Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Early life metal dysregulation in amyotrophic lateral sclerosis

ObjectiveDeficiencies and excess of essential elements and toxic metals are implicated in amyotrophic lateral sclerosis (ALS), but the age when metal dysregulation appears remains unknown. This study aims to determine whether metal uptake is dysregulated during childhood in individuals eventually diagnosed with ALS.MethodsLaser ablation- inductively coupled plasma- mass spectrometry was used to obtain time series data of metal uptake using biomarkers in teeth from autopsies or dental extractions of ALS (n = 36) and control (n = 31) participants. Covariate data included sex, smoking, occupational exposures, and ALS family history. Case- control differences were identified in temporal profiles of metal uptake for individual metals using distributed lag models. Weighted quantile sum (WQS) regression was used for metals mixture analyses. Similar analyses were performed on an ALS mouse model to further verify the relevance of dysregulation of metals in ALS.ResultsMetal levels were higher in cases than in controls: 1.49 times for chromium (1.11- 1.82; at 15 years), 1.82 times for manganese (1.34- 2.46; at birth), 1.65 times for nickel (1.22- 2.01; at 8 years), 2.46 times for tin (1.65- 3.30; at 2 years), and 2.46 times for zinc (1.49- 3.67; at 6 years). Co- exposure to 11 elements indicated that childhood metal dysregulation was associated with ALS. The mixture contribution of metals to disease outcome was likewise apparent in tooth biomarkers of an ALS mouse model, and differences in metal distribution were evident in ALS mouse brains compared to brains from littermate controls.InterpretationOverall, our study reveals direct evidence that altered metal uptake during specific early life time windows is associated with adult- onset ALS.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155978/1/acn351006_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155978/2/acn351006.pd

The adoption of generic immunosuppressant medications in kidney, liver, and heart transplantation among recipients in Colorado or nationally with Medicare part D

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144701/1/ajt14722.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144701/2/ajt14722_am.pd

Development and Validation of a Clinical Risk-Assessment Tool Predictive of All-Cause Mortality

In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient's health and overall function of multiple organ systems. We have developed a statistical procedure that condenses the information from a variety of health biomarkers into a composite index, which could be used as a risk score for predicting all-cause mortality. It could also be viewed as a holistic measure of overall physiological health status. This health status metric is computed as a function of standardized values of each biomarker measurement, weighted according to their empirically determined relative strength of association with mortality. The underlying risk model was developed using the biomonitoring and mortality data of a large sample of US residents obtained from the National Health and Nutrition Examination Survey (NHANES) and the National Death Index (NDI). Biomarker concentration levels were standardized using spline-based Cox regression models, and optimization algorithms were used to estimate the weights. The predictive accuracy of the tool was optimized by bootstrap aggregation. We also demonstrate how stacked generalization, a machine learning technique, can be used for further enhancement of the prediction power. The index was shown to be highly predictive of all-cause mortality and long-term outcomes for specific health conditions. It also exhibited a robust association with concurrent chronic conditions, recent hospital utilization, and current health status as assessed by self-rated health

Frequency and Prioritization of Patient Health Risks from a Structured Health Risk Assessment

PurposeTo describe the frequency and patient-reported readiness to change, desire to discuss, and perceived importance of 13 health risk factors in a diverse range of primary care practices.MethodsPatients (n = 1,707) in 9 primary care practices in the My Own Health Report (MOHR) trial reported general, behavioral, and psychosocial risk factors (body mass index [BMI], health status, diet, physical activity, sleep, drug use, stress, anxiety or worry, and depression). We classified responses as "at risk" or "healthy" for each factor, and patients indicated their readiness to change and/or desire to discuss identified risk factors with providers. Patients also selected 1 of the factors they were ready to change as most important. We then calculated frequencies within and across these factors and examined variation by patient characteristics and across practices.ResultsOn average, patients had 5.8 (SD = 2.12; range, 0-13) unhealthy behaviors and mental health risk factors. About 55% of patients had more than 6 risk factors. On average, patients wanted to change 1.2 and discuss 0.7 risks. The most common risks were inadequate fruit/vegetable consumption (84.5%) and overweight/obesity (79.6%). Patients were most ready to change BMI (33.3%) and depression (30.7%), and most wanted to discuss depression (41.9%) and anxiety or worry (35.2%). Overall, patients rated health status as most important.ConclusionsImplementing routine comprehensive health risk assessments in primary care will likely identify a high number of behavioral and psychosocial health risks. By soliciting patient priorities, providers and patients can better manage counseling and behavior change

