Abstract
Background:
Intravenous albumin reduces mortality in SBP. We sought to characterize albumin use for SBP over time and investigate patient and hospital-level factors associated with use.
Methods:
A retrospective cohort study in the Veterans Health Administration between 2008 and 2021 evaluated trends and patient, practice-, and facility-level factors associated with use among patients with cirrhosis hospitalized for SBP confirmed with ascitic fluid criteria.
Results:
Among 3,871 Veterans with SBP, 803 (20.7%) did not receive albumin, 1,119 (28.9%) received albumin but not per guidelines and 1,949 (50.3%) received albumin per guidelines; use increased from 66% in 2008 to 88% in 2022. Veterans who identified as Black compared to white were less likely to receive guideline-recommended albumin (OR 0.76, 95%CI 0.59–0.98) in all analyses. Guideline-recommended albumin was more likely to be administered to Veterans with CTP class B (OR 1.39, 95% CI 1.17–1.64) and C (OR 2.21, 95% CI 1.61–3.04) compared to CTP A; and AKI Stage 1 (OR 1.48, 95%CI 1.22 −1.79), Stage 2 (OR 2.17, 95%CI 1.62–2.91), and Stage 3 (OR 1.68, 95%CI 1.18 – 2.40) compared to no AKI. GI/Hepatology consultation (OR 1.60, 95% CI 1.29−−1.99), nephrology consultation (OR 1.60, 95%CI 1.23–2.07) and having both GI/hep and nephrology consultations (OR 2.17, 95%CI 1.60–2.96) were associated with higher albumin administration. In exploratory analyses accounting for interactions between model for end stage liver disease sodium (MELD-Na) and albumin, guideline-recommended albumin was associated with lower in-hospital mortality (HR 0.90, 95% CI 0.85 – 0.96).
Conclusion:
Future studies should investigate optimizing albumin use for SBP to reduce variability and mitigate healthcare disparities.
Keywords: Cirrhosis, health equity, decompensation, acute kidney injury
Introduction
Spontaneous bacterial peritonitis (SBP) is a common infection in cirrhosis resulting in 20–40% in-hospital mortality. SBP is commonly complicated by acute kidney injury (AKI), which is the largest contributor to morbidity and mortality in SBP.1–3 Intravenous (IV) albumin has been shown to lower the rates of AKI, mitigating risk of renal injury and death in patients with cirrhosis and SBP when added to antimicrobial therapy.4–6 The American Association for the Study of Liver Diseases (AASLD) and the European Association for the study of the Liver (EASL) recommend administration of intravenous (IV) albumin on day 1 and 3 of SBP diagnosis.4,7
Despite the accepted recommendation for albumin and its acceptance as a key cirrhosis quality measure,8 detailed factors associated with utilization have not been widely studied; however suboptimal adherence to use has been highlighted. A recent single center study of 110 patients with SBP showed that adherence to day 3 albumin was only 43% and a multicenter database study of nearly 300 SBP hospitalizations with AKI showed lack of timely albumin administration in about half of cases.9,10 Given the current gaps in evidence, we conducted a retrospective national cohort study of U.S. Veterans hospitalized with SBP. Our study objectives were to characterize IV albumin over time and investigate patient and hospital-level factors associated with guideline-recommended use. We explored associations between guideline-recommended albumin use and in-hospital mortality.
Methods
Study Design and Cohort Creation
This was a retrospective cohort study using data from the Veterans Outcomes and Costs Associated with Liver Disease (VOCAL) dataset. VOCAL contains detailed data from the Veterans Health Administration (VHA) Corporate Data Warehouse (CDW), and its derivation has been described previously.11–14 In brief, VOCAL contains data on more than 120,000 patients with cirrhosis identified between 2008 and 2021, using a previously validated algorithm. Patients are cared for across 128 VHA centers. In this study, we identified all adult patients with cirrhosis who were hospitalized for spontaneous bacterial peritonitis (SBP, see details below). If patients had multiple SBP hospitalizations over time, only the first was included in the cohort such that all observations represented unique patients. We excluded patients who received liver transplantation and those from low-volume centers with <10 SBP hospitalizations during the entire study window and hospitalizations where albumin could have been contraindicated, i.e. admissions with volume overload, identified using International Classification of Diseases (ICD) discharge codes for fluid overload, pulmonary edema, or heart failure (Supplemental Table 1). Patients were also excluded if they had prior ICD9/10 codes for SBP.
Ascertainment of SBP Hospitalizations
We first identified all completed paracentesis procedures using inpatient and outpatient Current Procedure Terminology (CPT) codes (49080, 49081). In the VHA CDW, we then ascertained all ascitic fluid analyses include white blood cell counts (WBC) and fluid differentials. Absolute polymorphonuclear (PMN) cell counts were obtained by multiplying WBC counts by percentage (%) PMNs. To be included, patients had to have absolute PMN count >250 per mm3 on a paracentesis performed during the inpatient stay or within 7 days prior to hospitalization (to capture hospitalizations that may have resulted from SBP diagnosed in the outpatient setting). Only the first paracentesis meeting criteria for SBP was included in the analysis. To evaluate the performance of this approach, we randomly sampled 30 SBP positive paracenteses and performed manual chart validation (M.S.). The positive predictive value (PPV) for SBP was greater than 97 percent.
Exposure Variables
For each patient, we obtained detailed data on demographics (age, sex, race/ethnicity), body mass index (BMI), selected comorbidities using ICD codes (diabetes, heart failure, coronary artery disease, atrial fibrillation) based on prior work.11,14 Data on etiology of liver disease and baseline Child-Turcotte-Pugh (CTP) class were obtained prior to hospitalizations using previously-validated VHA algorithms.15,16 Model for End Stage Liver Disease-Sodium (MELD-Na) prior to hospitalization as well as labs on day one of hospitalization (sodium [mEq/L], albumin [g/dL], total bilirubin [mg/dL], serum creatinine - sCr [mg/dL], estimated glomerular filtration rate - eGFR [mL/min/1.73m2], hemoglobin (g/dL), international normalized ratio - INR) and systolic blood pressure were obtained where available; MELD-Na on day 1 of hospitalization was also calculated. Acute kidney injury (AKI) stage was classified relative to most recent pre-hospitalization creatinine baseline using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria as stage 1 (≥0.3mg/dL increase in sCr within 48 hours or increase 1.5–2-fold from baseline), stage 2 (>2 to 3-fold increase), stage 3 (>3-fold increase or sCr >4.0 mg/dL as surrogate for imminent renal replacement therapy), or stage 0 (no AKI).17,18 Appropriate antibiotic use within 48 hours of SBP diagnosis was ascertained and classified using CDW inpatient pharmacy tables. VHA center-level variables included academic vs. community affiliation, rurality (urban vs. large rural), distance to closest transplant center, volume of cirrhosis admissions tertile (low, medium, high), and geographic region (northeast, southeast, midsouth, central, west). Hospital clinical complexity, a VHA classification measure was based on the patient population served, complexity of clinical services offered, and the presence of medical education and research activities, ranging from 1a (most complex: large-volume, high-acuity patients, broad specialty expertise) to 3 (least complex: small volume, low-risk patients, few specialists).19 Data on completed inpatient consultations to gastroenterology/hepatology (GI/Hep) and nephrology specialty services were obtained from the CDW consultation tables. These events were captured with structured query language (SQL) queries that were then manually reviewed and cleaned to ensure accuracy. Consultation requests to outpatient services after hospital discharge were not counted. Inpatient consultations were then classified as: none, GI/Hep alone, nephrology alone, and both GI/Hep and nephrology. Finally, for descriptive purposes we obtained culture data from peritoneal fluid studies to report culture positive versus negative SBP as well as associated microbiology data.
