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Article • Braz. J. Pharm. Sci. 62 • 2026 • https://doi.org/10.1590/s2175-97902026e24622 linkcopy

Open-access Triggers of drug-related bleeding events: a systematic review

AuthorshipSCIMAGO INSTITUTIONS RANKINGS

Abstract

Adverse drug events (ADEs), such as drug-related bleeding (DRB), represent a serious public health issue and highlight the role of triggers in pharmacovigilance. This study aimed to identify the triggers used to detect inpatient DRB. A systematic review was conducted and registered in PROSPERO (CRD42023440337). The search included the databases PubMed, LILACS, Cochrane Library, Embase, and Web of Science, covering published studies on ADEs in hospitalized adults focused on DRB triggers, with no language restrictions, up to September 2024. Methodological quality was assessed using the Joanna Briggs Institute tool, and trigger performance was evaluated by Positive Predictive Value (PPV). Twenty-three studies were included, identifying 28 triggers for DRB with PPV ranging from 0% to 100%. Prolonged INR and PTT were the most high-performing laboratory triggers, while textual triggers have demonstrated their potential, despite requiring adjustments to avoid false positives. A multimodal approach of laboratory and textual triggers combined emerges as beneficial to ADEs surveillance. Standardized and rigorous methodologies are recommended to strengthen evidence and guide clinical interventions.

Keywords:
Drug-related side effects and adverse reactions; Quality indicators; Health care; Hemorrhage; International normalized ratio; Pharmacovigilance

INTRODUCTION

Medications are crucial for achieving beneficial health outcomes, and reaching important sanitary, social, and economic impacts. On the other hand, their use can lead to the development of Adverse Drug Events (ADEs), portrayed as undesirable occurrences with the potential to cause harm, disability, and death (Otero, Domínguez-Gil, 2000; WHO, 2005; WHO, 2004).

Bleeding ADEs, in particular, are a significant concern in hospital settings, as they lead to emergency visits, prolonged hospitalizations, and increased healthcare costs, threatening the quality of patient care (Núñez et al., 2023; Laureau et al., 2021; Patel et al., 2023; Silva et al., 2022). The worldwide hospital incidence of ADEs range from 0.03% to 7.3%, and in outpatient settings, they lead to hospitalizations at a rate of 9.7 to 383 per 100,000 inhabitants (Silva et al., 2022).

Early identification of drug-related bleeding (DRB) is central for effective interventions to prevent serious complications, but its identification remains a challenge, due to the vast complexity and variety of individual reactions and the overlap of symptoms with other clinical conditions (Mitra et al., 2021). To improve ADE identification, the Institute for Healthcare Improvement (IHI) developed the Global Trigger Tool (GTT), an active surveillance tool that uses triggers to identify and monitor adverse events in healthcare institutions (Griffin, Resar, Classen, 2009). The triggers are identifiable signs or traces from patient electronic records that may indicate the occurrence of an ADE. The GTT includes a medication module with 13 triggers, including Partial Thromboplastin Time (PTT) greater than 100 seconds, International Normalized Ratio (INR) greater than 6, and the administration of vitamin K, which allow early detection of DRB and facilitate the assessment of the causal relationship between the administered drug and the occurrence of the adverse event (Griffin, Resar, Classen, 2009).

Despite GTT applicability in large-scale data analysis and its enhancing of systematic pharmacovigilance (Romeu et al., 2011), its implementation varies across healthcare institutions, which can lead to inconsistencies in ADEs detection (Rawson, 2015). Recent studies showed that hemorrhagic events, particularly those associated with antithrombotic medications, continue to be a leading cause of ADE-associated deaths in hospitals (Haerdtlein et al., 2023; Patel, Patel, 2019). Admissions related to ADEs, especially among the elderly, are often secondary to bleeding complications caused by these medications (Oscanoa, Lizaraso, Carvajal, 2017; Kauppila et al., 2024). Although studies have investigated triggers for ADEs in general, there is a lack of research specifically focused on triggers associated with DRB in hospitalized adult patients.

DRB in hospitalized adult patients remains an underexplored area. Therefore, this systematic review aimed to identify which triggers are used in clinical practice to detect drug-related bleeding events. Specifically, we sought to answer the research question: “What are the triggers associated with drug-related bleeding events in hospitalized adult patients?’’

