Total robotic liver transplant: the final frontier of minimally invasive surgery.

The use of robotic surgery in transplantation is increasing; however, robotic liver transplantation (RLT) remains a challenging undertaking. To our knowledge, this is a report of the first RLT in North America and the first RLT using a whole graft from a deceased donor in the world. This paper describes the preparation leading to the RLT and the surgical technique of the operation. The operation was performed in a 62-year-old man with hepatitis C cirrhosis and hepatocellular carcinoma with a native Model for End-Stage Liver Disease score of 10. The total console time for the operation was 8 hours 30 minutes, and the transplant hepatectomy took 3 hours 30 minutes. Warm ischemia time was 77 minutes. Biliary reconstruction was performed in a primary end-to-end fashion and took 19 minutes to complete. The patient had an uneventful recovery without early allograft dysfunction or surgical complications and continues to do well after 6-months follow-up. This paper demonstrates the feasibility of this operation in highly selected patients with chronic liver disease. Additional experience is required to fully understand the role of RLT in the future of transplant surgery. Narrated video is available at https://youtu.be/TkjDwLryd3I.

Multi-view representation learning for tabular data integration using inter-feature relationships.

OBJECTIVE: An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and only partially overlapping features is a prerequisite to developing and testing robust, generalizable algorithms, especially in healthcare. This integrating is usually resolved using meta-data such as feature names, which may be unavailable or ambiguous. Our goal is to design methods that create a mapping between structured tabular datasets derived from electronic health records independent of meta-data. METHODS: We evaluate methods in the challenging case of numeric features without reliable and distinctive univariate summaries, such as nearly Gaussian and binary features. We assume that a small set of features are a priori mapped between two datasets, which share unknown identical features and possibly many unrelated features. Inter-feature relationships are the main source of identification which we expect. We compare the performance of contrastive learning methods for feature representations, novel partial auto-encoders, mutual-information graph optimizers, and simple statistical baselines on simulated data, public datasets, the MIMIC-III medical-record changeover, and perioperative records from before and after a medical-record system change. Performance was evaluated using both mapping of identical features and reconstruction accuracy of examples in the format of the other dataset. RESULTS: Contrastive learning-based methods overall performed the best, often substantially beating the literature baseline in matching and reconstruction, especially in the more challenging real data experiments. Partial auto-encoder methods showed on-par matching with contrastive methods in all synthetic and some real datasets, along with good reconstruction. However, the statistical method we created performed reasonably well in many cases, with much less dependence on hyperparameter tuning. When validating feature match output in the EHR dataset we found that some mistakes were actually a surrogate or related feature as reviewed by two subject matter experts. CONCLUSION: In simulation studies and real-world examples, we find that inter-feature relationships are effective at identifying matching or closely related features across tabular datasets when meta-data is not available. Decoder architectures are also reasonably effective at imputing features without an exact match.

User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications.

BACKGROUND: Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. METHODS: Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis. RESULTS: During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5). CONCLUSIONS: Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.

Abnormal preoperative cognitive screening in aged surgical patients: a retrospective cohort analysis.

BACKGROUND: Preoperative cognitive dysfunction has been associated with adverse postoperative outcomes. There are limited data characterising the epidemiology of preoperative cognitive dysfunction in older surgical patients. METHODS: This retrospective cohort included all patients ≥65 yr old seen at the Washington University preoperative clinic between January 2013 and June 2018. Cognitive screening was performed using the Short-Blessed Test (SBT) and Eight-Item Interview to Differentiate Aging and Dementia (AD8) screen. The primary outcome of abnormal cognitive screening was defined as SBT score ≥5 or AD8 score ≥2. Multivariable logistic regression was used to identify associated factors. RESULTS: Overall, 21 666 patients ≥65 yr old completed screening during the study period; 23.5% (n=5099) of cognitive screens were abnormal. Abnormal cognitive screening was associated with increasing age, decreasing BMI, male sex, non-Caucasian race, decreased functional independence, and decreased metabolic functional capacity. Patients with a history of stroke or transient ischaemic attack, chronic obstructive pulmonary disease, diabetes mellitus, hepatic cirrhosis, and heavy alcohol use were also more likely to have an abnormal cognitive screen. Predictive modelling showed no combination of patient factors was able to reliably identify patients who had a <10% probability of abnormal cognitive screening. CONCLUSIONS: Routine preoperative cognitive screening of unselected aged surgical patients often revealed deficits consistent with cognitive impairment or dementia. Such deficits were associated with increased age, decreased function, decreased BMI, and several common medical comorbidities. Further research is necessary to characterise the clinical implications of preoperative cognitive dysfunction and identify interventions that may reduce related postoperative complications.