Outcome Classification
The primary outcome in this study was intravenous (IV) albumin use. We obtained data on all albumin administrations during inpatient hospitalization for all patients through the Barcode Medication Administration (BCMA) tables, intravenous (IV), and inpatient pharmacy CDW tables via an iterative process with three rounds of chart-validated quality checks (M.S.). These data were reviewed extensively and cleaned to ascertain precise timing (hospital day) of administration. Per the AASLD guidelines, patients hospitalized with SBP should receive either 1.5g/kg albumin on day 1 and 1g/kg on day 3 (D1/D3), or they may receive albumin per AKI guidance in cirrhosis with albumin 1g/kg on days 1 and 2 (D1/D2). The timing of albumin was based on the date of ascitic fluid analysis meeting SBP criteria if occurring between the admission date and discharge date. If SBP was diagnosed within seven days prior to admission, guideline-directed albumin day 1 was considered to be hospital day 1. Based on this guidance, patient albumin administration for SBP was then classified as: none given, guideline-recommended albumin use (D1/D2 or D1/D3), and non-guideline recommended albumin use. A +/− 1 day grace period was also used for D1 classification, recognizing that some patients may have SBP-positive fluid results that are not acted upon fully until the next day. Of note, the total grams of albumin administered was not systematically captured for many patients (missing in ~75% of cases), and thus the outcome of this study focuses on days of administration rather than days plus total grams. For selected analyses below we also utilized a variable called “any albumin given” which included hospitalizations with either guideline-recommended albumin or non-guideline recommended albumin use.
Statistical Analysis
Descriptive statistics for exposure variables were stratified by albumin administration category (no albumin given, guideline-recommended albumin use, non-guideline-recommended albumin); continuous data were presented as medians with interquartile ranges (IQRs) and categorical data as frequencies with percentages. Hypothesis tests were performed using the Kruskal-Wallis and Chi-squared tests for continuous and categorical data, respectively. To evaluate whether albumin administration practices changed over time, we plotted the proportion of SBP hospitalizations where (1) any albumin was given or (2) D1/D2 or D1/D3 albumin was given over calendar year, weighted by volume of annual SBP hospitalizations. Weighted linear regression models were fit to test the significance of trends, and associated beta coefficients were presented with 95% confidence intervals (CIs) and p-values. Next, we pooled data from 2008 to 2021 to evaluate unadjusted VA regional and center-level differences in albumin administration. These data were presented as scatterplots weighted by center SBP hospitalization volume and shown by individual center and stratified by VHA geographic region. Means were presented for (1) any albumin given and (2) D1/D2 or D1/D3 albumin.
To evaluate factors associated with albumin administration, we fit three-level mixed effects models where patients (level 1) are clustered within geographic regions (level 2) and within VHA centers (level 3) with regions and VHA centers treated as random intercepts. In this framework, logistic regression models were fit with the following patient and center-level variables chosen a-priori based on plausible association with albumin administration: age, sex, race/ethnicity, pre-hospitalization CTP class, systolic blood pressure and serum albumin o day 1 of hospitalization, AKI stage upon hospitalization, length of stay (LOS), inpatient consultation specialty, academic affiliation, and hospitalization year (to account for secular trends). Center rurality was considered in models, however resulted in outsized standard errors likely from collinearity with academic center affiliation and was later removed. Model outcomes were: (1) any albumin administration (guideline and non-guideline recommended) and (2) guideline-recommended albumin administration. Odds ratios (ORs) and 95% CIs were presented with p-values. From these models, adjusted estimates for regional center-level differences in albumin administration were then plotted, with similar presentation to the unadjusted scatterplots detailed above. In a sensitivity analysis, we excluded patients with gastrointestinal bleeding discharge codes (Supplemental Table 2) given that provision of blood transfusions could substitute for albumin administration.14
Model Sensitivity Analyses for Findings of Race/Ethnicity Disparities
Based on initial findings of differences in albumin administration by race/ethnicity, we performed further sensitivity analyses through sequential modeling for the outcome of any albumin administration to account for plausible mechanisms of confounding between race and albumin administration. In addition to the covariates in the primary model, in the first sensitivity model (S1), we added MELD-Na and estimated GFR upon hospitalization. In the second sensitivity analysis model (S2), we restricted the cohort to after 2014 onward to reflect the contemporary era of direct-acting antivirals. In the third sensitivity analysis model (S3), we additionally adjusted for center SBP hospitalization volume tertile and VHA center complexity. Results of the sensitivity models (S1, S2, S3) with respect to association between race/ethnicity and albumin administration were displayed using coefficient plots of ORs and 95% CIs.
Exploratory Analyses
To identify potential drivers of race/ethnicity disparities in albumin administration, we displayed a series of box plots for the predicted probability of any albumin administration (generated post-hoc from the primary regression model) stratified by (1) geographic region, (2) academic vs. non-academic center, (3) center SBP hospitalization volume, and (4) AKI stage at initial hospitalization. To better understand potential drivers of regional and center-level disparities in albumin administration for SBP hospitalizations related to specialty consultation, we created bar graphs for proportions of hospitalizations with completed inpatient specialty consultations stratified by (1) geographic region and (2) category variables representing percent of hospitalizations with any albumin administration based on variable distribution (<65%, 65–90%, >90%). Chi-squared tests evaluated significant associations between these variables. Finally, we fit a logistic regression model for in-hospital mortality using a-priori variables including an interaction between MELD-Na upon hospitalization and albumin administration category (no albumin, non-guideline recommended albumin, guideline-recommended albumin) based on previous data from a multicenter trial demonstrating lower mortality with IV albumin in SBP in a subgroup of patients with AKI and bilirubin >4 mg/dL.20
In an additional exploratory analysis, we investigated the possibility that albumin administration could be associated with an increased likelihood of respiratory failure, which was ascertained and defined using CPT codes for mechanical ventilation or non-invasive mechanical ventilation occurring after the diagnosis of SBP. Proportions of patients developing this outcome were compared using the Fisher’s exact test. This analysis was performed in the primary cohort as well as in an expanded cohort where previously excluded hospitalizations for “volume overload” were included.
