Systematic review and meta-analysis of algorithms used to identify drug-induced liver injury (DILI) in health record databases

Epidemiology and Risk Determinants of Drug-Induced Liver Injury: Current Knowledge and Future Research Needs

AIMS

Drug-induced liver injury (DILI) is a major global health concern resulting from adverse reactions to medications, supplements or herbal medicines. The relevance of DILI has grown with an aging population, the rising prevalence of chronic diseases and the increased use of biologics, including checkpoint inhibitors. This article aims to summarise current knowledge on DILI epidemiology and risk factors.

METHODS

This review critically appraises available evidence on DILI frequency, outcomes and risk determinants, focusing on drug properties and non-genetic host factors that may influence susceptibility.

RESULTS

DILI incidence varies across populations, with hospitalised patients experiencing notably higher rates than outpatients or the general population. Increased medication use, particularly among older adults and women, may partly explain age- and sex-based disparities in DILI incidence and reporting. Physiological changes associated with aging likely increase susceptibility to DILI in older adults, though further exposure-based studies are needed for definitive conclusions. Current evidence does not strongly support that women are inherently more susceptible to DILI than men; rather, susceptibility appears to depend on specific drugs. However, once DILI occurs, older age and female sex are associated with greater severity and poorer outcomes. Other less-studied host-related risk factors are also discussed based on available evidence.

CONCLUSIONS

This article summarises existing data on DILI frequency, outcomes, drug properties affecting hepatotoxicity and non-genetic host risk factors while identifying critical knowledge gaps. Addressing these gaps through future research could enhance understanding and support preventive measures.

Phenotyping Hepatic Immune-Related Adverse Events in the Setting of Immune Checkpoint Inhibitor Therapy

PURPOSE

We present and validate a rule-based algorithm for the detection of moderate to severe liver-related immune-related adverse events (irAEs) in a real-world patient cohort. The algorithm can be applied to studies of irAEs in large data sets.

METHODS

We developed a set of criteria to define hepatic irAEs. The criteria include: the temporality of elevated laboratory measurements in the first 2-14 weeks of immune checkpoint inhibitor (ICI) treatment, steroid intervention within 2 weeks of the onset of elevated laboratory measurements, and intervention with a duration of at least 2 weeks. These criteria are based on the kinetics of patients who experienced moderate to severe hepatotoxicity (Common Terminology Criteria for Adverse Events grades 2-4). We applied these criteria to a retrospective cohort of 682 patients diagnosed with hepatocellular carcinoma and treated with ICI. All patients were required to have baseline laboratory measurements before and after the initiation of ICI.

RESULTS

A set of 63 equally sampled patients were reviewed by two blinded, clinical adjudicators. Disagreements were reviewed and consensus was taken to be the ground truth. Of these, 25 patients with irAEs were identified, 16 were determined to be hepatic irAEs, 36 patients were nonadverse events, and two patients were of indeterminant status. Reviewers agreed in 44 of 63 patients, including 19 patients with irAEs (0.70 concordance, Fleiss' kappa: 0.43). By comparison, the algorithm achieved a sensitivity and specificity of identifying hepatic irAEs of 0.63 and 0.81, respectively, with a test efficiency (percent correctly classified) of 0.78 and outcome-weighted F1 score of 0.74.

CONCLUSION

The algorithm achieves greater concordance with the ground truth than either individual clinical adjudicator for the detection of irAEs.

Drug-induced liver injury and COVID-19: Use of artificial intelligence and the updated Roussel Uclaf Causality Assessment Method in clinical practice

BACKGROUND

Liver injury is a relevant condition in coronavirus disease 2019 (COVID-19) inpatients. Pathophysiology varies from direct infection by virus, systemic inflammation or drug-induced adverse reaction (DILI). DILI detection and monitoring is clinically relevant, as it may contribute to poor prognosis, prolonged hospitalization and increase indirect healthcare costs. Artificial Intelligence (AI) applied in data mining of electronic medical records combining abnormal liver tests, keyword searching tools, and risk factors analysis is a relevant opportunity for early DILI detection by automated algorithms.

AIM

To describe DILI cases in COVID-19 inpatients detected from data mining in electronic medical records (EMR) using AI and the updated Roussel Uclaf Causality Assessment Method (RUCAM).

