scholarly journals Discovering Outliers of Potential Drug Toxicities Using a Large-scale Data-driven Approach

2016 ◽  
Vol 15 ◽  
pp. CIN.S39549
Author(s):  
Jake Luo ◽  
Ron A. Cisler
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Adverse Event ◽  
Adverse Effects ◽  
Adverse Events ◽  
Large Scale ◽  
Data Driven ◽  
Cancer Drugs ◽  
Data Driven Approach ◽  
Grubbs Test

We systematically compared the adverse effects of cancer drugs to detect event outliers across different clinical trials using a data-driven approach. Because many cancer drugs are toxic to patients, better understanding of adverse events of cancer drugs is critical for developing therapies that could minimize the toxic effects. However, due to the large variabilities of adverse events across different cancer drugs, methods to efficiently compare adverse effects across different cancer drugs are lacking. To address this challenge, we present an exploration study that integrates multiple adverse event reports from clinical trials in order to systematically compare adverse events across different cancer drugs. To demonstrate our methods, we first collected data on 186,339 clinical trials from ClinicalTrials.gov and selected 30 common cancer drugs. We identified 1602 cancer trials that studied the selected cancer drugs. Our methods effectively extracted 12,922 distinct adverse events from the clinical trial reports. Using the extracted data, we ranked all 12,922 adverse events based on their prevalence in the clinical trials, such as nausea 82%, fatigue 77%, and vomiting 75.97%. To detect the significant drug outliers that could have a statistically high possibility of causing an event, we used the boxplot method to visualize adverse event outliers across different drugs and applied Grubbs’ test to evaluate the significance. Analyses showed that by systematically integrating cross-trial data from multiple clinical trial reports, adverse event outliers associated with cancer drugs can be detected. The method was demonstrated by detecting the following four statistically significant adverse event cases: the association of the drug axitinib with hypertension (Grubbs’ test, P < 0.001), the association of the drug imatinib with muscle spasm ( P < 0.001), the association of the drug vorinostat with deep vein thrombosis ( P < 0.001), and the association of the drug afatinib with paronychia ( P < 0.01).

Reconsenting patients with cancer on clinical trials: Does added risk influence continued participation?

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e15610-e15610
Author(s):  
A. Elegbede ◽  
A. Andrei ◽  
A. Andrei ◽  
K. D. Holen
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Adverse Event ◽  
Side Effects ◽  
Adverse Events ◽  
Study Participant ◽  
P Value ◽  
Study Participation ◽  
Grade 5 ◽  
Study Results

e15610 Background: The general policy endorsed by multiple professional societies and cooperative groups regarding patients on cancer clinical trials states that subjects should be informed of new adverse events or significant developments during study participation and re-consented to continue on study. However, no information is known as to the effect of re-consenting on a patients’ decision to continue study participation. Our research question addresses how the severity of reported risk to other study participants will impact the subjects’ decision to continue participation in a clinical trial. Methods: We surveyed 34 patients with gastrointestinal (GI) tumors all of whom were currently enrolled in a clinical trial. The survey portrayed hypothetical adverse reactions affecting another study participant ranging from Grade 1 to Grade 5 according to the National Cancer Institutes Common Terminology Criteria for Adverse Effects v. 3.0. The survey asked about subjects’ opinions of the theoretical adverse event categorized as “would not be concerned,” “would be concerned, but would continue the study,” and “would discontinue the study.” Results: Patients willingness to continue the study was highest at Grade 1 with 97% of all participants. However, willingness to continue participation progressively declined as the severity of adverse events increased such that only 44% of participants would continue participation with a reported Grade 5 adverse event. Conclusions: Among surveyed GI cancer patients, willingness to continue participation in a clinical trial declined significantly as the severity of adverse events increased from Grade 1 to Grade 3 - 5 (p-value < 0.001. This could be due to multiple factors, including the terminal nature of the patients’ cancer, the side effects of study therapy and the patients’ response to study treatment. This data could produce a reasonable adverse event grade cut-off for re-consenting patients regarding new side effects. No significant financial relationships to disclose.

