Transcriptome Analysis Reveals Nonfoamy Rather Than Foamy Plaque Macrophages Are Proinflammatory in Atherosclerotic Murine Models.

RATIONALE: Monocyte infiltration into the subintimal space and its intracellular lipid accumulation are the most prominent features of atherosclerosis. To understand the pathophysiology of atherosclerotic disease, we need to understand the characteristics of lipid-laden foamy macrophages in the subintimal space during atherosclerosis. OBJECTIVE: We sought to examine the transcriptomic profiles of foamy and nonfoamy macrophages isolated from atherosclerotic intima. METHODS AND RESULTS: Single-cell RNA sequencing analysis of CD45+ leukocytes from murine atherosclerotic aorta revealed that there are macrophage subpopulations with distinct differentially expressed genes involved in various functional pathways. To specifically characterize the intimal foamy macrophages of plaque, we developed a lipid staining-based flow cytometric method for analyzing the lipid-laden foam cells of atherosclerotic aortas. We used the fluorescent lipid probe BODIPY493/503 and assessed side-scattered light as an indication of cellular granularity. BODIPYhiSSChi foamy macrophages were found residing in intima and expressing CD11c. Foamy macrophage accumulation determined by flow cytometry was positively correlated with the severity of atherosclerosis. Bulk RNA sequencing analysis showed that compared with nonfoamy macrophages, foamy macrophages expressed few inflammatory genes but many lipid-processing genes. Intimal nonfoamy macrophages formed the major population expressing IL (interleukin)-1ß and many other inflammatory transcripts in atherosclerotic aorta. CONCLUSIONS: RNA sequencing analysis of intimal macrophages from atherosclerotic aorta revealed that lipid-loaded plaque macrophages are not likely the plaque macrophages that drive lesional inflammation.

Poor Oral Health Predicts Higher 10-Year Cardiovascular Risk: A Propensity Score Matching Analysis.

BACKGROUND: Although the association between periodontitis and cardiovascular disease (CVD) has been reported, whether periodontitis and poor oral health behavior influence CVD risk remains unclear. OBJECTIVE: The aim of this study was to examine whether periodontal disease and poor oral health behavior predict 10-year general CVD risk using the Framingham Risk Score. METHODS: Patients older than 30 years with no CVD history (n = 8370) were selected using cross-sectional study data from the Korean National Health and Nutrition Examination Survey in 2013 and 2014. To reduce selection bias in this population-based study, propensity score matching analysis was used with SPSS and R programs to compare CVD risk. RESULTS: Overall, 39.2% of the study population (n = 3277) had a global CVD risk of  10% or greater. In the low- and at-risk groups, 20.7% and 45.3% of patients, respectively, were diagnosed with periodontal disease by a dentist. Moreover, 43.2% and 62.8% of the low- and at-risk group patients, respectively, brushed teeth less than 3 times a day. After 1:1 propensity score matching of the low-risk (n = 1135) and at-risk (n = 1135) groups, bivariate analyses showed that a diagnosis of periodontal disease and less frequent toothbrushing were associated with a higher CVD risk (P < .001). Logistic regression analysis also showed that patients having periodontal disease and who brushed teeth less frequently were 1.38 and 1.33 times, respectively, more likely to be at risk of CVD (P < .001). CONCLUSION: Education on periodontitis management and oral hygiene behavior should be included, when strategies for public risk reduction of CVD are developed.

Prestroke Antiplatelet Agents in First-Time Ischemic Stroke Are Related to Subtypes.

