Research Spotlight

Posted June 24th 2020

Identifying an Optimal Liver Frailty Index Cutoff to Predict Waitlist Mortality in Liver Transplant Candidates.

Robert S. Rahimi M.D.

Robert S. Rahimi M.D.

Kardashian, A., J. Ge, C. E. McCulloch, M. R. Kappus, M. A. Dunn, A. Duarte-Rojo, M. L. Volk, R. S. Rahimi, E. C. Verna, D. R. Ganger, D. Ladner, J. L. Dodge, B. Boyarsky, M. McAdams-DeMarco, D. L. Segev and J. C. Lai (2020). “Identifying an Optimal Liver Frailty Index Cutoff to Predict Waitlist Mortality in Liver Transplant Candidates.” Hepatology June 3. [Epub ahead of print].

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BACKGROUND & AIMS: Frailty, as measured by the liver frailty index (LFI), is associated with liver transplant (LT) waitlist mortality. We sought to identify an optimal LFI cutoff that predicts waitlist mortality. APPROACH&RESULTS: Adults with cirrhosis awaiting LT without hepatocellular carcinoma at 9 LT centers in the United States with LFI assessments were included. Multivariable competing risk analysis assessed the relationship between LFI and waitlist mortality. We identified a single LFI cutoff by evaluating the fit of the competing risk models, searching for the cutoff that gave the best model fit (as judged by the pseudo-log-likelihood). We ascertained the area under the curve (AUC) in an analysis of waitlist mortality to find optimal cutoffs at 3, 6, or 12 months. We used the AUC to compare the discriminative ability of LFI+Model for End Stage Liver Disease-sodium (MELDNa) versus MELDNa alone in 3-month waitlist mortality prediction. Of 1,405 patients, 37(3%), 82(6%), and 135(10%) experienced waitlist mortality at 3, 6, and 12 months, respectively. LFI was predictive of waitlist mortality across a broad LFI range: 3.7-5.2. We identified an optimal LFI cutoff of 4.4 (95%CI:4.0-4.8) for 3-month, 4.2 (95%CI:4.1-4.4) for 6-month, and 4.2 (95%CI:4.1-4.4) for 12-month mortality. The AUC for prediction of 3-month mortality for MELDNa was 0.73; the addition of LFI to MELDNa improved the AUC to 0.79. CONCLUSIONS: LFI is predictive of waitlist mortality across a wide spectrum of LFI values. The optimal LFI cutoff for waitlist mortality was 4.4 at 3 months and 4.2 at 6 and 12 months. The discriminative performance of LFI+MELDNa was greater than MELDNa alone. Our data suggest that incorporating LFI with MELDNa can more accurately represent waitlist mortality in LT candidates.


Posted June 24th 2020

Identification of Serum miRNA Signature and Establishment of a Nomogram for Risk Stratification in Patients With Pancreatic Ductal Adenocarcinoma.

Raju Kandimalla Ph.D.

Raju Kandimalla Ph.D.

Kandimalla, R., T. Shimura, S. Mallik, F. Sonohara, S. Tsai, D. B. Evans, S. C. Kim, H. Baba, Y. Kodera, D. Von Hoff, X. Chen and A. Goel (2020). “Identification of Serum miRNA Signature and Establishment of a Nomogram for Risk Stratification in Patients With Pancreatic Ductal Adenocarcinoma.” Ann Surg May 8. [Epub ahead of print].

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OBJECTIVE: The aim of the study was to perform mRNA-miRNA regulatory network analyses to identify a miRNA panel for molecular subtype identification and stratification of high-risk patients with pancreatic ductal adenocarcinoma (PDAC). BACKGROUND: Recent transcriptional profiling effort in PDAC has led to the identification of molecular subtypes that associate with poor survival; however, their clinical significance for risk stratification in patients with PDAC has been challenging. METHODS: By performing a systematic analysis in The Cancer Genome Atlas and International Cancer Genome Consortium cohorts, we discovered a panel of miRNAs that associated with squamous and other poor molecular subtypes in PDAC. Subsequently, we used logistic regression analysis to develop models for risk stratification and Cox proportional hazard analysis to determine survival prediction probability of this signature in multiple cohorts of 433 patients with PDAC, including a tissue cohort (n = 199) and a preoperative serum cohort (n = 51). RESULTS: We identified a panel of 9 miRNAs that were significantly upregulated (miR-205-5p and -934) or downregulated (miR-192-5p, 194-5p, 194-3p, 215-5p, 375-3p, 552-3p, and 1251-5p) in PDAC molecular subtypes with poor survival [squamous, area under the receiver operating characteristic curve (AUC) = 0.90; basal, AUC = 0.89; and quasimesenchymal, AUC = 0.83]. The validation of this miRNA panel in a tissue clinical cohort was a significant predictor of overall survival (hazard ratio = 2.48, P < 0.0001), and this predictive accuracy improved further in a risk nomogram which included key clinicopathological factors. Finally, we were able to successfully translate this miRNA predictive signature into a liquid biopsy-based assay in preoperative serum specimens from PDAC patients (hazard ratio: 2.85, P = 0.02). CONCLUSION: We report a novel miRNA risk-stratification signature that can be used as a noninvasive assay for the identification of high-risk patients and potential disease monitoring in patients with PDAC.


