Developing a cancer-specific trigger tool to identify treatment-related adverse events using administrative data

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.

Improving infectious adverse event reporting for children and adolescents enrolled in clinical trials for acute lymphoblastic leukemia: A report from the Children's Oncology Group

Infections cause substantial morbidity for children with acute lymphoblastic leukemia (ALL). Therefore, accurate characterization of infectious adverse events (AEs) reported on clinical trials is imperative to defining, comparing, and managing safety and toxicity. Here, we describe key processes implemented to improve reporting of infectious AEs on two active phase III Children's Oncology Group (COG) ALL trials. Processes include: (a) identifying infections as a targeted toxicity, (b) incorporation of infection-specific case report form questions, and (c) physician review of AEs with real-time data cleaning. Preliminary assessment of these processes suggests improved reporting, as well as opportunities for further improvement.

Utility of a Clinically Guided Data-Driven Approach for Predicting Breast Cancer Complications: An Application Using a Population-Based Claims Data Set

PURPOSE

With earlier detection and an increasing number of breast cancer (BCa) survivors, more women are living with side effects of BCa treatment. A predictive approach to studying treatment-related adverse events (AEs) may generate proactive strategies; however, many studies are descriptive in nature. Focusing on short-term AEs, we determine the performance of prediction models of disease- or treatment-related AEs among women diagnosed with BCa.

METHODS

We used administrative claims data from the Blue Health Intelligence National Data Repository. The study sample included female individuals age 18 years and older who were diagnosed with BCa and received cancer-directed treatment between January 1, 2014, and August 1, 2019. Using the information available in the claims data, we constructed longitudinal patient histories and identified disease- and treatment-related AEs occurring within 6 months of treatment. The following prediction models were developed: logistic regression, Lasso regression, gradient boosted tree (GBT), and random forest (RF). We compared models using the area under the receiver operating characteristic curve and its CI, among other metrics.

RESULTS

Data were extracted for 267,473 members meeting study inclusion criteria. The area under the curve for the logistic regression model was 0.82 (0.82-0.86), compared with 0.89 (0.87-0.90) for the Lasso, 0.91 (0.89-0.93) for the GBT, and 0.90 (0.93-0.89) for the RF models. The sensitivity was 0.96 for the GBT, Lasso, and RF models, whereas the specificity was 0.42, 0.44, and 0.39 for the GBT, Lasso, and RF models, respectively. Positive predictive values were 0.96 across all three models.

CONCLUSION

Prediction models developed using big data methods and grounded in a clinically guided framework have the potential to reliably predict short-term treatment-related AEs among women diagnosed with BCa.

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.

The Trigger Tool Method for Routine Pharmacovigilance: A Retrospective Cohort Study of the Medical Records of Hospitalized Geriatric Patients

OBJECTIVE

The main objective was to assess the feasibility of the trigger tool method for the retrospective detection of adverse drug reactions (ADRs) in the Rennes University Hospital. The secondary objective was to describe the performance of the method in terms of positive predictive values (PPVs) and severity or preventability of ADRs.

METHODS

Using the Rennes University Hospital clinical data warehouse, pharmacovigilance experts performed a retrospective review of a random sample of 30 inpatient hospital medical records per month using the triggers "fall" and "delirium" to identify related ADRs among patients 65 years and older in 2018 in the geriatrics department. Using the Z test, we compared the proportion of medical records with a positive (identified) trigger related to an ADR, which were reviewed within 20 minutes using the reference of 50% reviewed within 20 minutes.

RESULTS

Among the 355 medical records reviewed, 222 had at least 1 trigger and 98 at least 1 related ADR. Among the 222 positive trigger medical records, 99.6% were reviewed in under 20 minutes (P < 0.001). The pharmacovigilance assessment took 3 months. The PPVs reached 53.9% (46.0%-61.7%) for falls and 21.0% (14.3%-27.5%) for delirium. Among the ADRs, 80% were serious and 53% were preventable.

