Introduction: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally, with treatment outcomes closely tied to liver function. This study evaluates the prognostic utility of the albumin-bilirubin (ALBI) score compared to the traditional Child-Pugh (CP) grading, leveraging real-world evidence from a large-scale, multi-center database. Methods: The Liver Cancer IN Korea (LINK) research network, a multi-center initiative, retrospectively collected electronic health records from three academic hospitals in South Korea, encompassing HCC patients diagnosed between 2015 and 2020. Inclusion criteria mandated at least one HCC treatment and excluded patients with other primary cancer diagnoses. The study followed patients until death, the last visit, or June 2021, employing standardized data processing and rule-based algorithms for data consistency. The prognostic efficacy of ALBI scores and CP scores was compared through time-dependent receiver operating characteristic (ROC) curves and the inverse probability censoring weighting method. Results: From 25,248 newly diagnosed patients, 10,297 were included, with 65.82% having hepatitis B etiology and a mean follow-up of 27.49 months. Patients’ classification by modified ALBI (mALBI) grade at diagnosis revealed: grade 1 (48.87%), 2a (20.50%), 2b (24.54%), and 3 (5.17%), with a minimal percentage missing (0.92%). Transarterial therapy (54.07%) and tyrosine kinase inhibitors (84.14% as the first-line systemic therapy) were predominant treatments. The ALBI score demonstrated greater prognostic efficacy than the CP score in long-term outcomes, with time-dependent area under the ROC curve analysis showing a score of 0.71 for ALBI versus 0.67 for CP at 60 months. Furthermore, higher mALBI grades were significantly associated with poorer survival outcomes, as indicated by both univariate and multivariate Cox proportional regression model analyses (p < 0.001). Conclusions: The study confirmed the ALBI score’s superior prognostic ability over the CP score, especially evident in long-term outcomes, suggesting a shift toward more nuanced liver function assessment tools in real-world clinical practice.

Liver cancer, primarily hepatocellular carcinoma (HCC), is a major global health challenge, ranking as the sixth most commonly diagnosed cancer and the third leading cause of cancer-related deaths worldwide. In 2020, liver cancer was responsible for over 905,000 new cases and 830,000 deaths, underscoring its lethal nature [1, 2]. Particularly in South Korea, HCC is a predominant concern, being the second leading cause of cancer deaths and notably frequent in diagnoses [3]. Despite advancements in the therapeutic landscape of HCC, the prognosis for affected patients remains poor, emphasizing the critical need for a deeper understanding of real-world treatment patterns and outcomes [3].

Recent years have seen significant progress in HCC treatment options, including the advent of emerging therapies that have reshaped the treatment landscape [4, 5]. However, a critical gap persists in the literature regarding the analysis of real-world treatment patterns and outcomes with more recent data. This study aims to bridge this gap by providing a comprehensive analysis of the HCC treatment landscape, leveraging real-world evidence to inform healthcare decisions.

The management of HCC is closely linked to the hepatic function of patients, highlighting the importance of employing accurate clinical grading systems for outcome prediction and treatment optimization [6]. While the Child-Pugh (CP) classification has been traditionally utilized, its reliance on subjective assessments introduces variability [7]. Alternatively, the albumin-bilirubin (ALBI) score offers an evidence-based, objective approach, demonstrating accuracy in reflecting hepatic function [8]. Moreover, the modified ALBI (mALBI) grading system, which divided the ALBI score into four subgrades, enables more precise, discriminative classification of patient prognoses compared to the ALBI grades [9‒12]. Recognizing these advantages, the 2022 update to the Barcelona Clinic Liver Cancer (BCLC) strategy also incorporated the ALBI and Model for End-Stage Liver Disease scores, moving beyond the conventional CP staging to assess liver function with greater granularity [13].

This study validates the prognostic value of the ALBI score using a large-scale real-world data by developing a longitudinal cohort of newly diagnosed HCC patients in South Korea. Through this, it seeks to elucidate current treatment patterns and outcomes, offering vital insights into the efficacy of clinical grading systems in a real-world context, ultimately aiming to improve HCC management.

Study Database and Populations

The Liver Cancer IN Korea (LINK) research network was established to generate real-world evidence on HCC from three leading academic hospitals in South Korea [14]. LINK operates as a large-scale longitudinal database that is regularly updated to include ongoing treatment data for existing patients and to capture newly diagnosed HCC patients. All adult patients (≥18 years) registered in the participating hospitals’ database with newly diagnosed HCC between January 1, 2015, and December 31, 2020, were included in the LINK database. Of those patients, patients were excluded if they had no treatment record for HCC within 4 months of the index date or until June 30, 2021, had only surgical records, had the International Classification of Diseases for Oncology-Third Edition (ICD-O-3) code for the diagnosis other than HCC, or had any other primary cancer. The initial date of HCC diagnosis was defined as the index date. The follow-up period for assessing real-world overall survival (rwOS) was from the index date to the earliest of event (or death), end of data period (June 30, 2021), or last known visit. Patients without event or death were censored at their last known activity.

Source Data Selection and Feasibility

A comprehensive feasibility assessment was performed to confirm the availability and validity of data for this study. Initially, questionnaires were distributed to evaluate the logistics and data accessibility across potential sources, aiming to align with our research goals. This preliminary step was supplemented by an in-depth qualitative and quantitative evaluation of each data source, focusing on key metrics such as the annual volume of HCC patients treated and the presence of essential clinical characteristics as structured entries in electronic health records (EHRs).

Upon meticulous evaluation, the clinical research data warehouses (CDWs) of Asan Medical Center (AMC), Samsung Medical Center (SMC), and Severance Hospital (SVC) were selected to contribute to the LINK database. These institutions are distinguished tertiary healthcare centers, with bed capacities ranging between approximately 2,200 to 2,700, providing comprehensive care from outpatient services to end-of-life support for cancer patients. Notably, all three institutes utilize an integrated EHR system that mirrors clinical data into the CDW. This setup ensures that the CDW serves as a centralized repository for de-identified patient data across various modalities, thereby serving as a pivotal resource for secondary research analyses.

