Wieczorek, Mikolaj A.

Background: Identifying the projected incidence of hepatobiliary cancers and recognizing patient cohorts at increased risk can help develop targeted interventions and resource allocation. The expected incidence of subtypes of hepatobiliary cancers in different age groups, races, and genders remains unknown. Methods: Historical epidemiological data from the Surveillance, Epidemiology, and End Results (SEER) database was used to project future incidence of hepatobiliary malignancies in the United States and identify trends by age, race, and gender. Patients ≥18 years of age diagnosed with a hepatobiliary malignancy between 2001 and 2017 were included. US Census Bureau 2017 National Population projects provided the projected population from 2017 to 2029. Age–Period–Cohort forecasting model was used to estimate future births cohort-specific incidence. All analyses were completed using R Statistical Software. Results: We included 110381 historical patients diagnosed with a hepatobiliary malignancy between 2001 and 2017 with the following subtypes: hepatocellular cancer (HCC) (68%), intrahepatic cholangiocarcinoma (iCCA) (11.5%), gallbladder cancer (GC) (8%), extrahepatic cholangiocarcinoma (eCCA) (7.6%), and ampullary cancer (AC) (4%). Our models predict the incidence of HCC to double (2001 to 2029) from 4.5 to 9.03 per 100,000, with the most significant increase anticipated in patients 70–79 years of age. In contrast, incidence is expected to continue to decline among the Asian population. Incidence of iCCA is projected to increase, especially in the white population, with rates in 2029 double those in 2001 (2.13 vs. 0.88 per 100,000, respectively; p < 0.001). The incidence of GC among the black population is expected to increase. The incidence of eCCA is expected to significantly increase, especially among the Hispanic population, while that of AC will remain stable. Discussion: The overall incidence of hepatobiliary malignancies is expected to increase in the coming years, with certain groups at increased risk. These findings may help with resource allocation when considering screening, treatment, and research in the coming years.

BACKGROUND: The incidence and prognosis of right bundle branch block (RBBB) following transcatheter aortic valve replacement (TAVR) are unknown. Hence, we sought to characterize the incidence of post-TAVR RBBB and determine associated risks of permanent pacemaker (PPM) implantation and mortality. METHODS: All patients 18 years and above without preexisting RBBB or PPM who underwent TAVR at US Mayo Clinic sites and Mayo Clinic Health Systems from June 2010 to May 2021 were evaluated. Post-TAVR RBBB was defined as new-onset RBBB in the postimplantation period. The risks of PPM implantation (within 90 days) and mortality following TAVR were compared for patients with and without post-TAVR RBBB using Kaplan-Meier analysis and Cox proportional hazards modeling. The risks of PPM implantation (within 90 days) and mortality following TAVR were compared for patients with and without post-TAVR RBBB using Kaplan-Meier analysis and Cox proportional hazards modeling. RESULTS: Of 1992 patients, 15 (0.75%) experienced new RBBB post-TAVR. There was a higher degree of valve oversizing among patients with new RBBB post-TAVR versus those without (17.9% versus 10.0%; P =0.034). Ten patients (66.7%) with post-TAVR RBBB experienced high-grade atrioventricular block and underwent PPM implantation (median 1 day; Q1, 0.2 and Q3, 4), compared with 268/1977 (13.6%) without RBBB. Following propensity score adjustment for covariates (age, sex, balloon-expandable valve, annulus diameter, and valve oversizing), post-TAVR RBBB was significantly associated with PPM implantation (hazard ratio, 8.36 [95% CI, 4.19–16.7]; P <0.001). No statistically significant increase in mortality was seen with post-TAVR RBBB (hazard ratio, 0.83 [95% CI, 0.33–2.11]; P =0.69), adjusting for age and sex. CONCLUSIONS: Although infrequent, post-TAVR RBBB was associated with elevated PPM implantation risk. The mechanisms for its development and its clinical prognosis require further study.