How Accurate Is Clinician Reporting of Chemotherapy Adverse Effects? A Comparison With Patient-Reported Symptoms From the Quality-of-Life Questionnaire C30

2004 ◽  
Vol 22 (17) ◽  
pp. 3485-3490 ◽  
Author(s):  
Erik K. Fromme ◽  
Kristine M. Eilers ◽  
Motomi Mori ◽  
Yi-Ching Hsieh ◽  
Tomasz M. Beer
Keyword(s):  
Quality Of Life ◽  
Clinical Trials ◽  
Adverse Event ◽  
Adverse Effects ◽  
Adverse Events ◽  
Life Questionnaire ◽  
European Organization ◽  
Quality Of Life Questionnaire ◽  
Patient Reported

Purpose Adverse events in chemotherapy clinical trials are assessed and reported by clinicians, yet clinician accuracy in assessing symptoms has been questioned. We compared patient reporting of eight symptoms using a validated instrument, the European Organization for the Research and Treatment of Cancer Quality-of-Life Questionnaire C30 (QLQ-C30 or QLQ) with physicians' reporting of the same symptoms in the study's adverse events log. Patients and Methods Thirty-seven men with metastatic, androgen-independent prostate cancer enrolled onto a phase II trial of weekly calcitriol and docetaxel completed the QLQ every 4 weeks for up to 28 weeks. A patient-reported symptom was defined as an increase in a QLQ symptom score by at least 10 points (0 to 100 scale), sustained for at least 4 weeks. A physician-reported symptom was considered present if it was ever documented in the adverse event log. Results Forty-nine (new or worsened) symptoms were detected by both physician and QLQ, 48 symptoms were detected by the physician alone, and 55 symptoms were detected by the QLQ alone. They agreed on the absence of a symptom in 102 instances of 254 possible opportunities. Their uncorrected agreement was 59.4%, but Cohen's κ, a coefficient of agreement that corrects for chance, was 0.15, indicating only slight agreement. Using the QLQ as the standard, overall physician sensitivity and specificity was 47% and 68%, respectively, although it varied considerably among symptoms. Conclusion Even in a tightly controlled clinical trial, physician reporting was neither sensitive nor specific in detecting common chemotherapy adverse effects. Tools for collecting patient-reported adverse event data in chemotherapy clinical trials should be developed.

scholarly journals Characteristics of voluntary reporting of adverse drug events related to antipsychotics in Australia: 14-year analysis

2021 ◽  
Vol 12 ◽  
pp. 204209862110128
Author(s):  
Hanan Khalil ◽  
Dimi Hoppe ◽  
Nabil Ameen
Keyword(s):  
Adverse Event ◽  
Adverse Effects ◽  
Adverse Events ◽  
Neuroleptic Malignant Syndrome ◽  
Antipsychotic Drug ◽  
Antipsychotic Drugs ◽  
Common Adverse Event ◽  
Drug Misuse ◽  
Large Databases ◽  
Chi Squared

Background: Retrospective analyses of large databases of treated patients can provide useful links to the presence of drug misuse or rare and infrequent adverse effects, such as agranulocytosis, diabetic ketoacidosis or neuroleptic malignant syndrome. The aim of this study is to describe the adverse effects to antipsychotics reported in the Australian Database of Adverse Event Notifications (DAEN). Methods: Data were collected from the DAEN – a spontaneous reporting database. The database, which covered the period from January 2004 to December 2017, was obtained from the Therapeutic Goods Administration (TGA) website ( www.TGA.gov ). The drugs selected for this investigation are the following: aripiprazole, clozapine, olanzapine, paliperidone, risperidone, ziprasidone, quetiapine, haloperidol and pimozide. All data were analysed descriptively. Comparison of reporting and management of adverse events between adults (older than 20 years) and children (5–19 years) was undertaken using chi squared test, where p < 0.05 is significant. Results: A total of 7122 adverse events associated with the antipsychotics aripiprazole, clozapine, haloperidol, olanzapine, paliperidone, pimozide, quetiapine and risperidone were reported to the TGA between January 2004 and December 2017. On average, there were 2.6 adverse events reported for each case. The most common adverse event reported for antipsychotics was neuroleptic malignant syndrome. There were no significant differences in the number of co-medications, formulations, indications, therapeutic dose, hospital admission and overdose among the antipsychotics between paediatric and adult populations. However, there were significant differences between causality, death and the management of adverse events between adult and paediatric populations (5–19 years) ( p < 0.05, chi squared test). Conclusion: The antipsychotic drug associated with the highest adverse events in adults was clozapine, followed by olanzapine. The most common adverse event in adults, and reported with a number of antipsychotic drugs, was neuroleptic malignant syndrome. In children, the highest numbers of adverse events reported in the database were associated with risperidone, clozapine and olanzapine. Plain language summary Adverse events reported of antipsychotics Background: Retrospective analyses of large databases of treated patients can provide useful clues to the presence of drug misuse or rare and infrequent adverse effects associated with antipsychotics. The drugs selected for this investigation are the following: aripiprazole, clozapine, olanzapine, paliperidone, risperidone, ziprasidone, quetiapine, haloperidol and pimozide. Methods: All data were analysed descriptively and investigated for any associations between the variables collected. Comparison of reporting and management of adverse events between adults (older than 20 years) and children (5–19 years) was undertaken using chi squared test, where p < 0.05 is significant. Results: The antipsychotic drug associated with the highest adverse events was clozapine, followed by olanzapine. In children, the highest numbers of adverse events reported in the database were associated with risperidone, clozapine and olanzapine. The most common adverse event in adults, and reported with a number of antipsychotic drugs, was neuroleptic malignant syndrome. Conclusion: There were significant differences between causality, death and the management of adverse events between adult and paediatric populations (5–19 years).Keywords: Antipsychotics, adverse effects, adverse events, safety