Data Pre-Processing and Transformation

Data from each hospital’s CDW were harmonized for inclusion in the LINK database, with structured data directly extracted and unstructured data enhanced through rule-based algorithms designed to accurately classify and interpret clinical information (Fig. 1). An analytical framework was established to ensure data consistency across sites, guided by common data specifications and programming scripts for data quality assessment, including conformance, completeness, and plausibility checks [15]. Discrepancies were addressed through medical adjudication and technology-based data abstraction. Data handling adhered to de-identification protocols per institutional policies.

Fig. 1.

Overview of the LINK research network design. LINK, Liver Cancer IN Korea; AMC, Asan Medical Center; SMC, Samsung Medical Center; SVC, Severance Hospital.

Fig. 1.

Overview of the LINK research network design. LINK, Liver Cancer IN Korea; AMC, Asan Medical Center; SMC, Samsung Medical Center; SVC, Severance Hospital.

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To categorize initial HCC treatments and systemic anti-cancer therapy (SACT) lines of therapy (LoTs), rule-based algorithms were developed reflecting local guidelines. These were rigorously tested and refined in consultation with clinical experts to ensure they accurately mirrored real-world practices. Initial treatment modalities covering both surgical and non-surgical interventions were classified as in online supplementary Table 1 (for all online suppl. material, see https://doi.org/10.1159/000539724). SACT LoT identification followed algorithms accounting for drug class, combination therapies, and gap days detailed in online supplementary Figure 1 using prescription data of drugs listed in online supplementary Table 2.

Statistical Analysis and Data Access Model

Descriptive statistics described patient characteristics and treatment patterns, with Sankey diagrams visualizing SACT sequences. The prognostic efficacy of ALBI scores and CP scores was compared using time-dependent ROC curves and the inverse probability censoring weighting method. Sensitivity and specificity were calculated using the Youden index. The Kaplan-Meier method assessed rwOS, with differences evaluated via the log-rank test and Benjamini-Hochberg correction. Cox proportional hazards models estimated hazard ratios for risk factors related to rwOS. All statistical analyses were performed using R, with two-sided tests employed and a significance level set at 0.05.

The analysis utilized a distributed research network model across three hospitals, enhancing data security by retaining patient-level data on-site and analyzing only aggregated statistics. This approach effectively minimizes the need to share confidential or proprietary information since the distributed research network’s querying capability enables research partners to provide results primarily as aggregated data counts. This remote querying method significantly reduces legal, regulatory, privacy, and technical challenges associated with data sharing for research purposes. Such a strategy is especially relevant for studies employing common data models (e.g., Sentinel Common Data Model, Observational Medical Outcomes Partnership (OMOP) Common Data Model), where datasets are not publicly accessible but data codes are available [16‒18].

Demographic and Clinical Characteristics at Diagnosis

Our study identified 25,248 patients newly diagnosed with HCC between January 1, 2015, and December 31, 2020. Of these, 10,297 patients met the inclusion criteria and were included into the LINK database, as detailed in Figure 2.

Fig. 2.

Patient selection flow. HCC, hepatocellular carcinoma; ICD-10, International Classification of Diseases-10th Edition; ICD-O-3, International Classification of Diseases for Oncology-3rd Edition.

Fig. 2.

Patient selection flow. HCC, hepatocellular carcinoma; ICD-10, International Classification of Diseases-10th Edition; ICD-O-3, International Classification of Diseases for Oncology-3rd Edition.

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The baseline characteristics of patients at diagnosis within the LINK database are summarized in Table 1. The cohort predominantly comprised male patients (80.76%, n = 8,316), with a median age of 60 years (interquartile range: 53–67). The majority (65.82%) had a disease etiology of hepatitis B, with liver cirrhosis (14.21%), hypertension (4.67%), and diabetes mellitus (3.90%) as prevalent comorbidities. Metastasis was present in 7.22% of patients, and 28.75% had alpha fetoprotein (AFP) levels greater than 200 ng/mL. The average follow-up duration was 27.49 months (standard error: 0.21).

Table 1.

Demographic and clinical characteristics of HCC patients in LINK database

VariablesTotalmALBI grade 1mALBI grade 2amALBI grade 2bmALBI grade 3Unknown/missing
N%N%N%N%N%N%
10,297100.005,03248.872,11120.502,52724.545325.17950.92
Age 
 Mean (SE) 60.18 (0.10) 59.72 (0.15) 61.13 (0.23) 60.72 (0.20) 58.33 (0.41) 59.42 (0.98) 
 Median (Q1–Q3) 60.00 (53.00–67.00) 60.00 (53.00–67.00) 61.00 (54.00–68.00) 60.00 (54.00–68.00) 57.00 (51.00–66.75) 58.00 (53.00–64.00) 
Sex 
 Male 8,316 80.76 4,070 80.88 1,691 80.10 2,030 80.33 439 82.52 86 90.53 
 Female 1,981 19.24 962 19.12 420 19.90 497 19.67 93 17.48 9.47 
BMI 
 <18.5 224 2.18 94 1.87 45 2.13 63 2.49 21 3.95 1.05 
 18.5–22.9 3,086 29.97 1,444 28.70 654 30.98 811 32.09 169 31.77 8.42 
 23.0–24.9 2,599 25.24 1,305 25.93 502 23.78 653 25.84 135 25.38 4.21 
 ≥25.0 4,176 40.56 2,145 42.63 873 41.35 950 37.59 202 37.97 6.32 
 Unknown/missing 212 2.06 44 0.87 37 1.75 50 1.98 0.94 76 80.00 
Smoking status 
 Never smoked 3,831 37.21 1,909 37.94 791 37.47 935 37.00 185 34.77 11 11.58 
 Former smoker 4,338 42.13 2,167 43.06 891 42.21 1,017 40.25 236 44.36 27 28.42 
 Current smoker 1,852 17.99 882 17.53 376 17.81 483 19.11 100 18.80 11 11.58 
 Unknown/missing 276 2.68 74 1.47 53 2.51 92 3.64 11 2.07 46 48.42 
Drinking status 
 Not a drinker 3,279 31.84 1,638 32.55 682 32.31 819 32.41 132 24.81 8.42 
 Former drinker 5,112 49.65 2,436 48.41 1,057 50.07 1,280 50.65 304 57.14 35 36.84 
 Current drinker 1,643 15.96 890 17.69 321 15.21 340 13.45 86 16.17 6.32 
 Unknown/missing 263 2.55 68 1.35 51 2.42 88 3.48 10 1.88 46 48.42 
Clinical trial participation 
 Yes 377 3.66 256 5.09 71 3.36 46 1.82 0.38 2.11 
 No 2,447 23.76 1,092 21.70 502 23.78 692 27.38 124 23.31 37 38.95 
 Did not receive SACT 7,473 72.57 3,684 73.21 1,538 72.86 1,789 70.80 406 76.32 56 58.95 
Follow-up duration, months 
 Mean (SE) 27.49 (0.21) 31.12 (0.29) 27.57 (0.44) 22.08 (0.39) 19.15 (0.85) 17.01 (1.71) 
 Median (Q1–Q3) 22.67 (9.63–41.89) 25.53 (12.14–47.10) 18.40 (7.84–35.77) 15.05 (5.60–30.16) 10.32 (4.30–31.92) 14.72 (7.18–33.71) 
ECOG PS 
 0 3,832 37.21 2,394 47.58 663 31.41 682 26.99 93 17.48 0.00 
 1 1,513 14.69 609 12.10 291 13.78 503 19.91 106 19.92 4.21 
 2 255 2.48 102 2.03 38 1.80 83 3.28 32 6.02 0.00 
 3 38 0.37 0.12 0.14 17 0.67 12 2.26 0.00 
 4 12 0.12 0.04 0.00 0.28 0.56 0.00 
 Unknown/missing 4,647 45.13 1,919 38.14 1,116 52.87 1,235 48.87 286 53.76 91 95.79 
BCLC stage 
 Stage 0 456 4.43 337 6.70 73 3.46 44 1.74 0.38 0.00 
 Stage A 679 6.59 414 8.23 112 5.31 135 5.34 18 3.38 0.00 
 Stage B 180 1.75 99 1.97 41 1.94 36 1.42 0.75 0.00 
 Stage C 3,259 31.65 1,644 32.67 610 28.90 852 33.72 145 27.26 8.42 
 Stage D 191 1.85 0.16 0.14 50 1.98 130 24.44 0.00 
 Unknown/missing 5,532 53.72 2,530 50.28 1,272 60.26 1,410 55.80 233 43.80 87 91.58 
CP class 
 Class A 7,635 74.15 4,423 87.90 1,809 85.69 1,398 55.32 0.75 1.05 
 Class B 1,595 15.49 86 1.71 157 7.44 961 38.03 391 73.50 0.00 
 Class C 146 1.42 0.00 0.00 26 1.03 120 22.56 0.00 
 Unknown/missing 921 8.94 523 10.39 145 6.87 142 5.62 17 3.20 94 98.95 
Disease etiology 
 Hepatitis B 6,778 65.82 3,519 69.93 1,371 64.95 1,545 61.14 312 58.65 31 32.63 
 Hepatitis C 936 9.09 382 7.59 208 9.85 287 11.36 50 9.40 9.47 
 Alcohol liver disease 1,490 14.47 520 10.33 335 15.87 467 18.48 147 27.63 21 22.11 
 Others 369 3.58 254 5.05 48 2.27 62 2.45 0.94 0.00 
 Unknown/missing 1,275 12.38 531 10.55 279 13.22 341 13.49 82 15.41 42 44.21 
Comorbidities 
 Liver cirrhosis 1,463 14.21 694 69.19 271 76.99 399 79.01 98 88.29 11.11 
 Hypertension 481 4.67 246 24.53 86 24.43 121 23.96 20 18.02 88.89 
 Diabetes mellitus 402 3.90 192 19.14 68 19.32 115 22.77 27 24.32 0.00 
Number of tumors 
 1 4,227 41.05 2,216 44.04 906 42.92 920 36.41 179 33.65 6.32 
 2 1,164 11.30 582 11.57 250 11.84 268 10.61 56 10.53 8.42 
 3 367 3.56 173 3.44 85 4.03 83 3.28 26 4.89 0.00 
 4–5 124 1.20 42 0.83 33 1.56 34 1.35 13 2.44 2.11 
 6+ 30 0.29 0.00 11 0.05 0.03 0.06 0.00 
 Unknown/missing 4,385 42.59 2,010 39.94 826 39.13 1,215 48.08 255 47.93 79 83.16 
Presence of metastasis 
 No 9,554 92.78 4,716 93.72 1,957 92.70 2,304 91.18 503 94.55 74 77.89 
 Yes 743 7.22 316 6.28 154 7.30 223 8.82 29 5.45 21 22.11 
AFP, ng/mL 
 Mean (SE) 5,776.52 (323.90) 4,208.56 (340.33) 9,182.69 (927.11) 6,919.12 (787.37) 8,470.82 (1,731.25) 26,740.47 (20,578.56) 
 Median (Q1–Q3) 22.20 (5.50–435.85) 17.70 (4.80–256.60) 38.61 (8.16–985.06) 31.00 (7.20–654.02) 30.78 (8.35–215.22) 9,194.15 (1,884.63–34,050.00) 
N 10,103 98.12 4,995 99.26 2,090 99.01 2,492 98.61 522 98.12 4.21 
 Unknown/missing 194 1.88 37 0.74 21 0.99 35 1.39 10 1.88 91 95.79 
AFP group, ng/mL 
 <200 7,143 69.37 3,684 73.21 1,448 68.59 1,649 65.26 362 68.05 0.00 
 ≥200 2,960 28.75 1,311 26.05 642 30.41 843 33.36 160 30.08 4.21 
