Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers.

Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative macular neovascularization (MNV). Optical Coherence Tomography (OCT) is the gold standard to identify fluid at different levels within the retina. The presence of fluid is considered the hallmark to define the presence of disease activity. Anti-vascular growth factor (anti-VEGF) injections can be used to treat exudative MNV. However, given the limitations of anti-VEGF treatment, as burdensome need for frequent visits and repeated injections to sustain efficacy, limited durability of the treatment, poor or no response, there is a great interest in detecting early biomarkers associated with a higher risk for AMD progression to exudative forms in order to optimize the design of early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision

Serum adiponectin levels and mortality after kidney transplantation.

Background and objectivesAdiponectin (ADPN), an adipose tissue-derived hormone, has protective properties with respect to atherogenesis, inflammation, and energy homeostasis. Its beneficial role has not been consistent in patients with CKD or those undergoing dialysis.Design, setting, participants, & measurementsThis study examined the association of plasma ADPN levels in 987 prevalent kidney transplant recipients (mean age ± SD, 51.0±12.8 years; estimated GFR, 52.8±21.9 ml/min per 1.73 m(2); median time since transplant, 78 months) on all-cause mortality and death-censored graft failure. Patients were enrolled between February and August 2007 and were followed for a median of 51 months (interquartile range, 49-53 months). Using Cox proportional hazard models, the association of log-transformed plasma adiponectin was studied, with and without adjustment for demographic variables, baseline GFR, markers of inflammation, and cardiovascular risk factors.ResultsAt baseline, patients in the lowest ADPN tertile were significantly more likely to be male; to be smokers; to have a higher baseline GFR, lower systolic BP, and lower HDL cholesterol level; and to have higher body mass index, abdominal circumference, C-reactive protein level, and total cholesterol level. The adjusted hazard ratio for death with elevated plasma ADPN (per natural log) was 1.44, and there was no significant interaction with any relevant cardiovascular risk subgroups (i.e., advanced age; diabetes; or elevated body mass index, waist circumference, C-reactive protein, or Framingham risk score). The hazard for death-censored graft failure was nonsignificant at 1.03.ConclusionElevated ADPN levels are associated with higher risk for death but not allograft failure in prevalent kidney transplant recipients

Serum adiponectin levels and mortality after kidney transplantation.

Background and objectivesAdiponectin (ADPN), an adipose tissue-derived hormone, has protective properties with respect to atherogenesis, inflammation, and energy homeostasis. Its beneficial role has not been consistent in patients with CKD or those undergoing dialysis.Design, setting, participants, & measurementsThis study examined the association of plasma ADPN levels in 987 prevalent kidney transplant recipients (mean age ± SD, 51.0±12.8 years; estimated GFR, 52.8±21.9 ml/min per 1.73 m(2); median time since transplant, 78 months) on all-cause mortality and death-censored graft failure. Patients were enrolled between February and August 2007 and were followed for a median of 51 months (interquartile range, 49-53 months). Using Cox proportional hazard models, the association of log-transformed plasma adiponectin was studied, with and without adjustment for demographic variables, baseline GFR, markers of inflammation, and cardiovascular risk factors.ResultsAt baseline, patients in the lowest ADPN tertile were significantly more likely to be male; to be smokers; to have a higher baseline GFR, lower systolic BP, and lower HDL cholesterol level; and to have higher body mass index, abdominal circumference, C-reactive protein level, and total cholesterol level. The adjusted hazard ratio for death with elevated plasma ADPN (per natural log) was 1.44, and there was no significant interaction with any relevant cardiovascular risk subgroups (i.e., advanced age; diabetes; or elevated body mass index, waist circumference, C-reactive protein, or Framingham risk score). The hazard for death-censored graft failure was nonsignificant at 1.03.ConclusionElevated ADPN levels are associated with higher risk for death but not allograft failure in prevalent kidney transplant recipients

Serum erythropoietin level and mortality in kidney transplant recipients.