616 Background: Cholangiocarcinoma (CCA) is an aggressive, heterogenous group of neoplasms that arise from the epithelial lining of bile ducts that have dismal poor prognosis. A recent epidemiological study in the United States demonstrated a steady increase in the incidence of cholangiocarcinoma from 2001 to 2017 with a predominant increase in the intrahepatic sub-type (iCCA). We sought to evaluate the future incidence of cholangiocarcinoma stratified based on the subtype, age, sex, and race, that could possibly help plan prevention, control, and treatment strategies for this cancer. Methods: We obtained histologically confirmed cases of cholangiocarcinoma (ICD-0-3 Site: C220, C21, and C240) from National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) 18 registries between 2000 to 2017 and predicted the incidence between the years 2018-2029. We applied age-period-cohort models to estimate future cholangiocarcinoma incidence rates (per 100, 000) and the estimated disease burden by multiplying incidence forecasts by corresponding US Census population projections. Results: A total of 12,737 and 8,395 patients with iCCA and extra-hepatic cholangiocarcinoma (eCCA) were included in the final analysis. The median age of iCCA cohort was 64 (range: 20-79) years and 46.4% were females whereas in eCCA the median age was 66 (range: 20-79) years and 43.8% were females. The overall observed age-adjusted incidence of iCCA between 2000-2017 was 1.07, which is predicted to increase to 2.13 by 2029 (99% increase) with a predominant increase in incidence rate observed in the age groups 70-79 and White males and females. Interestingly, incidence rate remained stable in Black and Asian males and in Asian females. Similarly, as compared to 2001-2017, the incidence of eCCA is projected to increase from 0.74 to 1.01 by 2029 (36.5% increase) with predominant increase noted in Black and Hispanic cohorts. In contrast, we noticed a relatively stable and decreasing incidence rate in White and Asian females, respectively. Conclusions: We predict an overall increased incidence of both iCCA and eCCA over the next decade especially in certain subgroups warranting the urgent need for preventive strategies. Further epidemiological studies are needed to determine the etiological factors contributing to predicted increase in incidence rate.

615 Background: Accounting for 90% of primary liver cancers, HCC is the fourth-most common cause of cancer-related deaths in the United States. While the vaccination against hepatitis B and awareness on hepatitis C transmission possibly decreased the incidence of HCC in Eastern World, there was an uptrend in the HCC incidence the Western World over the past few years possibly related to metabolic causes. With this changing paradigm of hepatocellular carcinoma, it is important to estimate its future burden stratified based on the age, sex, and race, that could possibly help plan prevention, control, and treatment strategies for this cancer. Methods: We obtained histologically confirmed cases of HCC (ICD-0-3 Site: C220 and 228; codes: 8170-8175) from National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) 18 registries between 2000 to 2017 and predicted the incidence between the years 2018-2029. We applied age-period-cohort models to estimate future HCC incidence rates (per 100, 000) and the estimated disease burden by multiplying incidence forecasts by corresponding US Census population projections. Results: A total of 76029 patients with HCC were included in the final analysis. The median age was 62 (range: 20-79) years with male predominance (70%). The overall observed age-adjusted incidence of HCC between 2000-2017 was 6.22, which is predicted to increase to 9.03 by 2029 with a predominant increase in incidence rate observed in the age groups 70-79 and White males. Interestingly, we noticed a progressive decrease in incidence rate in Asian cohort (both males and females) and Black males. Incidence rates by sub-groups based on the age, sex, and race are summarized in the Table. Conclusions: We predict an overall increased incidence of hepatocellular carcinoma over the next decade in the United States, predominantly in 70-79 years age group irrespective sex and race, with an exception to Asian cohort probably representing differential risk factors. Further epidemiological studies are needed to determine the etiological factors contributing to predicted increase in incidence rate.[Table: see text]

Abstract Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

Background: About 4 out of 10 fibromyalgia patients suffer from depression. The European Alliance of Associations for Rheumatology (EULAR) guidelines recommend using antidepressants to treat fibromyalgia. Objective: To determine predictors of improved outcomes following a multicomponent treatment program. Design: We designed this longitudinal treatment outcome study to evaluate the prevalence of depression symptoms in patients diagnosed with fibromyalgia at a tertiary care facility, and the impact of depression on functional outcomes after completing a multicomponent fibromyalgia treatment program. Setting: Tertiary care center. Patients: This study included 411 adult patients with fibromyalgia who completed a multicomponent treatment program for fibromyalgia. Expert physicians performed comprehensive evaluations following American College of Rheumatology (ACR) criteria to confirm fibromyalgia before referral to the program. Intervention: An intensive outpatient multicomponent treatment program consisting of 16 hours of cognitive behavioral strategies served as the intervention. Measurements: Functional status was assessed using the Fibromyalgia Impact Questionnaire Revised (FIQR). Depression was evaluated with the Center for Epidemiologic Study of Depression (CES-D) measure. Measures were administered prior to participation in the program and approximately 5 months following completion of the program. Results: The cohort had a high prevalence of depressive symptoms (73.2% had depression at admission). Higher depression scores at baseline predicted poorer outcomes following multi-component treatment. Effectively treated depression resulted in improved functioning at follow-up. Limitations: Findings limited to tertiary care center cohort of fibromyalgia patients. Patients did not undergo a structured clinical diagnostic interview to diagnose depression. Conclusions: The current data links depression to poorer outcomes in patients with fibromyalgia. Depression is an important modifiable factor in the management of fibromyalgia. Guidelines should reflect the importance of assessing and effectively treating depression at the time of diagnosis of fibromyalgia, to improve functional outcomes. Registration: Specific registry and specific study registration number—Institutional Review Board—(IRB# 19-000495). Funding Source: No funding.

