Acute Kidney Injury and Mortality in Cirrhosis: A Nationwide Analysis of Clinical Outcomes, Resource Utilization, and Effect Modification

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However, contemporary national data quantifying the impact of AKI severity on mortality and healthcare utilization, as well as potential effect modification across patient subgroups, remain limited. Methods We conducted a retrospective cohort study using the 2023 Nationwide Inpatient Sample. Adult hospitalizations with cirrhosis were identified using ICD-10-CM codes. AKI was categorized as no AKI, non–dialysis-requiring AKI, and dialysis-requiring AKI (AKI-D). Survey-weighted multivariable regression models evaluated associations between AKI and in-hospital mortality, length of stay (LOS), and hospital costs. Sensitivity analyses and interaction testing by age and chronic kidney disease (CKD) were performed. Results A total of 70,219 unweighted hospitalizations, representing approximately 351,095 nationally, were included. AKI occurred in 32.6% of hospitalizations. In-hospital mortality was higher among patients with AKI compared with those without AKI (13.0% vs 2.5%, p < 0.001). In adjusted analyses, non–dialysis-requiring AKI was associated with increased mortality (adjusted odds ratio [OR] 4.34, 95% CI 3.99–4.72), while AKI-D was associated with markedly higher mortality (OR 16.37, 95% CI 14.18–18.91). AKI was also associated with increased LOS (ratio 1.45 for non-dialysis AKI; 2.78 for AKI-D) and higher hospital costs (ratio 1.43 and 3.75, respectively). Findings were consistent across multiple sensitivity analyses. Significant interactions were observed by CKD and age, with stronger relative effects in patients without CKD and in younger individuals (p for interaction < 0.001). Conclusions AKI is common among hospitalized patients with cirrhosis and is strongly associated with increased mortality and healthcare utilization, with a clear graded relationship by severity. The impact of AKI varies across patient subgroups, highlighting the importance of individualized risk assessment. Strategies targeting early recognition and prevention of AKI may improve outcomes and reduce healthcare burden in this population. acute kidney injury cirrhosis in-hospital mortality dialysis-requiring AKI health disparities hospital outcomes length of stay healthcare utilization administrative data national inpatient sample Figures Figure 1 Figure 2 Study Design / Description This study evaluated how acute kidney injury (AKI) affects outcomes among hospitalized patients with cirrhosis using data from the 2023 Nationwide Inpatient Sample, a large, nationally representative database of U.S. hospitalizations. We included adult patients with cirrhosis and identified those who developed AKI, including a subgroup with severe AKI requiring dialysis. We then compared outcomes between patients with and without AKI, focusing on in-hospital mortality, length of stay, and hospitalization costs. Statistical models were used to account for differences in patient characteristics and hospital factors. Additional analyses were performed to ensure that the findings were consistent and not driven by underlying conditions such as chronic kidney disease or sepsis. Background Cirrhosis is a major cause of morbidity and mortality worldwide and is associated with frequent hospitalizations and substantial healthcare utilization [ 1 – 3 ]. Patients with cirrhosis are particularly vulnerable to acute decompensation and multi-organ dysfunction, which contribute significantly to adverse clinical outcomes [ 4 – 5 ]. Among hospitalized patients, complications such as infection, hemodynamic instability, and renal dysfunction are common and often portend poor prognosis [ 6 – 7 ]. Acute kidney injury (AKI) is one of the most frequent and clinically significant complications in patients with cirrhosis. The development of AKI reflects complex pathophysiologic processes, including systemic vasodilation, reduced effective arterial blood volume, and activation of neurohormonal pathways, and may manifest as hepatorenal syndrome or intrinsic renal injury [ 7 – 9 ]. Prior studies have consistently demonstrated that AKI in cirrhosis is associated with increased mortality; however, the magnitude of this association varies across clinical settings and patient populations, and the relationship between AKI severity and outcomes remains incompletely characterized. Despite existing evidence, several important gaps remain. Many prior studies have been limited to single-center cohorts or specific clinical settings, such as intensive care units, which may not reflect broader patient populations [ 10 – 11 ]. National-level data quantifying the impact of AKI severity on mortality and healthcare utilization are limited. In addition, the extent to which the association between AKI and mortality varies across key patient subgroups, including those with chronic kidney disease (CKD) and differing age profiles, is not well defined. Furthermore, the economic burden associated with AKI in cirrhosis has not been fully characterized in contemporary, nationally representative datasets. In this study, we used the Nationwide Inpatient Sample to evaluate the association between AKI and in-hospital mortality among patients hospitalized with cirrhosis. We further assessed the impact of AKI severity on healthcare utilization, including length of stay and hospital costs. Additionally, we examined the robustness of these associations across multiple sensitivity analyses and evaluated potential effect modification by CKD status and age. We hypothesized that AKI would be associated with a graded increase in mortality and resource utilization, with variation in effect across patient subgroups Methods Study Design and Data Source We conducted a retrospective cohort study using the 2023 Nationwide Inpatient Sample (NIS), part of the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest publicly available all-payer inpatient database in the United States and provides nationally representative estimates of hospitalizations [ 13 ]. It includes patient- and hospital-level data and incorporates discharge weights to generate national estimates. The dataset is de-identified and publicly available. The institutional review board at BayCare Health System reviewed and approval of this project was not required. Study Population Adult hospitalizations (age ≥ 18 years) with cirrhosis were identified using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes across all available diagnosis fields. Cirrhosis was defined using codes for alcoholic cirrhosis, biliary cirrhosis, and other cirrhosis-related conditions. Hospitalizations with missing key demographic or outcome variables were excluded. Exposure Definition The primary exposure was acute kidney injury (AKI), identified using ICD-10-CM codes (N17.x). AKI severity was categorized into three groups: no AKI, AKI not requiring dialysis, and AKI requiring dialysis (AKI-D). Dialysis was identified using ICD-10-PCS procedure codes for renal replacement therapy. This classification allowed evaluation of a graded relationship between AKI severity and clinical outcomes. Definitions of all study variables, including ICD-10-CM and ICD-10-PCS codes used to identify exposures, outcomes, comorbidities, and cirrhosis-related complications, are provided in Supplementary Table 1. Outcomes The primary outcome was in-hospital mortality, defined using the HCUP variable indicating death during hospitalization. Secondary outcomes included length of stay (LOS) and hospital cost. Hospital costs were estimated by applying HCUP-provided cost-to-charge ratios to total hospital charges. LOS was analyzed as a continuous variable and log-transformed to account for right-skewed distribution. Covariates Covariates included demographic characteristics (age and sex), socioeconomic factors (primary payer and median household income quartile), and admission characteristics (elective status and emergency department admission). Clinical comorbidities, including diabetes mellitus, congestive heart failure, hypertension, and chronic kidney disease, were identified using ICD-10-CM codes. Cirrhosis-related complications, including ascites, hepatic encephalopathy, and varices, were also included to account for liver disease severity. Sepsis was identified using ICD-10-CM codes (A40–A41) and included as a covariate in adjusted models. Statistical Analysis All analyses accounted for the complex survey design of the NIS using discharge weights (DISCWT), stratification (NIS_STRATUM), and clustering (HOSP_NIS) to generate nationally representative estimates. Continuous variables were summarized as means or medians, and categorical variables as proportions. Survey-weighted multivariable logistic regression models were used to assess the association between AKI and in-hospital mortality. Linear regression models with log-transformed outcomes were used to evaluate LOS and hospital costs, exponentiated coefficients were interpreted as relative percentage differences. Sensitivity analyses were performed to assess the robustness of findings, including removal of emergency department admission from the model, restriction to non-elective admissions, exclusion of patients with sepsis, and restriction to hospitalizations with cirrhosis as the primary diagnosis. Interaction analyses were conducted to evaluate effect modification by age and chronic kidney disease. Statistical significance was defined as a two-sided p-value < 0.05. All analyses were performed using R software (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria) [ 