Other Considerations
This study received Institutional Review Board approval from the Corporal Michael J. Crescenz Philadelphia Veterans Affairs Medical Center. All data management and analyses were performed using structured query language, RStudio (version 2023.12.1+402), and Stata/BE 17.0 (College Station, TX). For all hypothesis tests, an alpha level of 5% was chosen as the threshold for statistical significance.
Results
Cohort Characteristics and Temporal Trends
After application of selection criteria as shown in Supplemental Figure 1, we identified 3,871 hospitalizations for SBP. A total of 803 Veterans hospitalized with SBP (20.7%) did not receive any albumin, 1,088 (28.1%) received non-guideline recommended albumin and 1,980 (51.1%) received guideline-recommended albumin (D1/D2 or D1/D3).
Table 1 presents demographics, clinical, and facility-level characteristics stratified by albumin administration. The median cohort age was 62 (IQR 57–67) and greater than 97% of patients were male (Supplemental Table 3). Predominant liver disease etiologies were hepatitis C virus (HCV) and alcohol-associated liver disease (ALD). The overall median MELD-Na was 17 (IQR 11–23); greater than half of patients had prior cirrhosis decompensations. Nearly 70% of the cohort had absolute PMN count >500. Greater than half of the cohort had diabetes. At baseline, greater than half of patients did not have AKI (Stage 0) upon hospitalization; the prevalence of AKI was 25.4% for stage 1, 9.5% for Stage 2, and 6.2% for Stage 3.
Table 1 –
Cohort Characteristics Stratified by Albumin being Given for SBP Hospitalization
| Any Albumin Given (N=3,068) | ||||
|---|---|---|---|---|
| Factor Mean, IQR or N (%) |
No Albumin Given (N=803) |
Non guideline-recommended albumin (N=1,088) |
Guideline-recommended albumin (D1/D2 or D1/D3) (N=1,980) |
p-value |
| Baseline and pre-hospitalization factors | ||||
| Age, median (IQR) | 62 (57, 67) | 61 (57, 66) | 62 (57, 67) | 0.16 |
| Male Sex | 783 (97.5%) | 1069 (98.3%) | 1945 (98.2%) | 0.40 |
| Race | 0.080 | |||
| White | 473 (58.9%) | 677 (62.2%) | 1259 (63.6%) | |
| Black | 134 (16.7%) | 143 (13.1%) | 253 (12.8%) | |
| Hispanic | 81 (10.1%) | 122 (11.2%) | 181 (9.1%) | |
| Asian | 11 (1.4%) | 16 (1.5%) | 21 (1.1%) | |
| Other | 104 (13.0%) | 130 (11.9%) | 266 (13.4%) | |
| BMI, median (IQR) | 26.3 (23.0, 30.6) | 26.7 (23.3, 31.2) | 27.6 (24.0, 31.5) | <0.001 |
| Etiology of Liver Disease | 0.002 | |||
| HCV | 102 (12.7%) | 109 (10.0%) | 217 (11.0%) | |
| HBV | 2 (0.2%) | 9 (0.8%) | 14 (0.7%) | |
| EtOH | 298 (37.1%) | 492 (45.2%) | 918 (46.4%) | |
| HCV+EtOH | 258 (32.1%) | 315 (29.0%) | 561 (28.3%) | |
| MASLD | 118 (14.7%) | 138 (12.7%) | 230 (11.6%) | |
| Other | 25 (3.1%) | 25 (2.3%) | 40 (2.0%) | |
| MELD-Na Pre-Hospitalization, median (IQR) | 15 (9, 21) | 16 (11, 22) | 18 (12, 23) | <0.001 |
| CTP Class Pre-Hospitalization | <0.001 | |||
| A | 422 (52.6%) | 456 (41.9%) | 730 (36.9%) | |
| B | 349 (43.5%) | 561 (51.6%) | 1058 (53.4%) | |
| C | 32 (4.0%) | 71 (6.5%) | 192 (9.7%) | |
| Prior Cirrhosis Decompensation | 458 (57.0%) | 755 (69.4%) | 1491 (75.3%) | <0.001 |
| Diabetes Mellitus | 459 (57.2%) | 591 (54.3%) | 1129 (57.0%) | 0.30 |
| Coronary Artery Disease | 187 (23.3%) | 252 (23.2%) | 427 (21.6%) | 0.47 |
| History of Heart Failure | 100 (12.5%) | 135 (12.4%) | 251 (12.7%) | 0.97 |
| Atrial Fibrillation | 75 (9.3%) | 97 (8.9%) | 178 (9.0%) | 0.94 |
| Myocardial Infarction | 31 (3.9%) | 44 (4.0%) | 66 (3.3%) | 0.56 |
| Clinical parameters at baseline hospitalization | ||||
| Fluid WBC count X106/L, median (IQR) | 1800 (880, 4950) | 1579 (832, 4000) | 1800 (846, 4400) | 0.050 |
| Absolute PMN count, median (IQR) | 968 (416, 3154) | 928 (405, 2769) | 1108 (468, 3524) | 0.003 |
| PMN >500 | 550 (68.5%) | 741 (68.1%) | 1433 (72.4%) | 0.020 |
| MELD-Na on Hospital Day 1, median (IQR) | 17 (12, 22) | 20 (15, 25) | 21 (16, 27) | <0.001 |
| Sodium on Hospital Day 1, median (IQR) | 134 (130, 137) | 133 (129, 136) | 132 (128, 135) | <0.001 |
| Albumin on Hospital Day 1, median (IQR) | 2.6 (2.2, 3) | 2.5 (2.1, 2.8) | 2.5 (2, 2.8) | <0.001 |
| T. Bilirubin on Hospital Day 1, median (IQR) | 2.1 (1, 4.415) | 3.1 (1.5, 6.075) | 3.2 (1.7, 6.4) | <0.001 |
| Creatinine on Hospital Day 1, median (IQR) | 1.0 (0.8, 1.6) | 1.2 (0.9, 2.0) | 1.4 (1.0, 2.3) | <0.001 |
| AKI Stage | <0.001 | |||
| No AKI | 508 (76.9%) | 520 (58.1%) | 904 (52.3%) | |
| Stage 1 | 108 (16.3%) | 238 (26.6%) | 490 (28.3%) | |
| Stage 2 | 29 (4.4%) | 80 (8.9%) | 203 (11.7%) | |
| Stage 3 | 16 (2.4%) | 57 (6.4%) | 132 (7.6%) | |
| eGFR on Hospital Day 1, median (IQR) | 67.95 (35.1, 96.5) | 54 (31.4, 86) | 44 (27, 70.8) | <0.001 |
| Hemoglobin on Hospital Day 1, median (IQR) | 11.0 (9.2, 12.5) | 10.6 (9.0, 12.3) | 10.5 (9.0, 12.3) | 0.020 |
