Trigger Tool-Based Detection of Adverse Drug

Reactions – A Prospective Observational Study

Anjali Anand K.¹, Ralph Winson Pereira¹, Runi D. Shetty¹, Praneetha Jain²*,

Supriya P. S.³, Shraddha Shetty⁴

¹Pharm D Interns, Department of Pharmacy Practice, NGSM Institute of Pharmaceutical Sciences,

Nitte (Deemed to be University), Deralakatte, Mangaluru - 575018, India.

²Assistant Professor, Department of Pharmacy Practice, NGSM Institute of Pharmaceutical Sciences,

Nitte (Deemed to be University), Deralakatte, Mangaluru - 575018, India.

³Associate Professor, Department of General Medicine,

Father Muller Medical College, Mangaluru-575002, India.

⁴Assistant Professor, Department of Community Medicine, KS Hegde Medical Academy,

Nitte (Deemed to be University), Deralakatte, Mangaluru -575018, India.

*Corresponding Author E-mail: jain.pranitha10@gmail.com

ABSTRACT:

Background: To prevent medication-related patient harm and improve healthcare safety and quality, the hospital needs to detect, report, and review adverse drug reactions (ADR) so that specific target safety interventions can be done. Triggers are clues or tools used to identify adverse events. Methodology: A prospective review of patient (n = 366) records using the Global Trigger Tool method was undertaken to detect ADRs for 6 months. The presence of ADRs was reviewed by three independent authors using 39 triggers, and the findings were validated by a physician and a clinical pharmacist. Data collected was entered in Excel and analyzed by using SPSS Version 29.0 Results: Among 366 patient records reviewed, 203 times triggers were observed; the most common trigger was antiemetics (32.02%). However, the most common trigger related to adverse effects was using the other medication module trigger (25.12%). A total of 47 ADRs were observed, of which 44 were associated with triggers, and three were reported spontaneously. The most common ADR was found to be constipation in 11 patients (23.40%), and the most common drug class associated with ADRs was observed to be antibiotics in 16 patients (34.04%). There was no association observed between the ADR and the patient-specific factors. Conclusion: The trigger tool can be a feasible method for identifying ADRs compared to the traditional ADR identification methods. To improve the quality of patient safety,trigger-tool-based identification of ADR can be used in routine settings.

KEYWORDS: Adverse drug events, The global trigger tool, Pharmacovigilance, Patient Safety.

INTRODUCTION: 

Assessing the quality of health care service is an important issue of international relevance. Patient safety can be defined as a healthcare discipline that aims to reduce and prevent harm, risks, and errors that can happen in patients.^1,2 Every year, adverse events affect millions of patients.

In most cases, the main hurdle in assuring patient safety is that adverse events are not being reported due to various reasons.^3,4,5 World Health Organization (WHO) defines adverse drug reaction as “'any response to a drug which is noxious and unintentional, and which occurs at doses normally used in man for prophylaxis, diagnosis or therapy of disease or the modification of physiological function.”'⁶

To prevent medication-related patient harm and improve healthcare safety and quality, the hospital needs to detect, report, and review Adverse drug event (ADE) such that interventions specific to target safety can be done.^7,8,9 There are conventional methods like voluntary incident reports and retrospective or concurrent record reviews to track adverse events, but all these methods are not achieving the objective effectively.¹⁰ Less than 5% of events are found using the conventional methods of detecting ADR.¹¹

Triggers are clues or tools used to detect adverse events and measure the total level of harm from the treatment in a hospital.¹² The Institution of Healthcare Improvement developed a relatively improved method (IHI) using a ‘trigger tool’ to highlight adverse events by manually reviewing the patient's medical records. The Global trigger tool (GTT)            is a quality improvement tool primarily used for clinical practice to estimate and track ADR rates over time at a clinic or hospital.⁷

Even though the IHI Global trigger tool is not used for identifying every ADR in the inpatient data, several hospitals have used this tool to assess the severity of harm due to each Adverse Event (AE). Improving efforts helps determine whether the adverse events could be reduced over time. The triggers used in the IHI Global trigger tool have been developed from reviewing the literature from AE, which has occurred in different settings of the hospitals.^13,14

The current study hospital is a tertiary care teaching hospital, and this setting has a traditional voluntary adverse drug reaction reporting system to identify adverse drug reactions. Thus, this current study aimed to assess the IHI global trigger tool’s feasibility in the prospective detection of adverse drug reactions in General Medicine department patients.

