Development and Validation of a Nomogram for Predicting Prognosis in Patients with Drug-Induced Liver Injury: A Multicenter Retrospective Study

Development and Validation of a Nomogram for Predicting Prognosis in Patients with Drug-Induced Liver Injury: A Multicenter Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Nomogram for Predicting Prognosis in Patients with Drug-Induced Liver Injury: A Multicenter Retrospective Study Shimei Wang, Na Wang, Zhao Qian, Chunlin Xu, Xiaoren Wang, Zhenghan Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8643882/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Drug-induced liver injury (DILI) has a highly variable clinical course, making it challenging to predict individual patient outcomes. This study aimed to identify independent risk factors for adverse outcomes in DILI and to develop and validate a clinically practical nomogram for individualized prognosis prediction. Methods In this multicenter, retrospective cohort study, clinical data from 471 patients diagnosed with DILI between 2019 and 2024 were analyzed. Patients were randomly divided into a training set (n = 282) and a validation set (n = 189) in a 6:4 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of clinical outcomes (favorable vs. adverse prognosis). A nomogram was constructed based on the significant variables identified in the multivariate analysis. The model's performance was evaluated in both sets in terms of calibration (calibration curve), clinical utility (decision curve analysis, DCA), and discrimination (area under the curve, AUC). Results Traditional Chinese medicine (TCM)-natural medicine (NM)-health products (HP)-herbs-dietary supplements (HDS) were the most common causative agents. Multivariate analysis identified eleven predictors incorporated into the nomogram: sex, platelet (PLT), cholinesterase (Che), globulin (Glo), age, antinuclear antibody (ANA), autoantibodies, alanine aminotransferase (ALT), model for end-stage liver disease (MELD) score, prothrombin time activity (PTA), and prealbumin (PA). The nomogram demonstrated good predictive accuracy in the training set (AUC = 0.793) and excellent calibration (mean absolute error = 0.029). DCA confirmed the model's clinical utility across a wide range of threshold probabilities in both sets. Conclusions The proposed nomogram provides an effective and visually intuitive tool for predicting individual patient outcomes in DILI. It exhibits strong discriminatory ability and calibration, facilitating early risk stratification and informed clinical decision-making to help improve patient prognosis. Drug-induced liver injury Nomogram model Prediction Retrospective analysis Figures Figure 1 Figure 2 Figure 3 Background Drug-induced liver injury (DILI) is an important cause of liver diseases worldwide, and in severe cases, it can lead to acute liver failure.[ 1 – 3 ] Therefore, DILI is one of the major public health challenges caused by adverse drug reactions. Given the extensive list of causative agents and multifactorial pathogenesis of DILI, identifying prognostic risk factors is clinically imperative for physicians to make accurate outcome predictions. Recent studies have identified cytokeratin 18 (CK-18), macrophage colony-stimulating factor receptor 1 (MCSFR1), and osteopontin (OPN) as candidate biomarkers for predicting adverse prognosis in DILI.[ 4 ] However, these preliminary investigations lack robust validation through multicenter clinical trials, which limits their current clinical utility. Hence, there is an urgent need to establish a rapid, simple, and effective predictive prognostic model to evaluate the clinical outcomes of patients with DILI. Nomograms have emerged as indispensable tools in prognostic research, which graphically translate multivariate regression analyses into intuitive scoring systems to enhance the clinical interpretability of predictive models.[ 5 , 6 ] These visual decision aids enable clinicians to stratify patient risks objectively and to optimize therapeutic strategies through real-time probability quantification. In the past, nomograms have been widely applied to predict diagnoses and prognoses across various cancers and assess the probability of clinical outcome events in patients.[ 7 – 9 ] With advancements in medical research and technological progress, potential applications of nomograms in the medical field are expected to expand significantly. In recent years, nomograms have been increasingly used to predict the prognosis of non-neoplastic diseases. Clinicians can employ the predictors identified by this model to implement prospective and preventive interventions.[ 10 – 12 ] This study aims to investigate the risk factors influencing the prognosis of patients with DILI. Meanwhile, a nomogram prognostic model for quantitatively evaluating the individual risks of DILI patients based on clinical characteristics is established. Methods Patients This multicenter retrospective study analyzed real-world clinical data from four hospitals in Northeast China (2019–2024). All enrolled patients were newly diagnosed with DILI and initially hospitalized in the Department of Infectious Diseases during the study period. All enrolled patients had documented drug exposure histories prior to disease onset. Clinical management included immediately discontinuing the suspected drugs after the diagnosis of DILI and providing individualized treatment. The research protocol strictly adhered to the ethical standards established in the Declarations of Helsinki and the Istanbul Consensus Guidelines. Given the retrospective design of the study, the committee waived the need for written informed consent. This study was approved by the Ethics Committees of the Fourth Affiliated Hospital of Harbin Medical University (No. 2024–108), Heilongjiang Provincial Hospital (No. 2024–074), the First Affiliated Hospital of Harbin Medical University (No. 202562), and the Second Affiliated Hospital of Harbin Medical University (No. KY2024–280). Inclusion and exclusion criteria The inclusion criteria were as follows: (1) age ≥ 18 years, (2) clear history of drug exposure, (3) Roussel Uclaf causality assessment method (RUCAM) score ≥ 3 points, (4) diagnosed with DILI according to the Chinese guideline for the diagnosis and treatment of DILI.[ 3 ] The exclusion criteria were as follows: (1) viral hepatitis (including hepatitis A, B, C, E, cytomegalovirus, and Epstein-Barr virus, etc.); (2) alcoholic liver disease (ALD); (3) non-alcoholic fatty liver disease (NAFLD); (4) autoimmune hepatitis (AIH); (5) primary biliary cholangitis (PBC); (6) primary sclerosing cholangitis (PSC); (7) hepatolenticular degeneration (HLD); (8) hemochromatosis (HC); and (9) other causes of liver dysfunctions. Composition and types of drugs causing DILI The suspected causative agents for DILI were systematically categorized into 11 distinct groups based on their therapeutic class and composition: (1) traditional Chinese medicine (TCM)-natural medicine (NM)-health products (HP)-herbs-dietary supplements (HDS); (2) antibiotic drugs (including anti-tuberculosis drugs); (3) non-steroidal anti-inflammatory drugs (NSAIDs); (4) cardiovascular drugs; (5) drugs for metabolic diseases; (6) anti-tumor drugs; (7) chemical agents/industrial poisons; (8) central nervous system drugs; (9) biologics/immunosuppressants; (10) hormones; and (11) others. This classification is consistent with methodologies employed in large-scale DILI studies and guidelines.[ 1 – 3 ] Data collection Demographic characteristics and laboratory parameters of enrolled DILI patients were documented. Demographic details included sex, age, body mass index (BMI), hospitalization days, and history of allergy. The following laboratory parameters were recorded at peak disease severity : white blood cell (WBC), eosinophils (EO), hemoglobin (HGB), platelet (PLT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), cholinesterase (Che), creatinine (Cr), total bile acid (TBA), total bilirubin (TBIL), direct bilirubin (DBIL), albumin (ALB), globulin (Glo), prealbumin (PA), total cholesterol (TC), prothrombin time (PT), prothrombin time activity (PTA), international normalized ratio (INR), antinuclear antibody (ANA), and autoantibodies. In addition, various clinical manifestations following the onset of DILI have been documented, including fever, fatigue, nausea, vomiting, abdominal pain, abdominal distension, loss of appetite, jaundice, skin itching, and pale fecal color. Definition The RUCAM score was assessed based on the guideline for the diagnosis and treatment of DILI.[ 3 ] Based on the R ratio, acute DILI was classified as follows[ 13 , 14 ]: (1) hepatocellular injury type R ≥ 5, (2) cholestatic type R ≤ 2, and (3) mixed type 2 < R < 5. The R ratio was calculated using the following formula: [ALT/upper normal limit (ULN) of ALT]/[ALP/ULN of ALP]. If ALT levels were unavailable, AST levels were used for the calculation. Upon diagnosis of acute DILI, the severity of the condition was assessed as follows[ 3 ]: (1) mild: ALT ≥ 5 × ULN or ALP ≥ 2 × ULN with TBIL < 2 × ULN; (2) moderate: ALT ≥ 5 × ULN or ALP ≥ 2 × ULN with TBIL ≥ 2 × ULN or symptomatic hepatitis; (3) severe: ALT ≥ 5 × ULN or ALP ≥ 2 × ULN with TBIL ≥ 2 × ULN, or symptomatic hepatitis accompanied by any of the following criteria: INR ≥ 1.5, ascites, and/or hepatic encephalopathy; disease duration < 26 weeks without cirrhosis or other organ failure attributable to DILI; and (4) fatal: death resulting from DILI or necessitating liver transplantation for survival. The model for end-stage liver disease (MELD) was calculated as follows[ 15 , 16 ]: [3.78 × In(TBIL in mg/dL) + 11.2 × In(INR) + 9.57 × In(Cr in mg/dL) + 6.43 (Biliary or alcoholic 0, other1)]. Clinical outcomes Patients with DILI were classified into two groups according to clinical outcomes: (1) favorable outcome group: patients showed significant recovery in clinical symptoms and signs, and liver function returned to normal within 1 year after discontinuation of the suspected drug; (2) adverse outcome group: chronic DILI (1 year after the DILI event, biochemical indicators did not return to normal or baseline levels) or death. Follow up Patient outcomes were assessed retrospectively over a one-year period following DILI onset using outpatient electronic medical records supplemented by telephone interviews. The clinical endpoint for each patient was determined based on their status at the last available evaluation within this period and was categorized as complete recovery, progression to chronic DILI, or death. All enrolled patients had a definitive outcome recorded, and no patients were lost to follow-up by this retrospective ascertainment method. Statistical analysis Normally distributed measurement data are presented as mean ± standard deviation (SD), with between-group comparisons performed using the Student’s t-test. Non-normally distributed data are expressed as medians with interquartile ranges (IQR, P 25 -P 75 ) and compared using the Mann-Whitney U test. Categorical data are presented as percentages and analyzed with the Chi-square or Fisher’s exact test. A P -value < 0.05 was considered statistically significant. All analyses were conducted using SPSS 27.0 and R 4.3.0. Independent risk factors influencing clinical outcomes in patients with DILI were identified using univariate and multivariate logistic regression, with results reported as odds ratios (OR) and 95% confidence intervals (CI). Variables with P < 0.1 in univariate analysis were considered candidates for the multivariate model. Final predictors were selected via stepwise logistic regression optimized by minimizing the Akaike Information Criterion (AIC), aiming for a parsimonious model with strong predictive performance. All retained variables were incorporated into a nomogram. The nomogram was developed using the rms package in R, converting regression coefficients into a 0–100 point scale. Missing data were handled by multiple imputation using the mice package. Model performance was internally validated via calibration curves, decision curve analysis (DCA), and receiver operating characteristic (ROC) curves. Results Clinical characteristics A total of 1885 patients were screened for this study. Finally, 471 individuals diagnosed with DILI who met the inclusion and exclusion criteria were enrolled. Among these participants, 352 were classified into the favorable outcome group, while 119 were classified into the adverse outcome group ( Figure. 1 ). Notably, there were five fatalities recorded in the adverse outcome group. All five fatalities in the adverse outcome group were attributed to DILI-induced liver failure. The median age of the participants was 53.0 years (IQR, 45.0–61.0), with females constituting the majority, 321 (68.2%). Hepatocellular injury (272/471, 57.8%) was the predominant type affecting the participants, followed by the cholestatic type (115/471, 24.4%) and the mixed type (84/471, 17.8%). A comparative analysis between the two groups revealed statistically significant differences in variables such as severity, clinical phenotype, ALT, AST, ALP, GGT, Che, ALB, Glo, PT, PTA, INR, HGB, PLT, and the presence of autoantibodies ( P < 0.05) ( Table S1 ). Subsequently, the patients were randomly divided into a training set (n = 282) and a validation set (n = 189) in a ratio of 6:4 for subsequent analysis (Table 1) . The training set was used for model development and parameter estimation, while the validation set was used to independently verify the predictive performance of the model. Suspected causative agents for DILI In this study, TCM-NM-HP-HDS was the most common category associated with DILI, closely followed by antibiotics (including anti-tuberculosis drugs) ( Table 2 ). The specific drug names and representative compositions within each category are provided in Supplementary Table S2 . This supplementary table details the drug names and their compositions for all 471 patients. Clinical symptoms and signs The primary symptom and sign of DILI was fatigue, affecting 79.4% (374/471) of the patients. Other symptoms and signs included loss of appetite (60.1%, 283/471), nausea (40.1%, 189/471), abdominal distension (38.0%, 179/471), jaundice (37.2%, 175/471), fever (24.8%, 117/471), vomiting (16.1%, 76/471), skin itching (12.1%, 57/471), abdominal pain (7.2%, 34/471), and pale fecal color (5.9%, 28/471) ( Figure. S1 ). Fortunately, the associated signs and symptoms gradually diminish as the patients with DILI recover. Additionally, ten patients with DILI showed no significant signs of discomfort or symptoms from the onset to recovery. Univariate and multivariate logistic regression analysis Univariate logistic regression analysis showed that sex, elevated ALT, decreased Che, decreased ALB, decreased Glo, decreased PTA, decreased HGB, decreased PLT, and positive autoantibodies were associated with adverse outcomes in patients with DILI ( P < 0.05) ( Table 3 ). Subsequently, multiple stepwise logistic regression was used to identify the factors influencing the individual clinical outcomes of DILI patients and establish the optimal multiple regression model. Multivariate analysis screened out 11 variables, including sex, PLT, Che, Glo, age, ANA, autoantibodies, ALT, MELD score, PTA, and PA ( Table 3 ). Among them, sex, increased age, increased MELD score, elevated ALT, decreased PTA, positive ANA, and positive autoantibody are independent risk factors affecting the clinical outcome of DILI patients ( P < 0.05). Establishment and verification of nomogram The 11 factors identified through the multivariate stepwise logistic regression analysis were incorporated into a nomogram ( Figure. 