American Association for Thoracic Surgery Quality Gateway: A Surgeon Case Study of Its Application in Adult Cardiac Surgery for Quality Assurance.

OBJECTIVE: To demonstrate applying American Association for Thoracic Surgery Quality Gateway (AQG) outcomes models to a Surgeon Case Study of quality assurance in adult cardiac surgery. METHODS: The case study includes 6,989 cardiac and thoracic aorta operations performed in adults at Cleveland Clinic by one surgeon from 2001 to 2023. AQG models were used to predict expected probabilities for operative mortality and major morbidity, and to compare hospital outcomes, surgery type, risk profile, and individual risk-factor levels using virtual (digital) twin causal inference. These models were based on postoperative procedural outcomes after 52,792 cardiac operations performed in 19 hospitals of 3 high-performing hospital systems with overall hospital mortality of 2.0%, analyzed by advanced machine learning for rare events. RESULTS: For individual surgeons, their patients, hospitals, and hospital systems, the Surgeon Case Study demonstrated that AQG provides expected outcomes across the entire spectrum of cardiac surgery, from single-component primary operations to complex multi-component reoperations. Actionable opportunities for quality improvement based on virtual twins is illustrated for patients, surgeons, hospitals, risk profile groups, operations, and risk factors vis-à-vis other hospitals. CONCLUSIONS: Using minimal data collection and models developed using advanced machine learning, this case study shows that probabilities can be generated for operative mortality and major morbidity after virtually all adult cardiac operations. It demonstrates the utility of 21st century causal inference (virtual [digital] twin) tools for assessing quality for surgeons asking "How am I doing?" their patients asking "What are my chances?" and the profession asking "How can we get better?"

Development of American Association for Thoracic Surgery Quality Gateway outcome models, analytics, and visualizations for quality assurance.

OBJECTIVE: The study objective was to develop comprehensive quality assurance models for procedural outcomes after adult cardiac surgery. METHODS: Based on 52,792 cardiac operations in adults performed in 19 hospitals of 3 high-performing hospital systems, models were developed for operative mortality (n = 1271), stroke (n = 895), deep sternal wound infection (n = 122), prolonged intubation (6182), renal failure (1265), prolonged postoperative stay (n = 5418), and reoperations (n = 1693). Random forest quantile classification, a method tailored for challenges of rare events, and model-free variable priority screening were used to identify predictors of events. RESULTS: A small set of preoperative variables was sufficient to model procedural outcomes for virtually all cardiac operations, including older age; advanced symptoms; left ventricular, pulmonary, renal, and hepatic dysfunction; lower albumin; higher acuity; and greater complexity of the planned operation. Geometric mean performance ranged from .63 to .76. Calibration covered large areas of probability. Continuous risk factors provided high information content, and their association with outcomes was visualized with partial plots. These risk factors differed in strength and configuration among hospitals, as did their risk-adjusted outcomes according to patient risk as determined by counterfactual causal inference within a framework of virtual (digital) twins. CONCLUSIONS: By using a small set of variables and contemporary machine-learning methods, comprehensive models for procedural operative mortality and major morbidity after adult cardiac surgery were developed based on data from 3 exemplary hospital systems. They provide surgeons, their patients, and hospital systems with 21st century tools for assessing their risks compared with these advanced hospital systems and improving cardiac surgery quality.

Cancer cells resistant to immune checkpoint blockade acquire interferon-associated epigenetic memory to sustain T cell dysfunction.

Prolonged interferon (IFN) signaling in cancer cells can promote resistance to immune checkpoint blockade (ICB). How cancer cells retain effects of prolonged IFN stimulation to coordinate resistance is unclear. We show that, across human and/or mouse tumors, immune dysfunction is associated with cancer cells acquiring epigenetic features of inflammatory memory. Here, inflammatory memory domains, many of which are initiated by chronic IFN-γ, are maintained by signal transducer and activator of transcription (STAT)1 and IFN regulatory factor (IRF)3 and link histone 3 lysine 4 monomethylation (H3K4me1)-marked chromatin accessibility to increased expression of a subset of IFN-stimulated genes (ISGs). These ISGs include the RNA sensor OAS1 that amplifies type I IFN (IFN-I) and immune inhibitory genes. Abrogating cancer cell IFN-I signaling restores anti-programmed cell death protein 1 (PD1) response by increasing IFN-γ in immune cells, promoting dendritic cell and CD8+ T cell interactions, and expanding T cells toward effector-like states rather than exhausted states. Thus, cancer cells acquire inflammatory memory to augment a subset of ISGs that promote and predict IFN-driven immune dysfunction.

