Understanding Hispanic/Latino Participation in Clinical Trials and Observational Studies, and Strategies to Increase Participation: A Targeted Literature Review.

Hispanic/Latino representation in medical research remains poor. We describe factors affecting rates of recruitment, participation, adherence, and retention of Hispanics/Latinos in clinical studies in the United States and characterize proposed strategies to improve these rates. A targeted literature review was conducted. Relevant studies were identified from Embase, MEDLINE®, and CENTRAL from January 1, 2010 to September 4, 2020. Sixty-eight studies were included. Key facilitators to research involvement were establishing trust between research staff and participants, incorporating familism, and using culturally appropriate language. Common elements of successful strategies for improving research involvement included incorporating community partners, bilingual and culturally competent research staff, continuous engagement and building relationships between participants and staff, and incorporating Hispanic/Latino cultural values. There is no universal strategy to improve research involvement of Hispanics/Latinos. The best strategy is likely a combination of key elements from several strategies, tailored to each unique study population. Further research is needed.

Individual Case Safety Report Replication: An Analysis of Case Reporting Transmission Networks.

INTRODUCTION: The basis of pharmacovigilance is provided by the exchange of Individual Case Safety Reports (ICSRs) between the recipient of the original report and other interested parties, which include Marketing Authorization Holders (MAHs) and Health Authorities (HAs). Different regulators have different reporting requirements for report transmission. This results in replication of each ICSR that will exist in multiple locations. Adding in the fact that each case will go through multiple versions, different recipients may receive different case versions at different times, potentially influencing patient safety decisions and potentially amplifying or obscuring safety signals inappropriately. OBJECTIVE: The present study aimed to investigate the magnitude of replication, the variability among recipients, and the subsequent divergence across recipients of ICSRs. METHODS: Seven participating TransCelerate Member Companies (MCs) queried their respective safety databases covering a 3-year period and provided aggregate ICSR submission statistics for expedited safety reports to an independent project manager. As measured in the US Food and Drug Administration (FDA)'s Adverse Event Reporting System (FAERS), ICSR volume for these seven MCs makes up approximately 20% of the total case volume. Aggregate metrics were calculated from the company data, specifically: (i) number of ICSR transmissions, (ii) average number of recipients (ANR) per case version transmitted, (iii) a submission selectivity metric, which measures the percentage of recipients not having received all sequential case version numbers, and (iv) percent of common ISCRs residing in two or more MAH databases. RESULTS: The analysis reflects 2,539,802 case versions, distributed through 7,602,678 submissions. The overall mean replication rate is 3.0 submissions per case version. The distribution of the ANR replication measure was observed to be very long-tailed, with a significant fraction of case versions (~ 12.4% of all transmissions) being sent to ten or more HA recipients. Replication is higher than average for serious, unlisted, and literature cases, ranging from 3.5 to 6.1 submissions per version. Within the subset of ICSR versions sent to three recipients, a significant degree of variability in the actual recipients (i.e., HAs) was observed, indicating that there is not one single combination of the same three HAs predominantly receiving an ICSR. Submission selectivity increases with the case version. For case version 6, the range of the submission selectivity for the MAHs ranges from ~ 10% to over 50%, with a median of 30.2%. Within the participating MAHs, the percentage of cases that reside within at least two safety databases is approximately 2% across five databases. Further analysis of the data from three MAHs showed percentages of 13.4%, 15.6%, and 27.9% of ICSRs originating from HAs and any other partners such as other MAHs and other institutions. CONCLUSION: Replication of ICSRs and the variation of available safety information in recipient databases were quantified and shown to be substantial. Our work shows that multiple processors and medical reviewers will likely handle the same original ICSR as a result of replication. Aside from the obvious duplicate work, this phenomenon could conceivably lead to differing clinical assessments and decisions. If replication could be reduced or even eliminated, this would enable more focus on activities with a benefit for patient safety.

Adverse events and clinical outcomes in patients treated with PD-(L)1 blockade for advanced non-small-cell lung cancer.

Aims: To describe, in patients with advanced/metastatic non-small-cell lung cancer, the relationship between baseline immunosuppressive drug (ISD)/corticosteroid (CS) use, as well as the incidence of mild/moderate adverse events (AEs), and the clinical effectiveness of PD (L)-1 blockade. Patients & methods: This was a retrospective cohort study of patients with no evidence (n = 131) or positive evidence (n = 269) of ISD/CS use. Results: Duration of treatment, time to next treatment, progression-free survival and overall survival were significantly reduced for patients with evidence of prior ISD/CS use. Occurrence of mild/moderate AEs did not affect any clinical outcomes. Conclusion: Prior ISD/CS use was associated with a poorer prognosis in advanced/metastatic non-small-cell lung cancer patients treated with PD-(L)1 inhibitors, but the occurrence of AEs had no effect.