METHODS

This is a systematic literature review with a narrative synthesis. The writing ofthe study followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) checklist (Page et al., 2021). The review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (www.crd.york.ac.uk/prospero/) under identification number CRD42023440337.

Search Strategy

For constructing the research question and defining the search terms, the PVO acronym was utilized, (P = population, V = variables, and O = outcome) (Biruel, Pinto, 2011). Therefore, the following research question was defined: “What are the triggers associated with drug-related bleeding events in adult hospitalized patients?’.

To identify potentially relevant documents, the PubMed, LILACS, Cochrane Library, Embase and Web of Science databases were consulted, using the descriptors “Hemorrhage” AND “Precipitating Factors” OR “Quality Indicators Health Care” OR “Global Trigger Tool” AND “Drug-Related Side Effects and Adverse Reactions” AND “Inpatients”, together with their respective synonyms. The main search strategy was developed for PubMed and subsequently translated for each database according to their specificities (Supplementary Table I).

Eligibility Criteria

Studies investigating ADEs in hospital settings, focusing on the adult population and triggers related to DRB, were included. Studies published up to September 2024 and without language restrictions were considered. The inclusion and exclusion criteria are presented in Table I.

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TABLE I
PVO model alongside design inclusion/exclusion criteria

Study selection

After searching the databases, the studies were imported into the Rayyan IA® (2022), and duplicate records were removed. Eligibility criteria were applied by screening titles and abstracts by two independent reviewers (M. B. R and L. E). Full texts were assessed when the title and abstract did not provide sufficient information to apply the inclusion criteria. Discrepancies were resolved through consensus and, when necessary, by involving a third reviewer (D.P). The reference lists of included articles were also screened for potential additional studies.

Data extraction

A data extraction form was developed, tested, and standardized by the researchers in advance. Two independent reviewers (M. B. R and K. A.) conducted data extraction from the included studies, resolving discrepancies through consensus and, when necessary, a third reviewer (D.P) was consulted.

The following information was collected: general study details (publication year, country of origin, and study design); characteristics of the patient records assessed (sample size, age, gender, and average length of hospital stay); frequency of ADEs and DRB, data on triggers (number of triggers identified, healthcare professionals responsible for chart review or trigger tool application, methods and results of performance assessment); suspected drug classes of the adverse event, causality, severity, and preventability.

The performance of each trigger was evaluated based on the Positive Predictive Value (PPV), expressed as a percentage. PPVs were categorized as low (<30%), intermediate (30% to 70%), and high (>70%) performance (Yu et al., 2023).

Quality assessment

The critical appraisal of the included studies was conducted by two independent reviewers (M. B. R. and K. A.) using the Joanna Briggs Institute tool, which consists of eight items (JBI, 2017). These items assess the methodological quality of the studies based on the following aspects: clarity of sample inclusion criteria; detailed description of study subjects and setting; validity and reliability of exposure measurement; use of objective and standardized criteria for condition measurement; identification and handling of confounding factors; validity and reliability of results; and appropriateness of the statistical analysis employed.

The assessment tool provides three response options for each criterion: “yes,” indicating that the criterion was met; “no,” indicating that the criterion was not met; and “unclear.” Each positive response (“yes”) receives one point, while “no” and “unclear” responses receive zero points. The total score reflects the internal quality and risk of methodological bias of the study: studies with fewer than 50% positive responses are classified as having low methodological quality; those with between 50% and 75% positive responses are considered to have medium methodological quality; and studies with 75% or more positive responses are rated as having high methodological quality.

This tool was chosen due to its suitability for assessing the methodological quality of observational studies. Any disagreements between reviewers were resolved by consensus and, when necessary, by a third experienced reviewer (D.P.).

Data presentation and synthesis

The information derived from the data extraction process was presented in a narrative form, tables, or figures to facilitate the understanding of the results. The study characteristics, population characteristics, trigger tools, and assessment of ADEs were described, along with the assessment of bias quality. Performance results for bleeding triggers that were not expressed as a percentage were calculated based on available data, ensuring comparability between the different studies included in the analysis.