Effect of electroencephalogram-guided anaesthesia administration on 1-yr mortality: follow-up of a randomised clinical trial.

BACKGROUND: Intraoperative EEG suppression duration has been associated with postoperative delirium and mortality. In a clinical trial testing anaesthesia titration to avoid EEG suppression, the intervention did not decrease the incidence of postoperative delirium, but was associated with reduced 30-day mortality. The present study evaluated whether the EEG-guided anaesthesia intervention was also associated with reduced 1-yr mortality. METHODS: This manuscript reports 1 yr follow-up of subjects from a single-centre RCT, including a post hoc secondary outcome (1-yr mortality) in addition to pre-specified secondary outcomes. The trial included subjects aged 60 yr or older undergoing surgery with general anaesthesia between January 2015 and May 2018. Patients were randomised to receive EEG-guided anaesthesia or usual care. The previously reported primary outcome was postoperative delirium. The outcome of the current study was all-cause 1-yr mortality. RESULTS: Of the 1232 subjects enrolled, 614 subjects were randomised to EEG-guided anaesthesia and 618 subjects to usual care. One-year mortality was 57/591 (9.6%) in the guided group and 62/601 (10.3%) in the usual-care group. No significant difference in mortality was observed (adjusted absolute risk difference, -0.7%; 99.5% confidence interval, -5.8% to 4.3%; P=0.68). CONCLUSIONS: An EEG-guided anaesthesia intervention aiming to decrease duration of EEG suppression during surgery did not significantly decrease 1-yr mortality. These findings, in the context of other studies, do not provide supportive evidence for EEG-guided anaesthesia to prevent intermediate term postoperative death. CLINICAL TRIAL REGISTRATION: NCT02241655.

Preoperative Cognitive Abnormality, Intraoperative Electroencephalogram Suppression, and Postoperative Delirium: A Mediation Analysis.

BACKGROUND: Postoperative delirium is a common complication that hinders recovery after surgery. Intraoperative electroencephalogram suppression has been linked to postoperative delirium, but it is unknown if this relationship is causal or if electroencephalogram suppression is merely a marker of underlying cognitive abnormalities. The hypothesis of this study was that intraoperative electroencephalogram suppression mediates a nonzero portion of the effect between preoperative abnormal cognition and postoperative delirium. METHODS: This is a prespecified secondary analysis of the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) randomized trial, which enrolled patients age 60 yr or older undergoing surgery with general anesthesia at a single academic medical center between January 2015 and May 2018. Patients were randomized to electroencephalogram-guided anesthesia or usual care. Preoperative abnormal cognition was defined as a composite of previous delirium, Short Blessed Test cognitive score greater than 4 points, or Eight Item Interview to Differentiate Aging and Dementia score greater than 1 point. Duration of intraoperative electroencephalogram suppression was defined as number of minutes with suppression ratio greater than 1%. Postoperative delirium was detected via Confusion Assessment Method or chart review on postoperative days 1 to 5. RESULTS: Among 1,113 patients, 430 patients showed evidence of preoperative abnormal cognition. These patients had an increased incidence of postoperative delirium (151 of 430 [35%] vs.123 of 683 [18%], P < 0.001). Of this 17.2% total effect size (99.5% CI, 9.3 to 25.1%), an absolute 2.4% (99.5% CI, 0.6 to 4.8%) was an indirect effect mediated by electroencephalogram suppression, while an absolute 14.8% (99.5% CI, 7.2 to 22.5%) was a direct effect of preoperative abnormal cognition. Randomization to electroencephalogram-guided anesthesia did not change the mediated effect size (P = 0.078 for moderation). CONCLUSIONS: A small portion of the total effect of preoperative abnormal cognition on postoperative delirium was mediated by electroencephalogram suppression. Study precision was too low to determine if the intervention changed the mediated effect.