Adjuvant Therapy After Neoadjuvant Therapy for Esophageal Cancer: Who Needs It?

OBJECTIVE: We hypothesized that, on average, patients do not benefit from additional adjuvant therapy after neoadjuvant therapy for locally advanced esophageal cancer, although subsets of patients might. Therefore, we sought to identify profiles of patients predicted to receive the most survival benefit or greatest detriment from adding adjuvant therapy. BACKGROUND: Although neoadjuvant therapy has become the treatment of choice for locally advanced esophageal cancer, the value of adding adjuvant therapy is unknown. METHODS: From 1970 to 2014, 22,123 patients were treated for esophageal cancer at 33 centers on 6 continents (Worldwide Esophageal Cancer Collaboration), of whom 7731 with adenocarcinoma or squamous cell carcinoma received neoadjuvant therapy; 1348 received additional adjuvant therapy. Random forests for survival and virtual-twin analyses were performed for all-cause mortality. RESULTS: Patients received a small survival benefit from adjuvant therapy (3.2±10 months over the subsequent 10 years for adenocarcinoma, 1.8±11 for squamous cell carcinoma). Consistent benefit occurred in ypT3-4 patients without nodal involvement and those with ypN2-3 disease. The small subset of patients receiving most benefit had high nodal burden, ypT4, and positive margins. Patients with ypT1-2N0 cancers had either no benefit or a detriment in survival. CONCLUSIONS: Adjuvant therapy after neoadjuvant therapy has value primarily for patients with more advanced esophageal cancer. Because the benefit is often small, patients considering adjuvant therapy should be counseled on benefits versus morbidity. In addition, given that the overall benefit was meaningful in a small number of patients, emerging modalities such as immunotherapy may hold more promise in the adjuvant setting.

Identifying long-term and imminent suicide predictors in a general population and a clinical sample with machine learning.

BACKGROUND: Machine learning (ML) is increasingly used to predict suicide deaths but their value for suicide prevention has not been established. Our first objective was to identify risk and protective factors in a general population. Our second objective was to identify factors indicating imminent suicide risk. METHODS: We used survival and ML models to identify lifetime predictors using the Cohort of Norway (n=173,275) and hospital diagnoses in a Saskatoon clinical sample (n=12,614). The mean follow-up times were 17 years and 3 years for the Cohort of Norway and Saskatoon respectively. People in the clinical sample had a longitudinal record of hospital visits grouped in six-month intervals. We developed models in a training set and these models predicted survival probabilities in held-out test data. RESULTS: In the general population, we found that a higher proportion of low-income residents in a county, mood symptoms, and daily smoking increased the risk of dying from suicide in both genders. In the clinical sample, the only predictors identified were male gender and older age. CONCLUSION: Suicide prevention probably requires individual actions with governmental incentives. The prediction of imminent suicide remains highly challenging, but machine learning can identify early prevention targets.

Unsupervised random forests.

sidClustering is a new random forests unsupervised machine learning algorithm. The first step in sidClustering involves what is called sidification of the features: staggering the features to have mutually exclusive ranges (called the staggered interaction data [SID] main features) and then forming all pairwise interactions (called the SID interaction features). Then a multivariate random forest (able to handle both continuous and categorical variables) is used to predict the SID main features. We establish uniqueness of sidification and show how multivariate impurity splitting is able to identify clusters. The proposed sidClustering method is adept at finding clusters arising from categorical and continuous variables and retains all the important advantages of random forests. The method is illustrated using simulated and real data as well as two in depth case studies, one from a large multi-institutional study of esophageal cancer, and the other involving hospital charges for cardiovascular patients.