METHODS

The study was conducted in March 2021 in a hospital in southern Brazil. The NoHarm^© system uses AI to support decision making in clinical pharmacy. Hospital admissions were 100523 during this period, of which 478 met the inclusion criteria. From these, 290 inpatients were excluded due to alternative causes of liver injury and/or due to not having COVID-19. We manually reviewed the EMR of 188 patients for DILI investigation. Absence of clinical information excluded most eligible patients. The DILI assessment causality was possible via the updated RUCAM in 17 patients.

RESULTS

Mean patient age was 53 years (SD ± 18.37; range 22-83), most were male (70%), and admitted to the non-intensive care unit sector (65%). Liver injury pattern was mainly mixed, mean time to normalization of liver markers was 10 d, and mean length of hospitalization was 20.5 d (SD ± 16; range 7-70). Almost all patients recovered from DILI and one patient died of multiple organ failure. There were 31 suspected drugs with the following RUCAM score: Possible (n = 24), probable (n = 5), and unlikely (n = 2). DILI agents in our study were ivermectin, bicalutamide, linezolid, azithromycin, ceftriaxone, amoxicillin-clavulanate, tocilizumab, piperacillin-tazobactam, and albendazole. Lack of essential clinical information excluded most patients. Although rare, DILI is a relevant clinical condition in COVID-19 patients and may contribute to poor prognostics.

CONCLUSION

The incidence of DILI in COVID-19 inpatients is rare and the absence of relevant clinical information on EMR may underestimate DILI rates. Prospects involve creation and validation of alerts for risk factors in all DILI patients based on RUCAM assessment causality, alterations of liver biomarkers and AI and machine learning.

Liver Damage and COVID-19: At Least a “Two-Hit” Story in Systematic Review

COVID-19 pandemic waves have hit on our lives with pulmonary and, also, gastrointestinal symptoms. The latter also includes acute liver damage linked to direct SARS-CoV-2 action and/or drug-induced (DILI) in the frame of pre-existing chronic liver disease. We aimed to review literature data regarding liver damage during COVID-19. We conducted a systematic search on the main medical databases for original articles, reviews, meta-analyses, randomized clinical trials and case series using the following keywords and acronyms and their associations: liver disease, COVID-19, acute liver damage, drug-induced liver injury, antivirals. Acute liver damage due to SARS-CoV-2 infection is common among COVID-19 patients and is generally self-limiting. However, chronic hepatic diseases, such as metabolic-associated fatty liver disease (MAFLD), are associated with a less favorable prognosis, especially when alkaline phosphatases show a significant rise. Pathophysiology of COVID-19 liver damage is multifaceted and helps understand differences in liver derangement among patients. Thus, early recognition, monitoring and treatment of liver damage are crucial in these patients. In the frame of a not-ending pandemic sustained by SARS-CoV-2, it is crucial to recognize acute hepatic decompensation due to the virus and/or drugs used for COVID-19 treatment.

Combining K-72 Hepatic Failure with 15 Individual T-Codes to Identify Patients with Idiosyncratic Drug-Induced Liver Injury in the Electronic Medical Record

BACKGROUND

The aim of this study was to determine the utility of combining three K72 codes (hepatic failure) with 15 individual T-Codes (drug toxicity/poisoning) to identify potential DILI cases.

METHODS

The EMR was searched for encounters that had a K72 code combined with a T-code that also met minimal liver injury laboratory criteria between 10/1/15 and 9/30/18. After manual chart review, a DILIN expert opinion causality score (1-5) was assigned to each case.

RESULTS

Among the 345 patient encounters identified, mean age was 57 years, 53% were male, and 89% Caucasian. Thirty-seven cases (10.7%) were adjudicated as probable DILI with antibiotics being the most frequently identified suspect drugs. Of the 308 non-DILI cases, liver injury was most commonly due to congestive hepatopathy (38%) and hepatic metastases (15%). The probable-DILI cases were significantly more likely to have hepatocellular liver injury (57% vs 32.5%, p = 0.01), higher total bilirubin levels (7.7 vs 4.6 mg/dl, p = 0.03), and more severe liver injury scores (p < 0.01). The K72.0 (acute/ subacute hepatic failure) yielded the most DILI cases (29) compared to K72.9 (13) and K72.1 (0). The positive predictive value of the searching algorithm was 10.7% and improved to 15% when using only the K72.0 codes.

CONCLUSIONS

K72 codes combined with drug poisoning T-codes had a low positive predictive value in identifying patients with idiosyncratic DILI. These data support further refinement of ICD-10-based algorithms to detect DILI cases in the EMR.