Deep-learning model for predicting 30-day postoperative mortality.

BACKGROUND: Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. METHODS: We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data. RESULTS: Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835-0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790-0.860), random forest (0.848; 95% CI: 0.815-0.882), support vector machine (0.836; 95% CI: 0.802-870), and logistic regression (0.837; 95% CI: 0.803-0.871). CONCLUSIONS: A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.

Radical Prostatectomy, External Beam Radiotherapy, or External Beam Radiotherapy With Brachytherapy Boost and Disease Progression and Mortality in Patients With Gleason Score 9-10 Prostate Cancer.

Importance: The optimal treatment for Gleason score 9-10 prostate cancer is unknown. Objective: To compare clinical outcomes of patients with Gleason score 9-10 prostate cancer after definitive treatment. Design, Setting, and Participants: Retrospective cohort study in 12 tertiary centers (11 in the United States, 1 in Norway), with 1809 patients treated between 2000 and 2013. Exposures: Radical prostatectomy (RP), external beam radiotherapy (EBRT) with androgen deprivation therapy, or EBRT plus brachytherapy boost (EBRT+BT) with androgen deprivation therapy. Main Outcomes and Measures: The primary outcome was prostate cancer-specific mortality; distant metastasis-free survival and overall survival were secondary outcomes. Results: Of 1809 men, 639 underwent RP, 734 EBRT, and 436 EBRT+BT. Median ages were 61, 67.7, and 67.5 years; median follow-up was 4.2, 5.1, and 6.3 years, respectively. By 10 years, 91 RP, 186 EBRT, and 90 EBRT+BT patients had died. Adjusted 5-year prostate cancer-specific mortality rates were RP, 12% (95% CI, 8%-17%); EBRT, 13% (95% CI, 8%-19%); and EBRT+BT, 3% (95% CI, 1%-5%). EBRT+BT was associated with significantly lower prostate cancer-specific mortality than either RP or EBRT (cause-specific HRs of 0.38 [95% CI, 0.21-0.68] and 0.41 [95% CI, 0.24-0.71]). Adjusted 5-year incidence rates of distant metastasis were RP, 24% (95% CI, 19%-30%); EBRT, 24% (95% CI, 20%-28%); and EBRT+BT, 8% (95% CI, 5%-11%). EBRT+BT was associated with a significantly lower rate of distant metastasis (propensity-score-adjusted cause-specific HRs of 0.27 [95% CI, 0.17-0.43] for RP and 0.30 [95% CI, 0.19-0.47] for EBRT). Adjusted 7.5-year all-cause mortality rates were RP, 17% (95% CI, 11%-23%); EBRT, 18% (95% CI, 14%-24%); and EBRT+BT, 10% (95% CI, 7%-13%). Within the first 7.5 years of follow-up, EBRT+BT was associated with significantly lower all-cause mortality (cause-specific HRs of 0.66 [95% CI, 0.46-0.96] for RP and 0.61 [95% CI, 0.45-0.84] for EBRT). After the first 7.5 years, the corresponding HRs were 1.16 (95% CI, 0.70-1.92) and 0.87 (95% CI, 0.57-1.32). No significant differences in prostate cancer-specific mortality, distant metastasis, or all-cause mortality (≤7.5 and >7.5 years) were found between men treated with EBRT or RP (cause-specific HRs of 0.92 [95% CI, 0.67-1.26], 0.90 [95% CI, 0.70-1.14], 1.07 [95% CI, 0.80-1.44], and 1.34 [95% CI, 0.85-2.11]). Conclusions and Relevance: Among patients with Gleason score 9-10 prostate cancer, treatment with EBRT+BT with androgen deprivation therapy was associated with significantly better prostate cancer-specific mortality and longer time to distant metastasis compared with EBRT with androgen deprivation therapy or with RP.

