The Use of Administrative Data to Investigate the Population Burden of Hepatic Encephalopathy

Diagnostic Performance of the ICD-10 Code K76.82 for Hepatic Encephalopathy in Patients With Cirrhosis

INTRODUCTION

Hepatic encephalopathy (HE) is prevalent and is associated with increased morbidity and mortality among patients with cirrhosis. On October 1, 2022, a new, specific International Classification of Diseases-10 code for HE, K76.82, was introduced. We aimed to analyze the diagnostic accuracy of K76.82.

METHODS

Diagnostic performance of K76.82 for HE (sensitivity, specificity, positive predictive ratio, and negative predictive ratio) was evaluated in 2 large health systems compared with lactulose, rifaximin, and K72.90.

RESULTS

A total of 2,483 patients were analyzed. The combination term "lactulose or rifaximin" showed the highest sensitivity of >98% while K76.82 demonstrated a specificity of >87% in all cohorts.

DISCUSSION

Although K76.82 is promising, the combination term "lactulose or rifaximin" identified patients with HE more accurately.

Hospital frailty risk score is superior to legacy comorbidity indices for risk adjustment of in-hospital cirrhosis cases

Background & Aims

The hospital frailty risk score (HFRS) identifies older patients at risk of poor outcomes and may have value in cirrhosis. We compared the Charlson (CCI), Elixhauser (ECI), and cirrhosis (CirCom) comorbidity indices with the HFRS in predicting outcomes for cirrhosis hospitalisations.

Methods

Using the National Inpatient Sample (quarter 4 of 2015-2019), we analysed cirrhosis hospitalisations. For each index, we described the prevalence of comorbid conditions and inpatient mortality. We compared the ability of CCI, ECI, CirCom, and HFRS to predict inpatient mortality. Raw and adjusted models predicting inpatient mortality were compared using the area under the receiver operating characteristic curve and the Akaike information criterion.

Results

The cohort's (N = 626,553) median age was 61 years (IQR 52-68 years), 60% were male, cirrhosis was caused by alcohol in 43%, and 38% had ascites. The median comorbidity scores are as follows: ECI 4 (IQR 3-6), CCI 5 (IQR 4-8), and HFRS 5.6 (IQR 3.0-8.6). The most common CirCom score was 0 + 0 (44%). Across the range of values of each index, we observed different mortality ranges: CCI 1.9-13.1%, ECI 3.2-8.7%, CirCom 4.9-13.8%, and HFRS 1.0-15.2%. An adjusted model with HFRS had the highest area under the receiver operating characteristic curve in predicting mortality (HFRS 0.782 vs. ECI 0.689, CCI 0.695, and CirCom 0.692). We observed substantial variation in mortality with HFRS within each level of CCI, ECI, and CirCom. For example, for ECI 4, mortality increased from 0.6 to 16.4%, as HFRS increased from 0 to 15.

Conclusions

Comorbidity indices predict inpatient cirrhosis mortality, but HFRS performs better than CCI, ECI, and CirCom. HFRS is an ideal tool for measuring comorbidity burden and disease severity risk adjustment in cirrhosis-related administrative database studies.

Impact and Implications

We compared commonly used comorbidity indices to a more recently described risk score (hospital frailty risk score [HFRS]) in patients with cirrhosis using a national sample of hospital records. Comorbid conditions are common in hospitalised patients with cirrhosis. There is significant variability in mortality across the range of each index. HFRS outperforms the Charlson comorbidity index, Elixhauser comorbidity index, and CirCom (cirrhosis-specific comorbidity scoring system) in predicting inpatient mortality. HFRS is a valuable index for risk adjustment in inpatient administrative database studies.

Accurate Identification of Patients with Cirrhosis and Its Complications in the Electronic Health Record

BACKGROUND

Cirrhosis represents a significant health burden; administrative data provide an important tool for research studies.

AIMS

We aimed to understand the validity of current ICD-10 codes compared to previously used ICD-9 codes to identify patients with cirrhosis and its complications.

METHODS

We identified 1981 patients presenting to MUSC between 2013 and 2019 with a diagnosis of cirrhosis. To validate the sensitivity of ICD codes, we reviewed the medical records of 200 patients for each associated ICD 9 and 10 codes. Sensitivity, specificity, and positive predictive value for each ICD code (individually or when combined) were calculated and univariate binary logistic models, for cirrhosis and its complications, predicted probabilities were used to calculate C-statistics.

