Validating the Chinese geriatric trigger tool and analyzing adverse drug event associated risk factors in elderly Chinese patients: A retrospective review

Application of trigger tools for detecting adverse drug events in older people: A systematic review and meta-analysis

OBJECTIVE

To identify trigger tools applied to detect adverse drug events (ADEs) in older people and describe their utility and performance.

METHODS

A systematic review was conducted in the PubMed, Lilacs, and Scopus databases (January 2024). Studies that developed, applied, or validated trigger tools and evaluated their utility and/or performance for detecting ADEs in older people were considered. Direct proportion meta-analyses using the inverse-variance method were performed for prevalence of ADEs and positive predictive value (PPV).

RESULTS

Twenty-four studies (25 publications) were included. Twelve trigger tools were identified, of which six were developed for detecting ADEs in older population, four developed for general population and modified for older people, and two developed for general population. No tools for detecting ADEs in older people receiving palliative care or hospitalized in intensive or surgical care units were found. The performance of triggers was presented through PPV (11.5-71%), negative predictive values (83.3%), and sensitivity (30-94.8%). The overall PPV was 33.3% (95%CI: 32.5-34.2%). Triggers with good performance were changes in plasma levels of digoxin, glucose, and potassium; changes in international normalized ratio; abrupt medication stop; hypotension; and constipation. The prevalence of ADEs ranged from 2.8 to 66%, with overall prevalence of ADEs of 20% (95%CI: 19.3-20.8%). Preventability ranged from 8.4 to 94.4%. Metabolic or electrolyte disturbances induced by diuretics, constipation induced by opioids, and falls and delirium induced by benzodiazepines were the most prevalent ADEs.

CONCLUSION

The trigger tools are flexible and easy to apply, and they can contribute to the detection of ADEs, their associated risk factors, the level of harm, and preventability in different health settings. However, there is no consensus on good or poor values of PPV, which indicate the performance of triggers. Furthermore, there is limited evidence regarding the evaluation of performance through negative predictive value, sensitivity, and specificity.

PROSPERO

CRD42022379893.

Active monitoring of antifungal adverse events in hospitalized patients based on Global Trigger Tool method

Background

The increasing prevalence of fungal infections necessitates broader use of antifungal medications. However, the prevalence of adverse drug events (ADEs) restricts their clinical application. This study aimed to develop a reliable ADEs trigger for antifungals to enable proactive ADEs monitoring, serving as a reference for ADEs prevention and control.

Methods

This investigation comprises two phases. Initially, the trigger was established via a literature review, extraction of relevant items, and refinement through Delphi expert consultation. Subsequently, the validity of the trigger was assessed by analyzing hospital records of antifungal drug users from 1 January 2019 to 31 December 2020. The correlation between each trigger signal and ADEs occurrence was examined, and the sensitivity and specificity of the trigger were evaluated through the spontaneous reporting system (SRS) and Global Trigger Tool (GTT). Additionally, risk factors contributing to adverse drug events (ADEs) resulting from antifungal use were analyzed. Results: Twenty-one preliminary triggers were refined into 21 final triggers after one expert round. In the retrospective analysis, the positive trigger rate was 65.83%, with a positive predictive value (PPV) of 28.75%. The incidence of ADEs in inpatients was 28.75%, equating to 44.58 ADEs per 100 admissions and 33.04 ADEs per 1,000 patient days. Predominant ADEs categories included metabolic disturbances, gastrointestinal damage, and skin rashes. ADEs severity was classified into 36 cases at grade 1, 160 at grade 2, and 18 at grade 3. The likelihood of ADEs increased with longer stays, more positive triggers, and greater comorbidity counts.

Conclusion

This study underscores the effectiveness of the GTT in enhancing ADEs detection during antifungal medication use, thereby confirming its value as a monitoring tool.

The application of Global Trigger Tool in monitoring antineoplastic adverse drug events: a retrospective study

Objective

This study aimed to establish an antineoplastic drugs trigger tool based on Global Trigger Tool (GTT), to examine the performance by detecting adverse drug events (ADEs) in patients with cancer in a Chinese hospital (a retrospective review), and to investigate the factors associating with the occurrence of antineoplastic ADEs.

Methods

Based on the triggers recommended by the GTT and those used in domestic and foreign studies and taking into account the scope of biochemical indexes in our hospital, some of them were adjusted. A total of 37 triggers were finally developed. Five hundred medical records of oncology patients discharged in our hospital from 1 June 2020 to 31 May 2021 were randomly selected according to the inclusion and exclusion criteria. These records were reviewed retrospectively by antineoplastic drugs trigger tool. The sensitivity and specificity of the triggers were analyzed, as well as the characteristics and risk factors for the occurrence of ADEs.