Value of Lymphadenectomy in Patients Receiving Neoadjuvant Therapy for Esophageal Adenocarcinoma.

OBJECTIVE: The aim of this study was to assess the effect on survival of extent of lymphadenectomy during esophagectomy for patients undergoing multimodality (neoadjuvant) therapy for adenocarcinoma of the esophagus and esophagogastric junction using Worldwide Esophageal Cancer Collaboration data. SUMMARY BACKGROUND DATA: Previous worldwide data demonstrated that optimum lymphadenectomy during esophagectomy alone for esophageal cancer provides accurate staging and maximum survival. However, for patients undergoing neoadjuvant therapy for locally advanced adenocarcinoma, its value is unclear, leading to wide practice variability. METHODS: A total of 3859 patients with adenocarcinoma of the esophagus or esophagogastric junction received neoadjuvant therapy. The endpoint was all-cause mortality, reported as gain or loss of lifetime within 10 years. Lifetime predicted for each regional lymph node resected used quantile survival random forest methodology. RESULTS: Across all post-neoadjuvant ypTNM cancer categories, some degree of lymphadenectomy was associated with longer lifetime, but in a nonlinear fashion. For patients with ypN0 cancers, there was a modest gain in lifetime up to 25 lymph nodes resected and an incremental loss in lifetime as >25 were resected. For patients with ypN+ cancers, there was a robust gain in lifetime up to 30 lymph nodes resected and then an incremental loss in lifetime. CONCLUSIONS: Worldwide data for adenocarcinoma of the esophagus and esophagogastric junction demonstrate that lymphadenectomy during esophagectomy is a valuable component of neoadjuvant therapy. Survival is maximized when an optimum range of nodes is resected.

Opposing Functions of Interferon Coordinate Adaptive and Innate Immune Responses to Cancer Immune Checkpoint Blockade.

Interferon-gamma (IFNG) augments immune function yet promotes T cell exhaustion through PDL1. How these opposing effects are integrated to impact immune checkpoint blockade (ICB) is unclear. We show that while inhibiting tumor IFNG signaling decreases interferon-stimulated genes (ISGs) in cancer cells, it increases ISGs in immune cells by enhancing IFNG produced by exhausted T cells (TEX). In tumors with favorable antigenicity, these TEX mediate rejection. In tumors with neoantigen or MHC-I loss, TEX instead utilize IFNG to drive maturation of innate immune cells, including a PD1+TRAIL+ ILC1 population. By disabling an inhibitory circuit impacting PD1 and TRAIL, blocking tumor IFNG signaling promotes innate immune killing. Thus, interferon signaling in cancer cells and immune cells oppose each other to establish a regulatory relationship that limits both adaptive and innate immune killing. In melanoma and lung cancer patients, perturbation of this relationship is associated with ICB response independent of tumor mutational burden.

Precision Surgical Therapy for Adenocarcinoma of the Esophagus and Esophagogastric Junction.

INTRODUCTION: To facilitate the initial clinical decision regarding whether to use esophagectomy alone or neoadjuvant therapy in surgical care for individual patients with adenocarcinoma of the esophagus and esophagogastric junction-information not available from randomized trials-a machine-learning analysis was performed using worldwide real-world data on patients undergoing different therapies for this rare adenocarcinoma. METHODS: Using random forest technology in a sequential analysis, we (1) identified eligibility for each of four therapies among 13,365 patients: esophagectomy alone (n = 6649), neoadjuvant therapy (n = 4706), esophagectomy and adjuvant therapy (n = 998), and neoadjuvant and adjuvant therapy (n = 1022); (2) performed survival analyses incorporating interactions of patient and cancer characteristics with therapy; (3) determined optimal therapy as that predicted to maximize lifetime within 10 years (restricted mean survival time; RMST) for each patient; and (4) compared lifetime gained from optimal versus actual therapies. RESULTS: Actual therapy was optimal in 61% of those receiving esophagectomy alone; neoadjuvant therapy was optimal for 36% receiving neoadjuvant therapy. Many patients were predicted to benefit from postoperative adjuvant therapy. Total RMST for actual therapy received was 58,825 years. Had patients received optimal therapy, total RMST was predicted to be 62,982 years, a 7% gain. CONCLUSIONS: Average treatment effect for adenocarcinoma of the esophagus yields only crude evidence-based therapy guidelines. However, patient response to therapy is widely variable, and survival after data-driven predicted optimal therapy often differs from actual therapy received. Therapy must address an individual patient's cancer and clinical characteristics to provide precision surgical therapy for adenocarcinoma of the esophagus and esophagogastric junction.

Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality.

The prelisting variables essential for creating an accurate heart transplant allocation score based on survival are unknown. To identify these we studied mortality of adults on the active heart transplant waiting list in the Scientific Registry of Transplant Recipients database from January 1, 2004 to August 31, 2015. There were 33 069 candidates awaiting heart transplantation: 7681 UNOS Status 1A, 13 027 Status 1B, and 12 361 Status 2. During a median waitlist follow-up of 4.3 months, 5514 candidates died. Variables of importance for waitlist mortality were identified by machine learning using Random Survival Forests. Strong correlates predicting survival were estimated glomerular filtration rate (eGFR), serum albumin, extracorporeal membrane oxygenation, ventricular assist device, mechanical ventilation, peak oxygen capacity, hemodynamics, inotrope support, and type of heart disease with less predictive variables including antiarrhythmic agents, history of stroke, vascular disease, prior malignancy, and prior tobacco use. Complex interactions were identified such as an additive risk in mortality based on renal function and serum albumin, and sex-differences in mortality when eGFR >40 mL/min/1.73 m. Most predictive variables for waitlist mortality are in the current tiered allocation system except for eGFR and serum albumin which have an additive risk and complex interactions.

Biatrial maze procedure versus pulmonary vein isolation for atrial fibrillation during mitral valve surgery: New analytical approaches and end points.

OBJECTIVE: To use novel statistical methods for analyzing the effect of lesion set on (long-standing) persistent atrial fibrillation (AF) in the Cardiothoracic Surgical Trials Network trial of surgical ablation during mitral valve surgery (MVS). METHODS: Two hundred sixty such patients were randomized to MVS + surgical ablation or MVS alone. Ablation was randomized between pulmonary vein isolation and biatrial maze. During 12 months postsurgery, 228 patients (88%) submitted 7949 transtelephonic monitoring (TTM) recordings, analyzed for AF, atrial flutter (AFL), or atrial tachycardia (AT). As previously reported, more ablation than MVS-alone patients were free of AF or AF/AFL at 6 and 12 months (63% vs 29%; P < .001) by 72-hour Holter monitoring, without evident difference between lesion sets (for which the trial was underpowered). RESULTS: Estimated freedom from AF/AFL/AT on any transmission trended higher after biatrial maze than pulmonary vein isolation (odds ratio, 2.31; 95% confidence interval, 0.95-5.65; P = .07) 3 to 12 months postsurgery; estimated AF/AFL/AT load (ie, proportion of TTM strips recording AF/AFL/AT) was similar (odds ratio, 0.90; 95% confidence interval, 0.57-1.43; P = .6). Within 12 months, estimated prevalence of AF/AFL/AT by TTM was 58% after MVS alone, and 36% versus 23% after pulmonary vein isolation versus biatrial maze (P < .02). CONCLUSIONS: Statistical modeling using TTM recordings after MVS in patients with (long-standing) persistent AF suggests that a biatrial maze is associated with lower AF/AFL/AT prevalence, but not a lower load, compared with pulmonary vein isolation. The discrepancy between AF/AFL/AT prevalence assessed at 2 time points by Holter monitoring versus weekly TTM suggests the need for a confirmatory trial, reassessment of definitions for failure after ablation, and validation of statistical methods for assessing atrial rhythms longitudinally.

Tumor Immunity and Survival as a Function of Alternative Neopeptides in Human Cancer.

