Survival Tree Provides Individualized Estimates of Survival After Lung Transplant.

INTRODUCTION: Identifying contributors to lung transplant survival is vital in mitigating mortality. To enhance individualized mortality estimation and determine variable interaction, we employed a survival tree algorithm utilizing recipient and donor data. METHODS: United Network Organ Sharing data (2000-2021) were queried for single and double lung transplants in adult patients. Graft survival time <7 d was excluded. Sixty preoperative and immediate postoperative factors were evaluated with stepwise logistic regression on mortality; final model variables were included in survival tree modeling. Data were split into training and testing sets and additionally validated with 10-fold cross validation. Survival tree pruning and model selection was based on Akaike information criteria and log-likelihood values. Estimated survival probabilities and log-rank pairwise comparisons between subgroups were calculated. RESULTS: A total of 27,296 lung transplant patients (8175 single; 19,121 double lung) were included. Stepwise logistic regression yielded 47 significant variables associated with mortality. Survival tree modeling returned six significant factors: recipient age, length of stay from transplant to discharge, recipient ventilator duration post-transplant, double lung transplant, recipient reintubation post-transplant, and donor cytomegalovirus status. Eight subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves. CONCLUSIONS: Survival trees provide the ability to understand the effects and interactions of covariates on survival after lung transplantation. Individualized survival probability with this technique found that preoperative and postoperative factors influence survival after lung transplantation. Thus, preoperative patient counseling should acknowledge a degree of uncertainty given the influence of postoperative factors.

Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery.

Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently.

Rare Presentation of Umbilical Condyloma.

Syphilis is associated with 3 stages of infection-primary, secondary, and tertiary-each with their own associated clinical findings. Secondary syphilis manifests with condyloma lata and other cutaneous findings, and typically occurs several months after the initial infection. Condyloma lata are primarily found in the genital area, but may also be found in other locations such as the umbilicus, axilla, and neck. This brief report describes an umbilical condyloma lata discovered in a patient with secondary syphilis and HIV co-infection and discusses surgical excision and fulguration as an option for definitive management.

Model to predict major complications following liver resection for HCC in patients with metabolic syndrome.

BACKGROUND: Metabolic syndrome (MS) is rapidly growing as risk factor for HCC. Liver resection for HCC in patients with MS is associated with increased postoperative risks. There are no data on factors associated with postoperative complications. AIMS: The aim was to identify risk factors and develop and validate a model for postoperative major morbidity after liver resection for HCC in patients with MS, using a large multicentric Western cohort. MATERIALS AND METHODS: The univariable logistic regression analysis was applied to select predictive factors for 90 days major morbidity. The model was built on the multivariable regression and presented as a nomogram. Performance was evaluated by internal validation through the bootstrap method. The predictive discrimination was assessed through the concordance index. RESULTS: A total of 1087 patients were gathered from 24 centers between 2001 and 2021. Four hundred and eighty-four patients (45.2%) were obese. Most liver resections were performed using an open approach (59.1%), and 743 (68.3%) underwent minor hepatectomies. Three hundred and seventy-six patients (34.6%) developed postoperative complications, with 13.8% major morbidity and 2.9% mortality rates. Seven hundred and thirteen patients had complete data and were included in the prediction model. The model identified obesity, diabetes, ischemic heart disease, portal hypertension, open approach, major hepatectomy, and changes in the nontumoral parenchyma as risk factors for major morbidity. The model demonstrated an AUC of 72.8% (95% CI: 67.2%-78.2%) ( https://childb.shinyapps.io/NomogramMajorMorbidity90days/ ). CONCLUSIONS: Patients undergoing liver resection for HCC and MS are at high risk of postoperative major complications and death. Careful patient selection, considering baseline characteristics, liver function, and type of surgery, is key to achieving optimal outcomes.

A chronological review of 500 minimally invasive liver resections in a North American institution: overcoming stagnation and toward consolidation.