RESULTS

Single ICD 9 and 10 codes were similarly insensitive for detection of cirrhosis, with sensitivity ranging from 5 to 94%. However, ICD-9 code combinations (when used as either/or) had high sensitivity and specificity for the detection of cirrhosis, with the combination of either 571.5 (or 456.21) or 571.2 codes having a C-statistic of 0.975. Combinations of ICD-10 codes were only slightly less sensitive and specific than ICD-9 codes for detection of cirrhosis (K76.6, or K70.31, plus K74.60 or K74.69, and K70.30 had a C-statistic of 0.927).

CONCLUSIONS

ICD-9 and ICD-10 codes when used alone were inaccurate for identifying cirrhosis. ICD-10 and ICD-9 codes had similar performance characteristics. Combinations of ICD codes exhibited the greatest sensitivity and specificity for detection of cirrhosis, and thus should be used to accurately identify cirrhosis.

Current Challenges and Future Direction in Surveillance for Hepatocellular Carcinoma in Patients with Nonalcoholic Fatty Liver Disease

The burden for hepatocellular carcinoma (HCC) attributed to nonalcoholic fatty liver disease (NAFLD) continues to grow in parallel with rising global trends in obesity. The risk of HCC is elevated among patients with NAFLD-related cirrhosis to a level that justifies surveillance based on cost-effectiveness argument. The quality of current evidence for HCC surveillance in all patients with chronic liver disease is poor, and even lower in those with NAFLD. For a lack of more precise risk-stratification tools, current approaches to defining a target population in noncirrhotic NAFLD are limited to noninvasive tests for liver fibrosis, as a proxy for liver-related morbidity and mortality. Beyond etiology and severity of liver disease, traditional and metabolic risk factors, such as diabetes mellitus, older age, male gender and tobacco smoking, are not enough for HCC risk stratification for surveillance efficacy and effectiveness in NAFLD. There is an association between molecular and genetic factors and HCC risk in NAFLD, and risk models integrating both clinical and genetic factors will be key to personalizing HCC risk. In this review, we discuss concerns regarding defining a target population, surveillance test accuracy, surveillance underuse, and other cost-effective considerations for HCC surveillance in individuals with NAFLD.

Identifying Ascites in Patients with Cirrhosis Using Administrative Codes and Diuretic Use: A Multicenter Study

BACKGROUND

Ascites is associated with significantly increased morbidity, mortality, and health care costs. Large population studies are necessary to determine the burden and impact of ascites; however, ascites ICD-10 codes perform poorly in the identification of patients.

METHODS

We utilized three independent retrospective cohorts at the University of Michigan (cohorts 1 and 2) and Duke University (cohort 3). Cohort 1: Child A5-6 patients followed up to 10 years (n = 150); cohort 2: Child A5-B7 patients with portal hypertension followed for up to 1 year (n = 65); cohort 3: cross-sectional cohort of patients evaluated for liver transplant (n = 100). We computed performance characteristics for ascites-related ICD-10 codes (K70.31, K70.11, K71.51, R18.8), as well as loop and/or potassium-sparing diuretics.

RESULTS

A total of 315 patients were included across three cohorts. Algorithms including any ascites code provided better sensitivity and equivalent specificity to R18.8 alone for all cohorts. In cohort 2, we found that loop diuretics, potassium-sparing diuretics, and a combination of both with a cirrhosis code were highly sensitive (82.3% for each) and specific (89.1-93.5%). In contrast, ascites codes were insensitive. In patients with moderate-severe ascites, a combination of recorded diuretics showed high sensitivity and specificity (95.2% and 86.8%). In Cohort 3's transplant evaluation patients, we found that loop diuretics, potassium-sparing diuretics, and a combination of both with a cirrhosis code were highly sensitive (90.4%, 78.8% and 75.0%, respectively) and specific (85.0%, 90.0% and 95.0%, respectively). For moderate-severe cirrhosis, loop diuretics and R18.8 showed higher sensitivity (77.8%) and specificity (88.9%), respectively.

CONCLUSION

Diuretic records with a cirrhosis code improve the identification of ascites. This method for identifying ascites should be used in future large dataset studies.