Epidemiology and Outcomes of Hospitalizations Due to Hepatocellular Carcinoma

Mullangi, Sanjana; Keesari, Praneeth Reddy; Zaher, Anas; Pulakurthi, Yashwitha Sai; Poku, Frank Adusei; Rajeev, Arathi; Vidiyala, Prasanna Lakshmi; Guntupalli, Asha Latha; Desai, Mihir J.; Ohemeng-Dapaah, Jessica; Asare, Yaw; Patel, Achint; Lekkala, Manidhar Reddy

doi:10.7759/cureus.20089

Cited by 5 publications

(5 citation statements)

References 51 publications

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“…Patients with HCC who cannot undergo surgical resection often present with varying degrees of comorbidity burdens 20 , 21 . These include multimorbidity disease related to the liver tumour itself as well as nonhepatic comorbidities, such as hypertension, diabetes, and renal failure 21 , 22 . In our study, a diverse range of comorbid conditions accompanying HCC were exhibited in substantial proportions of patients.…”

Section: Discussionmentioning

confidence: 99%

“…This trend highlights the multifaceted challenges in managing HCC, particularly in the context of an ageing population. The presence and type of these comorbidities are key factors affecting the adverse prognosis in HCC patients 22 . The variety and cumulative effect of these comorbidities can increase the risk of longer hospital stays, in-hospital mortality, and postoperative complications for HCC patients 25 .…”

Section: Discussionmentioning

confidence: 99%

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Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy

Yao,

Zheng,

et al. 2024

Sci Rep

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Patients diagnosed with hepatocellular carcinoma (HCC) often present with multimorbidity, significantly contributing to adverse outcomes, particularly in-hospital mortality. This study aimed to develop a predictive nomogram to assess the impact of comorbidities on in-hospital mortality risk in HCC patients undergoing palliative locoregional therapy. We retrospectively analyzed data from 345 hospitalized HCC patients who underwent palliative locoregional therapy between January 2015 and December 2022. The nomogram was constructed using independent risk factors such as length of stay (LOS), hepatitis B virus (HBV) infection, hypertension, chronic obstructive pulmonary disease (COPD), anemia, thrombocytopenia, liver cirrhosis, hepatic encephalopathy (HE), N stage, and microvascular invasion. The model demonstrated high predictive accuracy with an AUC of 0.908 (95% CI: 0.859–0.956) for the overall dataset, 0.926 (95% CI: 0.883–0.968) for the training set, and 0.862 (95% CI: 0.728–0.994) for the validation set. Calibration curves indicated a strong correlation between predicted and observed outcomes, validated by statistical tests. Decision curve analysis (DCA) and clinical impact curves (CIC) confirmed the model's clinical utility in predicting in-hospital mortality. This nomogram offers a practical tool for personalized risk assessment in HCC patients undergoing palliative locoregional therapy, facilitating informed clinical decision-making and improving patient management.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy

Yao,

Zheng,

et al. 2024

Sci Rep

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“…The NIS, being an administrative database, has a potential for misclassification of diseases using ICD-10 codes or missing codes. However, previous studies have used similar ICD-10 codes to obtain information regarding patients with obesity and HCC [ 25 , 26 ]. Second, obesity in our study was not stratified into different categories such as morbidly obese (BMI: >40 kg/m 2 ) or obese (30 ≤ BMI ≤40 kg/m 2 ), which have significant clinical implications with regard to outcomes of hospitalization, which could have confounded our results.…”

Section: Discussionmentioning

confidence: 99%

A Retrospective Cohort Study From the National Inpatient Sample Database (2016-2019): Does Obesity Affect the Outcomes of Hospitalization Due to Hepatocellular Carcinoma?