Posted June 24th 2020

Modeling Personalized Adjuvant TreaTment in EaRly stage coloN cancer (PATTERN).

Raju Kandimalla Ph.D.

Raju Kandimalla Ph.D.

Jongeneel, G., M. J. E. Greuter, F. N. van Erning, M. Koopman, J. P. Medema, R. Kandimalla, A. Goel, L. Bujanda, G. A. Meijer, R. J. A. Fijneman, M. G. H. van Oijen, J. Ijzermans, C. J. A. Punt, G. R. Vink and V. M. H. Coupé (2020). “Modeling Personalized Adjuvant TreaTment in EaRly stage coloN cancer (PATTERN).” Eur J Health Econ May 26. [Epub ahead of print].

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AIM: To develop a decision model for the population-level evaluation of strategies to improve the selection of stage II colon cancer (CC) patients who benefit from adjuvant chemotherapy. METHODS: A Markov cohort model with a one-month cycle length and a lifelong time horizon was developed. Five health states were included; diagnosis, 90-day mortality, death other causes, recurrence and CC death. Data from the Netherlands Cancer Registry were used to parameterize the model. Transition probabilities were estimated using parametric survival models including relevant clinical and pathological covariates. Subsequently, biomarker status was implemented using external data. Treatment effect was incorporated using pooled trial data. Model development, data sources used, parameter estimation, and internal and external validation are described in detail. To illustrate the use of the model, three example strategies were evaluated in which allocation of treatment was based on (A) 100% adherence to the Dutch guidelines, (B) observed adherence to guideline recommendations and (C) a biomarker-driven strategy. RESULTS: Overall, the model showed good internal and external validity. Age, tumor growth, tumor sidedness, evaluated lymph nodes, and biomarker status were included as covariates. For the example strategies, the model predicted 83, 87 and 77 CC deaths after 5 years in a cohort of 1000 patients for strategies A, B and C, respectively. CONCLUSION: This model can be used to evaluate strategies for the allocation of adjuvant chemotherapy in stage II CC patients. In future studies, the model will be used to estimate population-level long-term health gain and cost-effectiveness of biomarker-based selection strategies.


Posted June 24th 2020

Guidelines for standardized nomenclature and reporting in uterus transplantation: An opinion from the United States Uterus Transplant Consortium.

Liza Johannesson, M.D.

Liza Johannesson, M.D.

Johannesson, L., G. Testa, R. Flyckt, R. Farrell, C. Quintini, A. Wall, K. O’Neill, A. Tzakis, E. G. Richards, S. M. Gordon and P. M. Porrett (2020). “Guidelines for standardized nomenclature and reporting in uterus transplantation: An opinion from the United States Uterus Transplant Consortium.” Am J Transplant May 7. [Epub ahead of print].

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Uterus transplantation is a nascent but growing field. To support this growth, the United States Uterus Transplant Consortium proposes guidelines for nomenclature related to operative technique, vascular anatomy, and donor, recipient, and offspring outcomes. In terms of anatomy, the group recommends reporting donor arterial inflow and recipient anastomotic site delivering inflow to the graft and offers standardization of the names for the 4 veins originating from the uterus because of current inconsistency in this particular nomenclature. Seven progressive stages with milestones of success are defined for reporting on uterus transplantation outcomes: (1) technical, (2) menstruation, (3) embryo implantation, (4) pregnancy, (5) delivery, (6) graft removal, and (7) long-term follow-up. The 3 primary metrics for success are recipient survival (as reported for other organ transplant recipients), graft survival, and uterus transplant live birth rate (defined as live birth per transplanted recipient). A number of secondary outcomes should also be reported, most of which capture stage-specific milestones, as well as data on graft failure. Outcome metrics for living donors include patient survival, survival free of operative intervention, and data on complications and hospitalizations. Finally, we make specific recommendations on follow-up for offspring born from uterine grafts, which includes specialty surveillance as well as collection and reporting of routine pediatric outcomes. The goal of standardization in reporting is to create consistency and improve the quality of evidence available on the efficacy and value of the procedure.


Posted June 24th 2020

Racial disparities and democratization of health care: A focus on TAVR in the United States.

Michael J. Mack M.D.

Michael J. Mack M.D.

Holmes, D. R., Jr., M. J. Mack, M. Alkhouli and S. Vemulapalli (2020). “Racial disparities and democratization of health care: A focus on TAVR in the United States.” Am Heart J 224: 166-170.

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What can be said with certainty is that there are real and documented disparities in care involving TAVR in the increasing population of patients with aortic stenosis.1–8,28–32These disparities may become more prominent as TAVR becomes the standard of care. There are multiple issues relating to this disparity including socioeconomic,genetic, personal valuations and expectations, access to care, and follow-up. Approaches to resolution need to take into consideration of these multiple issues from many angles and include multiple stakeholders – hospital systems to promote culturally competent team based care, reimbursement agencies, patient education, family support systems access to community based educational programs, industry resources working to develop trials with specific focus and recruitment goals to include racialand ethnic groups, and social services.29-32The CMS Accountable Health Communities Model project has been implemented and could potentially be used by the centers involved to focus on one limb of unmet needs. [No abstract; excerpt from article p.169-170].