Chronic liver disease (CLD), culminating in cirrhosis and primary liver cancer (PLC), is highly prevalent, affecting approximately 1.5 billion individuals worldwide [1-3]. The main etiologies are well established and include alcohol-related liver disease (ALD), metabolic dysfunction–associated steatotic liver disease (MASLD), and chronic viral infections caused by hepatitis B virus (HBV) and hepatitis C virus (HCV). Although viral infections are better controlled in developed countries due to HBV vaccination and effective antiviral therapies, alcohol misuse remains widespread, and the incidence of metabolic syndrome is rising, leading to a global increase in CLD prevalence, particularly MASLD. In Europe, the estimated prevalence of CLD is 833 cases per 100,000 inhabitants (range 447-1100), with an estimated cirrhosis incidence of 26.0 per 100,000 inhabitants [4,5]. Altogether, CLD remains associated with poor outcomes, accounting for approximately 2 million deaths annually worldwide [6].

In France, data on the epidemiology of CLD and its natural history are limited, and previous national studies predominantly focused on PLC (especially hepatocellular carcinoma [HCC]) and MASLD [7,8] through cancer registries [9,10] (lacking clinical data) or cohort studies (lacking representativeness) [11,12]. In the French Network of Cancer Registries (Francim), 11,658 new cases of PLC were declared in 2023 (8874 in men and 2784 in women) with a 12.3 standardized incidence rate for men and 3.1/100,000 for women [13]. Mortality related to CLD is poorly documented; however, it is estimated that cirrhosis causes approximately 15,000 deaths annually [14], with PLC responsible for 8700 deaths in 2018, accompanied by a dismal 5-year standardized net survival rate of 18% according to Institut National du Cancer in 2023 [10,15]. Cirrhosis is most often identified during a severe complication such as variceal bleeding, ascites, liver failure, or PLC [16-20]. Furthermore, more than half of PLC cases are diagnosed at advanced stages, precluding curative treatment options, particularly in patients with ALD [21-24]. This delayed diagnosis is related both to underrecognition of the underlying liver disease and to suboptimal implementation of HCC surveillance. Although international guidelines recommend semiannual surveillance using liver ultrasound with or without alpha-fetoprotein (AFP) in case of cirrhosis [25-27], real-world surveillance rates remain critically low, with only approximately 20%-25% of eligible patients undergoing appropriate surveillance [28]. Delayed diagnosis of advanced fibrosis or PLC markedly worsens patient prognosis.

Early medical intervention in at-risk populations is essential for preventing CLD onset. Such interventions include managing obesity, treating type 2 diabetes, screening for alcohol misuse, and providing addiction care. Even after CLD has developed, controlling the underlying causes can slow disease progression to advanced fibrosis stages and/or PLC, thereby reducing liver-related and overall mortality [11,29-32]. Since advanced fibrosis is the strongest predictor of clinical outcomes and mortality, early identification of significant fibrosis is critical, especially in high-risk groups such as individuals with alcohol misuse, diabetes, obesity, or sleep apnea syndrome. Several noninvasive tests have been developed to identify patients at risk of advanced fibrosis [33], with the European Association for the Study of the Liver (EASL) recommending the use of the FIB-4 score in high-risk populations [34].

Despite well-known etiologies and new tools for early fibrosis identification, CLD is often diagnosed at late stages of fibrosis or at the onset of PLC, indicating gaps in health care pathways.

A better understanding of CLD epidemiology, natural history, and recent changes (increase in MASLD, for instance [35], or a new therapeutic era for PLC with the advent of immunotherapy [36-38]), and health care trajectories is crucial to identify factors driving its poor prognosis [39].

To address this knowledge gap, we established a cohort of patients with CLD leveraging data from the French National Health Data System (Système National des Données de Santé [SNDS]). This paper describes the procedures used to target the population to be included in the cohort, construct the variables for the analyses, and define the research objectives.

RESONANCE is an historical cohort derived from the SNDS, including both incident and prevalent cases of CLD from 2015 to 2021.

