Effect of human H3N2 influenza virus reassortment on influenza incidence and severity during the 2017-18 influenza season in the USA: a retrospective observational genomic analysis.

BACKGROUND: During the 2017-18 influenza season in the USA, there was a high incidence of influenza illness and mortality. However, no apparent antigenic change was identified in the dominant H3N2 viruses, and the severity of the season could not be solely attributed to a vaccine mismatch. We aimed to investigate whether the altered virus properties resulting from gene reassortment were underlying causes of the increased case number and disease severity associated with the 2017-18 influenza season. METHODS: Samples included were collected from patients with influenza who were prospectively recruited during the 2016-17 and 2017-18 influenza seasons at the Johns Hopkins Hospital Emergency Departments in Baltimore, MD, USA, as well as from archived samples from Johns Hopkins Health System sites. Among 647 recruited patients with influenza A virus infection, 411 patients with whole-genome sequences were available in the Johns Hopkins Center of Excellence for Influenza Research and Surveillance network during the 2016-17 and 2017-18 seasons. Phylogenetic trees were constructed based on viral whole-genome sequences. Representative viral isolates of the two seasons were characterised in immortalised cell lines and human nasal epithelial cell cultures, and patients' demographic data and clinical outcomes were analysed. FINDINGS: Unique H3N2 reassortment events were observed, resulting in two predominant strains in the 2017-18 season: HA clade 3C.2a2 and clade 3C.3a, which had novel gene segment constellations containing gene segments from HA clade 3C.2a1 viruses. The reassortant re3C.2a2 viruses replicated with faster kinetics and to a higher peak titre compared with the parental 3C.2a2 and 3C.2a1 viruses (48 h vs 72 h). Furthermore, patients infected with reassortant 3C.2a2 viruses had higher Influenza Severity Scores than patients infected with the parental 3C.2a2 viruses (median 3·00 [IQR 1·00-4·00] vs 1·50 [1·00-2·00]; p=0·018). INTERPRETATION: Our findings suggest that the increased severity of the 2017-18 influenza season was due in part to two intrasubtypes, cocirculating H3N2 reassortant viruses with fitness advantages over the parental viruses. This information could help inform future vaccine development and public health policies. FUNDING: The Center of Excellence for Influenza Research and Response in the US, National Science and Technology Council, and Chang Gung Memorial Hospital in Taiwan.

Diagnostic accuracy of clinical signs and symptoms of COVID-19: A systematic review and meta-analysis to investigate the different estimates in a different stage of the pandemic outbreak.

Background: The coronavirus (COVID-19) pandemic caused enormous adverse socioeconomic impacts worldwide. Evidence suggests that the diagnostic accuracy of clinical features of COVID-19 may vary among different populations. Methods: We conducted a systematic review and meta-analysis of studies from PubMed, Embase, Cochrane Library, Google Scholar, and the WHO Global Health Library for studies evaluating the accuracy of clinical features to predict and prognosticate COVID-19. We used the National Institutes of Health Quality Assessment Tool to evaluate the risk of bias, and the random-effects approach to obtain pooled prevalence, sensitivity, specificity, and likelihood ratios. Results: Among the 189 included studies (53 659 patients), fever, cough, diarrhoea, dyspnoea, and fatigue were the most reported predictors. In the later stage of the pandemic, the sensitivity in predicting COVID-19 of fever and cough decreased, while the sensitivity of other symptoms, including sputum production, sore throat, myalgia, fatigue, dyspnoea, headache, and diarrhoea, increased. A combination of fever, cough, fatigue, hypertension, and diabetes mellitus increases the odds of having a COVID-19 diagnosis in patients with a positive test (positive likelihood ratio (PLR) = 3.06)) and decreases the odds in those with a negative test (negative likelihood ratio (NLR) = 0.59)). A combination of fever, cough, sputum production, myalgia, fatigue, and dyspnea had a PLR = 10.44 and an NLR = 0.16 in predicting severe COVID-19. Further updating the umbrella review (1092 studies, including 3 342 969 patients) revealed the different prevalence of symptoms in different stages of the pandemic. Conclusions: Understanding the possible different distributions of predictors is essential for screening for potential COVID-19 infection and severe outcomes. Understanding that the prevalence of symptoms may change with time is important to developing a prediction model.

Using time-course as an essential factor to accurately predict sepsis-associated mortality among patients with suspected sepsis.

