of data mining algorithms (i.e., technique for big data analysis) are available; they are classified into 2 main categories: supervised and unsupervised learning [12]. Supervised learning algorithms produce a model using classification or regression that can predict the response values for a particular outcome or behavior of interest. Unsupervised learning algorithms describe the form and hidden structure of data, using methods such as clustering, anomaly detection, and association rule mining (ARM), which has been applied for detecting co-prescription patterns in many studies [1317].

The Apriori algorithm is a classical ARM technique, based on the principle of frequent pattern mining [1821]. First, a candidate set is generated to identify items that occur with a frequency that exceeds a pre-specified threshold (i.e., defined as the support measure). Second, the association rules are derived by indicating conditional probabilities between a pair of items; groups are defined if the conditional probability value exceeds a user-defined threshold (called the confidence measure).

Our study aimed to assess associations within the gastro-protective agents (H2RAs and PPIs), and NSAIDs (including COX-2 inhibitors), as well as between these two drug classes using ARM. Once associations were identified, prescription patterns were explored for congruence with guidelines.

Methods

An electronic database of outpatients records at Ramathibodi Hospital between October 1st, 2013 and September 30th, 2015 was extracted from the hospital data warehouse focusing on H2RAs and PPIs (A02A and A02B codes), and NSAIDs and COX-2 inhibitors (M01A). Only fields for patient demographics, prescriptions, drug utilization, and diagnoses were retrieved. Two steps of data manipulation and analysis were then performed using R software version 3.3.0 in RStudio® version 0.99.902 (RStudio Inc., Boston, MA, USA). First, the data frame was constructed and then data was analyzed to identify association rules and evaluate rational drug use.

Data retrieval and manipulation

Five tables in the hospital data warehouse were retrieved as follows: 1) physician prescriptions, 2) master drug lists, 3) drug utilization, 4) diagnosis data, and 5) patient demographic data. The study protocol was approved by the ethics committee of Ramathibodi Hospital without requirement of consent for participation. As for our hospital’s rule, data were not available for public and thus we could not provide and share individual patient data.

The physician prescriptions over 2 fiscal years were retrieved. These data had been already cleaned through an “Extract, Transform, Load” (ETL) process while being loaded into the data warehouse on a daily basis [22]. Master drug lists from the data warehouse were also loaded and merged in RStudio®. To manipulate the data frame, R commands were constructed and run to select ambulatory or outpatient prescriptions with Anatomical Therapeutic Chemical (ATC) classification system codes of A02A: Antacids, A02B: Drug for peptic ulcer and GORD, and M01A: Anti-inflammatory and anti-rheumatic products, non-steroids or NSAIDs (see Table 1).
Table 1

Drug code of 1A and 4 L drugs and their names

Drug code

Name

Drug code

Name

A02A - Antacids

M01AA to M01AG, M01AX – conventional NSAIDs

 ALGY-T-

Alginic acid tablet

 ASPT-T-

Acetylsalicylic acid 300 mg

 ALHY-T-

Aluminium hydroxide 500 mg

 ASA.1 T-

Acetylsalicylic acid 81 mg

 ALHY-N2

Aluminium hydroxide 6.10%

 CAPN-T-

Acetylsalicylic acid 100 mg

 ANTT-T-

Aluminium hydroxide tablet

 CLIR-T-

Sulindac 200 mg

 ANTC-N2

Aluminium hydroxide 1000 ml

 DICF-T-

Diclofenac 25 mg

 ANTC-N1

Aluminium hydroxide 240 ml

 FAFX-T-

Nabumetone 500 mg

 GAVD-T-

Sodium alginate Dual Action

 FLAM-C-

Piroxicam 10 mg

 GAVI-N-

Sodium alginate Liquid

 IBUP-S-

Ibuprofen (100 mg/5 ml)