Pandey,

Changela,

Manvar

et al. 2024

Cureus

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Introduction: Obesity is commonly reported to be associated with hepatocellular carcinoma (HCC) along with higher risks of mortality. However, there is a significant research gap regarding the outcomes of hospitalization due to HCC among obese patients compared to those without obesity. This study compares the outcomes of hospitalization among those two groups. Methods: A total of 50,845 patients admitted from 2016 to 2019 with a principal admission diagnosis of HCC were identified using the International Classification of Disease 10 (ICD-10) coding from the National Inpatient Sample (NIS) database. Patients with a body mass index (BMI) >30 were stratified into the obese cohort, and those with BMI ≤30 into the non-obese cohort as per the ICD-10 coding criteria for obesity based on BMI. The primary outcome of the study was mortality, whereas the length of stay, total hospitalization charges, acute kidney injury (AKI), sepsis, and shock were the secondary outcomes. We also compared additional complications such as ascites, portal hypertension, acute liver failure, disseminated intravascular coagulation (DIC), hepatic encephalopathy, and hepatorenal syndrome between the two groups. A multivariate regression model was used to estimate the effect of obesity on outcomes of hospitalization due to HCC. Results: The obese cohort comprised 10.64% of the study population, whereas the non-obese cohort comprised 89.36% of the study population. Compared to the non-obese cohort, the obese cohort of patients with HCC were more likely to have a higher comorbidity index (CCI ≥4: 79.76% in the obese vs 71.17 % in the non-obese cohort). Obesity was found to be a protective factor for in-hospital mortality; that is, the odds of in-hospital mortality among the obese cohort was 0.713 times than that of the non-obese group of patients with HCC. The obese cohort had a higher mean length of stay (6.3 days vs 5.6 days; p value: <0.001) and total hospitalization charges (109,108$ vs 85,406$; p value: <0.001), which was further validated on multivariate analysis. The obese cohort had 1.26 times odds of developing AKI compared to the non-obese cohort (p value: 0.005). Sepsis, shock, and other complications such as acute liver failure, DIC, hepatic encephalopathy, hepatorenal syndrome, and portal hypertension were not significantly different between the two groups. Conclusion: Obesity was associated with reduced in-hospital mortality among patients with HCC. However, obese patients with HCC were found to have higher healthcare resource utilization in terms of length of stay and total hospitalization charge along with the development of AKI. Clinicians should be mindful of the potential longer length of stay and associated complications such as AKI while managing obese patients with HCC. Contrary to commonly held notions, obesity and its relation with in-hospital mortality reported in this study warrants further explorative research.

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“…Finally, it is important to highlight the recent decrease in the hospital burden associated with the LT population observed in our study. Although there are studies that explore hospitalization trends in patients with other digestive and liver diseases 19 , 35 , 36 or hepatocellular carcinoma, 37 there are no data about the temporal changes in LT recipients. There are several possible explanations for this finding.…”

Section: Discussionmentioning

confidence: 99%

Demographic Trends in Liver Transplant Survivors After 3 Decades of Program Implementation: The Impact of Cohort and Period Effects on Life Expectancy

Romero-Cristóbal,

Díaz-Fontenla,

Fernández-Yunquera

et al. 2024

Transplantation Direct

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Background. Demographic analyses may reveal current patterns of change in the outcomes of rapidly developing medical procedures because they incorporate the period perspective. Methods. We analyzed the changes in size, age structure, and hospitalizations in the population of liver transplantation (LT) survivors in our center during the last 30 y (n = 1114 patients) and generated projections, including life expectancy (LE), considering cohort and period effects. Life tables were used to project the complete LE (overall 1990–2020 experience), the cohort LE (according to the decade of surgery: 1990–2000, 2000–2010, and 2010–2020), and the period LE (current 2015–2020 experience). Results. The population of LT recipients in follow-up continued to experience progressive growth and aging since 1990 (492 patients [41.9% >65 y] in 2020), and the magnitude of these phenomena may double in the next 30 y. However, the number of admissions and days of admission has been decreasing. The complete LE at LT was 12.4 y, whereas the period LE was 15.8 y. The cohort LE (limited to 10 y) was 5.3, 6.3, and 7.3 y for the 1990–2000, 2000–2010, and 2010–2020 cohorts, respectively. Conclusions. The target population of our medical care after LT is growing and aging. The prevalence of both of these phenomena is expected to increase in the coming years and is associated with a current improvement in LE. However, the hospitalization burden associated with LT survivors is declining. The period effect should be considered for generating up-to-date information on these current trends, which are crucial when designing health policies for LT survivors.

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Epidemiology and Outcomes of Hospitalizations Due to Hepatocellular Carcinoma

Cited by 5 publications

References 51 publications

Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy

Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy

A Retrospective Cohort Study From the National Inpatient Sample Database (2016-2019): Does Obesity Affect the Outcomes of Hospitalization Due to Hepatocellular Carcinoma?

Demographic Trends in Liver Transplant Survivors After 3 Decades of Program Implementation: The Impact of Cohort and Period Effects on Life Expectancy

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