 Unknown/missing 194 1.88 37 0.74 21 0.99 35 1.39 10 1.88 91 95.79 
VariablesTotalmALBI grade 1mALBI grade 2amALBI grade 2bmALBI grade 3Unknown/missing
N%N%N%N%N%N%
10,297100.005,03248.872,11120.502,52724.545325.17950.92
Age 
 Mean (SE) 60.18 (0.10) 59.72 (0.15) 61.13 (0.23) 60.72 (0.20) 58.33 (0.41) 59.42 (0.98) 
 Median (Q1–Q3) 60.00 (53.00–67.00) 60.00 (53.00–67.00) 61.00 (54.00–68.00) 60.00 (54.00–68.00) 57.00 (51.00–66.75) 58.00 (53.00–64.00) 
Sex 
 Male 8,316 80.76 4,070 80.88 1,691 80.10 2,030 80.33 439 82.52 86 90.53 
 Female 1,981 19.24 962 19.12 420 19.90 497 19.67 93 17.48 9.47 
BMI 
 <18.5 224 2.18 94 1.87 45 2.13 63 2.49 21 3.95 1.05 
 18.5–22.9 3,086 29.97 1,444 28.70 654 30.98 811 32.09 169 31.77 8.42 
 23.0–24.9 2,599 25.24 1,305 25.93 502 23.78 653 25.84 135 25.38 4.21 
 ≥25.0 4,176 40.56 2,145 42.63 873 41.35 950 37.59 202 37.97 6.32 
 Unknown/missing 212 2.06 44 0.87 37 1.75 50 1.98 0.94 76 80.00 
Smoking status 
 Never smoked 3,831 37.21 1,909 37.94 791 37.47 935 37.00 185 34.77 11 11.58 
 Former smoker 4,338 42.13 2,167 43.06 891 42.21 1,017 40.25 236 44.36 27 28.42 
 Current smoker 1,852 17.99 882 17.53 376 17.81 483 19.11 100 18.80 11 11.58 
 Unknown/missing 276 2.68 74 1.47 53 2.51 92 3.64 11 2.07 46 48.42 
Drinking status 
 Not a drinker 3,279 31.84 1,638 32.55 682 32.31 819 32.41 132 24.81 8.42 
 Former drinker 5,112 49.65 2,436 48.41 1,057 50.07 1,280 50.65 304 57.14 35 36.84 
 Current drinker 1,643 15.96 890 17.69 321 15.21 340 13.45 86 16.17 6.32 
 Unknown/missing 263 2.55 68 1.35 51 2.42 88 3.48 10 1.88 46 48.42 
Clinical trial participation 
 Yes 377 3.66 256 5.09 71 3.36 46 1.82 0.38 2.11 
 No 2,447 23.76 1,092 21.70 502 23.78 692 27.38 124 23.31 37 38.95 
 Did not receive SACT 7,473 72.57 3,684 73.21 1,538 72.86 1,789 70.80 406 76.32 56 58.95 
Follow-up duration, months 
 Mean (SE) 27.49 (0.21) 31.12 (0.29) 27.57 (0.44) 22.08 (0.39) 19.15 (0.85) 17.01 (1.71) 
 Median (Q1–Q3) 22.67 (9.63–41.89) 25.53 (12.14–47.10) 18.40 (7.84–35.77) 15.05 (5.60–30.16) 10.32 (4.30–31.92) 14.72 (7.18–33.71) 
ECOG PS 
 0 3,832 37.21 2,394 47.58 663 31.41 682 26.99 93 17.48 0.00 
 1 1,513 14.69 609 12.10 291 13.78 503 19.91 106 19.92 4.21 
 2 255 2.48 102 2.03 38 1.80 83 3.28 32 6.02 0.00 
 3 38 0.37 0.12 0.14 17 0.67 12 2.26 0.00 
 4 12 0.12 0.04 0.00 0.28 0.56 0.00 
 Unknown/missing 4,647 45.13 1,919 38.14 1,116 52.87 1,235 48.87 286 53.76 91 95.79 
BCLC stage 
 Stage 0 456 4.43 337 6.70 73 3.46 44 1.74 0.38 0.00 
 Stage A 679 6.59 414 8.23 112 5.31 135 5.34 18 3.38 0.00 
 Stage B 180 1.75 99 1.97 41 1.94 36 1.42 0.75 0.00 
 Stage C 3,259 31.65 1,644 32.67 610 28.90 852 33.72 145 27.26 8.42 
 Stage D 191 1.85 0.16 0.14 50 1.98 130 24.44 0.00 
 Unknown/missing 5,532 53.72 2,530 50.28 1,272 60.26 1,410 55.80 233 43.80 87 91.58 
CP class 
 Class A 7,635 74.15 4,423 87.90 1,809 85.69 1,398 55.32 0.75 1.05 
 Class B 1,595 15.49 86 1.71 157 7.44 961 38.03 391 73.50 0.00 
 Class C 146 1.42 0.00 0.00 26 1.03 120 22.56 0.00 
 Unknown/missing 921 8.94 523 10.39 145 6.87 142 5.62 17 3.20 94 98.95 
Disease etiology 
 Hepatitis B 6,778 65.82 3,519 69.93 1,371 64.95 1,545 61.14 312 58.65 31 32.63 
 Hepatitis C 936 9.09 382 7.59 208 9.85 287 11.36 50 9.40 9.47 
 Alcohol liver disease 1,490 14.47 520 10.33 335 15.87 467 18.48 147 27.63 21 22.11 
 Others 369 3.58 254 5.05 48 2.27 62 2.45 0.94 0.00 
 Unknown/missing 1,275 12.38 531 10.55 279 13.22 341 13.49 82 15.41 42 44.21 
Comorbidities 
 Liver cirrhosis 1,463 14.21 694 69.19 271 76.99 399 79.01 98 88.29 11.11 
 Hypertension 481 4.67 246 24.53 86 24.43 121 23.96 20 18.02 88.89 
 Diabetes mellitus 402 3.90 192 19.14 68 19.32 115 22.77 27 24.32 0.00 
Number of tumors 
 1 4,227 41.05 2,216 44.04 906 42.92 920 36.41 179 33.65 6.32 
 2 1,164 11.30 582 11.57 250 11.84 268 10.61 56 10.53 8.42 
 3 367 3.56 173 3.44 85 4.03 83 3.28 26 4.89 0.00 
 4–5 124 1.20 42 0.83 33 1.56 34 1.35 13 2.44 2.11 
 6+ 30 0.29 0.00 11 0.05 0.03 0.06 0.00 
 Unknown/missing 4,385 42.59 2,010 39.94 826 39.13 1,215 48.08 255 47.93 79 83.16 
Presence of metastasis 
 No 9,554 92.78 4,716 93.72 1,957 92.70 2,304 91.18 503 94.55 74 77.89 
 Yes 743 7.22 316 6.28 154 7.30 223 8.82 29 5.45 21 22.11 
AFP, ng/mL 
 Mean (SE) 5,776.52 (323.90) 4,208.56 (340.33) 9,182.69 (927.11) 6,919.12 (787.37) 8,470.82 (1,731.25) 26,740.47 (20,578.56) 
 Median (Q1–Q3) 22.20 (5.50–435.85) 17.70 (4.80–256.60) 38.61 (8.16–985.06) 31.00 (7.20–654.02) 30.78 (8.35–215.22) 9,194.15 (1,884.63–34,050.00) 
N 10,103 98.12 4,995 99.26 2,090 99.01 2,492 98.61 522 98.12 4.21 
 Unknown/missing 194 1.88 37 0.74 21 0.99 35 1.39 10 1.88 91 95.79 
AFP group, ng/mL 
 <200 7,143 69.37 3,684 73.21 1,448 68.59 1,649 65.26 362 68.05 0.00 
 ≥200 2,960 28.75 1,311 26.05 642 30.41 843 33.36 160 30.08 4.21 
 Unknown/missing 194 1.88 37 0.74 21 0.99 35 1.39 10 1.88 91 95.79 