MATERIALS AND METHODS:

This was a prospective observational study carried out in the Department of General Medicine in a tertiary care teaching Hospital, which spanned over a duration of 6 months (August 2022-February 2023). Before initiating the study, it received the approval of the Institutional Ethics Committee (Ref. No: NGSMIPS/IEC/016/2022).

The convenience sampling method was employed based on specific inclusion and exclusion criteria. The study included 366 in-patients from the General Medicine department, aged 18 years or older, with a minimum length of stay of 24 hours and complete medical records. The IHI Trigger tool consisting of 39 triggers was utilized after excluding the perinatal module, Emergency department, and Intensive Care Triggers module according to the needs of the study. Participants with psychiatric/rehabilitation diagnoses were excluded.

Three independent authors recorded the basic patient demographic details and reviewed the records for the presence of triggers, Records with the positive trigger were further evaluated in depth to determine the presence of ADR. Also, the participants and treating physicians were consulted so that the ADR not captured in the clinical notes would not be missed. The findings were validated and reviewed by a clinical pharmacist and a physician. ADR without the presence of a trigger was also reported spontaneously.

The ADRs were analyzed using the causality assessment scale-Naranjo's scale¹⁵ (definite, probable, or possible) and the WHO causality scale¹⁶ (possible, probable or certain), the severity assessment scale-Modified Hartwig and Siegel scale¹⁷ (mild, moderate, severe), preventability assessment scale - Modified Schumock and Thornton scale¹⁸ (Definitely preventable, probably preventable, and not preventable). Predictability: Using Rawlins and Thompson's classification, the ADRs were classified as predictable (Type A reactions) or unpredictable (Type B reactions).¹⁹

Data was summarized using descriptive statistics, frequency, and percentage. The association between the patient-specific factors and ADR was analyzed by Chi-square test using Statistical software SPSS of version 29.0

RESULT:

A total of 366 randomly selected patient records were analyzed. Of the patient records analyzed, 199(54.4%) were male, and 167(45.6%) were female. Most patients had a hospital stay of 1-10 days and were 51-60 years old. Out of the 366 patients enrolled, 97.5% (n=357) were found to be non-smokers, and 2.5% (n=9) were smokers.

Triggers Observed:

From the 366 patient records, 12 triggers were observed 203 times. Antiemetics, which has the trigger code M10, was the most common trigger observed. The most common trigger related to adverse effects is the use of other medication module triggers, which had the trigger code M13.  Triggers Observed are summarized in Table 1.