Results

Thirty-three of the 37 triggers had positive trigger, and the sensitivity rate was 91.8% (459/500). For the specificity, the positive predictive value of overall ADEs was 46.0% (715/1556), the detection rate of ADEs was 63.0% (315/500), the rate of ADEs per 100 admissions was 136.0 (95% CI, 124.1-147.9), and the rate of ADEs per 1,000 patient days was 208.33 (95% CI, 201.2-215.5). The top three antineoplastic drugs related to ADEs were antimetabolic drugs (29.1%), plant sources and derivatives (27.1%), and metal platinum drugs (26.3%). The hematologic system was most frequently involved (507 cases, 74.6%), followed by gastrointestinal system (89 cases, 13.1%). Multivariate logistic regression analysis showed that the number of combined drugs (OR = 1.14; 95% CI, 1.07-1.22; P < 0.001) and the previous history of adverse drug reaction (ADR) (OR = 0.38; 95% CI, 0.23-0.60; P < 0.001) were the risk factors for ADEs. The length of hospital stay (OR = 0.40; 95% CI, 0.14-1.12; P < 0.05) and the previous history of ADR (OR = 2.18; 95% CI, 1.07-4.45; P < 0.05) were the risk factors for serious adverse drug events (SAE).

Conclusion

The established trigger tool could be used to monitor antineoplastic drugs adverse events in patients with tumor effectively but still needs to be optimized. This study may provide some references for further research in order to improve the rationality and safety of antineoplastic medications.

Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning

An accurate prediction of the hepatotoxicity associated with low-dose methotrexate can provide evidence for a reasonable treatment choice. This study aimed to develop a machine learning-based prediction model to predict hepatotoxicity associated with low-dose methotrexate and explore the associated risk factors. Eligible patients with immune system disorders, who received low-dose methotrexate at West China Hospital between 1 January 2018, and 31 December 2019, were enrolled. A retrospective review of the included patients was conducted. Risk factors were selected from multiple patient characteristics, including demographics, admissions, and treatments. Eight algorithms, including eXtreme Gradient Boosting (XGBoost), AdaBoost, CatBoost, Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Tree-based Pipeline Optimization Tool (TPOT), Random Forest (RF), and Artificial Neural Network (ANN), were used to establish the prediction model. A total of 782 patients were included, and hepatotoxicity was detected in 35.68% (279/782) of the patients. The Random Forest model with the best predictive capacity was chosen to establish the prediction model (receiver operating characteristic curve 0.97, accuracy 64.33%, precision 50.00%, recall 32.14%, and F1 39.13%). Among the 15 risk factors, the highest score was a body mass index of 0.237, followed by age (0.198), the number of drugs (0.151), and the number of comorbidities (0.144). These factors demonstrated their importance in predicting hepatotoxicity associated with low-dose methotrexate. Using machine learning, this novel study established a predictive model for low-dose methotrexate-related hepatotoxicity. The model can improve medication safety in patients taking methotrexate in clinical practice.

Impact of suspected adverse drug reactions on mortality and length of hospital stay in the hospitalised patients: a meta-analysis

PURPOSE

To estimate the risk of mortality and length of stay in hospitalised patients who have experienced suspected adverse drug reactions (ADRs) as compared to patients who did not experience suspected ADRs.

METHODS

A systematic literature search was conducted on databases for observational and randomised controlled studies conducted in any inpatient setting that reported deaths and/or length of hospital stay in patients who had suspected ADRs and did not have suspected ADRs during hospitalisation. PRISMA guidelines were strictly followed during the review. The methodological quality of included studies was assessed using a tool designed by Smyth et al. for the studies of adverse drug reactions. The meta-analytic summary of all-cause mortality was estimated using odds ratio-OR (95% CI) and length of stay using mean difference-MD (95% CI). Both outcomes were pooled using a random effect model (DerSimonian and Laird method). Subgroup and meta-regression were performed based on study variables: study design, age group, study ward, study region, types of suspected ADRs (ADR_Ad-suspected ADRs that lead to hospitalisation and ADR_In-suspected ADRs that occur following hospitalisation), study duration, sample size and study period. The statistical analysis was conducted through the 'Review manager software version 5.4.1 and JASP (Version 0.14.1)'.

RESULTS

After screening 475 relevant articles, 55 studies were included in this meta-analysis. Patients having suspected ADRs had reported significantly higher odds of all-cause mortality [OR: 1.50 (95% CI: 1.21-1.86; I² = 100%) than those patients who did not have suspected ADRs during hospitalisation. Study wards, types of suspected ADRs and sample size were observed as significant predictors of all-cause mortality (p < 0.05). Patients having suspected ADRs had reported significantly higher mean difference in hospital stay [MD: 3.98 (95% CI: 2.91, 5.05; I² = 99%) than those patients who did not have suspected ADRs during hospitalisation. Types of suspected ADRs and study periods were observed as significant predictors of length of stay (p < 0.05).

CONCLUSION

Suspected ADRs significantly increase the risk of mortality and length of stay in hospitalised patients.

SYSTEMATIC REVIEW REGISTRATION

CRD42020176320.

