Lorenzo L. Pesce

EuroSCORE performance in valve surgery: a meta-analysis.

BACKGROUND The European System for Cardiac Operative Risk Evaluation (EuroSCORE) was developed to predict immediate outcomes after adult cardiac operations, but less than 30% of the cases used to develop this score were valve procedures. We studied EuroSCORE performance in valve procedures. METHODS We performed a meta-analysis of published studies reporting the assessment of discriminatory power of the EuroSCORE by receiver operating characteristics (ROC) curve analysis in adult valve operations. A comparison of observed and predicted mortality rates was also performed. RESULTS A literature search identified 37 potentially eligible studies, and 12 were selected for meta-analysis comprising 26,621 patients with 1250 events (mortality rate, 4.7%). Meta-analysis of these studies provided an average area under the curve (AUC) value of 0.730 (95% confidence interval [CI], 0.717 to 0.743). The same results were obtained when meta-analyses were performed separately in studies categorized on reliability of uncertainty estimation: in the seven studies reporting reliable uncertainty estimation (8175 patients with 358 events; mortality rate, 4.4%), the ROC curve provided an average AUC value of 0.724 (95% CI, 0.699 to 0.749). The five studies not reporting reliable uncertainty estimation (18,446 patients with 892 events; mortality rate, 4.8%) had an average AUC of 0.732 (95% CI, 0.717 to 0.747). We documented a constant trend to overpredict mortality by EuroSCORE, both in the additive and especially in the logistic form. CONCLUSIONS The EuroSCORE has low discrimination ability for valve surgery, and it sensibly overpredicts risk. Alternative risk scoring algorithms should be seriously considered.

Performance of EuroSCORE in CABG and off-pump coronary artery bypass grafting: Single institution experience and meta-analysis

AIMS To assess EuroSCORE performance in predicting in-hospital mortality in on-pump coronary artery bypass grafting (CABG) and off-pump coronary artery bypass grafting (OPCAB). METHODS AND RESULTS Additive and logistic EuroSCORE were computed for consecutive patients undergoing CABG (n = 3440, 75%) or OPCAB (n = 1140, 25%) at our hospital from 1999 to September 2007. The areas under the receiver operating characteristic (ROC) curves (AUCs) were used to describe performance and accuracy. No difference in performance between CABG and OPCAB and between additive and logistic EuroSCORE (additive EuroSCORE AUCs of 0.808 and 0.779 for CABG and OPCAB, respectively; logistic EuroSCORE AUCs of 0.813 and of 0.773 for CABG and OPCAB, respectively) was found, although a marked tendency to overpredict mortality by both models was evident. A meta-analysis of previously published data was done, and a total of eight studies representing 19 212 and 5461 patients undergoing CABG and OPCAB, respectively, met inclusion criteria. Meta-analysis confirmed similar performance of EuroSCORE in CABG and OPCAB: estimated AUCs were 0.767 and 0.766 for CABG and OPCAB, respectively, with an estimated difference of 0.001 (95% CI -0.061 to 0.063). CONCLUSION Additive and logistic EuroSCORE algorithms performed similarly, and cumulative evidence suggests comparable performance in CABG and OPCAB procedures; both risk models, however, significantly overestimated mortality.

Safety of Pregnancy After Primary Breast Carcinoma in Young Women: A Meta-Analysis to Overcome Bias of Healthy Mother Effect Studies