Background and objectivesPosttransplant anemia is frequently reported in kidney transplant recipients and is associated with worsened patient survival. Similar to high erythropoiesis-stimulating agent requirements, resistance to endogenous erythropoietin may be associated with worse clinical outcomes in patients with ESRD. We examined the association between serum erythropoietin levels and mortality among kidney transplant recipients.Design, setting, participants, & measurementsWe collected sociodemographic, clinical, medical, and transplant history and laboratory data at baseline in 886 prevalent kidney transplant recipients (mean age 51 ± 13 [SD] years, 60% men, 21% diabetics). A solid-phase chemiluminescent immunometric assay was used to measure serum erythropoietin. Cox proportional hazards regression was used to model the association between baseline serum erythropoietin levels and all-cause mortality risk.ResultsDuring the median 39-month follow-up, 99 subjects died. The median serum erythropoietin level was 10.85 U/L and hemoglobin was 137 ± 16 g/L. Mortality rates were significantly higher in patients with higher erythropoietin levels (crude mortality rates in the highest to lowest erythropoietin tertiles were 51.7, 35.5, and 24.0 per 1000 patient-years, respectively [P = 0.008]). In unadjusted and also in adjusted Cox models each SD higher serum erythropoietin level significantly predicted all-cause mortality: HR(1SD increase) 1.22 and 1.28, respectively. In adjusted Cox models each SD higher serum erythropoietin/blood hemoglobin ratio also significantly predicted all-cause mortality: HR(1SD increase) 1.32. Serum erythropoietin predicted mortality in all analyzed subgroups.ConclusionsIn this sample of prevalent kidney transplant recipients, higher serum erythropoietin levels were associated with increased mortality

Serum erythropoietin level and mortality in kidney transplant recipients.

Background and objectivesPosttransplant anemia is frequently reported in kidney transplant recipients and is associated with worsened patient survival. Similar to high erythropoiesis-stimulating agent requirements, resistance to endogenous erythropoietin may be associated with worse clinical outcomes in patients with ESRD. We examined the association between serum erythropoietin levels and mortality among kidney transplant recipients.Design, setting, participants, & measurementsWe collected sociodemographic, clinical, medical, and transplant history and laboratory data at baseline in 886 prevalent kidney transplant recipients (mean age 51 ± 13 [SD] years, 60% men, 21% diabetics). A solid-phase chemiluminescent immunometric assay was used to measure serum erythropoietin. Cox proportional hazards regression was used to model the association between baseline serum erythropoietin levels and all-cause mortality risk.ResultsDuring the median 39-month follow-up, 99 subjects died. The median serum erythropoietin level was 10.85 U/L and hemoglobin was 137 ± 16 g/L. Mortality rates were significantly higher in patients with higher erythropoietin levels (crude mortality rates in the highest to lowest erythropoietin tertiles were 51.7, 35.5, and 24.0 per 1000 patient-years, respectively [P = 0.008]). In unadjusted and also in adjusted Cox models each SD higher serum erythropoietin level significantly predicted all-cause mortality: HR(1SD increase) 1.22 and 1.28, respectively. In adjusted Cox models each SD higher serum erythropoietin/blood hemoglobin ratio also significantly predicted all-cause mortality: HR(1SD increase) 1.32. Serum erythropoietin predicted mortality in all analyzed subgroups.ConclusionsIn this sample of prevalent kidney transplant recipients, higher serum erythropoietin levels were associated with increased mortality

Evaluation of the Malnutrition-Inflammation Score in Kidney Transplant Recipients

BackgroundChronic protein-energy wasting, termed malnutrition-inflammation complex syndrome, is frequent in patients with chronic kidney disease and is associated with anemia, morbidity, and mortality in patients on maintenance dialysis therapy. The Malnutrition-Inflammation Score (MIS) recently has been developed and validated in dialysis patients.Study designObservational cross-sectional study.Setting & participants993 prevalent kidney transplant recipients.PredictorMIS computed from change in body weight, dietary intake, gastrointestinal symptoms, functional capacity, comorbid conditions, decreased fat store/Systemic Global Assessment, signs of muscle wasting/Systemic Global Assessment, body mass index, serum albumin level, and serum transferrin level.OutcomesMarkers of inflammation and malnutrition, including serum C-reactive protein, interleukin 6, tumor necrosis factor alpha, serum leptin, prealbumin, body mass index, and abdominal circumference. The relationship was modeled by using structural equation models.ResultsMean age was 51 +/- 13 years, 57% were men, and 21% had diabetes. Median time from transplant was 72 months. MIS significantly correlated with abdominal circumference (r = -0.144), serum C-reactive protein level (r = 0.094), serum interleukin 6 level (r = 0.231), and serum tumor necrosis factor alpha level (r = 0.102; P < 0.01 for all). A structural equation model with 2 latent variables (malnutrition and inflammation factor) showed good fit to the observed data.LimitationsSingle-center study, lack of information about vascular access, presence of nonfunctioning kidney transplant, relatively high refusal rate.ConclusionsOur results confirm that MIS reflects both energy-protein wasting and inflammation in kidney transplant recipients. This simple instrument appears to be a useful tool to assess the presence of protein-energy wasting in this patient population

Associations between Serum Leptin Level and Bone Turnover in Kidney Transplant Recipients

Background and objectives: Obesity is associated with increased parathyroid hormone (PTH) in the general population and in patients with chronic kidney disease (CKD). A direct effect of adipose tissue on bone turnover through leptin production has been suggested, but such an association has not been explored in kidney transplant recipients

A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity.

Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable