scholarly journals One size does not fit all: a summary of signal detection methods

2021 ◽  
Vol 2 (3) ◽  
pp. 4-6
Author(s):  
Dr. Rajrajeshwari R. Patil ◽  
Dr. Vivek Singh
Keyword(s):  
Adverse Events ◽  
Signal Detection ◽  
Health Care Professionals ◽  
Quantitative Methods ◽  
Detection Methods ◽  
Universal Method ◽  
Spontaneous Reports ◽  
Safety Alert ◽  
The Right ◽  
Safety Signals

The selection of an appropriate signal detection method is pivotal in the identification process of safety signals in pharmacovigilance. Nevertheless, the early detection of safety signals is even more important to prevent the occurrence of another thalidomide tragedy in humans. Spontaneous reports, follow-up studies, scientific literature, preclinical & clinical studies, are valuable sources of adverse events; but on the other hand, these reported adverse events are extremely diverse, hence comprehending this can result in formulating the right signal detection and evaluation strategies. Broadly, signal detection methods fall into two categories: qualitative and quantitative, each having its significance; while the quantitative methods help to handle the voluminous data during signal detection, the qualitative one does its part to pick the rare signals. Hence, there is no single universal method that would be a perfect fit to identify safety signals from all data sources or for all types of adverse events. Further, the signal detection process involves a series of steps right from signal detection to its final assessment & submission, to regulatory authorities confirming a signal as a 'possible safety alert'. Finally, the completed task of finding a confirmed safety alert would be meaningless if it does not reach the end-users of the drug concerned. Therefore, effective communication to health care professionals, patients including clinical trial subjects, pharmaceutical companies, and other stakeholders is equally important.

scholarly journals Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals

2021 ◽  
Vol 11 (22) ◽  
pp. 10828
Author(s):  
Jianxiang Wei ◽  
Jimin Dai ◽  
Yingya Zhao ◽  
Pu Han ◽  
Yunxia Zhu ◽  
...  
Keyword(s):  
Data Mining ◽  
Signal Detection ◽  
Association Rules ◽  
Public Health Problem ◽  
Detection Methods ◽  
Drug Risk ◽  
Spontaneous Reports ◽  
Optimal Thresholds ◽  
Data Source ◽  
Association Rules Analysis

Adverse drug reactions (ADRs) are increasingly becoming a serious public health problem. Spontaneous reporting systems (SRSs) are an important way for many countries to monitor ADRs produced in the clinical use of drugs, and they are the main data source for ADR signal detection. The traditional signal detection methods are based on disproportionality analysis (DPA) and lack the application of data mining technology. In this paper, we selected the spontaneous reports from 2011 to 2018 in Jiangsu Province of China as the research data and used association rules analysis (ARA) to mine signals. We defined some important metrics of the ARA according to the two-dimensional contingency table of ADRs, such as Confidence and Lift, and constructed performance evaluation indicators such as Precision, Recall, and F1 as objective standards. We used experimental methods based on data to objectively determine the optimal thresholds of the corresponding metrics, which, in the best case, are Confidence = 0.007 and Lift = 1. We obtained the average performance of the method through 10-fold cross-validation. The experimental results showed that F1 increased from 31.43% in the MHRA method to 40.38% in the ARA method; this was a significant improvement. To reduce drug risk and provide decision making for drug safety, more data mining methods need to be introduced and applied to ADR signal detection.

scholarly journals Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis (Preprint)

2018 ◽  
Author(s):  
Marie-Laure Kürzinger ◽  
Stéphane Schück ◽  
Nathalie Texier ◽  
Redhouane Abdellaoui ◽  
Carole Faviez ◽  
...  
Keyword(s):  
Signal Detection ◽  
Predictive Value ◽  
Empirical Bayes ◽  
Geometric Mean ◽  
Area Under The Curve ◽  
Detection Methods ◽  
Operating Characteristics ◽  
Web Based ◽  
Spontaneous Reports ◽  
Reporting Systems

BACKGROUND While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs). OBJECTIVE This study aimed (1) to assess the consistency of SDRs detected from patients’ medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems. METHODS Messages posted on patients’ forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided. RESULTS The comparison analysis showed that the sensitivity ranged from 29% to 50.6%, the specificity from 86.1% to 95.5%, the PPV from 51.2% to 75.4%, the NPV from 68.5% to 91.6%, and the accuracy from 68% to 87.7%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase. CONCLUSIONS The specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients’ medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals.

scholarly journals Data-driven methodology for discovery and response to pulmonary symptomology in hypertension through AI and machine learning: Application to COVID-19 related pharmacovigilance