BACKGROUND: Although interest in expanding the application of minimally invasive liver resection (MILR) is high the world over, most of the extensive experience in MILR has been reported from Far East Asia and Europe and its adoption in North America is limited. The aim of this study was to review the experience of MILR in a single North American institute over a 15-year period, highlighting both the obstacles encountered and strategies adopted to overcome the stagnation in its uptake. METHODS: This study included 500 MILR cases between 2006 and 2020. Patient demographics, disease characteristics, surgical technique, and perioperative outcomes are summarized. The major hepatectomy rate and conversion rate were assessed according to case numbers (first 100, 101-300, and 301-500 cases) to assess chronological trends. RESULTS: Of 500, 402 MILRs were done by pure laparoscopic (80.4%), 67 were hand assisted (13.4%), and 31 were robotic (6.2%). The majority (64%) of cases were performed for malignancy (n = 320; 100 Hepatocellular carcinoma, 153 Colorectal metastases, 27 Intrahepatic cholangiocarcinoma, and others, 40, 64%). A total of 71 cases were converted to open (14.2%). The annual case number gradually increased over the first few years; however, case numbers stayed around 30 between 2009 and 2017. In this period, despite accumulating MILR experience, open conversion rates increased despite no change in major hepatectomy rate. After this period of long-term stagnation, we introduced crucial changes in team composition and laparoscopic instrumentation. Our MILR case number and major hepatectomy rate thereafter increased significantly without increasing conversion or complication rates. CONCLUSION: Our recovery from long-term stagnation by instituting key changes as detailed in this study could be used as a guidepost for programs that are contemplating transitioning their MILR program from minor to advanced resections. Establishing a formal MILR training model through proper mentorship/proctorship and building a dedicated MILR team would be imperative to this strategy.

Nontumor related risk score: A new tool to improve prediction of prognosis after hepatectomy for colorectal liver metastases.

BACKGROUND: Prognostic stratification of patients with colorectal cancer liver metastasis based solely on tumor-related factors has only moderate discriminatory ability. We hypothesized that the inclusion of nontumor related factors can improve prediction of long-term prognosis of patients with colorectal cancer liver metastasis. METHODS: Nontumor related laboratory markers were assessed utilizing a training cohort from 2 U.S. institutions (n = 1,205). Factors independently associated with prognosis were used to develop a nontumor related prognostic score. The discriminatory ability, assessed by Harrell's C-statistics (C-index) and net reclassification improvement, was validated and compared with 3 commonly used tumor-related clinical risk scores: Fong clinical risk scores, m-clinical risk scores, and Genetic and Morphological Evaluation (GAME) score in an external validation cohort from 5 Asian (n = 1,307) and 3 European (n = 1,058) institutions. The discriminatory ability of nontumor related prognostic score combined with each of these 3 tumor-related prognostic scores was also estimated. RESULTS: Alkaline phosphatase (hazard ratio 1.43; 95% confidence interval, 1.11-1.84), albumin (hazard ratio 0.71; 95% confidence interval, 0.57-0.89), and mean corpuscular volume (hazard ratio 19.0, per log unit; 95% confidence interval, 4.79-75.0) were each independently associated with increased risk of death after resection of colorectal cancer liver metastasis (all P < .05). In turn, alkaline phosphatase, albumin, and mean corpuscular volume were combined to form a nontumor related prognostic score (2.942 × mean corpuscular volume + 0.399 × alkaline phosphatase-0.339 × albumin-12) × 10 (median, 16; range, 1-30). The nontumor related prognostic score had good-to-modest discriminatory ability in the external cohort (C-index = 0.58), which was comparable to the 3 established tumor-related prognostic scores (C-index: Fong clinical risk scores, 0.53, m-clinical risk scores, 0.55, GAME, 0.58). The addition of the nontumor related prognostic score to the tumor-related prognostic scores enhanced the discriminatory ability in the entire study cohort (C-index: nontumor related score+Fong, 0.60, nontumor related score+m-clinical risk scores, 0.61, nontumor related score+GAME, 0.64), as well reclassification improvement (42.5, 42.7%, and 21.2%, respectively). CONCLUSION: Nontumor related prognostic information may help improve the prognostic stratification of patients after resection of colorectal cancer liver metastasis. The nontumor related prognostic score may be combined with tumor-related prognostic tools to enhance prognostic stratification of patients with colorectal cancer liver metastasis.