Due to the significant variability among the identified triggers, reflecting the complexity of the topic, a meta-analysis was not conducted. The diversity in the studies complicates the application of a consistent statistical approach, so we opted for a narrative synthesis of the data.

RESULTS

A total of 3,497 studies were identified in the databases. After removing duplicates, titles and abstracts were screened, and a total of 84 were selected for full-text analysis. Twenty-one studies met the inclusion criteria, and two additional studies were included based on the review of reference lists from the selected articles, resulting in a total of 23 studies. Details are presented in Figure 1. A list of excluded studies and the reason for their exclusion is available in Supplementary Table II.

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FIGURE 1
Flowchart of the study selection process according to the PRISMA Flow Diagram.

Table II presents the characteristics of the included studies published between 2010 and 2024. Most studies were conducted in Europe (n=10), followed by South America (n=4), East Asia (n=4), North America (n=3), Middle East (n=1), and Oceania (n=1). Nearly all studies (95.6%, n=22) employed a cross-sectional study design (CSS), with retrospective data collection (n=19), while two studies used prospective data collection, and another used both types at different phases of the study. Sample sizes ranged from 46 patients (De Souza et al., 2014) to over 16,000 (Kennerly et al., 2013). Participant age also varied, with some studies focusing on those over 60 years old (Hu et al., 2020; Hu et al., 2019), 65 years old (Guzmán et al., 2021; Otero et al., 2021; Wang et al., 2024), and 75 years old (Noorda et al., 2022). The proportion of female participants varied across studies, with some showing a majority of women, while others reported a more balanced gender distribution. Overall, women accounted for 50.9%.

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TABLE II
Characteristics of studies and population

The average length of hospital stay was reported in 16 studies (69.5%). Most studies (n=17) required a minimum length of stay, with 10 studies (58.8%) setting this threshold at 24 hours (Yu et al., 2023; Hu et al., 2020; Hu et al., 2019; Tchijevitch, Nielsen, Lisby, 2021; El Saghir et al., 2021; Grossmann et al., 2019; Hwang, Chin, Chang, 2014; Valkonen et al., 2023) and six studies (35.3%) at 48 hours (De Souza et al., 2014; Guzmán et al., 2021; Otero et al., 2021; Roque, Melo, 2010; Franklin et al., 2010; Zimlichman et al., 2018). One study (5.9%) required a minimum of 72 hours (Kennerly et al., 2013).

The GTT was used in most studies (n=17; 73.9%), and the identification of ADEs was carried out by different healthcare professionals, employing multidisciplinary approaches in 13 studies (56.5%). Most studies did not assess causality (n=14; 60.8%) or event preventability (n=13; 56.5%). Among the studies that assessed causality, three methods were used: clinical judgment (n=2), relying on clinicians’ expertise and consensus; the WHO-UMC system (n=4), a standardized classification ranging from “Certain” to “Unassessable”; and the Naranjo algorithm (n=3), a weighted questionnaire evaluating factors like temporal relationship and dechallenge/rechallenge to categorize events as “Definite,” “Probable,” “Possible,” or “Doubtful. However, severity was evaluated in 78.2% of the studies (n=18), with the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) framework being predominantly used (n=12; 66.6%).

Figure 2 shows the distribution of the drug classes identified in the included studies, based on the Anatomical Therapeutic Chemical (ATC) classification (WHO, 2021), along with their respective citation frequencies. However, the total number of citations exceeds the total number of included studies (n=23) because multiple drug classes were reported in some studies. Specifically, 8 studies (34.8%) cited more than one class. Three studies (13.0%) cited only one drug class (1 referring to acenocoumarol and 2 to warfarin), and 12 studies (52.2%) did not specify the drug classes used.

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FIGURE 2
Distribution of drug classes by ATC classification and citation frequency in included studies.

This systematic review identified 28 triggers, with a wide range of PPV (0% to 100%). Table III presents the frequency of ADE, DRB, identified triggers, and their performance based on IHI. In 15 studies (65.2%), no data on DRB were reported. Among the remaining studies, DRB cases ranged from 3 to 25, with gastrointestinal bleeding reported separately in one study (Noorda et al., 2022).

