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Microcomputer-assisted bayesian differential diagnosis of severe adverse reactions to new drugs
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Microcomputer-assisted Bayesian differential diagnosis of severe adverse reactions to new dru~s Lanct6t, K.L. and Naranjo, C.A. Clinical Pharmacology Program, Addiction Research Foundation Ciinical Institute and Departments of Pharmacology and Medicine, University of Toronto, Toronto, Canada
Severe idiosyncratic adverse drug reactions (IADRs) are an important source of morbidity and mortality. For example, they are frequently responsible for the discontinuation of newly marketed drugs. The adverse events associated with new drugs are usually detected by assessing a series of single case reports (Naranjo, 1986). The Bayesian Adverse Reaction Diagnostic Instrument (BARDI) can be used for the differential diagnosis of these adverse events. BARDI treats the differential diagnosis of an adverse event as a special case of conditional probability evaluation (Lane et al 1987). BARDI calculates the posterior odds (PsO) ~nd the posterior probability (PsP) in favour of a specific drug versus other drug or nondrug etiologies of an adverse event based on background (e.g. epidemiological) and case information (e.g. time of onset). The PsO is the ratio between the probability that a drug caused an adverse event and the probability that a drug did not cause that event, given the same background and case information. BARDI decomposes the PsO into six more easily assessed factors: the prior odds [(PRO), which is based on background information] and five likelihood ratios [(LR) based just on case information]. The 5 LRs consider i~formation that is of differential diagnostic value in the patient history (Hi), time of onset of the adverse event (Ti), characteristics of the event (Ch), and evidence during drug dechallenge (De) at~d rechallenge (Re). The product of the PrO and five LRs is the PsO as follows: PsO - PrO x LR(Hi) × LR(Ti) × LR(Ch) × LR(De) x LR(Re). The PsO can also be expressed as a PsP using the equation PsP -- PsO/(PsO + 1). Studies show at BARDI discriminates between drug and non-drug induced adverse events, and between several possible drug etiologies. However, its widespread use has been limited by the complexity and time require for ns. Sixty-two percent of all drugs discontinued from the market due to toxicity between 1964 and 1987 were associated with four ~ypes of adverse events: neurotoxicity, hepatotoxicity, hematological toxicity and h)~ersensitivity reactions (Lanct6t, Busto and Naranjo, 1989). Therefore, we expanded a previously developed prototype program (MacBARDI) designed for assessing neutropcnia with a Macintosh SE microcomputer to also calculate the PsP in favour of drug causation for examples of the remaining three types of adverse events. We will illustrate the application of MacBARDI to two examples of severe reactions associated with new drugs: 1) nine cases of Guillain-Barr6 Syndrome (GBS) associated with the antidepressant zimeldine, and 2) nine cases of neutropenia occurring during the administration of the antiarrhythmic mexiletine. BARDI indicated that the probabilities that the nine cases of GBS were caused by zimeldine were very high (range PsP = 0.97-0.99), whereas the probabilities that the nine cases of neutropenia were caused by mexiletine were low (range PsP = 0.0001-0.2). In the latter series of cases the PsP were in favour of procainamide for six cases (range PsP0 - 0.8-0.999), captopril for two cases (PsP = 0.7 and 0.99) and cyclic neutropenia in one case (PsP = 0.92). The progran~ decreased the time required to complete computations and facilitated the incorporation of new information. Also MacBARDI allowed us to model the impact of changes in the value of various BARDI factors. These examples illustrate that MacBARDI makes this method more readily available for postmarketing surveillance studies of severe idiosyncratic reactions to new drugs.
References Lanct6t, K.L., Busto, U, Naranjo, C.A., 1989, Cfin. Pharmacol. Ther. 45, !2.'!. Lane, D.A., Kramer, M.S., Hutchinson, T.A., Jones, J.K., Naranjo, C.A., 1987, Pharmaceut. Med. 2, 265. Naranjo, C.A., 1986, Drug Inf. J. 20, 387.