2021 ◽  
Author(s):  
Xuan Xu ◽  
Jessica Kawakami ◽  
Nuwan Indika Millagaha Gedara ◽  
Jim Riviere ◽  
Emma Meyer ◽  
...  
Keyword(s):  
High Risk ◽  
Signal Detection ◽  
Quantitative Methods ◽  
Randomized Clinical Trials ◽  
Single Agent ◽  
Respiratory Illness ◽  
Detection Methods ◽  
Drug Event ◽  
Increased Risk ◽  
Medical Histories

Potential therapy and confounding factors including typical co-administered medications, patient's disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials. In this study, we implemented adaptive signal detection approaches to correct spurious association, hidden factors, and confounder misclassification when the covariates are unknown or unmeasured on medications affecting the renin-angiotensin system (RAS), potentially creating an increased risk of life-threatening outcomes in high-risk patients. We consider pulmonary ADE (pADE) profiles in a long-standing group of therapeutics, RAS-acting agents, in patients with hypertension associated with high-risk for COVID-19. Using these techniques, we confirmed our hypothesis that drugs from the same drug class could have very different pADE profiles affecting outcomes in acute respiratory illness. Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pADEs. Macitentan and bosentan were associates with 64% and 56% of pADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by our signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy.

Phosphene perceptions and safety of chronic visual cortex stimulation in a blind subject

2020 ◽  
Vol 132 (6) ◽  
pp. 2000-2007 ◽  
Author(s):  
Soroush Niketeghad ◽  
Abirami Muralidharan ◽  
Uday Patel ◽  
Jessy D. Dorn ◽  
Laura Bonelli ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Adverse Events ◽  
Clinical Intervention ◽  
Occipital Cortex ◽  
Neuronal Population ◽  
Serious Adverse Events ◽  
Stimulation Parameters ◽  
The Right ◽  
Visual Cortical ◽  
Stimulation Of

Stimulation of primary visual cortices has the potential to restore some degree of vision to blind individuals. Developing safe and reliable visual cortical prostheses requires assessment of the long-term stability, feasibility, and safety of generating stimulation-evoked perceptions.A NeuroPace responsive neurostimulation system was implanted in a blind individual with an 8-year history of bare light perception, and stimulation-evoked phosphenes were evaluated over 19 months (41 test sessions). Electrical stimulation was delivered via two four-contact subdural electrode strips implanted over the right medial occipital cortex. Current and charge thresholds for eliciting visual perception (phosphenes) were measured, as were the shape, size, location, and intensity of the phosphenes. Adverse events were also assessed.Stimulation of all contacts resulted in phosphene perception. Phosphenes appeared completely or partially in the left hemifield. Stimulation of the electrodes below the calcarine sulcus elicited phosphenes in the superior hemifield and vice versa. Changing the stimulation parameters of frequency, pulse width, and burst duration affected current thresholds for eliciting phosphenes, and increasing the amplitude or frequency of stimulation resulted in brighter perceptions. While stimulation thresholds decreased between an average of 5% and 12% after 19 months, spatial mapping of phosphenes remained consistent over time. Although no serious adverse events were observed, the subject experienced mild headaches and dizziness in three instances, symptoms that did not persist for more than a few hours and for which no clinical intervention was required.Using an off-the-shelf neurostimulator, the authors were able to reliably generate phosphenes in different areas of the visual field over 19 months with no serious adverse events, providing preliminary proof of feasibility and safety to proceed with visual epicortical prosthetic clinical trials. Moreover, they systematically explored the relationship between stimulation parameters and phosphene thresholds and discovered the direct relation of perception thresholds based on primary visual cortex (V1) neuronal population excitation thresholds.

scholarly journals An overview of the rationale for qualitative research methods in social health

2013 ◽  
Vol 01 (01) ◽  
pp. 006-008
Author(s):  
Heather Stuckey
Keyword(s):  
Qualitative Research ◽  
Health Care Professionals ◽  
Quantitative Methods ◽  
Diabetes Management ◽  
Social Health ◽  
Diabetes Research ◽  
Medical Outcomes ◽  
Qualitative And Quantitative ◽  
Qualitative And Quantitative Methods ◽  
Beliefs And Attitudes

Qualitative research is a primary way to understand the context of diabetes in a person′s life, beyond the medical outcomes. Identifying the qualitative issues such as patients′ knowledge about diabetes, their beliefs and attitudes, and their relationship with health care professionals can serve as data to determine the obstacles and, in turn, resolutions to those issues in diabetes management. Characteristics of qualitative and quantitative methods are described, with the discussion that both methods are complementary, not conflicting, to further the field of diabetes research.

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