BACKGROUND: It is not well known whether prestroke antiplatelet agents (PAs) are associated with the subtypes of ischemic stroke. METHODS: We screened patients in a hospital-based stroke registry. Patients who were admitted with a diagnosis of first-time ischemic stroke within 5 days of symptom onset were included. Ischemic stroke subtypes were classified in accordance with the Trial of ORG 10172 in Acute Stroke Treatment classification based on stroke mechanism: large-artery atherosclerosis (LA), cardioembolism (CE), small vessel occlusion (SVO), other determined (OC) or undetermined causes (UC). Multinomial logistic regression analyses were performed to evaluate the effect of PA on stroke subtypes before and after propensity score matching. RESULTS: Among 3,025 patients, 748 (24.7%) were taking antiplatelet agents prior to stroke. After propensity score matching, 1,190 patients were ultimately included. The PA group was associated with strokes caused by SVO rather than LA in multinomial logistic regression of an unmatched dataset. However, multivariable analysis after propensity score matching demonstrated that PA use was associated with a higher probability of SVO and CE (OR 2.05, p < 0.001 and OR 1.62, p = 0.05, respectively) compared with LA. CONCLUSIONS: PAs were associated with specific index stroke subtypes.

Risk of adenomas with high-risk characteristics based on two previous colonoscopy.

BACKGROUND AND AIM: The recommended intervals between surveillance colonoscopies are based on the most recent examination findings. However, whether the two previous colonoscopies affect second surveillance colonoscopic findings is not established. The aim of this study is to estimate the risk of obtaining high-risk findings (HRF) on the next surveillance colonoscopy using the results of two previous colonoscopies, and to estimate the appropriate time interval for the next surveillance colonoscopy. METHODS: Among subjects who underwent screening colonoscopy during January 2002-December 2009, patients who underwent second surveillance colonoscopy before June 2012 were enrolled. "No adenoma" was defined as a hyperplastic polyp or no polyp, "low-risk findings (LRF)" as one or two small (< 1 cm) tubular adenomas, and "HRF" as advanced adenoma, cancer, or any sized multiple (≥ 3) adenomas. RESULTS: Among enrolled 852 subjects, 65 (7.6%) had HRF at second surveillance colonoscopy. Multivariate analysis showed that HRF on second surveillance colonoscopy were associated with male and HRF on screening colonoscopy (all, P < 0.01). In subjects with LRF on first surveillance colonoscopy, HRF on the screening colonoscopy significantly affected the detection of HRF on second surveillance colonoscopy (P < 0.01). Patients with HRF on screening colonoscopy and LRF on the first surveillance colonoscopy had no different risk of HRF on second surveillance colonoscopy from those with HRF on first surveillance colonoscopy (P > 0.05). CONCLUSIONS: The HRF on second surveillance are significantly associated with previous two colonoscopic results. In patients with LRF on first surveillance, screening colonoscopic findings should be considered to determine the optimal surveillance interval.

Patterns of decrease in multidirectional myocardial deformations in patients with fluctuating left ventricular ejection fraction.

Few studies have examined the variations in longitudinal/circumferential/radial strain (LS/CS/RS) and strain rate (LSr/CSr/RSr) in individual hearts when the left ventricular ejection fraction (LVEF) has changed. We hypothesized the relationships of strain/strain rate and LVEF are not linear, but vary with multiple inflection points (IPs) in individual hearts.Twenty-five patients with fluctuating LVEF (ΔLVEF > 10%) who had 2-D speckle tracking echocardiography available for analysis were enrolled. After models of best fit were obtained from the 'collective' plots to determine inflection points, the decrements of slopes above inflection points (IP) were compared with those below IPs in the 'individual hearts' plots.In the 'collective' plots, both LS and LSr linearly decreased in proportion to LVEF when LVEF ≥ 40% but remained constant regardless of LVEF when LVEF < 40% (IPs when LVEF = 40%, P < 0.0001). The RS-LVEF relationship was sigmoid with two IPs when LVEF = 30% and 50% (P < 0.0001). However, in the 'individual hearts' plots, the decrements of slopes above and below IPs were not different for LS-LVEF and LSr-LVEF, and marginally different for RS-LVEF (P = 0.049, across IP when LVEF = 50%).Collectively, the relationship of LS/LSr/RS and LVEF seemed to be not linear, but inflective, however, we could not prove the inflective relationship in individual hearts with fluctuating LVEF. Further study with more patients is needed to prove our hypothesis.