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals Improving the management of type 2 diabetes through large-scale general practice: the role of a data-driven and technology-enabled education programme

2021 ◽  
Vol 10 (1) ◽  
pp. e001087
Author(s):  
Tarek F Radwan ◽  
Yvette Agyako ◽  
Alireza Ettefaghian ◽  
Tahira Kamran ◽  
Omar Din ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Large Scale ◽  
Education Programme ◽  
Educational Programme ◽  
Data Driven ◽  
Treatment Targets ◽  
Care Processes ◽  
Data Driven Approach

A quality improvement (QI) scheme was launched in 2017, covering a large group of 25 general practices working with a deprived registered population. The aim was to improve the measurable quality of care in a population where type 2 diabetes (T2D) care had previously proved challenging. A complex set of QI interventions were co-designed by a team of primary care clinicians and educationalists and managers. These interventions included organisation-wide goal setting, using a data-driven approach, ensuring staff engagement, implementing an educational programme for pharmacists, facilitating web-based QI learning at-scale and using methods which ensured sustainability. This programme was used to optimise the management of T2D through improving the eight care processes and three treatment targets which form part of the annual national diabetes audit for patients with T2D. With the implemented improvement interventions, there was significant improvement in all care processes and all treatment targets for patients with diabetes. Achievement of all the eight care processes improved by 46.0% (p<0.001) while achievement of all three treatment targets improved by 13.5% (p<0.001). The QI programme provides an example of a data-driven large-scale multicomponent intervention delivered in primary care in ethnically diverse and socially deprived areas.

scholarly journals A Bayesian meta-analytic approach for safety signal detection in randomized clinical trials

2017 ◽  
Vol 14 (2) ◽  
pp. 192-200 ◽  
Author(s):  
Motoi Odani ◽  
Satoru Fukimbara ◽  
Tosiya Sato
Keyword(s):  
Clinical Trials ◽  
Adverse Event ◽  
Adverse Events ◽  
Hierarchical Structure ◽  
Meta Analysis ◽  
Event Data ◽  
Analysis Model ◽  
Exact Test ◽  
Adverse Event Data ◽  
Fisher’S Exact Test