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TABLE III
Adverse Drug Events and Trigger Performance based on IHI

Regarding triggers, the number identified ranged from 64 (Silva et al., 2018) to 14,184 triggers (Kennerly et al., 2013). In two studies, the frequency of triggers was not reported (De Souza et al., 2014; Buckley et al., 2018). The trigger “administration of vitamin K” was the most frequent, with low performance in 14 studies and intermediate in three studies. The trigger “INR > 6” showed high performance in three studies, intermediate performance in 2, and low performance in 2. The “PTT > 100 seconds” had a performance of 100% in 3 studies, meaning that every time the trigger was positive, there was an ADE; and it had intermediate performance in 4 studies (Yu et al., 2023; Otero et al., 2021; Kane-Gill et al., 2016; Buckley et al., 2018).

Other triggers used to identify bleeding are shown in Table IV. Six studies adapted the GTT, five used non-IHI triggers, and two evaluated keywords (also called trigger words) from medical records to track potential DRB.

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TABLE IV
Trigger performance

Some triggers stood out for their better performance. For example, an Activated Partial Thromboplastin Time ratio greater than 3 and INR > 5 showed a 100% PPV (Franklin et al., 2010). The trigger “Supratherapeutic INR” also had a 100% PPV in one study (Noorda et al., 2022). INR thresholds varied, with mixed results. Specifically, INR > 5 trigger achieved 100% performance (Hu et al. 2020), the same trigger got 32% (Otero et al. 2021; Guzmán et al., 2021).

One study evaluated the increase in INR due to warfarin treatment or dose changes and found intermediate performance, with PPV of 42% and 32%, respectively (Buckley et al., 2018). Other studies employed different triggers for bleeding detection, such as discontinuation of the suspected medication (n=2), a decline of more than 25% in hemoglobin or hematocrit (n=1), administration of protamine sulfate (n=1), and transfer to intensive care units (n=1).

The critical assessment of the methodological quality of the studies revealed that the majority (n=15; 65.2%) presented moderate methodological quality. Although these studies met essential criteria, such as clarity in the inclusion criteria and valid and reliable measurement of exposure, they exhibited shortcomings in identifying and managing confounding factors, as well as in applying appropriate statistical analyses. Five studies (21.7%) were classified as having low methodological quality. These studies showed significant deficiencies in several criteria, including defining inclusion criteria, describing the study subjects and setting, and validating measurements. Three studies (13.0%) were classified as having high methodological quality. These results are described in Supplementary Table III.

DISCUSSION

This review analyzed the triggers used to identify DRB. The diversity in the characteristics of the studies and triggers found highlights the complexity involved in achieving consistent and comparable analyses.

Considering that the inclusion criteria encompassed adults aged 18 years or older, elderly patients were particularly more susceptible to ADEs due to comorbidities, polypharmacy, and age-related pharmacokinetic and pharmacodynamic changes (Hu et al., 2020; Hu et al., 2019; Guzmán et al., 2021; Otero et al., 2021; Noorda et al., 2022).

Gender differences were observed, with women generally more susceptible to ADEs due to pharmacokinetic and pharmacodynamic variations, as well as body weight and hormonal differences (Roque, Melo, 2010; Silva et al., 2018; Wang et al., 2024). However, some studies had a higher male representation, possibly because they were conducted in settings or with conditions more prevalent among men (Yu et al., 2023; Hu et al., 2020; Hu et al., 2019; Tchijevitch, Nielsen, Lisby, 2021; El Saghir et al., 2021; Grossmann et al., 2019; Kane-Gill et al., 2016; Härkänen et al., 2015; Zimlichman et al., 2018; Buckley et al., 2018).

The GTT was widely used, demonstrating its reliability in identifying ADEs. However, the use of alternative tools indicates the need for adaptation to specific populations and clinical contexts, which may contribute to variability in trigger performance across studies (Hu et al., 2020; Noorda et al., 2022; Wang et al., 2024).

Except for one study (Hwang, Chin, Chang, 2014), all other studies involved collaboration among physicians, pharmacists, and nurses in identifying triggers, highlighting the multidisciplinary approach to ADEs surveillance. This collaborative focus is considered effective in improving accuracy in the detection and prevention of ADEs, as professionals from various fields contribute complementary perspectives, enriching the assessment and promoting better patient safety outcomes (Oscanoa, Lizaraso, Carvajal, 2017).