What is the article about? Patients with advanced/metastatic non-small-cell lung cancer (aNSCLC) are often treated with a class of drugs known as checkpoint inhibitors. There have been previous reports that treatment with corticosteroids and other drugs that suppress the immune system in the period leading up to treatment with checkpoint inhibitors may result in poorer outcomes, but most of these reports focus on serious adverse events leading to hospitalizations or emergency room visits that result from treatment. This study aimed to determine whether treatment with corticosteroids in these patients had any impact on the occurrence of mild or moderate adverse events and long-term treatment outcomes. What were the results? By looking back at deidentified medical insurance claims from patients with aNSCLC, we found that patients who were treated with corticosteroids or other immunosuppressive drugs (vs those who did not receive these drugs) in the months leading up to treatment with checkpoint inhibitors had poorer treatment outcomes (e.g., shorter overall survival). What do the results of the study mean? This study investigated the real-world outcomes in aNSCLC patients treated with checkpoint inhibitors and found that the use of corticosteroids or other immunosuppressive drugs may have an adverse effect. However, we are unable to rule out the possibility that there was an underlying difference between these two sets of patients that caused the difference in treatment outcomes. Further studies with larger sample sizes are needed.

Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach.

BACKGROUND: Treatment with immune checkpoint inhibitors (ICIs) has been associated with an increased rate of cardiac events. There are limited data on the risk factors that predict cardiac events in patients treated with ICIs. Therefore, we created a machine learning (ML) model to predict cardiac events in this at-risk population. METHODS: We leveraged the CancerLinQ database curated by the American Society of Clinical Oncology and applied an XGBoosted decision tree to predict cardiac events in patients taking programmed death receptor-1 (PD-1) or programmed death ligand-1 (PD-L1) therapy. All curated data from patients with non-small cell lung cancer, melanoma, and renal cell carcinoma, and who were prescribed PD-1/PD-L1 therapy between 2013 and 2019, were used for training, feature interpretation, and model performance evaluation. A total of 356 potential risk factors were included in the model, including elements of patient medical history, social history, vital signs, common laboratory tests, oncological history, medication history and PD-1/PD-L1-specific factors like PD-L1 tumor expression. RESULTS: Our study population consisted of 4960 patients treated with PD-1/PD-L1 therapy, of whom 418 had a cardiac event. The following were key predictors of cardiac events: increased age, corticosteroids, laboratory abnormalities and medications suggestive of a history of heart disease, the extremes of weight, a lower baseline or on-treatment percentage of lymphocytes, and a higher percentage of neutrophils. The final model predicted cardiac events with an area under the curve-receiver operating characteristic of 0.65 (95% CI 0.58 to 0.75). Using our model, we divided patients into low-risk and high-risk subgroups. At 100 days, the cumulative incidence of cardiac events was 3.3% in the low-risk group and 6.1% in the high-risk group (p<0.001). CONCLUSIONS: ML can be used to predict cardiac events in patients taking PD-1/PD-L1 therapy. Cardiac risk was driven by immunological factors (eg, percentage of lymphocytes), oncological factors (eg, low weight), and a cardiac history.

Application of natural language processing techniques to identify off-label drug usage from various online health communities.

OBJECTIVE: Outcomes mentioned on online health communities (OHCs) by patients can serve as a source of evidence for off-label drug usage evaluation, but identifying these outcomes manually is tedious work. We have built a natural language processing model to identify off-label usage of drugs mentioned in these patient posts. MATERIALS AND METHODS: Single patient posts from 4 major OHCs were considered for this study. A text classification model was built to classify the posts as either relevant or not relevant based on patient experience. The relevant posts were passed through a spelling correction tool, CSpell, and then medications and indications from these posts were identified using cTAKES (clinical Text Analysis and Knowledge Extraction System), a named entity recognition tool. Drug and indication pairs were identified using a dependency parser. Finally, if the paired indication was not mentioned on the label of the drug approved by U.S. Food and Drug Administration, it was tagged as off-label use of that drug. RESULTS: Using this algorithm, we identified 289 off-label indications, achieving a recall of 76%. CONCLUSIONS: The method designed in this study identifies and extracts the semantic relationship between drugs and indications from demotic posts in OHCs. The results demonstrate the feasibility of using natural language processing techniques in identifying off-label drug usage across online health forums for a variety of drugs. Understanding patients' off-label use of drugs may be able to help manufacturers innovate to better address patients' needs and assist doctors' prescribing decisions.

Investigating Overlap in Signals from EVDAS, FAERS, and VigiBase®.