BACKGROUND: Biomarker dynamics in different time-courses might be the primary reason why a static measurement of a single biomarker cannot accurately predict sepsis outcomes. Therefore, we conducted this prospective hospital-based cohort study to simultaneously evaluate the performance of several conventional and novel biomarkers of sepsis in predicting sepsis-associated mortality on different days of illness among patients with suspected sepsis. METHODS: We evaluated the performance of 15 novel biomarkers including angiopoietin-2, pentraxin 3, sTREM-1, ICAM-1, VCAM-1, sCD14 and 163, E-selectin, P-selectin, TNF-alpha, interferon-gamma, CD64, IL-6, 8, and 10, along with few conventional markers for predicting sepsis-associated mortality. Patients were grouped into quartiles according to the number of days since symptom onset. Receiver operating characteristic curve (ROC) analysis was used to evaluate the biomarker performance. RESULTS: From 2014 to 2017, 1,483 patients were enrolled, of which 78% fulfilled the systemic inflammatory response syndrome criteria, 62% fulfilled the sepsis-3 criteria, 32% had septic shock, and 3.3% developed sepsis-associated mortality. IL-6, pentraxin 3, sCD163, and the blood gas profile demonstrated better performance in the early days of illness, both before and after adjusting for potential confounders (adjusted area under ROC curve [AUROC]:0.81-0.88). Notably, the Sequential Organ Failure Assessment (SOFA) score was relatively consistent throughout the course of illness (adjusted AUROC:0.70-0.91). CONCLUSION: IL-6, pentraxin 3, sCD163, and the blood gas profile showed excellent predictive accuracy in the early days of illness. The SOFA score was consistently predictive of sepsis-associated mortality throughout the course of illness, with an acceptable performance.

Applying symptom dynamics to accurately predict influenza virus infection: An international multicenter influenza-like illness surveillance study.

BACKGROUND: Public health organizations have recommended various definitions of influenza-like illnesses under the assumption that the symptoms do not change during influenza virus infection. To explore the relationship between symptoms and influenza over time, we analyzed a dataset from an international multicenter prospective emergency department (ED)-based influenza-like illness cohort study. METHODS: We recruited patients in the US and Taiwan between 2015 and 2020 with: (1) flu-like symptoms (fever and cough, headache, or sore throat), (2) absence of any of the respiratory infection symptoms, or (3) positive laboratory test results for influenza from the current ED visit. We evaluated the association between the symptoms and influenza virus infection on different days of illness. The association was evaluated among different subgroups, including different study countries, influenza subtypes, and only patients with influenza. RESULTS: Among the 2471 recruited patients, 45.7% tested positive for influenza virus. Cough was the most predictive symptom throughout the week (odds ratios [OR]: 7.08-11.15). In general, all symptoms were more predictive during the first 2 days (OR: 1.55-10.28). Upper respiratory symptoms, such as sore throat and productive cough, and general symptoms, such as body ache and fatigue, were more predictive in the first half of the week (OR: 1.51-3.25). Lower respiratory symptoms, such as shortness of breath and wheezing, were more predictive in the second half of the week (OR: 1.52-2.52). Similar trends were observed for most symptoms in the different subgroups. CONCLUSIONS: The time course is an important factor to be considered when evaluating the symptoms of influenza virus infection.

Developing and validating clinical features-based machine learning algorithms to predict influenza infection in influenza-like illness patients.

BACKGROUND: Seasonal influenza poses a significant risk, and patients can benefit from early diagnosis and treatment. However, underdiagnosis and undertreatment remain widespread. We developed and compared clinical feature-based machine learning (ML) algorithms that can accurately predict influenza infection in emergency departments (EDs) among patients with influenza-like illness (ILI). MATERIAL AND METHODS: We conducted a prospective cohort study in five EDs in the US and Taiwan from 2015 to 2020. Adult patients visiting the EDs with symptoms of ILI were recruited and tested by real-time RT-PCR for influenza. We evaluated seven ML algorithms and compared their results with previously developed clinical prediction models. RESULTS: Out of the 2189 enrolled patients, 1104 tested positive for influenza. The eXtreme Gradient Boosting achieved superior performance with an area under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI] = 0.79-0.85), with a sensitivity of 0.92 (95% CI = 0.88-0.95), specificity of 0.89 (95% CI = 0.86-0.92), and accuracy of 0.72 (95% CI = 0.69-0.76) in the testing set over cut-offs of 0.4, 0.6 and 0.5, respectively. These results were superior to those of previously proposed clinical prediction models. The model interpretation revealed that body temperature, cough, rhinorrhea, and exposure history were positively associated with and the days of illness and influenza vaccine were negatively associated with influenza infection. We also found the week of the influenza season, pulse rate, and oxygen saturation to be associated with influenza infection. CONCLUSIONS: The clinical feature-based ML model outperformed conventional models for predicting influenza infection.