Although causality assessment methods, such as WHO-UMC and the Naranjo Algorithm, were used, these methods align with another study where they were also the most employed (Oscanoa, Lizaraso, Carvajal, 2017). However, more than 60% of studies did not assess causality, and over 56% did not assess preventability, limiting the depth of analyses and understanding of ADEs origins and prevention opportunities.

Additionally, the severity of ADEs was addressed in 78.2% ofthe studies, with the NCC MERP framework being widely used, showing emphasis on severity classification and facilitating the interpretation of results and communication between health professionals and researchers (De Souza et al., 2014; Hu et al., 2020; Guzmán et al., 2021; Otero et al., 2021; Roque, Melo, 2010; Carnevali et al., 2013; El Saghir et al., 2021; Grossmann et al., 2019; Kane-Gill et al., 2016; Valkonen et al., 2023; Härkänen et al., 2015; Zimlichman et al., 2018). Hemorrhagic events were categorized as F (temporary damage to the patient, requiring hospitalization or prolonged hospitalization) or H (need for intervention to maintain life), indicating critical support measures due to the severity of the ADE, which could cause disability (De Souza et al., 2014).

Anticoagulants and antithrombotic agents were the medications most associated with ADEs, reflecting their high risk of bleeding. These medications are widely prescribed to treat conditions such as atrial fibrillation, venous thromboembolism, and cardiovascular diseases. This highlights the importance of an individualized approach to bleeding risk assessment to guide clinical decisions and improve patient safety.

The reviewed studies varied significantly in terms of the number of detected ADEs and applied triggers. Reporting the highest number of ADEs (2,772) with many triggers (14,184), demonstrated the ability to track large volumes of hospital data with intermediate performance (Kennerly et al., 2013). Conversely, studies with smaller samples, detected fewer ADEs with a lower number of triggers but had a high predictive performance of100% for INR > 6 (Tchijevitch, Nielsen, Lisby, 2021; Franklin et al., 2010). While tracking large volumes of data can identify more ADEs, applying triggers in smaller contexts may result in greater accuracy and efficiency in diagnosing critical events.

In addition to INR > 6, other triggers showed high performance, such as INR > 5 (Varallo et al., 2017), “Supratherapeutic INR”, “Gastrointestinal bleeding”, “Other bleedings” (Noorda et al., 2022), PTT > 100 seconds (Varallo et al., 2017; Grossmann et al., 2019), activated partial thromboplastin time ratio >3 (Hu et al., 2020), abrupt discontinuation of the medication (Varallo et al., 2017).

Overall, the use of triggers such as INR > 6 and PTT > 100 is promising, as they are directly related to coagulation and provide a precise approach for monitoring patients at risk of bleeding. However, the variation in PPV values across studies indicates that trigger thresholds may need adjustments based on population characteristics and clinical context. The integration of multiple triggers and combining with textual triggers may enhance ADEs detection.

The administration of vitamin K was a common trigger but showed low performance in most studies (88.2%). This may be attributed to its frequent isolated assessment without considering prolonged INR as recommended by IHI, reducing specificity due to other indications for vitamin K use.

Some triggers, such as INR > 4 in patients using vitamin K antagonists, had low PPV, indicating that isolated INR levels may be insufficient to predict bleeding (Kane-Gill et al., 2016). Other triggers, such as transfer to ICU (4.3%), use of protamine (8.7%), and abrupt medication discontinuation (14%) (Roque, Melo, 2010), showed low performance, likely due to low specificity, as they can be triggered by various conditions. This reinforces the need to integrate multiple and specific triggers and consider the clinical context to improve ADEs detection. Furthermore, terms such as “intracranial hemorrhage” and other expressions in patient records have proven ineffective as triggers (Noorda et al., 2022; Kane-Gill et al., 2016). The mere presence of trigger words does not guarantee the identification of hemorrhagic events, reinforcing the need for careful selection.

In summary, the diversity of approaches and variability in trigger effectiveness highlight the complexity of DRB detection. Future research should focus on standardizing trigger definitions, adjusting thresholds based on population characteristics, and rigorously evaluating trigger combinations to enhance DRB detection and prevention.