CONCLUSIONS

Given the low PPV of the triggers used and the considerable need for technical and human resources, the trigger tool method cannot be used as a routine tool at the pharmacovigilance center. However, it could be implemented occasionally for specific purposes such as monitoring the impact of risk minimization measures to prevent ADRs.

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.

Evolution of Hematology Clinical Trial Adverse Event Reporting to Improve Care Delivery

PURPOSE OF REVIEW

Reporting of adverse events on hematology clinical trials is crucial to understanding the safety of standard treatments and novel agents. However, despite the importance of understanding toxicities, challenges in capturing and reporting accurate adverse event data exist.

RECENT FINDINGS

Currently, adverse events are reported manually on most hematology clinical trials. Especially on phase III trials, the highest grade of each adverse event during a reporting period is typically reported. Despite the effort committed to AE reporting, studies have identified underreporting of adverse events on hematologic malignancy clinical trials, which raises concern about the true understanding of safety of treatment that clinicians have in order to guide patients about what to expect during therapy. In order to address these concerns, recent studies have piloted alternative methods for identification of adverse events. These methods include automated extraction of adverse event data from the electronic health record, implementation of trigger or alert tools into the medical record, and analytic tools to evaluate duration of adverse events rather than only the highest adverse event grade. Adverse event reporting is a crucial component of clinical trials. Novel tools for identifying and reporting adverse events provide opportunities for honing and refining methods of toxicity capture and improving understanding of toxicities patients experience while enrolled on clinical trials.

Postoperative Morbidity After Resection of Recurrent Retroperitoneal Sarcoma: A Report from the Transatlantic Australasian RPS Working Group (TARPSWG)

BACKGROUND

This study aimed to evaluate perioperative morbidity after surgery for first locally recurrent (LR1) retroperitoneal sarcoma (RPS). Data concerning the safety of resecting recurrent RPS are lacking.

METHODS

Data were collected on all patients undergoing resection of RPS-LR1 at 22 Trans-Atlantic Australasian Retroperitoneal Sarcoma Working Group (TARPSWG) centers from 2002 to 2011. Uni- and multivariable logistic models were fitted to study the association between major (Clavien-Dindo grade ≥ 3) complications and patient/surgery characteristics as well as outcome. The resected organ score, a method of standardizing the number of organs resected, as previously described by the TARPSWG, was used.

RESULTS

The 681 patients in this study had a median age of 59 years, and 51.8% were female. The most common histologic subtype was de-differentiated liposarcoma (43%), the median resected organ score was 1, and 83.3% of the patients achieved an R0 or R1 resection. Major complications occurred for 16% of the patients, and the 90-day mortality rate was 0.4%. In the multivariable analysis, a transfusion requirement was found to be a significant predictor of major complications (p < 0.001) and worse overall survival (OS) (p = 0.010). However, having a major complication was not associated with a worse OS or a higher incidence of local recurrence or distant metastasis.

CONCLUSIONS

A surgical approach to recurrent RPS is relatively safe and comparable with primary RPS in terms of complications and postoperative mortality when performed at specialized sarcoma centers. Because alternative effective therapies still are lacking, when indicated, resection of a recurrent RPS is a reasonable option. Every effort should be made to minimize the need for blood transfusions.

Describing adverse events in Swiss hospitalized oncology patients using the Global Trigger Tool

BACKGROUND AND AIMS

The occurrence rate of adverse events (AEs) related to care among hospitalized oncology patients in Switzerland remains unknown. The primary objective of this study was to describe, for the first time, the occurrence rate, type, severity of harm, and preventability of AEs related to care, reported in health records of hospitalized hematological and solid-tumor cancer patients in three Swiss hospitals.

METHODS

Using an adapted version of the validated Global Trigger Tool (GTT) from the Institute for Healthcare Improvement, we conducted a retrospective record review of patients discharged from oncology units over a 6-week period during 2018. Our convenience sample included all records from adult patients (≥18 years of age), diagnosed with cancer, and hospitalized (>24 hours). Per the GTT method, two trained nurses independently assessed patient records to identify AEs using triggers, and physicians from the included units analyzed the consensus of the two nurses. Together, they assessed the severity and preventability of each AE.

