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.

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.

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.

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.

Association of a Perioperative Multicomponent Fall Prevention Intervention With Falls and Quality of Life After Elective Inpatient Surgical Procedures.

Importance: Falls after elective inpatient surgical procedures are common and have physical, emotional, and financial consequences. Close interactions between patients and health care teams before and after surgical procedures may offer opportunities to address modifiable risk factors associated with falls. Objective: To assess whether a multicomponent intervention that incorporates education, home medication review, and home safety assessment is associated with reductions in the incidence of falls after elective inpatient surgical procedures. Design, Setting, and Participants: This prospective propensity score-matched cohort study was a prespecified secondary analysis of data from the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) randomized clinical trial, which was conducted at a single academic medical center between January 16, 2015, and May 7, 2018. Patients in the intervention group of the present study were enrolled in either arm of the ENGAGES clinical trial. Patients in the control group were selected from the Systematic Assessment and Targeted Improvement of Services Following Yearly Surgical Outcomes Surveys prospective observational cohort study, which created a registry of patient-reported postoperative outcomes at the same single center. The propensity score-matched cohort in the present study included 1396 patients (698 pairs) selected from a pool of 2013 eligible patients. All patients underwent elective surgical procedures with general anesthesia and had a hospital stay of 2 or more days. Data were analyzed from January 2, 2020, to January 11, 2022. Interventions: The multicomponent safety intervention (offered to all patients in the ENGAGES clinical trial) included patient education on fall prevention techniques, home medication review by a geriatric psychiatrist (with communication of recommended changes to the surgeon), a self-administered home safety assessment, and targeted occupational therapy home visits with home hazard removal (offered to patients with a preoperative history of falls). Main Outcomes and Measures: The primary outcome was patient-reported falls within 1 year after an elective inpatient surgical procedure. The secondary outcome was quality of life 1 year after an elective surgical procedure, which was measured using the physical and mental composite summary scores on the Veterans RAND 12-item health survey (score range, 0-100 points, with 0 indicating lowest quality of life and 100 indicating highest quality of life). Results: Among 1396 patients, the median age was 69 years (IQR, 64-75 years), and 739 patients (52.9%) were male. With regard to race, 5 patients (0.4%) were Asian, 97 (6.9%) were Black or African American, 2 (0.1%) were Native Hawaiian or Pacific Islander, 1237 (88.6%) were White, 3 (0.2%) were of other race, and 52 (3.7%) were of unknown race; with regard to ethnicity, 12 patients (0.9%) were Hispanic or Latino, 1335 (95.6%) were non-Hispanic or non-Latino, and 49 (3.5%) were of unknown ethnicity. Adherence to individual intervention components was modest (from 22.9% for completion of the self-administered home safety assessment to 28.2% for implementation of the geriatric psychiatrist's recommended medication changes). Falls within 1 year after surgical procedures were reported by 228 of 698 patients (32.7%) in the intervention group and 225 of 698 patients (32.2%) in the control group. No significant difference was found in falls between the 2 groups (standardized risk difference, 0.4%; 95% CI, -4.5% to 5.3%). After adjusting for preoperative quality of life, patients in the intervention group had higher physical composite summary scores (3.8 points; 95% CI, 2.4-5.1 points) and higher mental composite summary scores (5.7 points; 95% CI, 4.7-6.7 points) at 1 year compared with patients in the control group. Conclusions and Relevance: In this cohort study, a multicomponent safety intervention was not associated with reductions in falls within the first year after an elective surgical procedure; however, an increase in quality of life at 1 year was observed. These results suggest a need for other interventions, such as those designed to increase adherence, to lower the incidence of falls after surgical procedures.

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.

Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications.

