Frailty and In-Hospital Mortality Risk Using EHR Nursing Data

Purpose The purpose of this study was to evaluate four definitions of a Frailty Risk Score (FRS) derived from EHR data that includes combinations of biopsychosocial risk factors using nursing flowsheet data or International Classification of Disease, 10th revision (ICD-10) codes and blood biomarkers and its predictive properties for in-hospital mortality in adults ≥50 years admitted to medical-surgical units. Methods In this retrospective observational study and secondary analysis of an EHR dataset, survival analysis and Cox regression models were performed with sociodemographic and clinical covariates. Integrated area under the ROC curve (iAUC) across follow-up time based on Cox modeling was estimated. Results The 46,645 patients averaged 1.5 hospitalizations (SD = 1.1) over the study period and 63.3% were emergent admissions. The average age was 70.4 years (SD = 11.4), 55.3% were female, 73.0% were non-Hispanic White (73.0%), mean comorbidity score was 3.9 (SD = 2.9), 80.5% were taking 1.5 high risk medications, and 42% recorded polypharmacy. The best performing FRS-NF-26-LABS included nursing flowsheet data and blood biomarkers (Adj. HR = 1.30, 95% CI [1.28, 1.33]), with good accuracy (iAUC = .794); the reduced model with age, sex, and FRS only demonstrated similar accuracy. The poorest performance was the ICD-10 code-based FRS. Conclusion The FRS captures information about the patient that increases risk for in-hospital mortality not accounted for by other factors. Identification of frailty enables providers to enhance various aspects of care, including increased monitoring, applying more intensive, individualized resources, and initiating more informed discussions about treatments and discharge planning.

Factors Affecting Long-Term Compliance of CPAP Treatment—A Single Centre Experience

Continuous positive airway pressure (CPAP) has been the standard treatment of obstructive sleep apnoea/hypopnoea syndrome (OSA) for almost four decades. Though usually effective, this treatment suffers from poor long-term compliance. Therefore, the aim of our one centre retrospective study was to assess factors responsible for treatment failure and long-term compliance. Four hundred subsequent patients diagnosed with OSA and qualified for CPAP treatment were chosen from our database and compliance data were obtained from medical charts. Many differing factors kept patients from starting CPAP or led to termination of treatment. Overall, almost half of patients ended treatment during the mean time of observation of 3.5 years. Survival analysis revealed that 25% of patients failed at a median time of 38.2 months. From several demographic and clinical covariates in Cox’s hazard model, only the presence of a mild OSA, i.e., AHI (apnoea/hypopnoea index) below 15/h was a factor strongly associated with long term CPAP failure. The compliance results of our study are in line with numerous studies addressing this issue. Contrary to them, some demographic or clinical variables that we used in our survival model were not related to CPAP adherence.

Adjustment for SNPs instead of demographic and clinical covariates in GWAS

Genome-wide association study (GWAS) is fundamentally designed to detect disease-causing genes. To reduce spurious associations or improve statistical power, about 80% of GWASs arbitrarily adjusted for demographic and clinical covariates. However, adjustment strategies in GWASs have not achieved consistent conclusions. Given the initial aim of GWAS that is to identify the causal association between a specific causal single-nucleotide polymorphism (SNP) and disease trait, we summarized all complex relationships of the target SNP, covariate and disease trait into 15 causal diagrams according to various roles of the covariate. Following each causal diagram, we conducted a series of theoretical justifications and statistical simulations. Our results demonstrate that it is unadvisable to adjust for any demographic or clinical covariates. We illustrate our point by applying GWASs for body mass index (BMI) and breast cancer, including adjusting and non-adjusting for age and smoking status. Genetic effects and P values might vary across different strategies. Instead, adjustments for SNPs (G') should be strongly recommended when G' are in linkage disequilibrium with the target SNP, and correlated with disease trait conditional on the target SNP. Specifically, adjustment for such G' can block all the confounding paths between the target SNP and disease trait, and avoid over-adjusting for colliders or intermediaries.