Using Machine Learning to Develop and Validate an In-Hospital Mortality Prediction Model for Patients with Suspected Sepsis.

BACKGROUND: Early recognition of sepsis and the prediction of mortality in patients with infection are important. This multi-center, ED-based study aimed to develop and validate a 28-day mortality prediction model for patients with infection using various machine learning (ML) algorithms. METHODS: Patients with acute infection requiring intravenous antibiotic treatment during the first 24 h of admission were prospectively recruited. Patient demographics, comorbidities, clinical signs and symptoms, laboratory test data, selected sepsis-related novel biomarkers, and 28-day mortality were collected and divided into training (70%) and testing (30%) datasets. Logistic regression and seven ML algorithms were used to develop the prediction models. The area under the receiver operating characteristic curve (AUROC) was used to compare different models. RESULTS: A total of 555 patients were recruited with a full panel of biomarker tests. Among them, 18% fulfilled Sepsis-3 criteria, with a 28-day mortality rate of 8%. The wrapper algorithm selected 30 features, including disease severity scores, biochemical parameters, and conventional and few sepsis-related biomarkers. Random forest outperformed other ML models (AUROC: 0.96; 95% confidence interval: 0.93-0.98) and SOFA and early warning scores (AUROC: 0.64-0.84) in the prediction of 28-day mortality in patients with infection. Additionally, random forest remained the best-performing model, with an AUROC of 0.95 (95% CI: 0.91-0.98, p = 0.725) after removing five sepsis-related novel biomarkers. CONCLUSIONS: Our results demonstrated that ML models provide a more accurate prediction of 28-day mortality with an enhanced ability in dealing with multi-dimensional data than the logistic regression model.

Longitudinal changes in body mass index Z-scores during infancy and risk of childhood allergies.

BACKGROUND: Few studies address the dynamic changes of body mass index (BMI) Z-scores during infancy with breastfeeding and their impact on childhood atopic diseases. METHODS: A total of 183 children from a birth cohort regularly followed-up for 4 years were enrolled in this study. Time series data of BMI Z-scores from 1 month to 2 years of age was clustered using K-means method in R software. Breastfeeding status during the first 6 months of life was recorded and classified. The total serum and specific immunoglobulin E (IgE) levels to food and inhalant allergens were measured at age 0.5, 1, 1.5, and 2 years. RESULTS: Using K-means clustering, the dynamic changes in BMI Z-scores were classified into three clusters (cluster A, increasing, n = 62; cluster B; decreasing, n = 62; cluster C, constant low, n = 59). Despite having no statistical association with atopic diseases, a decreasing trend in infantile BMI Z-scores was significantly associated with a higher prevalence of IgE sensitization at age 1 which increased the risk of rhinitis development at age 4 (P = 0.007). No difference in BMI Z-scores was determined between different breastfeeding patterns. However, exclusive formula feeding ≥6 months was found to be significantly associated with mite sensitization at age 1.5 years which risks asthma development at age 4 (P = 0.001). CONCLUSIONS: A decreasing trend of BMI Z-scores during infancy is determined to be inversely associated with IgE and allergen sensitization, which may potentially increase the risk of allergies in early childhood.

Current Evidence and Limitation of Biomarkers for Detecting Sepsis and Systemic Infection.

Sepsis was recently redefined as a life-threatening disease involving organ dysfunction caused by a dysregulated host response to infection. Biomarkers play an important role in early detection, diagnosis, and prognostication. We reviewed six promising biomarkers for detecting sepsis and systemic infection, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), CD64, presepsin, and sTREM-1. Among the recent studies, we found the following risks of bias: only a few studies adopted the random or consecutive sampling strategy; extensive case-control analysis, which worsened the over-estimated performance; most of the studies used post hoc cutoff values; and heterogeneity with respect to the inclusion criteria, small sample sizes, and different quantitative synthesis methods applied in meta-analyses. We recommend that CD64 and presepsin should be considered as the most promising biomarkers for diagnosing sepsis. Future studies should enroll a larger sample size with a cohort rather than a case-control study design. A random or consecutive study design with a pre-specified laboratory threshold, consistent sampling timing, and an updated definition of sepsis will also increase the reliability of the studies. Further investigations of appropriate specimens, testing assays, and cutoff levels for specific biomarkers are also warranted.

Effectiveness of Treatments and Diagnostic Tools and Declining Mortality in Patients With Severe Sepsis: A 12-Year Population-Based Cohort Study.