Salivary miRNAs as non-invasive biomarkers of hepatocellular carcinoma: a pilot study.

BACKGROUND: Improved detection of hepatocellular carcinoma (HCC) is needed, as current detection methods, such as alpha fetoprotein (AFP) and ultrasound, suffer from poor sensitivity. MicroRNAs (miRNAs) are small, non-coding RNAs that regulate many cellular functions and impact cancer development and progression. Notably, miRNAs are detectable in saliva and have shown potential as non-invasive biomarkers for a number of cancers including breast, oral, and lung cancers. Here, we present, to our knowledge, the first report of salivary miRNAs in HCC and compare these findings to patients with cirrhosis, a high-risk cohort for HCC. METHODS: We performed small RNA sequencing in 20 patients with HCC and 19 with cirrhosis. Eleven patients with HCC had chronic liver disease, and analyses were performed with these samples combined and stratified by the presence of chronic liver disease. P values were adjusted for multiple comparisons using a false discovery rate (FDR) approach and miRNA with FDR P < 0.05 were considered statistically significant. Differential expression of salivary miRNAs was compared to a previously published report of miRNAs in liver tissue of patients with HCC vs cirrhosis. Support vector machines and leave-one-out cross-validation were performed to determine if salivary miRNAs have predictive potential for detecting HCC. RESULTS: A total of 4,565 precursor and mature miRNAs were detected in saliva and 365 were significantly different between those with HCC compared to cirrhosis (FDR P < 0.05). Interestingly, 283 of these miRNAs were significantly downregulated in patients with HCC. Machine-learning identified a combination of 10 miRNAs and covariates that accurately classified patients with HCC (AUC = 0.87). In addition, we identified three miRNAs that were differentially expressed in HCC saliva samples and in a previously published study of miRNAs in HCC tissue compared to cirrhotic liver tissue. CONCLUSIONS: This study demonstrates, for the first time, that miRNAs relevant to HCC are detectable in saliva, that salivary miRNA signatures show potential to be highly sensitive and specific non-invasive biomarkers of HCC, and that additional studies utilizing larger cohorts are needed.

Validation of the IWATE criteria as a laparoscopic liver resection difficulty score in a single North American cohort.

BACKGROUND: Laparoscopic liver resection (LLR) involves a difficult learning curve, for which multiple difficulty scores have been proposed to assist with safe adaptation. The IWATE Criteria is a 4-level difficulty score shown to correlate with conversion to open surgery, estimated blood loss (EBL), and operative time in Japanese and French cohorts. We set out to validate the IWATE Criteria in a North American cohort, describe the evolution of our LLR program, and analyze the IWATE Criteria's ability to predict conversion to open surgery. METHODS: Patients that underwent LLR between January 2006 and December 2019 were selected from a prospectively maintained database. Difficulty outcomes, including conversion to open surgery, EBL, operative time, and post-operative complications were analyzed according to IWATE difficulty level, both overall and between chronological eras. The IWATE Criteria's ability to predict conversion to open surgery was assessed with a receiver operating characteristic (ROC) analysis. RESULTS: A total of 426 patients met inclusion criteria. Operative time, EBL, and conversion to open surgery increased in concordance with low to advanced IWATE difficulty. ROC analysis for conversion to open surgery demonstrated an overall area under the curve (AUC) of 0.694. Predictive performance was superior during the first two eras, with AUCs of 0.771 and 0.775; predictive value decreased as the LLR program gained experience, with AUCs of 0.708 and 0.551 for eras three and four. CONCLUSIONS: This study validated the IWATE Criteria in a North American population distinct from previous Japanese and French cohorts, based on its correlation with operative time, EBL, and conversion to open surgery. The IWATE Criteria may be of utility for identification of LLR cases appropriate for surgeon experience, as well as determination of laparoscopic feasibility. Interval difficulty score recalibration may be warranted as surgeon perception of difficulty evolves.