| INR on Hospital Day 1, median (IQR) | 1.4 (1.2, 1.8) | 1.6 (1.3, 1.9) | 1.6 (1.4, 2.0) | <0.001 |
| Systolic Blood Pressure on Hospital Day 1, median (IQR) | 113 (101, 126) | 109 (96, 122) | 106 (95, 120) | <0.001 |
| Length of Stay (days), median (IQR) | 5 (3, 9) | 7 (4, 15) | 8 (5, 15) | <0.001 |
| Clinical care and hospital-level factors | ||||
| Inpatient Specialty Consultation | <0.001 | |||
| None | 587 (73.1%) | 603 (55.4%) | 944 (47.7%) | |
| GI/Hepatology Alone | 139 (17.3%) | 233 (21.4%) | 454 (22.9%) | |
| Nephrology Alone | 52 (6.5%) | 150 (13.8%) | 318 (16.1%) | |
| Both GI/Hepatology and Nephrology | 25 (3.1%) | 102 (9.4%) | 264 (13.3%) | |
| Appropriate Antibiotics Given for SBP | 578 (72.0%) | 915 (84.1%) | 1866 (94.2%) | <0.001 |
| Academic Center Affiliation | 514 (64.0%) | 728 (66.9%) | 1433 (72.4%) | <0.001 |
| Center Rurality | 0.028 | |||
| Urban | 775 (96.5%) | 1053 (96.8%) | 1941 (98.0%) | |
| Large rural | 28 (3.5%) | 35 (3.2%) | 39 (2.0%) | |
| VHA Region | <0.001 | |||
| Northeast | 98 (12.2%) | 134 (12.3%) | 261 (13.2%) | |
| Southeast | 166 (20.7%) | 261 (24.0%) | 358 (18.1%) | |
| Midsouth | 218 (27.1%) | 294 (27.0%) | 454 (22.9%) | |
| Central | 127 (15.8%) | 173 (15.9%) | 384 (19.4%) | |
| West | 194 (24.2%) | 226 (20.8%) | 523 (26.4%) | |
| VHA Center Cirrhosis Volume Tertile | 0.008 | |||
| Low | 62 (7.7%) | 61 (5.6%) | 87 (4.4%) | |
| Medium | 260 (32.4%) | 349 (32.1%) | 683 (34.5%) | |
| High | 481 (59.9%) | 678 (62.3%) | 1210 (61.1%) | |
| VHA Center Complexity | <0.001 | |||
| 1a | 391 (48.7%) | 643 (59.1%) | 1185 (59.8%) | |
| 1b | 209 (26.0%) | 235 (21.6%) | 481 (24.3%) | |
| 1c | 155 (19.3%) | 147 (13.5%) | 217 (11.0%) | |
| 2 | 48 (6.0%) | 63 (5.8%) | 97 (4.9%) | |
Abbreviations:
AKI Stage 1: >SCr increase ≥ 0.3 mg/dL from baseline; AKI Stage 2: SCr increase >2.0–3.0 × from baseline; AKI Stage 3: SCr increase >3.0 × from baseline or renal replacement therapy
With regards to clnical care factors, the median hospital LOS was 7 days (IQR 4–14). Greater than half of patients received no specialty care consultation, 21.3% had GI/Hep consults, 13.4% had nephrology consults, and 10.1% had GI/Hep and nephrology consults completed. Most of the care was received at urban hospitals that were academically-affiliated centers with high complexity. A higher proportion of patients with higher MELD-Na, AKI, and subspecialty consultation received any albumin and guideline-recommended albumin. Regarding microbiology data, a total 690 patients had culture-positive SBP (17.8%) with 914 organisms isolated, the most common of which were E. coli (24.5%), Enterococcus (15.7%), Klebsiella (14.6%), Streptococcus (13.0%), and S. aureus (8.5%; Supplemental Figure 2). Temporal trends in any albumin or guideline-recommended albumin administration are in Figure 1 and show an increase in albumin use for Veterans hospitalized with SBP from 2008 to 2022.
Figure 1 –
Temporal Trends in Albumin Administration for Veterans Hospitalized with Spontaneous Bacterial Peritonitis
Demographics, Clinical and Facility-Level Factors Associated with Albumin Administration
After adjusting for clinical and facility-level factors in mixed-effects logistic regression models (Table 2), Veterans who identified as Black compared to White were less likely to receive any or guideline-recommended albumin (any albumin: OR 0.62, 95% CI 0.45–0.85; guideline-recommended albumin: OR 0.76, 95% CI 0.59–0.98). Any albumin was more likely to be administered to Veterans with CTP class B (OR 1.72, 95% CI 1.39 – 2.13) and CTP class C (OR 2.89, 95% CI 1.80 – 4.65) compared to CTP A; and AKI Stage 1 (OR 2.22, 95% CI 1.70 – 2.90), Stage 2 (OR 3.34, 95% CI 2.09 – 5.35), and Stage 3 (OR 3.08, 95% CI 1.66 – 5.71) compared to no AKI. Compared to no specialty care consultation, GI/Hep consultation alone (OR 1.97, 95% CI 1.48–2.62), nephrology consultation alone (OR 1.94, 95% CI 1.32–2.85), and both GI/Hep and nephrology consultation (OR 4.48, 95% CI 2.53–7.94) were associated with higher albumin administration. Academic center affiliation was also associated with higher albumin use (OR 1.63, 95% CI 1.20–2.21). Similar associations were noted for guideline-recommended albumin. Figure 2 presents unadjusted and adjusted estimates derived from mixed-effects models stratified by VA region with each dot representing a VA medical center with the size of the dot representing SBP hospitalization volume. Variation in any albumin and guideline-recommended (D1/D2 or D1/D3) was noted across each VA region and SBP hospitalization volume. In a sensitivity analysis excluding hospitalizations with gastrointestinal bleeding (n=314 excluded), major effects in models were similar for both any albumin and guideline-recommended albumin (Supplemental Table 4).