Background: An increased number of women are expected to conceive after the diagnosis of early breast cancer. Most physicians recommend that pregnancy be delayed by 2 to 3 years after diagnosis of early breast cancer, but this recommendation is based on data from trials with small patient cohorts. Furthermore, a healthy mother effect (HME) selection bias may be operative in most of these studies, because women undergoing childbearing after treatment were healthier when compared with the control group. Aim: To perform a systematic review and meta-analysis of published trials corrected for HME bias so as to assess the effect of pregnancy (at least 10 months after diagnosis) versus no pregnancy on overall survival of primary breast cancer patients less than 45 years. Methods: We searched MEDLINE and Thomson Reuters (ISI) Web of Knowledge for eligible studies. From each study we extracted the relative hazard ratio or, if not provided, all the necessary data to impute it. In cases where the duration from diagnosis to pregnancy was not reported, we extracted relevant data to estimate it. Results: Our electronic search strategy yielded 1623 hits pertaining to 20 potentially eligible studies involving 49,370 premenopausal breast cancer patients. Ten studies were eligible after considering HME potential bias in matching controls. Among these, 9 studies (pregnant 1089, matched-controls 13051) contained data appropriate for analysis. Overall survival was statistically higher among patients who became pregnant compared to controls: fixed effect model estimated pooled hazard ratio for death 0.51 (95% confidence interval: 0.42–0.62). No study heterogeneity was observed: Q = 10.4, P = 0.17; I2 = 48%. Conclusion: The pooled available evidence indicates that in early breast cancer patients, pregnancy that occurs at least 10 months after diagnosis does not jeopardize prognosis and may actually confer significant survival benefit. Target Audience: Obstetricians & Gynecologists, Family Physicians Learning Objectives: After completing this CME activity, physicians should be better able to assess the effect pregnancy has on long-term survival in primary breast cancer patients under age 45; counsel patients on the safety of pregnancy after breast cancer diagnosis and treatment; and interpret how pregnancy may be associated with improved breast cancer survival.

Targeted Analysis of Whole Genome Sequence Data to Diagnose Genetic Cardiomyopathy

Background—Cardiomyopathy is highly heritable but genetically diverse. At present, genetic testing for cardiomyopathy uses targeted sequencing to simultaneously assess the coding regions of >50 genes. New genes are routinely added to panels to improve the diagnostic yield. With the anticipated

Supercomputing for the parallelization of whole genome analysis.

1000 genome, it is expected that genetic testing will shift toward comprehensive genome sequencing accompanied by targeted gene analysis. Therefore, we assessed the reliability of whole genome sequencing and targeted analysis to identify cardiomyopathy variants in 11 subjects with cardiomyopathy. Methods and Results—Whole genome sequencing with an average of 37× coverage was combined with targeted analysis focused on 204 genes linked to cardiomyopathy. Genetic variants were scored using multiple prediction algorithms combined with frequency data from public databases. This pipeline yielded 1 to 14 potentially pathogenic variants per individual. Variants were further analyzed using clinical criteria and segregation analysis, where available. Three of 3 previously identified primary mutations were detected by this analysis. In 6 subjects for whom the primary mutation was previously unknown, we identified mutations that segregated with disease, had clinical correlates, and had additional pathological correlation to provide evidence for causality. For 2 subjects with previously known primary mutations, we identified additional variants that may act as modifiers of disease severity. In total, we identified the likely pathological mutation in 9 of 11 (82%) subjects. Conclusions—These pilot data demonstrate that ≈30 to 40× coverage whole genome sequencing combined with targeted analysis is feasible and sensitive to identify rare variants in cardiomyopathy-associated genes.

'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine.

MOTIVATION The declining cost of generating DNA sequence is promoting an increase in whole genome sequencing, especially as applied to the human genome. Whole genome analysis requires the alignment and comparison of raw sequence data, and results in a computational bottleneck because of limited ability to analyze multiple genomes simultaneously. RESULTS We now adapted a Cray XE6 supercomputer to achieve the parallelization required for concurrent multiple genome analysis. This approach not only markedly speeds computational time but also results in increased usable sequence per genome. Relying on publically available software, the Cray XE6 has the capacity to align and call variants on 240 whole genomes in ∼50 h. Multisample variant calling is also accelerated. AVAILABILITY AND IMPLEMENTATION The MegaSeq workflow is designed to harness the size and memory of the Cray XE6, housed at Argonne National Laboratory, for whole genome analysis in a platform designed to better match current and emerging sequencing volume.

Breast US Computer-aided Diagnosis System: Robustness across Urban Populations in South Korea and the United States

Background The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method ‘N-of-1-pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. Results Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). Conclusions The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies. Software http://lussierlab.org/publications/N-of-1-pathways

Performance of breast ultrasound computer-aided diagnosis: dependence on image selection.