Causality imputation between herbal products and HILI: An algorithm evaluation in a systematic review

Algorithms can have several purposes in the clinical practice. There are different scales for causality imputation in DILI (Drug-Induced Liver Injury), but the applicability and validity of these for the HILI (Herb-Induced Liver Injury) evaluation is questionable for some scales. The purpose of the study was to determine the clinical and demographic profile of the patients with HILI, and the main algorithmic scales used in its causality assessment. The methodology was a systematic review of articles in English, Spanish, or Portuguese language, from 1979 to 2019, involving humans, with descriptors related to HILI. Qualitative and quantitative statistical analysis were performed. As a result, from a total of 60 articles, 203 HILI reports were selected: 59.9% were women, similar with other studies, and the average age was 45.8 years. Jaundice was the most frequent symptom and regarding the type of lesion, the hepatocellular was the most frequent. In regard to HILI severity, 3.0% were severe and 7.6% were fatal or required liver transplantation. In 72.3% of the cases, the most used algorithm was RUCAM (Roussel Uclaf Causality Assessment Method). The conclusion of the study is that RUCAM was the most used algorithm for causality assessment in HILI. The patients were predominantly female, jaundice was the main symptom, and HILI is reversible in the majority of cases.

Using an Automated Algorithm to Identify Potential Drug-Induced Liver Injury Cases in a Pharmacovigilance Database

INTRODUCTION

Drug-induced liver injury (DILI) is the most frequent cause of acute liver failure in North America and Europe, but it is often missed because of unstandardized diagnostic methods and criteria. This study aimed to develop and validate an automated algorithm to identify potential DILI cases in routine pharmacovigilance (PV) activities.

METHODS

Post-marketing hepatic adverse events reported for a potentially hepatotoxic drug in a global PV database from 19 March 2017 to 18 June 2018 were assessed manually and with the automated algorithm. The algorithm provided case assessments by applying pre-specified criteria to all case data and narratives simultaneously.

RESULTS

A total of 1456 cases were included for analysis and assessed manually. Sufficient data for algorithm assessment were available for 476 cases (32.7%). Of these cases, manual assessment identified 312 (65.5%) potential DILI cases while algorithm assessment identified 305 (64.1%) potential DILI cases. Comparison of manual and algorithm assessments demonstrated a sensitivity of 97.8% and a specificity of 79.3% for the algorithm. Given the prevalence of potential DILI cases in the population studied, the algorithm was calculated to have positive predictive value 56.3% and negative predictive value 99.2%. The time required for manual review compared to algorithm review suggested that application of the algorithm prior to manual screening would have resulted in a time savings of 42.2%.

CONCLUSION

An automated algorithm to identify potential DILI cases was developed and successfully implemented. The algorithm demonstrated a high sensitivity, a high negative predictive value, along with significant efficiency and utility in a real-time PV database.

Utility of a Computerized ICD-10 Algorithm to Identify Idiosyncratic Drug-Induced Liver Injury Cases in the Electronic Medical Record

INTRODUCTION

Idiosyncratic drug-induced liver injury (DILI) is an important cause of liver injury that is difficult to diagnose and identify in the electronic medical record (EMR).

OBJECTIVE

Our objective was to develop a computerized algorithm that can reliably identify DILI cases from the EMR.

METHODS

The EMR was searched for all encounters with an International Classification of Diseases, Tenth Revision (ICD-10) T code for drug toxicity and a K-71 code for toxic liver injury between 1 October 2015 and 30 September 2018. Clinically significant liver injury was defined using predetermined laboratory values. An expert opinion causality score (1-3 = probable DILI, 4/5 = non-DILI), Roussel Uclaf Causality Assessment Method (RUCAM) score, and severity score was assigned to each case.

RESULTS

Among the 1,211,787 encounters searched, 517 had both an ICD-10 T code and a K-71 code, with 257 patients meeting the laboratory criteria. After excluding 75 cases of acetaminophen hepatotoxicity, the final study sample included 182 cases of potential DILI, with antineoplastics and antibiotics being the most frequently implicated agents. Causality assessment identified probable DILI in 121 patients (66.5%), whereas 61 (33.5%) had an alternative cause of liver injury. Although age, sex, race, and suspect drugs were similar, the probable DILI cases were more likely to present with a hepatocellular injury profile and have more severe liver injury than the non-DILI cases (p < 0.05).