Mapping the incidence rate of typhoid fever in sub-Saharan Africa.

BACKGROUND: With more than 1.2 million illnesses and 29,000 deaths in sub-Saharan Africa in 2017, typhoid fever continues to be a major public health problem. Effective control of the disease would benefit from an understanding of the subnational geospatial distribution of the disease incidence. METHOD: We collated records of the incidence rate of typhoid fever confirmed by culture of blood in Africa from 2000 to 2022. We estimated the typhoid incidence rate for sub-Saharan Africa on 20 km × 20 km grids by exploring the association with geospatial covariates representing access to improved water and sanitation, health conditions of the population, and environmental conditions. RESULTS: We identified six published articles and one pre-print representing incidence rate estimates in 22 sites in 2000-2022. Estimated incidence rates showed geospatial variation at sub-national, national, and regional levels. The incidence rate was high in Western and Eastern African subregions followed by Southern and Middle African subregions. By age, the incidence rate was highest among 5-14 yo followed by 2-4 yo, > 14 yo, and 0-1 yo. When aggregated across all age classes and grids that comprise each country, predicted incidence rates ranged from 43.7 (95% confidence interval: 0.6 to 591.2) in Zimbabwe to 2,957.8 (95% CI: 20.8 to 4,245.2) in South Sudan per 100,000 person-years. Sub-national heterogeneity was evident with the coefficient of variation at the 20 km × 20 km grid-level ranging from 0.7 to 3.3 and was generally lower in high-incidence countries and widely varying in low-incidence countries. CONCLUSION: Our study provides estimates of 20 km × 20 km incidence rate of typhoid fever across sub-Saharan Africa based on data collected from 2000 through 2020. Increased understanding of the subnational geospatial variation of typhoid fever in Africa may inform more effective intervention programs by better targeting resources to heterogeneously disturbed disease risk.

Bayesian semiparametric model with spatially-temporally varying coefficients selection.

In spatiotemporal analysis, the effect of a covariate on the outcome usually varies across areas and time. The spatial configuration of the areas may potentially depend on not only the structured random intercept but also spatially varying coefficients of covariates. In addition, the normality assumption of the distribution of spatially varying coefficients could lead to potential biases of estimations. In this article, we proposed a Bayesian semiparametric space-time model where the spatially-temporally varying coefficient is decomposed as fixed, spatially varying, and temporally varying coefficients. We nonparametrically modeled the spatially varying coefficients of space-time covariates by using the area-specific Dirichlet process prior with weights transformed via a generalized transformation. We modeled the temporally varying coefficients of covariates through the dynamic model. We also took into account the uncertainty of inclusion of the spatially-temporally varying coefficients by variable selection procedure through determining the probabilities of different effects for each covariate. The proposed semiparametric approach shows its improvement compared with the Bayesian spatial-temporal models with normality assumption on spatial random effects and the Bayesian model with the Dirichlet process prior on the random intercept. We presented a simulation example to evaluate the performance of the proposed approach with the competing models. We used an application to low birth weight data in South Carolina as an illustration.

Effect of characteristics on the clinical course at the initiation of treatment for human immunodeficiency virus infection using dimensionality reduction.

The beginning of human immunodeficiency virus (HIV) infection treatment depends on various factors, which are significantly correlated with the initial CD4 cell number. However, a covariate correlation between these factors may not reflect the correct outcome variable. Thus, we evaluated the effects of a combination of fixed factors (reduced dimensions), which determine when to start treatment for the first time, on short-term outcome, long-term outcome, and survival, considering correlations between factors. Multiple correspondence analysis was performed on variables obtained from 925 patients who participated in a Korean HIV/acquired immunodeficiency syndrome cohort study (2006-2017). Five reduced dimension groups were derived according to clinical data, viral load, CD4 cell count at diagnosis, initial antiretroviral therapy, and others. The dimension group with high initial viral loads (55,000 copies/mL) and low CD4 cell counts (< 200 cells/mm3) should start treatment promptly after diagnosis. Groups with high initial CD4 cell counts (> 350 cells/mm3) that did not require immediate treatment according to previous guidelines had a higher failure rate for long-term relative CD4 recovery. Our results highlight the importance of early diagnosis and treatment to positively influence long-term disease outcomes, even if the initial immune status is poor, given the patient's combination of early diagnostic symptoms.

