As minimally invasive operations evolve, it is imperative to evaluate the advantages and risks involved. The aim of our study was to evaluate our institution's experience in incorporating a robotic platform for transhiatal esophagectomy (THE). Patients undergoing robotic THE were prospectively followed. Data are presented as median (mean ± SD). Forty-five patients were of 67 (67 ± 6.9) years and BMI 26 (27 ± 5.5) kg/m2. Nine per cent of operations were converted to “open,” but none in the last 25 operations. Operative duration of robotic THE was 334 (364 ± 108.8) minutes and estimated blood loss was 200 (217 ± 144.0) mL, which decreased with time ( P = 0.017). Length of stay was 8 (12 ± 11.1) days. Twenty per cent had respiratory failure requiring intubation that resolved, 4 per cent developed pneumonia, 11 per cent developed a surgical site infection, 2 per cent developed renal insufficiency, and 2 per cent developed a UTI. Two per cent (one patient) died within 30 days postoperatively, because of cardiac arrest. Our experience with robotic THE promotes robotic application because we endeavor to achieve high-level proficiency. With experience, we improved estimated blood loss and converted fewer transhiatal esophagectomies to “open.” Our length of hospital stay seems long but reflects the ill-health of patients, as does the variety of complications. Our data support the evolving future of THE, which will integrally include a robotic approach.

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Quantifying heterogeneity in Alzheimers disease (AD) risk is critical for individualized care and management. Recent attempts to assess AD heterogeneity have used structural (magnetic resonance imaging (MRI)-based) or functional (Ab-42; or tau) imaging, which focused on generating quartets of atrophy patterns and protein spreading, respectively. Here we present a computational framework that facilitated the identification of subtypes based on their risk of progression to AD. We used cerebrospinal fluid (CSF) measures of Ab-42; from the Alzheimers Disease Neuroimaging Initiative (ADNI) (n=544, discovery cohort) as well as the National Alzheimer's Coordinating Center (NACC) (n=508, validation cohort), and risk-stratified individuals with mild cognitive impairment (MCI) into quartiles (high-risk (H), intermediate-high risk (IH), intermediate-low risk (IL), and low-risk (L)). Patients were divided into subgroups utilizing patterns of brain atrophy found in each of these risk-stratified quartiles. We found H subjects to have a greater risk of AD progression compared to the other subtypes at 2- and 4-years in both the discovery and validation cohorts (ADNI: H subtype versus all others, p < 0.05 at 2 and 4 years; NACC: H vs. IL and LR at 2 years, p < 0.05, and a trend toward higher risk vs. IH, and p < 0.05 vs. IH, and L risk groups at 48 months with a trend toward lower survival vs. IL). Using MRI-based neural models that fused various deep neural networks with survival analysis, we then predicted MCI to AD conversion. We used these models to identify subtype-specific regions that demonstrate the largest levels of atrophy-related importance, which had minimal overlap (Average pairwise Jaccard Similarity in regions between the top 5 subtypes, 0.25+/-0.05 (+/- std)). Neuropathologic changes characteristic of AD were present across all subtypes in comparable proportions (Chi-square test, p>0.05 for differences in ADNC, n=31). Our risk-based approach to subtyping individuals provides an objective means to intervene and tailor care management strategies at early stages of cognitive decline.