CONCLUSION

A computerized algorithm based on a combination of ICD-10 codes identified 182 potential DILI cases with 121 true positives, 61 false positives, and a positive predictive value of 66.5%. Future studies incorporating natural language processing may further improve the utility of this algorithm in identifying high-causality idiosyncratic DILI cases.

The validity of diagnostic algorithms to identify asthma patients in healthcare administrative databases: a systematic literature review

OBJECTIVES

To review the available evidence supporting the validity of algorithms to identify asthma patients in healthcare administrative databases.

METHODS

A systematic literature search was conducted on multiple databases from inception to March 2020 to identify studies that reported the validity of case-finding asthma algorithms applied to healthcare administrative data. Following an initial screening of abstracts, two investigators independently assessed the full text of studies which met the pre-determined eligibility criteria. Data on study population and algorithm characteristics were extracted. A revised version of the Quality Assessment of Diagnostic Accuracy Studies tool was used to evaluate the risk of bias and generalizability of studies.

RESULTS

A total of 20 studies met the eligibility criteria. Algorithms which incorporated ≥1 diagnostic code for asthma over a 1-year period appeared to be valid in both adult and pediatric populations (sensitivity ≥ 85%; specificity ≥ 89%; PPV ≥ 70%). The validity was enhanced when: (1) the time frame to capture asthma cases was increased to two years; (2) ≥2 asthma diagnostic codes were considered; and (3) when diagnoses were recorded by a pulmonologist. Algorithms which integrated pharmacy claims data appeared to correctly identify asthma patients; however, the extent to which asthma medications can improve the validity remains unclear. The quality of several studies was high, although disease progression bias and biases related to self-reported data was observed in some studies.

CONCLUSIONS

Healthcare administrative databases are adequate sources to identify asthma patients. More restrictive definitions based on both asthma diagnoses and asthma medications may enhance validity, although further research is required to confirm this hypothesis.

Comparison of laboratory threshold criteria in drug-induced liver injury detection algorithms for use in pharmacovigilance

PURPOSE

For the purpose of pharmacovigilance, we sought to determine the best performing laboratory threshold criteria to detect drug-induced liver injury (DILI) in the electronic medical records (EMR).

METHODS

We compared three commonly used liver chemistry criteria from the DILI expert working group (DEWG), DILI network (DILIN), and Council for International Organizations of Medical Sciences (CIOMS), based on hospital EMR for years 2010 and 2011 (42 176 admissions), using independent medical record review. The performance characteristics were compared in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, F-measure, and area under the receiver operating characteristic curve (AUROC).

RESULTS

DEWG had the highest PPV (5.5%, 95% CI: 4.1%-7.2%), specificity (97.0%, 95% CI: 96.8%-97.2%), accuracy (96.8%, 95% CI: 96.6%-97.0%) and F-measure (0.099). CIOMS had the highest sensitivity (74.0%, 95% CI: 64.3%-82.3%) and AUROC (85.2%, 95% CI: 80.8%-89.7%). Besides the laboratory criteria, including additional keywords in the classification algorithm improved the PPV and F-measure to a maximum of 29.0% (95% CI: 22.3%-36.5%) and 0.379, respectively.

CONCLUSIONS

More stringent criteria (DEWG and DILIN) performed better in terms of PPV, specificity, accuracy and F-measure. CIOMS performed better in terms of sensitivity. An algorithm with high sensitivity is useful in pharmacovigilance for detecting rare events and to avoid missing cases. Requiring at least two abnormal liver chemistries during hospitalization and text-word searching in the discharge summaries decreased false positives without loss in sensitivity.

Evaluation of Drug-Induced Liver Injury Developed During Hospitalization Using Electronic Health Record (EHR)-Based Algorithm

Purpose

The incidence of drug-induced liver injury (DILI) has been increasing; however, few algorithms are available to identify DILI in electronic health records (EHRs). We aimed to identify and evaluate DILI with an appropriate screening algorithm.

Methods

We collected data from 3 university hospitals between June 2015 and May 2016 using our newly developed algorithm for identifying DILI. Among patients with alanine transferase (ALT) ≤ 120 IU/L and total bilirubin (TB) ≤ 2.4 mg/dL in blood test results within 48 hours of admission, those who either had 1) ALT > 120 IU/L and TB > 2.4 mg/dL or 2) ALT > 200 IU/L at least once during hospitalization were identified. After excluding patients with liver disease-related diagnosis at discharge, medical records were retrospectively reviewed to evaluate epidemiological characteristics of DILI.

