A new Poisson and Bayesian-based method to assign risk and causality in patients with suspected hepatic adverse drug reactions: a report of two new cases of ticlopidine-induced hepatotoxicity

A challenge for diagnosing acute liver injury with concomitant/sequential exposure to multiple drugs: can causality assessment scales be utilized to identify the offending drug?

Drug-induced hepatotoxicity most commonly manifests as an acute hepatitis syndrome and remains the leading cause of drug-induced death/mortality and the primary reason for withdrawal of drugs from the pharmaceutical market. We report a case of acute liver injury in a 12-year-old Hispanic boy, who received a series of five antibiotics (amoxicillin, ceftriaxone, vancomycin, ampicillin/sulbactam, and clindamycin) for cervical lymphadenitis/retropharyngeal cellulitis. Histopathology of the liver biopsy specimen revealed acute cholestatic hepatitis. All known causes of acute liver injury were appropriately excluded and (only) drug-induced liver injury was left as a cause of his cholestasis. Liver-specific causality assessment scales such as Council for the International Organization of Medical Sciences/Roussel Uclaf Causality Assessment Method scoring system (CIOMS/RUCAM), Maria and Victorino scale, and Digestive Disease Week-Japan were applied to seek the most likely offending drug. Although clindamycin is the most likely cause by clinical diagnosis, none of causality assessment scales aid in the diagnosis.

Hepatotoxicity induced by trimethoprim-sulfamethoxazole in a child with cystic fibrosis

Patients with cystic fibrosis (CF) have chronic and progressive lung infections with various bacterial organisms that require treatment with oral and intravenous antibiotics on a regular basis. Trimethoprim-sulfamethoxazole (TMP-SMX) is one of the medications used to treat acute pulmonary infectious exacerbations in patients with CF. Hepatic toxicity secondary to TMP-SMX was previously described in normal subjects but has not been reported in children with CF. Here we describe a 14-year-old female child with CF who was given oral TMP-SMX for an acute pulmonary infectious exacerbation. She developed a rash, severe constitutional symptoms, and significant elevation of liver enzyme concentrations secondary to immunity-mediated reaction to TMP-SMX. Discontinuation of TMP-SMX and supportive treatment led to resolution of her symptoms and normalization of liver enzyme concentrations.

An algorithm to detect adverse drug reactions in the neonatal intensive care unit

Critically ill newborns in neonatal intensive care units (NICUs) are at greater risk of developing adverse drug reactions (ADRs). Differentiation of ADRs from reactions associated with organ dysfunction/immaturity is difficult. Current ADR algorithm scoring was established arbitrarily without validation in infants. The study objective was to develop a valid and reliable algorithm to identify ADRs in the NICU. Algorithm development began with a 24-item questionnaire for data collection on 100 previously suspected ADRs. Five pediatric pharmacologists independently rated cases as definite, probable, possible, and unlikely ADRs. Consensus "gold standard" was reached via teleconference. Logistic regression and iterative C programs were used to derive the scoring system. For validation, 50 prospectively collected ADR cases were assessed by 3 clinicians using the new algorithm and the Naranjo algorithm. Weighted kappa and intraclass correlation coefficient (ICC) were used to compare validity and reliability of algorithms. The new algorithm consists of 13 items. Kappa and ICC of the new algorithm were 0.76 and 0.62 versus 0.31 and 0.43 for the Naranjo algorithm. The new algorithm developed using actual patient data is more valid and reliable than the Naranjo algorithm for identifying ADRs in the NICU population. Because of the relatively small and nonrandom samples, further refinement and additional testing are needed.