13 ]. Results Baseline Characteristics A total of 70,219 unweighted hospitalizations with cirrhosis were identified, representing approximately 351,095 hospitalizations nationwide after applying survey weights. Acute kidney injury was present in 114,620 (32.6%) weighted hospitalizations. Patients with AKI were older than those without AKI (mean age 61.5 vs 58.6 years, p < 0.001) and had a slightly lower proportion of females (38.2% vs 40.3%, p = 0.02). Patients with AKI also demonstrated greater disease severity. Cirrhosis-related complications were more common in the AKI group, including ascites (15.5% vs 10.3%), hepatic encephalopathy (19.7% vs 10.0%), and slightly lower prevalence of varices (21.5% vs 23.4%) (all p < 0.05). Baseline characteristics were presented in Table 1 . Table 1 Baseline Characteristics of hospitalizations with cirrhosis, stratified by AKI status Characteristic No AKI (n = 47,295) AKI (n = 22,924) p-value Age, mean (SE) 58.6 (0.10) 61.5 (0.14) < 0.001 Female, % 40.3% 38.2% 0.02 In-hospital mortality, % 2.49% 13.04% < 0.001 Length of stay, median (IQR), days 4 (2–7) 6 (3–11) < 0.001 Hospital cost, median (IQR), $ 12,242 (7,710 − 20,518) 18,920 (10,940 − 35,710) < 0.001 Primary payer, % < 0.001 Medicare 41.4% 46.9% Medicaid 26.5% 22.4% Private insurance 22.0% 22.2% Self-pay 5.7% 4.2% No charge 0.5% 0.3% Other 3.7% 3.7% Median household income quartile, % 0.01 Q1 (lowest) 32.9% 31.7% Q2 26.3% 26.3% Q3 22.1% 23.0% Q4 (highest) 14.6% 15.2% Missing 4.1% 3.8% Cirrhosis-related complication, % Ascites 10.3% 15.5% < 0.001 Hepatic encephalopathy 10.0% 19.7% < 0.001 Varices 23.4% 21.5% 0.03 Table 1 describes the baseline demographic, clinical, and socioeconomic characteristics of cirrhosis hospitalizations stratified by AKI status. Values are reported as survey-weighted means, medians, or percentages, as appropriate. Costs were estimated using HCUP cost-to-charge ratios Association of AKI With In-Hospital Mortality In multivariable analysis adjusting for demographic characteristics, socioeconomic factors, admission characteristics, comorbidities, cirrhosis-related complications, and sepsis, AKI remained strongly associated with increased in-hospital mortality (Table 2 ). Compared with patients without AKI, non–dialysis-requiring AKI was associated with a more than fourfold increase in mortality (adjusted odds ratio [OR] 4.34, 95% CI 3.99–4.72), while dialysis-requiring AKI was associated with over a sixteen fold increase (OR 16.37, 95% CI 14.18–18.91). Table 2 Adjusted Association of AKI With In-Hospital Mortality Variable Adjusted OR 95% CI p-value AKI severity < 0.001 No AKI Reference — — AKI (non-dialysis) 4.34 3.99–4.72 < 0.001 AKI requiring dialysis (AKI-D) 16.4 14.2–18.9 < 0.001 Sepsis 4.90 4.36–5.51 < 0.001 Age (per year) 1.02 1.02–1.02 < 0.001 Female (vs male) 0.96 0.90–1.03 0.29 Primary payer Medicaid (vs Medicare) 0.80 0.72–0.89 < 0.001 Private insurance 0.90 0.81–1.00 0.04 Self-pay 1.33 1.14–1.55 < 0.001 Other 1.27 1.08–1.50 0.004 Median household income quartile 0.001 Q1 (lowest) Reference — — Q2 0.77 0.63–0.94 0.01 Q3 0.72 0.60–0.88 0.001 Q4 (highest) 0.68 0.56–0.82 < 0.001 Admission characteristics Admission via emergency department 0.65 0.59–0.72 < 0.001 Comorbidities Diabetes mellitus 0.72 0.64–0.80 < 0.001 Congestive heart failure 0.89 0.82–0.98 0.02 Hypertension 0.68 0.62–0.74 < 0.001 Chronic kidney disease 0.56 0.52–0.62 < 0.001 Cirrhosis-related complications Ascites 1.34 1.21–1.49 < 0.001 Hepatic encephalopathy 2.73 2.53–2.95 < 0.001 Varices 0.92 0.84–0.99 0.04 Table 2 evaluates the independent association between AKI and in-hospital mortality after adjustment for demographic and socioeconomic factors. Survey-weighted multivariable logistic regression evaluating the independent association between AKI severity and in-hospital mortality among cirrhosis hospitalizations, adjusted for demographic characteristics, socioeconomic factors, admission characteristics, comorbidities, cirrhosis-related complications, and sepsis. Sepsis was independently associated with increased mortality (OR 4.90, 95% CI 4.36–5.51). Increasing age was also associated with higher mortality, while higher household income was associated with lower mortality. Several comorbidities, including diabetes, hypertension, and chronic kidney disease, were associated with lower mortality. These findings likely reflect residual confounding, coding bias, survivor bias, or differences in baseline disease severity and treatment patterns. Importantly, sensitivity analyses excluding comorbidities yield consistent results, reinforcing that the association between AKI and mortality is robust and not driven by model specification. Among cirrhosis-related complications, hepatic encephalopathy demonstrated the strongest association with mortality (OR 2.73, 95% CI 2.53–2.95), followed by ascites (OR 1.34, 95% CI 1.21–1.49). Findings were consistent in a parsimonious model with limited covariate adjustment (Supplementary Table 2), demonstrating a similar graded association between AKI severity and in-hospital mortality. Length of Stay and Hospital Cost AKI was associated with increased healthcare utilization (Table 3 ). Non–dialysis-requiring AKI was associated with a 45% increase in length of stay (ratio 1.45, 95% CI 1.43–1.47), while dialysis-requiring AKI was associated with nearly a threefold increase (ratio 2.78, 95% CI 2.64–2.92). Table 3 Adjusted Association of AKI Severity with Length of Stay and Cost A. Length of stay Variable Ratio (exp β) 95% CI Interpretation AKI (non-D) 1.45 1.43–1.47 + 45% LOS AKI-D 2.78 2.64–2.92 ~ 3× LOS B. Hospital cost (CCR-adjusted) Variable Ratio (exp β) 95% CI Interpretation AKI (non-D) 1.43 1.41–1.46 + 43% cost AKI-D 3.75 3.53–3.98 3.7× cost Table 3 quantifies the impact of acute kidney injury (AKI) severity on hospital resource utilization, including length of stay (LOS) and total hospital costs. Survey-weighted multivariable regression models assessing the association of AKI severity with length of stay and total hospital costs, adjusted for demographic, socioeconomic, clinical, and cirrhosis-related factors. Results are presented as exponentiated coefficients representing relative differences. Similarly, AKI was associated with increased hospital costs. Non–dialysis-requiring AKI was associated with a 43% increase in cost (ratio 1.43, 95% CI 1.41–1.46), while dialysis-requiring AKI was associated with approximately a 3.7-fold increase (ratio 3.75, 95% CI 3.53–3.98). Sensitivity Analyses Sensitivity analyses demonstrate consistent findings across multiple model specifications (Supplementary Table 3). Excluding emergency department admission from the model did not materially change the results (AKI non–dialysis OR 4.79, 95% CI 4.40–5.21; AKI-D OR 20.8, 95% CI 18.1–24.0). Similarly, restricting the cohort to non-elective admissions yielded slightly higher effect estimates (AKI non–dialysis OR 4.92, 95% CI 4.51–5.36; AKI-D OR 21.9, 95% CI 19.0–25.3). Excluding patients with sepsis resulted in similar effect estimates (AKI non–dialysis OR 4.52, 95% CI 4.13–4.95; AKI-D OR 19.5, 95% CI 16.8–22.7). In analyses restricted to hospitalizations with cirrhosis as the primary diagnosis, the magnitude of association was substantially higher (AKI non–dialysis OR 7.98, 95% CI 6.84–9.30; AKI-D OR 35.5, 95% CI 28.3–44.6). The consistency of the association between AKI and in-hospital mortality across multiple sensitivity analyses is illustrated in Fig. 1 . Effect estimates remained stable across all model specifications, including exclusion of sepsis, removal of admission-related variables, and restriction to primary cirrhosis hospitalizations, supporting the robustness of the observed association. Figure 1 demonstrates the association between AKI severity and in-hospital mortality across primary and sensitivity analyses. The consistency of effect estimates across models, including exclusion of sepsis, comorbidities, and CKD, as well as restriction to primary cirrhosis hospitalizations, supports the robustness of the observed association and suggests that findings are not driven by residual confounding or model specification. Interaction Analyses Significant interactions were observed between AKI and both chronic kidney disease (CKD) and age (Supplementary Table 4). The association between AKI and mortality was attenuated among patients with CKD compared with those without CKD (p for interaction < 0.001). Similarly, the relative effect of AKI was weaker among patients aged ≥ 65 years, with a stronger association observed in younger patients (p for interaction < 0.001). The interaction between AKI severity and CKD status is visually demonstrated in Fig. 2 . The relative increase in mortality associated with AKI was attenuated among patients with CKD, whereas a steeper gradient was observed among patients without CKD, indicating a stronger association between AKI and mortality in those without underlying kidney disease. Figure 2 illustrates the effect modification of the association between AKI severity and in-hospital mortality by CKD status. Odds ratios are shown relative to patients without AKI within each subgroup. The steeper increase in mortality observed among patients without CKD indicates a stronger association between AKI and mortality compared with those with CKD Discussion In this nationally representative study of hospitalizations with cirrhosis, acute kidney injury (AKI) was common and strongly associated with adverse clinical outcomes. Approximately one-third (32.7%) of hospitalizations were complicated by AKI, which was associated with a marked increase in in-hospital mortality (13.0% vs 2.5%), longer length of stay, and higher hospital costs. A clear graded relationship was observed, with progressively higher mortality risk from non–dialysis-requiring AKI (adjusted OR 4.34) to dialysis-requiring AKI (adjusted OR 16.4). Importantly, these findings were consistent across multiple sensitivity analyses and model specifications, supporting the robustness of the observed association. We also identified significant heterogeneity in the association between AKI and mortality by chronic kidney disease (CKD) status and age, highlighting important differences in risk across patient subgroups. Together, these findings highlight AKI as a central determinant of both clinical outcomes and healthcare utilization in cirrhosis. The strong association between AKI and mortality in cirrhosis likely reflects the complex pathophysiology of advanced liver disease. Cirrhosis is characterized by systemic vasodilation, reduced effective arterial blood volume, and activation of neurohormonal pathways that predispose to renal hypoperfusion [ 14 – 15 ]. In this setting, AKI often represents a manifestation of multi-organ dysfunction, including processes such as hepatorenal syndrome and ischemic acute tubular necrosis, rather than an isolated renal insult. The markedly elevated mortality observed in dialysis-requiring AKI suggests that progression to severe kidney injury reflects advanced circulatory failure and limited physiologic reserve. These findings reinforce the concept that AKI in cirrhosis is both a marker of disease severity and a mediator of worse outcomes. Our findings extend prior literature in several important ways. While previous studies have demonstrated an association between AKI and mortality in cirrhosis, many have been limited to single-center cohorts or specific clinical settings. For example, a single-center study of 179 hospitalized cirrhosis patients reported an AKI prevalence of 27.9%, with a 64% mortality rate among affected patients, accounting for 41% of all in-hospital deaths [ 16 ]. Similarly, Staufer et al. [ 17 ] and Allegretti et al. [ 18 ] evaluated critically ill cirrhosis patients requiring renal replacement therapy and demonstrated extremely high mortality, highlighting the severe prognosis associated with advanced kidney injury in highly selected ICU populations. By leveraging a large, nationally representative dataset, our study provides contemporary estimates of the burden and impact of AKI across diverse hospital settings. The magnitude of association observed in this study, particularly for dialysis-requiring AKI, was striking, with effect estimates that appear higher than those reported in prior general cirrhosis cohorts, where AKI has typically been associated with approximately 2–6-fold increases in mortality depending on severity [ 19 – 21 ]. These findings underscore the severe prognostic implications of advanced kidney injury in cirrhosis. Furthermore, the clear dose–response relationship across AKI severity categories and the consistency of findings across multiple sensitivity analyses—including exclusion of comorbidities, CKD, and sepsis—strengthen the validity and generalizability of our results and reduce the likelihood that findings are driven by residual confounding or model specification. Beyond its impact on mortality, AKI was associated with substantial increases in healthcare utilization. Patients with AKI experienced significantly longer hospitalizations and higher costs, with dialysis-requiring AKI associated with nearly a threefold increase in length of stay and a more than threefold increase in hospital costs. These findings highlight AKI as a major driver of inpatient resource utilization in cirrhosis. The magnitude of these effects suggests that AKI contributes not only to clinical deterioration but also to substantial strain on healthcare systems. Given the high prevalence of cirrhosis-related hospitalizations, even modest reductions in AKI incidence or severity may translate into meaningful improvements in healthcare efficiency and cost containment. These findings position AKI as a key target for interventions aimed at improving both clinical outcomes and healthcare resource utilization. An important novel finding of this study is the presence of significant heterogeneity in the association between AKI and mortality across patient subgroups. The relative impact of AKI was attenuated among patients with underlying CKD compared with those without CKD, suggesting that AKI may represent a greater relative physiological insult in patients with lower baseline renal dysfunction. This finding is consistent with our sensitivity analyses, in which exclusion of CKD resulted in higher effect estimates, further supporting this interpretation. Similarly, the association between AKI and mortality was stronger among younger patients, indicating that AKI may represent a greater deviation from baseline health status in this population. These findings highlight the importance of considering baseline patient characteristics when assessing AKI-related risk and suggest that risk stratification strategies should account for underlying patient heterogeneity. Interestingly, several comorbidities, including diabetes, hypertension, and chronic kidney disease, were associated with lower mortality in adjusted analyses. While this finding may appear counterintuitive, it likely reflects residual confounding, coding bias, survivor bias, and differences in baseline disease severity or treatment patterns inherent to administrative data [ 22 – 23 ]. Importantly, sensitivity analyses excluding comorbidities demonstrated similar effect estimates for AKI, indicating that the primary association between AKI and mortality is robust and not driven by these factors. From a clinical and policy perspective, our findings emphasize the importance of early recognition and prevention of AKI in patients with cirrhosis. Strategies such as careful volume management, avoidance of nephrotoxic medications (e.g., nonsteroidal anti-inflammatory drugs and contrast agents), and early identification of high-risk patients may help mitigate the development and progression of AKI. The strong graded association between AKI severity and outcomes suggests that AKI staging could be incorporated into clinical risk stratification tools to guide management decisions. At the health system level, targeted interventions aimed at reducing AKI incidence may represent an important opportunity to improve outcomes while reducing costs. In addition, implementation of standardized AKI prevention protocols and early specialty consultation may further improve outcomes. Future studies should evaluate whether targeted AKI prevention strategies can reduce morbidity and healthcare costs in this population. This study has several important strengths. It utilizes a large, nationally representative dataset, allowing for broad generalizability of findings across the United States. The inclusion of detailed demographic, socioeconomic, and clinical variables enabled comprehensive adjustment for potential confounders. Additionally, the consistency of findings across multiple sensitivity analyses and interaction analyses supports the robustness and internal validity of the results. However, several limitations should be acknowledged. As an administrative database study, misclassification of diagnoses based on ICD-10 coding is possible. The NIS lacks granular clinical data, including laboratory values such as serum creatinine and measures of liver disease severity (e.g., MELD score), which limits the ability to distinguish between different etiologies of AKI. Additionally, the timing and duration of AKI during hospitalization cannot be determined. Residual confounding may persist despite adjustment, and the observational design precludes causal inference. Temporal relationships between AKI onset and clinical outcomes cannot be established in this dataset. Conclusion In summary, acute kidney injury is a common and clinically significant complication among patients hospitalized with cirrhosis and is associated with markedly increased mortality and healthcare utilization. The strong graded relationship between AKI severity and outcomes, the robustness of findings across multiple sensitivity analyses, and the observed heterogeneity across patient subgroups underscore the need for targeted strategies to prevent and manage AKI in this population. Efforts to improve early detection and management of AKI represent a critical opportunity to improve outcomes while reducing healthcare costs. Declarations Ethics approval and consent to participate BayCare Health System Institutional Review Board (IRB) Office assessed that the proposed activity does not constitute research involving human subjects as defined by DHHS and FDA regulations. IRB review and approval of this project is not required (Inquiry determination dated October 22, 2025). The analysis used fully de-identified data from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample, and no identifiable private information or intervention involving human participants occurred. All study procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki. Because no human subjects were involved, informed consent was not required per principal. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. AI assistance During the preparation of this work, Large Language Models (LLMs) were not used. The authors reviewed and revised the material and took full responsibility for the content. Funding The authors declare that they received no funding. Author Contribution BT and CO conceived of the presented idea. BT implemented, collected, analyzed, and interpreted the data. CO conducted literature reviews. AR supervised the project, provided insights, and assisted with revisions. All authors discussed the results and contributed to the final manuscript. Acknowledgement We wanted to acknowledge all the HCUP Data Partners that contribute to HCUP. A link to the HCUP-US web page that contains the list of State organizations is here. (hcup-us.ahrq.gov/db/hcupdatapartners.jsp). We also wish to thank Dr. Yu-Jun Chang and Dr. Xun Guo Huang for assistance with data analysis. Data Availability The data that support the findings of this study are available from the Agency for Healthcare Research and Quality, Department of Health and Human Services of the United States. However, restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the author upon reasonable request and with permission of the Agency for Healthcare Research and Quality. References Mokdad AA, Lopez AD, Shahraz S, Lozano R, Mokdad AH, Stanaway J, Murray CJ, Naghavi M. Liver cirrhosis mortality in 187 countries between 1980 and 2010: a systematic analysis. BMC Med. 2014;12(1):145. Ye F, Zhai M, Long J, Gong Y, Ren C, Zhang D, Lin X, Liu S. 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Piano S, Rosi S, Maresio G, Fasolato S, Cavallin M, Romano A, Morando F, Gola E, Frigo AC, Gatta A, Angeli P. Evaluation of the Acute Kidney Injury Network criteria in hospitalized patients with cirrhosis and ascites. J Hepatol. 2013;59(3):482–9. Iezzoni LI. Assessing quality using administrative data. Ann Intern Med. 1997;127(8 Pt 2):666–74. 10.7326/0003-4819-127-8_part_2-199710151-00048 . Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–98. 10.1001/jama.2011.1515 . Additional Declarations No competing interests reported. Supplementary Files SuppTable1.docx SuppTable2.docx SuppTable3.docx SuppTable4.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 29 Mar, 2026 Editor assigned by journal 29 Mar, 2026 Editor invited by journal 28 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 27 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9228608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614726417,"identity":"5ec33b38-fe01-4136-84d7-b7b998431ae8","order_by":0,"name":"Brent Tai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACexiDjb2HSC2GDUDiAIMBAxvPGSK1GByAamGQyCHWltmHjz3+wPBHnk/y7cHPPDV2+fwMzMc+fsGjxZ4vLR1ok4Fhm3ResjTPsWTLmQ1sybNl8NnSw2MmAdTC2CadYyDN28BsYHCAx5hZAp9fzvB/A2mxb5M8Y/ybt6HewJ6wFh42kJbENgkeM6Athw0MGHiMGT/gdRibmcQZA+PkNp68NMs5x44bSBxmS2bGo4PBnof5mURFhZzt/Pazh2+8qak24G9vPsz4A58eiPMgFBMPiARawcxDUAsUwA0nwpZRMApGwSgYQQAARjtCuw5Ft+YAAAAASUVORK5CYII=","orcid":"","institution":"BayCare Health System","correspondingAuthor":true,"prefix":"","firstName":"Brent","middleName":"","lastName":"Tai","suffix":""},{"id":614726418,"identity":"85415090-967e-40f9-a251-2b8f17d76fc8","order_by":1,"name":"Chijioke Okonkwo","email":"","orcid":"","institution":"BayCare Health System","correspondingAuthor":false,"prefix":"","firstName":"Chijioke","middleName":"","lastName":"Okonkwo","suffix":""},{"id":614726419,"identity":"2efdf8eb-b5ac-4fe3-b8cc-f85c26f8d04c","order_by":2,"name":"Arturo Riviera","email":"","orcid":"","institution":"BayCare Health System","correspondingAuthor":false,"prefix":"","firstName":"Arturo","middleName":"","lastName":"Riviera","suffix":""}],"badges":[],"createdAt":"2026-03-26 03:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9228608/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9228608/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106004504,"identity":"c08baa7a-a35a-4b8b-86ea-ad8b7ac8bae6","added_by":"auto","created_at":"2026-04-02 10:31:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88951,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation Between AKI Severity and In-Hospital Mortality Across Primary and Sensitivity Analyses\u003c/p\u003e\n\u003cp\u003eFigure 1 demonstrates the association between AKI severity and in-hospital mortality across primary and sensitivity analyses. The consistency of effect estimates across models, including exclusion of sepsis, comorbidities, and CKD, as well as restriction to primary cirrhosis hospitalizations, supports the robustness of the observed association and suggests that findings are not driven by residual confounding or model specification.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9228608/v1/8e2517f8378726dcf5cde076.png"},{"id":106004501,"identity":"55fae21a-ed49-4b15-8114-64a7283a64f5","added_by":"auto","created_at":"2026-04-02 10:31:30","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99764,"visible":true,"origin":"","legend":"\u003cp\u003eEffect Modification of the Association Between AKI Severity and In-Hospital Mortality by CKD Status\u003c/p\u003e\n\u003cp\u003eFig. 2 illustrates the effect modification of the association between AKI severity and in-hospital mortality by CKD status. Odds ratios are shown relative to patients without AKI within each subgroup. The steeper increase in mortality observed among patients without CKD indicates a stronger association between AKI and mortality compared with those with CKD\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9228608/v1/a005f475b60563c2f7da7c5c.jpeg"},{"id":106095772,"identity":"3d56c970-3e65-4835-b0ea-c0271c948bb7","added_by":"auto","created_at":"2026-04-03 11:50:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1016920,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9228608/v1/bd435b3c-cfce-4479-92dc-e9c31c63d814.pdf"},{"id":106093652,"identity":"fe220a0e-6b13-4455-a132-b803ca02f927","added_by":"auto","created_at":"2026-04-03 11:38:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18734,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9228608/v1/f9f435a2104acaa153f39088.docx"},{"id":106094208,"identity":"8bc6e4b8-345c-456e-92eb-7ef4427d86d8","added_by":"auto","created_at":"2026-04-03 11:41:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14838,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9228608/v1/b04c4b1771f14bd42c727000.docx"},{"id":106094119,"identity":"63c22d77-d93e-4e0d-bc2a-038ecdec3ce9","added_by":"auto","created_at":"2026-04-03 11:41:08","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15038,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9228608/v1/f12432cb887ad28634c98d8e.docx"},{"id":106004506,"identity":"d081df25-61f6-4b19-b4bb-bd889152dd2e","added_by":"auto","created_at":"2026-04-02 10:31:30","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15133,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-9228608/v1/b361adfa0c1747bd0eee26aa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Acute Kidney Injury and Mortality in Cirrhosis: A Nationwide Analysis of Clinical Outcomes, Resource Utilization, and Effect Modification","fulltext":[{"header":"Study Design / Description","content":"\u003cp\u003eThis study evaluated how acute kidney injury (AKI) affects outcomes among hospitalized patients with cirrhosis using data from the 2023 Nationwide Inpatient Sample, a large, nationally representative database of U.S. hospitalizations. We included adult patients with cirrhosis and identified those who developed AKI, including a subgroup with severe AKI requiring dialysis. We then compared outcomes between patients with and without AKI, focusing on in-hospital mortality, length of stay, and hospitalization costs. Statistical models were used to account for differences in patient characteristics and hospital factors. Additional analyses were performed to ensure that the findings were consistent and not driven by underlying conditions such as chronic kidney disease or sepsis.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eCirrhosis is a major cause of morbidity and mortality worldwide and is associated with frequent hospitalizations and substantial healthcare utilization [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. Patients with cirrhosis are particularly vulnerable to acute decompensation and multi-organ dysfunction, which contribute significantly to adverse clinical outcomes [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among hospitalized patients, complications such as infection, hemodynamic instability, and renal dysfunction are common and often portend poor prognosis [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAcute kidney injury (AKI) is one of the most frequent and clinically significant complications in patients with cirrhosis. The development of AKI reflects complex pathophysiologic processes, including systemic vasodilation, reduced effective arterial blood volume, and activation of neurohormonal pathways, and may manifest as hepatorenal syndrome or intrinsic renal injury [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Prior studies have consistently demonstrated that AKI in cirrhosis is associated with increased mortality; however, the magnitude of this association varies across clinical settings and patient populations, and the relationship between AKI severity and outcomes remains incompletely characterized.\u003c/p\u003e \u003cp\u003eDespite existing evidence, several important gaps remain. Many prior studies have been limited to single-center cohorts or specific clinical settings, such as intensive care units, which may not reflect broader patient populations [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. National-level data quantifying the impact of AKI severity on mortality and healthcare utilization are limited. In addition, the extent to which the association between AKI and mortality varies across key patient subgroups, including those with chronic kidney disease (CKD) and differing age profiles, is not well defined. Furthermore, the economic burden associated with AKI in cirrhosis has not been fully characterized in contemporary, nationally representative datasets.