 GAST-T-

Bismuth subsalicylate 524 mg

 IBUP1T-

Ibuprofen 200 mg

 MUCT-T-

Rebamipide 100 mg

 IBUP2T-

Ibuprofen 400 mg

 ULCF-N-

Sucralfate 240 ml

 INDM-C-

Indomethacin 25 mg

 ULCF-N1

Sucralfate 60 ml

 MEFN1C-

Mefenamic acid 250 mg

 ULSN-T-

Sucralfate 500 mg

 MEFN2T-

Mefenamic acid 500 mg

 ULSN1T-

Sucralfate 1000 mg

 MELO-T-

Meloxicam 7.5 mg

A02B – Drug for peptic ulcer and GORD

 MELO1T-

Meloxicam 15 mg

 CYTT-T-

Misoprostol 200 mcg

 NAPS-T-

Naproxen LE 250 mg

 XAND-T-

Ranitidine 150 mg

 NAPX-T-

Naproxen 250 mg

 COTL2T-

Pantoprazole 20 mg

 REMT-T-

Diclofenac 100 mg

 COTL-T-

Pantoprazole 40 mg

 VOLS1T-

Diclofenac SR 75 mg

 DEXI1C-

Dexlansoprazole 60 mg

 VOLS-T-

Diclofenac SR 100 mg

 DEXI-C-

Dexlansoprazole 30 mg

M01AH – COX-2 inhibitors

 LOSC-C-

Omeprazole MUPS 20 mg

 ARCX4T-

Etoricoxib 30 mg

 NEXM1T-

Esomeprazole 20 mg

 ARCX1T-

Etoricoxib 60 mg

 NEXM2T-

Esomeprazole 40 mg

 ARCX2T-

Etoricoxib 90 mg

 OMPZ-C-

Omeprazole 20 mg

 CELB-C-

Celecoxib 200 mg

 PARI-T-

Rabeprazole 10 mg

 CELB1C-

Celecoxib 400 mg

 PARI1T-

Rabeprazole 20 mg

  

 PRVF1T-

Lansoprazole 15 mg

  

 PRVF2T-

Lansoprazole 30 mg

  

Note: every patient in the cohort was prescribed at least one of the listed drugs

Two years of data were combined and drug strength and dosage were ascertained from the left 4 digits of the drug code substring, e.g. IBUP1T- (Ibuprofen 200 mg tablet), IBUP2T- (Ibuprofen 400 mg tablet), IBUP-S- (Ibuprofen 100 mg/5 ml) syrup transformed to the same code - IBUP for Ibuprofen. HN (patient’s hospital number) and date were joined to create HNDate, to represent visit date. Data frame was reshaped from long to wide format e.g.

And records with only one drug item per patient per day were excluded.

Drug utilization, diagnosis data, and patients’ demographic data were also retrieved from tables in the hospital data warehouse to get each prescription’s dose and frequency, primary/secondary diagnosis of each visit (with International Classification of Disease, Tenth Edition ICD-10), date of birth (to calculate age), and gender. All data were merged with physician prescriptions by HNDate.

Data analysis

Patient age and number of OPD visits/person/year were described using mean (SD) and number of male and number of diagnoses, defined by ICD-10 codes: K20-K29.9, K30-K38.9, K90-K93.8 for gastrointestinal complications. The Apriori algorithm with ARM was applied to assess the pattern of associations within the same drug classes (i.e., gastro-protective agents, NSAIDs) and between different drug classes (i.e., gastro-protective agents and NSAIDs).

Association rules were derived based on prescription data. The rules were aimed to detect prescribing patterns of NSAIDs and gastro-protective agents for individual patients in the same visit with detail as follows: Let I be a set of prescribed drug items (i.e., NSAIDs and gastro-protective agents) listed in the database and P = {P 1, P 2,…, P n} be a set of number of prescriptions, where P i (1 ≤ i ≤ n) is a set of drugs in prescription i. Given X and Y as non-overlapping sets of drug items (i.e., X ∩ Y = ), the ARM is used to measure how often X (called antecedent or left-hand-side or LHS) and Y (called consequent or right-hand-side or RHS) occurred/appeared together in the same prescription (P i). The association rules use 3 probability estimations: support, confidence, and lift without adjusting for derivation of multiple sets of drug items. Support is defined as the probability of prescriptions in P contains X and Y, i.e., support(X➔Y) = P(XY). Confidence is defined as the conditional probability of having Y given X; confidence(X➔Y) = P(Y|X). Lift is the deviation of the support parameter from what would be expected if X and Y were independent; lift(X➔Y) = P(X,Y) / P(X) x P(Y); lift values of <1, >1, and 1 refer to negative, positive, and independent associations between X and Y, respectively [20, 21, 23].

The Apriori algorithm in R was used for analyzing the ARM parameters with the command [24] as
$$ \mathrm{Apriori}\ \left(\mathrm{data},\mathrm{parameter}=\mathrm{NULL},\mathrm{appearance}=\mathrm{NULL},\mathrm{control}=\mathrm{NULL}\right) $$

From ARM, related data in 3 tables including drug utilization, diagnosis data, and patients’ demographic data, were explored and assessed to evaluate rational use of 2 concomitant drugs. In the first group - concomitant use of H2RAs and PPIs - dose and frequency appearing in each prescription along with clinic data were cross-checked for drug interaction or over-dosage. Number and percentage of prescriptions for any concomitant use of H2RAs and PPIs were compared with GORD (described in primary/secondary diagnosis).

In the second group - concomitant use of COX-2 inhibitors and PPIs - patients’ characteristics, number and percentage of prescriptions by age groups, co-therapy with Aspirin, and GI complication were described.

Results

A total of 2,575,331 outpatient visits over 2 fiscal years were retrieved. The mean age and number of OPD visits were 48.4 (SD = 21.4) years and 4.7 (SD = 4.4) per person per year, respectively, and the majority were females (66%). The percentages with GI complications and arthritis were 1.80% and 0.74%, respectively. Among them, 134,285 prescriptions had at least one oral antacid (A02A), drug for peptic ulcer and GORD (A02B), or NSAIDs (M01A) in the same day. A total of 128,117 (95.4%) observations were omitted due to prescription of only one drug per visit, leaving 6168 observations for ARM analysis.

The ARM was applied starting with a threshold of 1% for both support and confidence parameters, and increasing the threshold until association rules were found. Twelve rules were identified and pass the thresholds of 1% and 50% for support and confidence parameters, respectively (see Table 2). The strongest support parameter (0.2244) was between Aspirin and Omeprazole. The strongest confidence parameter (0.9738) was between Naproxen and Omeprazole. Lift values of <1, >1, and 1 refer to negative, positive, and independent associations between antecedent and consequent, respectively, the larger of the value indicates the more significant of the association. The most significant association was between Omeprazole and Ranitidine with highest lift of 7.6153. The rest was low associations between other drugs and Omeprazole.
Table 2

LHS, RHS, support, confidence and lift of 12 rules

Rule no.

LHS

RHS

Support

Confidence

Lift

1

OMPZ

XAND

0.0552

0.7944

7.6153

2

XAND

OMPZ

0.0552

0.5288

7.6153

3

NAPX

OMPZ

0.1085

0.9738

1.4363

4

MELO

OMPZ

0.0315

0.9557

1.4096

5

IBUP

OMPZ

0.0362

0.9028

1.3317

6

DICF

OMPZ

0.0109

0.8933

1.3177

7

ASPT

OMPZ

0.0399

0.8723

1.2867

8

ASA.

OMPZ

0.2244

0.7840

1.1564

9

CELB

OMPZ

0.0483

0.7582

1.1184

10

MOBC

OMPZ

0.0133

0.7522

1.1096

11

ARCX

OMPZ

0.0860

0.6901

1.0179

12

ANTC

OMPZ

0.0315

0.5543

0.8176

Among these 12 association rules, the number of prescriptions of concomitant use for the first and second lifts (i.e., H2RAs and PPIs and COX-2 inhibitors and PPIs) were next calculated. For H2RAs and PPIs (i.e., Ranitidine and Omeprazole), the support and numbers of observations were 0.0552 and 6168, respectively. As a result, 340 (0.0552 × 6168) visits were prescribed with Omeprazole and Ranitidine on the same day.