ObjectiveTo validate a new baseline estimated glomerular filtration rate (NB‐GFR) formula in a cohort of robotic‐assisted partial nephrectomies (RAPN).MethodsNB‐GFR = 35 + preoperative GFR (× 0.65) – 18 (if radical nephrectomy) – age (× 0.25) + 3 (if tumor size >7 cm) – 2 (if diabetes). NB‐GFR was calculated in 464 consecutive RAPN from a single surgeon cohort. 143 patients were excluded secondary to insufficient eGFR follow up. We analyzed NB‐GFR accuracy utilizing the last observed eGFR 3–12 months post RAPN. Categorical variables were summarized with the frequency and percentage of patients. Numerical variables were summarized with the median, 25th percentile, and 75th percentile.ResultsThe mean difference between observed and predicted NB‐GFR was 4.6 ml/min/1.73m2 (95% CI −6.9 to 16.1 ml/min/1.73m2). There was a pattern of higher observed NB‐GFRs being underestimated by the NB‐GFR equation while lower observed NB‐GFRs were overestimated by the NB‐GFR equation. The NB‐GFR formula had a high level of accuracy with 98.8% of predicted NB‐GFRs falling within 30% of the observed NB‐GFR (95% CI 86.8% to 99.5%). The median and interquartile range of the difference between observed and predicted NB‐GFR was 3.9 ml/min/1.73m2 (IQR 0.7 to 8.2 ml/min/1.73m2). The sensitivity, specificity, positive predictive value, and negative predictive value for the ability of predicted NB‐GFR to identify those with an observed NB‐GFR <60 ml/min/1.73m2 after RAPN was 98%, 92%, 88%, and 99%, respectively.ConclusionThe NB‐GFR equation developed with partial and radical nephrectomy cohorts is accurate in predicting post‐operative eGFR 3–12 months following RAPN.

Abstract Objective: To analyze operating room (OR) efficiency by evaluating fixed and variable OR times for open (OPN) and robotic-assisted partial nephrectomies (RAPN).Methods: We analyzed consecutive OPN and RAPN performed by one surgeon over a 24-month period. All patients were placed in the lateral decubitus position and secured with a beanbag regardless of approach. Fixed (nonprocedural) OR times were prospectively collected and defined as: in room to anesthesia release time (IRAT), anesthesia release to cut time (ARCT), and close to wheels out time (CTWO). Variable OR time was procedural cut to close time (CTCT). Comparison of fixed and variable OR time points between OPN and RAPN were performed using the Wilcoxon rank sum test. Results: 146 RAPN and 31 OPN were evaluated from 2019-2020. Median IRAT was similar for RAPN versus OPN [20 min (IQR: 16-25) vs. 20 min (IQR: 16-26), P=0.57]. Median ARCT was longer for RAPN than it was for OPN [40 min (IQR: 36-46) vs. 34 min (IQR: 30-39), P<0.001]. Median CTWO was similar for OPN (12 min, IQR: 9-14) and RAPN (11 min, IQR: 7-15) (P=0.89). Median CTCT was longer for RAPN (202 min, IQR: 170-236) compared to OPN (164 min, IQR: 154-184) (P<0.001).Conclusions: In a single surgeon partial nephrectomy series with the same patient positioning, utilization of robotic technology was associated with longer surgeon operating time as well as less efficient fixed OR times, specifically ARCT.

Abstract IntroductionLiver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds.MethodsWe utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis.ResultsA 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted.ConclusionCNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.