Table 1: Triggers Observed

Number	Trigger name	Trigger code	The number of time trigger was observed N=203 N (%)	The number of time triggers was associated with an ADR N=44 N(%)
1	Anti-emetic use	M10	65 (32.02)	5 (11.36)
2	Other Medication Module Triggers	M13	51 (25.12)	34 (77.27)
3	Transfusion or use of blood products	C1	24 (11.82)	0
4	Vitamin K administration	M6	20 (9.85)	0
5	Anti-emetic use + Vitamin K administration	M10 + M6	9 (4.43)	1 (2.27)
6	Transfer to a higher level of care	C13	5 (2.46)	0
7	Anti-emetic use + Transfusion or use of blood products	M10 + C1	5 (2.46)	0
8	X-ray or Doppler studies for emboli or DVT	C5	4 (1.97)	0
9	Rise in BUN or serum creatinine greater than 2 times baseline	M5	3 (1.48)	0
10	Anti-emetic use + Vitamin K administration + Transfusion or use of blood products	M10 + M6 + C1	2 (0.99)	0
11	Anti-emetic use + Vitamin K administration + Other Medication Module Triggers	M10 + M6 + M13	1 (0.49)	1 (2.27)
12	Anti-emetic use + Other Medication Module Triggers	M10 + M13	1 (0.49)	1 (2.27)
13	Restraint use	C10	1(0.49)	0
14	Anti-emetic use + Vitamin K administration + Readmission within 30 days	M10 + M6 + C9	1(0.49)	1 (2.27)
15	Anti-emetic use + Vitamin K administration + Code/arrest/rapid response team	M10 + M6 + C2	1(0.49)	1 (2.27)
16	Patient fall	C7	1(0.49)	0
17	Readmission within 30 days	C9	1(0.49)	0
18	Transfusion or use of blood products + Other Medication Module Triggers	C1 + M13	1(0.49)	0
19	Transfusion or use of blood products + Transfer to higher level of care	C1 + C13	1(0.49)	0 42
20	Vitamin K administration + Readmission within 30 davs	M6 + C9	1(0.49)	0
21	Rising BUN or serum creatinine greater than 2 times baseline + Vitamin K administration	M5 + M6	1(0.49)	0
22	Anti-emetic use + Readmission within 30 davs	M10 + C9	1(0.49)	0
23	Transfusion or use of blood products + X-ray or Doppler studies for emboli or DVI	C1 + C5	1(0.49)	0
24	Vitamin K administration + Transfusion or use of blood products	M6 + C1	1(0.49)	0
25	Positive blood culture	C4	1(0.49)	0

Detected Adverse Drug Reactions:

Among the 366 patient records observed for triggers, 47 ADRs were observed. Among these, 44 were detected from the triggers, and 3 ADRs were reported spontaneously.

Most ADRs developed were observed among the age range of 51 – 60 years 13(13.7%). Male predominance 28(14.1%) was observed among the ADRs developed with a length of stay of 1 to 10 days (11.7%).

Figure 1: Illustration of distribution of patients according to the presence and absence of adverse drug reactions (ADRs)

Distribution of ADR Observed:

The drug class that was suspected of causing the greatest number of ADRs was observed to be antibiotics 16 (34.04%), followed by antiemetics 5 (10.64%).  The most common adverse drug reaction was found to be Constipation, which was found in 11 (23.40%) patients, followed by Vomiting in 6 (12.76%), Diarrhoea in 5 (10.63%) and Drowsiness in 5 (10.63%) patients respectively.

Table 2: Distribution of ADR Observed

Drug Class	Drug Name	Type Of ADR	Occurrence Of ADRs (N=47) N(%)
Antibiotics	Meropenem	Seizures	1 (2.13)
	Ciprofloxacin	Vomiting	1(2.13)
	Ceftriaxone	Diarrhoea	5 (10.64)
		Vomiting	2 (4.26)
		Skin Rash	1 (2.13)
		Anemia	1 (2.13)
		Vomiting	1 (2.13)
	Piperacillin + Tazobactum	Vomiting	1 (2.13)
		Fever	1 (2.13)
	Cefpodoxime	Constipation	1 (2.13)
	Cefuroxime Sulbactam	Anemia	1 (2.13)
Anti Emetic	Ondansetron	Constipation	2 (4.26)
		Drowsiness	1 (2.13)
		Headache	1 (2.13)
	Dexamethasone	Hyperglycemia	1 (2.13)
Iron Supplements	Ferrous Ascorbate	Breathlessness	1 (2.13)
	Ferrous Ascorbate + Folic Acid	Constipation	1 (2.13)
Bezodiazepines	Lorazepam	Drowsiness	2 (4.26)
	Clonazepam	Drowsiness	1 (2.13)
Anti-Psychotics	Quetiapine Fumarate	Constipation	1 (2.13)
	Resperidone	Sedation	1 (2.13)
Beta Blockers	Propranolol	Constipation	1 (2.13)
		Drowsiness	1 (2.13)
Proton pump Inhibitors	Pantoprazole	Constipation	2 (4.26)
		Vomiting	1 (2.13)
Insulin	Insulin	Hypoglycemia	2 (4.26)
Cardiovascular	Ivabradine	Systemic Hypertension	1 (2.13)
H1 Antagonist	Chlorpheniramine+ Codeine	Constipation	1 (2.13)
Calcium Channel Blockers	Amlodipine	Abdominal Pain	1 (2.13)
Vitamin Supplement	Optineuron	Constipation	1 (2.13)
Corticosteroid	Budesonide	Tachycardia	1 (2.13)
Electrolyte	Potassium Chloride	Chest Discomfort	1 (2.13)
NSAIDS	Aspirin	Abdominal Pain	1 (2.13)
Anti Diabetic	Metformin	Palpitation	1 (2.13)
Analgesic	Tramadol	Constipation	1 (2.13)
Anti Cholinergic	Atropine	blurred vision	1 (2.13)
Loop Diuretic	Torsemide	Hypokalemia	1 (2.13)
Angiotensin Receptor Blocker	Telmisartan	Anemia	1 (2.13)