2 ). These variables were ranked according to the size of their regression coefficients; thus, the degree of contribution progressively increased from top to bottom. Each variable within the model was projected upward onto the first line of the scoring scale for evaluation purposes, and the score values for all 11 variables were summed up to derive the total score. This total score serves as a predictor of clinical outcomes in patients with DILI: the higher the score, the more likely the adverse outcome for patients with DILI. To evaluate the performance of the predictive nomogram, we assessed its calibration, clinical utility, and discriminative ability in both the training and validation sets. The calibration curves indicated excellent agreement between predicted and observed probabilities in the training set, with a mean absolute error of 0.029 (based on 1000 Bootstrap repetitions). In the validation set, the model maintained acceptable overall calibration, albeit with slight deviations in the mid-to-high probability range ( Figure. 3A and B ). DCA demonstrated that the use of the nomogram provided a higher net benefit than the “treat-all” or “treat-none” strategies across threshold probabilities of 0.1–0.7 in the training set and 0.2–0.5 in the validation set, supporting its clinical utility ( Figure. 3C and D ). Regarding discriminative ability, the model achieved an area under the ROC curve of 0.793 in the training set ( Figure. 3E ), indicating good performance. However, the area under the curve (AUC) decreased to 0.664 in the independent validation set ( Figure. 3F ), suggesting potential overfitting and limited generalizability of the model. Discussion Most patients with DILI can recover within six months following discontinuation of the offending drugs, and their prognosis is generally favorable. However, some patients may experience adverse clinical outcomes, potentially progressing to chronic DILI, acute liver failure, or even death.[ 1 – 3 ] A clinical prediction model is a statistical framework developed based on multiple factors, employing mathematical language or formulas to articulate the relationships between various elements. This model estimates the probability of developing a disease or predicts the likelihood of specific future outcomes, such as recurrence, deterioration, or mortality. Clinical prediction models can be categorized into two types: clinical diagnosis and clinical prediction models. By analyzing the patients’ clinical data, including demographic information, genetic factors, symptoms, signs, laboratory results, imaging findings, and histopathological results, clinical prediction models can assist clinicians in gaining a deeper understanding of disease progression and provide personalized treatment plans for their patients. Several predictive models have been proposed to assess DILI, including biochemical non-resolution-6 (BNR-6)[ 17 ] and drug-induced acute liver failure-5 (DIALF-5).[ 18 ] It is important to note that BNR-6 serves as a scoring system specifically for chronic DILI and necessitates a liver pathology score, whereas DIALF-5 primarily functions as a prognostic scoring tool for non-acetaminophen drug-induced acute liver failure. Consequently, both scoring systems have limitations in clinical practice. The nomogram model developed in this study is designed to be broadly applicable to patients with DILI. It serves as a convenient, efficient, and practical scoring tool that enables clinicians to assess the clinical outcomes of patients effectively. The developed nomogram is intended as a general prognostic tool for DILI. Its predictive variables are derived from the patient’s clinical and biochemical response to the injury, making it broadly applicable regardless of the specific causative agent. A multicenter retrospective study conducted across four hospitals in northeast China summarized the demographic characteristics and prognosis of patients with DILI in this region. These findings indicate that DILI was more prevalent among women, with hepatocellular injury being the predominant clinical type observed. The primary drug implicated in DILI was TCM-NM-HP-HDS. Multivariate logistic regression analysis revealed that factors such as sex, PLT, Che, Glo, age, ANA, autoantibodies, ALT, MELD score, PTA, and PA were significantly associated with the clinical outcomes in patients with DILI. A nomogram model was subsequently developed based on the variables identified through the multivariate logistic regression analysis, and internal validation and evaluation were also performed. The predominance of TCM-NM-HP-HDS as the primary causative agents of DILI in our cohort aligns with epidemiological patterns observed across China and much of Asia[ 19 , 20 ], underscoring a critical public health concern. The hepatotoxic mechanisms of TCM/HDS are notably complex and multifactorial, often involving intrinsic toxicity, idiosyncratic reactions, and external factors such as contamination. Certain herbs contain inherently hepatotoxic compounds; for example, pyrrolizidine alkaloids (found in some Heliotropium species) can cause hepatic sinusoidal obstruction syndrome via metabolic activation into damaging pyrrolic derivatives, while glycosides in Polygonum multiflorum —a frequently implicated herb—are associated with mitochondrial dysfunction and oxidative stress, leading to hepatocellular apoptosis or necrosis[ 2 , 3 , 21 ]. Furthermore, idiosyncratic reactions, which are unpredictable and not dose-dependent, are common and may involve the metabolic activation of herbal constituents by cytochrome P450 enzymes into reactive metabolites. These metabolites can act as haptens, triggering an adaptive immune response, or directly induce oxidative stress and mitochondrial injury[ 2 , 3 , 20 ]. Complicating this picture are issues of herb-drug interactions and potential contamination with heavy metals, pesticides, or undeclared synthetic drugs, which can independently or synergistically provoke liver injury[ 21 ]. The high prevalence of TCM/HDS-related DILI highlights the imperative for enhanced regulatory oversight, rigorous quality control of herbal products, and continued research into the specific toxic components and host genetic factors that predispose individuals to these injuries. In Western countries, NSAIDs such as acetaminophen, antibiotics such as amoxicillin-clavulanate, anti-tuberculosis drugs, and HDS are among the most common agents responsible for DILI.[ 22 ] Over the past 25 years, data from the United States Drug-Induced Liver Injury Network (DILIN) prospective study have indicated an increase in DILI cases attributed to HDS from 7% to 17%.[ 22 ] Furthermore, there has been an eight-fold increase in the number of individuals awaiting liver transplantation due to liver failure caused by these substances.[ 23 ] In Asian countries, including China,[ 19 ] South Korea,[ 20 ] Japan,[ 24 ] India,[ 25 ] and Malaysia[ 26 ], the primary drugs responsible for DILI include TCM, anti-tuberculosis drugs, and antibiotics, which is consistent with our results. Clinicians should exercise heightened vigilance regarding the medication history of patients using TCM/HDS, as well as anti-tuberculosis drugs and antibiotics, in conjunction with both Eastern and Western susceptible drugs when identifying potential agents for DILI. Similarly, controversy remains over whether female sex is a risk factor for susceptibility to DILI. Epidemiological data derived from three prospective cohort studies conducted in Spain, the United States, and Iceland revealed a relatively balanced sex distribution.[ 22 , 27 , 28 ] Women exhibit higher susceptibility to certain drugs such as minocycline- and nitrofurantoin-induced AIH.[ 29 ] This may be because women are more susceptible to PBC and AIH, whereas men are more susceptible to PSC.[ 30 ] Our findings also indicated that age was an independent risk factor for the prognosis of DILI ( P 0.05). Therefore, although age and sex are significant factors influencing DILI, a definitive conclusion remains elusive owing to the diverse range of drugs implicated in DILI.[ 28 ] When considering the pathogenic effects of these two factors on patients, clinicians should adopt a flexible approach. With increasing age, the body’s excretion and metabolic function of drugs gradually declines, resulting in prolonged retention time of drugs in the body, and the risk of adverse drug reactions increases. It is important to note that certain drugs may not be influenced by advancing age. Research has demonstrated that advanced age increases the risk of liver damage associated with isoniazid, amoxicillin-clavulanate potassium, and nitrofurantoin use.[ 31 – 33 ] Conversely, sodium valproate has been associated with a higher risk of DILI in younger children.[ 34 , 35 ] Data from most retrospective studies[ 36 – 38 ] indicate that age is a predisposing factor for DILI. However, the findings of a large-scale prospective study[ 22 , 28 ] on DILI do not corroborate this assertion. Hepatocellular injury, cholestasis, and mixed types are the most prevalent clinical types of DILI. Among these, hepatocellular injury accounts for approximately 42–59%, primarily characterized by elevated levels of ALT or AST. The cholestatic type constitutes approximately 20–32%, predominantly marked by increased ALP or GGT.[ 3 ] Hepatocytes possess robust regenerative capacity; thus, mild hepatocellular injury often leads to recovery. Therefore, patients with hepatocellular injury and mixed DILI typically have favorable prognoses. However, a subset of patients with hepatocellular injury may progress to fulminant liver failure, with a significant proportion being attributed to TCM/HDS. These individuals frequently require liver transplantation as a lifesaving intervention.[ 21 ] In contrast to hepatocellular injury and mixed types of DILI, patients with the cholestatic type more commonly experience involvement of bile duct cells, resulting in damage to the bile duct epithelium, leading to cholestasis, and even in severe cases, vanishing bile duct syndrome. The disease course of patients exhibiting this phenotype is often prolonged, placing them at an elevated risk for chronic conditions or delayed recovery.[ 39 , 40 ] A multicenter, prospective, large-scale cohort study conducted in the United States included 363 patients diagnosed with DILI via liver biopsy.[ 39 ] The results revealed that 26 patients (26/363, 7.2%) exhibited varying degrees of bile duct disappearance, and their clinical type was cholestatic without exception. Among these patients, five (5/26, 19.2%) succumbed to their condition, and 2 (2/26, 7.7%) underwent liver transplantation. Consequently, it is imperative that clinicians closely monitor patients with cholestatic DILI, paying particular attention to their laboratory indicators and clinical outcomes. If the disease persists without remission or even progresses further, consideration must be given to the possibility that these patients may require liver transplantation to preserve their lives. ANA and autoantibodies play a critical role in autoimmune diseases by targeting proteins or tissues.[ 41 , 42 ] Normally, the immune system can distinguish between self and non-self substances, thereby preventing attacks on healthy cells. However, this mechanism malfunctions in autoimmune diseases, leading to the production of autoantibodies. Therefore, positive ANA and autoantibody results suggest the potential presence of immune-mediated liver injury and serve as auxiliary diagnostic indicators for the progression of DILI to chronic disease. Moreover, patients positive for ANA and autoantibodies are at a higher risk of developing chronic liver injury, cirrhosis, and even liver failure, which is significant for patient prognosis. Multivariate logistic regression analysis revealed that ANA and autoantibodies were independent risk factors that influenced the prognosis of patients with DILI. However, it is important to note that these antibody positivity markers are not the sole determinants of DILI progression to a chronic condition or patient prognosis; they are also influenced by factors such as drug type, dosage, treatment duration, drug interactions, sex, and individual genetic background.[ 1 – 3 , 43 , 44 ] Despite the rigorous methodology and internal validation, this study has several limitations that should be acknowledged. First, the development and validation of our nomogram were based on a multicenter cohort from Northeast China. Although robust internal validation via bootstrap resampling demonstrated good model performance, the absence of external validation with an independent, geographically diverse cohort limits the generalizability of our findings. The model’s performance in other populations with different genetic backgrounds, healthcare systems, and prescribing habits remains uncertain and warrants future confirmation. Second, as a retrospective study, it is inherently subject to potential selection bias and unmeasured confounding factors. Despite our efforts to collect comprehensive data, some variables of potential interest, such as specific genetic markers or detailed dietary information, were not available for analysis, which might have further refined the predictive accuracy of the model. Third, the categorization of causative agents, particularly the broad TCM-NM-HP-HDS group, encompasses a highly heterogeneous mixture of substances. The specific components and dosages within this category were often unclear, which precludes a more nuanced analysis of the risks associated with individual agents. Conclusions In summary, the nomogram model developed in this study demonstrated satisfactory clinical calibration, discriminative ability, and practical value in assessing the clinical outcomes of patients with DILI. It is anticipated that this model will offer new insights and methodologies for the clinical management of patients with DILI and will provide clinicians with important information on the developmental trend of the disease. This capability facilitates timely adjustments to treatment strategies, enhances therapeutic efficacy, and improves patient prognosis. In the future, prospective validation studies‌ incorporating emerging biomarkers and multimodal data integration ‌will further refine the diagnostic precision‌ and expand the applicability across diverse patient cohorts‌༎ Abbreviations AIH autoimmune hepatitis Alb albumin ALD alcoholic liver disease ALP alkaline phosphatase ALT alanine aminotransferase ANA antinuclear antibody AST aspartate aminotransferase AUC area under curve BMI body mass index BNR-6 biochemical resolution or not-6 Che cholinesterase CI confidence interval CK-18 cytokeratin 18 Cr creatinine DBIl direct bilirubin DCA decision curve analysis DIALF-5 drug-induced acute liver failure-5 DILI drug-induced liver injury DILIN Drug-Induced Liver Injury Network EO eosinophils GGT gamma-glutamyl transferase Glo globulin HC hemochromatosis HDS herbs and dietary supplements HGB hemoglobin HLD hepatolenticular degeneration HP health products INR international normalized ratio IQR interquartile range MCSFR1 macrophage colony stimulating factor receptor 1 MELD model for end-stage liver disease NAFLD non-alcoholic fatty liver disease NM natural medicine NSAIDs non-steroidal anti-inflammatory drugs OPN osteopontin OR odds ratio PA prealbumin PBC primary biliary cholangitis PBS primary sclerosing cholangitis PLT platelet PT prothrombin time PTA prothrombin time activity ROC receiver operating characteristic RUCAM Roussel Uclaf causality assessment method SD standard deviation TBA total bile acid TBIl total bilirubin TC total cholesterol TCM traditional Chinese medicine ULN upper normal limit WBC white blood cell. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committees of the Fourth Affiliated Hospital of Harbin Medical University (No. 2024-108), Heilongjiang Provincial Hospital (No. 2024-074), the First Affiliated Hospital of Harbin Medical University (No. 202562), and the Second Affiliated Hospital of Harbin Medical University (No. KY2024-280). Human Ethics and Consent to Participate declarations: not applicable. Clinical trial number: not applicable. Consent for publication All authors have reviewed the full text and agree to its publication. Availability of data and materials The data used to support the findings of this study are available upon request from the corresponding author. Access to the data is subject to approval by the institutional review board and compliance with applicable privacy regulations. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study. Funding This work was supported by the Fourth Affiliated Hospital of Harbin Medical University Specially Funded Research Project (No. HYDSYTB202206) and Scientific Research Project of Health Commission of Heilongjiang Province (No.20241313050210). Author contributions’ The study concept and design were primarily undertaken by SW and NW. Data collection was carried out by ZQ, CX, XW, ZL, TA, ZZ, and XX. Statistical analysis and data interpretation were conducted by SW and NW. Manuscript drafting was primarily performed by SW and NW. Critical revisions of the manuscript for important intellectual content were conducted by LY. All authors read and approved the final manuscript. 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Tables Table 1 Model performance in the training and the internal validation sets Characteristic Total (n = 471) Training set (n = 282 ) Validation set (n = 1 89 ) Sex, n (%) Male 150 (31.8) 93 (33.0) 57 (30.2) Female 321 (68.2) 189 (67.0) 132 (69.8) Age (years) 53.0 (45.0, 61.0) 52.0 (43.0, 61.0) 54.0 (48.0, 62.0) BMI (kg/m 2 ) 23.2 (20.8, 25.0) 23.2 (20.8, 25.0) 23.1 (21.1, 25.0) Hospitalization (days) 12 (7, 18) 12 (8, 18) 11 (7, 16) Allergic, n (%) Yes 34 (7.2) 19 (6.7) 15 (7.9) No 437 (92.8) 263 (93.3) 174 (92.1) Severity, n (%) 1 (Mild) 234 (49.7) 138 (48.9) 96 (50.8) 2 (Moderate) 203 (43.1) 120 (42.6) 83 (43.9) 3 (Severe) 29 (6.2) 20 (7.1) 9 (4.8) 4 (Fatal) 5 (1.0) 4 (1.4) 1 (0.5) Clinical phenotype, n (%) Hepatocellular 272 (57.8) 170 (60.3) 102 (54.0) Cholestatic 115 (24.4) 59 (20.9) 56 (29.6) Mixed 84 (17.8) 53 (18.8) 31 (16.5) RUCAM score 8.0 (8.0, 9.0) 8.0 (8.0, 9.0) 8.0 (8.0, 9.0) MELD score 12.0 (7.0, 16.0) 12.0 (7.0, 17.0) 12.0 (7.0, 16.0) ALT (U/L) 372.5 (145.2, 776.0) 361.0 (154.3, 832.0) 390.0 (144.4, 708.0) AST (U/L) 242.0 (99.0, 596.0) 245.7 (104.0, 572.3) 237.0 (95.8, 610.9) ALP (U/L) 169.0 (119.0, 259.0) 164.0 (111.0, 254.0) 178.0 (136.0, 263.2) GGT (U/L) 218.2 (113.0, 382.6) 214.0 (115.0, 346.0) 232.0 (99.0, 477.0) Che (U/L) 6781.0 (5077.0, 8244.0) 6707.5 (5077.0, 8193.0) 6997.0 (5114.0, 8423.0) Cr (μmol/L) 58.0 (50.0, 68.0) 58.0 (49.4, 68.5) 58.8 (51.0, 67.0) TBA (μmol/L) 33.2 (7.4, 139.1) 32.6 (7.4, 144.7) 33.5 (7.5, 131.1) TBIL (μmol/L) 45.9 (18.7, 135.5) 48.7 (18.3, 139.8) 43.0 (19.4, 132.8) DBIL (μmol/L) 24.2 (6.7, 106.8) 24.0 (6.6, 108.9) 24.9 (7.0, 102.9) ALB (g/L) 40.2 (35.9, 43.7) 40.2 (36.4, 43.6) 40.1 (35.5, 43.9) Glo (g/L) 28.1 (24.8, 32.5) 27.8 (24.4, 32.3) 28.6 (25.1, 32.5) PA (mg/dL) 7.4 (1.4, 18.5) 8.3 (1.6, 18.5) 5.7 (1.2, 18.3) TC (mmol/L) 3.73 (1.81, 4.92) 3.69 (1.77, 4.98) 3.73 (2.18, 4.90) PT (s) 11.9 (10.9, 13.1) 11.8 (11.0, 13.4) 11.9 (10.9, 12.8) PTA (%) 88.0 (73.0, 100.2) 88.0 (70.0, 100.3) 88.8 (76.8, 98.6) INR 1.05 (0.97, 1.19) 1.06 (0.97, 1.20) 1.05 (0.97, 1.16) WBC (´ 10 9 /L) 5.4 (4.3, 6.7) 5.3 (4.2, 6.7) 5.4 (4.4, 6.7) EO (´ 10 9 /L) 0.08 (0.04, 0.16) 0.09 (0.04, 0.16) 0.08 (0.04, 0.15) HGB (g/L) 132.0 (121.0, 142.0) 130.0 (120.0, 143.0) 133.0 (123.0, 140.0) PLT (´ 10 9 /L) 205.0 (158.0, 268.0) 204.0 (155.0, 265.0) 208.0 (164.0, 268.0) ANA, n (%) Positive 222 (47.1) 133 (47.2) 89 (47.1) Negative 249 (52.9) 149 (52.8) 100 (52.9) Autoantibody, n (%) Positive 70 (14.9) 46 (16.3) 24 (12.7) Negative 401 (85.1) 236 (83.7) 165 (87.3) Abbreviations: ALB, albumin; ALP, alkaline phosphatase; ALT, alanine aminotransferase; ANA, antinuclear antibody; AST, aspartate aminotransferase; BMI, body mass index; Che, cholinesterase; Cr, creatinine; DBIL, direct bilirubin; EO, eosinophils; GGT, gamma-glutamyl transferase; Glo, globulin; HGB, hemoglobin; INR, international normalized ratio; MELD, model for end-stage liver disease; PA, prealbumin; PLT, platelet; PT, prothrombin time; PTA, prothrombin time activity; RUCAM, Roussel Uclaf causality assessment method; TBA, total bile acid; TBIL, total bilirubin; TC, total cholesterol; WBC, white blood cell. Table 2 Categories of medications associated with liver injury in 471 patients diagnosed with DILI Drug classification Main drugs TCM-NM-HP-HDS · Alumen , a ndrographolide injection, angelica sinensis, anshenbunao oral solution, baguniu pulvis, baizi yangxin pills, Compound l iquorice t ablets ; cortex dictamni , c ortex phellodendri , cortex pseudolaricis , danqi, mo luo dan, dictamni cortex, diet pill, Endoconcha Sepiae ; enzyme jelly, Flos Carthami ; fructus aurantii , fuyankang capsules, fructus cnidii , fructus kochiae , gentina scabra bunge, ginseng; antler, guizhifuling capsules, Guchang zhixie wan; health care products, hundred-pace viper, jinshuibao capsules, jinwugutong capsules, kunbao pills, miao nationality herbal medicines, mongolian medicines, Multivitamin; Niaoduqing Granules ; pericarpium zanthoxyli , powder of antelope’s horn for clearing lung-heat, Radix s ngelicae s inensis ; r adix a stragali , radix bupleuri, Rhizoma c orydalis ; R adix curcumae ; Rhizoma c urcumae l ongae ; Rhizoma Cyperi ; Radix et r hizoma r hodiolae ; R adix glycyrrhizae ; Radix n otoginseng ; R adix p aeoniae alba , r adix polygoni multiflori, radix sophorae flavescentis , Runzao zhiyang capsules ; rhizoma corydalis , sedum aizoon, Salvia miltiorrhiza ; selfheal medicinal extract, selfheal, shenqijingukang capsules, Shensong y angxin c apsules ; sijunzi decoction, sophora, turtle shell decocted pills, Urticaria Pills ; vitex negundo, wangbi tablets, Wuling c apsules ; xihuang pills, yangxue qingnao granules, yangxueshengfa capsules, yaobitong capsules, Yougui wan; etc.‌ Antibiotic drugs (including anti-tuberculosis drugs) Amikacin, amoxicillin, antiretroviral therapy drugs, aztreonam, Cefixime ; Cefpodoxime p roxetil ; clindamycin, ethambutol, ganciclovir, isoniazid, levofloxacin, metronidazole, Norfloxacin ; pyrazinamide, rifampicin, Rifapentine ; roxithromycin, voriconazole NSAIDs Acetaminophen ; Aspirin; celecoxib, compound aminopyrine phenacetin, compound paracetamol and amantadine, etoricoxib, ibuprofen, Lguratimod; Metamizde sodium; paracetamol, sodium aminosalicylate Cardiovascular drugs Amlodipine, irbesartan hydrochlorothiazide, nifedipine, reserpine Drugs for metabolic diseases Allopurinol, atorvastatin, colchicine, dapagliflozin, febuxostat, metformin, propylthiouracil, rosuvastatin, thiamazole Anti-tumor drugs Camatinib; Carboplatin ; Cisplatin, cyclophosphamide, Docetaxel; doxorubicin, etoposide, flumatinib, fulvestrant, gefitinib, lenalidomide, letrozol, methotrexate, nilotinib, Osimertinib; Paclitaxel, Pemetrexed ; exemestane, tamoxifen Chemical agents/industrial poisons Disinfecting agent, hair dye, industrial, nail enamel, range hood cleaning agent Central nervous system drugs Agomelatine, buspirone, carbamazepine, citalopram, clozapine, diazepam, Escitalopram o xalate t ablets ; gastrodin, levetiracetam, mirtazapine, olanzapine, paroxetine, risperidone, sodium valproate, venlafaxine, zopiclone Biologics/immunosuppressants Atezolizumab ; Bevacizumab ; Cyclosporin; Leflunomide, obinutuzumab, other programmed death-1, pertuzumab, spleen aminopeptide, transfer factor, trastuzumab Hormone Danazol, ethinylestradiol cyproteron, fulvestrant, levothyroxine, methylprednisolone, stanazol, triamcinolone Others Acitretin, azelastine, betahistine, cetirizine, clopidogrel, ebastine, Efavirenz , iguratimod, Lamivudine , loratadine, rivaroxaban, Tenofovir , ubenimex; Ursodeoxycholic acid Abbreviations: TCM, traditional Chinese medicine; NM, natural medicine; HP, health products; HDS, herbs and dietary supplements; NSAIDs, non-steroidal anti-inflammatory drugs. Table 3 Univariate and multivariate Logistic analysis of 471 patients with DILI Characteristics Univariate analysis Multivariate analysis OR 95% CI P OR 95% CI P Sex 1.98 (1.08, 3.78) 0.032* 2.07 (1.02, 4.40) 0.050* Age (years) 1.02 (1.00, 1.04) 0.080 1.03 (1.01, 1.06) 0.019* BMI (kg/m 2 ) 0.95 (0.86, 1.04) 0.259 Hospitalization (days) 1.01 (0.98, 1.04) 0.527 Allergic 0.71 (0.27, 2.09) 0.507 Severity 0.67 (0.38, 1.21) 0.188 Clinical phenotype 1.72 (0.89, 3.27) 0.099 RUCAM score 1.08 (0.89, 1.32) 0.438 MELD score 1.02 (0.98, 1.06) 0.280 0.92 (0.85, 0.99) 0.022* ALT (U/L) 1.00 (1.00, 1.00) 0.006** 1.00 (1.00, 1.00) 0.023* AST (U/L) 1.00 (1.00, 1.00) 0.121 ALP (U/L) 1.00 (1.00, 1.00) 0.127 GGT (U/L) 1.00 (1.00, 1.00) 0.053 Che (U/L) 1.00 (1.00, 1.00) 0.001*** 1.00 (1.00, 1.00) 0.082 Cr (μmol/L) 1.00 (0.99, 1.00) 0.415 TBA (μmol/L) 1.00 (1.00, 1.00) 0.491 TBIL (μmol/L) 1.00 (1.00, 1.00) 0.657 DBIL (μmol/L) 1.00 (1.00, 1.00) 0.763 ALB (g/L) 0.92 (0.88, 0.97) 0.002** Glo (g/L) 1.04 (1.01, 1.08) 0.013* 1.02 (1.00, 1.06) 0.341 PA (mg/dL) 0.98 (0.96, 1.00) 0.064 0.98 (0.96, 1.00) 0.070 TC (mmol/L) 0.97 (0.87, 1.04) 0.516 PT (s) 1.03 (0.99, 1.09) 0.169 PTA (%) 0.98 (0.96, 0.99) 0.001*** 0.97 (0.95, 0.99) 0.007** INR 1.26 (0.90, 2.08) 0.228 WBC (×10 9 /L) 0.95 (0.85, 1.04) 0.276 EO (×10 9 /L) 0.89 (0.36, 1.31) 0.672 HGB (g/L) 0.98 (0.96, 0.99) 0.001*** PLT (×10 9 /L) 0.99 (0.99, 1.00) 0.001*** 1.00 (0.99, 1.00) 0.068 ANA 1.04 (0.61, 1.78) 0.894 2.27 (1.15, 4.63) 0.020* Autoantibody 0.29 (0.15, 0.55) ＜0.001*** 0.30 (0.13, 0.67) 0.004** Note: Defined Logistic multivariable analysis for incorporated into the Nomogram model. * P < 0.05，** P < 0.01，*** P < 0.001. Abbreviations: ALB, albumin; ALP, alkaline phosphatase; ALT, alanine aminotransferase; ANA, antinuclear antibody; AST, aspartate aminotransferase; BMI, body mass index; Che, cholinesterase; Cr, creatinine; DBIL, direct bilirubin; EO, eosinophils; GGT, gamma-glutamyl transferase; Glo, globulin; HGB, hemoglobin; INR, international normalized ratio; MELD, model for end-stage liver disease; PA, prealbumin; PLT, platelet; PT, prothrombin time; PTA, prothrombin time activity; RUCAM, Roussel Uclaf causality assessment method; TBA, total bile acid; TBIL, total bilirubin; TC, total cholesterol; WBC, white blood cell. Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoren","middleName":"","lastName":"Wang","suffix":""},{"id":640018669,"identity":"b5cae116-f9da-485f-a3a2-ba28c00267d9","order_by":5,"name":"Zhenghan Li","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenghan","middleName":"","lastName":"Li","suffix":""},{"id":640018670,"identity":"e9d2b929-ea87-4648-a6c2-5309d02cf2d6","order_by":6,"name":"Tomii Ayaka","email":"","orcid":"","institution":"the Fourth Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tomii","middleName":"","lastName":"Ayaka","suffix":""},{"id":640018671,"identity":"79cbcb7b-4dca-4b8d-a895-1e8a83543e40","order_by":7,"name":"Zhu Zhu","email":"","orcid":"","institution":"the Fourth Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Zhu","suffix":""},{"id":640018672,"identity":"dccfe05f-f53f-42bd-bf15-6c0eb5e506a5","order_by":8,"name":"Xiaotong Xie","email":"","orcid":"","institution":"the Fourth Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Xie","suffix":""},{"id":640018673,"identity":"5dd11fbf-5635-42e4-9275-606985de3a90","order_by":9,"name":"Lei Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoElEQVRIiWNgGAWjYBACgxvMxxgYGyBsYrWwpZGoRXIGjxmJWvgleL49rtyxLbGBvXmbBEPNHcJa2CR4txuePXM7sYHnWJkEw7FnRGnZJtnYBtQikWMmwdhwmBgtPM8gWuTfEK+FDWoLD9Fa2MwkG8/cNm7jSSu2SDhGjBb5w0CH7bgt289+eOONDzVEaEHoBREJJGgYBaNgFIyCUYAHAAD5ojZLjvmJ6QAAAABJRU5ErkJggg==","orcid":"","institution":"the Fourth Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2026-01-20 02:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8643882/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8643882/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109331726,"identity":"97447267-73e9-4e28-a06f-2be27af2921b","added_by":"auto","created_at":"2026-05-15 16:10:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1249392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study cohort selection for patients with DILI.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8643882/v1/1ed70f8cfafc27dd84257225.png"},{"id":109331727,"identity":"59d3256b-0d64-4c80-95b0-379c755c5fc1","added_by":"auto","created_at":"2026-05-15 16:10:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5776397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting the risk of adverse outcomes in patients with DILI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, locate the patient's value for each predictor on the corresponding \"Points\" axis and draw a line upward to determine the score for that variable. Sum the scores for all variables and locate the resulting value on the \"Total Points\" axis. Finally, project this total score downward to the \"Predictions\" axis to read the estimated probability of an adverse outcome. Note: Variables marked with an asterisk (*) or double asterisks (**) in the Figure indicate different levels of statistical significance in the multivariable analysis (the specific \u003cem\u003eP\u003c/em\u003e-value thresholds should be defined in the Methods section). Abbreviations: ALT, alanine aminotransferase; ANA, antinuclear antibody; Che, cholinesterase; Glo, globulin; MELD, model for end-stage liver disease; PA, prealbumin; PLT, platelet; PTA, prothrombin time activity.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8643882/v1/fda89c2481711e8413044bfc.png"},{"id":109331732,"identity":"9c22a596-de3a-434f-a3c6-0852f71189a2","added_by":"auto","created_at":"2026-05-15 16:10:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4424758,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance evaluation of the predictive nomogram model in the training and validation sets. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Calibration curve in the training set. The plot depicts the \"Apparent\" prediction, the \"Bias-corrected\" curve, and the \"Ideal\" reference line representing perfect prediction. Internal validation was performed using the Bootstrap method (1000 repetitions, n = 282). The mean absolute error of 0.029 indicates high agreement between predicted and observed outcomes. (\u003cstrong\u003eB\u003c/strong\u003e) Calibration curve in the validation set. The deviation of the curve from the ideal line illustrates the calibration performance on the independent dataset. (\u003cstrong\u003eC\u003c/strong\u003e) DCA in the training set. The plot compares the net benefit of the \"Nomogram Model\" against the \"All\" and \"None\" strategies across various threshold probabilities. The nomogram provides clinical net benefit when its curve is above the two reference lines. (\u003cstrong\u003eD\u003c/strong\u003e) DCA in the validation set. This result validates the clinical utility of the model in an independent cohort. (\u003cstrong\u003eE\u003c/strong\u003e) ROC curve in the training set. The AUC of 0.793 indicates good discriminative ability in the training set. (\u003cstrong\u003eF\u003c/strong\u003e) ROC curve in the validation set. The AUC of 0.664 demonstrates the model's discriminative performance in the independent validation set. Abbreviations: AUC, area under the curve; DCA, decision curve analysis; ROC, receiver operating characteristic.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8643882/v1/9f77f138b57438cf81a2ab95.png"},{"id":109331813,"identity":"fba3c4b8-e4b5-4228-bed1-10d8f90b1902","added_by":"auto","created_at":"2026-05-15 16:10:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12129603,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8643882/v1/e15a1061-27c0-4b87-9047-65a1ad5eca4d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Nomogram for Predicting Prognosis in Patients with Drug-Induced Liver Injury: A Multicenter Retrospective Study","fulltext":[{"header":"Background","content":"\u003cp\u003eDrug-induced liver injury (DILI) is an important cause of liver diseases worldwide, and in severe cases, it can lead to acute liver failure.[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Therefore, DILI is one of the major public health challenges caused by adverse drug reactions. Given the extensive list of causative agents and multifactorial pathogenesis of DILI, identifying prognostic risk factors is clinically imperative for physicians to make accurate outcome predictions. Recent studies have identified cytokeratin 18 (CK-18), macrophage colony-stimulating factor receptor 1 (MCSFR1), and osteopontin (OPN) as candidate biomarkers for predicting adverse prognosis in DILI.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] However, these preliminary investigations lack robust validation through multicenter clinical trials, which limits their current clinical utility. Hence, there is an urgent need to establish a rapid, simple, and effective predictive prognostic model to evaluate the clinical outcomes of patients with DILI.\u003c/p\u003e \u003cp\u003eNomograms have emerged as indispensable tools in prognostic research, which graphically translate multivariate regression analyses into intuitive scoring systems to enhance the clinical interpretability of predictive models.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] These visual decision aids enable clinicians to stratify patient risks objectively and to optimize therapeutic strategies through real-time probability quantification. In the past, nomograms have been widely applied to predict diagnoses and prognoses across various cancers and assess the probability of clinical outcome events in patients.[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] With advancements in medical research and technological progress, potential applications of nomograms in the medical field are expected to expand significantly. In recent years, nomograms have been increasingly used to predict the prognosis of non-neoplastic diseases. Clinicians can employ the predictors identified by this model to implement prospective and preventive interventions.[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis study aims to investigate the risk factors influencing the prognosis of patients with DILI. Meanwhile, a nomogram prognostic model for quantitatively evaluating the individual risks of DILI patients based on clinical characteristics is established.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThis multicenter retrospective study analyzed real-world clinical data from four hospitals in Northeast China (2019\u0026ndash;2024). \u003cem\u003eAll enrolled patients were newly diagnosed with DILI and initially hospitalized in the Department of Infectious Diseases during the study period.\u003c/em\u003e All enrolled patients had documented drug exposure histories prior to disease onset. Clinical management included immediately discontinuing the suspected drugs after the diagnosis of DILI and providing individualized treatment. The research protocol strictly adhered to the ethical standards established in the Declarations of Helsinki and the Istanbul Consensus Guidelines. Given the retrospective design of the study, the committee waived the need for written informed consent.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committees of the Fourth Affiliated Hospital of Harbin Medical University (No. 2024\u0026ndash;108), Heilongjiang Provincial Hospital (No. 2024\u0026ndash;074), the First Affiliated Hospital of Harbin Medical University (No. 202562), and the Second Affiliated Hospital of Harbin Medical University (No. KY2024\u0026ndash;280).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003e The inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, (2) clear history of drug exposure, (3) Roussel Uclaf causality assessment method (RUCAM) score\u0026thinsp;\u0026ge;\u0026thinsp;3 points, (4) diagnosed with DILI according to the Chinese guideline for the diagnosis and treatment of DILI.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows: (1) viral hepatitis (including hepatitis A, B, C, E, cytomegalovirus, and Epstein-Barr virus, etc.); (2) alcoholic liver disease (ALD); (3) non-alcoholic fatty liver disease (NAFLD); (4) autoimmune hepatitis (AIH); (5) primary biliary cholangitis (PBC); (6) primary sclerosing cholangitis (PSC); (7) hepatolenticular degeneration (HLD); (8) hemochromatosis (HC); and (9) other causes of liver dysfunctions.\u003c/p\u003e\n\u003ch3\u003eComposition and types of drugs causing DILI\u003c/h3\u003e\n\u003cp\u003eThe suspected causative agents for DILI were systematically categorized into 11 distinct groups based on their therapeutic class and composition: (1) traditional Chinese medicine (TCM)-natural medicine (NM)-health products (HP)-herbs-dietary supplements (HDS); (2) antibiotic drugs (including anti-tuberculosis drugs); (3) non-steroidal anti-inflammatory drugs (NSAIDs); (4) cardiovascular drugs; (5) drugs for metabolic diseases; (6) anti-tumor drugs; (7) chemical agents/industrial poisons; (8) central nervous system drugs; (9) biologics/immunosuppressants; (10) hormones; and (11) others. This classification is consistent with methodologies employed in large-scale DILI studies and guidelines.[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eDemographic characteristics and laboratory parameters of enrolled DILI patients were documented. Demographic details included sex, age, body mass index (BMI), hospitalization days, and history of allergy. \u003cem\u003eThe following laboratory parameters were recorded at peak disease severity\u003c/em\u003e: white blood cell (WBC), eosinophils (EO), hemoglobin (HGB), platelet (PLT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), cholinesterase (Che), creatinine (Cr), total bile acid (TBA), total bilirubin (TBIL), direct bilirubin (DBIL), albumin (ALB), globulin (Glo), prealbumin (PA), total cholesterol (TC), prothrombin time (PT), prothrombin time activity (PTA), international normalized ratio (INR), antinuclear antibody (ANA), and autoantibodies. In addition, various clinical manifestations following the onset of DILI have been documented, including fever, fatigue, nausea, vomiting, abdominal pain, abdominal distension, loss of appetite, jaundice, skin itching, and pale fecal color.\u003c/p\u003e\n\u003ch3\u003eDefinition\u003c/h3\u003e\n\u003cp\u003e The RUCAM score was assessed based on the guideline for the diagnosis and treatment of DILI.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eBased on the R ratio, acute DILI was classified as follows[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]: (1) hepatocellular injury type R\u0026thinsp;\u0026ge;\u0026thinsp;5, (2) cholestatic type R\u0026thinsp;\u0026le;\u0026thinsp;2, and (3) mixed type 2 \u0026lt; \u003cem\u003eR\u003c/em\u003e \u0026lt; 5. The R ratio was calculated using the following formula: [ALT/upper normal limit (ULN) of ALT]/[ALP/ULN of ALP]. If ALT levels were unavailable, AST levels were used for the calculation.\u003c/p\u003e \u003cp\u003eUpon diagnosis of acute DILI, the severity of the condition was assessed as follows[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]: (1) mild: ALT\u0026thinsp;\u0026ge;\u0026thinsp;5 \u0026times; ULN or ALP\u0026thinsp;\u0026ge;\u0026thinsp;2 \u0026times; ULN with TBIL \u0026lt; 2 \u0026times; ULN; (2) moderate: ALT\u0026thinsp;\u0026ge;\u0026thinsp;5 \u0026times; ULN or ALP\u0026thinsp;\u0026ge;\u0026thinsp;2 \u0026times; ULN with TBIL\u0026thinsp;\u0026ge;\u0026thinsp;2 \u0026times; ULN or symptomatic hepatitis; (3) severe: ALT\u0026thinsp;\u0026ge;\u0026thinsp;5 \u0026times; ULN or ALP\u0026thinsp;\u0026ge;\u0026thinsp;2 \u0026times; ULN with TBIL\u0026thinsp;\u0026ge;\u0026thinsp;2 \u0026times; ULN, or symptomatic hepatitis accompanied by any of the following criteria: INR\u0026thinsp;\u0026ge;\u0026thinsp;1.5, ascites, and/or hepatic encephalopathy; disease duration \u0026lt; 26 weeks without cirrhosis or other organ failure attributable to DILI; and (4) fatal: death resulting from DILI or necessitating liver transplantation for survival.\u003c/p\u003e \u003cp\u003eThe model for end-stage liver disease (MELD) was calculated as follows[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]: [3.78 \u0026times; In(TBIL in mg/dL) + 11.2 \u0026times; In(INR) + 9.57 \u0026times; In(Cr in mg/dL) + 6.43 (Biliary or alcoholic 0, other1)].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical outcomes\u003c/h2\u003e \u003cp\u003ePatients with DILI were classified into two groups according to clinical outcomes: (1) favorable outcome group: patients showed significant recovery in clinical symptoms and signs, and liver function returned to normal within 1 year after discontinuation of the suspected drug; (2) adverse outcome group: chronic DILI (1 year after the DILI event, biochemical indicators did not return to normal or baseline levels) or death.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFollow up\u003c/h3\u003e\n\u003cp\u003ePatient outcomes were assessed retrospectively over a one-year period following DILI onset using outpatient electronic medical records supplemented by telephone interviews. The clinical endpoint for each patient was determined based on their status at the last available evaluation within this period and was categorized as complete recovery, progression to chronic DILI, or death. All enrolled patients had a definitive outcome recorded, and no patients were lost to follow-up by this retrospective ascertainment method.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNormally distributed measurement data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), with between-group comparisons performed using the Student\u0026rsquo;s t-test. Non-normally distributed data are expressed as medians with interquartile ranges (IQR, P\u003csub\u003e25\u003c/sub\u003e-P\u003csub\u003e75\u003c/sub\u003e) and compared using the Mann-Whitney U test. Categorical data are presented as percentages and analyzed with the Chi-square or Fisher\u0026rsquo;s exact test. A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were conducted using SPSS 27.0 and R 4.3.0.\u003c/p\u003e \u003cp\u003eIndependent risk factors influencing clinical outcomes in patients with DILI were identified using univariate and multivariate logistic regression, with results reported as odds ratios (OR) and 95% confidence intervals (CI). Variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate analysis were considered candidates for the multivariate model. Final predictors were selected via stepwise logistic regression optimized by minimizing the Akaike Information Criterion (AIC), aiming for a parsimonious model with strong predictive performance. All retained variables were incorporated into a nomogram.\u003c/p\u003e \u003cp\u003eThe nomogram was developed using the rms package in R, converting regression coefficients into a 0\u0026ndash;100 point scale. Missing data were handled by multiple imputation using the mice package. Model performance was internally validated via calibration curves, decision curve analysis (DCA), and receiver operating characteristic (ROC) curves.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics\u003c/h2\u003e \u003cp\u003eA total of 1885 patients were screened for this study. Finally, 471 individuals diagnosed with DILI who met the inclusion and exclusion criteria were enrolled. Among these participants, 352 were classified into the favorable outcome group, while 119 were classified into the adverse outcome group (\u003cb\u003eFigure. 1\u003c/b\u003e). Notably, there were five fatalities recorded in the adverse outcome group. All five fatalities in the adverse outcome group were attributed to DILI-induced liver failure.