Background/Aim: Meta-analyses are frequently performed on adverse event data and are primarily used for improving statistical power to detect safety signals. However, in the evaluation of drug safety for New Drug Applications, simple pooling of adverse event data from multiple clinical trials is still commonly used. We sought to propose a new Bayesian hierarchical meta-analytic approach based on consideration of a hierarchical structure of reported individual adverse event data from multiple randomized clinical trials. Methods: To develop our meta-analysis model, we extended an existing three-stage Bayesian hierarchical model by including an additional stage of the clinical trial level in the hierarchical model; this generated a four-stage Bayesian hierarchical model. We applied the proposed Bayesian meta-analysis models to published adverse event data from three premarketing randomized clinical trials of tadalafil and to a simulation study motivated by the case example to evaluate the characteristics of three alternative models. Results: Comparison of the results from the Bayesian meta-analysis model with those from Fisher’s exact test after simple pooling showed that 6 out of 10 adverse events were the same within a top 10 ranking of individual adverse events with regard to association with treatment. However, more individual adverse events were detected in the Bayesian meta-analysis model than in Fisher’s exact test under the body system “Musculoskeletal and connective tissue disorders.” Moreover, comparison of the overall trend of estimates between the Bayesian model and the standard approach (odds ratios after simple pooling methods) revealed that the posterior median odds ratios for the Bayesian model for most adverse events shrank toward values for no association. Based on the simulation results, the Bayesian meta-analysis model could balance the false detection rate and power to a better extent than Fisher’s exact test. For example, when the threshold value of the posterior probability for signal detection was set to 0.8, the false detection rate was 41% and power was 88% in the Bayesian meta-analysis model, whereas the false detection rate was 56% and power was 86% in Fisher’s exact test. Limitations: Adverse events under the same body system were not necessarily positively related when we used “system organ class” and “preferred term” in the Medical Dictionary for Regulatory Activities as a hierarchical structure of adverse events. For the Bayesian meta-analysis models to be effective, the validity of the hierarchical structure of adverse events and the grouping of adverse events are critical. Conclusion: Our proposed meta-analysis models considered trial effects to avoid confounding by trial and borrowed strength from both within and across body systems to obtain reasonable and stable estimates of an effect measure by considering a hierarchical structure of adverse events.

scholarly journals RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID

PLoS Genetics ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. e1009315
Author(s):  
Ardalan Naseri ◽  
Junjie Shi ◽  
Xihong Lin ◽  
Shaojie Zhang ◽  
Degui Zhi
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Scale Up ◽  
Data Driven ◽  
Genome Wide Association Studies ◽  
Inference Method ◽  
Genome Wide ◽  
Familial Relationship ◽  
Kinship Coefficients ◽  
Data Driven Approach

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

scholarly journals Modeling Clinical Trial Attrition Using Machine Intelligence: A driver analytics case study using 1,325 trials representing one million patients

2021 ◽  
Author(s):  
Emmette Hutchison ◽  
Sreenath Nampally ◽  
Imran Khan Neelufer ◽  
Youyi Zhang ◽  
Jim Weatherall ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Machine Intelligence ◽  
Duration Of Treatment ◽  
Trial Duration ◽  
Major Barrier ◽  
Clinical Phase ◽  
Successful Execution ◽  
Patient Attrition ◽  
Data Driven Approach

The amount of time and resources invested in bringing novel therapeutics to market has increased year over year with fewer successful treatments reaching patients. In the lifecycle of drug development, the clinical phase is a major contributor to this decreasing efficiency in the development of clinical trials. One major barrier to the successful execution of a randomized control trial (RCT) is the attrition of patients who no longer participate in a trial either following enrollment or randomization. To address this problem, we have assembled a unique dataset by integrating multiple public databases including ClinicalTrials.gov and Aggregate Analysis of ClincalTrials.gov (AACT) to assemble a trial sponsor-independent dataset. This data spans 20 years of clinical trials and over 1 million patients (3,175 cohorts consisting of 1,020,085 patients and 79 curated features) in the respiratory domain and enabled a data-driven approach to identify top features influencing patient attrition in a trial. Top Features included Duration of Trial, Duration of Treatment, Indication, and Number of Adverse Events. We evaluated multiple machine learning models and found the best performance on the Test Set with Random Forest (Test subset: n=637 cohorts; RMSE 6.64). We envisage that our work will enable clinical trial sponsors to optimize trial run time by better anticipating and correcting for potential patient attrition using patient-centric strategies to improve patient engagement, thus enabling new therapies to be delivered to patients more quickly.

