Identification of Long Non-Coding RNA Expression Profiles and Co-Expression Genes in Thyroid Carcinoma Based on The Cancer Genome Atlas (TCGA) Database.

BACKGROUND Thyroid carcinoma is a malignancy with high morbidity and mortality. Genetic alterations play pivot roles in the pathogenesis of thyroid carcinoma, where long noncoding RNA (lncRNA) have been identified to be crucial. This study sought to investigate the biological functions of lncRNA expression profiles in thyroid carcinoma. MATERIAL AND METHODS The lncRNAs expression profiles were acquired from The Cancer Genome Atlas (TCGA) database according to 510 thyroid cancer tissues and 58 normal thyroid tissues. By using R package edgeR, differentially expressed RNAs were obtained. Also, an overall survival model was established based on Cox regression and clinical data then testified by Kaplan-Meier plot, receiver operating characteristic (ROC)-curve and C-index analysis. We investigated the co-expressed genes with lncRNAs involved in the prognostic model, as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted R package clusterProfile. RESULTS A total of 352 lncRNAs were identified as differentially expressed in thyroid carcinoma, and an overall survival model consisting of 8 signature lncRNAs was proposed (ROC=0.862, C-index=0.893, P<0.05), 3 of which (DOCK9-DT, FAM111A-DT, and LINC01736) represent co-expressed mRNAs. However, as an oncogene, only FAM111A-DT increased the prognostic risk in thyroid carcinoma. Furthermore, we found differential genes LINC01016, LHX1-DT, IGF2-AS, ND MIR1-1HG-AS1, significantly related to lymph node metastasis (P<0.05). CONCLUSIONS In this study, we clarified the differential lncRNA expression profiles which were related to the tumorigenesis and prognosis in thyroid carcinoma. Our results provide new rationale and understandings to the pathogenesis and regulatory mechanisms of thyroid carcinoma.

Exponential Family Functional data analysis via a low-rank model.

In many applications, non-Gaussian data such as binary or count are observed over a continuous domain and there exists a smooth underlying structure for describing such data. We develop a new functional data method to deal with this kind of data when the data are regularly spaced on the continuous domain. Our method, referred to as Exponential Family Functional Principal Component Analysis (EFPCA), assumes the data are generated from an exponential family distribution, and the matrix of the canonical parameters has a low-rank structure. The proposed method flexibly accommodates not only the standard one-way functional data, but also two-way (or bivariate) functional data. In addition, we introduce a new cross validation method for estimating the latent rank of a generalized data matrix. We demonstrate the efficacy of the proposed methods using a comprehensive simulation study. The proposed method is also applied to a real application of the UK mortality study, where data are binomially distributed and two-way functional across age groups and calendar years. The results offer novel insights into the underlying mortality pattern.

Effect of a Multifaceted Quality Improvement Intervention on Hospital Personnel Adherence to Performance Measures in Patients With Acute Ischemic Stroke in China: A Randomized Clinical Trial.

Importance: In China and other parts of the world, hospital personnel adherence to evidence-based stroke care is limited. Objective: To determine whether a multifaceted quality improvement intervention can improve hospital personnel adherence to evidence-based performance measures in patients with acute ischemic stroke (AIS) in China. Design, Setting, and Participants: A multicenter, cluster-randomized clinical trial among 40 public hospitals in China that enrolled 4800 patients hospitalized with AIS from August 10, 2014, through June 20, 2015, with 12-month follow-up through July 30, 2016. Interventions: Twenty hospitals received a multifaceted quality improvement intervention (intervention group; 2400 patients), including a clinical pathway, care protocols, quality coordinator oversight, and performance measure monitoring and feedback. Twenty hospitals participated in the stroke registry with usual care (control group; 2400 patients). Main Outcomes and Measures: The primary outcome was hospital personnel adherence to 9 AIS performance measures, with co-primary outcomes of a composite of percentage of performance measures adhered to, and as all-or-none. Secondary outcomes included in-hospital mortality and long-term outcomes (a new vascular event, disability [modified Rankin Scale score, 3-5], and all-cause mortality) at 3, 6, and 12 months. Results: Among 4800 patients with AIS enrolled from 40 hospitals and randomized (mean age, 65 years; women, 1757 [36.6%]), 3980 patients (82.9%) completed the 12-month follow-up of the trial. Patients in intervention group were more likely to receive performance measures than those in the control groups (composite measure, 88.2% vs 84.8%, respectively; absolute difference, 3.54% [95% CI, 0.68% to 6.40%], P = .02). The all-or-none measure did not significantly differ between the intervention and control groups (53.8% vs 47.8%, respectively; absolute difference, 6.69% [95% CI, -0.41% to 13.79%], P = .06). New clinical vascular events were significantly reduced in the intervention group compared with the control group at 3 months (3.9% vs 5.3%, respectively; difference, -2.03% [95% CI, -3.51% to -0.55%]; P = .007), 6 months (6.3% vs 7.8%, respectively; difference, -2.18% [95% CI, -4.0% to -0.35%]; P = .02) and 12 months (9.1% vs 11.8%, respectively; difference, -3.13% [95% CI, -5.28% to -0.97%]; P = .005). Conclusions and Relevance: Among 40 hospitals in China, a multifaceted quality improvement intervention compared with usual care resulted in a statistically significant but small improvement in hospital personnel adherence to evidence-based performance measures in patients with acute ischemic stroke when assessed as a composite measure, but not as an all-or-none measure. Further research is needed to understand the generalizability of these findings. Trial Registration: ClinicalTrials.gov Identifier: NCT02212912.