\u003c/p\u003e \u003cp\u003eThe median age of the participants was 53.0 years (IQR, 45.0\u0026ndash;61.0), with females constituting the majority, 321 (68.2%). Hepatocellular injury (272/471, 57.8%) was the predominant type affecting the participants, followed by the cholestatic type (115/471, 24.4%) and the mixed type (84/471, 17.8%). A comparative analysis between the two groups revealed statistically significant differences in variables such as severity, clinical phenotype, ALT, AST, ALP, GGT, Che, ALB, Glo, PT, PTA, INR, HGB, PLT, and the presence of autoantibodies (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, the patients were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;282) and a validation set (n\u0026thinsp;=\u0026thinsp;189) in a ratio of 6:4 for subsequent analysis \u003cb\u003e(Table\u0026nbsp;1)\u003c/b\u003e. The training set was used for model development and parameter estimation, while the validation set was used to independently verify the predictive performance of the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSuspected causative agents for DILI\u003c/h2\u003e \u003cp\u003eIn this study, TCM-NM-HP-HDS was the most common category associated with DILI, closely followed by antibiotics (including anti-tuberculosis drugs) (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). The specific drug names and representative compositions within each category are provided in Supplementary \u003cb\u003eTable S2\u003c/b\u003e. This supplementary table details the drug names and their compositions for all 471 patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical symptoms and signs\u003c/h2\u003e \u003cp\u003eThe primary symptom and sign of DILI was fatigue, affecting 79.4% (374/471) of the patients. Other symptoms and signs included loss of appetite (60.1%, 283/471), nausea (40.1%, 189/471), abdominal distension (38.0%, 179/471), jaundice (37.2%, 175/471), fever (24.8%, 117/471), vomiting (16.1%, 76/471), skin itching (12.1%, 57/471), abdominal pain (7.2%, 34/471), and pale fecal color (5.9%, 28/471) (\u003cb\u003eFigure. S1\u003c/b\u003e). Fortunately, the associated signs and symptoms gradually diminish as the patients with DILI recover. Additionally, ten patients with DILI showed no significant signs of discomfort or symptoms from the onset to recovery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and multivariate logistic regression analysis\u003c/h2\u003e \u003cp\u003eUnivariate logistic regression analysis showed that sex, elevated ALT, decreased Che, decreased ALB, decreased Glo, decreased PTA, decreased HGB, decreased PLT, and positive autoantibodies were associated with adverse outcomes in patients with DILI (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (\u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e). Subsequently, multiple stepwise logistic regression was used to identify the factors influencing the individual clinical outcomes of DILI patients and establish the optimal multiple regression model. Multivariate analysis screened out 11 variables, including sex, PLT, Che, Glo, age, ANA, autoantibodies, ALT, MELD score, PTA, and PA (\u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e). Among them, sex, increased age, increased MELD score, elevated ALT, decreased PTA, positive ANA, and positive autoantibody are independent risk factors affecting the clinical outcome of DILI patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment and verification of nomogram\u003c/h2\u003e \u003cp\u003eThe 11 factors identified through the multivariate stepwise logistic regression analysis were incorporated into a nomogram (\u003cb\u003eFigure. 2\u003c/b\u003e). These variables were ranked according to the size of their regression coefficients; thus, the degree of contribution progressively increased from top to bottom. Each variable within the model was projected upward onto the first line of the scoring scale for evaluation purposes, and the score values for all 11 variables were summed up to derive the total score. This total score serves as a predictor of clinical outcomes in patients with DILI: the higher the score, the more likely the adverse outcome for patients with DILI.\u003c/p\u003e \u003cp\u003eTo evaluate the performance of the predictive nomogram, we assessed its calibration, clinical utility, and discriminative ability in both the training and validation sets.\u003c/p\u003e \u003cp\u003eThe calibration curves indicated excellent agreement between predicted and observed probabilities in the training set, with a mean absolute error of 0.029 (based on 1000 Bootstrap repetitions). In the validation set, the model maintained acceptable overall calibration, albeit with slight deviations in the mid-to-high probability range (\u003cb\u003eFigure. 3A and B\u003c/b\u003e). DCA demonstrated that the use of the nomogram provided a higher net benefit than the \u0026ldquo;treat-all\u0026rdquo; or \u0026ldquo;treat-none\u0026rdquo; strategies across threshold probabilities of 0.1\u0026ndash;0.7 in the training set and 0.2\u0026ndash;0.5 in the validation set, supporting its clinical utility (\u003cb\u003eFigure. 3C and D\u003c/b\u003e). Regarding discriminative ability, the model achieved an area under the ROC curve of 0.793 in the training set (\u003cb\u003eFigure. 3E\u003c/b\u003e), indicating good performance. However, the area under the curve (AUC) decreased to 0.664 in the independent validation set (\u003cb\u003eFigure. 3F\u003c/b\u003e), suggesting potential overfitting and limited generalizability of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMost patients with DILI can recover within six months following discontinuation of the offending drugs, and their prognosis is generally favorable. However, some patients may experience adverse clinical outcomes, potentially progressing to chronic DILI, acute liver failure, or even death.[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eA clinical prediction model is a statistical framework developed based on multiple factors, employing mathematical language or formulas to articulate the relationships between various elements. This model estimates the probability of developing a disease or predicts the likelihood of specific future outcomes, such as recurrence, deterioration, or mortality. Clinical prediction models can be categorized into two types: clinical diagnosis and clinical prediction models. By analyzing the patients\u0026rsquo; clinical data, including demographic information, genetic factors, symptoms, signs, laboratory results, imaging findings, and histopathological results, clinical prediction models can assist clinicians in gaining a deeper understanding of disease progression and provide personalized treatment plans for their patients. Several predictive models have been proposed to assess DILI, including biochemical non-resolution-6 (BNR-6)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and drug-induced acute liver failure-5 (DIALF-5).[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] It is important to note that BNR-6 serves as a scoring system specifically for chronic DILI and necessitates a liver pathology score, whereas DIALF-5 primarily functions as a prognostic scoring tool for non-acetaminophen drug-induced acute liver failure. Consequently, both scoring systems have limitations in clinical practice. The nomogram model developed in this study is designed to be broadly applicable to patients with DILI. It serves as a convenient, efficient, and practical scoring tool that enables clinicians to assess the clinical outcomes of patients effectively. The developed nomogram is intended as a general prognostic tool for DILI. Its predictive variables are derived from the patient\u0026rsquo;s clinical and biochemical response to the injury, making it broadly applicable regardless of the specific causative agent.\u003c/p\u003e \u003cp\u003eA multicenter retrospective study conducted across four hospitals in northeast China summarized the demographic characteristics and prognosis of patients with DILI in this region. These findings indicate that DILI was more prevalent among women, with hepatocellular injury being the predominant clinical type observed. The primary drug implicated in DILI was TCM-NM-HP-HDS. Multivariate logistic regression analysis revealed that factors such as sex, PLT, Che, Glo, age, ANA, autoantibodies, ALT, MELD score, PTA, and PA were significantly associated with the clinical outcomes in patients with DILI. A nomogram model was subsequently developed based on the variables identified through the multivariate logistic regression analysis, and internal validation and evaluation were also performed.\u003c/p\u003e \u003cp\u003eThe predominance of TCM-NM-HP-HDS as the primary causative agents of DILI in our cohort aligns with epidemiological patterns observed across China and much of Asia[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], underscoring a critical public health concern. The hepatotoxic mechanisms of TCM/HDS are notably complex and multifactorial, often involving intrinsic toxicity, idiosyncratic reactions, and external factors such as contamination. Certain herbs contain inherently hepatotoxic compounds; for example, pyrrolizidine alkaloids (found in some \u003cem\u003eHeliotropium\u003c/em\u003e species) can cause hepatic sinusoidal obstruction syndrome via metabolic activation into damaging pyrrolic derivatives, while glycosides in \u003cem\u003ePolygonum multiflorum\u003c/em\u003e\u0026mdash;a frequently implicated herb\u0026mdash;are associated with mitochondrial dysfunction and oxidative stress, leading to hepatocellular apoptosis or necrosis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, idiosyncratic reactions, which are unpredictable and not dose-dependent, are common and may involve the metabolic activation of herbal constituents by cytochrome P450 enzymes into reactive metabolites. These metabolites can act as haptens, triggering an adaptive immune response, or directly induce oxidative stress and mitochondrial injury[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Complicating this picture are issues of herb-drug interactions and potential contamination with heavy metals, pesticides, or undeclared synthetic drugs, which can independently or synergistically provoke liver injury[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The high prevalence of TCM/HDS-related DILI highlights the imperative for enhanced regulatory oversight, rigorous quality control of herbal products, and continued research into the specific toxic components and host genetic factors that predispose individuals to these injuries.\u003c/p\u003e \u003cp\u003eIn Western countries, NSAIDs such as acetaminophen, antibiotics such as amoxicillin-clavulanate, anti-tuberculosis drugs, and HDS are among the most common agents responsible for DILI.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Over the past 25 years, data from the United States Drug-Induced Liver Injury Network (DILIN) prospective study have indicated an increase in DILI cases attributed to HDS from 7% to 17%.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Furthermore, there has been an eight-fold increase in the number of individuals awaiting liver transplantation due to liver failure caused by these substances.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] In Asian countries, including China,[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] South Korea,[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Japan,[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] India,[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and Malaysia[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the primary drugs responsible for DILI include TCM, anti-tuberculosis drugs, and antibiotics, which is consistent with our results. Clinicians should exercise heightened vigilance regarding the medication history of patients using TCM/HDS, as well as anti-tuberculosis drugs and antibiotics, in conjunction with both Eastern and Western susceptible drugs when identifying potential agents for DILI.\u003c/p\u003e \u003cp\u003eSimilarly, controversy remains over whether female sex is a risk factor for susceptibility to DILI. Epidemiological data derived from three prospective cohort studies conducted in Spain, the United States, and Iceland revealed a relatively balanced sex distribution.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Women exhibit higher susceptibility to certain drugs such as minocycline- and nitrofurantoin-induced AIH.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] This may be because women are more susceptible to PBC and AIH, whereas men are more susceptible to PSC.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOur findings also indicated that age was an independent risk factor for the prognosis of DILI (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). DILI was more prevalent among women; however, this observation was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Therefore, although age and sex are significant factors influencing DILI, a definitive conclusion remains elusive owing to the diverse range of drugs implicated in DILI.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] When considering the pathogenic effects of these two factors on patients, clinicians should adopt a flexible approach.\u003c/p\u003e \u003cp\u003eWith increasing age, the body\u0026rsquo;s excretion and metabolic function of drugs gradually declines, resulting in prolonged retention time of drugs in the body, and the risk of adverse drug reactions increases. It is important to note that certain drugs may not be influenced by advancing age. Research has demonstrated that advanced age increases the risk of liver damage associated with isoniazid, amoxicillin-clavulanate potassium, and nitrofurantoin use.[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] Conversely, sodium valproate has been associated with a higher risk of DILI in younger children.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Data from most retrospective studies[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] indicate that age is a predisposing factor for DILI. However, the findings of a large-scale prospective study[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] on DILI do not corroborate this assertion.\u003c/p\u003e \u003cp\u003eHepatocellular injury, cholestasis, and mixed types are the most prevalent clinical types of DILI. Among these, hepatocellular injury accounts for approximately 42\u0026ndash;59%, primarily characterized by elevated levels of ALT or AST. The cholestatic type constitutes approximately 20\u0026ndash;32%, predominantly marked by increased ALP or GGT.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Hepatocytes possess robust regenerative capacity; thus, mild hepatocellular injury often leads to recovery. Therefore, patients with hepatocellular injury and mixed DILI typically have favorable prognoses. However, a subset of patients with hepatocellular injury may progress to fulminant liver failure, with a significant proportion being attributed to TCM/HDS. These individuals frequently require liver transplantation as a lifesaving intervention.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] In contrast to hepatocellular injury and mixed types of DILI, patients with the cholestatic type more commonly experience involvement of bile duct cells, resulting in damage to the bile duct epithelium, leading to cholestasis, and even in severe cases, vanishing bile duct syndrome. The disease course of patients exhibiting this phenotype is often prolonged, placing them at an elevated risk for chronic conditions or delayed recovery.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] A multicenter, prospective, large-scale cohort study conducted in the United States included 363 patients diagnosed with DILI via liver biopsy.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] The results revealed that 26 patients (26/363, 7.2%) exhibited varying degrees of bile duct disappearance, and their clinical type was cholestatic without exception. Among these patients, five (5/26, 19.2%) succumbed to their condition, and 2 (2/26, 7.7%) underwent liver transplantation. Consequently, it is imperative that clinicians closely monitor patients with cholestatic DILI, paying particular attention to their laboratory indicators and clinical outcomes. If the disease persists without remission or even progresses further, consideration must be given to the possibility that these patients may require liver transplantation to preserve their lives.\u003c/p\u003e \u003cp\u003eANA and autoantibodies play a critical role in autoimmune diseases by targeting proteins or tissues.