PURPOSE To evaluate the robustness of a breast ultrasonographic (US) computer-aided diagnosis (CAD) system in terms of its performance across different patient populations. MATERIALS AND METHODS Three US databases were analyzed for this study: one South Korean and two United States databases. All three databases were utilized in an institutional review board-approved and HIPAA-compliant manner. Round-robin analysis and independent testing were performed to evaluate the performance of a computerized breast cancer classification scheme across the databases. Receiver operating characteristic (ROC) analysis was used to evaluate performance differences. RESULTS The round-robin analyses of each database demonstrated similar results, with areas under the ROC curve ranging from 0.88 (95% confidence interval [CI]: 0.820, 0.918) to 0.91 (95% CI: 0.86, 0.95). The independent testing of each database, however, indicated that although the performances were similar, the range in areas under the ROC curve (from 0.79 [95% CI: 0.730, 0.842] to 0.87 [95% CI: 0.794, 0.923]) was wider than that with the round-robin tests. However, the only instances in which statistically significant differences in performance were demonstrated occurred when the Korean database was used in a testing capacity in independent testing. CONCLUSION The few observed statistically significant differences in performance indicated that while the US features used by the system were useful across the databases, their relative importance differed. In practice, this means that a CAD system may need to be adjusted when applied to a different population.

Improved detection of focal pneumonia by chest radiography with bone suppression imaging

RATIONALE AND OBJECTIVES The automated classification of sonographic breast lesions is generally accomplished by extracting and quantifying various features from the lesions. The selection of images to be analyzed, however, is usually left to the radiologist. Here we present an analysis of the effect that image selection can have on the performance of a breast ultrasound computer-aided diagnosis system. MATERIALS AND METHODS A database of 344 different sonographic lesions was analyzed for this study (219 cysts/benign processes, 125 malignant lesions). The database was collected in an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant manner. Three different image selection protocols were used in the automated classification of each lesion: all images, first image only, and randomly selected images. After image selection, two different protocols were used to classify the lesions: (a) the average feature values were input to the classifier or (b) the classifier outputs were averaged together. Both protocols generated an estimated probability of malignancy. Round-robin analysis was performed using a Bayesian neural network-based classifier. Receiver-operating characteristic analysis was used to evaluate the performance of each protocol. Significance testing of the performance differences was performed via 95% confidence intervals and noninferiority tests. RESULTS The differences in the area under the receiver-operating characteristic curves were never more than 0.02 for the primary protocols. Noninferiority was demonstrated between these protocols with respect to standard input techniques (all images selected and feature averaging). CONCLUSION We have proved that our automated lesion classification scheme is robust and can perform well when subjected to variations in user input.

A study of the effect of noise injection on the training of artificial neural networks

AbstractObjectiveTo evaluate radiologists’ ability to detect focal pneumonia by use of standard chest radiographs alone compared with standard plus bone-suppressed chest radiographs.MethodsStandard chest radiographs in 36 patients with 46 focal airspace opacities due to pneumonia (10 patients had bilateral opacities) and 20 patients without focal opacities were included in an observer study. A bone suppression image processing system was applied to the 56 radiographs to create corresponding bone suppression images. In the observer study, eight observers, including six attending radiologists and two radiology residents, indicated their confidence level regarding the presence of a focal opacity compatible with pneumonia for each lung, first by use of standard images, then with the addition of bone suppression images. Receiver operating characteristic (ROC) analysis was used to evaluate the observers’ performance.ResultsThe mean value of the area under the ROC curve (AUC) for eight observers was significantly improved from 0.844 with use of standard images alone to 0.880 with standard plus bone suppression images (P < 0.001) based on 46 positive lungs and 66 negative lungs.ConclusionUse of bone suppression images improved radiologists’ performance for detection of focal pneumonia on chest radiographs.Key Points• Bone suppression image processing can be applied to conventional digital radiography systems. • Bone suppression imaging (BSI) produces images that appear similar to dual-energy soft tissue images. • BSI improves the conspicuity of focal lung disease by minimizing bone opacity. • BSI can improve the accuracy of radiologists in detecting focal pneumonia.