Importance: Postoperative complications can significantly impact perioperative care management and planning. Objectives: To assess machine learning (ML) models for predicting postoperative complications using independent and combined preoperative and intraoperative data and their clinically meaningful model-agnostic interpretations. Design, Setting, and Participants: This retrospective cohort study assessed 111 888 operations performed on adults at a single academic medical center from June 1, 2012, to August 31, 2016, with a mean duration of follow-up based on the length of postoperative hospital stay less than 7 days. Data analysis was performed from February 1 to September 31, 2020. Main Outcomes and Measures: Outcomes included 5 postoperative complications: acute kidney injury (AKI), delirium, deep vein thrombosis (DVT), pulmonary embolism (PE), and pneumonia. Patient and clinical characteristics available preoperatively, intraoperatively, and a combination of both were used as inputs for 5 candidate ML models: logistic regression, support vector machine, random forest, gradient boosting tree (GBT), and deep neural network (DNN). Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Model interpretations were generated using Shapley Additive Explanations by transforming model features into clinical variables and representing them as patient-specific visualizations. Results: A total of 111 888 patients (mean [SD] age, 54.4 [16.8] years; 56 915 [50.9%] female; 82 533 [73.8%] White) were included in this study. The best-performing model for each complication combined the preoperative and intraoperative data with the following AUROCs: pneumonia (GBT), 0.905 (95% CI, 0.903-0.907); AKI (GBT), 0.848 (95% CI, 0.846-0.851); DVT (GBT), 0.881 (95% CI, 0.878-0.884); PE (DNN), 0.831 (95% CI, 0.824-0.839); and delirium (GBT), 0.762 (95% CI, 0.759-0.765). Performance of models that used only preoperative data or only intraoperative data was marginally lower than that of models that used combined data. When adding variables with missing data as input, AUROCs increased from 0.588 to 0.905 for pneumonia, 0.579 to 0.848 for AKI, 0.574 to 0.881 for DVT, 0.5 to 0.831 for PE, and 0.6 to 0.762 for delirium. The Shapley Additive Explanations analysis generated model-agnostic interpretation that illustrated significant clinical contributors associated with risks of postoperative complications. Conclusions and Relevance: The ML models for predicting postoperative complications with model-agnostic interpretation offer opportunities for integrating risk predictions for clinical decision support. Such real-time clinical decision support can mitigate patient risks and help in anticipatory management for perioperative contingency planning.

Ascertaining Design Requirements for Postoperative Care Transition Interventions.

BACKGROUND: Handoffs or care transitions from the operating room (OR) to intensive care unit (ICU) are fragmented and vulnerable to communication errors. Although protocols and checklists for standardization help reduce errors, such interventions suffer from limited sustainability. An unexplored aspect is the potential role of developing personalized postoperative transition interventions using artificial intelligence (AI)-generated risks. OBJECTIVES: This study was aimed to (1) identify factors affecting sustainability of handoff standardization, (2) utilize a human-centered approach to develop design ideas and prototyping requirements for a sustainable handoff intervention, and (3) explore the potential role for AI risk assessment during handoffs. METHODS: We conducted four design workshops with 24 participants representing OR and ICU teams at a large medical academic center. Data collection phases were (1) open-ended questions, (2) closed card sorting of handoff information elements, and (3) scenario-based design ideation and prototyping for a handoff intervention. Data were analyzed using thematic analysis. Card sorts were further tallied to characterize handoff information elements as core, flexible, or unnecessary. RESULTS: Limited protocol awareness among clinicians and lack of an interdisciplinary electronic health record (EHR)-integrated handoff intervention prevented long-term sustainability of handoff standardization. Clinicians argued for a handoff intervention comprised of core elements (included for all patients) and flexible elements (tailored by patient condition and risks). They also identified unnecessary elements that could be omitted during handoffs. Similarities and differences in handoff intervention requirements among physicians and nurses were noted; in particular, clinicians expressed divergent views on the role of AI-generated postoperative risks. CONCLUSION: Current postoperative handoff interventions focus largely on standardization of information transfer and handoff processes. Our design approach allowed us to visualize accurate models of user expectations for effective interdisciplinary communication. Insights from this study point toward EHR-integrated, "flexibly standardized" care transition interventions that can automatically generate a patient-centered summary and risk-based report.

Underutilization of Androgen Deprivation Therapy with External Beam Radiotherapy in Men with High-grade Prostate Cancer.