Optimal Timing of Administration of Direct-acting Antivirals for Patients With Hepatitis C-associated Hepatocellular Carcinoma Undergoing Liver Transplantation.

OBJECTIVE: To investigate the optimal timing of direct acting antiviral (DAA) administration in patients with hepatitis C-associated hepatocellular carcinoma (HCC) undergoing liver transplantation (LT). SUMMARY OF BACKGROUND DATA: In patients with hepatitis C (HCV) associated HCC undergoing LT, the optimal timing of direct-acting antivirals (DAA) administration to achieve sustained virologic response (SVR) and improved oncologic outcomes remains a topic of much debate. METHODS: The United States HCC LT Consortium (2015-2019) was reviewed for patients with primary HCV-associated HCC who underwent LT and received DAA therapy at 20 institutions. Primary outcomes were SVR and HCC recurrence-free survival (RFS). RESULTS: Of 857 patients, 725 were within Milan criteria. SVR was associated with improved 5-year RFS (92% vs 77%, P < 0.01). Patients who received DAAs pre-LT, 0-3 months post-LT, and ≥3 months post-LT had SVR rates of 91%, 92%, and 82%, and 5-year RFS of 93%, 94%, and 87%, respectively. Among 427 HCV treatment-naïve patients (no previous interferon therapy), patients who achieved SVR with DAAs had improved 5-year RFS (93% vs 76%, P < 0.01). Patients who received DAAs pre-LT, 0-3 months post-LT, and ≥3 months post-LT had SVR rates of 91%, 93%, and 78% (P < 0.01) and 5-year RFS of 93%, 100%, and 83% (P = 0.01). CONCLUSIONS: The optimal timing of DAA therapy appears to be 0 to 3 months after LT for HCV-associated HCC, given increased rates of SVR and improved RFS. Delayed administration after transplant should be avoided. A prospective randomized controlled trial is warranted to validate these results.

Salivary Metabolites are Promising Non-Invasive Biomarkers of Hepatocellular Carcinoma and Chronic Liver Disease.

BACKGROUND: Hepatocellular carcinoma (HCC) is a leading causes of cancer mortality worldwide. Improved tools are needed for detecting HCC so that treatment can begin as early as possible. Current diagnostic approaches and existing biomarkers, such as alpha-fetoprotein (AFP) lack sensitivity, resulting in too many false negative diagnoses. Machine-learning may be able to identify combinations of biomarkers that provide more robust predictions and improve sensitivity for detecting HCC. We sought to evaluate whether metabolites in patient saliva could distinguish those with HCC, cirrhosis, and those with no documented liver disease. METHODS AND RESULTS: We tested 125 salivary metabolites from 110 individuals (43 healthy, 37 HCC, 30 cirrhosis) and identified 4 metabolites that displayed significantly different abundance between groups (FDR P <.2). We also developed four tree-based, machine-learning models, optimized to include different numbers of metabolites, that were trained using cross-validation on 99 patients and validated on a withheld test set of 11 patients. A model using 12 metabolites -octadecanol, acetophenone, lauric acid, 1-monopalmitin, dodecanol, salicylaldehyde, glycyl-proline, 1-monostearin, creatinine, glutamine, serine and 4-hydroxybutyric acid- had a cross-validated sensitivity of 84.8%, specificity of 92.4% and correctly classified 90% of the HCC patients in the test cohort. This model outperformed previously reported sensitivities and specificities for AFP (20-100ng/ml) (61%, 86%) and AFP plus ultrasound (62%, 88%). CONCLUSIONS AND IMPACT: Metabolites detectable in saliva may represent products of disease pathology or a breakdown in liver function. Notably, combinations of salivary metabolites derived from machine-learning may serve as promising non-invasive biomarkers for the detection of HCC.

Performance of two prognostic scores that incorporate genetic information to predict long-term outcomes following resection of colorectal cancer liver metastases: An external validation of the MD Anderson and JHH-MSK scores.

