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

Validation of diagnoses of liver disorders in users of systemic azole antifungal medication in Sweden

BACKGROUND

Liver disorders are important adverse effects associated with antifungal drug treatment. However, the accuracy of Clinical International Classification of Diseases (ICD)-10 codes in identifying liver disorders for register based research is not well-established. This study aimed to determine the positive predictive value (PPV) of the ICD-10 codes for identifying patients with toxic liver disease, hepatic failure, and jaundice among patients with systemic antifungal treatment.

METHODS

Data from the Swedish Prescribed Drug Register and the National Patient Register were utilized to identify adult patients who received systemic azole antifungal drugs and had a recorded diagnosis of toxic liver disease (K71.0, K71.1, K71.2, K71.6, K71.8, K71.9), hepatic failure (K72.0, K72.9), or jaundice (R17) between 2005 and 2016. The medical records of all included patients were reviewed. Prespecified criteria were used to re-evaluate and confirm each diagnosis, serving as the gold standard to calculate PPVs with 95% confidence intervals (95% CI) for each diagnostic group.

RESULTS

Among the 115 included patients, 26 were diagnosed with toxic liver disease, 58 with hepatic failure, and 31 with jaundice. Toxic liver disease was confirmed in 14 out of 26 patients, yielding a PPV of 53.8% (95% CI 33.4-73.4%). Hepatic failure was confirmed in 26 out of 38 patients, resulting in a PPV of 62.1% (95% CI 48.4-74.5%). The highest PPV was found in jaundice, with 30 confirmed diagnoses out of 31, yielding a PPV of 96.8% (95% CI 83.3-99.9%).

CONCLUSION

Among patients who received azole antifungal treatment and were subsequently diagnosed with a liver disorder, the PPV for the diagnosis of jaundice was high, while the PPVs for toxic liver disease and hepatic failure were lower.

MetaLAB-HOI: Template standardization of health outcomes enable massive and accurate detection of adverse drug reactions from electronic health records

PURPOSE

This study aimed to advance the MetaLAB algorithm and verify its performance with multicenter data to effectively detect major adverse drug reactions (ADRs), including drug-induced liver injury.

METHODS

Based on MetaLAB, we created an optimal scenario for detecting ADRs by considering demographic and clinical records. MetaLAB-HOI was developed to identify ADR signals using common model-based multicenter electronic health record (EHR) data from the clinical health outcomes of interest (HOI) template and design for drug-exposed and nonexposed groups. In this study, we calculated the odds ratio of 101 drugs for HOI in Konyang University Hospital, Seoul National University Hospital, Chungbuk National University Hospital, and Seoul National University Bundang Hospital.

RESULTS

The overlapping drugs in four medical centers are amlodipine, aspirin, bisoprolol, carvedilol, clopidogrel, clozapine, digoxin, diltiazem, methotrexate, and rosuvastatin. We developed MetaLAB-HOI, an algorithm that can detect ADRs more efficiently using EHR. We compared the detection results of four medical centers, with drug-induced liver injuries as representative ADRs.

CONCLUSIONS

MetaLAB-HOI's strength lies in fully utilizing the patient's clinical information, such as prescription, procedure, and laboratory results, to detect ADR signals. Considering changes in the patient's condition over time, we created an algorithm based on a scenario that accounted for each drug exposure and onset period supervised by specialists for HOI. We determined that when a template capable of detecting ADR based on clinical evidence is developed and manualized, it can be applied in medical centers for new drugs with insufficient data.

Prior drug allergies are associated with worse outcome in patients with idiosyncratic drug-induced liver injury: A machine learning approach for risk stratification

The impact of prior drug allergies (PDA) on the clinical features and outcomes of patients who develop idiosyncratic drug-induced liver injury (DILI) is largely unknown. We aimed to assess the clinical presentation and outcomes of DILI patients based on the presence or absence of PDA and explore the association between culprit drugs responsible for DILI and allergy. We analysed a well-vetted cohort of DILI cases enrolled from the Spanish DILI Registry. Bootstrap-enhanced least absolute shrinkage operator procedure was used in variable selection, and a multivariable logistic model was fitted to predict poor outcomes in DILI. Of 912 cases with a first episode of DILI, 61 (6.7%) had documented PDA. Patients with PDA were older (p = 0.009), had higher aspartate aminotransferase (AST) levels (p = 0.047), lower platelet count (p = 0.011) and higher liver-related mortality than those without a history of drug allergies (11% vs. 1.6%, p < 0.001). Penicillin was the most common drug associated with PDA in DILI patients (32%). A model including PDA, nR-based type of liver injury, female sex, AST, total bilirubin, and platelet count showed an excellent performance in predicting poor outcome in patients from the Spanish DILI Registry (area under the ROC curve [AUC] 0.887; 95% confidence interval [CI] 0.794 - 0.981) and the LATINDILI Network (AUC 0.932; 95% CI 0.884 - 0.981). Patients with suspected DILI should be screened for PDA as they would require a close monitoring for early detection of worsening clinical course.

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.

Paracetamol (acetaminophen) overdose and hepatotoxicity: mechanism, treatment, prevention measures, and estimates of burden of disease

INTRODUCTION

Paracetamol is one of the most used medicines worldwide and is the most common important poisoning in high-income countries. In overdose, paracetamol causes dose-dependent hepatotoxicity. Acetylcysteine is an effective antidote, however despite its use hepatotoxicity and many deaths still occur.

AREAS COVERED

This review summarizes paracetamol overdose and toxicity (including mechanisms, risk factors, risk assessment, and treatment). In addition, we summarize the epidemiology of paracetamol overdose worldwide. A literature search on PubMed for poisoning epidemiology and mortality from 1 January 2017 to 26 October 2022 was performed to estimate rates of paracetamol overdose, liver injury, and deaths worldwide.

EXPERT OPINION

Paracetamol is widely available and yet is substantially more toxic than other analgesics available without prescription. Where data were available, we estimate that paracetamol is involved in 6% of poisonings, 56% of severe acute liver injury and acute liver failure, and 7% of drug-induced liver injury. These estimates are limited by lack of available data from many countries, particularly in Asia, South America, and Africa. Harm reduction from paracetamol is possible through better identification of high-risk overdoses, and better treatment regimens. Large overdoses and those involving modified-release paracetamol are high-risk and can be targeted through legislative change.

Landscape of DILI-related adverse drug reaction in China Mainland

Drug-induced liver injury (DILI) is a type of bizarre adverse drug reaction (ADR) damaging liver (L-ADR) which may lead to substantial hospitalizations and mortality. Due to the general low incidence, detection of L-ADR remains an unsolved public health challenge. Therefore, we used the data of 6.673 million of ADR reports from January 1st, 2012 to December 31st, 2016 in China National ADR Monitoring System to establish a new database of L-ADR reports for future investigation. Results showed that totally 114,357 ADR reports were retrieved by keywords searching of liver-related injuries from the original heterogeneous system. By cleaning and standardizing the data fields by the dictionary of synonyms and English translation, we resulted 94,593 ADR records reported to liver injury and then created a new database ready for computer mining. The reporting status of L-ADR showed a persistent 1.62-fold change over the past five years. The national population-adjusted reporting numbers of L-ADR manifested an upward trend with age increasing and more evident in men. The annual reporting rate of L-ADR in age group over 80 years old strikingly exceeded the annual DILI incidence rate in general population, despite known underreporting situation in spontaneous ADR reporting system. The percentage of herbal and traditional medicines (H/TM) L-ADR reports in the whole number was 4.5%, while 80.60% of the H/TM reports were new findings. There was great geographical disparity of reported agents, i.e. more cardiovascular and antineoplastic agents were reported in higher socio-demographic index (SDI) regions and more antimicrobials, especially antitubercular agents, were reported in lower SDI regions. In conclusion, this study presented a large-scale, unbiased, unified, and computer-minable L-ADR database for further investigation. Age-, sex- and SDI-related risks of L-ADR incidence warrant to emphasize the precise pharmacovigilance policies within China or other regions in the world.

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.

Drug-Induced Liver Injury: Highlights and Controversies in the Recent Literature

Drug-induced liver injury (DILI) remains an important, yet challenging diagnosis for physicians. Each year, additional drugs are implicated in DILI and this year was no different, with more than 1400 articles published on the subject. This review examines some of the most significant highlights and controversies in DILI-related research over the past year and their implications for clinical practice. Several new drugs were approved by the US Food and Drug Administration including a number of drugs implicated in causing DILI, particularly among the chemotherapeutic classes. The COVID-19 pandemic was also a major focus of attention in 2020 and we discuss some of the notable aspects of COVID-19-related liver injury and its implications for diagnosing DILI. Updates in diagnostic and causality assessments related to DILI such as the Roussel Uclaf Causality Assessment Method are included, mindful that there is still no single biomarker or diagnostic tool to unequivocally diagnose DILI. Glutamate dehydrogenase received renewed attention as being more specific than alanine aminotransferase. There were a few new reports of previously unrecognized hepatotoxins, including immune modulators and novel gene therapy drugs that we highlight. Updates and new developments of previously described hepatotoxins, such as immune checkpoint inhibitors and anti-tuberculosis drugs are reviewed. Finally, novel technologies such as organoid culture systems to better predict DILI preclinically may be coming of age and determinants of hepatocyte loss, such as calculating P_ALT are poised to improve our current means of estimating DILI severity and the risk of acute liver failure.