This systematic review addresses specific triggers for DRB in hospitalized patients, highlighting the need for consistent approaches in ADEs identification, adaptation to clinical contexts, and the importance of triggers such as INR and PTT. However, this review has an important limitation: the included studies investigated multiple triggers, which prevented a meta-analysis due to heterogeneity in the definitions and criteria used, making quantitative synthesis of the results challenging. Furthermore, the variability in the methodological quality of included studies impacts the interpretation of results. Moderate-quality studies with limitations in confounding control may have compromised trigger effectiveness accuracy, emphasizing the need for cautious interpretation. Methodological limitations, such as inadequate statistical analyses and lack of confounding control, weaken inferences from the data, making it difficult to precisely identify triggers associated with bleeding events. The limited number of high-quality studies underscores the need for future research with rigorous methodology to increase the reliability of findings and inform clinical interventions and safety policies related to bleeding event prevention.

CONCLUSION

This study identified 28 triggers associated with drug-related bleeding in hospitalized adult patients, with PPV ranging from 0% to 100%. Triggers, such as prolonged INR and elevated PTT, are essential tools aiming for ADEs detection, especially in patients using anticoagulants, yet their effectiveness may be influenced by overlapping other clinical and laboratory factors. The inclusion of textual triggers, such as keywords emerged from electronic records, may also enhance the detection of ADEs.

The findings of the study support future research and clinical practice interventions. The implementation of multimodal approaches that integrate laboratory and textual triggers is essential to enhance ADEs surveillance. Standardizing trigger definitions and employing rigorous methodologies are essential to strengthen patient safety.

SUPPLEMENTAL TABLES

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SUPPLEMENTARY TABLE I
Search strategy used for the systematic literature review in electronic databases, consulted in October 2024, including studies published up to September 30, 2024
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SUPPLEMENTARY TABLE II
Excluded studies
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SUPPLEMENTARY TABLE III
Results of the critical assessment of the methodological quality of the included studies, using the JBI tool for analytical cross-sectional studies

DATA AVAILABILITY STATEMENT

All data is available within the article or its supplementary materials.