Table 2 –
Mixed-Effects Logistic Regression Models* for (1) Any Albumin Administration and (2) Guideline-Recommended (Day1/Day 2 or Day 1/Day 3) Albumin Administration
| Any Albumin | Guideline-Recommended Albumin | |||
|---|---|---|---|---|
| Variable | Odds Ratio (95% CI) | p-value | Odds Ratio (95% CI) | p-value |
| Age | 1.00 (0.99 – 1.01) | 0.94 | 1.01 (0.99 – 1.02) | 0.240 |
| Male Sex (ref=female) | 1.99 (0.97 – 4.07) | 0.06 | 1.15 (0.63 – 2.10) | 0.646 |
| Race/Ethnicity | ||||
| White | (ref) | (ref) | ||
| Black | 0.62 (0.45 – 0.85) | 0.003 | 0.76 (0.59 – 0.98) | 0.032 |
| Hispanic | 0.89 (0.62 – 1.27) | 0.52 | 0.72 (0.54 – 0.95) | 0.020 |
| Asian | 0.63 (0.26 – 1.51) | 0.30 | 0.60 (0.29 – 1.22) | 0.155 |
| Other | 0.85 (0.62 – 1.19) | 0.35 | 0.93 (0.72 – 1.20) | 0.567 |
| Etiology of Liver Disease | ||||
| Hepatitis C Virus | (ref) | (ref) | ||
| Hepatitis B Virus | 2.73 (0.56 – 13.34) | 0.22 | 0.98 (0.36 – 2.65) | 0.964 |
| Alcohol | 1.20 (0.85 – 1.70) | 0.29 | 0.99 (0.76 – 1.30) | 0.958 |
| Hepatitis C Virus + Alcohol | 1.03 (0.72 – 1.46) | 0.88 | 1.08 (0.81 – 1.42) | 0.602 |
| NAFLD | 0.77 (0.51 – 1.16) | 0.21 | 0.73 (0.52 – 1.02) | 0.063 |
| Other | 0.76 (0.36 – 1.58) | 0.46 | 0.74 (0.41 – 1.35) | 0.323 |
| Pre-Hospital CTP Class | ||||
| A | (ref) | (ref) | ||
| B | 1.72 (1.39 – 2.13) | <0.001 | 1.39 (1.17 – 1.64) | <0.001 |
| C | 2.89 (1.80 – 4.65) | <0.001 | 2.21 (1.61 – 3.04) | <0.001 |
| AKI Stage | ||||
| 0 | (ref) | (ref) | ||
| 1 | 2.22 (1.70 – 2.90) | <0.001 | 1.48 (1.22 – 1.79) | <0.001 |
| 2 | 3.34 (2.09 – 5.35) | <0.001 | 2.17 (1.62 – 2.91) | <0.001 |
| 3 | 3.08 (1.66 – 5.71) | <0.001 | 1.68 (1.18 – 2.40) | 0.004 |
| Albumin, day 1 | 0.76 (0.64 – 0.91) | 0.003 | 0.93 (0.81 – 1.07) | 0.305 |
| Systolic BP, day 1 | 0.99 (0.99 – 1.00) | 0.02 | 0.99 (0.99 – 1.00) | 0.013 |
| Length of Stay (per day) | 1.05 (1.03 – 1.06) | <0.001 | 1.01 (1.01 – 1.02) | 0.001 |
| Inpatient Consultation | ||||
| None | (ref) | (ref) | ||
| GI/Hep Alone | 1.97 (1.48 – 2.62) | <0.001 | 1.60 (1.29 – 1.99) | <0.001 |
| Nephrology Alone | 1.94 (1.32 – 2.85) | 0.001 | 1.60 (1.23 – 2.07) | <0.001 |
| Both GI/Hep and Nephrology | 4.48 (2.53 – 7.94) | <0.001 | 2.17 (1.60 – 2.96) | <0.001 |
| Academic Center Affiliation | 1.63 (1.20 – 2.21) | 0.002 | 1.51 (1.18 – 1.94) | 0.001 |
| Hospitalization Year (per year) | 1.04 (1.01 – 1.08) | 0.016 | 1.04 (1.02 – 1.07) | <0.001 |
Models account for a three-level mixed effect where patients (level 1) are clustered within regions (level 2) and within centers (level 3). VA regions and VHA centers are treated as random intercepts in the above models.
Figure 2 –
(A) Unadjusted and (B) Adjusted* Proportions of SBP Hospitalizations with Any Albumin or Guideline-Recommended Albumin Given, Stratified by VHA Center and VA Region
*Adjusted estimates are derived from mixed-effects logistic regression models in Table 2 that account for VA region and VHA center as random intercepts. Bubble size reflects center volume.
Sensitivity and Exploratory Analyses
Racial Disparities in Albumin Use
Given the finding of racial differences in albumin administration, we further explored associations between race and the outcome in 3 sensitivity analyses shown in Figure 3. In model S1, which included MELD-Na and estimated GFR upon hospitalization, Black race relative to White was associated with lower odds of any albumin administration (OR 0.62, 95% CI 0.45–0.85) with a stronger association than in the primary model. In models S2, which restricted the cohort to after 2014 and model S3, which adjusted for SBP hospitalization volume tertile and VHA center complexity, results demonstrated an association between Black race and lower albumin administration results, with Black race associated with half the odds of any albumin administration. Figure 4 shows stratified analyses plotting the adjusted predicted probabilities of any albumin administration stratified by race/ethnicity and VA region (Panel A), academic vs. non-academic medical center (Panel B), center SBP hospitalization volume (Panel C), and AKI Stage at hospitalization (Panel D). The probability of any albumin administration was numerically lower among Black Veterans compared to white in all geographic regions with the difference being statistically significant in the Southeast and West. The predicted probability of any albumin administration was significantly lower for Black Veterans compared to white in academic medical centers, among high-volume centers, and among Veterans with no AKI, stage 1, and stage 3 AKI.
Figure 3 –
Primary and Sensitivity Analyses* Exploring Association between Black Race and Odds of Any Albumin Administration for SBP Hospitalization
* Estimates generated from adjusted mixed-effects logistic regression models noted in methods, including primary, S1, S2, and S3 models. Beyond the primary model, S1 is additionally adjusted for MELD-Na and eGFR. S2 restricts the cohort to after 2014 and is adjusted for all covariates in the primary model and S1. S3 restricts the cohort to after 2014, is adjusted for all covariates in the primary model and S1, and is additionally adjusted for SBP hospitalization volume tertile and Veterans Health Administration center complexity.
Figure 4 –
Exploratory Analysis of Race/Ethnicity Disparities
Regional Variation in Albumin Administration and Facility-level Differences in Specialty Care Consultation
Given the findings that inpatient specialty care consultation was associated with higher odds of albumin administration, we sought to further understand differences in the percent of Veterans with SBP hospitalization that had received GI/Hep, nephrology consultation or both services by VA geographic region (Figure 5, Panel A) and percent of hospitalizations with any albumin administration (<65% (low), 65–90% (medium), >90% (high)). Regional variation was noted in specialty care consultation with GI/Hep consultation being more likely in the Northeast (26%), Southeast (27%), and Central (28%) regions compared to less than 20% in the Midsouth and West. Regional differences in nephrology consultation were less pronounced; GI/Hep and nephrology consultations were relatively uncommon and ranged from 5 to 15% depending on the region. As shown in Figure 5, Panel B, centers with the highest albumin administration categories had a greater proportion of GI/Hep consultation. We evaluated facility level differences in specialty consultation rates (Supplemental Table 5) and noted that academically-affiliated, higher-volume, and higher complexity centers were more likely to request both GI/hep and nephrology consultations. No significant association was noted with rurality but most VHA centers are in urban settings.
