Development and Validation of a Claims-Based Model to Predict Categories of Obesity.

We developed and validated a claims-based algorithm that classifies patients into obesity categories. Using Medicare (2007-2017) and Medicaid (2000-2014) claims data linked to 2 electronic health record (EHR) systems in Boston, Massachusetts, we identified a cohort of patients with an EHR-based body mass index (BMI) measurement (calculated as weight (kg)/height (m)2). We used regularized regression to select from 137 variables and built generalized linear models to classify patients with BMIs of ≥25, ≥30, and ≥40. We developed the prediction model using EHR system 1 (training set) and validated it in EHR system 2 (validation set). The cohort contained 123,432 patients in the Medicare population and 40,736 patients in the Medicaid population. The model comprised 97 variables in the Medicare set and 95 in the Medicaid set, including BMI-related diagnosis codes, cardiovascular and antidiabetic drugs, and obesity-related comorbidities. The areas under the receiver-operating-characteristic curve in the validation set were 0.72, 0.75, and 0.83 (Medicare) and 0.66, 0.66, and 0.70 (Medicaid) for BMIs of ≥25, ≥30, and ≥40, respectively. The positive predictive values were 81.5%, 80.6%, and 64.7% (Medicare) and 81.6%, 77.5%, and 62.5% (Medicaid), for BMIs of ≥25, ≥30, and ≥40, respectively. The proposed model can identify obesity categories in claims databases when BMI measurements are missing and can be used for confounding adjustment, defining subgroups, or probabilistic bias analysis.

Precision psychiatry: predicting predictability.

Precision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.

Effective sample size: A measure of individual uncertainty in predictions.

Clinical prediction models are estimated using a sample of limited size from the target population, leading to uncertainty in predictions, even when the model is correctly specified. Generally, not all patient profiles are observed uniformly in model development. As a result, sampling uncertainty varies between individual patients' predictions. We aimed to develop an intuitive measure of individual prediction uncertainty. The variance of a patient's prediction can be equated to the variance of the sample mean outcome in n ∗ $$ {n}_{\ast } $$ hypothetical patients with the same predictor values. This hypothetical sample size n ∗ $$ {n}_{\ast } $$ can be interpreted as the number of similar patients n eff $$ {n}_{\mathrm{eff}} $$ that the prediction is effectively based on, given that the model is correct. For generalized linear models, we derived analytical expressions for the effective sample size. In addition, we illustrated the concept in patients with acute myocardial infarction. In model development, n eff $$ {n}_{\mathrm{eff}} $$ can be used to balance accuracy versus uncertainty of predictions. In a validation sample, the distribution of n eff $$ {n}_{\mathrm{eff}} $$ indicates which patients were more and less represented in the development data, and whether predictions might be too uncertain for some to be practically meaningful. In a clinical setting, the effective sample size may facilitate communication of uncertainty about predictions. We propose the effective sample size as a clinically interpretable measure of uncertainty in individual predictions. Its implications should be explored further for the development, validation and clinical implementation of prediction models.

Care Fragmentation, Social Determinants of Health, and Postoperative Mortality in Older Veterans.