REFERENCES

  • Biruel EP, Pinto R. Bibliotecário um profissional a serviço da pesquisa. In: Anais do XXIV Congresso Brasileiro de Biblioteconomia, Documentação e Ciência da Informação Maceió, Alagoas, Brasil. 2011.
  • Buckley MS, Rasmussen JR, Bikin DS, Richards EC, Berry AJ, Culver MA, et al. Trigger alerts associated with laboratory abnormalities on identifying potentially preventable adverse drug events in the intensive care unit and general ward. Ther Adv Drug Saf. 2018;9(4):207-17.
  • Carnevali L, Krug B, Amant F, Van Pee D, Gérard V, de Béthune X, et al. Performance of the Adverse Drug Event Trigger Tool and the Global Trigger Tool for Identifying Adverse Drug Events: Experience in a Belgian Hospital. Ann Pharmacother. 2013;47(11):1414-9.
  • De Souza AC, Almeida FVS, Elias SC, de Castilho SR. Uso da vitamina K como rastreador de eventos adversos hemorrágicos por varfarina: um estudo de caso. Rev Ciênc Farm Básica Apl. 2014;35(3).
  • El Saghir A, Dimitriou G, Scholer M, Istampoulouoglou I, Heinrich P, Baumgartl K, et al. Development and implementation of an e-trigger tool for adverse drug events in a Swiss University Hospital. Drug Healthc Patient Saf. 2021;251-63.
  • Franklin BD, Birch S, Schachter M, Barber N. Testing a trigger tool as a method of detecting harm from medication errors in a UK hospital: A pilot study. Int J Pharm Pract. 2010;18(5):305-11.
  • Griffin F, Resar R, Classen D. IHI Global Trigger Tool for measuring adverse events. IHI Innov Ser white Pap. 2009;1-44. Available from: http://www.ihi.org/resources/Pages/IHIWhitePapers/IHIGlobalTriggerToolWhitePaper.aspx
    » http://www.ihi.org/resources/Pages/IHIWhitePapers/IHIGlobalTriggerToolWhitePaper.aspx
  • Grossmann N, Gratwohl F, Musy SN, Nielen NM, Donzé J, Simon M. Describing adverse events in medical inpatients using the Global Trigger Tool. Swiss Med Wkly. 2019;149(w20149):w20149.
  • Guzmán MDT, Banqueri MG, Otero MJ, Fidalgo SS, Noguera IF, Guerrero MCP. Validating a trigger tool for detecting adverse drug events in elderly patients with multimorbidity (TRIGGER-CHRON). J Patient Saf. 2021;17(8):e976-82.
  • Haerdtlein A, Debold E, Rottenkolber M, Boehmer AM, Pudritz YM, Shahid F, et al. Which Adverse Events and Which Drugs Are Implicated in Drug-Related Hospital Admissions? A Systematic Review and Meta-Analysis. J Clin Med. 2023;12(4):1320.
  • Härkänen M, Kervinen M, Ahonen J, Voutilainen A, Turunen H, Vehviläinen-Julkunen K. Patient-specific risk factors of adverse drug events in adult inpatients-evidence detected using the Global Trigger Tool method. J Clin Nurs. 2015;24(3-4):582-91.
  • Hu Q, Qin Z, Zhan M, Chen Z, Wu B, Xu T. Validating the Chinese geriatric trigger tool and analyzing adverse drug event associated risk factors in elderly Chinese patients: A retrospective review. PLoS One. 2020;15(4):e0232095.
  • Hu Q, Wu B, Zhan M, Jia W, Huang Y, Xu T. Adverse events identified by the global trigger tool at a university hospital: A retrospective medical record review. J Evid Based Med. 2019;12(2):91-7.
  • Hwang J, Chin HJ, Chang Y. Characteristics associated with the occurrence of adverse events: a retrospective medical record review using the Global Trigger Tool in a fully digitalized tertiary teaching hospital in Korea. J Eval Clin Pract. 2014;20(1):27-35.
  • Joanna Briggs Institute (JBI). The Joanna Briggs Institute critical appraisal tools for use in JBI systematic reviews checklist for analytical cross sectional studies. North Adelaide, Aust Joanna Briggs Inst. 2017.
  • Kane-Gill SL, MacLasco AM, Saul MI, Smith TRP, Kloet MA, Kim C, et al. Use of text searching for trigger words in medical records to identify adverse drug reactions within an intensive care unit discharge summary. Appl Clin Inform. 2016;7(03):660-71.
  • Kauppila M, Backman JT, Niemi M, Lapatto-Reiniluoto O. Drug-related deaths in a university hospital: Comparison to previous decades. Basic Clin Pharmacol Toxicol. 2024;134(1):165-174.
  • Kennerly DA, Saldaña M, Kudyakov R, Graca B da, Nicewander D, Compton J. Description and evaluation of adaptations to the global trigger tool to enhance value to adverse event reduction efforts. J Patient Saf. 2013;9(2):87-95.
  • Laureau M, Vuillot O, Gourhant V, Perier D, Pinzani V, Lohan L, et al. Adverse drug events detected by clinical pharmacists in an emergency department: A prospective monocentric observational study. J Patient Saf. 2021;17(8):E1040-9.
  • Mitra A, Rawat B, McManus D, Yu H. Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study. JMIR Med Inf. 2021;9(7)e27527.