Figure 5 –
Association between Inpatient Consultations and (A) VA Region and (B) Proportion of SBP Hospitalizations with Albumin Administration
In-hospital Mortality and Respiratory Failure Outcomes
In an exploratory analysis, we modeled in-hospital mortality with a prespecified interaction term between albumin administration category and MELD-Na on hospital admission as a continuous variable. Model results are in Supplemental Table 6 with interaction results plotted in Figure 6. After adjustment for age, sex, race, ethnicity, etiology of liver disease, pre-hospital CTP class, AKI stage, appropriate antibiotics for SBP, and hospitalization year, the interaction between MELD-Na and the albumin categorical variable was statistically significant (p<.001). In that model, guideline-recommended albumin was associated with lower odds of in-hospital mortality (HR 0.90, 95% 0.85 – 0.96).
Figure 6 -.
Interaction between Presentation MELD-Na and Albumin Administration in Adjusted Model for In-Hospital Mortality
In a final exploratory analysis, there was no significant difference in incidence of respiratory failure between any albumin and no albumin groups in either the primary cohort (0.16% vs. 0.00%, p=0.31) or in the expanded cohort that included volume overload admissions (0.16% vs. 0.00%, p=0.23).
Discussion
In a national cohort study, we identified patient- and system-level factors associated with albumin use, a guideline-recommended therapy and quality measure in SBP, a high-morbidity and mortality condition.7,8,21 We highlight three main findings. First, we identified that despite increasing albumin use over time, there was notable regional and risk-adjusted facility-level variation for any albumin. Notably, albumin use for SBP in the VHA was found to be higher on average than reported in the private healthcare sector.9,10 Second, we noted that Black Veterans had lower odds of receiving albumin therapy relative to White Veterans. This effect persisted after adjusting for a multitude of clinical factors including liver disease severity and kidney injury, evaluating data from a more contemporary era after 2014, and adjusting for facility-level volume and ability to delivery complex care. Furthermore, the effect persisted across most of the regions at academic and non-academic hospitals. Albumin is recommended by AASLD and EASL guidelines for all cases of SBP, however, the recent American Gastroenterological Association Clinical Practice Guidance only recommends IV albumin as the volume expander of choice in hospitalized patients with cirrhosis and AKI.22 However, even in subgroups with AKI, the racial disparity in albumin use was persistent in Stage 1 and Stage 3 AKI. Third, specialty care consultation and particularly GI/Hep or GI/Hep in combination with nephrology consultation were associated with higher albumin use. Studies previously showed that GI/Hep or Hepatology consultation alone were associated with higher odds of receipt of guideline-recommended care for cirrhosis, HCC, and in some cases, mortality,12,23–25 SBP is another clinical scenario where specialty care access is associated with guideline-recommended care.
Our findings must be interpreted in a larger context. Previous large-scale studies of the U.S. healthcare system have found persistent variation in inpatient processes and patient safety outcomes after adjusting for comorbidities that could not be explained by variation in healthcare spending or facility-level factors.26 In fact, population factors, comorbidity, and facility-level factors explained less than half of the variability in most outcomes. A national study of cirrhosis admissions found that only 4% of hospital-level variation in mortality could be explained by hospital-level factors27 and an investigation evaluating care delivery for ascites in the VA showed receipt of guideline-recommended care less than 2/3 of the time.25 Our findings suggest that targeted and systematic efforts to improve care by reducing variation are needed for patients hospitalized with ascites and SBP. Most hospital systems are equipped with robust electronic health records whereby high-risk, high-acuity conditions can trigger best practice alerts (BPAs) for albumin when a fluid ascitic analysis rules in SBP. This can be implemented within virtually any health system.
Our analysis uncovered healthcare disparities, broadly defined as inequitable differences between groups in access to care or quality of care: in this case, lower odds of receiving a guideline-recommended indicator for SBP among Veterans who identified as Black. Care variation has long been recognized as a source of health disparities and is a key target to improve health.28 For SBP admissions, the quality of inpatient care or access to proper care could be related to hospital quality in under-resourced areas, however, we found variation across geographic and hospital complexity characteristics. This inpatient variation in SBP care appears to be driven by downstream health-system factors and thus modifiable29. Sources of care variation could have been due to lack of recognition of SBP, lack of guideline knowledge, or due to implicit or explicit bias exacerbated by unexplained variation. These inadequacies may readily be targeted with systematic quality improvement (QI) efforts detailed below.
As an integrated system of care, the VHA has been a leader in quality improvement and implementation through population-based tools, education, and dissemination strategies. As a single system with a robust and intra-operable electronic health record (EHR), measuring quality of care, a first cardinal rule of QI prior to intervention, may be readily implemented. This is feasible in most health systems given near universal use of EHRs and clinical decision support. Additional interventions can be deployed to improve inpatient cirrhosis care within the VHA and more widely. For example, the VHA has formed hepatic innovation teams, a group of clinicians to develop and disseminate evidence-based practices for cirrhosis and chronic liver disease nationally.30,31 A study by our group showed that adequate staffing and availability of specialty consultation were viewed by providers as facilitators for high quality cirrhosis care.19 The VA’s specialty care access network extension for community health-care outcomes (SCAN-ECHO) program has utilized telemedicine to pair front line primary care providers with specialists to improve access to care and quality of care for hepatitis C, cirrhosis, and liver trasplantation.19,32–34 Implementing guideline-based care for cirrhosis decompensations is a viable intervention target for such programs.
Cirrhosis care may also be improved by protocolizing care for uncommon but highly morbid conditions. A quality improvement program with a standardized protocol and emergency department workflow developed with clinician input reduced hospital admissions for uncomplicated ascites requiring paracentesis to nearly zero and decreased emergency department LOS by nearly one day; other QI initiatives successfully increased rates of early paracentesis for patients hospitalized with ascites and have reduced readmissions for hepatic encephalopathy.35–37 An intervention delivered to medical trainees to enhance knowledge and diagnostic criteria for SBP was effective in increasing understanding and management, however, the pilot study did not evaluate the effect of the intervention on patient outcomes and future studies need to be done in this area.38 Finally, best practice alerts embedded into the electronic health record (EHR) that prompt clinicians to order appropriate therapy are highly effective in changing behavior and automating clinical decisions and may be deployed at scale.39–41
There are study limitations that we must acknowledge. This was a retrospective cohort study among predominantly male Veterans with potential for misclassification, confounding, and limited generalizability. However, we present national data with validated biochemical rather than diagnosis code-based SBP definition after detailed manual validation. Although we cannot fully remove confounding by indication or practice setting, we excluded low-volume hospitals and patients admitted with volume overload where albumin might have been contraindicated. We did not have full data capture of albumin dosing; however, the dose is not specified in the guidelines. We only evaluated albumin use for patients diagnosed with SBP on the date of admission or during the hospitalization. It possible that we missed SBP episodes and subsequent albumin in the outpatient setting or emergency department. Our algorithm did not capture bacterascites as microbiology data were not readily available and we could not exclude secondary bacterial peritonitis, however, we would not expect these factors to be differentially present in different racial or ethnic groups. We could not account for SBP admissions if they occurred in other healthcare settings besides the VHA. We did not capture other colloid such as blood product or crystalloid use that may have been substituted for albumin, however, we would not expect to see systematic differences by race or clinical practice factors. We are planning future studies to evaluate the treatment effects of albumin and other quality measures for SBP.. This study was not constructed to evaluate the effects of albumin on clinical outcomes. A causal inference framework and clinical trial emulation study is further planned to evaluate these objectives. However, in early exploratory analyses guideline-recommended albumin attenuated in-hospital mortality at MELD-Na greater than 30 consistent with prior studies showing a benefit for albumin with higher degree of liver synthetic dysfunction.
