Use of hierarchical models to evaluate performance of cardiac surgery centres in the Italian CABG outcome study

<p>Abstract</p> <p>Background</p> <p>Hierarchical modelling represents a statistical method used to analyze nested data, as those concerning patients afferent to different hospitals. Aim of this paper is to build a hierarchical regression model using data from the "Italian CABG outcome study" in order to evaluate the amount of differences in adjusted mortality rates attributable to differences between centres.</p> <p>Methods</p> <p>The study population consists of all adult patients undergoing an isolated CABG between 2002–2004 in the 64 participating cardiac surgery centres.</p> <p>A risk adjustment model was developed using a classical single-level regression. In the multilevel approach, the variable "clinical-centre" was employed as a group-level identifier. The intraclass correlation coefficient was used to estimate the proportion of variability in mortality between groups. Group-level residuals were adopted to evaluate the effect of clinical centre on mortality and to compare hospitals performance. Spearman correlation coefficient of ranks (<it>ρ</it>) was used to compare results from classical and hierarchical model.</p> <p>Results</p> <p>The study population was made of 34,310 subjects (mortality rate = 2.61%; range 0.33–7.63). The multilevel model estimated that 10.1% of total variability in mortality was explained by differences between centres. The analysis of group-level residuals highlighted 3 centres (VS 8 in the classical methodology) with estimated mortality rates lower than the mean and 11 centres (VS 7) with rates significantly higher. Results from the two methodologies were comparable (<it>ρ </it>= 0.99).</p> <p>Conclusion</p> <p>Despite known individual risk-factors were accounted for in the single-level model, the high variability explained by the variable "clinical-centre" states its importance in predicting 30-day mortality after CABG.</p

Effects of Preoperative Aspirin on Cardiocerebral and Renal Complications in Non-Emergent Cardiac Surgery Patients: A Sub-Group and Cohort Study

BACKGROUND AND OBJECTIVE: Postoperative cardiocerebral and renal complications are a major threat for patients undergoing cardiac surgery. This study was aimed to examine the effect of preoperative aspirin use on patients undergoing cardiac surgery. METHODS: An observational cohort study was performed on consecutive patients (n = 1879) receiving cardiac surgery at this institution. The patients excluded from the study were those with preoperative anticoagulants, unknown aspirin use, or underwent emergent cardiac surgery. Outcome events included were 30-day mortality, renal failure, readmission and a composite outcome - major adverse cardiocerebral events (MACE) that include permanent or transient stroke, coma, perioperative myocardial infarction (MI), heart block and cardiac arrest. RESULTS: Of all patients, 1145 patients met the inclusion criteria and were divided into two groups: those taking (n = 858) or not taking (n = 287) aspirin within 5 days preceding surgery. Patients with aspirin presented significantly more with history of hypertension, diabetes, peripheral arterial disease, previous MI, angina and older age. With propensity scores adjusted and multivariate logistic regression, however, this study showed that preoperative aspirin therapy (vs. no aspirin) significantly reduced the risk of MACE (8.4% vs. 12.5%, odds ratio [OR] 0.585, 95% CI 0.355-0.964, P = 0.035), postoperative renal failure (2.6% vs. 5.2%, OR 0.438, CI 0.203-0.945, P = 0.035) and dialysis required (0.8% vs. 3.1%, OR 0.230, CI 0.071-0.742, P = 0.014), but did not significantly reduce 30-day mortality (4.1% vs. 5.8%, OR 0.744, CI 0.376-1.472, P = 0.396) nor it increased readmissions in the patients undergoing cardiac surgery. CONCLUSIONS: Preoperative aspirin therapy is associated with a significant decrease in the risk of MACE and renal failure and did not increase readmissions in patients undergoing non-emergent cardiac surgery

Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan

<p>Abstract</p> <p>Objective</p> <p>In-hospital mortality is an important performance measure for quality improvement, although it requires proper risk adjustment. We set out to develop in-hospital mortality prediction models for acute hospitalization using a nation-wide electronic administrative record system in Japan.</p> <p>Methods</p> <p>Administrative records of 224,207 patients (patients discharged from 82 hospitals in Japan between July 1, 2002 and October 31, 2002) were randomly split into preliminary (179,156 records) and test (45,051 records) groups. Study variables included Major Diagnostic Category, age, gender, ambulance use, admission status, length of hospital stay, comorbidity, and in-hospital mortality. ICD-10 codes were converted to calculate comorbidity scores based on Quan's methodology. Multivariate logistic regression analysis was then performed using in-hospital mortality as a dependent variable. C-indexes were calculated across risk groups in order to evaluate model performances.</p> <p>Results</p> <p>In-hospital mortality rates were 2.68% and 2.76% for the preliminary and test datasets, respectively. C-index values were 0.869 for the model that excluded length of stay and 0.841 for the model that included length of stay.</p> <p>Conclusion</p> <p>Risk models developed in this study included a set of variables easily accessible from administrative data, and still successfully exhibited a high degree of prediction accuracy. These models can be used to estimate in-hospital mortality rates of various diagnoses and procedures.</p

The European System for Cardiac Operative Risk Evaluation (EuroSCORE) is not appropriate for withholding surgery in high-risk patients with aortic stenosis: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>The European System for Cardiac Operative Risk Evaluation (EuroSCORE) is a widely used risk assessment tool in patients with severe aortic stenosis to determine operability and to select patients for alternative therapies such as transcatheter aortic valve implantation. The objective of this study was to determine the accuracy of the EuroSCORE in predicting mortality following aortic valve replacement (AVR).</p> <p>Methods</p> <p>The logistic EuroSCORE was determined for all consecutive patients that underwent conventional AVR between 1995 and 2005 at our institution. Provincial Vital Statistics were used to determine all-cause mortality. The accuracy of the prognostic risk prediction provided by logistic EuroSCORE was assessed by comparing observed and expected operative mortality.</p> <p>Results</p> <p>During the study period, a total of 1,421 patients underwent AVR including 237 patients (16.7%) that had a logistic EuroSCORE > 20. Among these patients, the mean predicted operative mortality was 38.7% (SD = 18.1). The actual mortality of these patients was significantly lower than that predicted by EuroSCORE (11.4% vs. 38.7%, observed/expected ratio 0.29, 95% CI 0.15–0.52, P < 0.05). The EuroSCORE overestimated mortality within all strata of predicted risk. Although medium-term mortality is significantly higher among patients with EuroSCORE > 20 (log rank P = 0.0001), approximately 60% are alive at five years.</p> <p>Conclusion</p> <p>Actual operative mortality in patients undergoing AVR is significantly lower than that predicted by the logistic EuroSCORE. Additionally, medium-term survival following AVR is acceptable in high-risk patients with EuroSCORE > 20. More accurate risk prediction models are needed for risk-stratifying patients with severe aortic stenosis.</p

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Utilizing Risk Scores in Determining the Optimal Revascularization Strategy for Complex Coronary Artery Disease

Percutaneous coronary intervention (PCI) of multivessel and/or left main stem disease have been shown to be potentially legitimate revascularization alternatives in appropriately selected patients. Risk stratification is an important component in guiding patients to identify the most appropriate revascularization modality (PCI or coronary artery bypass grafting [CABG]) in conjunction with the Heart Team. The aim of this paper is to give the clinician a concise overview of the important established and evolving contemporary risk models in aiding this decision-making process. Risk models, based on clinical and anatomical variables alone, the novel concept of functional anatomical risk scores, and risk models combining aspects from both clinical and anatomical scores, are all discussed. The emerging concepts of the patient-empowered risk/benefit tradeoff between PCI and CABG to help personalize the choice of revascularization modality are also explored