INTRODUCTION: The Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and VigiBase® are two established databases for safety monitoring of medicinal products, recently complemented with the EudraVigilance Data Analysis System (EVDAS). OBJECTIVE: Signals of disproportionate reporting (SDRs) can characterize the reporting profile of a drug, accounting for the distribution of all drugs and all events in the database. This study aims to quantify the redundancy among the three databases when characterized by two disproportionality-based analyses (DPA). METHODS: SDRs for 100 selected products were identified with two sets of thresholds (standard EudraVigilance SDR criteria for all vs Bayesian approach for FAERS and VigiBase®). Per product and database, the presence or absence of SDRs was determined and compared. Adverse events were considered at three levels: MedDRA® Preferred Term (PT), High Level Term (HLT), and HLT combined with Standardized MedDRA® Query (SMQ). Redundancy was measured in terms of recall (SDRs in EVDAS divided by SDRs from any database) and overlap (SDRs in EVDAS and at least one other database, divided by SDRs in EVDAS). Covariates with potential impact on results were explored with linear regression models. RESULTS: The median overlap between EVDAS and FAERS or VigiBase® was 85.0% at the PT level, 94.5% at the HLT level, and 97.7% at the HLT or SMQ level. The corresponding median recall of signals in EVDAS as a percentage of all signals generated in all three databases was 59.4%, 74.1%, and 87.9% at the PT, HLT, and HLT or SMQ levels, respectively. The overlap difference is partially explained by the relative number of EU cases in EudraVigilance and the ratio of EVDAS cases and FAERS cases, presumably due to differences in marketing authorizations, or market penetration in different regions. Products with few cases in EVDAS (< 1500) also display limited recall of signals relative to FAERs/VigiBase®. Time-on-market does not predict signal redundancy between the three databases. The choice of the DPA has an expected but somewhat small effect on redundancy. CONCLUSIONS: Organizations typically consider regulatory expectations, operating performance (e.g., positive predictive value), and procedural complexity when selecting databases for signal management. As SDRs can be seen as a proxy of general reporting characteristics identifiable in a systematic screening process, our results indicate that, for most products, these characteristics are largely similar in each of the databases.

Adverse events in patients with liver cancer.

This study aimed to further the understanding of the incidence of adverse events (AEs) in a population-based representative liver cancer population where there is currently a lack of knowledge. We carried out a retrospective cohort study using data from an administrative claims database between 1 January 2004 and 31 December 2010. Patients were included in the study if they had at least one primary liver cancer diagnosis [International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM): 155.0] and a metastatic diagnosis [ICD-9-CM: 196.x, 197.x (except 197.7), 198.x or 199.0]. We estimated the incidence rate (IR) and 95% confidence interval (CI) for each AE under study. Of the patients identified, 1292 fulfilled the inclusion and exclusion criteria. The most common AEs were nausea and vomiting (IR=878.5/1000 person-years; 95% CI=799.5-963.1). Other common AEs were hypertension (IR=648.7/1000 person-years; 95% CI=569.2-736.1) and hemorrhage (IR=580.0/1000 person-years; 95% CI=518.6-646.6). The least common AEs were rare dermatologic diseases such as Stevens-Johnson syndrome and toxic epidermal necrolysis where no cases were observed. The rates detailed in this analysis are helpful in understanding the benefit risk of treating patients with liver cancer in the real world. Although no formal comparisons were performed, the increased risk of certain events observed in sorafenib-treated patients from this analysis mirrors the risks reported on the label for sorafenib. Therefore, this analysis provided a reasonable assessment of the AEs that patients with liver cancer experience in the real world.

Selected adverse events in cancer patients treated with vascular endothelial growth factor inhibitors.

INTRODUCTION: To evaluate the safety profile of new drugs, it is important to quantify the rates of adverse events (AE). There has been little to no research on the safety of vascular endothelial growth factor (VEGF) inhibitors in population-based settings. The purpose of this study was to further the understanding of the incidence of AEs in a population-based representative cancer population receiving VEGF inhibitors where there currently is a deep lack of knowledge. METHODS: We conducted a retrospective cohort study using data from an administrative claims database between January 1, 2004 and December 31, 2010. Patients were included into the study if they had at least two malignant primary cancer diagnoses codes (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM]: 140-209 [not including 196-199.0]) from the same cancer site and a prescription for a VEGF inhibitor. We estimated the incidence rate (IR) and 95% confidence interval (CI) for each adverse event under study. RESULTS: 2326 patients met the inclusion and exclusion criteria. The mean age for the cohort was 57.3 where 79% of the patients were 50 years of age or older. The most common adverse event was nausea and vomiting (IR = 651.7/1000 person-years; 95% CI = 589.7-718.4). Other common adverse events were hypertension (IR = 452.9/1000 person-years; 95% CI = 394.9-517.1) and hemorrhage (IR = 375.2/1000 person-years; 95% CI = 332.2-422.3). The least common adverse events were rare dermatologic diseases such as Stevens-Johnson syndrome and toxic epidermal necrolysis where no cases were observed. CONCLUSIONS: The rates detailed in this analysis are helpful in understanding the benefit risk of VEGF inhibitors as they are prescribed in the real world. Although no formal comparisons were conducted, the VEGF inhibitors evaluated in this study appeared to have overlapping toxicity profiles; however, the manner in which these AEs are listed in the prescribing information was not always consistent.