INTRODUCTION: Veterans Affairs Surgical Quality Improvement Program (VASQIP) benchmarking algorithms helped the Veterans Health Administration (VHA) reduce postoperative mortality. Despite calls to consider social risk factors, these algorithms do not adjust for social determinants of health (SDoH) or account for services fragmented between the VHA and the private sector. This investigation examines how the addition of SDoH change model performance and quantifies associations between SDoH and 30-d postoperative mortality. METHODS: VASQIP (2013-2019) cohort study in patients ≥65 y old with 2-30-d inpatient stays. VASQIP was linked to other VHA and Medicare/Medicaid data. 30-d postoperative mortality was examined using multivariable logistic regression models, adjusting first for clinical variables, then adding SDoH. RESULTS: In adjusted analyses of 93,644 inpatient cases (97.7% male, 79.7% non-Hispanic White), higher proportions of non-veterans affairs care (adjusted odds ratio [aOR] = 1.02, 95% CI = 1.01-1.04) and living in highly deprived areas (aOR = 1.15, 95% CI = 1.02-1.29) were associated with increased postoperative mortality. Black race (aOR = 0.77, CI = 0.68-0.88) and rurality (aOR = 0.87, CI = 0.79-0.96) were associated with lower postoperative mortality. Adding SDoH to models with only clinical variables did not improve discrimination (c = 0.836 versus c = 0.835). CONCLUSIONS: Postoperative mortality is worse among Veterans receiving more health care outside the VA and living in highly deprived neighborhoods. However, adjusting for SDoH is unlikely to improve existing mortality-benchmarking models. Reduction efforts for postoperative mortality could focus on alleviating care fragmentation and designing care pathways that consider area deprivation. The adjusted survival advantage for rural and Black Veterans may be of interest to private sector hospitals as they attempt to alleviate enduring health-care disparities.

Statistical methods for discrimination of STR genotypes using high resolution melt curve data.

Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen's principal component analysis (PCA)-based ScreenClust® HRM® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene® Q qPCR instrument and EvaGreen® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.

Evaluating methods for risk prediction of Covid-19 mortality in nursing home residents before and after vaccine availability: a retrospective cohort study.

BACKGROUND: SARS-CoV-2 vaccines are effective in reducing hospitalization, COVID-19 symptoms, and COVID-19 mortality for nursing home (NH) residents. We sought to compare the accuracy of various machine learning models, examine changes to model performance, and identify resident characteristics that have the strongest associations with 30-day COVID-19 mortality, before and after vaccine availability. METHODS: We conducted a population-based retrospective cohort study analyzing data from all NH facilities across Ontario, Canada. We included all residents diagnosed with SARS-CoV-2 and living in NHs between March 2020 and July 2021. We employed five machine learning algorithms to predict COVID-19 mortality, including logistic regression, LASSO regression, classification and regression trees (CART), random forests, and gradient boosted trees. The discriminative performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) for each model using 10-fold cross-validation. Model calibration was determined through evaluation of calibration slopes. Variable importance was calculated by repeatedly and randomly permutating the values of each predictor in the dataset and re-evaluating the model's performance. RESULTS: A total of 14,977 NH residents and 20 resident characteristics were included in the model. The cross-validated AUCs were similar across algorithms and ranged from 0.64 to 0.67. Gradient boosted trees and logistic regression had an AUC of 0.67 pre- and post-vaccine availability. CART had the lowest discrimination ability with an AUC of 0.64 pre-vaccine availability, and 0.65 post-vaccine availability. The most influential resident characteristics, irrespective of vaccine availability, included advanced age (≥ 75 years), health instability, functional and cognitive status, sex (male), and polypharmacy. CONCLUSIONS: The predictive accuracy and discrimination exhibited by all five examined machine learning algorithms were similar. Both logistic regression and gradient boosted trees exhibit comparable performance and display slight superiority over other machine learning algorithms. We observed consistent model performance both before and after vaccine availability. The influence of resident characteristics on COVID-19 mortality remained consistent across time periods, suggesting that changes to pre-vaccination screening practices for high-risk individuals are effective in the post-vaccination era.

Interim recruitment prediction for multi-center clinical trials.

We introduce a general framework for monitoring, modeling, and predicting the recruitment to multi-center clinical trials. The work is motivated by overly optimistic and narrow prediction intervals produced by existing time-homogeneous recruitment models for multi-center recruitment. We first present two tests for detection of decay in recruitment rates, together with a power study. We then introduce a model based on the inhomogeneous Poisson process with monotonically decaying intensity, motivated by recruitment trends observed in oncology trials. The general form of the model permits adaptation to any parametric curve-shape. A general method for constructing sensible parameter priors is provided and Bayesian model averaging is used for making predictions which account for the uncertainty in both the parameters and the model. The validity of the method and its robustness to misspecification are tested using simulated datasets. The new methodology is then applied to oncology trial data, where we make interim accrual predictions, comparing them to those obtained by existing methods, and indicate where unexpected changes in the accrual pattern occur.

Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis.

BACKGROUND: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. AIMS: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models. METHODS: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models). RESULTS: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination. CONCLUSION: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.

The challenge of understanding and predicting phenotypic diversity in urea cycle disorders.

The Urea Cycle Disorders Consortium (UCDC) and the European registry and network for Intoxication type Metabolic Diseases (E-IMD) are the worldwide largest databases for individuals with urea cycle disorders (UCDs) comprising longitudinal data from more than 1100 individuals with an overall long-term follow-up of approximately 25 years. However, heterogeneity of the clinical phenotype as well as different diagnostic and therapeutic strategies hamper our understanding on the predictors of phenotypic diversity and the impact of disease-immanent and interventional variables (e.g., diagnostic and therapeutic interventions) on the long-term outcome. A new strategy using combined and comparative data analyses helped overcome this challenge. This review presents the mechanisms and relevant principles that are necessary for the identification of meaningful clinical associations by combining data from different data sources, and serves as a blueprint for future analyses of rare disease registries.

Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more.

INTRODUCTION: Fatigue is the most common and debilitating symptom experienced by cancer patients undergoing chemotherapy (CTX). Prediction of symptom severity can assist clinicians to identify high-risk patients and provide education to decrease symptom severity. The purpose of this study was to predict the severity of morning fatigue in the week following the administration of CTX. METHODS: Outpatients (n = 1217) completed questionnaires 1 week prior to and 1 week following administration of CTX. Morning fatigue was measured using the Lee Fatigue Scale (LFS). Separate prediction models for morning fatigue severity were created using 157 demographic, clinical, symptom, and psychosocial adjustment characteristics and either morning fatigue scores or individual fatigue item scores. Prediction models were created using two regression and five machine learning approaches. RESULTS: Elastic net models provided the best fit across all models. For the EN model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out," "exhausted") were the strongest predictors. CONCLUSIONS: This study is the first to use machine learning techniques to accurately predict the severity of morning fatigue from prior to through the week following the administration of CTX using total and individual item scores from the Lee Fatigue Scale (LFS). Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict morning fatigue severity.

Landscape of Human Immunodeficiency Virus Neutralization Susceptibilities Across Tissue Reservoirs.

BACKGROUND: Human immunodeficiency virus type 1 (HIV-1) sequence diversity and the presence of archived epitope muta-tions in antibody binding sites are a major obstacle for the clinical application of broadly neutralizing antibodies (bNAbs) against HIV-1. Specifically, it is unclear to what degree the viral reservoir is compartmentalized and if virus susceptibility to antibody neutralization differs across tissues. METHODS: The Last Gift cohort enrolled 7 people with HIV diagnosed with a terminal illness and collected antemortem blood and postmortem tissues across 33 anatomical compartments for near full-length env HIV genome sequencing. Using these data, we applied a Bayesian machine-learning model (Markov chain Monte Carlo-support vector machine) that uses HIV-1 envelope sequences and approximated glycan-occupancy information to quantitatively predict the half-maximal inhib-itory concentrations (IC50) of bNAbs, allowing us to map neutralization resistance pattern across tissue reservoirs. RESULTS: Predicted mean susceptibilities across tissues within participants were relatively homogenous, and the susceptibility pattern observed in blood often matched what was predicted for tissues. However, selected tissues, such as the brain, showed ev-idence of compartmentalized viral populations with distinct neutralization susceptibilities in some participants. Additionally, we found substantial heterogeneity in the range of neutralization susceptibilities across tissues within and between indi-viduals, and between bNAbs within individuals (standard deviation of log2(IC50) >3.4). CONCLUSIONS: Blood-based screening methods to determine viral susceptibility to bNAbs might underestimate the presence of resistant viral variants in tissues. The extent to which these resistant viruses are clinically relevant, that is, lead to bNAb therapeutic failure, needs to be further explored.

Contrast-enhanced CT radiomics for prediction of recurrence-free survival in gallbladder carcinoma after surgical resection.

OBJECTIVES: Gallbladder carcinoma (GBC) is the most common and aggressive biliary tract malignancy with high postoperative recurrence rates. This single-center study aimed to develop and validate a radiomics signature to estimate GBC recurrence-free survival (RFS). METHODS: This study retrospectively included 204 consecutive patients with pathologically diagnosed GBC and were randomly divided into development (n = 142) and validation (n = 62) cohorts (7:3). The radiomics features of tumor were extracted from preoperative contrast-enhanced CT imaging for each patient. In the development cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression was employed to develop a radiomics signature for RFS prediction. The patients were stratified into high-score or low-score groups according to their median value of radiomics score. A nomogram was established using multivariable Cox regression by incorporating significant pathological predictors and radiomics signatures. RESULTS: The radiomics signature based on 12 features could discriminate high-risk patients with poor RFS. Multivariate Cox analysis revealed that pT3/4 stage (hazard ratio, [HR] = 2.691), pN2 stage (HR = 3.60), poor differentiation grade (HR = 2.651), and high radiomics score (HR = 1.482) were independent risk variables associated with worse RFS and were incorporated to construct a nomogram. The nomogram displayed good prediction performance in estimating RFS with AUC values of 0.895, 0.935, and 0.907 at 1, 3, and 5 years, respectively. CONCLUSIONS: The radiomics signature and combined nomogram may assist in predicting RFS in GBC patients. KEY POINTS: • A radiomics signature extracted from preoperative contrast-enhanced CT can be a useful tool to preoperatively predict RFS of GBC. • T3/T4 stage, N2, poor tumor differentiation, and high radiomics score were positively associated with postoperative recurrence.

External Validation of the FIPS Score for Post-TIPS Mortality in a National Veterans Affairs Cohort.

BACKGROUND: The Freiburg index of post-TIPS survival (FIPS) score was recently demonstrated to improve prediction of post-TIPS mortality relative to existing standards. As this score was derived from a German cohort over an extended time period, it is unclear if performance will translate well to other settings. This study aimed to externally validate the FIPS score in a large Veterans Affairs (VA) cohort over two separate eras of TIPS-related care. METHODS: This was a retrospective cohort study of patients with cirrhosis who underwent TIPS placement in the VA from 2008 to 2020. Cox regression models for post-TIPS survival were constructed using FIPS, MELD, MELD-Na, or CTP scores as predictors. Discrimination (Harrell's C) and calibration (joint tests of calibration curve slope and intercept) were evaluated for each score. A stratified analysis was performed for time periods between 2008-2013 and 2014-2020. RESULTS: The cohort of 1,274 patients was 97.3% male with mean age 60.9 years and mean MELD-Na 14. The FIPS score demonstrated the highest overall discrimination versus MELD, MELD-Na, and CTP (0.634 vs. 0.585, 0.626, 0.612, respectively). However, in the modern treatment era (2014-2020), the FIPS score performed similarly to MELD-Na. Additionally, the FIPS score demonstrated poor calibration at one-month and six-month post-TIPS timepoints (joint p = 0.04 and 0.004, respectively). MELD, MELD-Na, and CTP were well-calibrated at each timepoint (each joint p > 0.05). CONCLUSION: The FIPS score performed similarly to MELD-Na in the modern TIPS treatment era and demonstrated regions of poor calibration. Future models derived with contemporary data may improve prediction of post-TIPS mortality.