Analysis of ADRs:

Both the WHO-UMC and Naranjo's scales were used to assess the causality of suspected adverse drug reactions (ADRs). In accordance with the WHO-UMC causality assessment, a larger portion of the ADRs was deemed probable 24 (51.06%), with a notable number classified as possible 22 (46.81%). According to Naranjo's scale, the ADRs were mostly classified as possible 26 (55.32%), followed by probable 19 (40.43%). The Modified Hartwig and Seigel Scale was used to assess the severity of ADRs. Out of 47 ADRs, the majority were mild 40(85.11%), followed by moderate 7(14.89%). A modified Schumock and Thornton scale was used to assess the preventability of ADRs. It was observed that all 47(100%) ADRs were probably preventable. In accordance with the Predictability Assessment scale, the majority of ADRs were predictable 27 (57.45%) than unpredictable ADRs 20 (42.55%).

Patient Factors Associated with Adverse Drug Reactions:

There seemed to be no statistical association between the occurrence of ADR and various patient-specific factors like gender, length of stay, smoking, number of drugs prescribed, and substance abuse. (Table 3)

Table 3: Patient factors associated to Adverse Drug Reactions

Variables		Patients with ADR N(%)	Patients without ADR N(%)	Chi-square/ Fisher’s exact Value	p-value
Gender	Male	28 (59.57)	171(53.61)	0.58	0.44
	Female	19(40.43)	148(46.39)
Length of stay	01-10 days	25(53.19)	189 (59.25)	0.64	0.72
	11-20 days	21(44.68)	123(38.56)
	21-30 days	1(2.13)	7(2.19)
Number of drugs prescribed	1-10	27(57.45)	165(51.72)	0.53	0.46
	>10	20(42.55)	154(48.28)
Age Group	18-30 years	5(10.64)	37(11.60)	7.84	0.35
	31-40 years	5(10.64)	31(9.72)
	41-50 years	5(10.64)	52(16.30)
	51-60 years	13(27.66)	82(25.71)
	61-70 years	11(23.40)	68(21.32)
	71-80 years	6(12.77)	42(13.17)
	>90	2(4.26)	7(2.19)

DISCUSSION:

Preventing the occurrence of ADEs is a major hurdle in ensuring patient safety in healthcare settings. IHI Global trigger tool uses triggers as clues to detect AEs and to measure the total level of harm from the treatment in a hospital. This study aimed to assess the feasibility of a trigger tool for the prospective detection of ADRs and to study the association of patient-specific factors and AEs.

