Review and Comparison of Computational Approaches for Joint Longitudinal and Time‐to‐Event Models

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151312/1/insr12322.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151312/2/insr12322_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151312/3/Supplement_ReviewComputationalJointModels_final.pd

The fallacy of indexed effective orifice area charts to predict prosthesis-patient mismatch alter prosthesis implantation

Aims Indexed effective orifice area (EOAi) charts are used to determine the likelihood of prosthesis-patient mismatch (PPM) after aortic valve replacement (AVR). The aim of this study is to validate whether these EOAi charts, based on echocardiographic normal reference values, can accurately predict PPM.Methods and results In the PERIcardial SurGical AOrtic Valve ReplacemeNt (PERIGON) Pivotal Trial, 986 patients with aortic valve stenosis/regurgitation underwent AVR with an Avalus valve. Patients were randomly split (50:50) into training and test sets. The mean measured EOAs for each valve size from the training set were used to create an Avalus EOAi chart. This chart was subsequently used to predict PPM in the test set and measures of diagnostic accuracy (sensitivity, specificity, and negative and positive predictive value) were assessed. PPM was defined by an EOAi <= 0.85 cm(2)/m(2) and severe PPM was defined as EOAi <= 0.65 cm(2)/m(2). The reference values obtained from the training set ranged from 1.27 cm(2) for size 19 mm up to 1.81 cm(2) for size 27 mm. The test set had an incidence of 66% of PPM and 24% of severe PPM. The EOAi chart inaccurately predicted PPM in 30% of patients and severe PPM in 22% of patients. For the prediction of PPM, the sensitivity was 87% and the specificity 37%. For the prediction of severe PPM, the sensitivity was 13% and the specificity 98%.Conclusion use of echocardiographic normal reference values for EOAi charts to predict PPM is unreliable due to the large proportion of misclassifications.Thoracic Surger

Statistical primer: checking model assumptions with regression diagnostics

Regression modelling is an important statistical tool frequently utilized by cardiothoracic surgeons. However, these models-including linear, logistic and Cox proportional hazards regression-rely on certain assumptions. If these assumptions are violated, then a very cautious interpretation of the fitted model should be taken. Here, we discuss several assumptions and report diagnostics that can be used to detect departures from these assumptions. Most of the diagnostics discussed are based on residuals: a measure of the difference between the observed and model fitted values. Reliable and generalizable results depend on correctly developed statistical models, and proper diagnostics should play an integral part in the model development

Micromagnetic disorder in antiparallel biased spin valves

The reorientation of antiferromagnetically coupled Co layers comprising the pinned layers of an antiparallel biased spin valve is reported. Initially, the lower Co layer is saturated in the growth field in the deposition chamber, but it reorients as the upper Co layer grows to be thicker than the lower one. We have investigated the nature of this reorientation by ex situ transport measurements and Lorentz microscopy, and found it highly inhomogeneous, leading to a complex in-plane domain pattern. This results in a reduction of the giant magnetoresistance of the spin valves close to the balance point, where the benefits of the antiparallel biasing are greatest

Dynamic prediction modeling approaches for cardiac surgery

Background: The calibration of several cardiac clinical prediction models has deteriorated over time. We compare different model fitting approaches for in-hospital mortality after cardiac surgery that adjust for cross-sectional case mix in a heterogeneous patient population. Methods and Results: Data from >300 000 consecutive cardiac surgery procedures performed at all National Health Service and some private hospitals in England and Wales between April 2001 and March 2011 were extracted from the National Institute for Cardiovascular Outcomes Research clinical registry. The study outcome was in-hospital mortality. Model approaches included not updating, periodic refitting, rolling window, and dynamic logistic regression. Covariate adjustment was made in each model using variables included in the logistic European System for Cardiac Operative Risk Evaluation model. The association between in-hospital mortality and some variables changed with time. Notably, the intercept coefficient has been steadily decreasing during the study period, consistent with decreasing observed mortality. Some risk factors, such as operative urgency and postinfarct ventricular septal defect, have been relatively stable over time, whereas other risk factors, such as left ventricular function and surgery on the thoracic aorta, have been associated with lower risk relative to the static model. Conclusions: Dynamic models or periodic model refitting is necessary to counteract calibration drift. A dynamic modeling framework that uses contemporary and available historic data can provide a continuously smooth update mechanism that also allows for inferences to be made on individual risk factors. Better models that withstand the effects of time give advantages for governance, quality improvement, and patient-level decision making