Optimizing an existing prediction model for quality of life one-year post-intensive care unit: An exploratory analysis.

BACKGROUND: This study aimed to improve the PREPARE model, an existing linear regression prediction model for long-term quality of life (QoL) of intensive care unit (ICU) survivors by incorporating additional ICU data from patients' electronic health record (EHR) and bedside monitors. METHODS: The 1308 adult ICU patients, aged ≥16, admitted between July 2016 and January 2019 were included. Several regression-based machine learning models were fitted on a combination of patient-reported data and expert-selected EHR variables and bedside monitor data to predict change in QoL 1 year after ICU admission. Predictive performance was compared to a five-feature linear regression prediction model using only 24-hour data (R2  = 0.54, mean square error (MSE) = 0.031, mean absolute error (MAE) = 0.128). RESULTS: The 67.9% of the included ICU survivors was male and the median age was 65.0 [IQR: 57.0-71.0]. Median length of stay (LOS) was 1 day [IQR 1.0-2.0]. The incorporation of the additional data pertaining to the entire ICU stay did not improve the predictive performance of the original linear regression model. The best performing machine learning model used seven features (R2  = 0.52, MSE = 0.032, MAE = 0.125). Pre-ICU QoL, the presence of a cerebro vascular accident (CVA) upon admission and the highest temperature measured during the ICU stay were the most important contributors to predictive performance. Pre-ICU QoL's contribution to predictive performance far exceeded that of the other predictors. CONCLUSION: Pre-ICU QoL was by far the most important predictor for change in QoL 1 year after ICU admission. The incorporation of the numerous additional features pertaining to the entire ICU stay did not improve predictive performance although the patients' LOS was relatively short.

Reinforcement learning evaluation of treatment policies for patients with hepatitis C virus.

BACKGROUND: Evaluation of new treatment policies is often costly and challenging in complex conditions, such as hepatitis C virus (HCV) treatment, or in limited-resource settings. We sought to identify hypothetical policies for HCV treatment that could best balance the prevention of cirrhosis while preserving resources (financial or otherwise). METHODS: The cohort consisted of 3792 HCV-infected patients without a history of cirrhosis or hepatocellular carcinoma at baseline from the national Veterans Health Administration from 2015 to 2019. To estimate the efficacy of hypothetical treatment policies, we utilized historical data and reinforcement learning to allow for greater flexibility when constructing new HCV treatment strategies. We tested and compared four new treatment policies: a simple stepwise policy based on Aspartate Aminotransferase to Platelet Ratio Index (APRI), a logistic regression based on APRI, a logistic regression on multiple longitudinal and demographic indicators that were prespecified for clinical significance, and a treatment policy based on a risk model developed for HCV infection. RESULTS: The risk-based hypothetical treatment policy achieved the lowest overall risk with a score of 0.016 (90% CI 0.016, 0.019) while treating the most high-risk (346.4 ± 1.4) and the fewest low-risk (361.0 ± 20.1) patients. Compared to hypothetical treatment policies that treated approximately the same number of patients (1843.7 vs. 1914.4 patients), the risk-based policy had more untreated time per patient (7968.4 vs. 7742.9 patient visits), signaling cost reduction for the healthcare system. CONCLUSIONS: Off-policy evaluation strategies are useful to evaluate hypothetical treatment policies without implementation. If a quality risk model is available, risk-based treatment strategies can reduce overall risk and prioritize patients while reducing healthcare system costs.

Prediction modeling of geogenic iodine contaminated groundwater throughout China.