In our study, from a total of 366 patients, 203 times triggers were observed. Similar to the study carried out by Priyanka P et al., where out of 180 patients, 21 triggers were observed. The most common trigger observed was the use of antiemetic drugs in 89 patients (43.84%). This is similar to a study conducted by Pandya AD et al., where out of 463 medical records analyzed, the most common triggers were found to be abrupt medication stoppage (34.98%), Antiemetic use (25.91%), and time in ED > 6hours (17.49%).^20,21 AS Khairnar et al. observedgastritis and antiemetics as the most common triggers.²²

In this study, among the patient records analyzed, a total of 203 times patients had one or more triggers, and 47 ADRs were observed. Among these, 44 were detected using triggers. These results parallel the observations from the study conducted by Urmila Menat et al., where 34 triggers were observed in 327 patients, of which 19 triggers led to the detection of 66 ADRs. And the study by Pandya ADet al., where among 463 patients, 62 ADRs were observed, of which 53 ADRs were identified by trigger tool and 9 ADRs were identified spontaneously.^23,21

In the present study most frequently observed ADR was constipation 11 (23.40%), which can also be seen in the study conducted by O Guzman-Ruiz et al., where the most frequent ADR was found to be pressure ulcer, delirium, constipation, nosocomial respiratory tract infection and altered level of consciousness. ²⁴ The most common class of drug class causing ADR was observed to be antibiotics. Similar to the observation by Raj Purnima et al.²⁵

Marja Harkanen et al., from reviewing 463 randomly selected discharged patient records, the risk of ADEs increased with the length of hospitalization and increased number of drugs patients used. This contrasts with the current study, where no association was found between length of stay and the number of drugs prescribed. ²⁶ This could be due to the difference in the patient population.

When the causality assessment of suspected ADRs was analyzed using the Naranjo scale, the ADRs were mainly possible 23(48.9%) followed by probable 14 (25.53%), which is similar to the study conducted by Silvana Maria de Almeida et al., which the ADRs detected using trigger tool when classified according to Naranjo scale, the majority fell under possible (71%) and probable (29%).²⁷

Similarly, in that same study by Silvana Maria de Almeida et al., Anti-infective agents (19%) were found to be the most common cause of adverse drug reactions, followed by Cardiovascular agents (14%) and Musculoskeletal drugs (14%). Similarly, in our study, Antibiotics (34%) were found to be the most common cause of ADRs observed. Also, the study found no association between ADRs and the age and sex of the patient, which is similar to our study, where no association between patient-specific factors such as age and gender with ADRs had been found. ²⁷

Even though an association between patient-specific factors and adverse events could not be found, it may be due to differences in the setting and patient population. ADRs were found to be most commonly associated with triggers, as found in multiple previous studies. ^24,28,29,30

More detailed studies are still needed to explore the possibilities of the trigger tool. However, we can conclude that IHI Global Trigger Tool can be a valuable asset in detecting adverse drug events if used by trained professionals

CONCLUSION:

Out of 366 patients, 203 triggers were observed; the most common trigger was the use of antiemetics with the trigger code M10. But the most common trigger related to adverse effects was using the other medication module trigger, which had the trigger code M13. A total of 47 ADRs were observed, of which 44 were associated with triggers, and three were reported spontaneously. The most common ADR was found to be constipation in 11 patients (23.40%), and the most common drug class associated with ADRs was observed to be antibiotics in 16 patients (34.04%). According to the WHO-UMC causality assessment, a larger portion of the ADRs was classified as probable 51.06%, and 46.81% as possible. The data obtained suggest that the Trigger Tool can be a feasible method for identifying the ADR compared to the traditional ADR identification methods. To improve patient safety and patient care quality, trigger tool-based identification can be used in routine healthcare settings.

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

ACKNOWLEDGMENTS:

The authors would like to express sincere gratitude to the authorities of Justice KS Hegde Charitable Hospital and Nitte (Deemed to be University), Mangalore, India for their constant support during the conduct of the study.

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Received on 23.08.2023            Modified on 21.12.2023

Accepted on 02.02.2024           © RJPT All right reserved

Research J. Pharm. and Tech 2024; 17(5):2339-2344.

DOI: 10.52711/0974-360X.2024.00366