Validity of administrative codes associated with cirrhosis in Sweden

Risk of severe COVID-19 and mortality in patients with established chronic liver disease: a nationwide matched cohort study

Background and aims

Some, but not all, prior studies have suggested that patients with chronic liver disease are at increased risk of contracting COVID-19 and developing more severe disease. However, nationwide data are lacking from well-phenotyped cohorts with liver histology and comparisons to matched general population controls.

Methods

We conducted a nationwide cohort study of all Swedish adults with chronic liver disease (CLD) confirmed by liver biopsy between 1966 and 2017 (n = 42,320), who were alive on February 1, 2020. CLD cases were matched to ≤ 5 population comparators by age, sex, calendar year and county (n = 182,147). Using Cox regression, we estimated multivariable-adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for COVID-19 hospitalization and severe COVID-19 (intensive care admission or death due to COVID-19).

Results

Between February 1 and July 31, 2020, 161 (0.38%) CLD patients and 435 (0.24%) general population controls were hospitalized with COVID-19 (aHR = 1.36, 95% CI = 1.11–1.66), while 65 (0.15%) CLD patients and 191 (0.10%) controls developed severe COVID-19 (aHR = 1.08, 95% CI = 0.79–1.48). Results were similar in patients with CLD due to alcohol use, nonalcoholic fatty liver disease, viral hepatitis, autoimmune hepatitis, and other etiologies. Among patients with cirrhosis (n = 2549), the aHRs for COVID-19 hospitalization and for severe COVID-19 were 1.08 (95% CI 0.48–2.40) and 1.23 (95% CI = 0.37–4.04), respectively, compared to controls. Moreover, among all patients diagnosed with COVID-19, the presence of underlying CLD was not associated with increased mortality (aHR = 0.85, 95% CI = 0.61–1.19).

Conclusions

In this nationwide cohort, patients with CLD had a higher risk of hospitalization for COVID-19 compared to the general population, but they did not have an increased risk of developing severe COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-02017-8.

Applying Administrative Data-Based Coding Algorithms for Frailty in Patients With Cirrhosis

Frailty is a powerful prognostic tool in cirrhosis. Claims-based frailty scores estimate the presence of frailty without the need for in-person evaluation. These algorithms have not been validated in cirrhosis. Whether they measure true frailty or perform as well as frailty in outcome prediction is unknown. We evaluated 2 claims-based frailty scores-Hospital Frailty Risk Score (HFRS) and Claims-Based Frailty Index (CFI)-in 3 prospective cohorts comprising 1100 patients with cirrhosis. We assessed differences in neuromuscular/neurocognitive capabilities in those classified as frail or nonfrail based on each score. We assessed the ability of the indexes to discriminate frailty based on the Fried Frailty Index (FFI), chair stands, activities of daily living (ADL), and falls. Finally, we compared the performance of claims-based frailty measures and physical frailty measures to predict transplant-free survival using competing risk regression and patient-reported outcomes. The CFI identified neuromuscular deficits (balance, chair stands, hip strength), whereas the HFRS only identified poor chair-stand performance. The CFI had areas under the receiver operating characteristic curve (AUROCs) for identifying frailty as measured by the FFI, ADL, and falls of 0.57, 0.60, and 0.68, respectively; similarly, the AUROCs were 0.66, 0.63, and 0.67, respectively, for the HFRS. Claims-based frailty scores were associated with poor quality of life and sleep but were outperformed by the FFI and chair stands. The HFRS, per 10-point increase (but not the CFI) predicted survival of patients in the liver transplantation (subdistribution hazard ratio [SHR], 1.08; 95% confidence interval [CI], 1.03-1.12) and non-liver transplantation cohorts (SHR, 1.13; 95% CI, 1.05-1.22). Claims-based frailty scores do not adequately associate with physical frailty but are associated with important cirrhosis-related outcomes.

Administrative Coding in Electronic Health Care Record-Based Research of NAFLD: An Expert Panel Consensus Statement

BACKGROUND AND AIMS

Electronic health record (EHR)-based research allows the capture of large amounts of data, which is necessary in NAFLD, where the risk of clinical liver outcomes is generally low. The lack of consensus on which International Classification of Diseases (ICD) codes should be used as exposures and outcomes limits comparability and generalizability of results across studies. We aimed to establish consensus among a panel of experts on ICD codes that could become the reference standard and provide guidance around common methodological issues.

APPROACH AND RESULTS

Researchers with an interest in EHR-based NAFLD research were invited to collectively define which administrative codes are most appropriate for documenting exposures and outcomes. We used a modified Delphi approach to reach consensus on several commonly encountered methodological challenges in the field. After two rounds of revision, a high level of agreement (>67%) was reached on all items considered. Full consensus was achieved on a comprehensive list of administrative codes to be considered for inclusion and exclusion criteria in defining exposures and outcomes in EHR-based NAFLD research. We also provide suggestions on how to approach commonly encountered methodological issues and identify areas for future research.

CONCLUSIONS

This expert panel consensus statement can help harmonize and improve generalizability of EHR-based NAFLD research.

Detecting non-alcoholic fatty liver disease and risk factors in health databases: accuracy and limitations of the ICD-10-AM

OBJECTIVE

The prevalence of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) cirrhosis is often underestimated in healthcare and administrative databases that define disease burden using International Classification of Diseases (ICD) codes. This retrospective audit was conducted to explore the accuracy and limitations of the ICD, Tenth Revision, Australian Modification (ICD-10-AM) to detect NAFLD, metabolic risk factors (obesity and diabetes) and other aetiologies of chronic liver disease.

DESIGN/METHOD

ICD-10-AM codes in 308 admitted patient encounters at two major Australian tertiary hospitals were compared with data abstracted from patients' electronic medical records. Accuracy of individual codes and grouped combinations was determined by calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and Cohen's kappa coefficient (κ).

RESULTS

The presence of an ICD-10-AM code accurately predicted the presence of NAFLD/NASH (PPV 91.2%) and obesity (PPV 91.6%) in most instances. However, codes underestimated the prevalence of NAFLD/NASH and obesity by 42.9% and 45.3%, respectively. Overall concordance between clinical documentation and 'grouped alcohol' codes (κ 0.75) and hepatitis C codes (κ 0.88) was high. Hepatitis B codes detected false-positive cases in patients with previous exposure (PPV 55.6%). Accuracy of codes to detect diabetes was excellent (sensitivity 95.8%; specificity 97.6%; PPV 94.9%; NPV 98.1%) with almost perfect concordance between codes and documentation in medical records (κ 0.93).

CONCLUSION

Recognition of the utility and limitations of ICD-10-AM codes to study the burden of NAFLD/NASH cirrhosis is imperative to inform public health strategies and appropriate investment of resources to manage this burgeoning chronic disease.

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

Hepatic encephalopathy (HE) is a devastating complication of cirrhosis with an increasing footprint in global public health. Although the condition is defined using a careful history and examination, we cannot accurately measure the true impact of HE relying on data collected exclusively from clinical studies. For this reason, administrative data sources are necessary to study the population burden of HE. Administrative data is generated with each health care encounter to account for health care resource utilization and is extracted into a dataset for the secondary purpose of research. In order to utilize such data for valid analysis, several pitfalls must be avoided—specifically, selecting the particular database capable of meeting the needs of the study’s aims, paying careful attention to the limits of each given database, and ensuring validity of case definition for HE specific to the dataset. In this review, we summarize the types of data available for and the results of administrative data studies of HE.