Ethics in Clinical Studies: The Dilemma of very Young and Old

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Avisek Dutta ◽  
Avisek Dutta ◽  
Avisek Dutta
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Human Physiology ◽  
Informed Consent ◽  
Adverse Effects ◽  
Age Groups ◽  
The Elderly ◽  
Infantile Spasms ◽  
Care Givers ◽  
Improve Health

The objectives of the research are to percolate knowledge which can improve health and improve understanding of human physiology. Pervasive exclusion of children and elderly in clinical trials as is happening today is not justified. Children have different physiology and pharmacology from adults; often adverse effects are also different and specific. Diseases like neonatal hyperbilirubinemia, infantile spasms are very age specific. Elderly too, have age specific issues like dementias, malignancies, weakened systems and polypharmacy that make them a special cohort. Clinical trials in these age groups are essential so as to gather comprehensive data about a medication across all age groups. Informed consent is a challenge in both these groups. It can be remedied by obtaining consent from parents, or legally acceptable representative in case of children and care givers and/or LARs in case of the elderly. Oral assent from 7 to 11 years, and written assent from 12 to 18 years and in the elderly, along with consent from the LAR, parents, care givers as the case may be, forms the bedrock of good clinical trial ethics.

Safety and efficacy outcomes with nivolumab plus ipilimumab in patients with advanced renal cell carcinoma and low Karnofsky performance status: Results from the CheckMate 920 trial.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 315-315
Author(s):  
Thomas E. Hutson ◽  
Bradley Curtis Carthon ◽  
Jeffrey Yorio ◽  
Sunil Babu ◽  
Heidi Ann McKean ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Renal Cell Carcinoma ◽  
Clinical Trials ◽  
Adverse Events ◽  
Clear Cell ◽  
Karnofsky Performance Status ◽  
Performance Status ◽  
Objective Response ◽  
Safety And Efficacy ◽  
Grade 3

315 Background: Combination therapy with nivolumab + ipilimumab (NIVO+IPI) has demonstrated long-term efficacy and tolerability for patients (pts) with previously untreated advanced renal cell carcinoma (aRCC). Most pivotal clinical trials in pts with aRCC have excluded pts with low Karnofsky performance status (KPS; < 70%). CheckMate 920 is a multi-arm, phase IIIb/IV, open-label clinical trial of NIVO+IPI treatment in pts enrolled in a community practice setting with aRCC and a high unmet medical need. We present safety and efficacy results for the cohort of pts with aRCC of any histology and KPS 50%–60% from CheckMate 920 (NCT02982954). Methods: Pts with previously untreated advanced/metastatic RCC and KPS 50%–60% received NIVO 3 mg/kg + IPI 1 mg/kg Q3W × 4 doses followed by 480 mg NIVO Q4W for ≤ 2 years or until disease progression/unacceptable toxicity. The primary endpoint was incidence of grade ≥ 3 immune-mediated adverse events (imAEs) within 100 days of last dose of study drug. Key secondary endpoints included progression-free survival (PFS) and objective response rate (ORR) by RECIST v1.1 (both per investigator). Exploratory endpoints included overall survival (OS). Results: Of 25 treated pts with KPS 50%–60%, 76% were men; median age was 67 years (range, 34–81). IMDC risk was favorable in 0%, intermediate in 32%, and poor in 68% of pts; 84% had clear cell and 16% had non-clear cell RCC histology. With a minimum follow-up of 25 months, median duration of therapy (95% CI) was 2.3 months (2.1–7.7) for NIVO and 2.1 months (2.1–2.1) for IPI. The median number of doses (range) received was 4 (1–27) for NIVO and 4 (1–4) for IPI; 76% of pts received ≥ 4 NIVO doses and 68% received all 4 IPI doses. The only grade 3–4 imAEs by category were hepatitis (4.0%) and adrenal insufficiency (4.0%). No grade 5 imAEs occurred. Overall, 4 (16%) pts discontinued due to any-grade adverse events (n = 1 each for elevated AST, malignant neoplasm progression, back pain, and acetabulum fracture). Of 18 evaluable pts, ORR was 33.3% (95% CI, 13.3–59.0); no pts had a complete response and 6 had partial response. Median time to objective response was 4.5 months (range, 2.5–24.7). Median duration of objective response was 20.6 months (range, 0.03+–24.2+). Median PFS was 4.6 months (95% CI, 2.5–14.8). Median OS was 15.6 months (95% CI, 5.3–25.1). Conclusions: NIVO+IPI demonstrated an acceptable safety profile and promising antitumor activity in pts with previously untreated aRCC and KPS 50%–60%. The combination was tolerated at a dose intensity similar to that observed in clinical trials conducted in pts with higher KPS (≥ 70%). These data support the value of NIVO+IPI in pts who may not be considered ideal candidates for this therapy and consequently may have limited treatment options. Clinical trial information: NCT02982954 .

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