Substantial Progress Yet Significant Opportunity for Improvement in Stroke Care in China.

BACKGROUND AND PURPOSE: Stroke is a leading cause of death in China. Yet the adherence to guideline-recommended ischemic stroke performance metrics in the past decade has been previously shown to be suboptimal. Since then, several nationwide stroke quality management initiatives have been conducted in China. We sought to determine whether adherence had improved since then. METHODS: Data were obtained from the 2 phases of China National Stroke Registries, which included 131 hospitals (12 173 patients with acute ischemic stroke) in China National Stroke Registries phase 1 from 2007 to 2008 versus 219 hospitals (19 604 patients) in China National Stroke Registries phase 2 from 2012 to 2013. Multiple regression models were developed to evaluate the difference in adherence to performance measure between the 2 study periods. RESULTS: The overall quality of care has improved over time, as reflected by the higher composite score of 0.76 in 2012 to 2013 versus 0.63 in 2007 to 2008. Nine of 13 individual performance metrics improved. However, there were no significant improvements in the rates of intravenous thrombolytic therapy and anticoagulation for atrial fibrillation. After multivariate analysis, there remained a significant 1.17-fold (95% confidence interval, 1.14-1.21) increase in the odds of delivering evidence-based performance metrics in the more recent time periods versus older data. The performance metrics with the most significantly increased odds included stroke education, dysphagia screening, smoking cessation, and antithrombotics at discharge. CONCLUSIONS: Adherence to stroke performance metrics has increased over time, but significant opportunities remain for further improvement. Continuous stroke quality improvement program should be developed as a national priority in China.

The Statistics and Mathematics of High Dimension Low Sample Size Asymptotics.

The aim of this paper is to establish several deep theoretical properties of principal component analysis for multiple-component spike covariance models. Our new results reveal an asymptotic conical structure in critical sample eigendirections under the spike models with distinguishable (or indistinguishable) eigenvalues, when the sample size and/or the number of variables (or dimension) tend to infinity. The consistency of the sample eigenvectors relative to their population counterparts is determined by the ratio between the dimension and the product of the sample size with the spike size. When this ratio converges to a nonzero constant, the sample eigenvector converges to a cone, with a certain angle to its corresponding population eigenvector. In the High Dimension, Low Sample Size case, the angle between the sample eigenvector and its population counterpart converges to a limiting distribution. Several generalizations of the multi-spike covariance models are also explored, and additional theoretical results are presented.

Mixture SNPs effect on phenotype in genome-wide association studies.

BACKGROUND: Recently mixed linear models are used to address the issue of "missing" heritability in traditional Genome-wide association studies (GWAS). The models assume that all single-nucleotide polymorphisms (SNPs) are associated with the phenotypes of interest. However, it is more common that only a small proportion of SNPs have significant effects on the phenotypes, while most SNPs have no or very small effects. To incorporate this feature, we propose an efficient Hierarchical Bayesian Model (HBM) that extends the existing mixed models to enforce automatic selection of significant SNPs. The HBM models the SNP effects using a mixture distribution of a point mass at zero and a normal distribution, where the point mass corresponds to those non-associative SNPs. RESULTS: We estimate the HBM using Gibbs sampling. The estimation performance of our method is first demonstrated through two simulation studies. We make the simulation setups realistic by using parameters fitted on the Framingham Heart Study (FHS) data. The simulation studies show that our method can accurately estimate the proportion of SNPs associated with the simulated phenotype and identify these SNPs, as well as adapt to certain model mis-specification than the standard mixed models. In addition, we analyze data from the FHS and the Health and Retirement Study (HRS) to study the association between Body Mass Index (BMI) and SNPs on Chromosome 16, and replicate the identified genetic associations. The analysis of the FHS data identifies 0.3% SNPs on Chromosome 16 that affect BMI, including rs9939609 and rs9939973 on the FTO gene. These two SNPs are in strong linkage disequilibrium with rs1558902 (Rsq =0.901 for rs9939609 and Rsq =0.905 for rs9939973), which has been reported to be linked with obesity in previous GWAS. We then replicate the findings using the HRS data: the analysis finds 0.4% of SNPs associated with BMI on Chromosome 16. Furthermore, around 25% of the genes that are identified to be associated with BMI are common between the two studies. CONCLUSIONS: The results demonstrate that the HBM and the associated estimation algorithm offer a powerful tool for identifying significant genetic associations with phenotypes of interest, among a large number of SNPs that are common in modern genetics studies.

Rationale and design of a cluster-randomized multifaceted intervention trial to improve stroke care quality in China: The GOLDEN BRIDGE-Acute Ischemic Stroke.

BACKGROUND: Prior studies have demonstrated a significant gap between guideline-based recommendations and clinical practice in the management of acute ischemic stroke (AIS) in China. AIMS: This study implements a targeted multifaceted quality improvement intervention in AIS patients and identifies the feasibility and efficacy of this intervention. DESIGN: This is a multicenter, 2-arm, open-label, cluster-randomized trial involving 40 clusters (hospitals) from China National Network of Stroke Research. Hospitals are randomized to receive a targeted multifaceted quality improvement intervention (experimental group) or routine standard of care (control group). The multifaceted intervention includes an evidence-based clinical pathway, written care protocols, a quality coordinator, and a monitoring and feedback system of performance measures. The number of enrolled patients in the trial will be 4,800. Primary outcome is the measure of the adherence to AIS evidence-based performance measures including the composite measure (defined as the total number of interventions performed among eligible patients divided by the total number of possible interventions among eligible patients) and the all-or-none measure (defined as the proportion of eligible patients who receive all of the performance measure interventions for which they are eligible). Secondary patient outcomes include inhospital death; a new vascular event; disability; and all-cause death at 3, 6, and 12 months after initial symptom onset. All analyses will be performed according to the intention-to-treatment principle and accounted for clustering using generalized estimating equations. CONCLUSIONS: If proven effective, this targeted multifaceted intervention model will be extended nationwide as a model to bridge the evidence-based gap in the AIS management in China.

The Genome-Wide Influence on Human BMI Depends on Physical Activity, Life Course, and Historical Period.

In this analysis, guided by an evolutionary framework, we investigate how the human genome as a whole interacts with historical period, age, and physical activity to influence body mass index (BMI). The genomic influence is estimated by (1) heritability or the proportion of variance in BMI explained by genome-wide genotype data, and (2) the random effects or the best linear unbiased predictors (BLUPs) of genome-wide association studies (GWAS) data on BMI. Data were used from the Framingham Heart Study (FHS) in the United States. The study was initiated in 1948, and the obesity data were collected repeatedly over the subsequent decades. The analyses draw analysis samples from a pool of >8,000 individuals in the FHS. The hypothesis testing based on Pitman test, permutation Pitman test, F test, and permutation F test produces three sets of significant findings. First, the genomic influence on BMI is substantially larger after the mid-1980s than in the few decades before the mid-1980s within each age group of 21-40, 41-50, 51-60, and >60. Second, the genomic influence on BMI weakens as one ages across the life course, or the genomic influence on BMI tends to be more important during reproductive ages than after reproductive ages within each of the two historical periods. Third, within the age group of 21-50 and not in the age group of >50, the genomic influence on BMI among physically active individuals is substantially smaller than the influence on those who are not physically active. In summary, this study provides evidence that the influence of human genome as a whole on obesity depends on historical period, age, and level of physical activity.