Causality assessment methods in drug induced liver injury: strengths and weaknesses

Diagnosis of drug-induced liver injury (DILI) remains a challenge and eagerly awaits the development of reliable hepatotoxicity biomarkers. Several methods have been developed in order to facilitate hepatotoxicity causality assessments. These methods can be divided into three categories: (1) expert judgement, (2) probabilistic approaches, and (3) algorithms or scales. The last category is further divided into general and liver-specific scales. The Council for International Organizations of Medical Sciences (CIOMS) scale, also referred to as the Roussel Uclaf Causality Assessment Method (RUCAM), although cumbersome and difficult to apply by physicians not acquainted with DILI, is used by many expert hepatologists, researchers, and regulatory authorities to assess the probability of suspected causal agents. However, several limitations of this scale have been brought to light, indicating that a number of adjustments are needed. This review is a detailed timely criticism to alert the readers of the limitations and give insight into what would be needed to improve the scale. Instructions on how to approach DILI diagnosis in practice are provided, using CIOMS as an aid to emphasize the topics to be addressed when assessing DILI cases. Amendments of the CIOMS scale in the form of applying authoritative evidence-based criteria, a simplified scoring system and appropriate weighting given to individual parameters based on statistical evaluations with large databases will provide wider applicability in the clinical setting.

The existence of a relationship between increased serum alanine aminotransferase levels detected in premarketing clinical trials and postmarketing published hepatotoxicity case reports

BACKGROUND

Drug-induced liver injury (DILI) profile in most drugs' available information is based on both the incidence of alanine aminotansferase (ALT) elevations in clinical trials and published case reports.

AIM

To assess the relationship between ALT elevations in clinical trials and the number of published case reports in the postmarketing setting.

METHODS

Hepatotoxic drugs were identified from product labelling and classified in high-medium risk (Black Box Warning or Precautions section) or low risk (a statement in the Adverse Reactions section). Incidence of ALT elevations (> or = 3 x ULN) for drug (I(D)) and placebo (I(C)) treated patients in premarketing clinical trials and DILI published case reports were retrieved from product labelling and MEDLINE.

RESULTS

The median I(C) was 10/1000. The high-medium-risk drugs' median I(D) was significantly higher compared with low-risk drugs (17/1000 vs. 10/1000; P = 0.046). Chi-squared test, absolute difference and odds ratio comparing I(D) and I(C) identified 35%, 51% and 77% of high-medium-risk drugs respectively. Less number of case reports were associated with low- than high-medium-risk drugs (1 vs. 7; P = 0.001). A high odds ratio in clinical trials (I(D) vs. I(C)) was the strongest predictor of published DILI case reports.

CONCLUSION

A relationship between increased ALT incidence in premarketing clinical trials and postmarketing published case reports exists.

Causality assessment of liver injury after chronic oral amiodarone intake

BACKGROUND/AIM

The number of patients receiving amiodarone will increase in future years. As clinically significant hepatotoxicity associated with oral amiodarone is infrequent and difficult to predict, a new Bayesian-developed model is proposed to help in the causality assessment of amiodarone-induced liver injury.

METHODS

Incidence of abnormal liver enzymes in patients receiving amiodarone was obtained from placebo controlled clinical trials. Published case reports of amiodarone-induced hepatotoxicity were identified through a literature search. Maximum number of expected hepatotoxicity cases in amiodarone and placebo-treated patients was calculated using Poisson distribution. The calculated odds ratio was used as a Prior Odds (PrO) to subsequent quantification, using a Bayesian-approach, of individual amiodarone-induced hepatotoxicity likelihood.

RESULTS

PrO of amiodarone-induced hepatotoxicity was 0.48. Thirty nine amiodarone-associated hepatotoxicity case reports were retrieved. Half of published case reports developed an irreversible damage. The amiodarone Bayesian model combining information about latency period and period of remission, together with analytical parameters properly defines the toxicity profile shown in published case reports. The analytical pattern defined by this model is different from the one expected if liver injury in published cases was caused by other etiologies.

CONCLUSIONS

A method based on a Bayesian-approach, which links information from clinical trials with clinical hepatotoxicity profile from published case reports can be a useful tool for amiodarone-induced liver injury causality assessment. At present, this method is limited due to scarcity and quality of available data. Further efforts are needed to improve model ability in order to identify amiodarone-induced liver injury.