Since Omeprazole and Ranitidine are in the same drug class (A02B) for treatment of GORD, rational concomitant drug uses for these 340 visits were therefore explored, see Table 3. Drug dose and frequency from each prescription were retrieved. Among these, one patient was prescribed both drugs from different clinics, 12 patients were prescribed Omeprazole and Ranitidine by the same physicians with taking both drugs at the same meals, while the rest of the patients received two drugs from one physician but for different meals. All GI related diagnoses were further explored among these 340 patients, see Table 4. The results indicate that in 118 visits or one-third of these patients, the combination was not prescribed for GORD.
Table 3

Drug’s dose and frequency of Omeprazole (OMPZ) and Ranitidine (XAND)

Code

Dose and frequency

Clinic

Code

Dose and frequency

Clinic

OMPZ

1

CAP

AM

SDORP11

XAND

1

TAB

BID

OFM18

OMPZ

1

CAP

AM

SDPMD02

XAND

1

TAB

BID

SDPMD02

OMPZ

1

CAP

AM

OGY111

XAND

1

TAB

BID

OGY111

OMPZ

2

CAP

AM

OPS01

XAND

1

TAB

BID

OPS01

OMPZ

1

CAP

AM

SDOET11

XAND

1

TAB

BID

SDOET11

OMPZ

1

CAP

AM

SDPMD02

XAND

1

TAB

BID

SDPET01

OMPZ

1

CAP

BID

OPS02

XAND

1

TAB

BID

OPS02

OMPZ

1

CAP

BID

SDPET01

XAND

1

TAB

BID

SDPET01

OMPZ

1

CAP

BID

OEX01

XAND

1

TAB

BID

OEX01

OMPZ

1

CAP

BID

SDPET01

XAND

1

TAB

BID

SDPET01

OMPZ

1

CAP

BID

SDOSU05

XAND

1

TAB

BID

SDOSU05

OMPZ

1

CAP

BID

SDPRP03

XAND

2

TAB

PM

SDPRP03

OMPZ

2

CAP

BID

SDPRP03

XAND

1

TAB

PM

SDPRP03

CAP capsule, TAB tablet, AM in a morning, PM in an evening, BID twice a day, in morning and evening

Table 4

Diagnosis related to GI complications of visits prescribed Omeprazole and Ranitidine on the same day, frequency (%)

ICD10

Disease

N = 340

K219

Gastro-oesophageal reflux disease without oesophagitis

221 (65.0)

K259

Gastric ulcer Unspecified as acute or chronic, without haemorrhage or perforation

1 (0.3)

 

GORD

222

K30

Dyspepsia

38 (11.2)

K279

Peptic ulcer, site unspecified Unspecified as acute or chronic, without haemorrhage or perforation

9 (2.6)

K297

Gastritis, unspecified

5 (1.5)

K922

Gastrointestinal haemorrhage, unspecified

2 (0.6)

K921

Melaena

1 (0.3)

K319

Disease of stomach and duodenum, unspecified

1 (0.3)

K254

Gastric ulcer Chronic or unspecified with haemorrhage

1 (0.3)

K210

Gastro-oesophageal reflux disease with oesophagitis

1 (0.3)

K20

Oesophagitis

1 (0.3)

 

Non-GORD

118

In the second group, we looked at concomitant use of COX-2 inhibitors with PPIs, a combination that is indicated only in elderly patients or those who have GI complications or are taking Aspirin. From a total of 828 visits, there were no COX-2 inhibitors (i.e., Etoricoxib or Celecoxib) prescribed in the same visit. Of these, 295 (35.6%) visits (Table 5) did not comply with the clinical practice guidelines, i.e. for patients aged less than 60 years with no GI complication and no Aspirin taken.
Table 5

