BACKGROUND: The detrimental effects of adverse drug reactions (ADRs) are well established. Hence, precise and accurate assessment of ADRs' causality which can differentiate signal from noise is crucial in screening, management and minimisation of ADRs. OBJECTIVE: The current study reported our attempt to improve the scoring system of a previously published algorithm of ADR assessment by our group using a genetic algorithm approach so that the final score can measure the probability of ADR causality. DESIGN: Using ADR cases obtained from the Centre for Drug Administration, the national centre for pharmacovigilance in Singapore, with known causality probability values as reference points, rules were developed to define possible combinations of criteria for 'Definite' ADR cases and 'Probable' ADR cases. A new scoring system was developed using these parameters with the help of genetic algorithm, and tested on 37 'Definite' and 431 'Not Definite' ADR cases. In addition, sensitivity and specificity analysis were performed to allow a comparison of performance between our algorithm and that used by the Adverse Drug Reaction Advisory Committee in Australia (ADRAC). RESULTS: The new scoring system is able to provide a probability of the causality of an ADR by a suspected drug. When applied to the 'Definite' and 'Not Definite' ADR reports, the new algorithm gave a sensitivity of 83.8% and specificity of 71.0%. CONCLUSIONS: Using a quantitative method of assessing causality in the new algorithm allows rare and new ADRs to be more readily identified since a quantitative score can give a more precise degree of ADR causality. This scoring system that provides a probability score would help to make this algorithm more informative and assistive for clinicians, regulatory agencies or pharmaceutical companies to generate ADR alerts. The higher sensitivity value displayed by our algorithm also shows that it would be a good ADR screening tool.
BACKGROUND: The detrimental effects of adverse drug reactions (ADRs) are well established. Hence, precise and accurate assessment of ADRs' causality which can differentiate signal from noise is crucial in screening, management and minimisation of ADRs. OBJECTIVE: The current study reported our attempt to improve the scoring system of a previously published algorithm of ADR assessment by our group using a genetic algorithm approach so that the final score can measure the probability of ADR causality. DESIGN: Using ADR cases obtained from the Centre for Drug Administration, the national centre for pharmacovigilance in Singapore, with known causality probability values as reference points, rules were developed to define possible combinations of criteria for 'Definite' ADR cases and 'Probable' ADR cases. A new scoring system was developed using these parameters with the help of genetic algorithm, and tested on 37 'Definite' and 431 'Not Definite' ADR cases. In addition, sensitivity and specificity analysis were performed to allow a comparison of performance between our algorithm and that used by the Adverse Drug Reaction Advisory Committee in Australia (ADRAC). RESULTS: The new scoring system is able to provide a probability of the causality of an ADR by a suspected drug. When applied to the 'Definite' and 'Not Definite' ADR reports, the new algorithm gave a sensitivity of 83.8% and specificity of 71.0%. CONCLUSIONS: Using a quantitative method of assessing causality in the new algorithm allows rare and new ADRs to be more readily identified since a quantitative score can give a more precise degree of ADR causality. This scoring system that provides a probability score would help to make this algorithm more informative and assistive for clinicians, regulatory agencies or pharmaceutical companies to generate ADR alerts. The higher sensitivity value displayed by our algorithm also shows that it would be a good ADR screening tool.
Authors: Richard D Boyce; Patrick B Ryan; G Niklas Norén; Martijn J Schuemie; Christian Reich; Jon Duke; Nicholas P Tatonetti; Gianluca Trifirò; Rave Harpaz; J Marc Overhage; Abraham G Hartzema; Mark Khayter; Erica A Voss; Christophe G Lambert; Vojtech Huser; Michel Dumontier Journal: Drug Saf Date: 2014-08 Impact factor: 5.606
Authors: Konstantin L Makridis; Sebastian Triller; Deniz A Atalay; Christine Prager; Christian E Elger; Angela M Kaindl Journal: Front Neurol Date: 2021-11-30 Impact factor: 4.003
Authors: Andrew Wai Kei Ko; Arash Ghaffari-Rafi; Alvin Chan; William B Harris; Arcelita Imasa; Kore Kai Liow; Jason Viereck Journal: Hawaii J Health Soc Welf Date: 2021-06
Authors: Lateef M Khan; Sameer E Al-Harthi; Abdel-Moneim M Osman; Mai A Alim A Sattar; Ahmed S Ali Journal: Saudi Pharm J Date: 2015-01-10 Impact factor: 4.330