Cell-bound complement activation products associate with lupus severity in SLE.

To evaluate the association between lupus severity and cell-bound complement activation products (CB-CAPs) or low complement proteins C3 and C4. All subjects (n=495) fulfilled the American College of Rheumatology (ACR) classification criteria for SLE. Abnormal CB-CAPs (erythrocyte-bound C4d or B-lymphocyte-bound C4d levels &gt;99th percentile of healthy) and complement proteins C3 and C4 were determined using flow cytometry and turbidimetry, respectively. Lupus severity was estimated using the Lupus Severity Index (LSI). Statistical analysis consisted of multivariable linear regression and groups comparisons. Abnormal CB-CAPs were more prevalent than low complement values irrespective of LSI levels (62% vs 38%, respectively, p&lt;0.0001). LSI was low (median 5.44, IQR: 4.77-6.93) in patients with no complement abnormality, intermediate in patients with abnormal CB-CAPs (median 6.09, IQR: 5.31-8.20) and high in the group presenting with both abnormal CB-CAPs and low C3 and/or C4 (median 7.85, IQR: 5.51-8.37). Odds of immunosuppressant use was higher in subjects with LSI ≥5.95 compared with subjects with LSI &lt;5.95 (1.60 vs 0.53, p&lt;0.0001 for both). Multivariable regression analysis revealed that higher LSI scores associated with abnormal CB-CAPs-but not low C3/C4-after adjusting for younger age, race and longer disease duration (p=0.0001), which were also independent predictors of disease severity (global R2=0.145). Abnormalities in complement activation as measured by CB-CAPs are associated with increased LSI

Cell-bound complement activation products associate with lupus severity in SLE.

OBJECTIVES:To evaluate the association between lupus severity and cell-bound complement activation products (CB-CAPs) or low complement proteins C3 and C4. METHODS:All subjects (n=495) fulfilled the American College of Rheumatology (ACR) classification criteria for SLE. Abnormal CB-CAPs (erythrocyte-bound C4d or B-lymphocyte-bound C4d levels &gt;99th percentile of healthy) and complement proteins C3 and C4 were determined using flow cytometry and turbidimetry, respectively. Lupus severity was estimated using the Lupus Severity Index (LSI). Statistical analysis consisted of multivariable linear regression and groups comparisons. RESULTS:Abnormal CB-CAPs were more prevalent than low complement values irrespective of LSI levels (62% vs 38%, respectively, p&lt;0.0001). LSI was low (median 5.44, IQR: 4.77-6.93) in patients with no complement abnormality, intermediate in patients with abnormal CB-CAPs (median 6.09, IQR: 5.31-8.20) and high in the group presenting with both abnormal CB-CAPs and low C3 and/or C4 (median 7.85, IQR: 5.51-8.37). Odds of immunosuppressant use was higher in subjects with LSI ≥5.95 compared with subjects with LSI &lt;5.95 (1.60 vs 0.53, p&lt;0.0001 for both). Multivariable regression analysis revealed that higher LSI scores associated with abnormal CB-CAPs-but not low C3/C4-after adjusting for younger age, race and longer disease duration (p=0.0001), which were also independent predictors of disease severity (global R2=0.145). CONCLUSION:Abnormalities in complement activation as measured by CB-CAPs are associated with increased LSI