The immune system exerts antitumor activity via T cell-dependent recognition of tumor-specific antigens. Although the number of tumor neopeptides-peptides derived from somatic mutations-often correlates with immune activity and survival, most classically defined high-affinity neopeptides (CDNs) are not immunogenic, and only rare CDNs have been linked to tumor rejection. Thus, the rules of tumor antigen recognition remain incompletely understood. Here, we analyzed neopeptides, immune activity, and clinical outcome from 6,324 patients across 27 tumor types. We characterized a class of "alternatively defined neopeptides" (ADNs), which are mutant peptides predicted to bind MHC (class I or II) with improved affinity relative to their nonmutated counterpart. ADNs are abundant and molecularly distinct from CDNs. The load of ADNs correlated with intratumoral T-cell responses and immune suppression, and ADNs were also strong predictors of patient survival across tumor types. These results expand the spectrum of mutation-derived tumor antigens with potential clinical relevance. Cancer Immunol Res; 6(3); 276-87. ©2018 AACR.

Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model.

Atrial fibrillation is an arrhythmic disorder where the electrical signals of the heart become irregular. The probability of atrial fibrillation (binary response) is often time varying in a structured fashion, as is the influence of associated risk factors. A generalized nonlinear mixed effects model is presented to estimate the time-related probability of atrial fibrillation using a temporal decomposition approach to reveal the pattern of the probability of atrial fibrillation and their determinants. This methodology generalizes to patient-specific analysis of longitudinal binary data with possibly time-varying effects of covariates and with different patient-specific random effects influencing different temporal phases. The motivation and application of this model is illustrated using longitudinally measured atrial fibrillation data obtained through weekly trans-telephonic monitoring from an NIH sponsored clinical trial being conducted by the Cardiothoracic Surgery Clinical Trials Network.

Estimating the prevalence of atrial fibrillation from a three-class mixture model for repeated diagnoses.

Atrial fibrillation (AF) is an abnormal heart rhythm characterized by rapid and irregular heartbeat, with or without perceivable symptoms. In clinical practice, the electrocardiogram (ECG) is often used for diagnosis of AF. Since the AF often arrives as recurrent episodes of varying frequency and duration and only the episodes that occur at the time of ECG can be detected, the AF is often underdiagnosed when a limited number of repeated ECGs are used. In studies evaluating the efficacy of AF ablation surgery, each patient undergoes multiple ECGs and the AF status at the time of ECG is recorded. The objective of this paper is to estimate the marginal proportions of patients with or without AF in a population, which are important measures of the efficacy of the treatment. The underdiagnosis problem is addressed by a three-class mixture regression model in which a patient's probability of having no AF, paroxysmal AF, and permanent AF is modeled by auxiliary baseline covariates in a nested logistic regression. A binomial regression model is specified conditional on a subject being in the paroxysmal AF group. The model parameters are estimated by the Expectation-Maximization (EM) algorithm. These parameters are themselves nuisance parameters for the purpose of this research, but the estimators of the marginal proportions of interest can be expressed as functions of the data and these nuisance parameters and their variances can be estimated by the sandwich method. We examine the performance of the proposed methodology in simulations and two real data applications.

Cancer of the Esophagus and Esophagogastric Junction: An Eighth Edition Staging Primer.

This primer for eighth edition staging of esophageal and esophagogastric epithelial cancers presents separate classifications for the clinical (cTNM), pathologic (pTNM), and postneoadjuvant pathologic (ypTNM) stage groups, which are no longer shared. For pTNM, pT1 has been subcategorized as pT1a and pT1b for the subgrouping pStage I adenocarcinoma and squamous cell carcinoma. A new, simplified esophagus-specific regional lymph node map has been introduced. Undifferentiated histologic grade (G4) has been eliminated; additional analysis is required to expose histopathologic cell type. Location has been removed as a category for pT2N0M0 squamous cell cancer. The definition of the esophagogastric junction has been revised. ypTNM stage groups are identical for both histopathologic cell types, unlike those for cTNM and pTNM.