A SPARSE REDUCED RANK FRAMEWORK FOR GROUP ANALYSIS OF FUNCTIONAL NEUROIMAGING DATA.

In spatial-temporal neuroimaging studies, there is an evolving literature on the analysis of functional imaging data in order to learn the intrinsic functional connectivity patterns among different brain regions. However, there are only few efficient approaches for integrating functional connectivity pattern across subjects, while accounting for spatial-temporal functional variation across multiple groups of subjects. The objective of this paper is to develop a new sparse reduced rank (SRR) modeling framework for carrying out functional connectivity analysis across multiple groups of subjects in the frequency domain. Our new framework not only can extract both frequency and spatial factors across subjects, but also imposes sparse constraints on the frequency factors. It thus leads to the identification of important frequencies with high power spectra. In addition, we propose two novel adaptive criteria for automatic selection of sparsity level and model rank. Using simulated data, we demonstrate that SRR outperforms several existing methods. Finally, we apply SRR to detect group differences between controls and two subtypes of attention deficit hyperactivity disorder (ADHD) patients, through analyzing the ADHD-200 data.

Risk score to predict hospital-acquired pneumonia after spontaneous intracerebral hemorrhage.

BACKGROUND AND PURPOSE: We aimed to develop a risk score (intracerebral hemorrhage-associated pneumonia score, ICH-APS) for predicting hospital-acquired stroke-associated pneumonia (SAP) after ICH. METHODS: The ICH-APS was developed based on the China National Stroke Registry (CNSR), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Variables routinely collected at presentation were used for predicting SAP after ICH. For testing the added value of hematoma volume measure, we separately developed 2 models with (ICH-APS-B) and without (ICH-APS-A) hematoma volume included. Multivariable logistic regression was performed to identify independent predictors. The area under the receiver operating characteristic curve (AUROC), Hosmer-Lemeshow goodness-of-fit test, and integrated discrimination index were used to assess model discrimination, calibration, and reclassification, respectively. RESULTS: The SAP was 16.4% and 17.7% in the overall derivation (n=2998) and validation (n=2000) cohorts, respectively. A 23-point ICH-APS-A was developed based on a set of predictors and showed good discrimination in the overall derivation (AUROC, 0.75; 95% confidence interval, 0.72-0.77) and validation (AUROC, 0.76; 95% confidence interval, 0.71-0.79) cohorts. The ICH-APS-A was more sensitive for patients with length of stay >48 hours (AUROC, 0.78; 95% confidence interval, 0.75-0.81) than those with length of stay <48 hours (AUROC, 0.64; 95% confidence interval, 0.55-0.73). The ICH-APS-A was well calibrated (Hosmer-Lemeshow test) in the derivation (P=0.20) and validation (P=0.66) cohorts. Similarly, a 26-point ICH-APS-B was established. The ICH-APS-A and ICH-APS-B were not significantly different in discrimination and reclassification for SAP after ICH. CONCLUSION: The ICH-APSs are valid risk scores for predicting SAP after ICH, especially for patients with length of stay >48 hours.

Poisson factor models with applications to non-normalized microRNA profiling.

MOTIVATION: Next-generation (NextGen) sequencing is becoming increasingly popular as an alternative for transcriptional profiling, as is the case for micro RNAs (miRNA) profiling and classification. miRNAs are a new class of molecules that are regulated in response to differentiation, tumorigenesis or infection. Our primary motivating application is to identify different viral infections based on the induced change in the host miRNA profile. Statistical challenges are encountered because of special features of NextGen sequencing data: the data are read counts that are extremely skewed and non-negative; the total number of reads varies dramatically across samples that require appropriate normalization. Statistical tools developed for microarray expression data, such as principal component analysis, are sub-optimal for analyzing NextGen sequencing data. RESULTS: We propose a family of Poisson factor models that explicitly takes into account the count nature of sequencing data and automatically incorporates sample normalization through the use of offsets. We develop an efficient algorithm for estimating the Poisson factor model, entitled Poisson Singular Value Decomposition with Offset (PSVDOS). The method is shown to outperform several other normalization and dimension reduction methods in a simulation study. Through analysis of an miRNA profiling experiment, we further illustrate that our model achieves insightful dimension reduction of the miRNA profiles of 18 samples: the extracted factors lead to more accurate and meaningful clustering of the cell lines. AVAILABILITY: The PSVDOS software is available on request.

