Prevalence of Hepatic Encephalopathy from a Commercial Medical Claims Database in the United States
Bibliographic record
Abstract
Introduction. Hepatic encephalopathy (HE), a complication of cirrhosis, is associated with increased healthcare resource utilization and mortality, and impaired quality of life. Information on the prevalence of HE in the US general population is limited. Methods. Prevalence of HE was estimated by sequential stepwise data analysis of the Symphony Health anonymized patient-level data (APLD) claims database. First, <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mtext>patients</a:mtext> <a:mo>≥</a:mo> <a:mn>18</a:mn> </a:math> years with International Classification of Diseases ninth/tenth edition, clinical modification (ICD-9/10-CM), and codes for cirrhosis from 2018 medical and hospital claims were used to estimate prevalence of cirrhosis within the data set and number of patients with cirrhosis in the US general population. Second, patients diagnosed with cirrhosis in the APLD data set from 2015–2016 with an HE ICD-9/10-CM code within 1 year of cirrhosis diagnosis were used to deduce the prevalence of HE within the data set and estimate the number of patients with HE in the US general population. Last, US DiagnosticSource data on serum ammonia level laboratory results measured within ±2 days of a confirmed HE event were merged with the APLD HE data set, then applied to the US general population. Results. Medical and hospital claims data were available for 272,256 patients with cirrhosis in 2018. An estimated 536,856 US adults had a diagnosis of cirrhosis (prevalence of 0.21%) in 2018. This proportion applied to the estimated number of patients with cirrhosis in the United States resulted in a prevalence estimate of 201,858 cirrhosis patients with HE in 2018. When factoring in serum ammonia data, prevalence was conservatively estimated as approximately 196,000 cirrhosis patients with HE and serum ammonia <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mtext>levels</c:mtext> <c:mo>></c:mo> <c:mn>21</c:mn> <c:mtext> </c:mtext> <c:mi>μ</c:mi> <c:mtext>mol</c:mtext> <c:mo>/</c:mo> <c:mtext>L</c:mtext> </c:math> . Conclusions. In this longitudinal cohort–based study, it was estimated that ≈202,000 patients had HE in the United States in 2018, representing a considerable burden to society and payers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".