SE, standard error; BMI, body mass index; Q1, first quartile; Q3, third quartile; SACT, systemic anti-cancer therapy; ECOG PS, Eastern Cooperative Oncology Group Performance Status; BCLC, Barcelona Clinic Liver Cancer; CP, Child-Pugh; mALBI, modified albumin-bilirubin; AFP, alpha fetoprotein.

The distribution of CP class was highly skewed, where most patients were classified as CP class A (74.15%), followed by class B (15.49%), with very few in class C (1.42%). The distribution of mALBI grade was relatively more spread out than that of the CP class or traditional ALBI grade, with mALBI grade 1 (48.87%), grade 2a (20.50%), grade 2b (24.54%), and grade 3 (5.17%) at diagnosis.

The variables Eastern Cooperative Oncology Group (ECOG) performance score and number of tumors had a significant portion of missing data, 45.13% and 42.59%, respectively. This led to a high missing rate for the BCLC stage, which is commonly used as a reference staging system in clinical trials.

Initial Treatment Options and Systemic Therapy Regimens

Transarterial therapy was identified as the most common initial treatment (54.07%), followed by hepatectomy (14.71%) and local ablation therapy (12.57%), as outlined in Table 2. Transarterial therapy was prevalent across all mALBI grades, with hepatectomy being more common in grades 1 and 2a, while SACT was more frequent in grades 2b and 3.

Table 2.

Distributions of initial treatments options

TotalmALBI grade
totalgrade 1grade 2agrade 2bgrade 3unknown/missing
N%N%N%N%N%N%
10,297100.005,03248.872,11120.502,52724.545325.17950.92
Initial treatment 
 Hepatectomy 1,515 14.71 1,089 21.64 275 13.03 133 5.26 14 2.63 4.21 
 LT 628 6.10 206 4.09 109 5.16 225 8.90 83 15.60 5.26 
 Local ablation therapy 1,294 12.57 793 15.76 225 10.66 233 9.22 37 6.95 6.32 
 Transarterial therapy 5,568 54.07 2,514 49.96 1,243 58.88 1,475 58.37 285 53.57 51 53.68 
 EBRT 163 1.58 50 0.99 26 1.23 58 2.30 29 5.45 0.00 
 SACT 1,129 10.96 380 7.55 233 11.04 403 15.95 84 15.79 29 30.53 
TotalmALBI grade
totalgrade 1grade 2agrade 2bgrade 3unknown/missing
N%N%N%N%N%N%
10,297100.005,03248.872,11120.502,52724.545325.17950.92
Initial treatment 
 Hepatectomy 1,515 14.71 1,089 21.64 275 13.03 133 5.26 14 2.63 4.21 
 LT 628 6.10 206 4.09 109 5.16 225 8.90 83 15.60 5.26 
 Local ablation therapy 1,294 12.57 793 15.76 225 10.66 233 9.22 37 6.95 6.32 
 Transarterial therapy 5,568 54.07 2,514 49.96 1,243 58.88 1,475 58.37 285 53.57 51 53.68 
 EBRT 163 1.58 50 0.99 26 1.23 58 2.30 29 5.45 0.00 
 SACT 1,129 10.96 380 7.55 233 11.04 403 15.95 84 15.79 29 30.53 

mALBI, modified albumin-bilirubin; LT, liver transplantation; EBRT, external beam radiation therapy; SACT, systemic anti-cancer therapy.

Among the 2,824 patients who received at least one line of SACT during the follow-up period, tyrosine kinase inhibitors were the preferred regimen class for both LoT1 and LoT2. At LoT1, sorafenib (63.42%) was the most frequently received regimen, followed by lenvatinib (19.12%) and atezolizumab + bevacizumab (1.27%). For LoT2 (n = 888), regorafenib (41.55%) emerged as the leading choice, with nivolumab (18.47%) and sorafenib (17.23%) also commonly selected (Fig. 3).

Fig. 3.

Sankey diagram for SACT treatment sequence by drug class. TKI, tyrosine kinase inhibitor; ICI, immune checkpoint inhibitor; CCT, cytotoxic chemotherapy; mAb, monoclonal antibody; CT, clinical trials; Tx, treatment.

Fig. 3.

Sankey diagram for SACT treatment sequence by drug class. TKI, tyrosine kinase inhibitor; ICI, immune checkpoint inhibitor; CCT, cytotoxic chemotherapy; mAb, monoclonal antibody; CT, clinical trials; Tx, treatment.

Close modal

Real-World Outcome of HCC Patients by mALBI Grades

The median rwOS was not reached for patients classified as mALBI grade 1 and 2a during the follow-up period, whereas patients with mALBI grade 2b and 3 exhibited median rwOS of 41.36 months (95% confidence interval [CI]: 37.75–52.96) and 18.53 months (95% CI: 15.21–25.69), respectively. Significant differences in rwOS were observed across all mALBI grades (p < 0.001, Fig. 4), with a significant difference in post hoc pairwise comparisons among all grade combinations, including mALBI 2a versus 2b, using Bonferroni corrections (p < 0.001). This indicates a clear segregation and difference in survival outlook between mALBI grade 2a and 2b, which is grouped together as grade 2 in the original ALBI grade system. When categorized by CP class, median rwOS also varied significantly (p < 0.001), not reached for class A, 23.46 months for class B (95% CI: 18.96, 28.29), and 7.33 months for class C (95% CI: 5.09, 12.75).