Multiple randomized trials have shown a survival benefit to long durations of androgen deprivation therapy (ADT) in patients with Gleason grade group (GG) 4-5 (ie, Gleason score 8-10) prostate cancer (PCa) undergoing definitive external beam radiotherapy (EBRT). We conducted a population-based retrospective study utilizing the complete Surveillance, Epidemiology, and End Results (SEER)-Medicare-linked database from 2008 to 2011, extracting PCa patients of non-Hispanic white (NHW) and African-American (AA) race diagnosed with GG 4-5PCa who received EBRT with or without concomitant ADT. Of 961 patients receiving definitive EBRT, 225 (23.4%) received no ADT, 297 (30.9%) received 1-6mo of ADT, 313 (32.6) received 7-23mo of ADT, and 126 (13.1%) received ≥24mo of ADT. On multinomial logistic regression after inverse probability treatment weighting to balance for differences in other covariates, AA men still had significantly lower odds of receiving 1-6mo of ADT versus no ADT compared with NHW men (odds ratios 0.519 [95% confidence interval, 0.384-0.700]). In conclusion, long-duration ADT is underutilized, with nearly 90% of patients with GG 4-5PCa receiving <24mo of concomitant ADT, and AA men are less likely to receive ADT than NHW men. PATIENT SUMMARY: In this report, we examined the utilization of concomitant androgen deprivation therapy (ADT) among men with high-grade prostate cancer undergoing definitive external beam radiotherapy. We found that long-duration ADT was underutilized overall; moreover, African-American men were less likely to receive concomitant ADT than non-Hispanic white men.

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.

The intraprostatic immune environment after stereotactic body radiotherapy is dominated by myeloid cells.

BACKGROUND: Hundreds of ongoing clinical trials combine radiation therapy, mostly delivered as stereotactic body radiotherapy (SBRT), with immune checkpoint blockade. However, our understanding of the effect of radiotherapy on the intratumoral immune balance is inadequate, hindering the optimal design of trials that combine radiation therapy with immunotherapy. Our objective was to characterize the intratumoral immune balance of the malignant prostate after SBRT in patients. METHODS: Sixteen patients with high-risk, non-metastatic prostate cancer at comparable Gleason Grade disease underwent radical prostatectomy with (n = 9) or without (n = 7) neoadjuvant SBRT delivered in three fractions of 8 Gy over 5 days completed 2 weeks before surgery. Freshly resected prostate specimens were processed to obtain single-cell suspensions, and immune-phenotyped for major lymphoid and myeloid cell subsets by staining with two separate 14-antibody panels and multicolor flow cytometry analysis. RESULTS: Malignant prostates 2 weeks after SBRT had an immune infiltrate dominated by myeloid cells, whereas malignant prostates without preoperative treatment were more lymphoid-biased (myeloid CD45+ cells 48.4 ± 19.7% vs. 25.4 ± 7.0%; adjusted p-value = 0.11; and CD45+ lymphocytes 51.6 ± 19.7% vs. 74.5 ± 7.0%; p = 0.11; CD3+ T cells 35.2 ± 23.8% vs. 60.9 ± 9.7%; p = 0.12; mean ± SD). CONCLUSION: SBRT drives a significant lymphoid to myeloid shift in the prostate-tumor immune infiltrate. This may be of interest when combining SBRT with immunotherapies, particularly in prostate cancer.

Development and Validation of a Comprehensive Multivariate Dosimetric Model for Predicting Late Genitourinary Toxicity Following Prostate Cancer Stereotactic Body Radiotherapy.

Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials. Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network. Results: Median follow-up time was 32.3 months (range 3-98.9 months). Late grade ≥2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ≥2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses. Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.

Phase 1 Trial of Stereotactic Body Radiation Therapy Neoadjuvant to Radical Prostatectomy for Patients With High-Risk Prostate Cancer.