Web-based tool for dynamic functional outcome after acute ischemic stroke and comparison with existing models.

BACKGROUND: Acute ischemic stroke (AIS) is one of the leading causes of death and adult disability worldwide. In the present study, we aimed to develop a web-based risk model for predicting dynamic functional status at discharge, 3-month, 6-month, and 1-year after acute ischemic stroke (Dynamic Functional Status after Acute Ischemic Stroke, DFS-AIS). METHODS: The DFS-AIS was developed based on the China National Stroke Registry (CNSR), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Good functional outcome was defined as modified Rankin Scale (mRS) score ≤ 2 at discharge, 3-month, 6-month, and 1-year after AIS, respectively. Independent predictors of each outcome measure were obtained using multivariable logistic regression. The area under the receiver operating characteristic curve (AUROC) and plot of observed and predicted risk were used to assess model discrimination and calibration. RESULTS: A total of 12,026 patients were included and the median age was 67 (interquartile range: 57-75). The proportion of patients with good functional outcome at discharge, 3-month, 6-month, and 1-year after AIS was 67.9%, 66.5%, 66.9% and 66.9%, respectively. Age, gender, medical history of diabetes mellitus, stroke or transient ischemic attack, current smoking and atrial fibrillation, pre-stroke dependence, pre-stroke statins using, admission National Institutes of Health Stroke Scale score, admission blood glucose were identified as independent predictors of functional outcome at different time points after AIS. The DFS-AIS was developed from sets of predictors of mRS ≤ 2 at different time points following AIS. The DFS-AIS demonstrated good discrimination in the derivation and validation cohorts (AUROC range: 0.837-0.845). Plots of observed versus predicted likelihood showed excellent calibration in the derivation and validation cohorts (all r = 0.99, P < 0.001). When compared to 8 existing models, the DFS-AIS showed significantly better discrimination for good functional outcome and mortality at discharge, 3-month, 6-month, and 1-year after AIS (all P < 0.0001). CONCLUSION: The DFS-AIS is a valid risk model to predict functional outcome at discharge, 3-month, 6-month, and 1-year after AIS.

Risk score to predict gastrointestinal bleeding after acute ischemic stroke.

BACKGROUND: Gastrointestinal bleeding (GIB) is a common and often serious complication after stroke. Although several risk factors for post-stroke GIB have been identified, no reliable or validated scoring system is currently available to predict GIB after acute stroke in routine clinical practice or clinical trials. In the present study, we aimed to develop and validate a risk model (acute ischemic stroke associated gastrointestinal bleeding score, the AIS-GIB score) to predict in-hospital GIB after acute ischemic stroke. METHODS: The AIS-GIB score was developed from data in the China National Stroke Registry (CNSR). Eligible patients in the CNSR were randomly divided into derivation (60%) and internal validation (40%) cohorts. External validation was performed using data from the prospective Chinese Intracranial Atherosclerosis Study (CICAS). Independent predictors of in-hospital GIB were obtained using multivariable logistic regression in the derivation cohort, and ß-coefficients were used to generate point scoring system for the AIS-GIB. The area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively. RESULTS: A total of 8,820, 5,882, and 2,938 patients were enrolled in the derivation, internal validation and external validation cohorts. The overall in-hospital GIB after AIS was 2.6%, 2.3%, and 1.5% in the derivation, internal, and external validation cohort, respectively. An 18-point AIS-GIB score was developed from the set of independent predictors of GIB including age, gender, history of hypertension, hepatic cirrhosis, peptic ulcer or previous GIB, pre-stroke dependence, admission National Institutes of Health stroke scale score, Glasgow Coma Scale score and stroke subtype (Oxfordshire). The AIS-GIB score showed good discrimination in the derivation (0.79; 95% CI, 0.764-0.825), internal (0.78; 95% CI, 0.74-0.82) and external (0.76; 95% CI, 0.71-0.82) validation cohorts. The AIS-GIB score was well calibrated in the derivation (P = 0.42), internal (P = 0.45) and external (P = 0.86) validation cohorts. CONCLUSION: The AIS-GIB score is a valid clinical grading scale to predict in-hospital GIB after AIS. Further studies on the effect of the AIS-GIB score on reducing GIB and improving outcome after AIS are warranted.

