Customized Prediction of Short Length of Stay Following Elective Cardiac Surgery in Elderly Patients Using a Genetic Algorithm

Objective: To develop a customized short LOS (<6 days) prediction model for geriatric patients receiving cardiac surgery, using local data and a computational feature selection algorithm. Design: Utilization of a machine learning algorithm in a prospectively collected STS database consisting of patients who received cardiac surgery between January 2002 and June 2011. Setting: Urban tertiary-care center. Participants: Geriatric patients aged 70 years or older at the time of cardiac surgery. Interventions None. Measurements and Main Results Predefined morbidity and mortality events were collected from the STS database. 23 clinically relevant predictors were investigated for short LOS prediction with a genetic algorithm (GenAlg) in 1426 patients. Due to the absence of an STS model for their particular surgery type, STS risk scores were unavailable for 771 patients. STS prediction achieved an AUC of 0.629 while the GenAlg achieved AUCs of 0.573 (in those with STS scores) and 0.691 (in those without STS scores). Among the patients with STS scores, the GenAlg features significantly associated with shorter LOS were absence of congestive heart failure (CHF) (OR = 0.59, p = 0.04), aortic valve procedure (OR = 1.54, p = 0.04), and shorter cross clamp time (OR = 0.99, p = 0.004). In those without STS prediction, short LOS was significantly correlated with younger age (OR = 0.93, p < 0.001), absence of CHF (OR = 0.53, p = 0.007), no preoperative use of beta blockers (OR = 0.66, p = 0.03), and shorter cross clamp time (OR = 0.99, p < 0.001). Conclusion: While the GenAlg-based models did not outperform STS prediction for patients with STS risk scores, our local-data-driven approach reliably predicted short LOS for cardiac surgery types that do not allow STS risk calculation. We advocate that each institution with sufficient observational data should build their own cardiac surgery risk models

Regional geometric differences between regurgitant and non-regurgitant mitral valves in patients with coronary artery disease

Objective: Demonstrate that regional geometric differences exist between regurgitant and non-regurgitant mitral valves (MV's) in patients with coronary artery disease and due to the heterogenous and regional nature of ischemic remodeling in patients with coronary artery disease (CAD), that the available anatomical reserve and likelihood of developing mitral regurgitation (MR) is variable in non-regurgitant MV's in patients with CAD. Methods: In this retrospective, observational study intraoperative three-dimensional transesophageal echocardiographic data was analyzed in patients undergoing coronary revascularization with MR (IMR group) and without MR (NMR group). Regional geometric differences between both groups were assessed and MV reserve which was defined as the increase in antero-posterior (AP) annular diameter from baseline that would lead to coaptation failure was calculated in three zones of the MV from antero-lateral (zone 1), middle (zone 2), and posteromedial (zone 3). Measurements and Main Results: There were 31 patients in the IMR group and 93 patients in the NMR group. Multiple regional geometric differences existed between both groups. Most significantly patients in the NMR group had significantly larger coaptation length and MV reserve than the IMR group in zones 1 (p-value =.005,.049) and 2 (p-value =.00,.00), comparable between the two groups in zone 3 (p-value =.436,.513). Depletion of the MV reserve was associated with posterior displacement of the coaptation point in zones 2 and 3. Conclusions: There are significant regional geometric differences between regurgitant and non-regurgitant MV's in patients with coronary artery disease. Due to regional variations in available anatomical reserve and the risk of coaptation failure in patients with CAD, absence of MR is not synonymous with normal MV function.</p

Preliminary Biomarkers for Identification of Human Ascending Thoracic Aortic Aneurysm

Background: Human ascending thoracic aortic aneurysms (ATAAs) are life threatening and constitute a leading cause of mortality in the United States. Previously, we demonstrated that collagens α2(V) and α1(XI) mRNA and protein expression levels are significantly increased in ATAAs. Methods and Results: In this report, the authors extended these preliminary studies using high‐throughput proteomic analysis to identify additional biomarkers for use in whole blood real‐time RT‐PCR analysis to allow for the identification of ATAAs before dissection or rupture. Human ATAA samples were obtained from male and female patients aged 65±14 years. Both bicuspid and tricuspid aortic valve patients were included and compared with nonaneurysmal aortas (mean diameter 2.3 cm). Five biomarkers were identified as being suitable for detection and identification of ATAAs using qRT‐PCR analysis of whole blood. Analysis of 41 samples (19 small, 13 medium‐sized, and 9 large ATAAs) demonstrated the overexpression of 3 of these transcript biomarkers correctly identified 79.4% of patients with ATAA of ≥4.0 cm (P<0.001, sensitivity 0.79, CI=0.62 to 0.91; specificity 1.00, 95% CI=0.42 to 1.00). Conclusion: A preliminary transcript biomarker panel for the identification of ATAAs using whole blood qRT‐PCR analysis in men and women is presented