Bayesian latent structure models with space-time-dependent covariates.

Spatial-temporal data requires flexible regression models which can model the dependence of responses on space- and time-dependent covariates. In this paper, we describe a semiparametric space-time model from a Bayesian perspective. Nonlinear time dependence of covariates and the interactions among the covariates are constructed by local linear and piecewise linear models, allowing for more flexible orientation and position of the covariate plane by using time-varying basis functions. Space-varying covariate linkage coefficients are also incorporated to allow for the variation of space structures across the geographical location. The formulation accommodates uncertainty in the number and locations of the piecewise basis functions to characterize the global effects, spatially structured and unstructured random effects in relation to covariates. The proposed approach relies on variable selection-type mixture priors for uncertainty in the number and locations of basis functions and in the space-varying linkage coefficients. A simulation example is presented to evaluate the performance of the proposed approach with the competing models. A real data example is used for illustration.

An analysis of the dynamic spatial spread of COVID-19 across South Korea.

The first case of coronavirus disease 2019 (COVID-19) in South Korea was confirmed on January 20, 2020, approximately three weeks after the report of the first COVID-19 case in Wuhan, China. By September 15, 2021, the number of cases in South Korea had increased to 277,989. Thus, it is important to better understand geographical transmission and design effective local-level pandemic plans across the country over the long term. We conducted a spatiotemporal analysis of weekly COVID-19 cases in South Korea from February 1, 2020, to May 30, 2021, in each administrative region. For the spatial domain, we first covered the entire country and then focused on metropolitan areas, including Seoul, Gyeonggi-do, and Incheon. Moran's I and spatial scan statistics were used for spatial analysis. The temporal variation and dynamics of COVID-19 cases were investigated with various statistical visualization methods. We found time-varying clusters of COVID-19 in South Korea using a range of statistical methods. In the early stage, the spatial hotspots were focused in Daegu and Gyeongsangbuk-do. Then, metropolitan areas were detected as hotspots in December 2020. In our study, we conducted a time-varying spatial analysis of COVID-19 across the entirety of South Korea over a long-term period and found a powerful approach to demonstrating the current dynamics of spatial clustering and understanding the dynamic effects of policies on COVID-19 across South Korea. Additionally, the proposed spatiotemporal methods are very useful for understanding the spatial dynamics of COVID-19 in South Korea.

Investigating the spatio-temporal variation of hepatitis A in Korea using a Bayesian model.

Hepatitis A is a water-borne infectious disease that frequently occurs in unsanitary environments. However, paradoxically, those who have spent their infancy in a sanitary environment are more susceptible to hepatitis A because they do not have the opportunity to acquire natural immunity. In Korea, hepatitis A is prevalent because of the distribution of uncooked seafood, especially during hot and humid summers. In general, the transmission of hepatitis A is known to be dynamically affected by socioeconomic, environmental, and weather-related factors and is heterogeneous in time and space. In this study, we aimed to investigate the spatio-temporal variation of hepatitis A and the effects of socioeconomic and weather-related factors in Korea using a flexible spatio-temporal model. We propose a Bayesian Poisson regression model coupled with spatio-temporal variability to estimate the effects of risk factors. We used weekly hepatitis A incidence data across 250 districts in Korea from 2016 to 2019. We found spatial and temporal autocorrelations of hepatitis A indicating that the spatial distribution of hepatitis A varied dynamically over time. From the estimation results, we noticed that the districts with large proportions of males and foreigners correspond to higher incidences. The average temperature was positively correlated with the incidence, which is in agreement with other studies showing that the incidences in Korea are noticeable in spring and summer due to the increased outdoor activity and intake of stale seafood. To the best of our knowledge, this study is the first to suggest a spatio-temporal model for hepatitis A across the entirety of Korean. The proposed model could be useful for predicting, preventing, and controlling the spread of hepatitis A.