Fig. 4.

rwOS of HCC patients by mALBI grade (a) and by CP class (b). mALBI, modified albumin-bilirubin.

Fig. 4.

rwOS of HCC patients by mALBI grade (a) and by CP class (b). mALBI, modified albumin-bilirubin.

Close modal

Cumulative rwOS curves indicated no significant differences across mALBI grades for patients undergoing surgical treatments such as hepatectomy or liver transplantation. However, significant disparities were observed in non-surgical interventions, with cumulative rwOS varying by mALBI grade across all treatment groups (p < 0.001), as shown in Figure 5. Multiple comparisons using the Benjamini-Hochberg method confirmed significant differences between each grade, except for mALBI grade 2b versus 3 in the EBRT and SACT category (Table 3).

Fig. 5.

rwOS of HCC patients by mALBI grade according to initial treatments modality. Surgical interventions include hepatectomy (a) and LT (b); and non-surgical interventions include local ablation therapy (c), transarterial therapy (d), EBRT (e), and SACT (f).

Fig. 5.

rwOS of HCC patients by mALBI grade according to initial treatments modality. Surgical interventions include hepatectomy (a) and LT (b); and non-surgical interventions include local ablation therapy (c), transarterial therapy (d), EBRT (e), and SACT (f).

Close modal
Table 3.

mALBI multiple comparison log-rank tests within each initial treatment options

Initial treatmentp values for mALBI multiple comparison
1 versus 2a1 versus 2b1 versus 32a versus 2b2a versus 32b versus 3
Hepatectomy 0.470 0.480 0.470 0.470 0.470 0.470 
LT 0.960 0.910 0.910 0.910 0.910 0.910 
Local ablation therapy 0.002** <0.001*** <0.001*** 0.032* <0.001*** <0.001*** 
Transarterial therapy <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** 
EBRT 0.036* <0.001*** <0.001*** 0.036* 0.005** 0.090 
SACT <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** 0.158 
Initial treatmentp values for mALBI multiple comparison
1 versus 2a1 versus 2b1 versus 32a versus 2b2a versus 32b versus 3
Hepatectomy 0.470 0.480 0.470 0.470 0.470 0.470 
LT 0.960 0.910 0.910 0.910 0.910 0.910 
Local ablation therapy 0.002** <0.001*** <0.001*** 0.032* <0.001*** <0.001*** 
Transarterial therapy <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** 
EBRT 0.036* <0.001*** <0.001*** 0.036* 0.005** 0.090 
SACT <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** 0.158 

mALBI, modified albumin-bilirubin; LT, liver transplantation; EBRT, external beam radiation therapy; SACT, systemic anti-cancer therapy.

*p < 0.05.

**p < 0.01.

***p < 0.001.

Prognostic Efficacy of ALBI Score Compared to CP Score

The distribution of mALBI score within the CP score revealed a trend: as CP score increased from 5 to 14, there was a corresponding general escalation in mALBI grades (Fig. 6). Notably, within individual CP classes, a wide distribution of mALBI grades was observed, exhibiting considerable heterogeneity. For example, among patients with a CP score of 5, the majority were classified as mALBI grade 1 or 2a (72% and 24%, respectively), with a small fraction (3%) falling into grade 2b. Contrastingly, at CP score of 6, the majority (61%) were classified with grade 2b, with significant decrease in the proportions of grade 1 and 2a to 17% and 22%, respectively. This variability was also evident within CP class B, where a notable proportion of patients had higher mALBI grades, indicating heterogeneous liver function even among patients sharing the same CP classifications.

Fig. 6.

a Distribution of mALBI grades across CP scores. b Side-by-side boxplots of ALBI score distributions within each CP score. mALBI, modified albumin-bilirubin; ALBI, albumin-bilirubin.

Fig. 6.

a Distribution of mALBI grades across CP scores. b Side-by-side boxplots of ALBI score distributions within each CP score. mALBI, modified albumin-bilirubin; ALBI, albumin-bilirubin.

Close modal

Time-dependent area under the receiver operating characteristic curve (AUROC) analysis favored the ALBI score’s long-term prognostic efficacy for predicting rwOS over the CP score. Initially, the CP score showed a slightly better predictive ability for timepoints up to approximately 12 months (AUROC = 0.73), but the ALBI score’s prognostic capability became more pronounced over time, remaining consistent after 15 months post-diagnosis (Fig. 7). The AUROC for the ALBI score consistently remained around 0.71, while the AUROC for the CP score dropped to 0.67 at the 60-month mark.

Fig. 7.

Time-dependent AUROCs of ALBI score and CP score for predicting rwOS in HCC patients. AUROC, area under the receiver operating characteristic curve; ALBI, albumin-bilirubin.

Fig. 7.

Time-dependent AUROCs of ALBI score and CP score for predicting rwOS in HCC patients. AUROC, area under the receiver operating characteristic curve; ALBI, albumin-bilirubin.

Close modal

For the overall patient population, both univariate and multivariate Cox proportional hazards regression model analysis showed that higher CP class was significantly associated with decreased rwOS. Likewise, higher mALBI grades also had a significant association with less favorable survival outcome in both univariate and multivariate Cox proportional regression model analysis (Table 4). Furthermore, within the subgroup of patients with CP class A, both univariate and multivariate Cox proportional hazards regression model analyses indicated that mALBI grade 2a/2b was significantly associated with poor rwOS, suggesting that even within the same CP class A group, patients with different mALBI grades faced significantly different risks in terms of rwOS.

Table 4.