Predicting DNA methylation level across human tissues

Differences in methylation across tissues are critical to cell differentiation and are key to understanding the role of epigenetics in complex diseases. In this investigation, we found that locus-specific methylation differences between tissues are highly consistent across individuals. We developed a novel statistical model to predict locus-specific methylation in target tissue based on methylation in surrogate tissue. The method was evaluated in publicly available data and in two studies using the latest IlluminaBeadChips: a childhood asthma study with methylation measured in both peripheral blood leukocytes (PBL) and lymphoblastoid cell lines; and a study of postoperative atrial fibrillation with methylation in PBL, atrium and artery. We found that our method can greatly improve accuracy of cross-tissue prediction at CpG sites that are variable in the target tissue [R2 increases from 0.38 (original R2 between tissues) to 0.89 for PBL-to-artery prediction; from 0.39 to 0.95 for PBL-to-atrium; and from 0.81 to 0.98 for lymphoblastoid cell line-to-PBL based on cross-validation, and confirmed using cross-study prediction]. An extended model with multiple CpGs further improved performance. Our results suggest that large-scale epidemiology studies using easy-to-access surrogate tissues (e.g. blood) could be recalibrated to improve understanding of epigenetics in hard-to-access tissues (e.g. atrium) and might enable non-invasive disease screening using epigenetic profiles

Dynamic changes in the ischemic mitral annulus: Implications for ring sizing

Objectives: Contrary to the rest of the mitral annulus, inter-trigonal distance is known to be relatively less dynamic during the cardiac cycle. Therefore, intertrigonal distance is considered a suitable benchmark for annuloplasty ring sizing during mitral valve (MV) surgery. The entire mitral annulus dilates and flattens in patients with ischemic mitral regurgitation (IMR). It is assumed that the fibrous trigone of the heart and the intertrigonal distance does not dilate. In this study, we sought to demonstrate the changes in mitral annular geometry in patients with IMR and specifically analyze the changes in intertrigonal distance during the cardiac cycle. Methods: Intraoperative three-dimensional transesophageal echocardiographic data obtained from 26 patients with normal MVs undergoing nonvalvular cardiac surgery and 36 patients with IMR undergoing valve repair were dynamically analyzed using Philips Qlab ® software. Results: Overall, regurgitant valves were larger in area and less dynamic than normal valves. Both normal and regurgitant groups displayed a significant change in annular area (AA) during the cardiac cycle (P 0.05). Conclusions: Annular dimensions in regurgitant valves are dynamic and can be measured feasibly and accurately using echocardiography. The echocardiographically identified inter-trigonal distance does not change significantly during the cardiac cycle

Echocardiography derived three-dimensional printing of normal and abnormal mitral annuli

Aims and Objectives: The objective of this study was to assess the clinical feasibility of using echocardiographic data to generate three-dimensional models of normal and pathologic mitral valve annuli before and after repair procedures. Materials and Methods: High-resolution transesophageal echocardiographic data from five patients was analyzed to delineate and track the mitral annulus (MA) using Tom Tec Image-Arena software. Coordinates representing the annulus were imported into Solidworks software for constructing solid models. These solid models were converted to stereolithographic (STL) file format and three-dimensionally printed by a commercially available Maker Bot Replicator 2 three-dimensional printer. Total time from image acquisition to printing was approximately 30 min. Results: Models created were highly reflective of known geometry, shape and size of normal and pathologic mitral annuli. Post-repair models also closely resembled shapes of the rings they were implanted with. Compared to echocardiographic images of annuli seen on a computer screen, physical models were able to convey clinical information more comprehensively, making them helpful in appreciating pathology, as well as post-repair changes. Conclusions: Three-dimensional printing of the MA is possible and clinically feasible using routinely obtained echocardiographic images. Given the short turn-around time and the lack of need for additional imaging, a technique we describe here has the potential for rapid integration into clinical practice to assist with surgical education, planning and decision-making

Artificial Intelligence in Mitral Valve Analysis

Background: Echocardiographic analysis of mitral valve (MV) has become essential for diagnosis and management of patients with MV disease. Currently, the various software used for MV analysis require manual input and are prone to interobserver variability in the measurements. Aim: The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis. Settings and Design: Retrospective analysis of intraoperative three-dimensional transesophageal echocardiography data acquired from four patients with normal MV undergoing coronary artery bypass graft surgery in a tertiary hospital. Materials and Methods: Echocardiographic data were analyzed using the eSie Valve Software (Siemens Healthcare, Mountain View, CA, USA). Three examiners analyzed three end-systolic (ES) frames from each of the four patients. A total of 36 ES frames were analyzed and included in the study. Statistical Analysis: A multiple mixed-effects ANOVA model was constructed to determine if the examiner, the patient, and the loop had a significant effect on the average value of each parameter. A Bonferroni correction was used to correct for multiple comparisons, and P = 0.0083 was considered to be significant. Results: Examiners did not have an effect on any of the six parameters tested. Patient and loop had an effect on the average parameter value for each of the six parameters as expected (P < 0.0083 for both). Conclusion: We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention

Making three-dimensional echocardiography more tangible: a workflow for three-dimensional printing with echocardiographic data

Abstract Three-dimensional (3D) printing is a rapidly evolving technology with several potential applications in the diagnosis and management of cardiac disease. Recently, 3D printing (i.e. rapid prototyping) derived from 3D transesophageal echocardiography (TEE) has become possible. Due to the multiple steps involved and the specific equipment required for each step, it might be difficult to start implementing echocardiography-derived 3D printing in a clinical setting. In this review, we provide an overview of this process, including its logistics and organization of tools and materials, 3D TEE image acquisition strategies, data export, format conversion, segmentation, and printing. Generation of patient-specific models of cardiac anatomy from echocardiographic data is a feasible, practical application of 3D printing technology