A Risk-Factor Model for Antineoplastic Drug-Induced Serious Adverse Events in Cancer Inpatients: A Retrospective Study Based on the Global Trigger Tool and Machine Learning

The objective of this study was to apply a machine learning method to evaluate the risk factors associated with serious adverse events (SAEs) and predict the occurrence of SAEs in cancer inpatients using antineoplastic drugs. A retrospective review of the medical records of 499 patients diagnosed with cancer admitted between January 1 and December 31, 2017, was performed. First, the Global Trigger Tool (GTT) was used to actively monitor adverse drug events (ADEs) and SAEs caused by antineoplastic drugs and take the number of positive triggers as an intermediate variable. Subsequently, risk factors with statistical significance were selected by univariate analysis and least absolute shrinkage and selection operator (LASSO) analysis. Finally, using the risk factors after the LASSO analysis as covariates, a nomogram based on a logistic model, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), adaptive boosting (AdaBoost), light-gradient-boosting machine (LightGBM), random forest (RF), gradient-boosting decision tree (GBDT), decision tree (DT), and ensemble model based on seven algorithms were used to establish the prediction models. A series of indicators such as the area under the ROC curve (AUROC) and the area under the PR curve (AUPR) was used to evaluate the model performance. A total of 94 SAE patients were identified in our samples. Risk factors of SAEs were the number of triggers, length of stay, age, number of combined drugs, ADEs occurred in previous chemotherapy, and sex. In the test cohort, a nomogram based on the logistic model owns the AUROC of 0.799 and owns the AUPR of 0.527. The GBDT has the best predicting abilities (AUROC = 0.832 and AUPR = 0.557) among the eight machine learning models and was better than the nomogram and was chosen to establish the prediction webpage. This study provides a novel method to accurately predict SAE occurrence in cancer inpatients.

Chronic-pharma: New Platform for Chronic Patients Pharmacotherapy Optimization

We describe the technological development of a web platform named CHRONIC-PHARMA that integrates three prescription support tools for patients with chronic diseases: Anticholinergic Burden Calculator (ABC), LESS-CHRON criteria and TRIGGER-CHRON. They focus on the optimization and evaluation of pharmacotherapy in patients with chronic diseases, resulting in a useful, single platform that can facilitate the review of pharmacotherapy and improve the safety of chronically ill patients. This is achieved by estimating and reducing the anticholinergic risk (ABC), detecting opportunities for deprescribing drugs and monitoring its success (LESS-CHRON criteria), as well as calculating the risk of adverse drug events (TRIGGER-CHRON). The platform is freely accessible online (https://chronic-pharma.com/) as well as through a mobile application, and therefore easily accessible among the healthcare community.

Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study

BACKGROUND

In spite of the global implementation of standardized surgical safety checklists and evidence-based practices, general surgery remains associated with a high residual risk of preventable perioperative complications and adverse events. This study was designed to validate the hypothesis that a new "Trigger Tool" represents a sensitive predictor of adverse events in general surgery.

METHODS

An observational multicenter validation study was performed among 31 hospitals in Spain. The previously described "Trigger Tool" based on 40 specific triggers was applied to validate the predictive power of predicting adverse events in the perioperative care of surgical patients. A prediction model was used by means of a binary logistic regression analysis.

RESULTS

The prevalence of adverse events among a total of 1,132 surgical cases included in this study was 31.53%. The "Trigger Tool" had a sensitivity and specificity of 86.27% and 79.55% respectively for predicting these adverse events. A total of 12 selected triggers of overall 40 triggers were identified for optimizing the predictive power of the "Trigger Tool".

CONCLUSIONS

The "Trigger Tool" has a high predictive capacity for predicting adverse events in surgical procedures. We recommend a revision of the original 40 triggers to 12 selected triggers to optimize the predictive power of this tool, which will have to be validated in future studies.

Translation, Cross-Cultural Adaptation, and Measurement Properties of the Portuguese Version of the Global Trigger Tool for Adverse Events

Purpose

To adapt and validate the Global Trigger Tool (IHI-GTT), which identifies and analyzes adverse events (AE) in hospitalized patients and their measurement properties in the Portuguese context.

Methods

A retrospective cross-sectional study was based on a random sample of 90 medical records. The stages of translation and cross-cultural adaptation of the IHI-GTT were based on the Cross-Cultural Adaptation Protocol that originated from the Portuguese version, GTT-PT, for the hospital context in medical-surgical departments. Internal consistency, reliability, reproducibility, diagnostic tests, and discriminatory predictive value were investigated.

Results

The final phase of the GTT-PT showed insignificant inconsistencies. The pre-test phase confirmed translation accuracy, easy administration, effectiveness in identifying AEs, and relevance of integrating it into hospital risk management. It had a sensitivity of 97.8% and specificity of 74.8%, with a cutoff point of 0.5, an accuracy of 83%, and a positive predictive value of 69.8% and a negative predictive value of 0.98%.

Conclusion

The GTT-PT is a reliable, accurate, and valid tool to identify AE, with robust measurement properties.