PURPOSE: This study aimed to evaluate the feasibility and safety of prostate stereotactic body radiation therapy (SBRT) neoadjuvant to radical prostatectomy (RP) in a phase 1 trial. The primary endpoint was treatment completion rate without severe acute surgical complications. Secondary endpoints included patient-reported quality of life and physician-reported toxicities. METHODS AND MATERIALS: Patients with nonmetastatic high-risk or locally advanced prostate cancer received 24 Gy in 3 fractions to the prostate and seminal vesicles over 5 days, completed 2 weeks before RP. Patients with pN1 disease were treated after multidisciplinary discussion and shared decision making. Patient-reported quality of life (International Prostate Symptom Score and Expanded Prostate Cancer Index Composite 26-item version questionnaires) and physician-reported toxicity (Common Terminology Criteria for Adverse Events, version 4.03) were assessed before SBRT, immediately before surgery, and at 3-month intervals for 1 year. RESULTS: Twelve patients were enrolled, and 11 completed treatment (1 patient had advanced disease on prostate-specific membrane antigen positron emission tomography after enrollment but before treatment). There were no significant surgical complications. After RP, 2 patients underwent additional radiation therapy to nodes with androgen suppression for pN1 disease. Median follow-up after completion of treatment was 20.1 months, with 9 of 11 patients having a follow-up period of >12 months. Two patients had biochemical recurrence (prostate-specific antigen ≥0.05) within the first 12 months, with an additional 2 patients found to have biochemical recurrence after the 12-month period. The highest Common Terminology Criteria for Adverse Events genitourinary grades were 0, 1, 2, and 3 (n = 1, 4, 4, and 2, respectively), and the highest gastrointestinal grades were 0, 1, and 2 (n = 9, 1, and 1, respectively). At 12 months, incontinence was the only grade ≥2 toxicity. One and 2 of 9 patients had grade 2 and 3 incontinence, respectively. On the Expanded Prostate Cancer Index Composite (26-item version), the mean/median changes in scores from baseline to 12 months were -32.8/-31.1 for urinary incontinence, -1.6/-6.2 for urinary irritative/obstructive, -2.1/0 for bowel, -34.4/-37.5 for sexual function, and -10.6/-2.5 for hormonal. The mean/median change in International Prostate Symptom Score from baseline to 12 months was 0.5/0.5. CONCLUSIONS: RP after neoadjuvant SBRT appears to be feasible and safe at the dose tested. The severity of urinary incontinence may be higher than RP alone.

Dosimetric predictors of patient-reported toxicity after prostate stereotactic body radiotherapy: Analysis of full range of the dose-volume histogram using ensemble machine learning.

BACKGROUND AND PURPOSE: This study aims to evaluate the associations between dosimetric parameters and patient-reported outcomes, and to identify latent dosimetric parameters that most correlate with acute and subacute patient-reported urinary and rectal toxicity after prostate stereotactic body radiotherapy (SBRT) using machine learning methods. MATERIALS AND METHODS: Eighty-six patients who underwent prostate SBRT (40 Gy in 5 fractions) were included. Patient-reported health-related quality of life (HRQOL) outcomes were derived from bowel and bladder symptom scores on the Expanded Prostate Cancer Index Composite (EPIC-26) at 3 and 12 months post-SBRT. We utilized ensemble machine learning (ML) to interrogate the entire dose-volume histogram (DVH) to evaluate relationships between dose-volume parameters and HRQOL changes. The latent predictive dosimetric parameters that were most associated with HRQOL changes in urinary and rectal function were thus identified. An external cohort of 26 prostate SBRT patients was acquired to further test the predictive models. RESULTS: Bladder dose-volume metrics strongly predicted patient-reported urinary irritative and incontinence symptoms (area under the curves [AUCs] of 0.79 and 0.87, respectively) at 12 months. Maximum bladder dose, bladder V102.5%, bladder volume, and conformity indices (V50/VPTV and V100/VPTV) were most predictive of HRQOL changes in both urinary domains. No strong rectal toxicity dosimetric association was identified (AUC = 0.64). CONCLUSION: We demonstrated the application of advanced ML methods to identify a set of dosimetric variables that most highly correlated with patient-reported urinary HRQOL. DVH quantities identified with these methods may be used to achieve outcome-driven planning objectives to further reduce patient-reported toxicity with prostate SBRT.