The primary research objective is to assess the impact of nonoptimal care pathways on the incidence of severe forms of CLD. Secondary objectives include the following:

We plan to perform analyses dedicated to subpopulations of special interest such as patients with cirrhosis, PLC, and subtypes of PLC. In particular, we aim to describe the therapeutic management of HCC (first-line and subsequent strategies) and changes over time, especially in the immunotherapy era (since 2021).

For each objective, we will explore the role of sex, age, social environment, and access to care, as well as the COVID-19 pandemic period.

Permanent access to the SNDS is automatically granted to certain government agencies and public institutions, such as university-affiliated hospitals and public service authorities. The scope of data access is defined by a Council of State decree, based on the opinion of the National Commission for Information Technology and Civil Liberties (CNIL [French Data Protection Authority]). The RESONANCE cohort is promoted by Grenoble Alpes University Hospital, which holds permanent access to SNDS data, exempting it from additional regulatory procedures [41]. All necessary accreditations for secure data access have been obtained and RESONANCE is registered in the Health Data Hub under reference number F20230224144925 (project number 26266080). According to French regulation (Articles L1461-1 and R1461-13 of the French Public Health Code), studies conducted using pseudonymized SNDS data do not require approval from an institutional review board (IRB) or ethics committee, or patient individual consent, since no directly identifiable data are used and no contact with patients occurs.

ESND beneficiaries were included in the RESONANCE cohort as “2015-2021 incident cases” if they presented at least one condition suggesting the presence of a CLD according to the multicriteria algorithm detailed in the previous section, with the first occurrence identified from January 1, 2015, to December 31, 2021.

If ESND beneficiaries presented at least one condition of the targeting algorithm between January 1, 2013, and December 31, 2014, they were included in the cohort as “prevalent cases over the 2015-2021 period.” The initial identification code—the one that granted the patient entry into the cohort—corresponds to the inclusion (or index) date.

For each case, information on medical history and events of interest was retrieved from January 1, 2013, up to death or until December 31, 2023 (whichever came first). The minimal follow-up period was 2 years (Figure 2).

Extensive information about patient characteristics, comorbidities, and medical management has been collected from SNDS in PMSI, DCIR, and CépiDc tables as summarized in Figure 3.

All codes, sources, and period of search used are detailed in Multimedia Appendices 2 to 14.

For each patient, we collected the following data on medical management and follow-up: hospitalizations stays and consultations, outpatient visits to the general practitioner, nonhospital medical consultations to gastrointestinal (GI) specialists as well as specialists involved in the management of risk factors for CLD and comorbidities (cardiovascular pathology, internal medicine, pediatrics, pulmonology, physical medicine and rehabilitation, psychiatry, geriatrics, nephrology, endocrinology, and vascular medicine), liver diseases–related imaging and therapeutic procedures, biologic tests of interest (aspartate aminotransferase [AST], alanine aminotransferase [ALT], and platelets count; Multimedia Appendices 2 to 4). As the type of specialist is not available in case of outpatient visits conducted in hospital facilities, we used 2 proxies to identify GI consultations in the hospital setting: (1) all hospital consultations and (2) hospital consultations coupled with liver imaging or endoscopy or elastography or liver biopsy or transjugular intrahepatic portosystemic shunt (TIPS) within 6 months before or after.

Comorbidities of interest (diabetes, obesity, sleep apnea syndrome, dyslipidemia, arterial hypertension, metabolic syndrome, and HIV infection) and unhealthy behaviors (alcohol misuse, tobacco or drug use) identification was based on proxy algorithms using hospital diagnoses, LTC, procedures, medications, and reimbursed health care data. Detailed definitions and coding algorithms are provided in the Multimedia Appendices 5 to 9.

The Charlson Comorbidity Index (CCI) at the time of inclusion (Multimedia Appendix 10), assessing the level of comorbidity according to 17 predefined comorbid conditions, was computed according to previous studies [45-50]. As all patients in the RESONANCE cohort display a liver disease, the CCI was always ≥1 to ensure that CLD was automatically recorded as liver comorbidity. The use of a modified CCI excluding liver-related conditions will also allow a more accurate assessment of extrahepatic baseline comorbidities [51].

CLD causes (viral, alcoholic, metabolic, and rare diseases) have been defined according to combinations of ICD-10 codes, procedure codes, medication codes, or biology codes as detailed in Multimedia Appendix 11. Algorithms regarding CLD causes are consistent with previous studies conducted in the SNDS [52-59].

Main etiology of the CLD was categorized into mutually exclusive classes (Multimedia Appendix 12): ALD, viral B or C hepatitis-related liver disease (Viral), rare disease–related liver disease (RD), MASLD, metabolic dysfunction–associated alcohol-related liver disease (MetALD), other combinations of causes referred to as mixed causes of liver disease (MD) and undetermined when none of the previous etiologies were identified.

Cirrhosis and liver-related complications, including major clinical events reflecting disease progression, such as ascites, hepatic insufficiency, portal hypertension, hepato-renal syndrome, and refractory encephalopathy, were identified using ICD-10 and procedure codes listed in Multimedia Appendix 13. PLC (identified through ICD-10 codes) was also considered as a complication.

Liver transplantation was recorded using ICD-10 code and procedure code (Multimedia Appendix 2).

All-cause mortality was recorded through the date of death in various tables in SNDS (PMSI, DCIR, and CépiDc). When available, liver-related mortality will also be explored based on ICD-10 codes of main or associated causes of death recorded in the CépiDc tables implemented in the SNDS.

End points will be properly described in each upcoming manuscript focusing on specific research questions. To assess the primary research objective, impact of nonoptimal care pathways on the incidence of severe forms of CLD, the following definitions will be used:

Severe forms of CLD encompass:

Nonoptimal care pathways (summarized in Figure 4) were defined according to both disease severity and the timing of severe form onset:

Secondary end points will include, but are not limited to, the following:

Baseline demographic, socioeconomic, clinical, and health care use characteristics will be described overall and according to CLD subtype, sex, disease severity, and other clinically relevant subgroups. Continuous variables will be summarized using means and SDs or medians and IQRs, as appropriate, whereas categorical variables will be presented as counts and percentages. Comparisons between groups will be performed using appropriate parametric or nonparametric tests according to variable type and distribution, including chi-square tests or Fisher exact tests when sample sizes are small for categorical variables and Student t tests or Wilcoxon rank-sum tests for continuous variables, as appropriate. The distribution of major liver disease etiologies, risk factors, comorbidities, health care use indicators, and social deprivation measures will be specifically examined.

Due to the medico-administrative nature of the data, missing data are expected to be limited and concern only social deprivation measured through the FDep Index (3.8%), health care accessibility measured through the APL index (1.7%), and causes of death (8.5%). Given the low proportion of missing data and the administrative nature of the database, complete-case analyses will usually be performed without multiple imputation. Multiple imputation may nevertheless be considered for specific research questions or sensitivity analyses if the extent or pattern of missingness justifies it. All other study variables are complete.

Incidence, prevalence, and mortality rates will be estimated overall with their corresponding 95% CI, based on large-sample normal approximation and stratified according to age, sex, social deprivation (FDep Index), health care accessibility (APL), and French departments. Whenever appropriate, rates will be age-standardized using the World Health Organization (WHO) World Standard Population.

Time-to-event analyses will be conducted to assess the association between care pathways and the occurrence of severe forms of CLD. The time origin will be the date of cohort entry, and patients will be followed until the first event of interest, death, loss to follow-up, or administrative censoring. Survival probabilities will be estimated using Kaplan-Meier methods and compared using log-rank tests for outcomes without competing risks.

Multivariable Cox proportional hazards models will be used to estimate adjusted hazard ratios. Age, sex, social deprivation, and major clinical characteristics, including liver disease etiology, risk factors, comorbidities, and disease severity indicators, will be systematically considered as potential confounders. The proportional hazards assumption will be assessed using Schoenfeld residuals through the cox.zph() function in R (R Foundation for Statistical Computing). In the presence of nonproportional effects, alternative modeling approaches such as time-dependent Cox models using the timecox() function will be considered.

For analyses involving time-varying exposures or covariates, such as treatment initiation, disease progression, incident PLC, liver transplantation, cirrhosis decompensation, or changes in health care use during follow-up, appropriate longitudinal modeling strategies incorporating time-dependent variables will be applied. Depending on the research question and data structure, additional analytical approaches adapted to longitudinal data, including time-series analyses, may also be considered.

Competing risks will be explicitly considered when studying outcomes such as liver-related death, cirrhosis decompensation, PLC, or liver transplantation. Depending on the research question, cause-specific hazard models and Fine and Gray subdistribution hazard models will be used.

Potential collinearity between explanatory variables will be assessed using variance inflation factors (VIFs) and Cramer V statistics and considered.

Sensitivity analyses will explore the robustness of findings, including alternative definitions of exposures and outcomes, restrictive and expanded disease-identification algorithms, subgroup analyses, and restricted population studies. For example, the contribution of enhanced identification algorithms is illustrated in Multimedia Appendix 15.

Statistical significance will be assessed using a 2-sided alpha level of .05. All statistical analyses will be conducted using R software (R Foundation for Statistical Computing) implemented on the secure CNAM’s SNDS online platform (version 4.4.3, with versions 4.3.3 and 4.1.2 also available).

As RESONANCE is intended to support multiple research projects addressing distinct clinical questions, detailed statistical analysis plans, including model specifications and additional methodological considerations, will be further developed and reported in dedicated methodological and results manuscripts.

Data extraction, cohort construction, and data management were completed in January 2025, allowing initiation of cohort analyses.

In total, 26,663 incident and prevalent cases of CLD had been identified between 2015 and 2021 in the ESND, constituting the RESONANCE Cohort. CNAM mapping algorithm was the main source of inclusion in the cohort, contributing to 91.5% of case identification. The 3 other sources, beyond CNAM mapping, allowed to capture 8.5% additional cases (2262/26,663). Relative and incremental contributions of each source to CLD identification are summarized in Figure 5.

The RESONANCE cohort includes 19,645 incident and 7018 prevalent cases of CLD identified between 2015 and 2021. Preliminary descriptive analyses are shown in Table 1. The cohort encompasses a broad spectrum of etiologies, including ALD (7580/26,663, 28.4%), metabolic-associated liver disease (6030/26,663, 24.8%), viral hepatitis (3450/26,663, 12.9%), rare diseases (839/26,663, 3.1%), and mixed (2895/26,663, 10.9%) or unknown causes (5297/26,663, 19.9%). The cohort is characterized by an unbalanced sex distribution with 58.1% of men and a mean age of 59.9 (SD 17.4) years, consistent with previous reports [63,64]. During the entire follow-up (from January 1, 2015, to December 31, 2023), there were 10,216 deaths, reflecting the severity of the underlying conditions. At baseline, 43% (11,461/26,663) of the patients lived in a socially deprived environment (according to the 4th and 5th quintiles of the FDep Index), and most of the patients had good or intermediate access to primary care (23,726/26,663, 89%).

As detailed in Multimedia Appendix 15, the choice of algorithm definitions substantially influenced the estimated prevalence of each CLD subtype. Cirrhosis was identified in 42.5% (11,332/26,663) of patients when relying solely on ICD-10 cirrhosis codes, rising to 50.6% (13,488/26,663) of patients when an expanded algorithm incorporating cirrhosis-related complications was applied—representing a relative increase of 19%. Similarly, MASLD identification increased from 5500 to 6763 cases when the definition was broadened beyond ICD-10 codes K75.8 and K76.0 to additionally require the presence of metabolic risk factors in the absence of competing etiologies, yielding 1263 additional cases (+23%). For ALD, extending the definition beyond ICD-10 code K70 to encompass alcohol-related comorbidities and alcohol cessation treatments increased the total number of patients from 6146 to 8986 (+46.2%). Taken together, these findings underscore the sensitivity of epidemiological estimates to the practical definitions applied and highlight the importance of algorithm selection when conducting database studies on CLD, beyond the French setting.