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] Normally, the immune system can distinguish between self and non-self substances, thereby preventing attacks on healthy cells. However, this mechanism malfunctions in autoimmune diseases, leading to the production of autoantibodies. Therefore, positive ANA and autoantibody results suggest the potential presence of immune-mediated liver injury and serve as auxiliary diagnostic indicators for the progression of DILI to chronic disease. Moreover, patients positive for ANA and autoantibodies are at a higher risk of developing chronic liver injury, cirrhosis, and even liver failure, which is significant for patient prognosis. Multivariate logistic regression analysis revealed that ANA and autoantibodies were independent risk factors that influenced the prognosis of patients with DILI. However, it is important to note that these antibody positivity markers are not the sole determinants of DILI progression to a chronic condition or patient prognosis; they are also influenced by factors such as drug type, dosage, treatment duration, drug interactions, sex, and individual genetic background.[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDespite the rigorous methodology and internal validation, this study has several limitations that should be acknowledged. First, the development and validation of our nomogram were based on a multicenter cohort from Northeast China. Although robust internal validation via bootstrap resampling demonstrated good model performance, the absence of external validation with an independent, geographically diverse cohort limits the generalizability of our findings. The model\u0026rsquo;s performance in other populations with different genetic backgrounds, healthcare systems, and prescribing habits remains uncertain and warrants future confirmation. Second, as a retrospective study, it is inherently subject to potential selection bias and unmeasured confounding factors. Despite our efforts to collect comprehensive data, some variables of potential interest, such as specific genetic markers or detailed dietary information, were not available for analysis, which might have further refined the predictive accuracy of the model. Third, the categorization of causative agents, particularly the broad TCM-NM-HP-HDS group, encompasses a highly heterogeneous mixture of substances. The specific components and dosages within this category were often unclear, which precludes a more nuanced analysis of the risks associated with individual agents.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the nomogram model developed in this study demonstrated satisfactory clinical calibration, discriminative ability, and practical value in assessing the clinical outcomes of patients with DILI. It is anticipated that this model will offer new insights and methodologies for the clinical management of patients with DILI and will provide clinicians with important information on the developmental trend of the disease. This capability facilitates timely adjustments to treatment strategies, enhances therapeutic efficacy, and improves patient prognosis. In the future, prospective validation studies\u0026zwnj; incorporating emerging biomarkers and multimodal data integration \u0026zwnj;will further refine the diagnostic precision\u0026zwnj; and expand the applicability across diverse patient cohorts\u0026zwnj;༎\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eautoimmune hepatitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAlb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealbumin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealcoholic liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealkaline phosphatase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eantinuclear antibody\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBNR-6\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebiochemical resolution or not-6\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eChe\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echolinesterase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCK-18\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecytokeratin 18\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatinine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBIl\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edirect bilirubin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDIALF-5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edrug-induced acute liver failure-5\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDILI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edrug-induced liver injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDILIN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDrug-Induced Liver Injury Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eeosinophils\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGGT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egamma-glutamyl transferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGlo\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglobulin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehemochromatosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eherbs and dietary supplements\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHGB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehepatolenticular degeneration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehealth products\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einternational normalized ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCSFR1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emacrophage colony stimulating factor receptor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMELD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emodel for end-stage liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAFLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-alcoholic fatty liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enatural medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSAIDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-steroidal anti-inflammatory drugs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOPN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eosteopontin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprealbumin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprimary biliary cholangitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprimary sclerosing cholangitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eplatelet\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprothrombin time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprothrombin time activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRUCAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoussel Uclaf causality assessment method\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal bile acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBIl\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal bilirubin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etraditional Chinese medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eULN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eupper normal limit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewhite blood cell.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committees of the Fourth Affiliated Hospital of Harbin Medical University (No. 2024-108), Heilongjiang Provincial Hospital (No. 2024-074), the First Affiliated Hospital of Harbin Medical University (No. 202562), and the Second Affiliated Hospital of Harbin Medical University (No. KY2024-280).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate declarations: not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed the full text and agree to its publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of this study are available upon request from the corresponding author. Access to the data is subject to approval by the institutional review board and compliance with applicable privacy regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Fourth Affiliated Hospital of Harbin Medical University Specially Funded Research Project (No. HYDSYTB202206) and Scientific Research Project of Health Commission of Heilongjiang Province (No.20241313050210).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions’\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study concept and design were primarily undertaken by SW and NW. Data collection was carried out by ZQ, CX, XW, ZL, TA, ZZ, and XX. Statistical analysis and data interpretation were conducted by SW and NW. Manuscript drafting was primarily performed by SW and NW. Critical revisions of the manuscript for important intellectual content were conducted by LY. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the patients and their families for participating in this study, as well as the hospital staff for their valuable dedication and professional support.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEuropean Association for the Study of the Liver. Electronic address eee, Clinical Practice Guideline Panel C, Panel m, representative EGB: EASL Clinical Practice Guidelines: Drug-induced liver injury. J Hepatol. 2019;70(6):1222\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevarbhavi H, Aithal G, Treeprasertsuk S, Takikawa H, Mao Y, Shasthry SM, Hamid S, Tan SS, Philips CA, George J, et al. Drug-induced liver injury: Asia Pacific Association of Study of Liver consensus guidelines. Hepatol Int. 2021;15(2):258\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao Y, Ma S, Liu C, Liu X, Su M, Li D, Li Y, Chen G, Chen J, Chen J, et al. Chinese guideline for the diagnosis and treatment of drug-induced liver injury: an update. Hepatol Int. 2024;18(2):384\u0026ndash;419.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChurch RJ, Kullak-Ublick GA, Aubrecht J, Bonkovsky HL, Chalasani N, Fontana RJ, Goepfert JC, Hackman F, King NMP, Kirby S, et al. Candidate biomarkers for the diagnosis and prognosis of drug-induced liver injury: An international collaborative effort. Hepatology. 2019;69(2):760\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015;16(4):e173\u0026ndash;180.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Li J, Xia Y, Gong R, Wang K, Yan Z, Wan X, Liu G, Wu D, Shi L, et al. Prognostic nomogram for intrahepatic cholangiocarcinoma after partial hepatectomy. J Clin Oncol. 2013;31(9):1188\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastelli J, Depeursinge A, Ndoh V, Prior JO, Ozsahin M, Devillers A, Bouchaab H, Chajon E, de Crevoisier R, Scher N, et al. A PET-based nomogram for oropharyngeal cancers. Eur J Cancer. 2017;75:222\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGittleman H, Sloan AE, Barnholtz-Sloan JS. An independently validated survival nomogram for lower-grade glioma. Neuro Oncol. 2020;22(5):665\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan K, Chen J, Xu P, Zhang X, Gong X, Wu M, Xie Y, Wang H, Xu G, Liu X. A Nomogram for Predicting Stroke Recurrence Among Young Adults. Stroke. 2020;51(6):1865\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiuey EJ, Zhang Q, Rapuano CJ, Ayres BD, Hammersmith KM, Nagra PK, Syed ZA. Development of a Nomogram to Predict Graft Survival After Penetrating Keratoplasty. Am J Ophthalmol. 2021;226:32\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanna AK, Kelava M, Ahuja S, Makarova N, Liang C, Tanner D, Insler SR. A nomogram to predict postoperative pulmonary complications after cardiothoracic surgery. 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Gastroenterology. 2021;161(6):1887\u0026ndash;95. e1884.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiesner R, Edwards E, Freeman R, Harper A, Kim R, Kamath P, Kremers W, Lake J, Howard T, Merion RM, et al. Model for end-stage liver disease (MELD) and allocation of donor livers. Gastroenterology. 2003;124(1):91\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang CY, Deng Y, Li P, Zheng S, Chen G, Zhou G, Xu J, Chen YP, Wang Z, Jin X, et al. Prediction of biochemical nonresolution in patients with chronic drug-induced liver injury: A large multicenter study. Hepatology. 2022;75(6):1373\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan L, Huang A, Chen J, Teng G, Sun Y, Chang B, Liu HL, Xu M, Lan X, Liang Q, et al. Clinical characteristics and prognosis of non-APAP drug-induced acute liver failure: a large multicenter cohort study. Hepatol Int. 2024;18(1):225\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen T, Liu Y, Shang J, Xie Q, Li J, Yan M, Xu J, Niu J, Liu J, Watkins PB, et al. Incidence and Etiology of Drug-Induced Liver Injury in Mainland China. Gastroenterology. 2019;156(8):2230\u0026ndash;41. e2211.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuk KT, Kim DJ, Kim CH, Park SH, Yoon JH, Kim YS, Baik GH, Kim JB, Kweon YO, Kim BI, et al. A prospective nationwide study of drug-induced liver injury in Korea. Am J Gastroenterol. 2012;107(9):1380\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavarro VJ, Barnhart H, Bonkovsky HL, Davern T, Fontana RJ, Grant L, Reddy KR, Seeff LB, Serrano J, Sherker AH, et al. Liver injury from herbals and dietary supplements in the U.S. Drug-Induced Liver Injury Network. Hepatology. 2014;60(4):1399\u0026ndash;408.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalasani N, Bonkovsky HL, Fontana R, Lee W, Stolz A, Talwalkar J, Reddy KR, Watkins PB, Navarro V, Barnhart H, et al. Features and Outcomes of 899 Patients With Drug-Induced Liver Injury: The DILIN Prospective Study. Gastroenterology. 2015;148(7):1340\u0026ndash;52. e1347.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhabril M, Ma J, Patidar KR, Nephew L, Desai AP, Orman ES, Vuppalanchi R, Kubal S, Chalasani N. Eight-Fold Increase in Dietary Supplement-Related Liver Failure Leading to Transplant Waitlisting Over the Last Quarter Century in the United States. Liver Transpl. 2022;28(2):169\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAiso M, Takikawa H, Tsuji K, Kagawa T, Watanabe M, Tanaka A, Sato K, Sakisaka S, Hiasa Y, Takei Y, et al. Analysis of 307 cases with drug-induced liver injury between 2010 and 2018 in Japan. 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Gastroenterology. 2013;144(7):1419\u0026ndash;25. 1425 e1411-1413; quiz e1419-1420.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLucena MI, Andrade RJ, Kaplowitz N, Garcia-Cortes M, Fernandez MC, Romero-Gomez M, Bruguera M, Hallal H, Robles-Diaz M, Rodriguez-Gonzalez JF, et al. Phenotypic characterization of idiosyncratic drug-induced liver injury: the influence of age and sex. Hepatology. 2009;49(6):2001\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edeLemos AS, Foureau DM, Jacobs C, Ahrens W, Russo MW, Bonkovsky HL. Drug-induced liver injury with autoimmune features. Semin Liver Dis. 2014;34(2):194\u0026ndash;204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuy J, Peters MG. Liver disease in women: the influence of gender on epidemiology, natural history, and patient outcomes. 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Chest. 2005;128(1):116\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelker D, Lynn A, Wang S, Johnson DE. Evidence for a potential protective effect of carnitine-pantothenic acid co-treatment on valproic acid-induced hepatotoxicity. Expert Rev Clin Pharmacol. 