\u003c/p\u003e \u003cp\u003eIn this study, we used the Nationwide Inpatient Sample to evaluate the association between AKI and in-hospital mortality among patients hospitalized with cirrhosis. We further assessed the impact of AKI severity on healthcare utilization, including length of stay and hospital costs. Additionally, we examined the robustness of these associations across multiple sensitivity analyses and evaluated potential effect modification by CKD status and age. We hypothesized that AKI would be associated with a graded increase in mortality and resource utilization, with variation in effect across patient subgroups\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eStudy Design and Data Source\u003c/p\u003e\u003cp\u003eWe conducted a retrospective cohort study using the 2023 Nationwide Inpatient Sample (NIS), part of the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest publicly available all-payer inpatient database in the United States and provides nationally representative estimates of hospitalizations [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. It includes patient- and hospital-level data and incorporates discharge weights to generate national estimates. The dataset is de-identified and publicly available. The institutional review board at BayCare Health System reviewed and approval of this project was not required.\u003c/p\u003e\u003cp\u003eStudy Population\u003c/p\u003e\u003cp\u003eAdult hospitalizations (age ≥ 18 years) with cirrhosis were identified using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes across all available diagnosis fields. Cirrhosis was defined using codes for alcoholic cirrhosis, biliary cirrhosis, and other cirrhosis-related conditions. Hospitalizations with missing key demographic or outcome variables were excluded.\u003c/p\u003e\u003cp\u003eExposure Definition\u003c/p\u003e\u003cp\u003eThe primary exposure was acute kidney injury (AKI), identified using ICD-10-CM codes (N17.x). AKI severity was categorized into three groups: no AKI, AKI not requiring dialysis, and AKI requiring dialysis (AKI-D). Dialysis was identified using ICD-10-PCS procedure codes for renal replacement therapy. This classification allowed evaluation of a graded relationship between AKI severity and clinical outcomes. Definitions of all study variables, including ICD-10-CM and ICD-10-PCS codes used to identify exposures, outcomes, comorbidities, and cirrhosis-related complications, are provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003eOutcomes\u003c/p\u003e\u003cp\u003eThe primary outcome was in-hospital mortality, defined using the HCUP variable indicating death during hospitalization. Secondary outcomes included length of stay (LOS) and hospital cost. Hospital costs were estimated by applying HCUP-provided cost-to-charge ratios to total hospital charges. LOS was analyzed as a continuous variable and log-transformed to account for right-skewed distribution.\u003c/p\u003e\u003cp\u003eCovariates\u003c/p\u003e\u003cp\u003eCovariates included demographic characteristics (age and sex), socioeconomic factors (primary payer and median household income quartile), and admission characteristics (elective status and emergency department admission). Clinical comorbidities, including diabetes mellitus, congestive heart failure, hypertension, and chronic kidney disease, were identified using ICD-10-CM codes. Cirrhosis-related complications, including ascites, hepatic encephalopathy, and varices, were also included to account for liver disease severity. Sepsis was identified using ICD-10-CM codes (A40–A41) and included as a covariate in adjusted models.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll analyses accounted for the complex survey design of the NIS using discharge weights (DISCWT), stratification (NIS_STRATUM), and clustering (HOSP_NIS) to generate nationally representative estimates. Continuous variables were summarized as means or medians, and categorical variables as proportions. Survey-weighted multivariable logistic regression models were used to assess the association between AKI and in-hospital mortality. Linear regression models with log-transformed outcomes were used to evaluate LOS and hospital costs, exponentiated coefficients were interpreted as relative percentage differences.\u003c/p\u003e\u003cp\u003eSensitivity analyses were performed to assess the robustness of findings, including removal of emergency department admission from the model, restriction to non-elective admissions, exclusion of patients with sepsis, and restriction to hospitalizations with cirrhosis as the primary diagnosis. Interaction analyses were conducted to evaluate effect modification by age and chronic kidney disease. Statistical significance was defined as a two-sided p-value \u0026lt; 0.05. All analyses were performed using R software (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria) [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003cp\u003eA total of 70,219 unweighted hospitalizations with cirrhosis were identified, representing approximately 351,095 hospitalizations nationwide after applying survey weights. Acute kidney injury was present in 114,620 (32.6%) weighted hospitalizations. Patients with AKI were older than those without AKI (mean age 61.5 vs 58.6 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had a slightly lower proportion of females (38.2% vs 40.3%, p\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e \u003cp\u003ePatients with AKI also demonstrated greater disease severity. Cirrhosis-related complications were more common in the AKI group, including ascites (15.5% vs 10.3%), hepatic encephalopathy (19.7% vs 10.0%), and slightly lower prevalence of varices (21.5% vs 23.4%) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Baseline characteristics were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of hospitalizations with cirrhosis, stratified by AKI status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo AKI (n\u0026thinsp;=\u0026thinsp;47,295)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAKI (n\u0026thinsp;=\u0026thinsp;22,924)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, mean (SE)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.6 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn-hospital mortality, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of stay, median (IQR), days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital cost, median (IQR), $\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,242 (7,710\u0026thinsp;\u0026minus;\u0026thinsp;20,518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,920 (10,940\u0026thinsp;\u0026minus;\u0026thinsp;35,710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary payer, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-pay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo charge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian household income quartile, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (highest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCirrhosis-related complication, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic encephalopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVarices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the baseline demographic, clinical, and socioeconomic characteristics of cirrhosis hospitalizations stratified by AKI status. Values are reported as survey-weighted means, medians, or percentages, as appropriate. Costs were estimated using HCUP cost-to-charge ratios\u003c/p\u003e \u003cp\u003eAssociation of AKI With In-Hospital Mortality\u003c/p\u003e \u003cp\u003eIn multivariable analysis adjusting for demographic characteristics, socioeconomic factors, admission characteristics, comorbidities, cirrhosis-related complications, and sepsis, AKI remained strongly associated with increased in-hospital mortality (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with patients without AKI, non\u0026ndash;dialysis-requiring AKI was associated with a more than fourfold increase in mortality (adjusted odds ratio [OR] 4.34, 95% CI 3.99\u0026ndash;4.72), while dialysis-requiring AKI was associated with over a sixteen fold increase (OR 16.37, 95% CI 14.18\u0026ndash;18.91).