Geogenic iodine-contaminated groundwater represents a threat to public health in China. Identifying high-iodine areas is essential to guide the mitigation of this problem. Considering that traditional analytical techniques for iodine testing are generally time-consuming, laborious, and expensive, alternative methods are needed to supplement and enhance existing approaches. Therefore, we developed an artificial neural network (ANN) model and assessed its feasibility in terms of predicting high iodine levels in groundwater in China. A total of 22 indicators (including climate, topography, geology, and soil properties) and 3185 aggregated samples (measured groundwater iodine concentrations) were utilized to develop the ANN model. The results showed that the accuracy and area under the receiver operating characteristic curve of the model on the test dataset are 90.9% and 0.972, respectively, and climate and soil variables are the most effective predictors. Based on the prediction results, a high-resolution (1-km) nationwide prediction map of high-iodine groundwater was produced. The high-risk areas are mainly concentrated in the central provinces of Henan, Shaanxi, and Shanxi, the eastern provinces of Henan, Shandong, and Hebei, and the northeastern provinces of Liaoning, Jilin, and Heilongjiang. The total number of people estimated to potentially be at high-risk areas because they use untreated high-iodine groundwater as drinking water is approximately 30 million. Considering the growing demand for groundwater in China, this work can guide the prioritization of groundwater contamination mitigation efforts based on regional groundwater quality levels to enhance environmental management.

Prediction of steroid resistance and steroid dependence in nephrotic syndrome children.

BACKGROUND: Steroid resistant (SR) nephrotic syndrome (NS) affects up to 30% of children and is responsible for fast progression to end stage renal disease. Currently there is no early prognostic marker of SR and studied candidate variants and parameters differ highly between distinct ethnic cohorts. METHODS: Here, we analyzed 11polymorphic variants, 6 mutations, SOCS3 promoter methylation and biochemical parameters as prognostic markers in a group of 124 Polish NS children (53 steroid resistant, 71 steroid sensitive including 31 steroid dependent) and 55 controls. We used single marker and multiple logistic regression analysis, accompanied by prediction modeling using neural network approach. RESULTS: We achieved 92% (AUC = 0.778) SR prediction for binomial and 63% for multinomial calculations, with the strongest predictors ABCB1 rs1922240, rs1045642 and rs2235048, CD73 rs9444348 and rs4431401, serum creatinine and unmethylated SOCS3 promoter region. Next, we achieved 80% (AUC = 0.720) in binomial and 63% in multinomial prediction of SD, with the strongest predictors ABCB1 rs1045642 and rs2235048. Haplotype analysis revealed CD73_AG to be associated with SR while ABCB1_AGT was associated with SR, SD and membranoproliferative pattern of kidney injury regardless the steroid response. CONCLUSIONS: We achieved prediction of steroid resistance and, as a novelty, steroid dependence, based on early markers in NS children. Such predictions, prior to drug administration, could facilitate decision on a proper treatment and avoid diverse effects of high steroid doses.

Penalized regression calibration: A method for the prediction of survival outcomes using complex longitudinal and high-dimensional data.

Longitudinal and high-dimensional measurements have become increasingly common in biomedical research. However, methods to predict survival outcomes using covariates that are both longitudinal and high-dimensional are currently missing. In this article, we propose penalized regression calibration (PRC), a method that can be employed to predict survival in such situations. PRC comprises three modeling steps: First, the trajectories described by the longitudinal predictors are flexibly modeled through the specification of multivariate mixed effects models. Second, subject-specific summaries of the longitudinal trajectories are derived from the fitted mixed models. Third, the time to event outcome is predicted using the subject-specific summaries as covariates in a penalized Cox model. To ensure a proper internal validation of the fitted PRC models, we furthermore develop a cluster bootstrap optimism correction procedure that allows to correct for the optimistic bias of apparent measures of predictiveness. PRC and the CBOCP are implemented in the R package pencal, available from CRAN. After studying the behavior of PRC via simulations, we conclude by illustrating an application of PRC to data from an observational study that involved patients affected by Duchenne muscular dystrophy, where the goal is predict time to loss of ambulation using longitudinal blood biomarkers.

Risk Factors for and Outcomes Associated With Peri-Intubation Hypoxemia: A Multicenter Prospective Cohort Study.

