Journal of the American College of Surgeons
-
Multicenter Study
Are Current National Review Board Downstaging Protocols for Hepatocellular Carcinoma Too Restrictive?
Liver transplantation (LT) is an effective strategy for patients with unresectable hepatocellular carcinoma (HCC). To qualify for standardized LT model for end-stage liver disease exception points, the United Network for Organ Sharing National Liver Review Board (NLRB) requires that the presenting and final HCC tumor burden be within the University of California San Francisco criteria, which were recently expanded (within expanded UCSF [W-eUCSF]). Current NLRB criteria may be too restrictive because it has been shown previously that the initial burden does not predict LT failure when tumors downstage to UCSF. This study aims to assess LT outcomes for HCC initially presenting beyond expanded UCSF (B-eUCSF) criteria in a large multicenter collaboration. ⋯ Transplantation for patients initially presenting with HCC B-eUSCF criteria offers a survival advantage similar to those with tumors meeting W-eUCSF criteria at presentation. The current NLRB policy is too stringent, and considerations to expand criteria should be discussed.
-
Observational Study
Using Process Flow Disruption Analysis to Guide Quality Improvement.
Process flow describes the efficiency and consistency with which a process functions. Disruptions in surgical flow have been shown to be associated with an increase in error. Despite this, little experience exists in using surgical flow analysis to guide quality improvement (QI). ⋯ The use of process flow analysis to direct surgical quality initiatives is a novel approach that emphasizes system-level strategy. Resolving flow disruptions can lead to effective QI that embraces reliability by focusing attention on common processes rather than adverse events that may be unique and therefore difficult to apply broadly.
-
The American College of Surgeons (ACS) NSQIP risk calculator helps guide operative decision making. In patients with significant surgical risk, it may be unclear whether to proceed with "Hail Mary"-type interventions. To refine predictions, a local interpretable model-agnostic explanations machine (LIME) learning algorithm was explored to determine weighted patient-specific factors' contribution to mortality. ⋯ Through the application of machine learning algorithms (GBM and LIME), our model individualized predicted mortality and contributing factors with substantial ACS-NSQIP predicted mortality. USE of machine learning techniques may better inform operative decisions and family conversations in cases of significant surgical risk.
-
Recent large retrospective studies suggest that breast-conserving therapy (BCT) plus radiation yielded better outcomes than mastectomy (MST) for women with early-stage breast cancer (ESBC). Whether this is applicable to the different subtypes is unknown. We hypothesize that BCT yielded better outcomes than MST, regardless of subtypes of ESBC. ⋯ BCT yielded better survival rates than mastectomy for women with all subtypes of ESBC. The role of mastectomy for women with ESBC should be reassessed in future clinical trials.