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted Association of AKI With In-Hospital Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAKI severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo AKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI (non-dialysis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.99\u0026ndash;4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI requiring dialysis (AKI-D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2\u0026ndash;18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSepsis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.36\u0026ndash;5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (per year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale (vs male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary payer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid (vs Medicare)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026ndash;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-pay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026ndash;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian household income quartile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u0026ndash;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60\u0026ndash;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (highest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u0026ndash;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdmission characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission via emergency department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u0026ndash;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u0026ndash;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026ndash;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCirrhosis-related complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic encephalopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.53\u0026ndash;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVarices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e evaluates the independent association between AKI and in-hospital mortality after adjustment for demographic and socioeconomic factors. Survey-weighted multivariable logistic regression evaluating the independent association between AKI severity and in-hospital mortality among cirrhosis hospitalizations, adjusted for demographic characteristics, socioeconomic factors, admission characteristics, comorbidities, cirrhosis-related complications, and sepsis.\u003c/p\u003e \u003cp\u003eSepsis was independently associated with increased mortality (OR 4.90, 95% CI 4.36\u0026ndash;5.51). Increasing age was also associated with higher mortality, while higher household income was associated with lower mortality. Several comorbidities, including diabetes, hypertension, and chronic kidney disease, were associated with lower mortality. These findings likely reflect residual confounding, coding bias, survivor bias, or differences in baseline disease severity and treatment patterns. Importantly, sensitivity analyses excluding comorbidities yield consistent results, reinforcing that the association between AKI and mortality is robust and not driven by model specification.\u003c/p\u003e \u003cp\u003eAmong cirrhosis-related complications, hepatic encephalopathy demonstrated the strongest association with mortality (OR 2.73, 95% CI 2.53\u0026ndash;2.95), followed by ascites (OR 1.34, 95% CI 1.21\u0026ndash;1.49). Findings were consistent in a parsimonious model with limited covariate adjustment (Supplementary Table\u0026nbsp;2), demonstrating a similar graded association between AKI severity and in-hospital mortality.\u003c/p\u003e \u003cp\u003eLength of Stay and Hospital Cost\u003c/p\u003e \u003cp\u003eAKI was associated with increased healthcare utilization (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Non\u0026ndash;dialysis-requiring AKI was associated with a 45% increase in length of stay (ratio 1.45, 95% CI 1.43\u0026ndash;1.47), while dialysis-requiring AKI was associated with nearly a threefold increase (ratio 2.78, 95% CI 2.64\u0026ndash;2.92).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted Association of AKI Severity with Length of Stay and Cost\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eA. Length of stay\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatio (exp β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI (non-D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;45% LOS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64\u0026ndash;2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;3\u0026times; LOS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eB. Hospital cost (CCR-adjusted)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRatio (exp β)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eInterpretation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI (non-D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;43% cost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.53\u0026ndash;3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u0026times; cost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e quantifies the impact of acute kidney injury (AKI) severity on hospital resource utilization, including length of stay (LOS) and total hospital costs. Survey-weighted multivariable regression models assessing the association of AKI severity with length of stay and total hospital costs, adjusted for demographic, socioeconomic, clinical, and cirrhosis-related factors. Results are presented as exponentiated coefficients representing relative differences.\u003c/p\u003e \u003cp\u003eSimilarly, AKI was associated with increased hospital costs. Non\u0026ndash;dialysis-requiring AKI was associated with a 43% increase in cost (ratio 1.43, 95% CI 1.41\u0026ndash;1.46), while dialysis-requiring AKI was associated with approximately a 3.7-fold increase (ratio 3.75, 95% CI 3.53\u0026ndash;3.98).\u003c/p\u003e \u003cp\u003eSensitivity Analyses\u003c/p\u003e \u003cp\u003eSensitivity analyses demonstrate consistent findings across multiple model specifications (Supplementary Table\u0026nbsp;3). Excluding emergency department admission from the model did not materially change the results (AKI non\u0026ndash;dialysis OR 4.79, 95% CI 4.40\u0026ndash;5.21; AKI-D OR 20.8, 95% CI 18.1\u0026ndash;24.0). Similarly, restricting the cohort to non-elective admissions yielded slightly higher effect estimates (AKI non\u0026ndash;dialysis OR 4.92, 95% CI 4.51\u0026ndash;5.36; AKI-D OR 21.9, 95% CI 19.0\u0026ndash;25.3).\u003c/p\u003e \u003cp\u003eExcluding patients with sepsis resulted in similar effect estimates (AKI non\u0026ndash;dialysis OR 4.52, 95% CI 4.13\u0026ndash;4.95; AKI-D OR 19.5, 95% CI 16.8\u0026ndash;22.7). In analyses restricted to hospitalizations with cirrhosis as the primary diagnosis, the magnitude of association was substantially higher (AKI non\u0026ndash;dialysis OR 7.98, 95% CI 6.84\u0026ndash;9.30; AKI-D OR 35.5, 95% CI 28.3\u0026ndash;44.6).\u003c/p\u003e \u003cp\u003eThe consistency of the association between AKI and in-hospital mortality across multiple sensitivity analyses is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Effect estimates remained stable across all model specifications, including exclusion of sepsis, removal of admission-related variables, and restriction to primary cirrhosis hospitalizations, supporting the robustness of the observed association.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrates the association between AKI severity and in-hospital mortality across primary and sensitivity analyses. The consistency of effect estimates across models, including exclusion of sepsis, comorbidities, and CKD, as well as restriction to primary cirrhosis hospitalizations, supports the robustness of the observed association and suggests that findings are not driven by residual confounding or model specification.\u003c/p\u003e \u003cp\u003eInteraction Analyses\u003c/p\u003e \u003cp\u003eSignificant interactions were observed between AKI and both chronic kidney disease (CKD) and age (Supplementary Table\u0026nbsp;4). The association between AKI and mortality was attenuated among patients with CKD compared with those without CKD (p for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, the relative effect of AKI was weaker among patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, with a stronger association observed in younger patients (p for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe interaction between AKI severity and CKD status is visually demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The relative increase in mortality associated with AKI was attenuated among patients with CKD, whereas a steeper gradient was observed among patients without CKD, indicating a stronger association between AKI and mortality in those without underlying kidney disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the effect modification of the association between AKI severity and in-hospital mortality by CKD status. Odds ratios are shown relative to patients without AKI within each subgroup. The steeper increase in mortality observed among patients without CKD indicates a stronger association between AKI and mortality compared with those with CKD\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationally representative study of hospitalizations with cirrhosis, acute kidney injury (AKI) was common and strongly associated with adverse clinical outcomes. Approximately one-third (32.7%) of hospitalizations were complicated by AKI, which was associated with a marked increase in in-hospital mortality (13.0% vs 2.5%), longer length of stay, and higher hospital costs. A clear graded relationship was observed, with progressively higher mortality risk from non\u0026ndash;dialysis-requiring AKI (adjusted OR 4.34) to dialysis-requiring AKI (adjusted OR 16.4). Importantly, these findings were consistent across multiple sensitivity analyses and model specifications, supporting the robustness of the observed association. We also identified significant heterogeneity in the association between AKI and mortality by chronic kidney disease (CKD) status and age, highlighting important differences in risk across patient subgroups. Together, these findings highlight AKI as a central determinant of both clinical outcomes and healthcare utilization in cirrhosis.