A clinically actionable and explainable real-time risk assessment framework for stroke-associated pneumonia.

The current medical practice is more responsive rather than proactive, despite the widely recognized value of early disease detection, including improving the quality of care and reducing medical costs. One of the cornerstones of early disease detection is clinically actionable predictions, where predictions are expected to be accurate, stable, real-time and interpretable. As an example, we used stroke-associated pneumonia (SAP), setting up a transformer-encoder-based model that analyzes highly heterogeneous electronic health records in real-time. The model was proven accurate and stable on an independent test set. In addition, it issued at least one warning for 98.6 % of SAP patients, and on average, its alerts were ahead of physician diagnoses by 2.71 days. We applied Integrated Gradient to glean the model's reasoning process. Supplementing the risk scores, the model highlighted critical historical events on patients' trajectories, which were shown to have high clinical relevance.

Novel risk score to predict pneumonia after acute ischemic stroke.

BACKGROUND AND PURPOSE: To develop and validate a risk score (acute ischemic stroke-associated pneumonia score [AIS-APS]) for predicting in-hospital stroke-associated pneumonia (SAP) after AIS. METHODS: The AIS-APS was developed based on the China National Stroke Registry, in which eligible patients were randomly classified into derivation (60%) and internal validation cohort (40%). External validation was performed using the prospective Chinese Intracranial Atherosclerosis Study. Independent predictors of in-hospital SAP after AIS were obtained using multivariable logistic regression, and ß-coefficients were used to generate point scoring system of the AIS-APS. The area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively. RESULTS: The overall in-hospital SAP after AIS was 11.4%, 11.3%, and 7.3% in the derivation (n=8820), internal (n=5882) and external (n=3037) validation cohort, respectively. A 34-point AIS-APS was developed from the set of independent predictors including age, history of atrial fibrillation, congestive heart failure, chronic obstructive pulmonary disease and current smoking, prestroke dependence, dysphagia, admission National Institutes of Health Stroke Scale score, Glasgow Coma Scale score, stroke subtype (Oxfordshire), and blood glucose. The AIS-APS showed good discrimination (area under the receiver operating characteristic curve) in the internal (0.785; 95% confidence interval, 0.766-0.803) and external (0.792; 95% confidence interval, 0.761-0.823) validation cohort. The AIS-APS was well calibrated (Hosmer-Lemeshow test) in the internal (P=0.22) and external (P=0.30) validation cohort. When compared with 3 prior scores, the AIS-APS showed significantly better discrimination with regard to in-hospital SAP after AIS (all P<0.0001). CONCLUSIONS: The AIS-APS is a valid risk score for predicting in-hospital SAP after AIS.

Interrelationship among common medical complications after acute stroke: pneumonia plays an important role.

BACKGROUND AND PURPOSE: Medical complications are common among patients with stroke. However, little is known about the potential interrelationship among them. In the present study, we aimed to investigate the association between common in-hospital medical complications after acute ischemic stroke (AIS) and spontaneous intracerebral hemorrhage (ICH). METHODS: We analyzed patients enrolled in the China National Stroke Registry from 2007 to 2008. The occurrence of 11 common stroke-associated medical complications during acute hospitalization was prospectively registered. Multivariable analysis using generalized estimation equation was performed to assess association between medical complications in AIS and ICH cohort, respectively. RESULTS: A total of 14 702 patients with AIS and 5221 patients with ICH were enrolled. The median age was 65 years (interquartile range, 55-74 years), and 38.1% were female. The median length of hospital stay was 14 days (interquartile range, 10-20 days) for AIS and 18 days (interquartile range, 11-26 days) for ICH. Pneumonia was the most common medical complication after AIS (11.4%) and ICH (16.8%). In the AIS cohort, after adjusting for potential confounders, pneumonia was significantly associated with development of gastrointestinal bleeding (adjusted odds ratio [OR], 8.35; 95% confidence interval [CI], 6.27-11.1; P<0.001), decubitus ulcer (adjusted OR, 5.31; 95% CI, 3.39-8.31; P<0.001), deep vein thrombosis (adjusted OR, 4.27; 95% CI, 2.41-7.59; P<0.001), epileptic seizure (adjusted OR, 3.96; 95% CI, 2.67-5.88; P<0.001), urinary tract infection (adjusted OR, 3.34; 95% CI, 2.73-4.10; P<0.001), atrial fibrillation/flutter (adjusted OR, 3.17; 95% CI, 2.58-3.90; P<0.001), and recurrent stroke (adjusted OR, 2.65; 95% CI, 2.07-3.40; P<0.001). Similar significant association between pneumonia and development of several nonpneumonia medical complications was verified in ICH cohort as well. CONCLUSIONS: Pneumonia is closely associated with the development of several nonpneumonia medical complications after AIS and ICH.