Drug Induced Liver Injury in Geriatric Patients Detected by a Two-Hospital Prospective Pharmacovigilance Program: A Comprehensive Analysis Using the Roussel Uclaf Causality Assessment Method

Background/aim: A prospective evaluation of drug-induced liver injury (DILI) in two tertiary hospitals was conducted through a pharmacovigilance program from laboratory signals at hospital (PPLSH) to determine the principal characteristics of DILI in patients older than 65 years, a growing age group worldwide, which is underrepresented in the literature on DILI.

Methods: All DILI in patients older than 65 years detected by PPLSH in two hospitals were followed up for 8 years in the La Paz Hospital and 2 years in the Getafe Hospital. A descriptive analysis was conducted that determined the causality of DILI and suspected drugs, the incidence of DILI morbidities, DILI characteristics, laboratory patterns, evolution and outcomes.

Results: 458 DILI cases in 441 patients were identified, 31.0% resulting in hospitalisation and 69.0% developing during hospitalisation. The mean age was 76.61 years old (SD, 7.9), and 54.4% were women. The DILI incidence was 76.33/10,000 admissions (95%CI 60.78–95.13). Polypharmacy (taking >4 drugs) was present in 86.84% of patients, 39.68% of whom took >10 drugs. The hepatocellular phenotype was the most frequent type of DILI (53.29%), a higher proportion (65%) had a mild severity index, and, in 55.2% of the evaluated drugs the RUCAM indicated that the causal relationship was highly probable. The most frequently employed drugs were paracetamol (50-cases), amoxicillin-clavulanate (42-cases) and atorvastatin (37-cases). The incidence rate of in-hospital DILI per 10,000 DDDs was highest for piperacillin-tazobactam (66.96/10,000 DDDs). A higher risk of in-hospital DILI was associated with the therapeutic chemical group-J (antiinfectives for systemic use) (OR, 2.65; 95%CI 1.58–4.46) and group-N (central nervous system drugs) (OR, 2.33; 95%CI 1.26–4.31). The patients taking >4 medications presented higher maximum creatinine level (OR, 2.01; 95%CI 1.28–3.15), and the patients taking >10 medications had a higher use of group J drugs (OR, 2.08; 95%IC 1.31–3.32).

Conclusion: The incidence rate of DILI in the patients older than 65 years was higher than expected. DILI in elderly patients is mild, has a good outcome, has a hepatocellular pattern, develops during hospitalisation, and prolongs the hospital stay. Knowing the DILI incidence and explanatory factors will help improve the therapy of the elderly population.

Use of ICD-10-CM T codes in hospital claims data to identify adverse drug events in Taiwan

WHAT IS KNOWN AND OBJECTIVE

Adverse drug events (ADEs) are a major public health concern worldwide and may prolong hospital stays, causing a burden on the healthcare system and increasing the associated costs. Therefore, optimizing medication use and reducing ADEs are priorities for public health. Medication safety can be monitored and improved by identifying ADEs. The utilization of diagnoses coded according to the International Statistical Classification of Diseases and Related Health Problems (ICD) system for the identification of ADEs has been firmly established. In Taiwan, however, the validity of recording ADEs on the basis of inpatient ICD-10-CM T codes has not been evaluated. Therefore, this study investigated the potential usefulness of ICD-10-CM T codes in routine hospital data for the identification of ADEs and for increasing the rate of reporting.

METHODS

We use hospital claims data of hospitalized patients from one medical centre in northern Taiwan between 1 July 2016 and 30 June 2018. We defined an ADE to have taken place if an ICD-10-CM T code was present among the primary or secondary diagnosis codes. The inpatients who were discharged with T codes in a primary or secondary diagnosis were identified by the computerized T code information platform, and the retrospective review of the medical charts was performed by pharmacists to confirm the ADEs.

RESULTS AND DISCUSSION

1384 inpatients who were discharged with the relevant T codes in a primary or secondary diagnosis were identified during the study period. Code T36 (poisoning by, adverse effect of or underdosing of systemic antibiotics) was the most common code, accounting for 56.6%, followed by T42 (17.7%; poisoning by, adverse effect of or underdosing of antiepileptic, sedative-hypnotic or antiparkinsonism drug). Overall, 789 clinically significant ADEs were identified after medical chart review. The dermatologic system was the most commonly involved. The overall positive predictive value for a flagged code representing an ADE was 57%. Furthermore, the use of T codes to confirm the number of ADE cases increased the ADE reporting rate by 9.17%.

WHAT IS NEW AND CONCLUSION

The PPV of ICD-10-CM T codes analysed in our study was insufficient for identifying ADEs during hospitalization. The sensitivity and specificity of this were inadequate. However, the T code system can be used as an auxiliary resource for pharmacists to identify potential ADEs and report the information as prompts on the physician order entry system. When a physician prescribes a drug that may cause an ADE in a patient, an alert is issued to ensure medication safety. In conclusion, the T codes did not perform well in our study and caution should be exercised in their use to identify ADEs on their own.