Results

The total number of inpatients was 256,598, of whom 1,100 (0.43%) were selected by the algorithm as suspected DILI. Subsequently, 365 cases (0.14% of total inpatients, 95% confidence interval, 0.13–0.16) were identified as DILI, yielding a positive predictive value of 33.1%. Antibiotics (n = 214, 47.2%) were the major class of causative drug followed by chemotherapeutic agents (n = 87, 19.2%). The most common causative drug was piperacillin-tazobactam (n = 38, 8.4%); the incidence of DILI by individual agent was highest for methotrexate (19.4 cases/1,000 patients administered the drug). Common reasons for excluding suspected DILI cases were ischemic hepatitis and postoperative liver dysfunction.

Conclusions

Using our EHR-based algorithm, we identified that approximately 0.14% of patients developed DILI during hospitalization. Further studies are needed to modify criteria for more accurate identification of DILI.

Validity of hospital ICD-10-GM codes to identify acute liver injury in Germany

PURPOSE

Acute liver injury (ALI) is an important adverse drug reaction. We estimated the positive predictive values (PPVs) of ICD-10-GM codes of ALI used in an international postauthorisation safety study (PASS).

METHODS

Analyses used routine data (2007 to 2016, adults) from a German academic hospital in a cross-sectional design. Two algorithms from the PASS were applied to extract potential cases from the hospital information system: specific end point (A) (discharge diagnosis of liver disease-specific codes) and less specific end point (B) (discharge and outpatient-specific and nonspecific codes suggestive of liver injury). ALI cases were confirmed on the basis of plasma liver enzyme activity elevation. Secondary analysis was performed following exclusion of cases with known cancer, chronic liver, biliary and pancreatic disease, heart failure, and alcohol-related disorders, as applied in the PASS.

RESULTS

On the basis of ICD codes: outcome A, 154 cases (143 with case notes and lab data for case verification); outcome B, 485 cases (357 with case notes and lab data). ALI was confirmed in 71 outcome A cases, PPV of 49.7% (95% confidence interval [CI], 41.2%-58.1%), and 100 outcome B cases, PPV of 28.0% (95% CI, 23.4%-33.0%). Applying exclusion criteria increased PPV (95% CI) to 62.7% (50.0%-74.2%) for outcome A and 45.7% (37.2%-54.3%) for outcome B.

CONCLUSIONS

In safety studies on hepatotoxicity based on routine data using ICD-10-GM discharge codes and when validation of potential cases is not feasible, only the more specific codes should be used to describe ALI, and competing diagnoses for liver injury should be excluded to avoid substantial misclassification.

Roussel Uclaf Causality Assessment Method for Drug-Induced Liver Injury: Present and Future

Among the causality assessment methods used for the diagnosis of drug-induced liver injury (DILI), Roussel Uclaf Causality Assessment Method (RUCAM) remains the most widely used not only for individual cases but also for prospective and retrospective studies worldwide. This first place is justified by the characteristics of the method such as precise definition and classification of the liver injury, which determines the right scale in the scoring system, precise definition of the seven criteria, and the validation approach based on cases with positive rechallenge. RUCAM is used not only for any types of drugs but also for herbal medicines causing herb-induced liver injury, (HILI) and dietary supplements. In 2016, the updated RUCAM provided further specifications of criteria and instructions to improve interobserver variability. Although this method was criticized for criteria such as the age and alcohol consumption, recent consensus meeting of experts has recognized their value and recommended their incorporation into any method. While early studies searching for DILI in large databases especially in electronic medical records were based on codes of diseases or natural language without causality assessment, the recommendation is now to include RUCAM in the search for DILI/HILI. There are still studies on DILI detection or the identification of biomarkers that take into consideration the cases assessed as “possible,” although it is well known that these cases reduce the strength of the association between the cases and the offending compound or the new biomarker to be validated. Attempts to build electronic RUCAM or automatized application of this method were successful despite some weaknesses to be corrected. In the future, more reflections are needed on an expert system to standardize the exclusion of alternative causes according to the clinical context. Education and training on RUCAM should be encouraged to improve the results of the studies and the day-to-day work in pharmacovigilance departments in companies or in regulatory agencies. It is also expected to improve RUCAM with biomarkers or other criteria provided that the validation process replaces expert opinion by robust standards such as those used for the original method.