Treatment trends for patients with brain metastases: Does practice reflect the data?

BACKGROUND: Published guidelines regarding the optimal treatment strategies for brain metastases focus on patients with ≤3 lesions. As delivery techniques for stereotactic radiosurgery (SRS) improve, radiation oncologists are increasingly using it for patients with >3 metastases. In the current study, the authors sought to characterize practice patterns among practitioners to identify areas of controversy. METHODS: A survey of practicing radiation oncologists was distributed via e-mail. Responses were collected from April 1 to May 5, 2016. Survey data were analyzed. RESULTS: A total of 711 currently practicing radiation oncologists responded, for a response rate of 12.5%. Specialists in central nervous system tumors (CNS specialists) were more likely to treat higher numbers of patients with brain metastases with SRS. There was a significant difference in the optimal "cutoff number" used when deciding how many lesions to treat with SRS versus whole-brain radiotherapy. Cutoff numbers were significantly higher for high-volume CNS specialists (≥10 patients/month) than for either low-volume CNS specialists (5-9 patients/month) or high-volume, non-CNS specialists (8.1 vs 5.6 and 5.1, respectively; P<.001). A majority of respondents (56%) identified patients with 4 to 6 brain metastases as being the most challenging patients to treat. CONCLUSIONS: To the authors' knowledge, there appears to be no consensus regarding the optimal treatment strategy among patients with >3 brain metastases, and practice patterns are heterogeneous. Radiation oncologists, especially high-volume CNS specialists, are treating significantly more brain metastases with SRS than what currently is recommended by published consensus guidelines. Providers struggle with patients with a moderate intracranial disease burden. Further prospective studies are needed to support these practice patterns and guide decision making. Cancer 2017;123:2274-2282. © 2017 American Cancer Society.

Ultrasensitive prostate specific antigen after prostatectomy reliably identifies patients requiring postoperative radiotherapy.

PURPOSE: Integrating ultrasensitive prostate specific antigen with surveillance in patients at high risk after radical prostatectomy potentially optimizes treatment by correctly identifying recurrence, promoting an early salvage strategy and minimizing overtreatment. We tested the power of postoperative ultrasensitive prostate specific antigen to identify eventual biochemical failure. MATERIALS AND METHODS: We identified 247 patients at high risk with a median followup of 44 months who underwent radical prostatectomy from 1991 to 2013. Each patient had extraprostatic extension and/or a positive margin. Surgical technique, initial prostate specific antigen, pathology findings and postoperative prostate specific antigen were analyzed. The ultrasensitive prostate specific antigen assay threshold was 0.01 ng/ml. Conventional biochemical relapse was defined as prostate specific antigen 0.2 ng/ml or greater. Kaplan-Meier and Cox multivariate analyses were done to compare the rates of ultrasensitive prostate specific antigen recurrence and conventional biochemical relapse. RESULTS: Sensitivity analysis revealed that ultrasensitive prostate specific antigen 0.03 ng/ml or greater was the optimal threshold to identify recurrence. A first postoperative ultrasensitive value of 0.03 ng/ml or greater, Gleason grade, T stage, initial prostate specific antigen and margin status predicted conventional biochemical relapse. On multivariate analysis only a first postoperative ultrasensitive value of 0.03 ng/ml or greater, Gleason grade and T stage independently predicted conventional biochemical relapse. First postoperative ultrasensitive prostate specific antigen 0.03 ng/ml or greater conferred the highest risk (HR 8.5, p < 0.0001) and identified conventional biochemical relapse with greater sensitivity than undetectable first conventional prostate specific antigen (70% vs 46%). Any postoperative prostate specific antigen 0.03 ng/ml or greater captured all failures missed by the first postoperative value (100% sensitivity) with accuracy (96% specificity). Defining failure at an ultrasensitive value of 0.03 ng/ml or greater yielded a median lead time advantage of 18 months (mean 24) over the conventional definition of prostate specific antigen 0.2 ng/ml or greater. CONCLUSIONS: Ultrasensitive prostate specific antigen 0.03 ng/ml or greater is an independent factor that identifies biochemical relapse more accurately than any traditional risk factors and confers a significant lead time advantage. This factor enables critical decisions on the timing of and indication for postoperative radiotherapy in patients at high risk after radical prostatectomy.

Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial.

Background: Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown. Methods: This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operating characteristic curve (AUROC) for the clinician predictions was determined. Results: Among 5,071 patient cases reviewed by 89 clinicians, the observed incidence was 2% for postoperative death and 11% for acute kidney injury. Clinician predictions agreed with the models more strongly in the assisted versus unassisted group (weighted kappa 0.75 versus 0.62 for death [difference 0.13, 95%CI 0.10-0.17] and 0.79 versus 0.54 for kidney injury [difference 0.25, 95%CI 0.21-0.29]). Clinicians predicted death with AUROC of 0.793 in the assisted group and 0.780 in the unassisted group (difference 0.013, 95%CI -0.070 to 0.097). Clinicians predicted kidney injury with AUROC of 0.734 in the assisted group and 0.688 in the unassisted group (difference 0.046, 95%CI -0.003 to 0.091). Conclusions: Although there was evidence that the models influenced clinician predictions, clinician performance was not statistically significantly different with and without machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. Trial Registration: ClinicalTrials.gov NCT05042804.

Effect of Telemedicine Support for Intraoperative Anaesthesia Care on Postoperative Outcomes: The TECTONICS Randomised Clinical Trial.

Background: Novel applications of telemedicine can improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied. Methods: This single centre randomised clinical trial ( clinicaltrials.gov NCT03923699 ) of unselected adult surgical patients was conducted between July 1, 2019 and January 31, 2023. Patients received usual care or decision support from a telemedicine service, the Anesthesiology Control Tower (ACT). The ACT provided real-time recommendations to intraoperative anaesthesia clinicians based on case reviews, machine-learning forecasting, and physiologic alerts. ORs were randomised 1:1. Co-primary outcomes of 30-day all-cause mortality, respiratory failure, acute kidney injury (AKI), and delirium were analysed as intention-to-treat. Results: The trial completed planned enrolment with 71927 surgeries (35956 ACT; 35971 usual care). After multiple testing correction, there was no significant effect of the ACT vs. usual care on 30-day mortality [641/35956 (1.8%) vs 638/35971 (1.8%), risk difference 0.0% (95% CI -0.2% to 0.3%), p=0.96], respiratory failure [1089/34613 (3.1%) vs 1112/34619 (3.2%), risk difference -0.1% (95% CI -0.4% to 0.3%), p=0.96], AKI [2357/33897 (7%) vs 2391/33795 (7.1%), risk difference -0.1% (-0.6% to 0.4%), p=0.96], or delirium [1283/3928 (32.7%) vs 1279/3989 (32.1%), risk difference 0.6% (-2.0% to 3.2%), p=0.96]. There were no significant differences in secondary outcomes or in sensitivity analyses. Conclusions: In this large RCT of a novel application of telemedicine-based remote monitoring and decision support using real-time alerts and case reviews, we found no significant differences in postoperative outcomes. Large-scale intraoperative telemedicine is feasible, and we suggest future avenues where it may be impactful.