Laparoscopic resection of small gastric submucosal tumors.

BACKGROUND: No consensus exists regarding the necessity of operative resection for patients with small, asymptomatic gastric submucosal tumors (SMTs). The purpose of this study is to evaluate clinical outcomes of resection by minimally invasive surgery. METHODS: The medical records of 20 consecutive patients who had undergone laparoscopic or robotic wedge resection for small (<5 cm) gastric SMTs between March 2008 and February 2009 were reviewed. Operative indications included all SMTs unquestionably visible by endoscopy, irrespective of symptoms. The operative procedures, clinicopathologic features, and operative results were assessed. RESULTS: Out of a total of 20 patients, 17 were asymptomatic, and 3 presented with vague abdominal discomfort. One patient had two tumors, therefore 21 total lesions were resected and evaluated (19 by laparoscopy and 2 by robotic procedures). There were 12 exogastric and 9 transgastric wedge resections. Mean operative time was 84 ± 28 min, and mean length of hospitalization was 4.7 ± 1.6 days. There were no major peri- or postoperative complications or mortalities. Mean tumor size was 2.4 ± 1.2 cm (range 0.6-4.8 cm). All lesions had microscopically negative resection margins. There were 16 gastrointestinal tumors (GISTs) and 5 other benign lesions. Fifteen of the GISTs had mitotic count (MC) <5 per 50 high-power fields (HPFs), and one lesion measuring 2.5 cm in size had MC of 38 per 50 HPFs. CONCLUSIONS: Small size cannot guarantee a specific malignant risk for gastric SMTs. Laparoscopic/robotic wedge resection is safe and effective in treating small, asymptomatic lesions. Therefore, an active surgical approach should be considered for management of patients with small gastric SMTs.

No difference in early functional outcomes for mini-midvastus and limited medial parapatellar approaches in navigation-assisted total knee arthroplasty: a prospective randomized clinical trial.

Patients desire less pain and faster recovery of range of knee motion after total knee arthroplasty (TKA). While minimal invasive surgery (MIS) TKA appears to meet these needs, concerns exist regarding component malpositioning. Navigation systems can reduce the incidence of component misalignment. The present prospective randomized study compared limited medial parapatellar (LMPP) and mini-midvastus (MMV) approaches in 30 patients who had bilateral simultaneous primary TKAs. Each patient underwent LMPP on one knee and MMV on the other. Both approaches were navigation-assisted. We primarily analyzed postoperative pain (using visual analog scores), range of knee motion, and hospital for special surgery (HSS) scores in the first 6 postoperative months (i.e., the early postoperative period), and secondarily analyzed perioperative parameters and radiographic outcomes. The LMPP and MMV approaches were found to be similar in terms of pain measured at postoperative 3 days, 1, 2 weeks, 1, 3, and 6 months (P = NS). Similarly, the 2 approaches were found to be similar in terms of range of motion measured at 1, 2 weeks, 1, 3, and 6 months (P = NS), and in terms of HSS scores measured at 1, 3, and 6 months (P = NS). There were also no differences between groups in terms of total blood loss, approach preferred by patients, and radiographic component positioning. The present study found that navigation-assisted MMV and navigation-assisted LMPP resulted in similar outcomes in terms of pain, ROM, HSS score, and radiologic outcomes over the first six postoperative months. We recommend the LMPP approach over the MMV approach in navigation-assisted MIS TKA because it is more familiar to surgeons and is easier to convert to the conventional approach where necessary.

