Using time-to-onset for detecting safety signals in spontaneous reports of adverse events following immunization: a proof of concept study

2012 ◽  
Vol 21 (6) ◽  
pp. 603-610 ◽  
Author(s):  
Lionel Van Holle ◽  
Ziad Zeinoun ◽  
Vincent Bauchau ◽  
Thomas Verstraeten
Keyword(s):  
Adverse Events ◽  
Proof Of Concept ◽  
Concept Study ◽  
Spontaneous Reports ◽  
Time To Onset ◽  
Safety Signals

scholarly journals Baduanjin Qigong Intervention by Telerehabilitation (TeleParkinson): A Proof-of-Concept Study in Parkinson’s Disease

2021 ◽  
Vol 18 (13) ◽  
pp. 6990
Author(s):  
Livia P. Carvalho ◽  
Simon Décary ◽  
Isabelle Beaulieu-Boire ◽  
Rosalie Dostie ◽  
Isabelle Lalonde ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Patient Satisfaction ◽  
Adverse Events ◽  
Proof Of Concept ◽  
Concept Study ◽  
Supervised Exercise ◽  
Patient Reported

Many people living with Parkinson’s Disease (PD) face issues with healthcare services, including delays in diagnosis and treatment, as well as limited access to specialized care, including rehabilitation programs. Non-motor and motor signs and symptoms typically observed in people with PD, such as tremor, rigidity, postural instability, bradykinesia, and freezing are particularly disabling and have been associated with falls, fractures, hospitalizations, and a worse quality of life. Baduanjin Qigong (BDJ) programs have been proven potentially effective in improving physical outcomes and reducing the incidence of falls in PD. The aim of this case report, proof-of-concept, study was to explore the adherence, feasibility, acceptability, and potential efficacy of a BDJ program offered via telerehabilitation in people with PD living in the community. Two participants performed semi-supervised exercise sessions at home, twice a week (over eight weeks) using the TeraPlus platform. Adherence, adverse events, and feasibility (technical implementability), acceptability (patient satisfaction), patient-reported, self-reported, and performance outcomes were measured. Results were based on single-subject descriptive data, minimal detectable change, and anchor-based minimally important difference. Our findings suggest that the intervention seems feasible with no major technical issues or adverse events, and high adherence; acceptable (patient satisfaction); and potentially effective to improve markers of walking performance (gait speed, balance), and quality of life (activities of daily living, mobility).

Innovative Digital Tools and Surveillance Systems for the Timely Detection of Adverse Events at the Point of Care: A Proof-of-Concept Study

Drug Safety ◽  
2016 ◽  
Vol 39 (10) ◽  
pp. 977-988 ◽  
Author(s):  
Christian Hoppe ◽  
Patrick Obermeier ◽  
Susann Muehlhans ◽  
Maren Alchikh ◽  
Lea Seeber ◽  
...  
Keyword(s):  
Adverse Events ◽  
Point Of Care ◽  
Digital Tools ◽  
Surveillance Systems ◽  
Proof Of Concept ◽  
Concept Study

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.

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