Consequence of adjustments for demographic or clinical covariates and a recommended solution in genome-wide association studies

Genome-wide association study (GWAS) is fundamentally designed to detect disease-causing genes. To reduce spurious associations or improve statistical power, about 80% of GWASs arbitrarily adjusted for demographic or clinical covariates. However, adjustment strategies in GWASs have not achieved consistent conclusions. Given the initial aim of GWAS that is to identify the causal association between a specific causal single-nucleotide polymorphism (SNP) and disease trait, we summarized all complex relationships of the target SNP, covariate and disease trait into 15 causal diagrams according to various roles of the covariate. Following each causal diagram, we conducted a series of theoretical justifications and statistical simulations. Our results demonstrate that it is unadvisable to adjust for any demographic or clinical covariates. We illustrate our point by applying GWASs for body mass index (BMI) and breast cancer, including adjusting and non-adjusting for age and smoking status. Genetic effects and P values might vary across different strategies. Instead, adjustments for SNPs (G') should be strongly recommended when G' are in linkage disequilibrium with the target SNP, and correlated with disease trait conditional on the target SNP. Specifically, adjustment for such G' can block all the confounding paths between the target SNP and disease trait, and avoid over-adjusting for colliders or intermediaries.

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and present challenges to clinical decision-making. We generated a dataset of Optical Coherence Tomography (OCT) based high-resolution circular measurements on NRR phenotypes, along with other clinical covariates, of 3973 healthy eyes as part of an established clinical cohort of Asian Indian participants. We introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, followed by meta-clustering to characterize the clusters using clinical covariates, and presented a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40–49 years with predictive potential for glaucoma. Our dataset also addresses the disparity of representation of this particular population in normative OCT databases.

High dose intravenous vitamin C treatment in Sepsis: associations with acute kidney injury and mortality

Background The effects of vitamin C on clinical outcomes in critically ill patients remain controversial due to inconclusive studies. This retrospective observational cohort study evaluated the effects of vitamin C therapy on acute kidney injury (AKI) and mortality among septic patients. Methods Electronic medical records of 1390 patients from an academic hospital who were categorized as Treatment (received at least one dose of 1.5 g IV vitamin C, n = 212) or Comparison (received no, or less than 1.5 g IV vitamin C, n = 1178) were reviewed. Propensity score matching was conducted to balance a number of covariates between groups. Multivariate logistic regressions were conducted predicting AKI and in-hospital mortality among the full sample and a sub-sample of patients seen in the ICU. Results Data revealed that vitamin C therapy was associated with increases in AKI (OR = 2.07 95% CI [1.46–2.93]) and in-hospital mortality (OR = 1.67 95% CI [1.003–2.78]) after adjusting for demographic and clinical covariates. When stratified to examine ICU patients, vitamin C therapy remained a significant risk factor of AKI (OR = 1.61 95% CI [1.09–2.39]) and provided no protective benefit against mortality (OR = 0.79 95% CI [0.48–1.31]). Conclusion Ongoing use of high dose vitamin C in sepsis should be appraised due to observed associations with AKI and death.

Population Pharmacokinetics of Intravenous Amoxicillin Combined With Clavulanic Acid in Healthy and Critically Ill Dogs