INTRODUCTION: Two novel clinical risk scores (CRS) that incorporate KRAS mutation status were developed: modified CRS (mCRS) and GAME score. However, they have not been tested in large national and international cohorts. The aim of this study was to validate the prognostic discrimination utility and determine the clinical usefulness of the two novel CRS. METHODS: Patients undergoing hepatectomy for CRLM (2000-2018) in 10 centers were included. The discriminatory abilities of mCRS, GAME, and Fong CRS were evaluated using Harrell's C-index and Akaike's Information Criterion. RESULTS: In the entire cohort, the C-index of the GAME score (0.61) was significantly higher than those of Fong score (0.57) and mCRS (0.54), while the C-Index of mCRS was significantly lower than that of Fong score. When we compared the models in the various geographical regions, the C-index of GAME score was significantly higher than that of mCRS in North America, Europe, and South America. The AIC of Fong score, mCRS, and GAME score were 14 405, 14 447, and 14 319, respectively. CONCLUSION: In conclusion, using the largest and most heterogenous population of CRLM patients with known KRAS status, this independent, external validation demonstrated that the GAME score outperforms both the traditional Fong score and mCRS.

A case of 18 years disease-free survival after combined pancreatoduodenectomy and hemicolectomy for carcinosarcoma of the transverse colon.

BACKGROUND: Ascertaining the origin of large tumors located in the region of the pancreas head and adjacent mesocolon can pose a challenge preoperatively. En bloc pancreatoduodenectomy with hemicolectomy is often required towards curative tumor resection (R0) of malignant tumors in this region. CASE PRESENTATION: Herein we report a case of a 48-year-old man with two contiguous masses each 5 cm in size, located in the pancreatic head. The masses were detected incidentally by abdominal ultrasonography at an annual health check. Endoscopic biopsies revealed inflammation with no malignancy. Cross-sectional imaging showed the tumor direct invasion of the uncinate process of the pancreas, and the third portion of the duodenum. Based on imaging, a malignant submucosal tumor originating from mesenchymal cells in the mesentery of the transverse colon was made preoperatively. The mass required en bloc pancreatoduodenectomy, right hemicolectomy, and resection of the superior mesenteric vein. The final pathology was carcinosarcoma of the transverse colon. The patient survived 18 years after surgery without recurrence. CONCLUSIONS: Malignant tumors located in the region of the pancreas head should be considered for an en bloc curative tumor resection and adjuvant chemotherapy treatments offered that might be beneficial for carcinosarcoma.

Conditional probability of graft survival in liver transplantation using donation after circulatory death grafts - a retrospective study.

The use of livers from donation after circulatory death (DCD) is historically characterized by increased rates of biliary complications and inferior short-term graft survival (GS) compared to donation after brain death (DBD) allografts. This study aimed to evaluate the dynamic prognostic impact of DCD livers to reveal whether they remain an adverse factor even after patients survive a certain period following liver transplant (LT). This study used 74 961 LT patients including 4065 DCD LT in the scientific registry of transplant recipients from 2002-2017. The actual, 1 and 3-year conditional hazard ratio (HR) of 1-year GS in DCD LT were calculated using a conditional version of Cox regression model. The actual 1-, 3-, and 5-year GS of DCD LT recipients were 83.3%, 73.3%, and 66.3%, which were significantly worse than those of DBD (all P < 0.01). Actual, 1-, and 3-year conditional HR of 1-year GS in DCD compared to DBD livers were 1.87, 1.49, and 1.39, respectively. Graft loss analyses showed that those lost to biliary related complications were significantly higher in the DCD group even 3 years after LT. National registry data demonstrate the protracted higher risks inherent to DCD liver grafts in comparison to their DBD counterparts, despite survival through the early period after LT. These findings underscore the importance of judicious DCD graft selection at individual center level to minimize the risk of long-term biliary complications.

Machine learning predicts unpredicted deaths with high accuracy following hepatopancreatic surgery.