Social Vulnerability and Surgery Outcomes: A Cross-sectional Analysis.

Background: Post-operative complications present a challenge to the healthcare system due to the high unpredictability of their incidence. However, the socioeconomic factors that relate to postoperative complications are still unclear as they can be heterogeneous based on communities, types of surgical services, and sex and gender. Methods: In this study, we conducted a large population cross-sectional analysis of social vulnerability and the odds of various post-surgical complications. We built statistical logistic regression models of postsurgical complications with social vulnerability index as the independent variable along with sex interaction. Results: We found that social vulnerability was associated with abnormal heart rhythm with socioeconomic status and housing status being the main association factors. We also found associations of the interaction of social vulnerability and female sex with an increase in odds of heart attack and surgical wound infection. Conclusions: Our results indicate that social vulnerability measures such as socioeconomic status and housing conditions could be related to health outcomes. This suggests that the domain of preventive medicine should place social vulnerability as a priority to achieve its goals.

Masking the transmembrane region of the amyloid ß precursor protein as a safe means to lower amyloid ß production.

Introduction: Reducing brain levels of both soluble and insoluble forms of amyloid beta (Aß) remains the primary goal of most therapies that target Alzheimer's disease (AD). However, no treatment has so far resulted in patient benefit, and clinical trials of the most promising drug candidates have generally failed due to significant adverse effects. This highlights the need for safer and more selective ways to target and modulate Aß biogenesis. Methods: Peptide technology has advanced to allow reliable synthesis, purification, and delivery of once-challenging hydrophobic sequences. This is opening up new routes to target membrane processes associated with disease. Here we deploy a combination of atomic detail molecular dynamics (MD) simulations, living-cell Förster resonance energy transfer (FRET), and in vitro assays to elucidate the atomic-detail dynamics, molecular mechanisms, and cellular activity and selectivity of a membrane-active peptide that targets the Aß precursor protein (APP). Results: We demonstrate that Aß biogenesis can be downregulated selectively using an APP occlusion peptide (APPOP). APPOP inhibits Aß production in a dose-dependent manner, with a mean inhibitory concentration (IC50) of 450 nM toward exogenous APP and 50 nM toward endogenous APP in primary rat cortical neuronal cultures. APPOP does not impact the γ-secretase cleavage of Notch-1, or exhibit toxicity toward cultured primary rat neurons, suggesting that it selectively shields APP from proteolysis. Discussion: Drugs targeting AD need to be given early and for very long periods to prevent the onset of clinical symptoms. This necessitates being able to target Aß production precisely and without affecting the activity of key cellular enzymes such as γ-secretase for other substrates. Peptides offer a powerful way for targeting key pathways precisely, thereby reducing the risk of adverse effects. Here we show that protecting APP from proteolytic processing offers a promising route to safely and specifically lower Aß burden. In particular, we show that the amyloid pathway can be targeted directly and specificically. This reduces the risk of off-target effects and paves the way for a safe prophylactic treatment.

An Intraoperative Telemedicine Program to Improve Perioperative Quality Measures: The ACTFAST-3 Randomized Clinical Trial.