  • Noorda NMF, Sallevelt BTGM, Langendijk WL, Egberts TCG, van Puijenbroek EP, Wilting I, et al. Performance of a trigger tool for detecting adverse drug reactions in patients with polypharmacy acutely admitted to the geriatric ward. Eur Geriatr Med. 2022;13(4):837-47.
  • Núñez DB, Gómez MF, Suanzes J, Framiñán LM, Gómez MC, Orgueira JMF, et al. Prevalence of adverse drug reactions associated with emergency department visits and risk factors for hospitalization. Farm Hosp. 2023;47(1):T20-T25.
  • Oscanoa TJ, Lizaraso F, Carvajal A. Hospital admissions due to adverse drug reactions in the elderly. A meta-analysis. Eur J Clin Pharmacol. 2017; 73, 759-770.
  • Otero MJ, Domínguez-Gil A. Acontecimientos adversos por medicamentos: una patología emergente. Farm Hosp. 2000;24(4):258-66.
  • Otero MJ, Guzmán MDT, Galván-Banqueri M, Martinez-Sotelo J, Santos-Rubio MD. Utility of a trigger tool (TRIGGER-CHRON) to detect adverse events associated with high-alert medications in patients with multimorbidity. Eur J Hosp Pharm. 2021;28(e1):e41-6.
  • Page MJ, Mckenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. 2021 [cited 2022 Nov 4]; Available from: http://dx.doi.org/10.1136/bmj.n71
    » https://doi.org/10.1136/bmj.n71
  • Patel PB, Patel TK. Mortality among patients due to adverse drug reactions that occur following hospitalisation: a meta-analysis. Eur J Clin Pharmacol. 2019; 75, 1293-1307.
  • Patel TK, Patel PB, Bhalla HL, Dwivedi P, Bajpai V, Kishore S. Impact of suspected adverse drug reactions on mortality and length of hospital stay in the hospitalised patients: a meta-analysis. Eur J Clin Pharmacol. 2023;79(1):99-116.
  • Rawson NSB. Canada’s Adverse Drug Reaction Reporting System: A Failing Grade. J Popul Ther Clin Pharmacol. 2015;22(2).
  • Romeu GA, Távora MRF, Costa AKMD, Souza MOBD, Gondim APS. Notificação de reações adversas em um hospital sentinela de Fortaleza-Ceará. Rev Bras Farm Hosp Serv Saúde. 2011;2(1).
  • Roque KE, Melo ECP. Adjustment of evaluation criteria of adverse drug events for use in a public hospital in the state of Rio de Janeiro. Rev Bras Epidemiol. 2010;13(4):607-19.
  • Silva LT, Modesto ACF, Amaral RG, Lopes FM. Hospitalizations and deaths related to adverse drug events worldwide: Systematic review of studies with national coverage. Eur J Clin Pharmacol. 2022; 78 :435-466.
  • Silva M das DG, Martins MAP, Viana L de G, Passaglia LG, de Menezes RR, Oliveira JA de Q, et al. Evaluation of accuracy of IHI Trigger Tool in identifying adverse drug events: a prospective observational study. Br J Clin Pharmacol. 2018;84(10):2252-9.
  • Tchijevitch OA, Nielsen LP, Lisby M. Life-Threatening and fatal adverse drug events in a Danish university hospital. J Patient Saf. 2021;17(6):e562-7.
  • Valkonen V, Haatainen K, Saano S, Tiihonen M. Evaluation of Global trigger tool as a medication safety tool for adverse drug event detection—a cross-sectional study in a tertiary hospital. Eur J Clin Pharmacol. 2023;79(5):617-25.
  • Varallo FR, Dagli-Hernandez C, Pagotto C, de Nadai TR, Herdeiro MT, de Carvalho Mastroianni P. Confounding variables and the performance of triggers in detecting unreported adverse drug reactions. Clin Ther. 2017;39(4):686-96.
  • World Health Organization. (WHO). A importância da farmacovigilância. Organização Mundial da Saúde Brasília; 2005.
  • World Health Organization (WHO). La farmacovigilancia: garantía de seguridad en el uso de los medicamentos. Ginebra PP - Ginebra: Organización Mundial de la Salud; 2004.
  • World Health Organization (WHO). Anatomical therapeutic chemical (ATC) classification; 2021.
  • Yu N, Wu L, Yin Q, Du S, Liu X, Wu S, et al. Adverse drug events in Chinese elder inpatients: a retrospective review for evaluating the efficiency of the Global Trigger Tool. Front Med. 2023;10:1232334.
  • Zimlichman E, Gueta I, Daliyot D, Ziv A, Oberman B, Hochman O. Adverse Drug Event Rate in Israeli Hospitals: Validation of an International Trigger Tool and an International Comparison Study. Isr Med Assoc J. 2018;20(11):665-669.

Edited by

  • Associate Editor:
    Inajara Rotta

Publication Dates

  • Publication in this collection
    09 Mar 2026
  • Date of issue
    2026

History

  • Received
    15 Aug 2024
  • Accepted
    26 Jan 2025
Creative Common - by 4.0
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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E-mail: bjps@usp.br
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