2014;7(2):211\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBryant AE 3rd, Dreifuss FE. Valproic acid hepatic fatalities. III. U.S. experience since 1986. Neurology. 1996;46(2):465\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanan G, Benichou C. Causality assessment of adverse reactions to drugs\u0026ndash;I. A novel method based on the conclusions of international consensus meetings: application to drug-induced liver injuries. J Clin Epidemiol. 1993;46(11):1323\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoofnagle JH, Navarro VJ. Drug-induced liver injury: Icelandic lessons. Gastroenterology. 2013;144(7):1335\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore TJ, Cohen MR, Furberg CD. Serious adverse drug events reported to the Food and Drug Administration, 1998\u0026ndash;2005. Arch Intern Med. 2007;167(16):1752\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonkovsky HL, Kleiner DE, Gu J, Odin JA, Russo MW, Navarro VM, Fontana RJ, Ghabril MS, Barnhart H, Hoofnagle JH, et al. Clinical presentations and outcomes of bile duct loss caused by drugs and herbal and dietary supplements. Hepatology. 2017;65(4):1267\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoofnagle JH, Bjornsson ES. Drug-Induced Liver Injury - Types and Phenotypes. N Engl J Med. 2019;381(3):264\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSur LM, Floca E, Sur DG, Colceriu MC, Samasca G, Sur G. Antinuclear Antibodies: Marker of Diagnosis and Evolution in Autoimmune Diseases. Lab Med. 2018;49(3):e62\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiacomelli R, Afeltra A, Alunno A, Bartoloni-Bocci E, Berardicurti O, Bombardieri M, Bortoluzzi A, Caporali R, Caso F, Cervera R, et al. Guidelines for biomarkers in autoimmune rheumatic diseases - evidence based analysis. Autoimmun Rev. 2019;18(1):93\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldblatt F, O'Neill SG. Clinical aspects of autoimmune rheumatic diseases. Lancet. 2013;382(9894):797\u0026ndash;808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWahren-Herlenius M, Dorner T. Immunopathogenic mechanisms of systemic autoimmune disease. Lancet. 2013;382(9894):819\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Model performance in the training and the internal validation sets\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 471)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining set\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n =\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e282\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;set\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 1\u003c/strong\u003e\u003cstrong\u003e89\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e150 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e93\u0026nbsp;(33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e57\u0026nbsp;(30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e321 (68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e189 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e132\u0026nbsp;(69.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e53.0 (45.0, 61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e52.0 (43.0, 61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e54.0 (48.0, 62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e23.2 (20.8, 25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e23.2 (20.8, 25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e23.1 (21.1, 25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eHospitalization (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e12 (7, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e12 (8, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e11 (7, 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eAllergic, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e34 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e19\u0026nbsp;(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e15\u0026nbsp;(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e437 (92.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e263\u0026nbsp;(93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e174 (92.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSeverity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1 (Mild)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e234 (49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e138 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e96\u0026nbsp;(50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2 (Moderate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e203 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e120 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e83\u0026nbsp;(43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3 (Severe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e29 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e20\u0026nbsp;(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e9\u0026nbsp;(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e4 (Fatal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e5 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4\u0026nbsp;(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1\u0026nbsp;(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003eClinical phenotype, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eHepatocellular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e272 (57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e170\u0026nbsp;(60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e102\u0026nbsp;(54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eCholestatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e115 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e59\u0026nbsp;(20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e56\u0026nbsp;(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e84 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e53\u0026nbsp;(18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e31\u0026nbsp;(16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eRUCAM score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e8.0 (8.0, 9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e8.0 (8.0, 9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e8.0 (8.0, 9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMELD score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e12.0 (7.0, 16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e12.0 (7.0, 17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e12.0 (7.0, 16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e372.5 (145.2, 776.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e361.0 (154.3, 832.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e390.0 (144.4,\u0026nbsp;708.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e242.0 (99.0, 596.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e245.7 (104.0,\u0026nbsp;572.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e237.0 (95.8, 610.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e169.0 (119.0, 259.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e164.0 (111.0, 254.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e178.0 (136.0, 263.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eGGT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e218.2 (113.0, 382.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e214.0 (115.0, 346.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e232.0 (99.0,\u0026nbsp;477.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eChe (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e6781.0 (5077.0, 8244.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e6707.5 (5077.0, 8193.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e6997.0 (5114.0, 8423.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eCr (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e58.0 (50.0, 68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e58.0 (49.4, 68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e58.8 (51.0, 67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTBA (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e33.2 (7.4, 139.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e32.6 (7.4, 144.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e33.5 (7.5, 131.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTBIL\u0026nbsp;(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e45.9 (18.7, 135.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e48.7 (18.3, 139.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e43.0 (19.4,\u0026nbsp;132.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eDBIL\u0026nbsp;(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e24.2 (6.7, 106.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e24.0 (6.6, 108.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e24.9 (7.0,\u0026nbsp;102.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eALB\u0026nbsp;(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e40.2 (35.9, 43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e40.2 (36.4, 43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e40.1 (35.5, 43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eGlo (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e28.1 (24.8, 32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e27.8 (24.4, 32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e28.6 (25.1, 32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePA (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e7.4 (1.4, 18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e8.3 (1.6, 18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e5.7\u0026nbsp;(1.2, 18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3.73 (1.81,\u0026nbsp;4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e3.69 (1.77,\u0026nbsp;4.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3.73 (2.18, 4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePT (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e11.9 (10.9, 13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e11.8 (11.0, 13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e11.9 (10.9, 12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePTA (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e88.0 (73.0, 100.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e88.0 (70.0, 100.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e88.8 (76.8, 98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.05 (0.97, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1.06 (0.97, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.05 (0.97, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eWBC (\u0026acute;\u0026nbsp;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e5.4 (4.3, 6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e5.3 (4.2, 6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e5.4 (4.4, 6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eEO (\u0026acute;\u0026nbsp;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.08 (0.04, 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.09 (0.04, 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.08 (0.04, 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eHGB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e132.0 (121.0, 142.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e130.0 (120.0, 143.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e133.0 (123.0, 140.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePLT (\u0026acute;\u0026nbsp;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e205.0 (158.0, 268.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e204.0 (155.0, 265.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e208.0 (164.0, 268.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eANA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e222 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e133 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e89\u0026nbsp;(47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e249 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e149 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e100\u0026nbsp;(52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eAutoantibody, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e70\u0026nbsp;(14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e46\u0026nbsp;(16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e24\u0026nbsp;(12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e401 (85.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e236\u0026nbsp;(83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e165\u0026nbsp;(87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: ALB, albumin; ALP, alkaline phosphatase; ALT, alanine aminotransferase; ANA, antinuclear antibody; AST, aspartate aminotransferase; BMI, body mass index; Che, cholinesterase; Cr, creatinine; DBIL, direct bilirubin; EO, eosinophils; GGT, gamma-glutamyl transferase; Glo, globulin; HGB, hemoglobin; INR, international normalized ratio; MELD, model for end-stage liver disease; PA, prealbumin; PLT, platelet; PT, prothrombin time; PTA, prothrombin time activity; RUCAM, Roussel Uclaf causality assessment method; TBA, total bile acid; TBIL, total bilirubin; TC, total cholesterol; WBC, white blood cell.