Supplementary Material
WHAT IS KNOWN:
Intravenous (IV) albumin reduces acute kidney injury and mortality in spontaneous bacterial peritonitis (SBP). Multiple society guidelines recommend IV albumin in SBP on Day 1 and Day 3 of clinical presentation.
WHAT IS NEW HERE:
We have assembled one of the largest known national cohorts of SBP diagnosed with fluid analysis in the Veterans Health Administration.
IV albumin use increased over time.
Profound regional variation was noted in albumin use Patients with cirrhosis who identified as Black were less likely to receive IV albumin despite adjustments.
Patients receiving gastroenterology, hepatology and nephrology consultation were more likely to receive IV albumin.
Albumin mitigated associations between MELD-Na and in-hospital mortality at scores beyond 30.
Disclosures:
Dr. Kaplan reported receiving grants from the US Department of Veterans Affairs, AstraZeneca, Gilead Sciences, Glycotest, Exact Sciences, and Bayer Healthcare outside the submitted work. Dr. Serper reported receiving grants from the National Institutes of Health (NIH) and research funding from Grifols. Dr. Mahmud reported receiving grants from the National Institute of Diabetes and Digestive and Kidney Diseases. Dr. Taddei reported receiving grants from the US Department of Veterans Affairs. No other disclosures were reported.
Grant support:
MS and NM received unrestricted research funding from Grifols, SA. MS is funded by the National Institutes of Health: R01DK131547, R01DK132138, R01AA030963. NM is funded by the National Institutes of Health K08DK124577.
Abbreviations:
- SBP
Spontaneous bacterial peritonitis
- AKI
acute kidney injury
- AASLD
American Association for the Study of Liver Diseases
- EASL
European Association for the study of the Liver
- IV
intravenous
- VOCAL
Veterans Outcomes and Costs Associated with Liver Disease
- VHA
the Veterans Health Administration
- CDW
Corporate Data Warehouse
- ICD
International Classification of Diseases
- CPT
Current Procedure Terminology
- WBC
white blood cell counts
- PMN
polymorphonuclear
- PPV
positive predictive value
- CTP
Child-Turcotte-Pugh
- MELD-Na
Model for End Stage Liver Disease-Sodium
- MM
millimeter
- sCr
serum creatinine
- eGFR
estimated glomerular filtration rate
- INR
international normalized ratio
- KDIGO
the Kidney Disease: Improving Global Outcomes
- GI/Hep
gastroenterology/hepatology
- SQL
structured query language
- BCMA
Barcode Medication Administration
- CIs
confidence intervals
- IQRs
interquartile ranges
- LOS
length of stay
- ORs
Odds ratios
- HCV
hepatitis C virus
- ALD
alcohol-associated liver disease
- QI
quality improvement
Data Transparency Statement:
Data will be made available upon reasonable request.
References
- 1.Merli M, Lucidi C, Giannelli V, et al. Cirrhotic patients are at risk for health care-associated bacterial infections. Clin Gastroenterol Hepatol. 2010;8(11):979–985. [DOI] [PubMed] [Google Scholar]
- 2.Niu B, Kim B, Limketkai BN, et al. Mortality from Spontaneous Bacterial Peritonitis Among Hospitalized Patients in the USA. Dig Dis Sci. 2018;63(5):1327–1333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Nadim MK, Garcia-Tsao G. Acute Kidney Injury in Patients with Cirrhosis. N Engl J Med. 2023;388(8):733–745. [DOI] [PubMed] [Google Scholar]
- 4.Biggins SW, Angeli P, Garcia-Tsao G, et al. Diagnosis, Evaluation, and Management of Ascites, Spontaneous Bacterial Peritonitis and Hepatorenal Syndrome: 2021 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2021;74(2):1014–1048. [DOI] [PubMed] [Google Scholar]
- 5.Batool S, Waheed MD, Vuthaluru K, et al. Efficacy of Intravenous Albumin for Spontaneous Bacterial Peritonitis Infection Among Patients With Cirrhosis: A Meta-Analysis of Randomized Control Trials. Cureus. 2022;14(12):e33124. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Fernandez J, Claria J, Amoros A, et al. Effects of Albumin Treatment on Systemic and Portal Hemodynamics and Systemic Inflammation in Patients With Decompensated Cirrhosis. Gastroenterology. 2019;157(1):149–162. [DOI] [PubMed] [Google Scholar]
- 7.European Association for the Study of the Liver. Electronic address eee, European Association for the Study of the L. EASL Clinical Practice Guidelines for the management of patients with decompensated cirrhosis. J Hepatol. 2018;69(2):406–460. [DOI] [PubMed] [Google Scholar]
- 8.Kanwal F, Tapper EB, Ho C, et al. Development of Quality Measures in Cirrhosis by the Practice Metrics Committee of the American Association for the Study of Liver Diseases. Hepatology. 2019;69(4):1787–1797. [DOI] [PubMed] [Google Scholar]
- 9.Eberhard CM, Davis KA, Nisly SA. Adherence to goal-directed therapy for patients with cirrhosis hospitalized with spontaneous bacterial peritonitis. American Journal of Health-System Pharmacy. 2022;79(Supplement_2):S27–S32. [DOI] [PubMed] [Google Scholar]
- 10.Kim RW, Raghunathan K, Martin GS, et al. Timely Albumin Improves Survival in Patients With Cirrhosis on Diuretic Therapy Who Develop Acute Kidney Injury: Real-World Evidence in the United States. Gastro Hep Advances. 2023;2(2):252–260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Kaplan DE, Serper MA, Mehta R, et al. Effects of Hypercholesterolemia and Statin Exposure on Survival in a Large National Cohort of Patients With Cirrhosis. Gastroenterology. 2019;156(6):1693–1706 e1612. [DOI] [PubMed] [Google Scholar]
- 12.Serper M, Kaplan DE, Shults J, et al. Quality Measures, All-Cause Mortality, and Health Care Use in a National Cohort of Veterans With Cirrhosis. Hepatology. 2019;70(6):2062–2074. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Serper M, Kaplan DE, Taddei TH, Tapper EB, Cohen JB, Mahmud N. Nonselective beta blockers, hepatic decompensation, and mortality in cirrhosis: A national cohort study. Hepatology. 2023;77(2):489–500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Serper M, Weinberg EM, Cohen JB, Reese PP, Taddei TH, Kaplan DE. Mortality and Hepatic Decompensation in Patients With Cirrhosis and Atrial Fibrillation Treated With Anticoagulation. Hepatology. 2021;73(1):219–232. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Kaplan DE, Dai F, Aytaman A, et al. Development and Performance of an Algorithm to Estimate the Child-Turcotte-Pugh Score From a National Electronic Healthcare Database. Clin Gastroenterol Hepatol. 2015;13(13):2333–2341.e2331–2336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Beste LA, Leipertz SL, Green PK, Dominitz JA, Ross D, Ioannou GN. Trends in burden of cirrhosis and hepatocellular carcinoma by underlying liver disease in US veterans, 2001–2013. Gastroenterology. 2015;149(6):1471–1482 e1475; quiz e1417–1478. [DOI] [PubMed] [Google Scholar]