Shed blood re-transfusion provides no benefit in computer-assisted primary total knee arthroplasty.

PURPOSE: This matched case-cohort retrospective study examined the effectiveness of shed blood re-transfusion in reducing the need for allogeneic blood transfusion in computer-assisted primary cemented total knee arthroplasty (TKA). METHODS: The shed blood re-transfusion system used was the cell saver system. Data from 146 cases were analyzed (73 patients with cell saver, 73 patients without cell saver). RESULTS: The ABT rate was similar in each group. The mean allogenic blood transfusion volume was similar for each group (CS=214±453 ml, non-CS=288±447 ml). The only factors correlated with allogenic blood transfusion use were low preoperative hemoglobin and low body mass index. Two patients in cell saver group experienced shivering after re-transfusion. CONCLUSION: Shed blood re-transfusion provided no blood management benefits in computer-assisted primary TKA and is therefore recommended only for selected patients with low hemoglobin levels and low body mass index.

Evaluation of Bayesian spatio-temporal latent models in small area health data.

Health outcomes are linked to air pollution, demographic, or socioeconomic factors which vary across space and time. Thus, it is often found that relative risks in space-time health data have locally different temporal patterns. In such cases, latent modeling is useful in the disaggregation of risk profiles. In particular, spatio-temporal mixture models can help to isolate spatial clusters each of which has a homogeneous temporal pattern in relative risks. In mixture modeling, various weight structures can be used and two situations can be considered: the number of underlying components is known or unknown. In this paper, we compare spatio-temporal mixture models with different weight structures in both situations. In addition, spatio-temporal Dirichlet process mixture models are compared to them when the number of components is unknown. For comparison, we propose a set of spatial cluster detection diagnostics based on the posterior distribution of the weights. We also develop new accuracy measures to assess the recovery of true relative risks. Based on the simulation study, we examine the performance of various spatio-temporal mixture models in terms of proposed methods and goodness-of-fit measures. We apply our models to a county-level chronic obstructive pulmonary disease data set from the state of Georgia.

Objectively Measured Built Environments and Cardiovascular Diseases in Middle-Aged and Older Korean Adults.

This study assesses the association between the objectively measured built environment and cardiovascular diseases (CVDs) in 50,741 adults from the Korean Community Health Survey. The CVD outcomes of hypertension, diabetes, dyslipidemia, stroke, and myocardial infarction (MI) or angina were derived from self-reported histories of physician diagnoses. Using ArcGIS software and Korean government databases, this study measured the built environment variables for the 546 administrative areas of Gyeonggi province. A Bayesian spatial multilevel model was performed independently in two age groups (i.e., 40-59 years or ≥60 years). After adjusting for statistical significant individual- and community-level factors with the spatial associations, living far from public transit was associated with an increase in the odds of MI or angina in middle-aged adults, while living in neighborhoods in which fast-food restaurants were concentrated was associated with a decrease in the odds of hypertension and stroke. For adults 60 or older, living farther from public physical-activity (PA) facilities was associated with a 15% increased odds for dyslipidemia, compared with living in neighborhoods nearer to PA facilities. These findings suggest that creating a built environment that provides more opportunities to engage in PA in everyday life should be considered a strategy to reduce the prevalence of CVD.

Spatial epidemic dynamics of the COVID-19 outbreak in China.

BACKGROUND: On 31 December 2019 an outbreak of COVID-19 in Wuhan, China, was reported. The outbreak spread rapidly to other Chinese cities and multiple countries. This study described the spatio-temporal pattern and measured the spatial association of the early stages of the COVID-19 epidemic in mainland China from 16 January-06 February 2020. METHODS: This study explored the spatial epidemic dynamics of COVID-19 in mainland China. Moran's I spatial statistic with various definitions of neighbours was used to conduct a test to determine whether a spatial association of the COVID-19 infections existed. RESULTS: The spatial spread of the COVID-19 pandemic in China was observed. The results showed that most of the models, except medical-care-based connection models, indicated a significant spatial association of COVID-19 infections from around 22 January 2020. CONCLUSIONS: Spatial analysis is of great help in understanding the spread of infectious diseases, and spatial association was the key to the spatial spread during the early stages of the COVID-19 pandemic in mainland China.

