Biases in the reporting of hepatocellular carcinoma tumor sizes on the liver transplant waiting list

Abstract

We investigate the possibility that patients with hepatocellular carcinoma (HCC) listed for liver transplant with tumors just outside Stage T2 size criteria may be inaccurately reported as just meeting the tumor size criteria for transplant. The UNOS/STAR database identified 12,958 patients listed for liver transplants with HCC exception points from 2006–2013, 9,168 of whom were listed with one tumor. A logistic power peak function was fitted to the single-tumor size histogram, with the fitted values representing unbiased expected values. The difference between the observed and expected tumor counts for 2.0cm and 5.0cm was 238 (22%) and 66 (57%), respectively. This suggests that up to 304 (3.0%) patients with tumors outside of transplant criteria had their measurements recorded at the margins of eligibility. A risk-adjusted Poisson model evaluated the ratio of observed to expected (O:E) HCC recurrence by tumor size. There were 435 HCC recurrences among 6,049 transplants. Only 2.0cm tumors had O:E recurrence differing from 1 (ratio 0.73, 95%CI 0.57–0.94), indicating a 27% lower than expected rate of recurrence. Conclusion Higher than expected observed tumor counts at the lower transplant criteria margin were corroborated by lower than expected HCC recurrence, suggesting that tumor sizes at the margins of HCC transplant criteria may be subject to inaccurate reporting.

National Experience on Down‐Staging of Hepatocellular Carcinoma Before Liver Transplant: Influence of Tumor Burden, Alpha‐Fetoprotein, and Wait Time

“…We observed in the UNOS‐DS cohort a relatively high proportion of patients with explant tumor stage beyond Milan criteria (33%), reflecting either pre‐LT under‐staging or inadequate response to down‐staging, as well as a higher 3‐year post‐LT HCC recurrence rate of 13% in this group versus 7% in the Milan group. Samoylova et al recently reported inaccuracies and biases in reporting tumor size by LT centers for patients meeting conventional LT criteria . Adherence to the endpoint of down‐staging to be within Milan criteria before LT and observing disease stability for at least 3 to 6 months are essential in minimizing the risks for tumor recurrence.…”

“…Samoylova et al recently reported inaccuracies and biases in reporting tumor size by LT centers for patients meeting conventional LT criteria. (31) Adherence to the endpoint of down-staging to be within Milan criteria before LT and observing disease stability for at least 3 to 6 months are essential in minimizing the risks for tumor recurrence. We found that within the UNOS-DS group, those from SWR and MWR had significantly worse post-LT survival compared with LWR.…”

Predictors of low risk for dropout from the liver transplant waiting list for hepatocellular carcinoma in long wait time regions: Implications for organ allocation

“…We concluded that this subgroup at low risk for waitlist dropout did not warrant the same listing priority as others with T2 HCC. 37 43 then there is little doubt that bias in reporting radiographic response to LRT will emerge as well. Given that blood type was strongly associated with wait time and the MELD score at the time F I G U R E 3 Cumulative incidence of waitlist dropout with lowrisk group meeting all of the following at listing: MELD-Na < 15, Child-Pugh class A, single tumor 2-3 cm, and AFP ≤20 ng/mL TA B L E 4 Waitlist and explant characteristics between the low risk of dropout group and all other patients of LT, 31 we then restricted the analysis to patients with blood group O (longest expected wait time) who accounted for 45% of the overall cohort.…”

“…Furthermore, a patient with a T2 HCC may be granted MELD exception but could then be subjected to a reduction in MELD exception points based on a favorable response to LRT. If there are already inaccuracies in reporting tumor size by LT centers for patients meeting conventional LT criteria, 43 then there is little doubt that bias in reporting radiographic response to LRT will emerge as well. The focus of the current study is to identify a subgroup at low risk for dropout, but there are still problems concerning the rest of the cohort in that they represent a highly heterogeneous population with a wide range of dropout risks but receive the same priority for LT.…”

Modeling the Determinants of Meet-or-Beat Behavior in Distribution Discontinuity Tests

“…Their earnings management tests rely on less restrictive assumptions than tests of abnormal accruals (e.g., Jones, 1991) and real activities management (e.g., Roychowdhury, 2006), thus yielding more credible inferences. 1 The distribution discontinuity approach is widely used to detect earnings management (e.g., Degeorge et al, 1999;Matsumoto, 2002;Leuz et al, 2003;Barth et al, 2008), analyze properties of the "suspect" observations just above the zero earnings benchmark (e.g., Roychowdhury, 2006;Zang, 2012), and study manipulation of other metrics such as marathon running times (Allen et al, 2017) and reported tumor sizes (Samoylova et al, 2017). Burgstahler and Dichev's (1997) test is based on the empirical histogram of earnings and cannot easily incorporate multiple explanatory variables.…”