Univariate and multivariate Cox proportional hazards regression model analyses of CP class and mALBI grade at diagnosis

Number of events/patients, nUnivariate analysisMultivariate analysisa
HR95% CIp valueHR95% CIp value
(Overall patients) 2,234/7,773       
 CP class B 649/1,310 3.10 (2.02, 4.76) <0.001*** 3.03 (2.33, 3.94) <0.001*** 
 CP class C 89/128 3.87 (1.16, 12.93) 0.028* 4.37 (1.87, 10.23) <0.001*** 
 Ref: CP class A 1,496/6,335 1.00   1.00   
(Overall patients) 2,234/7,773       
 mALBI grade 2a 451/1,604 2.07 (1.84, 2.34) <0.001*** 1.88 (1.66, 2.13) <0.001*** 
 mALBI grade 2b 804/1,958 3.34 (2.29, 4.85) <0.001*** 3.38 (2.62, 4.36) <0.001*** 
 mALBI grade 3 232/422 4.67 (2.60, 8.37) <0.001*** 4.93 (3.10, 7.83) <0.001*** 
 Ref: mALBI grade 1 747/3,789 1.00   1.00   
(CP class A) 1,496/6,335       
 mALBI grade 2a 386/1,479 2.09 (1.69, 2.58) <0.001*** 1.77 (1.42, 2.20) <0.001*** 
 mALBI grade 2b 393/1,137 2.85 (2.30, 3.53) <0.001*** 2.71 (2.18, 3.38) <0.001*** 
 mALBI grade 3 1/2 2.53 (0.35, 18.03) 0.356 1.75 (0.24, 12.86) 0.585 
 Ref: mALBI grade 1 716/3,717 1.00   1.00   
Number of events/patients, nUnivariate analysisMultivariate analysisa
HR95% CIp valueHR95% CIp value
(Overall patients) 2,234/7,773       
 CP class B 649/1,310 3.10 (2.02, 4.76) <0.001*** 3.03 (2.33, 3.94) <0.001*** 
 CP class C 89/128 3.87 (1.16, 12.93) 0.028* 4.37 (1.87, 10.23) <0.001*** 
 Ref: CP class A 1,496/6,335 1.00   1.00   
(Overall patients) 2,234/7,773       
 mALBI grade 2a 451/1,604 2.07 (1.84, 2.34) <0.001*** 1.88 (1.66, 2.13) <0.001*** 
 mALBI grade 2b 804/1,958 3.34 (2.29, 4.85) <0.001*** 3.38 (2.62, 4.36) <0.001*** 
 mALBI grade 3 232/422 4.67 (2.60, 8.37) <0.001*** 4.93 (3.10, 7.83) <0.001*** 
 Ref: mALBI grade 1 747/3,789 1.00   1.00   
(CP class A) 1,496/6,335       
 mALBI grade 2a 386/1,479 2.09 (1.69, 2.58) <0.001*** 1.77 (1.42, 2.20) <0.001*** 
 mALBI grade 2b 393/1,137 2.85 (2.30, 3.53) <0.001*** 2.71 (2.18, 3.38) <0.001*** 
 mALBI grade 3 1/2 2.53 (0.35, 18.03) 0.356 1.75 (0.24, 12.86) 0.585 
 Ref: mALBI grade 1 716/3,717 1.00   1.00   

HR, hazard ratio; CI, confidence interval; CP, Child-Pugh; mALBI, modified albumin‐bilirubin.

aCovariates including age, sex, BMI, drinking status, smoking status, etiology, metastasis, and AFP group were adjusted.

*p < 0.05.

***p < 0.001.

This study leverages a large-scale, multi-center database to generate fast and reliable real-world evidence in HCC patients through systematic data extraction from EHR, providing a comprehensive analysis of HCC management. Our findings not only corroborate the superior long-term prognostic efficacy of the ALBI score over the CP score, as established in previous studies [8, 12, 19], but also introduce new insights into its applicability for liver function assessments in a real-world setting.

The observed heterogeneity in mALBI grades within the same CP score categories emphasizes the intricate complexity of liver function assessment in HCC patients, indicating that the use of CP scores may not be consistent across different contexts and settings. This inconsistency is particularly notable with the subjective grading of ascites and encephalopathy [20], suggesting that mALBI may offer additional granularity that could refine prognostic evaluations and treatment stratifications beyond what CP scores alone can provide. Despite a broader distribution of ALBI grade being more evenly spread out among grade 1 and grade 2 compared to the traditional CP class, it was still practically a dichotomous categorization system with very few patients identified as grade 3, a trend also noted in prior research [12]. The subdivision of grade 2 into subcategories 2a and 2b in mALBI allows for distinguishing between patient groups who otherwise might be homogenously grouped, offering distinct survival outcomes and underscoring the potential of the mALBI score to identify subtler distinctions in liver function that may have significant implications for patient management and outcomes.

As an objective measure, especially in the context of multi-center comparisons, the mALBI score could serve a similar role to that of the Model for End-Stage Liver Disease score used for liver transplant organ allocation [21]. The variation in mALBI grades within CP classes, particularly the notable shift in grade distributions between CP class A and B, advocates for incorporating the ALBI score more systematically in clinical practice to enhance the precision of prognosis and potentially guide more tailored therapeutic approaches.

This is further evidenced by our time-dependent AUROC analysis and the distinctive stratification of HCC patient prognosis across mALBI grades. The time-dependent AUROC analysis indicated that the ALBI score has a better long-term prognostic value than the CP score. This finding aligned with the previous studies where the predictive ability of the ALBI score was initially comparable but superior to that of CP grade at long-term time points [22]. Furthermore, the rwOS curves demonstrated distinctive stratification for the prognosis of HCC patients with each mALBI grade, including grade 2a and 2b, showing significant differences across the mALBI grades. This indicates that the mALBI grade could provide a more detailed assessment of hepatic function and prognosis for HCC patients compared to the ALBI grades [10, 23, 24]. Such findings advocate for the broader adoption of the ALBI grade in clinical settings [25], despite the current Korean reimbursement criteria’s reliance on the CP system [26].

Contrary to findings from the Liver Cancer Study Group of Japan [25, 27], our study revealed that the mALBI grade’s stratification performance was especially effective in non-curative modality (non-surgical interventions) but not in curative/surgical modality such as hepatectomy or transplantation. This discrepancy might highlight the overriding significance of surgical interventions over liver function grading by mALBI at diagnosis and invite further investigation into the mALBI grading system’s role across different HCC management strategies. We also demonstrated the significant retention of ALBI’s relevance when comparing multiple modalities simultaneously, maintaining its significance across a broad spectrum of noncurative treatment modalities. This offers novel insights into the prognostic utility of liver function assessments in real-world settings where various treatment modalities coexist, suggesting that the ALBI score is particularly useful in such contexts. The median rwOS was notably longer for patients with lower mALBI grades, highlighting the grade’s utility in prognostication. These findings provide a valuable benchmark for comparing the effectiveness of different treatment strategies and feature the potential of mALBI grades in guiding treatment decisions.