Importance: Telemedicine for clinical decision support has been adopted in many health care settings, but its utility in improving intraoperative care has not been assessed. Objective: To pilot the implementation of a real-time intraoperative telemedicine decision support program and evaluate whether it reduces postoperative hypothermia and hyperglycemia as well as other quality of care measures. Design, Setting, and Participants: This single-center pilot randomized clinical trial (Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments [ACTFAST-3]) was conducted from April 3, 2017, to June 30, 2019, at a large academic medical center in the US. A total of 26 254 adult surgical patients were randomized to receive either usual intraoperative care (control group; n = 12 980) or usual care augmented by telemedicine decision support (intervention group; n = 13 274). Data were initially analyzed from April 22 to May 19, 2021, with updates in November 2022 and February 2023. Intervention: Patients received either usual care (medical direction from the anesthesia care team) or intraoperative anesthesia care monitored and augmented by decision support from the Anesthesiology Control Tower (ACT), a real-time, live telemedicine intervention. The ACT incorporated remote monitoring of operating rooms by a team of anesthesia clinicians with customized analysis software. The ACT reviewed alerts and electronic health record data to inform recommendations to operating room clinicians. Main Outcomes and Measures: The primary outcomes were avoidance of postoperative hypothermia (defined as the proportion of patients with a final recorded intraoperative core temperature >36 °C) and hyperglycemia (defined as the proportion of patients with diabetes who had a blood glucose level ≤180 mg/dL on arrival to the postanesthesia recovery area). Secondary outcomes included intraoperative hypotension, temperature monitoring, timely antibiotic redosing, intraoperative glucose evaluation and management, neuromuscular blockade documentation, ventilator management, and volatile anesthetic overuse. Results: Among 26 254 participants, 13 393 (51.0%) were female and 20 169 (76.8%) were White, with a median (IQR) age of 60 (47-69) years. There was no treatment effect on avoidance of hyperglycemia (7445 of 8676 patients [85.8%] in the intervention group vs 7559 of 8815 [85.8%] in the control group; rate ratio [RR], 1.00; 95% CI, 0.99-1.01) or hypothermia (7602 of 11 447 patients [66.4%] in the intervention group vs 7783 of 11 672 [66.7.%] in the control group; RR, 1.00; 95% CI, 0.97-1.02). Intraoperative glucose measurement was more common among patients with diabetes in the intervention group (RR, 1.07; 95% CI, 1.01-1.15), but other secondary outcomes were not significantly different. Conclusions and Relevance: In this randomized clinical trial, anesthesia care quality measures did not differ between groups, with high confidence in the findings. These results suggest that the intervention did not affect the targeted care practices. Further streamlining of clinical decision support and workflows may help the intraoperative telemedicine program achieve improvement in targeted clinical measures. Trial Registration: ClinicalTrials.gov Identifier: NCT02830126.

MR spectroscopic imaging and diffusion-weighted imaging of prostate cancer with Gleason scores.

PURPOSE: To investigate functional changes in prostate cancer patients with three pathologically proven different Gleason scores (GS) (3+3, 3+4, and 4+3) using magnetic resonance spectroscopic imaging (MRSI) and diffusion-weighted imaging (DWI). MATERIALS AND METHODS: In this study MRSI and DWI data were acquired in 41 prostate cancer patients using a 1.5T MRI scanner with a body matrix combined with an endorectal coil. The metabolite ratios of (Cho+Cr)/Cit were calculated from the peak integrals of total choline (Cho), creatine (Cr), and citrate (Cit) in MRSI. Apparent diffusion coefficient (ADC) values were derived from DWI for three groups of Gleason scores. The sensitivity and specificity of MRSI and DWI in patients were calculated using receiver operating characteristic curve (ROC) analysis. RESULTS: The mean and standard deviation of (Cho+Cr)/Cit ratios of GS 3+3, GS 3+4, and GS 4+3 were: 0.44 ± 0.02, 0.56 ± 0.06, and 0.88 ± 0.11, respectively. For the DWI, the mean and standard deviation of ADC values in GS 3+3, GS 3+4, and GS 4+3 were: 1.13 ± 0.11, 0.97 ± 0.10, and 0.83 ± 0.08 mm(2) /sec, respectively. Statistical significances were observed between the GS and metabolite ratio as well as ADC values and GS. CONCLUSION: Combined MRSI and DWI helps identify the presence and the proportion of aggressive cancer (ie, Gleason grade 4) that might not be apparent on biopsy sampling. This information can guide subsequent rebiopsy management, especially for active surveillance programs.