\u003c/p\u003e \u003cp\u003eThe strong association between AKI and mortality in cirrhosis likely reflects the complex pathophysiology of advanced liver disease. Cirrhosis is characterized by systemic vasodilation, reduced effective arterial blood volume, and activation of neurohormonal pathways that predispose to renal hypoperfusion [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this setting, AKI often represents a manifestation of multi-organ dysfunction, including processes such as hepatorenal syndrome and ischemic acute tubular necrosis, rather than an isolated renal insult. The markedly elevated mortality observed in dialysis-requiring AKI suggests that progression to severe kidney injury reflects advanced circulatory failure and limited physiologic reserve. These findings reinforce the concept that AKI in cirrhosis is both a marker of disease severity and a mediator of worse outcomes.\u003c/p\u003e \u003cp\u003eOur findings extend prior literature in several important ways. While previous studies have demonstrated an association between AKI and mortality in cirrhosis, many have been limited to single-center cohorts or specific clinical settings. For example, a single-center study of 179 hospitalized cirrhosis patients reported an AKI prevalence of 27.9%, with a 64% mortality rate among affected patients, accounting for 41% of all in-hospital deaths [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, Staufer et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Allegretti et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] evaluated critically ill cirrhosis patients requiring renal replacement therapy and demonstrated extremely high mortality, highlighting the severe prognosis associated with advanced kidney injury in highly selected ICU populations. By leveraging a large, nationally representative dataset, our study provides contemporary estimates of the burden and impact of AKI across diverse hospital settings.\u003c/p\u003e \u003cp\u003eThe magnitude of association observed in this study, particularly for dialysis-requiring AKI, was striking, with effect estimates that appear higher than those reported in prior general cirrhosis cohorts, where AKI has typically been associated with approximately 2\u0026ndash;6-fold increases in mortality depending on severity [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These findings underscore the severe prognostic implications of advanced kidney injury in cirrhosis. Furthermore, the clear dose\u0026ndash;response relationship across AKI severity categories and the consistency of findings across multiple sensitivity analyses\u0026mdash;including exclusion of comorbidities, CKD, and sepsis\u0026mdash;strengthen the validity and generalizability of our results and reduce the likelihood that findings are driven by residual confounding or model specification.\u003c/p\u003e \u003cp\u003eBeyond its impact on mortality, AKI was associated with substantial increases in healthcare utilization. Patients with AKI experienced significantly longer hospitalizations and higher costs, with dialysis-requiring AKI associated with nearly a threefold increase in length of stay and a more than threefold increase in hospital costs. These findings highlight AKI as a major driver of inpatient resource utilization in cirrhosis. The magnitude of these effects suggests that AKI contributes not only to clinical deterioration but also to substantial strain on healthcare systems. Given the high prevalence of cirrhosis-related hospitalizations, even modest reductions in AKI incidence or severity may translate into meaningful improvements in healthcare efficiency and cost containment. These findings position AKI as a key target for interventions aimed at improving both clinical outcomes and healthcare resource utilization.\u003c/p\u003e \u003cp\u003eAn important novel finding of this study is the presence of significant heterogeneity in the association between AKI and mortality across patient subgroups. The relative impact of AKI was attenuated among patients with underlying CKD compared with those without CKD, suggesting that AKI may represent a greater relative physiological insult in patients with lower baseline renal dysfunction. This finding is consistent with our sensitivity analyses, in which exclusion of CKD resulted in higher effect estimates, further supporting this interpretation. Similarly, the association between AKI and mortality was stronger among younger patients, indicating that AKI may represent a greater deviation from baseline health status in this population. These findings highlight the importance of considering baseline patient characteristics when assessing AKI-related risk and suggest that risk stratification strategies should account for underlying patient heterogeneity.\u003c/p\u003e \u003cp\u003eInterestingly, several comorbidities, including diabetes, hypertension, and chronic kidney disease, were associated with lower mortality in adjusted analyses. While this finding may appear counterintuitive, it likely reflects residual confounding, coding bias, survivor bias, and differences in baseline disease severity or treatment patterns inherent to administrative data [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Importantly, sensitivity analyses excluding comorbidities demonstrated similar effect estimates for AKI, indicating that the primary association between AKI and mortality is robust and not driven by these factors.\u003c/p\u003e \u003cp\u003eFrom a clinical and policy perspective, our findings emphasize the importance of early recognition and prevention of AKI in patients with cirrhosis. Strategies such as careful volume management, avoidance of nephrotoxic medications (e.g., nonsteroidal anti-inflammatory drugs and contrast agents), and early identification of high-risk patients may help mitigate the development and progression of AKI. The strong graded association between AKI severity and outcomes suggests that AKI staging could be incorporated into clinical risk stratification tools to guide management decisions. At the health system level, targeted interventions aimed at reducing AKI incidence may represent an important opportunity to improve outcomes while reducing costs. In addition, implementation of standardized AKI prevention protocols and early specialty consultation may further improve outcomes. Future studies should evaluate whether targeted AKI prevention strategies can reduce morbidity and healthcare costs in this population.\u003c/p\u003e \u003cp\u003eThis study has several important strengths. It utilizes a large, nationally representative dataset, allowing for broad generalizability of findings across the United States. The inclusion of detailed demographic, socioeconomic, and clinical variables enabled comprehensive adjustment for potential confounders. Additionally, the consistency of findings across multiple sensitivity analyses and interaction analyses supports the robustness and internal validity of the results. However, several limitations should be acknowledged. As an administrative database study, misclassification of diagnoses based on ICD-10 coding is possible. The NIS lacks granular clinical data, including laboratory values such as serum creatinine and measures of liver disease severity (e.g., MELD score), which limits the ability to distinguish between different etiologies of AKI. Additionally, the timing and duration of AKI during hospitalization cannot be determined. Residual confounding may persist despite adjustment, and the observational design precludes causal inference. Temporal relationships between AKI onset and clinical outcomes cannot be established in this dataset.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, acute kidney injury is a common and clinically significant complication among patients hospitalized with cirrhosis and is associated with markedly increased mortality and healthcare utilization. The strong graded relationship between AKI severity and outcomes, the robustness of findings across multiple sensitivity analyses, and the observed heterogeneity across patient subgroups underscore the need for targeted strategies to prevent and manage AKI in this population. Efforts to improve early detection and management of AKI represent a critical opportunity to improve outcomes while reducing healthcare costs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eBayCare Health System Institutional Review Board (IRB) Office assessed that the proposed activity does not constitute research involving human subjects as defined by DHHS and FDA regulations. IRB review and approval of this project is not required (Inquiry determination dated October 22, 2025). The analysis used fully de-identified data from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample, and no identifiable private information or intervention involving human participants occurred. All study procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki. Because no human subjects were involved, informed consent was not required per principal.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAI assistance\u003c/h2\u003e \u003cp\u003eDuring the preparation of this work, Large Language Models (LLMs) were not used. The authors reviewed and revised the material and took full responsibility for the content.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that they received no funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBT and CO conceived of the presented idea. BT implemented, collected, analyzed, and interpreted the data. CO conducted literature reviews. AR supervised the project, provided insights, and assisted with revisions. All authors discussed the results and contributed to the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe wanted to acknowledge all the HCUP Data Partners that contribute to HCUP. A link to the HCUP-US web page that contains the list of State organizations is here. (hcup-us.ahrq.gov/db/hcupdatapartners.jsp). We also wish to thank Dr. Yu-Jun Chang and Dr. Xun Guo Huang for assistance with data analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the Agency for Healthcare Research and Quality, Department of Health and Human Services of the United States. However, restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the author upon reasonable request and with permission of the Agency for Healthcare Research and Quality.