A novel risk score to predict 1-year functional outcome after intracerebral hemorrhage and comparison with existing scores.

INTRODUCTION: Spontaneous intracerebral hemorrhage (ICH) is one of leading causes of mortality and morbidity worldwide. Several predictive models have been developed for ICH; however, none of them have been consistently used in routine clinical practice or clinical research. In the study, we aimed to develop and validate a risk score for predicting 1-year functional outcome after ICH (ICH Functional Outcome Score, ICH-FOS). Furthermore, we compared discrimination of the ICH-FOS and 8 existing ICH scores with regard to 30-day, 3-month, 6-month, and 1-year functional outcome and mortality after ICH. METHODS: The ICH-FOS was developed based on the China National Stroke Registry, in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Poor functional outcome was defined as modified Rankin Scale score (mRS) ≥3 at 1 year after ICH. Multivariable logistic regression was performed to determine independent predictors, and ß-coefficients were used to generate scoring system of the ICH-FOS. The area under the receiver operating characteristic curve (AUROC) and Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration. RESULTS: The overall 1-year poor functional outcome (mRS ≥ 3) was 46.7% and 44.9% in the derivation (n = 1,953) and validation (n = 1,302) cohorts, respectively. A 16-point ICH-FOS was developed from the set of independent predictors of 1-year poor functional outcome after ICH including age (P < 0.001), admission National Institutes of Health Stroke Scale score (P < 0.001), Glasgow Coma Scale score (P < 0.001), blood glucose (P = 0.002), ICH location (P < 0.001), hematoma volume (P < 0.001), and intraventricular extension (P < 0.001). The ICH-FOS showed good discrimination (AUROC) in the derivation (0.836, 95% CI: 0.819-0.854) and validation (0.830, 95% CI: 0.808-0.852) cohorts. The ICH-FOS was well calibrated (Hosmer-Lemeshow test) in the derivation (P = 0.42) and validation (P = 0.39) cohort. When compared to 8 prior ICH scores, the ICH-FOS showed significantly better discrimination with regard to 1-year functional outcome and mortality after ICH (all P < 0.0001). Meanwhile, the ICH-FOS also demonstrated either comparable or significantly better discrimination for poor functional outcome and mortality at 30-day, 3-month, and 6-month after ICH. CONCLUSION: The ICH-FOS is a valid clinical grading scale for 1-year functional outcome after ICH. Further validation of the ICH-FOS in different populations is needed.

Effect of the Shanghai Stroke Service System (4S) on the quality of stroke care and outcomes: A prospective quality improvement project.

BACKGROUND: In China, disparities in the quality of stroke care still exist and implementing quality improvement is still a challenge. AIM: The aim of the study was to determine whether the intervention by Shanghai Stroke Service System (4S) has helped improve adherence to stroke care guidelines and patient outcome. METHODS: The 4S is a regional stroke network with real-time data extraction among its 61 stroke centers in Shanghai. A total of 11 key performance indicators (KPIs) were evaluated. The primary outcomes were a composite measure and an all-or-none measure of adherence to 11 KPIs. The secondary outcomes were length of hospital stay and in-hospital mortality. RESULTS: The study enrolled 92,395 patients (mean age 69.0 ± 12.5 years, 65.2% men) with acute ischemic stroke hospitalized within 7 days of onset in Shanghai from January 2015 to December 2020. More patients received guideline recommended care between 2018 and 2020 than those between 2015 and 2017 (composite measure 87.1% vs 83.6%; absolute difference 2.9%, 95% confidence interval (CI) = [2.7%, 3.2%], p < 0.001; all-or-none measure 49.2% vs 44.8% patients; absolute difference 3.5%, 95% CI = [2.7%, 4.2%], p < 0.001). Further analysis of individual KPIs showed an absolute increase in six KPIs ranging from 3.4% to 8.9% (p < 0.001 for all comparisons). Compared with 2015-2017, hospital length of stay was shorter (10.95 vs 11.90 days; absolute difference -1.08, 95% CI = [-1.18, -0.99], p < 0.001), and in-hospital mortality was significantly reduced (risk ratio (RR) = 0.88, 95% CI = [0.79, 0.98], p = 0.01) in 2018-2020. CONCLUSION: The 4S intervention was associated with increased adherence to the stroke care guidelines, which further translated to improved clinical outcomes. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02735226.