BACKGROUND: Machine learning to predict morbidity and mortality-especially in a population traditionally considered low risk-has not been previously examined. We sought to characterize the incidence of death among patients with a low estimated morbidity and mortality risk based on the National Surgical Quality Improvement Program (NSQIP) estimated probability (EP), as well as develop a machine learning model to identify individuals at risk for "unpredicted death" (UD) among patients undergoing hepatopancreatic (HP) procedures. METHODS: The NSQIP database was used to identify patients who underwent elective HP surgery between 2012-2017. The risk of morbidity and mortality was stratified into three tiers (low, intermediate, or high estimated) using a k-means clustering method with bin sorting. A machine learning classification tree and multivariable regression analyses were used to predict 30-day mortality with a 10-fold cross validation. C statistics were used to compare model performance. RESULTS: Among 63,507 patients who underwent an HP procedure, median patient age was 63 (IQR: 54-71) years. Patients underwent either pancreatectomy (n=38,209, 60.2%) or hepatic resection (n=25,298, 39.8%). Patients were stratified into three tiers of predicted morbidity and mortality risk based on the NSQIP EP: low (n=36,923, 58.1%), intermediate (n=23,609, 37.2%) and high risk (n=2,975, 4.7%). Among 36,923 patients with low estimated risk of morbidity and mortality, 237 patients (0.6%) experienced a UD. According to the classification tree analysis, age was the most important factor to predict UD (importance 16.9) followed by preoperative albumin level (importance: 10.8), disseminated cancer (importance: 6.5), preoperative platelet count (importance: 6.5), and sex (importance 5.9). Among patients deemed to be low risk, the c-statistic for the machine learning derived prediction model was 0.807 compared with an AUC of only 0.662 for the NSQIP EP. CONCLUSIONS: A prognostic model derived using machine learning methodology performed better than the NSQIP EP in predicting 30-day UD among low risk patients undergoing HP surgery.

Quality of Care Among Medicare Patients Undergoing Pancreatic Surgery: Safety Grade, Magnet Recognition, and Leapfrog Minimum Volume Standards-Which Quality Benchmark Matters?

BACKGROUND: The association of national quality benchmarking metrics with postoperative outcomes following complex surgery remains unknown. We assessed the relationship between the "quality trifactor" of Leapfrog minimum volume standards, Hospital Safety Grade A, and Magnet Recognition with outcomes of Medicare patients undergoing pancreatectomy. METHODS: The Standard Analytic Files (SAF) merged with Leapfrog Hospital Survey and Leapfrog Safety Scores Denominator Files were reviewed to identify Medicare patients who underwent pancreatic procedures between 2013 and 2015. Primary outcomes were overall and serious complications, as well as 30- and 90-day mortality. Multivariable logistic regression analyses were conducted to evaluate possible associations among hospitals meeting the quality trifactor and short-term outcomes. RESULTS: Among 4853 Medicare patients, 909 (18.7%) underwent pancreatectomy at hospitals meeting the quality trifactor. Among 260 hospitals, 7.3% (n = 19) met the quality trifactor. Safety Grade A (48.8%, n = 127) was the most commonly met criterion followed by Magnet Recognition (36.2%, n = 94); the Leapfrog minimum volume standards were achieved by 25% (n = 65) of hospitals. Patients undergoing surgery at hospitals that were only Safety Grade A and Magnet designated, but did not meet Leapfrog criteria, had higher odds of serious complications (OR 1.59, 95% CI 1.00-2.51). In contrast, patients undergoing treatment at hospitals having all three designations (i.e., the quality trifactor) had 40% and 39% lower odds of both serious complications (OR 0.60, 95% CI 0.37-0.97) and 90-day mortality (OR 0.61, 95% CI 0.42-0.89), respectively. In turn, patients undergoing pancreatectomy at quality trifactor hospitals had higher odds of experiencing the composite quality measure textbook outcome (OR 1.28, 95% CI 1.03-1.59) versus patients undergoing pancreatectomy at non-trifactor hospitals. CONCLUSION: While Safety Grade A and Magnet designation alone were not associated with higher odds of an optimal composite outcome following pancreatectomy, compliance with Leapfrog criteria to achieve the "quality trifactor" metric was associated with lower odds of serious complications and mortality.