- 17.Angeli P, Ginès P, Wong F, et al. Diagnosis and management of acute kidney injury in patients with cirrhosis: revised consensus recommendations of the International Club of Ascites. Gut. 2015;64(4):531–537. [DOI] [PubMed] [Google Scholar]
- 18.Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO Clinical Practice Guideline for Acute Kidney Injury. Kidney inter., Suppl 2012; 2: 1–138. [Google Scholar]
- 19.Serper M, Agha A, Garren PA, et al. Multidisciplinary teams, efficient communication, procedure services, and telehealth improve cirrhosis care: A qualitative study. Hepatol Commun. 2023;7(6). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Sort P, Navasa M, Arroyo V, et al. Effect of intravenous albumin on renal impairment and mortality in patients with cirrhosis and spontaneous bacterial peritonitis. N Engl J Med. 1999;341(6):403–409. [DOI] [PubMed] [Google Scholar]
- 21.Biggins SW, Angeli P, Garcia-Tsao G, et al. Diagnosis, Evaluation, and Management of Ascites, Spontaneous Bacterial Peritonitis and Hepatorenal Syndrome: 2021 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2021;74(2):1014–1048. [DOI] [PubMed] [Google Scholar]
- 22.Garcia-Tsao G, Abraldes JG, Rich NE, Wong VW. AGA Clinical Practice Update on the Use of Vasoactive Drugs and Intravenous Albumin in Cirrhosis: Expert Review. Gastroenterology. 2024;166(1):202–210. [DOI] [PubMed] [Google Scholar]
- 23.Serper M, Taddei TH, Mehta R, et al. Association of Provider Specialty and Multidisciplinary Care With Hepatocellular Carcinoma Treatment and Mortality. Gastroenterology. 2017;152(8):1954–1964. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Serper M, Kaplan DE, Lin M, et al. Inpatient Gastroenterology Consultation and Outcomes of Cirrhosis-Related Hospitalizations in Two Large National Cohorts. Dig Dis Sci. 2022;67(6):2094–2104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Kanwal F, Kramer JR, Buchanan P, et al. The quality of care provided to patients with cirrhosis and ascites in the Department of Veterans Affairs. Gastroenterology. 2012;143(1):70–77. [DOI] [PubMed] [Google Scholar]
- 26.Rosenberg BL, Kellar JA, Labno A, et al. Quantifying Geographic Variation in Health Care Outcomes in the United States before and after Risk-Adjustment. PLoS One. 2016;11(12):e0166762. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Mellinger JL, Richardson CR, Mathur AK, Volk ML. Variation among United States hospitals in inpatient mortality for cirrhosis. Clin Gastroenterol Hepatol. 2015;13(3):577–584; quiz e530. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Wasserman J, Palmer RC, Gomez MM, Berzon R, Ibrahim SA, Ayanian JZ. Advancing Health Services Research to Eliminate Health Care Disparities. American Journal of Public Health. 2019;109(S1):S64–S69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kardashian A, Serper M, Terrault N, Nephew LD. Health disparities in chronic liver disease. Hepatology. 2023;77(4):1382–1403. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Rogal SS, Yakovchenko V, Gonzalez R, et al. The Hepatic Innovation Team Collaborative: A Successful Population-Based Approach to Hepatocellular Carcinoma Surveillance. Cancers (Basel). 2021;13(9). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Yakovchenko V, Morgan TR, Miech EJ, et al. Core implementation strategies for improving cirrhosis care in the Veterans Health Administration. Hepatology. 2022;76(2):404–417. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Su GL, Glass L, Tapper EB, Van T, Waljee AK, Sales AE. Virtual Consultations Through the Veterans Administration SCAN-ECHO Project Improves Survival for Veterans With Liver Disease. Hepatology. 2018;68(6):2317–2324. [DOI] [PubMed] [Google Scholar]
- 33.John BV, Love E, Dahman B, et al. Use of Telehealth Expedites Evaluation and Listing of Patients Referred for Liver Transplantation. Clin Gastroenterol Hepatol. 2020;18(8):1822–1830 e1824. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Konjeti VR, Heuman D, Bajaj JS, et al. Telehealth-Based Evaluation Identifies Patients Who Are Not Candidates for Liver Transplantation. Clin Gastroenterol Hepatol. 2019;17(1):207–209 e201. [DOI] [PubMed] [Google Scholar]
- 35.Siddique SM, Porges S, Lane-Fall M, et al. Reducing Hospital Admissions for Paracentesis: A Quality Improvement Intervention. Clin Gastroenterol Hepatol. 2019;17(13):2630–2633.e2632. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Jesudian A, Barraza L, Steel P, et al. Quality improvement initiative increases total paracentesis and early paracentesis rates in hospitalised cirrhotics with ascites. Frontline Gastroenterology. 2019:flgastro-2019–101199. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Tapper EB, Finkelstein D, Mittleman MA, Piatkowski G, Chang M, Lai M. A quality improvement initiative reduces 30-day rate of readmission for patients with cirrhosis. Clinical Gastroenterology and Hepatology. 2016;14(5):753–759. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Rawson TM, Bouri S, Allen C, et al. Improving the management of spontaneous bacterial peritonitis in cirrhotic patients: assessment of an intervention in trainee doctors. Clinical medicine (London, England). 2015;15(5):426–430. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Mehta SJ, Torgersen J, Small DS, et al. Effect of a Default Order vs an Alert in the Electronic Health Record on Hepatitis C Virus Screening Among Hospitalized Patients: A Stepped-Wedge Randomized Clinical Trial. JAMA Network Open. 2022;5(3):e222427–e222427. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Tapper EB, Parikh ND. The Future of Quality Improvement for Cirrhosis. Liver Transpl. 2021;27(10):1479–1489. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Spann A, Bishop KM, Weitkamp AO, Stenner SP, Nelson SD, Izzy M. Clinical decision support automates care gap detection among primary care patients with nonalcoholic fatty liver disease. Hepatol Commun. 2023;7(3):e0035. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data will be made available upon reasonable request.