BACKGROUND: Little is known about hypoxemia surrounding endotracheal intubation in the critically ill. Thus, we sought to identify risk factors associated with peri-intubation hypoxemia and its effects' on the critically ill. METHODS: Data from a multicenter, prospective, cohort study enrolling 1,033 critically ill adults who underwent endotracheal intubation across 16 medical/surgical ICUs in the United States from July 2015-January 2017 were used to identify risk factors associated with peri-intubation hypoxemia and its effects on patient outcomes. We defined hypoxemia as any pulse oximetry ≤ 88% during and up to 30 minutes following endotracheal intubation. RESULTS: In the full analysis (n = 1,033), 123 (11.9%) patients experienced the primary outcome. Five risk factors independently associated with our outcome were identified on multiple logistic regression: cardiac related reason for endotracheal intubation (OR 1.67, [95% CI 1.04, 2.69]); pre-intubation noninvasive ventilation (OR 1.66, [95% CI 1.09, 2.54]); emergency intubation (OR 1.65, [95% CI 1.06, 2.55]); moderate-severe difficult bag-mask ventilation (OR 2.68, [95% CI 1.72, 4.19]); and crystalloid administration within the preceding 24 hours (OR 1.24, [95% CI 1.07, 1.45]; per liter up to 4 liters). Higher baseline SpO2 was found to be protective (OR 0.93, [95% CI 0.91, 0.96]; per percent up to 97%). Consistent results were seen in a separate analysis on only stable patients (n = 921, 93 [10.1%]) (those without baseline hypoxemia ≤ 88%). Peri-intubation hypoxemia was associated with in-hospital mortality (OR 2.40, [95% CI 1.33, 4.31]; stable patients: OR 2.67, [95% CI 1.38, 5.17]) but not ICU length of stay (point estimate 0.9 days, [95% CI -1.0, 2.8 days]; stable patients: point estimate 1.5 days, [95% CI -0.4, 3.4 days]) after adjusting for age, body mass index, illness severity, airway related reason for intubation (i.e., acute respiratory failure), and baseline SPO2. CONCLUSIONS: Patients with pre-existing noninvasive ventilation and volume loading who were intubated emergently in the setting of hemodynamic compromise with bag-mask ventilation described as moderate-severe were at increased risk for peri-intubation hypoxemia. Higher baseline oxygenation was found to be protective against peri-intubation hypoxemia. Peri-intubation hypoxemia was associated with in-hospital mortality but not ICU length of stay. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT02508948 and Registered Report Identifier: RR2-10.2196/11101.

Modeling residential indoor concentrations of PM2.5 , NO2 , NOx , and secondhand smoke in the Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS) Air study.

Increased outdoor concentrations of fine particulate matter (PM2.5 ) and oxides of nitrogen (NO2 , NOx ) are associated with respiratory and cardiovascular morbidity in adults and children. However, people spend most of their time indoors and this is particularly true for individuals with chronic obstructive pulmonary disease (COPD). Both outdoor and indoor air pollution may accelerate lung function loss in individuals with COPD, but it is not feasible to measure indoor pollutant concentrations in all participants in large cohort studies. We aimed to understand indoor exposures in a cohort of adults (SPIROMICS Air, the SubPopulations and Intermediate Outcome Measures in COPD Study of Air pollution). We developed models for the entire cohort based on monitoring in a subset of homes, to predict mean 2-week-measured concentrations of PM2.5 , NO2 , NOx , and nicotine, using home and behavioral questionnaire responses available in the full cohort. Models incorporating socioeconomic, meteorological, behavioral, and residential information together explained about 60% of the variation in indoor concentration of each pollutant. Cross-validated R2 for best indoor prediction models ranged from 0.43 (NOx ) to 0.51 (NO2 ). Models based on questionnaire responses and estimated outdoor concentrations successfully explained most variation in indoor PM2.5 , NO2 , NOx , and nicotine concentrations.