RESULTS

From the sample of 224 reviewed records, we identified 661 triggers and 169 AEs in 94 of them (42%). Pain related to care was the most frequent AE (n = 29), followed by constipation (n = 17). AEs rates were 75.4 per 100 admissions and 106.6 per 1000 patient days. Most of the identified AEs (78%) caused temporary harm to the patient and required an intervention. Among AEs during hospitalization (n = 125), 76 (61%) were considered not preventable, 28 (22%) preventable, and 21 (17%) undetermined.

CONCLUSION

About half of the hospitalized oncology patients suffered from at least one AE related to care during their hospitalization. Pain, constipation, and nosocomial infections were the most frequent AEs. It is, therefore, essential to identify AEs to guide future clinical practice initiatives to ensure patient safety.

Association between cancer-specific adverse event triggers and mortality: A validation study

Background

As there are few validated measures of patient safety in clinical oncology, creating an efficient measurement instrument would create significant value. Accordingly, we sought to assess the validity of a novel patient safety measure by examining the association of oncology‐specific triggers and mortality using administrative claims data.

Methods

We examined a retrospective cohort of 322 887 adult cancer patients enrolled in commercial or Medicare Advantage products for one year after an initial diagnosis of breast, colorectal, lung, or prostate cancer in 2008‐2014. We used diagnosis and procedure codes to calculate the prevalence of 16 cancer‐specific "triggers"–events that signify a potential adverse event. We compared one‐year mortality rates among patients with and without triggers by cancer type and metastatic status using logistic regression models.

Results

Trigger events affected 19% of patients and were most common among patients with metastatic colorectal (41%) and lung (50%) cancers. There was increased one‐year mortality among patients with triggers compared to patients without triggers across all cancer types in unadjusted and multivariate analyses. The increased mortality rate among patients with trigger events was particularly striking for nonmetastatic prostate cancer (1.3% vs 7.5%, adjusted odds ratio 1.96 [95% CI 1.49‐2.57]) and nonmetastatic colorectal cancer (4.1% vs 11.7%, 1.44 [1.19‐1.75]).

Conclusions

The association between adverse event triggers and poor survival among a cohort of cancer patients supports the validity of a cancer‐specific, administrative claims‐based trigger tool.

Developing a cancer-specific trigger tool to identify treatment-related adverse events using administrative data

Background

As there are few validated tools to identify treatment‐related adverse events across cancer care settings, we sought to develop oncology‐specific “triggers” to flag potential adverse events among cancer patients using claims data.

Methods

322 887 adult patients undergoing an initial course of cancer‐directed therapy for breast, colorectal, lung, or prostate cancer from 2008 to 2014 were drawn from a large commercial claims database. We defined 16 oncology‐specific triggers using diagnosis and procedure codes. To distinguish treatment‐related complications from comorbidities, we required a logical and temporal relationship between a treatment and the associated trigger. We tabulated the prevalence of triggers by cancer type and metastatic status during 1‐year of follow‐up, and examined cancer trigger risk factors.

Results

Cancer‐specific trigger events affected 19% of patients over the initial treatment year. The trigger burden varied by disease and metastatic status, from 6% of patients with nonmetastatic prostate cancer to 41% and 50% of those with metastatic colorectal and lung cancers, respectively. The most prevalent triggers were abnormal serum bicarbonate, blood transfusion, non‐contrast chest CT scan following radiation therapy, and hypoxemia. Among patients with metastatic disease, 10% had one trigger event and 29% had two or more. Triggers were more common among older patients, women, non‐whites, patients with low family incomes, and those without a college education.

Conclusions

Oncology‐specific triggers offer a promising method for identifying potential patient safety events among patients across cancer care settings.