Category of visits prescribed COX-2 inhibitors (Etoricoxib or Celecoxib) with Omeprazole, frequency (%)

Category

N = 828

Age (years)

> = 60

498 (60.2)

50–59

233 (28.1)

< 40

97 (11.7)

GI complication (K20-K29.9, K30-K38.9, K90-K93.8)

Yes

141 (17.0)

No

687 (83.0)

Aspirin taken

Yes

11 (1.3)

No

917 (98.7)

Age < 60 years with no GI complication and no Aspirin taken

Yes

295 (35.6)

No

533 (64.4)

Discussion

The study applied ARM to find association rules in prescribing drugs that contained any of 2 drug groups in the same day, i.e., NSAIDs and gastro-protective agents. Data were manipulated and analyzed by Apriori algorithm in RStudio®. Twelve rules were found with >1% support and >50% confidence thresholds and revealed 2 non-guideline prescription patterns of NSAIDs and gastro-protective agents from a hospital data warehouse i.e., Omeprazole with Ranitidine, and COX-2 inhibitors with Omeprazole.

The overwhelming majority of prescriptions (95%) were only for single agents, indicating that rational drug prescriptions was occurring the majority of the time. However, the remaining 5% still represented over 6000 prescriptions and these need more analysis to ascertain whether they complied with clinical practice guidelines.

Among scripts with more than one drug, the strongest association was between Omeprazole and Ranitidine, both of which are in the same drug group, (A02B). Although their pharmacological pathways are different [5], most physicians prescribe either one or another. However, evidence from few studies indicated that taking these 2 drugs in the same meal can improve gastric acid control [10, 11].

The second prescription pattern was between COX-2 inhibitors and Omeprazole. There is no cost effectiveness study directly supporting the benefits of this combination strategy [25], and PPIs are clinically not indicated to prescribe with COX-2 inhibitors, except for high GI risk patients [5].

This study showed that ARM could detect possible poor quality of drug prescription patterns from a hospital data warehouse. Applying this ARM in a routine practice of drug prescriptions should support and lead to health care improvement. The ARM has also found benefits in other clinical studies to identify risk patterns for type 2 diabetes [26], analyze the records of patients diagnosed with essential hypertension [27], identify interesting patterns of infection control [28], find disease association rules from the national health insurance research database in Taiwan [29], and to identify product–multiple adverse event associations in the US Vaccine Adverse Event Reporting System (VAERS) [30]. Apriori is an algorithm for generating association rules; other ARM algorithms are Eclat and FP-Growth algorithms [31, 32].

Conclusion

This study used data in a hospital data warehouse to explore the prescription pattern of 2 drug groups. The method uses an existing algorithm (Apriori) within an open source package (R) for deriving the association rules. Twelve rules were found, representing around one-third of visits (i.e., 118 of 340 who were prescribed Omeprazole with Ranitidine and 295 from 828 who were prescribed Omeprazole with Etoricoxib or Celecoxib), where prescriptions were potentially not congruent with guidelines. This Apriori algorithm should be implemented in hospital monitoring systems in order to detect guideline-discordant use of medicines and routinely feedback to prescribers for increased patient safety.

Declarations

Acknowledgements

None.

Funding

None.

Availability of data and materials

As for our hospital’s rule, data were not available for public and thus we could not provide and share individual patient data.

Authors’ contributions

OP contributed in conception, acquire and analyze data. AT participated in design and interpret the result. MM and JA participated in discussing the results and revising the manuscript critically for important intellectual content. All authors read and approved the final manuscript.

Authors’ information

Described on the title page.

Ethics approval and consent to participate

The study protocol was reviewed and approved by the ethics committee of Ramathibodi Hospital without requirement of consent for participation.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Section for Clinical Epidemiology and Biostatistics, The Faculty of Medicine Ramathibodi Hospital, Mahidol University
(2)
Centre for Clinical Epidemiology and Biostatistics, and Hunter Medical Research Institute, The University of Newcastle

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