Idiosyncratic DILI: Analysis of 46,266 Cases Assessed for Causality by RUCAM and Published From 2014 to Early 2019

One of the most difficult challenges in clinical hepatology is the diagnosis of a drug-induced liver injury (DILI). The timing of the events, exclusion of alternative causes, and taking into account the clinical context should be systematically assessed and scored in a transparent manner. RUCAM (Roussel Uclaf Causality Assessment Method) is a well-established diagnostic algorithm and scale to assess causality in patients with suspected DILI. First published in 1993 and updated in 2016, RUCAM is now the worldwide most commonly used causality assessment method (CAM) for DILI. The following manuscript highlights the recent implementation of RUCAM around the world, by reviewing the literature for publications that utilized RUCAM, and provides a review of “best practices” for the use of RUCAM in cases of suspected DILI. The worldwide appreciation of RUCAM is substantiated by the current analysis of 46,266 DILI cases, all tested for causality using RUCAM. These cases derived from 31 reports published from 2014 to early 2019. Their first authors came from 10 countries, with China on top, followed by the US, and Germany on the third rank. Importantly, all RUCAM-based DILI reports were published in high profile journals. Many other reports were published earlier from 1993 up to 2013 in support of RUCAM. Although most of the studies were of high quality, the current case analysis revealed shortcomings in few studies, not at the level of RUCAM itself but rather associated with the work of the users. To ensure in future DILI cases a better performance by the users, a list of essential elements is proposed. As an example, all suspected DILI cases should be evaluated 1) by the updated RUCAM to facilitate result comparisons, 2) according to a prospective study protocol to ensure complete data sets, 3) after exclusion of cases with herb induced liver injury (HILI) from a DILI cohort to prevent confounding variables, and 4) according to inclusion of DILI cases with RUCAM-based causality gradings of highly probable or probable, in order to increase the specificity of the results. In conclusion, RUCAM benefits from its high appreciation and performs well provided the users adhere to published recommendations to prevent confounding variability.

Validity of ICD-9 and ICD-10 codes used to identify acute liver injury: A study in three European data sources

Purpose

Validating cases of acute liver injury (ALI) in health care data sources is challenging. Previous validation studies reported low positive predictive values (PPVs).

Methods

Case validation was undertaken in a study conducted from 2009 to 2014 assessing the risk of ALI in antidepressants users in databases in Spain (EpiChron and SIDIAP) and the Danish National Health Registers. Three ALI definitions were evaluated: primary (specific hospital discharge codes), secondary (specific and nonspecific hospital discharge codes), and tertiary (specific and nonspecific hospital and outpatient codes). The validation included review of patient profiles (EpiChron and SIDIAP) and of clinical data from medical records (EpiChron and Denmark). ALI cases were confirmed when liver enzyme values met a definition by an international working group.

Results

Overall PPVs (95% CIs) for the study ALI definitions were, for the primary ALI definition, 84% (60%‐97%) (EpiChron), 60% (26%‐88%) (SIDIAP), and 74% (60%‐85%) (Denmark); for the secondary ALI definition, 65% (45%‐81%) (EpiChron), 40% (19%‐64%) (SIDIAP), and 70% (64%‐77%) (Denmark); and for the tertiary ALI definition, 25% (18%‐34%) (EpiChron), 8% (7%‐9%) (SIDIAP), and 47% (42%‐52%) (Denmark). The overall PPVs were higher for specific than for nonspecific codes and for hospital discharge than for outpatient codes. The nonspecific code “unspecified jaundice” had high PPVs in Denmark.

Conclusions

PPVs obtained apply to patients using antidepressants without preexisting liver disease or ALI risk factors. To maximize validity, studies on ALI should prioritize hospital specific discharge codes and should include hospital codes for unspecified jaundice. Case validation is required when ALI outpatient cases are considered.