Adherence to Stroke Care Performance Measures in Different Regions in China, 2015-2019: Evidence From the Chinese Stroke Center Alliance.

BACKGROUND AND OBJECTIVES: To explore the regional discrepancy of the adherence to guideline-recommended stroke interventions for the stroke belt division (north vs south), the economic development division (east vs middle vs west), and potential interaction. METHODS: We conducted a retrospective observational study using data from the Chinese Stroke Center Alliance from August 2015 to August 2019. The primary outcome was hospital personnel adherence to 11 individual guideline-recommended treatments. The coprimary outcomes included 2 summary measures: a composite score (range, 0 [nonadherence] to 1 [perfect adherence]) and an all-or-none binary outcome for adherence to evidence-based stroke. Regional disparities were assessed according to the stroke belt division and the economic development division and the interaction between these 2 divisions. Multivariate regression models with generalized estimating equations were used to analyze the outcomes. RESULTS: This study included 838,229 patients with acute ischemic stroke from 1,473 hospitals. The overall quality of care in the nonbelt regions (southern China) was higher than in the stroke belt regions (northern China), as reflected by a higher composite score (0.77 vs 0.75; adjusted odds ratio 1.03 [95% CI 1.02-1.04]; p < 0.001) and a higher all-or-none measure (25.5% vs 22.0%; 1.32 [1.17-1.49], p < 0.001). Patients in the East and Central had higher odds of using intravenous tissue-type plasminogen activator (East: 1.81 [95% CI 1.51-2.18], p < 0.001; Central: 1.57 [95% CI 1.26-1.95], p < 0.001), early antithrombotic medications (East: 1.77 [1.49-2.11], p < 0.001; Central: 1.37 [1.12-1.66], p < 0.001), lipid-lowering medications (East: 1.29 [1.08-1.53], p < 0.001), and deep vein thrombosis prophylaxis (East: 1.28 [1.08-1.50], p = 0.003) compared with those in the West. Patients in the nonbelt regions had higher odds of getting dysphagia screening (1.82 [1.55-2.13], p < 0.001) and rehabilitation assessment (which though varied among different economic development levels). Reflected by significant interaction effects, for patients in the East, those in the nonbelt regions had greater odds of receiving anticoagulation (1.62 [1.34-1.96]; p < 0.001) but lower odds of receiving antithrombotic (0.63 [0.52-0.77]; p < 0.001) and antidiabetic medications (0.87 [0.77-0.99]; p = 0.03); for patients in the West, those in the nonbelt regions were less likely to receive antihypertensive (0.64 [0.46-0.88]; p = 0.004) and antidiabetic (0.66 [0.54-0.81]; p < 0.001) medications. DISCUSSION: Stroke care performance measures differed across regions, along the stroke belt division, and the economic development division. The overall quality of care in the non-stroke belt regions was higher than that in the stroke belt regions. The 2 divisions had interaction effects on several individual measures.

Sampling Properties of color Independent Component Analysis.

Independent Component Analysis (ICA) offers an effective data-driven approach for blind source extraction encountered in many signal and image processing problems. Although many ICA methods have been developed, they have received relatively little attention in the statistics literature, especially in terms of rigorous theoretical investigation for statistical inference. The current paper aims at narrowing this gap and investigates the statistical sampling properties of the colorICA (cICA) method. The cICA incorporates the correlation structure within sources through parametric time series models in the frequency domain and outperforms several existing ICA alternatives numerically. We establish the consistency and asymptotic normality of the cICA estimates, which then enables statistical inference based on the estimates. These asymptotic properties are further validated using simulation studies.