A new scoring system to determine thromboembolic risk after heart valve replacement

Objective— To determine the most important inflammatory and hematologic predictors of thromboembolism (TE) in patients undergoing valve replacement (VR) to be used in conjunction with clinical risk factors for preoperative risk profiling. Methods and Results— Preoperative and immediately postoperative clinical, echocardiographic, hematologic, biochemical and microbiological parameters were examined prospectively in 370 patients undergoing VR (249 AVR, 93 MVR, 28 DVR). Mean follow-up was 4.4 years (max 6.6 years; total 1566 pt/yrs), and 96 TE events were documented (28 major and 68 minor). INR data were collected on all patients. Laboratory values were considered elevated if they exceeded the 80th percentile of those of 70 controls with the same distribution of age and gender. IgA antibody to Chlamydia pneumoniae (CP)≥1:64 was considered indicative of significant infection. Predictors of TE on multivariate analysis following AVR were (hazard ratios): CP infection (2.6), previous TE (2.5), raised eosinophils (2.4), cancer history (2.1), postoperative infection (2.0), hypertension (2.0), CABG × 3/4 (2.0), and diabetes (1.9). Predictors of TE following MVR/DVR were raised mean platelet volume (4.0), raised factor VII (3.1), CP infection (2.7), previous mitral valvotomy (2.5), raised fibrinogen (2.2), and raised reticulocytes (2.0). These risk factors were additive when present in the same patient, enabling a scoring system to be developed that accurately predicted risk of TE based on number of risk factors. Conclusions— Selected blood tests and clinical risk factors provide a scoring system that accurately predicts TE risk and may guide prosthesis choice and antithrombotic management

Multiplex immunofluorescence to measure dynamic changes in tumor-infiltrating lymphocytes and PD-L1 in early-stage breast cancer.

BACKGROUND: The H&E stromal tumor-infiltrating lymphocyte (sTIL) score and programmed death ligand 1 (PD-L1) SP142 immunohistochemistry assay are prognostic and predictive in early-stage breast cancer, but are operator-dependent and may have insufficient precision to characterize dynamic changes in sTILs/PD-L1 in the context of clinical research. We illustrate how multiplex immunofluorescence (mIF) combined with statistical modeling can be used to precisely estimate dynamic changes in sTIL score, PD-L1 expression, and other immune variables from a single paraffin-embedded slide, thus enabling comprehensive characterization of activity of novel immunotherapy agents. METHODS: Serial tissue was obtained from a recent clinical trial evaluating loco-regional cytokine delivery as a strategy to promote immune cell infiltration and activation in breast tumors. Pre-treatment biopsies and post-treatment tumor resections were analyzed by mIF (PerkinElmer Vectra) using an antibody panel that characterized tumor cells (cytokeratin-positive), immune cells (CD3, CD8, CD163, FoxP3), and PD-L1 expression. mIF estimates of sTIL score and PD-L1 expression were compared to the H&E/SP142 clinical assays. Hierarchical linear modeling was utilized to compare pre- and post-treatment immune cell expression, account for correlation of time-dependent measurement, variation across high-powered magnification views within each subject, and variation between subjects. Simulation methods (Monte Carlo, bootstrapping) were used to evaluate the impact of model and tissue sample size on statistical power. RESULTS: mIF estimates of sTIL and PD-L1 expression were strongly correlated with their respective clinical assays (p \u3c .001). Hierarchical linear modeling resulted in more precise estimates of treatment-related increases in sTIL, PD-L1, and other metrics such as CD8+ tumor nest infiltration. Statistical precision was dependent on adequate tissue sampling, with at least 15 high-powered fields recommended per specimen. Compared to conventional t-testing of means, hierarchical linear modeling was associated with substantial reductions in enrollment size required (n = 25➔n = 13) to detect the observed increases in sTIL/PD-L1. CONCLUSION: mIF is useful for quantifying treatment-related dynamic changes in sTILs/PD-L1 and is concordant with clinical assays, but with greater precision. Hierarchical linear modeling can mitigate the effects of intratumoral heterogeneity on immune cell count estimations, allowing for more efficient detection of treatment-related pharmocodynamic effects in the context of clinical trials. TRIAL REGISTRATION: NCT02950259

Usefulness of microsimulation to translate valve performance into patient outcome: Patient prognosis after aortic valve replacement with the Carpentier–Edwards supra-annular valve

ObjectiveNumerous reports have been published documenting the results of aortic valve replacement. It is often not easy to translate these outcomes involving the condition of the valve into the actual consequences for the patient. We previously developed an alternative method to study outcome after aortic valve replacement that allows direct estimation of patient outcome after aortic valve replacement: microsimulation modeling. The goal of this article is to provide insight into microsimulation methodology and to give an overview of the advantages and disadvantages of simulation methods (in particular microsimulation) in comparison with standard methods of outcome analysis.MethodsBy using a primary dataset containing 1847 patients and 14,429 patient-years, advantages and disadvantages of standard methods of outcome analysis are discussed, and the potential role of microsimulation is illustrated by means of a step-by-step explanation of building, testing, and using such a model.ResultsTotal life expectancy, event-free life expectancy, and reoperation-free life expectancy for a 65-year-old male patient were 10.6 years, 9.2 years, and 9.8 years, respectively. Lifetime risk of reoperation due to structural valve deterioration was 13.3%.ConclusionsMicrosimulation is capable of providing accurate estimates of age-related life expectancy and lifetime risk of reoperation for patients who underwent aortic valve replacement with the Carpentier–Edwards supra-annular valve. It provides a useful tool to facilitate and optimize the choice for a specific heart valve prosthesis in a particular patient

Continuous Monitoring of Risk-Adjusted Outcomes: Excess Deaths vs. Lives Saved.

Reporting of risk-adjusted surgical outcomes is commonly used to compare providers and track changes over time. Preferred graphical methods use the relationship of the observed to the expected values of outcome events, including their ratio (O/E), cumulative sum (CUSUM) of their differences over time, called Risk-Adjusted CUSUM (RA-CUSUM) or Variable Life Adjusted Display (VLAD). We demonstrate these methods using operative mortality data for 7,255 isolated coronary artery bypass graft patients from January 2014 to June 2017. RA-CUSUM and VLAD are excellent techniques to display risk-adjusted outcomes and, unlike O/E, can provide continuous monitoring as performance varies over time

Bayesian stopping guidelines for heart valve premarket approval studies

ObjectivesThe Data Monitoring Committee (DMC) for the premarket approval (PMA) study of a new heart valve prosthesis convenes periodically to review the accumulating results of the study, and determines, among other things, whether there is enough concern with safety to stop the study. Their deliberations are largely subjective, based on their combined experience and expertise, but an objective aid to evaluating complication rates, usually called a stopping rule, is desirable.MethodsThe US Food and Drug Administration has designated objective performance criteria (OPC) for 7 heart valve complications. At the end of the PMA study, when approximately 800 patient-years have been accumulated, the complication rates must compare favorably with the OPC. Given the results to date at an interim review of the data, we use a Bayesian approach to compute the probability of passing the OPC test by the end of study.ResultsWe provide a method that the DMC can use to predict the probability of passing the OPC test for each complication, and a graphical aid for each number of events, observed at 100 patient-year intervals.ConclusionsAlthough the DMC ultimately uses combined experience and expertise to make the decision to stop a PMA valve study, we have provided an objective assessment of the probability of the valve ultimately passing the OPC test to aid in making that decision