Long-Term Outcomes after Spleen-Preserving Distal Pancreatectomy for Pancreatic Neuroendocrine Tumors: Results from the US Neuroendocrine Study Group.

BACKGROUND: The adoption of spleen-preserving distal pancreatectomy (SPDP) for malignant disease such as pancreatic neuroendocrine tumors (pNETs) has been controversial. The objective of the current study was to assess the impact of SPDP on outcomes of patients with pNETs. METHODS: Patients undergoing a distal pancreatectomy for pNET between 2002 and 2016 were identified in the US Neuroendocrine Tumor Study Group database. Propensity score matching (PSM) was used to compare short- and long-term outcomes of patients undergoing SPDP versus distal pancreatectomy with splenectomy (DPS). RESULTS: Among 621 patients, 103 patients (16.6%) underwent an SPDP. Patients who underwent SPDP were more likely to have lower BMI (median, 27.5 [IQR 24.0-31.2] vs. 28.7 [IQR 25.7-33.6]; p = 0.005) and have undergone minimally invasive surgery (n = 56, 54.4% vs. n = 185, 35.7%; p < 0.001). After PSM, while the median total number of lymph nodes examined among patients who underwent an SPDP was lower compared with DPS (3 [IQR 1-8] vs. 9 [5-13]; p < 0.001), 5-year overall survival (OS) and recurrence-free survival (RFS) were comparable (OS: 96.8 vs. 92.0%, log-rank p = 0.21, RFS: 91.1 vs. 84.7%, log-rank p = 0.93). In addition, patients undergoing SPDP had less intraoperative blood loss (median, 100 mL [IQR 10-250] vs. 150 mL [IQR 100-400]; p = 0.001), lower incidence of serious complications (n = 13, 12.8% vs. n = 28, 27.5%; p = 0.014), and shorter length of stay (median: 5 days [IQR 4-7] vs. 6 days [IQR 5-13]; p = 0.049) compared with patients undergoing DPS. CONCLUSION: SPDP for pNET was associated with acceptable perioperative and long-term outcomes that were comparable to DPS. SPDP should be considered for patients with pNET.

Variation in Drain Management Among Patients Undergoing Major Hepatectomy.

BACKGROUND: Although previous studies have suggested that drain management is highly variable, data on drain placement and timing of drain removal among patients undergoing hepatic resection remain scarce. The objective of the current study was to define the utilization of drain placement among patients undergoing major hepatic resection. METHODS: The ACS NSQIP-targeted hepatectomy database was used to identify patients who underwent major hepatectomy between 2014 and 2017. Association between day of drain removal, timing of discharge, and drain fluid bilirubin on postoperative day (POD) 3 (DFB-3) was assessed. Propensity score matching (PSM) was used to compare outcomes of patients with a drain removed before and after POD 3. RESULTS: Among 5330 patients, most patients had an abdominal drain placed at the time of hepatic resection (n = 3075, 57.7%). Of 2495 patients with data on timing of drain removal, only 380 patients (15.2%) had their drain removed by POD 3. Almost 1 in 6 patients (n = 441, 17.7%) were discharged home with the drain in place. DFB-3 values correlated poorly with POD of drain removal (R2 = 0.0049). After PSM, early drain removal (≤ POD 3) was associated with lower rates of grade B or C bile leakage (2.1% vs. 7.1%, p = 0.008) and prolonged length of hospital stay (6.0% vs. 12.7%, p = 0.009) compared with delayed drain removal (> POD 3). CONCLUSIONS: Roughly 3 in 5 patients had a drain placed at the time of major hepatectomy and only 1 in 7 patients had the drain removed early. This study demonstrated the potential benefits of early drain removal in an effort to improve the quality of care following major hepatectomy.

Predicting Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma.