Time-variant reproductive number of COVID-19 in Seoul, Korea.

OBJECTIVES: To estimate time-variant reproductive number (Rt) of coronavirus disease 19 based on either number of daily confirmed cases or their onset date to monitor effectiveness of quarantine policies. METHODS: Using number of daily confirmed cases from January 23, 2020 to March 22, 2020 and their symptom onset date from the official website of the Seoul Metropolitan Government and the district office, we calculated Rt using program R's package "EpiEstim". For asymptomatic cases, their symptom onset date was considered as -2, -1, 0, +1, and +2 days of confirmed date. RESULTS: Based on the information of 313 confirmed cases, the epidemic curve was shaped like 'propagated epidemic curve'. The daily Rt based on Rt_c peaked to 2.6 on February 20, 2020, then showed decreased trend and became <1.0 from March 3, 2020. Comparing both Rt from Rt_c and from the number of daily onset cases, we found that the pattern of changes was similar, although the variation of Rt was greater when using Rt_c. When we changed assumed onset date for asymptotic cases (-2 days to +2 days of the confirmed date), the results were comparable. CONCLUSIONS: Rt can be estimated based on Rt_c which is available from daily report of the Korea Centers for Disease Control and Prevention. Estimation of Rt would be useful to continuously monitor the effectiveness of the quarantine policy at the city and province levels.

Implementation of Complementary Model using Optimal Combination of Hematological Parameters for Sepsis Screening in Patients with Fever.

The early detection and timely treatment are the most important factors for improving the outcome of patients with sepsis. Sepsis-related clinical score, such as SIRS, SOFA and LODS, were defined to identify patients with suspected infection and to predict severity and mortality. A few hematological parameters associated with organ dysfunction and infection were included in the score although various clinical pathology parameters (hematology, serum chemistry and plasma coagulation) in blood sample have been found to be associated with outcome in patients with sepsis. The investigation of the parameters facilitates the implementation of a complementary model for screening sepsis to existing sepsis clinical criteria and other laboratory signs. In this study, statistical analysis on the multiple clinical pathology parameters obtained from two groups, patients with sepsis and patients with fever, was performed and the complementary model was elaborated by stepwise parameter selection and machine learning. The complementary model showed statistically better performance (AUC 0.86 vs. 0.74-0.51) than models built up with specific hematology parameters involved in each existing sepsis-related clinical score. Our study presents the complementary model based on the optimal combination of hematological parameters for sepsis screening in patients with fever.

Spatial-temporal association between fine particulate matter and daily mortality.

Fine particulate matter (PM(2.5)) is a mixture of pollutants that has been linked to serious health problems, including premature mortality. Since the chemical composition of PM(2.5) varies across space and time, the association between PM(2.5) and mortality could also change with space and season. In this work we develop and implement a statistical multi-stage Bayesian framework that provides a very broad, flexible approach to studying the spatiotemporal associations between mortality and population exposure to daily PM(2.5) mass, while accounting for different sources of uncertainty. In stage 1, we map ambient PM(2.5) air concentrations using all available monitoring data (IMPROVE and FRM) and an air quality model (CMAQ) at different spatial and temporal scales. In stage 2, we examine the spatial temporal relationships between the health end-points and the exposures to PM(2.5) by introducing a spatial-temporal generalized Poisson regression model. We adjust for time-varying confounders, such as seasonal trends. A common seasonal trends model is to use a fixed number of basis functions to account for these confounders, but the results can be sensitive to the number of basis functions. In this study, the number of the basis functions is treated as an unknown parameter in our Bayesian model and we use a space-time stochastic search variable selection approach. We apply our methods to a data set in North Carolina for the year 2001.