Background: Data regarding antimicrobial pharmacokinetics (PK) in critically ill dogs are lacking and likely differ from those of healthy dogs. The aim of this work is to describe a population PK model for intravenous (IV) amoxicillin–clavulanic acid (AMC) in both healthy and sick dogs and to simulate a range of clinical dosing scenarios to compute PK/PD cutoffs for both populations.Methods: This study used a prospective clinical trial in normal and critically ill dogs. Twelve client-owned dogs hospitalized in the intensive care unit (ICU) received IV AMC 20 mg/kg every 8 h (0.5-h infusion) during at least 48 h. Eight blood samples were collected at predetermined times, including four trough samples before the next administration. Clinical covariates and outcome were recorded, including survival to discharge and bacteriologic clinical failure. Satellite PK data were obtained de novo from a group of 12 healthy research dogs that were dosed with a single AMC 20 mg/kg IV. Non-linear mixed-effects model was used to estimate the PK parameters (and the effect of health upon them) together with variability within and between subjects. Monte Carlo simulations were performed with seven dosage regimens (standard and increased doses). The correlation between model-derived drug exposure and clinical covariates was tested with Spearman's non-parametric correlation analysis. Outcome was recorded including survival to discharge and bacteriologic clinical failure.Results: A total of 218 amoxicillin concentrations in plasma were available for healthy and sick dogs. A tricompartmental model best described the data. Amoxicillin clearance was reduced by 56% in sick dogs (0.147 L/kg/h) compared with healthy dogs (0.336 L/kg/h); intercompartmental clearance was also decreased (p <0.01). None of the clinical data covariates were significantly correlated with individual exposure. Monte Carlo simulations showed that higher PK/PD cutoff values of 8 mg/L could be reached in sick dogs by extending the infusion to 3 h or doubling the dose.Conclusions: The PK of AMC is profoundly different in critically ill dogs compared with normal dogs, with much higher interindividual variability and a lower systemic clearance. Our study allows to generate hypotheses with regard to higher AMC exposure in clinical dogs and provides supporting data to revise current AMC clinical breakpoint for IV administration.

Identification of Osteoporosis Using Ensemble Deep Learning Model with Panoramic Radiographs and Clinical Covariates

Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radiographs would be very cost-effective and useful. In this study, we investigate the use of deep learning to classify osteoporosis from dental panoramic radiographs. In addition, the effect of adding clinical covariate data to the radiographic images on the identification performance was assessed. For objective labeling, a dataset containing 778 images was collected from patients who underwent both skeletal-bone-mineral density measurement and dental panoramic radiography at a single general hospital between 2014 and 2020. Osteoporosis was assessed from the dental panoramic radiographs using convolutional neural network (CNN) models, including EfficientNet-b0, -b3, and -b7 and ResNet-18, -50, and -152. An ensemble model was also constructed with clinical covariates added to each CNN. The ensemble model exhibited improved performance on all metrics for all CNNs, especially accuracy and AUC. The results show that deep learning by CNN can accurately classify osteoporosis from dental panoramic radiographs. Furthermore, it was shown that the accuracy can be improved using an ensemble model with patient covariates.

FastMix: A Versatile Multi-Omics Data Integration Pipeline for Cell Type-Specific Biomarker Inference

We developed a novel analytic pipeline - FastMix - to integrate flow cytometry, bulk transcriptomics, and clinical covariates for statistical inference of cell type-specific gene expression signatures. FastMix addresses the ''large p, small n'' problem via a carefully designed linear mixed effects model (LMER), which is applicable for both cross-sectional and longitudinal studies. With a novel moment-based estimator, FastMix runs and converges much faster than competing methods for big data analytics. The pipeline also includes a cutting-edge flow cytometry data analysis method for identifying cell population proportions. Simulation studies showed that FastMix produced smaller type I/II errors with more accurate parameter estimation than competing methods. When applied to real transcriptomics and flow cytometry data in two vaccine studies, FastMix-identified cell type-specific signatures were largely consistent with those obtained from the single cell RNA-seq data, with some unique interesting findings.

Elastic Shape Analysis of Brain Structures for Predictive Modeling of PTSD

Predictive modeling involving brain morphological features and other covariates is of paramount interest in such heterogeneous mental disorders as PTSD. We propose a comprehensive shape analysis framework representing brain substructures, such as the hippocampus, amygdala, and putamen, as parameterized surfaces and quantifying their shape differences using an elastic shape metric. Under this metric, we compute shape summaries (mean, covariance, PCA) of subcortical data and represent individual shapes by their principal scores under a shape PCA basis. These representations are rich enough to allow visualizations of full 3D structures and help understand localized changes. Subsequently, we use these PCs, the auxiliary exposure variables, and their interactions for regression modeling and prediction. We apply our method to data from the Grady Trauma Project (GTP), where the goal is to predict clinical measures of PTSD. The framework seamlessly integrates accurate morphological features and other clinical covariates to yield superior predictive performance when modeling PTSD outcomes. This approach reveals considerably greater predictive power under the elastic shape analysis than the current approaches and helps identify local deformations in brain shapes associated with PTSD severity.