BACKGROUND: The objective of the current study was to develop a model to predict the likelihood of occult lymph node metastasis (LNM) prior to resection of intrahepatic cholangiocarcinoma (ICC). METHODS: Patients who underwent hepatectomy for ICC between 2000 and 2017 were identified using a multi-institutional database. A novel model incorporating clinical and preoperative imaging data was developed to predict LNM. RESULTS: Among 980 patients who underwent resection of ICC, 190 (19.4%) individuals had at least one LNM identified on final pathology. An enhanced imaging model incorporating clinical and imaging data was developed to predict LNM ( https://k-sahara.shinyapps.io/ICC_imaging/ ). The performance of the enhanced imaging model was very good in the training data set (c-index 0.702), as well as the validation data set with bootstrapping resamples (c-index 0.701) and outperformed the preoperative imaging alone (c-index 0.660). The novel model predicted both 5-year overall survival (OS) (low risk 48.4% vs. high risk 18.4%) and 5-year disease-specific survival (DSS) (low risk 51.9% vs. high risk 25.2%, both p < 0.001). When applied among Nx patients, 5-year OS and DSS of low-risk Nx patients was comparable with that of N0 patients, while high-risk Nx patients had similar outcomes to N1 patients (p > 0.05). CONCLUSION: This tool may represent an opportunity to stratify prognosis of Nx patients and can help inform clinical decision-making prior to resection of ICC.

Nutrition Support in Liver Transplantation and Postoperative Recovery: The Effects of Vitamin D Level and Vitamin D Supplementation in Liver Transplantation.

Vitamin D plays an important role in the arena of liver transplantation. In addition to affecting skeletal health significantly, it also clinically exerts immune-modulatory properties. Vitamin D deficiency is one of the nutritional issues in the perioperative period of liver transplantation (LT). Although vitamin D deficiency is known to contribute to higher incidences of acute cellular rejection (ACR) and graft failure in other solid organ transplantation, such as kidneys and lungs, its role in LT is not well understood. The aim of this study was to investigate the clinical implication of vitamin D deficiency in LT. LT outcomes were reviewed in a retrospective cohort of 528 recipients during 2014-2019. In the pre-transplant period, 55% of patients were vitamin-D-deficient. The serum vitamin D level was correlated with the model for end-stage liver disease (MELD-Na) score. Vitamin D deficiency in the post-transplant period was associated with lower survival after LT, and the post-transplant supplementation of vitamin D was associated with a lower risk of ACR. The optimal vitamin D status and vitamin D supplementation in the post-transplant period may prolong survival and reduce ACR incidence.

A Novel Machine-Learning Approach to Predict Recurrence After Resection of Colorectal Liver Metastases.

BACKGROUND: Surgical resection of hepatic metastases remains the only potentially curative treatment option for patients with colorectal liver metastases (CRLM). Widely adopted prognostic tools may oversimplify the impact of model parameters relative to long-term outcomes. METHODS: Patients with CRLM who underwent a hepatectomy between 2001 and 2018 were identified in an international, multi-institutional database. Bootstrap resampling methodology used in tandem with multivariable mixed-effects logistic regression analysis was applied to construct a prediction model that was validated and compared with scores proposed by Fong and Vauthey. RESULTS: Among 1406 patients who underwent hepatic resection of CRLM, 842 (59.9%) had recurrence. The full model (based on age, sex, primary tumor location, T stage, receipt of chemotherapy before hepatectomy, lymph node metastases, number of metastatic lesions in the liver, size of the largest hepatic metastases, carcinoembryonic antigen [CEA] level and KRAS status) had good discriminative ability to predict 1-year (area under the receiver operating curve [AUC], 0.693; 95% confidence interval [CI], 0.684-0.704), 3-year (AUC, 0.669; 95% CI, 0.661-0.677), and 5-year (AUC, 0.669; 95% CI, 0.661-0.679) risk of recurrence. Studies analyzing validation cohorts demonstrated similar model performance, with excellent model accuracy. In contrast, the AUCs for the Fong and Vauthey scores to predict 1-year recurrence were only 0.527 (95% CI, 0.514-0.538) and 0.525 (95% CI, 0.514-0.533), respectively. Similar trends were noted for 3- and 5-year recurrence. CONCLUSION: The proposed clinical score, derived via machine learning, which included clinical characteristics and morphologic data, as well as information on KRAS status, accurately predicted recurrence after CRLM resection with good discrimination and prognostic ability.