Our findings also highlight the LINK database’s capability to accurately mirror real-world treatment patterns, disease prognosis, and patient outcomes for HCC. The comprehensive longitudinal cohort dataset, developed through a meticulously organized ETL (extract, transform, load) process, underpins the LINK database’s role as a sustainable research platform, ensuring ongoing data relevance with continuous updates. Significantly, it covers a broad spectrum of treatment options, both surgical and non-surgical, thereby enriching the decision-making process for patient care. The representativeness of the LINK database for HCC patients in South Korea is underscored by its coverage of approximately a quarter (26.72%) of the nation’s newly diagnosed cases [28].

We observed that the choice of systemic therapy regimens aligns with clinical practice and reimbursement guidelines in Korea [26, 29] with sorafenib being the predominant first-line treatment for advanced unresectable HCC since 2007 [30]. However, newer treatments like lenvatinib and the combination of atezolizumab and bevacizumab, approved in 2018 and 2020, respectively [31, 32], suggest evolving treatment landscapes. Our database platform is an EHR-integrated patient registries that automate efforts to aggregate updated data on an interval basis [33, 34], which reflects launching of EHR-based registry across multiple institutes. As the present study spans data from 2015 to 2020, it has been iterated to include annual data to reflect newer therapies and warrant further investigation as treatment guidelines continue to change.

Despite these strengths, it is critical to recognize the inherent limitations associated with database studies, such as potential delays in incorporating the latest treatments and capturing out-of-hospital death data. While the LINK network undergoes annual updates, there is typically 1–2 year lag in extracting and integrating new data. Consequently, our current findings might not fully reflect the adoption of newer treatment regimens and may overestimate survival rates due to the absence of records on out-of-hospital deaths.

However, supporting evidence from recent studies using updated regimens suggests that our results remain relevant and robust. For instance, previous studies incorporating relatively recent regimens such as lenvatinib or atezolizumab + bevacizumab have shown similar outcomes to our findings, with a marked decrease in overall survival associated with higher mALBI grades in HCC patients. These studies reported a significant difference in survival outcomes for patients using lenvatinib (p < 0.001) [35] and for those using atezolizumab + bevacizumab (p < 0.001) [36], thereby validating the utility of the mALBI grade in reflecting patient outcomes across diverse treatment advancements.

Furthermore, database studies are limited to the information recorded in the EHR, meaning actual adherence to prescribed treatments or changes in regimen due to unforeseen adverse events might not be accurately captured or updated in the prescription data. Consequently, this limitation can lead to potential misclassification of LoT in some instances. Despite employing algorithms to categorize SACT treatments into LoTs and to identify patients’ initial treatments, it should be noted that not every scenario could be accounted for by these algorithms, leaving room for possible misclassification. Moreover, the heterogeneity across participating centers and the variable completeness of EHR data, as indicated by the presence of high percentages of missingness in certain variables, pose challenges to ensuring data consistency and reliability in interpretation.

In conclusion, the LINK database not only fills a critical gap in real-world HCC research but also serves as a foundational platform for ongoing and future studies aimed at refining treatment strategies and improving patient outcomes in South Korea. By continually updating and expanding its dataset, LINK supports the ongoing refinement of HCC management guidelines, ensuring they remain aligned with the latest evidence and therapeutic advancements. This study, therefore, establishes a solid basis for more nuanced, data-informed decision-making in HCC management, demonstrating the invaluable role of real-world evidence in bridging the gap between clinical research and everyday clinical practice.

The authors would like to extend our heartfelt thanks to Hyun-Jeong Kim, Na Won Ha, Yun Jung Kim, and Seung Hoon Lee from Asan Medical Center, as well as Sung Kyung Ju from Samsung Medical Center, for their contributions to data management and operational work.

This study protocol was reviewed and approved by the Institutional Review Boards of Asan Medical Center (AMC, S2022-0110-0001), Samsung Medical Center (SMC, 2022-01-132), and Severance Hospital (SVC, 4-2022-0010). This study has been granted an exemption from requiring written informed consent by the Institutional Review Boards of Asan Medical Center (AMC, S2022-0110-0001), Samsung Medical Center (SMC, 2022-01-132), and Severance Hospital (SVC, 4-2022-0010).

The authors have no conflicts of interest to declare.

This research was sponsored and funded by Eisai Korea Inc. The funders had no role in the study design, execution, and analysis. The decision to publish and manuscript preparation were conducted independently by the authors.

Kyu-Pyo Kim contributed to the conception of the work, design of the work, and interpretation of data for the work.

Kang Mo Kim, Baek-Yeol Ryoo, Mira Kang, Dong Hyun Sinn, and Do Young Kim contributed to design of the work and interpretation of data for the work.

Won-Mook Choi contributed to design of the work and acquisition of data for the work.

Won Chul Cha and DongKyu Kim contributed to the conception of the work.

Myung Ji Goh contributed to design of the work.

Min Ji Lee, Subin Lim, and Kyoungdae Baek contributed to acquisition of data for the work.

Joohyun Kim contributed to design of the work, acquisition of data for the work, analysis of data for the work, and interpretation of data for work.

Eui Jun Choi contributed to acquisition of data for the work and analysis of data for the work.

Doik Lee contributed to interpretation of data for the work.

Jung-Ae Kim and Ki-Hun Kim contributed to conception of the work and interpretation of data for the work.

The data that support the findings of this study are not publicly available due to their containing information that could compromise the privacy of research participants. Access to anonymized patient-level data is restricted to participating site staff who are registered and approved by Institutional Review Boards, and such data will be provided either as encrypted files or within an encrypted system. Aggregated data outputs, however, are available from the authors [Kyu-Pyo Kim, Won Chul Cha, Do Young Kim] upon reasonable request and with permission from Data Review Boards [DFIT@amc.seoul.kr; http://www.e-irb.com; irb@yuhs.ac].

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