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMokdad AA, Lopez AD, Shahraz S, Lozano R, Mokdad AH, Stanaway J, Murray CJ, Naghavi M. Liver cirrhosis mortality in 187 countries between 1980 and 2010: a systematic analysis. BMC Med. 2014;12(1):145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe F, Zhai M, Long J, Gong Y, Ren C, Zhang D, Lin X, Liu S. The burden of liver cirrhosis in mortality: Results from the global burden of disease study. Front public health. 2022;10:909455.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTapper EB, Parikh ND. Diagnosis and management of cirrhosis and its complications: a review. 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Acute kidney disease is common and associated with poor outcomes in patients with cirrhosis and acute kidney injury. J Hepatol. 2022;77(1):108\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCullaro G, Rubin JB, Fortune BE, Crawford CV, Verna EC, Hsu CY, Liu KD, Brown RS, Lai JC, Rosenblatt R. Association between kidney dysfunction types and mortality among hospitalized patients with cirrhosis. Dig Dis Sci. 2022;67(7):3426\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAttieh RM, Wadei HM. Acute kidney injury in liver cirrhosis. Diagnostics. 2023;13(14):2361.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBucsics T, Mandorfer M, Schwabl P, Bota S, Sieghart W, Ferlitsch A, Trauner M, Peck-Radosavljevic M, Reiberger T. Impact of acute kidney injury on prognosis of patients with liver cirrhosis and ascites: a retrospective cohort study. J Gastroenterol Hepatol. 2015;30(11):1657\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopes JA, Melo MJ, Costa AC, Raimundo M, Alexandrino P, Gomes da Costa A, Velosa J. Acute kidney injury and in-hospital mortality in critically ill patients with cirrhosis: a cohort study. Gut. 2012;61(6):955\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHCUP National Inpatient Sample (NIS). Healthcare Cost and Utilization Project (HCUP). 2023. Agency for Healthcare Research and Quality, Rockville, MD. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehcup-us.ahrq.gov/nisoverview.jsp\u003c/span\u003e\u003cspan address=\"http://hcup-us.ahrq.gov/nisoverview.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. \u003cem\u003eR: A language and environment for statistical computing\u003c/em\u003e. Version 4.3.1. Vienna (Austria). R Foundation for Statistical Computing; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNesci A, Ruggieri V, Manilla V, Spinelli I, Santoro L, Di Giorgio A, Santoliquido A, Ponziani FR. Endothelial dysfunction and liver cirrhosis: unraveling of a complex relationship. Int J Mol Sci. 2024;25(23):12859.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Nguyen HH, Hwang SY, Lee SS. Oxidative mechanisms and cardiovascular abnormalities of cirrhosis and portal hypertension. Int J Mol Sci. 2023;24(23):16805.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuah A, Duah F, Ampofo-Boobi D, Addo BP, Osei-Poku F, Agyei-Nkansah A. Acute Kidney Injury in Patients with Liver Cirrhosis: Prevalence, Predictors, and In-Hospital Mortality at a District Hospital in Ghana. Biomed Res Int. 2022;2022:4589767.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStaufer K, Roedl K, Kivaranovic D, Drolz A, Horvatits T, Rasoul-Rockenschaub S, Zauner C, Trauner M, Fuhrmann V. Renal replacement therapy in critically ill liver cirrhotic patients-outcome and clinical implications. Liver Int. 2017;37(6):843\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllegretti AS, Ortiz G, Wenger J, Deferio JJ, Wibecan J, Kalim S, Tamez H, Chung RT, Karumanchi SA, Thadhani RI. Prognosis of Acute Kidney Injury and Hepatorenal Syndrome in Patients with Cirrhosis: A Prospective Cohort Study. Int J Nephrol. 2015;2015:108139.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFagundes C, Barreto R, Guevara M, Garcia E, Sol\u0026agrave; E, Rodr\u0026iacute;guez E, Graupera I, Ariza X, Pereira G, Alfaro I, C\u0026aacute;rdenas A, Fern\u0026aacute;ndez J, Poch E, Gin\u0026egrave;s P. A modified acute kidney injury classification for diagnosis and risk stratification of impairment of kidney function in cirrhosis. J Hepatol. 2013;59(3):474\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelcher JM, Garcia-Tsao G, Sanyal AJ, Bhogal H, Lim JK, Ansari N, Coca SG, Parikh CR, TRIBE-AKI Consortium. Association of AKI with mortality and complications in hospitalized patients with cirrhosis. Hepatology. 2013;57(2):753\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiano S, Rosi S, Maresio G, Fasolato S, Cavallin M, Romano A, Morando F, Gola E, Frigo AC, Gatta A, Angeli P. Evaluation of the Acute Kidney Injury Network criteria in hospitalized patients with cirrhosis and ascites. J Hepatol. 2013;59(3):482\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIezzoni LI. Assessing quality using administrative data. Ann Intern Med. 1997;127(8 Pt 2):666\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7326/0003-4819-127-8_part_2-199710151-00048\u003c/span\u003e\u003cspan address=\"10.7326/0003-4819-127-8_part_2-199710151-00048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2011.1515\u003c/span\u003e\u003cspan address=\"10.1001/jama.2011.1515\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"acute kidney injury, cirrhosis, in-hospital mortality, dialysis-requiring AKI, health disparities, hospital outcomes, length of stay, healthcare utilization, administrative data, national inpatient sample","lastPublishedDoi":"10.21203/rs.3.rs-9228608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9228608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute kidney injury (AKI) is a common complication in patients with cirrhosis and is associated with poor outcomes. However, contemporary national data quantifying the impact of AKI severity on mortality and healthcare utilization, as well as potential effect modification across patient subgroups, remain limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using the 2023 Nationwide Inpatient Sample. Adult hospitalizations with cirrhosis were identified using ICD-10-CM codes. AKI was categorized as no AKI, non\u0026ndash;dialysis-requiring AKI, and dialysis-requiring AKI (AKI-D). Survey-weighted multivariable regression models evaluated associations between AKI and in-hospital mortality, length of stay (LOS), and hospital costs. Sensitivity analyses and interaction testing by age and chronic kidney disease (CKD) were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 70,219 unweighted hospitalizations, representing approximately 351,095 nationally, were included. AKI occurred in 32.6% of hospitalizations. In-hospital mortality was higher among patients with AKI compared with those without AKI (13.0% vs 2.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In adjusted analyses, non\u0026ndash;dialysis-requiring AKI was associated with increased mortality (adjusted odds ratio [OR] 4.34, 95% CI 3.99\u0026ndash;4.72), while AKI-D was associated with markedly higher mortality (OR 16.37, 95% CI 14.18\u0026ndash;18.91). AKI was also associated with increased LOS (ratio 1.45 for non-dialysis AKI; 2.78 for AKI-D) and higher hospital costs (ratio 1.43 and 3.75, respectively). Findings were consistent across multiple sensitivity analyses. Significant interactions were observed by CKD and age, with stronger relative effects in patients without CKD and in younger individuals (p for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAKI is common among hospitalized patients with cirrhosis and is strongly associated with increased mortality and healthcare utilization, with a clear graded relationship by severity. The impact of AKI varies across patient subgroups, highlighting the importance of individualized risk assessment. Strategies targeting early recognition and prevention of AKI may improve outcomes and reduce healthcare burden in this population.\u003c/p\u003e","manuscriptTitle":"Acute Kidney Injury and Mortality in Cirrhosis: A Nationwide Analysis of Clinical Outcomes, Resource Utilization, and Effect Modification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 10:31:24","doi":"10.21203/rs.3.rs-9228608/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-15T09:01:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T10:28:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T17:14:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114133870210838217691194496507020073481","date":"2026-03-30T16:01:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163853169581698239768974838648655847885","date":"2026-03-29T10:37:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T10:17:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-29T10:11:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-28T05:18:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T01:42:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2026-03-28T01:38:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9c6d459f-3990-4646-aafa-3e0e920f7f47","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T09:56:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 10:31:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9228608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9228608","identity":"rs-9228608","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}