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Categories of medications associated with liver injury in 471 patients diagnosed with DILI\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug classification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain drugs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTCM-NM-HP-HDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e· \u003cem\u003eAlumen\u003c/em\u003e\u003cem\u003e, a\u003c/em\u003endrographolide injection, angelica sinensis, anshenbunao oral solution, baguniu pulvis, baizi yangxin pills,\u0026nbsp;\u003cstrong\u003eCompound\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003el\u003c/strong\u003e\u003cstrong\u003eiquorice\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003eablets\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003e\u003cem\u003ecortex dictamni\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e c\u003cem\u003eortex phellodendri\u003c/em\u003e\u003cem\u003e,\u0026nbsp;\u003c/em\u003e\u003cem\u003ecortex pseudolaricis\u003c/em\u003e\u003cem\u003e,\u003c/em\u003edanqi, mo luo dan, dictamni cortex, diet pill,\u0026nbsp;\u003cem\u003eEndoconcha Sepiae\u003c/em\u003e\u003cem\u003e;\u003c/em\u003e enzyme jelly,\u0026nbsp;\u003cem\u003eFlos Carthami\u003c/em\u003e\u003cem\u003e;\u0026nbsp;\u003c/em\u003e\u003cem\u003efructus aurantii\u003c/em\u003e\u003cem\u003e,\u0026nbsp;\u003c/em\u003efuyankang capsules,\u0026nbsp;\u003cem\u003efructus cnidii\u003c/em\u003e\u003cem\u003e,\u0026nbsp;\u003c/em\u003e\u003cem\u003efructus kochiae\u003c/em\u003e\u003cem\u003e,\u003c/em\u003egentina scabra bunge, ginseng; antler, guizhifuling capsules,\u0026nbsp;Guchang zhixie wan;\u0026nbsp;health care products, hundred-pace viper, jinshuibao capsules, jinwugutong capsules, kunbao pills, miao nationality herbal medicines, mongolian medicines, Multivitamin;\u0026nbsp;\u003cstrong\u003eNiaoduqing Granules\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003e\u003cem\u003epericarpium zanthoxyli\u003c/em\u003e\u003cem\u003e,\u0026nbsp;\u003c/em\u003epowder of antelope’s horn for clearing lung-heat,\u0026nbsp;\u003cem\u003eRadix\u0026nbsp;\u003c/em\u003e\u003cem\u003es\u003c/em\u003e\u003cem\u003engelicae\u0026nbsp;\u003c/em\u003e\u003cem\u003es\u003c/em\u003e\u003cem\u003einensis\u003c/em\u003e\u003cem\u003e;\u003c/em\u003e\u003cem\u003er\u003c/em\u003e\u003cem\u003eadix\u0026nbsp;\u003c/em\u003e\u003cem\u003ea\u003c/em\u003e\u003cem\u003estragali\u003c/em\u003e\u003cem\u003e,\u0026nbsp;\u003c/em\u003eradix bupleuri,\u0026nbsp;\u003cem\u003eRhizoma\u0026nbsp;\u003c/em\u003e\u003cem\u003ec\u003c/em\u003e\u003cem\u003eorydalis\u003c/em\u003e\u003cem\u003e; R\u003c/em\u003e\u003cem\u003eadix curcumae\u003c/em\u003e\u003cem\u003e;\u003c/em\u003e \u003cem\u003eRhizoma\u0026nbsp;\u003c/em\u003e\u003cem\u003ec\u003c/em\u003e\u003cem\u003eurcumae\u0026nbsp;\u003c/em\u003e\u003cem\u003el\u003c/em\u003e\u003cem\u003eongae\u003c/em\u003e\u003cem\u003e;\u003c/em\u003e\u003cem\u003e\u0026nbsp;Rhizoma Cyperi\u003c/em\u003e\u003cem\u003e;\u0026nbsp;\u003c/em\u003e\u003cem\u003eRadix et\u0026nbsp;\u003c/em\u003e\u003cem\u003er\u003c/em\u003e\u003cem\u003ehizoma\u0026nbsp;\u003c/em\u003e\u003cem\u003er\u003c/em\u003e\u003cem\u003ehodiolae\u003c/em\u003e\u003cem\u003e; R\u003c/em\u003e\u003cem\u003eadix glycyrrhizae\u003c/em\u003e\u003cem\u003e;\u0026nbsp;\u003c/em\u003e\u003cem\u003eRadix\u0026nbsp;\u003c/em\u003e\u003cem\u003en\u003c/em\u003e\u003cem\u003eotoginseng\u003c/em\u003e\u003cem\u003e;\u003c/em\u003e\u003cem\u003eR\u003c/em\u003e\u003cem\u003eadix\u0026nbsp;\u003c/em\u003e\u003cem\u003ep\u003c/em\u003e\u003cem\u003eaeoniae alba\u003c/em\u003e\u003cem\u003e, r\u003c/em\u003eadix polygoni multiflori,\u0026nbsp;\u003cem\u003eradix sophorae flavescentis\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e \u003cstrong\u003eRunzao zhiyang capsules\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003e\u003cem\u003erhizoma corydalis\u003c/em\u003e\u003cem\u003e,\u0026nbsp;\u003c/em\u003esedum aizoon,\u0026nbsp;\u003cstrong\u003eSalvia miltiorrhiza\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eselfheal medicinal extract, selfheal, shenqijingukang capsules,\u0026nbsp;\u003cstrong\u003eShensong\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ey\u003c/strong\u003e\u003cstrong\u003eangxin\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eapsules\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003esijunzi decoction, sophora, turtle shell decocted pills,\u0026nbsp;\u003cstrong\u003eUrticaria Pills\u003c/strong\u003e\u003cstrong\u003e;\u003c/strong\u003e vitex negundo, wangbi tablets,\u0026nbsp;\u003cstrong\u003eWuling\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;c\u003c/strong\u003e\u003cstrong\u003eapsules\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003exihuang pills, yangxue qingnao granules, yangxueshengfa capsules, yaobitong capsules,\u0026nbsp;Yougui wan;\u0026nbsp;etc.‌\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAntibiotic drugs (including anti-tuberculosis drugs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmikacin, amoxicillin, antiretroviral therapy drugs, aztreonam,\u0026nbsp;\u003cstrong\u003eCefixime\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCefpodoxime\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eroxetil\u003c/strong\u003e\u003cstrong\u003e;\u003c/strong\u003e clindamycin, ethambutol,\u0026nbsp;ganciclovir, isoniazid, levofloxacin, metronidazole,\u0026nbsp;\u003cstrong\u003eNorfloxacin\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003epyrazinamide, rifampicin,\u0026nbsp;\u003cstrong\u003eRifapentine\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eroxithromycin, voriconazole\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNSAIDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcetaminophen\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eAspirin; celecoxib, compound aminopyrine phenacetin, compound paracetamol and amantadine, etoricoxib, ibuprofen, Lguratimod; Metamizde sodium; paracetamol, sodium aminosalicylate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCardiovascular drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAmlodipine, irbesartan hydrochlorothiazide, nifedipine, reserpine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDrugs for metabolic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAllopurinol, atorvastatin, colchicine, dapagliflozin, febuxostat, metformin, propylthiouracil,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erosuvastatin, thiamazole\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnti-tumor drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCamatinib;\u0026nbsp;\u003cstrong\u003eCarboplatin\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eCisplatin, cyclophosphamide, Docetaxel; doxorubicin, etoposide, flumatinib, fulvestrant, gefitinib, lenalidomide, letrozol, methotrexate, nilotinib,\u0026nbsp;Osimertinib;\u0026nbsp;Paclitaxel,\u0026nbsp;\u003cstrong\u003ePemetrexed\u003c/strong\u003e\u003cstrong\u003e;\u003c/strong\u003e exemestane, tamoxifen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChemical agents/industrial poisons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDisinfecting agent, hair dye, industrial, nail enamel, range hood cleaning agent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCentral nervous system drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAgomelatine, buspirone, carbamazepine, citalopram, clozapine, diazepam,\u0026nbsp;\u003cstrong\u003eEscitalopram\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eo\u003c/strong\u003e\u003cstrong\u003exalate\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003eablets\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003egastrodin, levetiracetam, mirtazapine, olanzapine, paroxetine, risperidone, sodium valproate, venlafaxine, zopiclone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBiologics/immunosuppressants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAtezolizumab\u003c/strong\u003e;\u0026nbsp;\u003cstrong\u003eBevacizumab\u003c/strong\u003e;\u0026nbsp;Cyclosporin; Leflunomide, obinutuzumab, other programmed death-1, pertuzumab, spleen aminopeptide, transfer factor, trastuzumab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHormone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDanazol, ethinylestradiol cyproteron, fulvestrant,\u0026nbsp;levothyroxine,\u0026nbsp;methylprednisolone,\u0026nbsp;stanazol, triamcinolone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAcitretin, azelastine, betahistine, cetirizine, clopidogrel, ebastine,\u0026nbsp;\u003cstrong\u003eEfavirenz\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e iguratimod,\u0026nbsp;\u003cstrong\u003eLamivudine\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e loratadine, rivaroxaban,\u0026nbsp;\u003cstrong\u003eTenofovir\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eubenimex;\u0026nbsp;Ursodeoxycholic acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: TCM, traditional Chinese medicine; NM, natural medicine; HP, health products; HDS, herbs and dietary supplements; NSAIDs, non-steroidal anti-inflammatory drugs.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Univariate and multivariate Logistic analysis of 471 patients with DILI\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.08, 3.78)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.032*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.02, 4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.04)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.01, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.86, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHospitalization (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.98, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAllergic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.27, 2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSeverity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.38, 1.21)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eClinical phenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.89, 3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRUCAM score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.89, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMELD score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.98, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.85, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.022*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.023*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.00) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGGT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.00) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChe (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCr (μmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.99, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTBA (μmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTBIL (μmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDBIL (μmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.88, 0.97)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlo (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.01, 1.08)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.00, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePA (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.96, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.96, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.87, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePT (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.99, 1.09)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePTA (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.96, 0.99)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.95, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.90, 2.08)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWBC (×10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.85, 1.04)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEO (×10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.36, 1.31)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHGB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.96, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePLT (×10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.99, 1.00)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.99, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eANA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.61, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.15, 4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.020*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAutoantibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.15, 0.55)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.13, 0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Defined Logistic multivariable analysis for incorporated into the Nomogram model. *\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05，**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01，***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ALB, albumin; ALP, alkaline phosphatase; ALT, alanine aminotransferase; ANA, antinuclear antibody; AST, aspartate aminotransferase; BMI, body mass index; Che, cholinesterase; Cr, creatinine; DBIL, direct bilirubin; EO, eosinophils; GGT, gamma-glutamyl transferase; Glo, globulin; HGB, hemoglobin; INR, international normalized ratio; MELD, model for end-stage liver disease; PA, prealbumin; PLT, platelet; PT, prothrombin time; PTA, prothrombin time activity; RUCAM, Roussel Uclaf causality assessment method; TBA, total bile acid; TBIL, total bilirubin; TC, total cholesterol; WBC, white blood cell.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Drug-induced liver injury, Nomogram model, Prediction, Retrospective analysis","lastPublishedDoi":"10.21203/rs.3.rs-8643882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8643882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDrug-induced liver injury (DILI) has a highly variable clinical course, making it challenging to predict individual patient outcomes. This study aimed to identify independent risk factors for adverse outcomes in DILI and to develop and validate a clinically practical nomogram for individualized prognosis prediction.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this multicenter, retrospective cohort study, clinical data from 471 patients diagnosed with DILI between 2019 and 2024 were analyzed. Patients were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;282) and a validation set (n\u0026thinsp;=\u0026thinsp;189) in a 6:4 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of clinical outcomes (favorable vs. adverse prognosis). A nomogram was constructed based on the significant variables identified in the multivariate analysis. The model's performance was evaluated in both sets in terms of calibration (calibration curve), clinical utility (decision curve analysis, DCA), and discrimination (area under the curve, AUC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTraditional Chinese medicine (TCM)-natural medicine (NM)-health products (HP)-herbs-dietary supplements (HDS) were the most common causative agents. Multivariate analysis identified eleven predictors incorporated into the nomogram: sex, platelet (PLT), cholinesterase (Che), globulin (Glo), age, antinuclear antibody (ANA), autoantibodies, alanine aminotransferase (ALT), model for end-stage liver disease (MELD) score, prothrombin time activity (PTA), and prealbumin (PA). The nomogram demonstrated good predictive accuracy in the training set (AUC\u0026thinsp;=\u0026thinsp;0.793) and excellent calibration (mean absolute error\u0026thinsp;=\u0026thinsp;0.029). DCA confirmed the model's clinical utility across a wide range of threshold probabilities in both sets.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe proposed nomogram provides an effective and visually intuitive tool for predicting individual patient outcomes in DILI. It exhibits strong discriminatory ability and calibration, facilitating early risk stratification and informed clinical decision-making to help improve patient prognosis.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Nomogram for Predicting Prognosis in Patients with Drug-Induced Liver Injury: A Multicenter Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 16:09:50","doi":"10.21203/rs.3.rs-8643882/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-06T12:51:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-09T16:23:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T10:55:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T10:54:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2026-01-20T01:53:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"299e0d03-1707-4ac0-ab7a-adddbb0331c0","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"30","date":"2026-05-06T12:51:30+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T16:09:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 16:09:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8643882","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8643882","identity":"rs-8643882","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}