Microcomputer-assisted Bayesian differential diagnosis of severe adverse reactions to new dru~s Lanct6t, K.L. and Naranjo, C.A. Clinical Pharmacology Program, Addiction Research Foundation Ciinical Institute and Departments of Pharmacology and Medicine, University of Toronto, Toronto, Canada
Severe idiosyncratic adverse drug reactions (IADRs) are an important source of morbidity and mortality. For example, they are frequently responsible for the discontinuation of newly marketed drugs. The adverse events associated with new drugs are usually detected by assessing a series of single case reports (Naranjo, 1986). The Bayesian Adverse Reaction Diagnostic Instrument (BARDI) can be used for the differential diagnosis of these adverse events. BARDI treats the differential diagnosis of an adverse event as a special case of conditional probability evaluation (Lane et al 1987). BARDI calculates the posterior odds (PsO) ~nd the posterior probability (PsP) in favour of a specific drug versus other drug or nondrug etiologies of an adverse event based on background (e.g. epidemiological) and case information (e.g. time of onset). The PsO is the ratio between the probability that a drug caused an adverse event and the probability that a drug did not cause that event, given the same background and case information. BARDI decomposes the PsO into six more easily assessed factors: the prior odds [(PRO), which is based on background information] and five likelihood ratios [(LR) based just on case information]. The 5 LRs consider i~formation that is of differential diagnostic value in the patient history (Hi), time of onset of the adverse event (Ti), characteristics of the event (Ch), and evidence during drug dechallenge (De) at~d rechallenge (Re). The product of the PrO and five LRs is the PsO as follows: PsO - PrO x LR(Hi) × LR(Ti) × LR(Ch) × LR(De) x LR(Re). The PsO can also be expressed as a PsP using the equation PsP -- PsO/(PsO + 1). Studies show at BARDI discriminates between drug and non-drug induced adverse events, and between several possible drug etiologies. However, its widespread use has been limited by the complexity and time require for ns. Sixty-two percent of all drugs discontinued from the market due to toxicity between 1964 and 1987 were associated with four ~ypes of adverse events: neurotoxicity, hepatotoxicity, hematological toxicity and h)~ersensitivity reactions (Lanct6t, Busto and Naranjo, 1989). Therefore, we expanded a previously developed prototype program (MacBARDI) designed for assessing neutropcnia with a Macintosh SE microcomputer to also calculate the PsP in favour of drug causation for examples of the remaining three types of adverse events. We will illustrate the application of MacBARDI to two examples of severe reactions associated with new drugs: 1) nine cases of Guillain-Barr6 Syndrome (GBS) associated with the antidepressant zimeldine, and 2) nine cases of neutropenia occurring during the administration of the antiarrhythmic mexiletine. BARDI indicated that the probabilities that the nine cases of GBS were caused by zimeldine were very high (range PsP = 0.97-0.99), whereas the probabilities that the nine cases of neutropenia were caused by mexiletine were low (range PsP = 0.0001-0.2). In the latter series of cases the PsP were in favour of procainamide for six cases (range PsP0 - 0.8-0.999), captopril for two cases (PsP = 0.7 and 0.99) and cyclic neutropenia in one case (PsP = 0.92). The progran~ decreased the time required to complete computations and facilitated the incorporation of new information. Also MacBARDI allowed us to model the impact of changes in the value of various BARDI factors. These examples illustrate that MacBARDI makes this method more readily available for postmarketing surveillance studies of severe idiosyncratic reactions to new drugs.
References Lanct6t, K.L., Busto, U, Naranjo, C.A., 1989, Cfin. Pharmacol. Ther. 45, !2.'!. Lane, D.A., Kramer, M.S., Hutchinson, T.A., Jones, J.K., Naranjo, C.A., 1987, Pharmaceut. Med. 2, 265. Naranjo, C.A., 1986, Drug Inf. J. 20, 387.