Roussel Uclaf Causality Assessment Method for Drug-Induced Liver Injury: Present and Future

Among the causality assessment methods used for the diagnosis of drug-induced liver injury (DILI), Roussel Uclaf Causality Assessment Method (RUCAM) remains the most widely used not only for individual cases but also for prospective and retrospective studies worldwide. This first place is justified by the characteristics of the method such as precise definition and classification of the liver injury, which determines the right scale in the scoring system, precise definition of the seven criteria, and the validation approach based on cases with positive rechallenge. RUCAM is used not only for any types of drugs but also for herbal medicines causing herb-induced liver injury, (HILI) and dietary supplements. In 2016, the updated RUCAM provided further specifications of criteria and instructions to improve interobserver variability. Although this method was criticized for criteria such as the age and alcohol consumption, recent consensus meeting of experts has recognized their value and recommended their incorporation into any method. While early studies searching for DILI in large databases especially in electronic medical records were based on codes of diseases or natural language without causality assessment, the recommendation is now to include RUCAM in the search for DILI/HILI. There are still studies on DILI detection or the identification of biomarkers that take into consideration the cases assessed as "possible," although it is well known that these cases reduce the strength of the association between the cases and the offending compound or the new biomarker to be validated. Attempts to build electronic RUCAM or automatized application of this method were successful despite some weaknesses to be corrected. In the future, more reflections are needed on an expert system to standardize the exclusion of alternative causes according to the clinical context. Education and training on RUCAM should be encouraged to improve the results of the studies and the day-to-day work in pharmacovigilance departments in companies or in regulatory agencies. It is also expected to improve RUCAM with biomarkers or other criteria provided that the validation process replaces expert opinion by robust standards such as those used for the original method.

Idiosyncratic DILI: Analysis of 46,266 Cases Assessed for Causality by RUCAM and Published From 2014 to Early 2019

One of the most difficult challenges in clinical hepatology is the diagnosis of a drug-induced liver injury (DILI). The timing of the events, exclusion of alternative causes, and taking into account the clinical context should be systematically assessed and scored in a transparent manner. RUCAM (Roussel Uclaf Causality Assessment Method) is a well-established diagnostic algorithm and scale to assess causality in patients with suspected DILI. First published in 1993 and updated in 2016, RUCAM is now the worldwide most commonly used causality assessment method (CAM) for DILI. The following manuscript highlights the recent implementation of RUCAM around the world, by reviewing the literature for publications that utilized RUCAM, and provides a review of "best practices" for the use of RUCAM in cases of suspected DILI. The worldwide appreciation of RUCAM is substantiated by the current analysis of 46,266 DILI cases, all tested for causality using RUCAM. These cases derived from 31 reports published from 2014 to early 2019. Their first authors came from 10 countries, with China on top, followed by the US, and Germany on the third rank. Importantly, all RUCAM-based DILI reports were published in high profile journals. Many other reports were published earlier from 1993 up to 2013 in support of RUCAM. Although most of the studies were of high quality, the current case analysis revealed shortcomings in few studies, not at the level of RUCAM itself but rather associated with the work of the users. To ensure in future DILI cases a better performance by the users, a list of essential elements is proposed. As an example, all suspected DILI cases should be evaluated 1) by the updated RUCAM to facilitate result comparisons, 2) according to a prospective study protocol to ensure complete data sets, 3) after exclusion of cases with herb induced liver injury (HILI) from a DILI cohort to prevent confounding variables, and 4) according to inclusion of DILI cases with RUCAM-based causality gradings of highly probable or probable, in order to increase the specificity of the results. In conclusion, RUCAM benefits from its high appreciation and performs well provided the users adhere to published recommendations to prevent confounding variability.

Drug-induced liver injury: a safety review

INTRODUCTION

Idiosyncratic drug-induced liver injury (DILI) remains one of the most important causes of drug attrition both in the early phases of clinical drug development and in the postmarketing scenario. This is because, in spite of emerging data on genetic susceptibility variants associated to the risk of hepatotoxicity, the precise identification of the individual who will develop DILI when exposed to a given drug remains elusive.

AREAS COVERED

In this review, we have addressed recent progress made and initiatives taken in the field of DILI from a safety perspective through a comprehensive search of the literature.

EXPERT OPINION

Despite the substantial progress made over this century, new approaches using big data analysis to characterize the true incidence of DILI are needed and to categorize the drugs' hepatotoxic potential. Genetic studies have highlighted the role of the adaptive immune system yet the mechanisms leading adaptation versus progression remain to be elucidated. There is a compelling need for development and qualification of sensitive, specific, and affordable biomarkers in DILI to foster drug development, patient treatment stratification and, improvement of causality assessment methods. Gaining mechanistic insights in DILI is essential to uncover therapeutic targets and design prospective clinical trials with appropriate endpoints.