Sepsis is a major cause of morbidity and mortality worldwide. With the advance of medical care, the mortality of sepsis has decreased in the past decades. Many treatments and diagnostic tools still lack supporting evidence. We conducted a retrospective population-based cohort study with propensity score matched subcohorts based on a prospectively collected national longitudinal health insurance database in Taiwan. Severe sepsis-associated hospital admissions from 2000 to 2011 based on International Classification of Diseases, Ninth Revision, Clinical Modification codes of infections and acute organ dysfunction were identified. To compare the effectiveness of treatment and diagnostic tool, propensity scores were generated to match the comparable control groups. During the 12-year period, 33 375 patients and 50 465 hospitalizations of severe sepsis were identified. The age-standardized 28-day in-hospital mortality decreased significantly from 21% in 2008 to 15% in 2011 with increasingly implemented treatment and diagnostic tool. After propensity score matching, procalcitonin (odds ratio [OR]: 0.70, 95% confidence interval [95% CI]: 0.61-0.81) and lactate testing (OR: 0.90, 95% CI: 0.84-0.97, respectively), transfusion of packed red blood cell (OR: 0.60, 95% CI: 0.52-0.69), albumin (OR: 0.72, 95% CI: 0.55-0.93), balanced crystalloid (OR: 0.29, 95% CI: 0.20-0.41), and use of dopamine (OR: 0.44, 95% CI: 0.39-0.49) were found to be significantly associated with lower mortality rate. However, inconsistent findings need to be further validated.

Optimal measuring timing of cystatin C for early detection of contrast-induced acute kidney injury: A systematic review and meta-analysis.

OBJECTIVE: The optimal measuring timing of serum/plasma Cystatin C (CysC) for early detection of contrast-induced acute kidney injury (CIAKI) remains un-studied. We elucidated further on this issue. METHODS: We searched PubMed, MEDLINE, and Embase from inception until March 2018 for studies evaluating diagnostic accuracy of CysC for detecting CIAKI in patients exposed to contrast agents during diagnostic examinations or cardiac/peripheral catheterizations. RESULTS: A total of 10 relevant studies, comprising 2554 patients, were included and divided into the <24 -h and 24 -h groups based on CysC measuring timing (i.e., hours after contrast agent exposure). Compared with creatinine, pooled diagnostic odds ratio of CysC for detecting CIAKI of the <24 -h and 24 -h groups was 7.59 (95 % confidence interval [CI]: 1.31-44.08) and 53.81 (95 % CI: 13.57-213.26). Pooled sensitivity of the <24 -h and 24 -h groups was 0.81 and 0.88. Pooled specificity of the <24 -h and 24 -h groups was 0.64 and 0.88, respectively. Area under the hierarchical summary receiver operating characteristic curve of the <24 -h and 24 -h groups was 0.75 and 0.93. CONCLUSIONS: Measuring CysC at 24 h after contrast agent exposure shows higher diagnostic accuracy for early detection of CIAKI than measuring CysC at <24 h after contrast agent exposure.

Could we employ the queueing theory to improve efficiency during future mass causality incidents?

BACKGROUND: Preparation for a disaster or accident-related mass casualty events is often based on experience. The objective measures or tools for evaluating decision-making and effectiveness during such events are underdeveloped. Queueing theory has been suggested to evaluate the effectiveness of mass causality incidents (MCI) plans. OBJECTIVE: Using different types of real MCI, we aimed to determine if a queueing network model could be used as a tool to assist in preparing plans to address mass causality incidents. METHODS: We collected information from two types of mass casualty events: a motor vehicle accident and a dust explosion. Patient characteristics, time intervals of every working station, numbers of physicians and nurses attending, and time required by physicians and nurses during these two MCIs were collected and used for calculation in a queueing network model. Balanced efficiency was determined by calculating the numbers of server, i.e., nurses and physicians, in the two MCIs. RESULTS: Efficient patient flows were found in both MCIs. However, excessive medical manpower supply was revealed when the queueing network model was applied to assess the MCIs. The best fitting result, i.e., the most efficient man power utilization, can be calculated by the queueing network models. Furthermore, balanced efficiency may be a more suitable condition than the highest efficiency man power utilization when faced with MCIs. CONCLUSION: The queueing network model is a flexible tool that could be used in different types of MCIs to observe the degree of efficiency when handling MCIs.

Comparison of the accuracy of neutrophil CD64, procalcitonin, and C-reactive protein for sepsis identification: a systematic review and meta-analysis.

BACKGROUND: Neutrophil CD64 is widely described as an accurate biomarker for the diagnosis of infection in patients with septic syndrome. We performed a systematic review and meta-analysis to evaluate the diagnostic accuracy of neutrophil CD64, comparing it with C-reactive protein (CRP) and procalcitonin (PCT) for the diagnosis of infection in adult patients with septic syndrome, based on sepsis-2 criteria. We searched the PubMed and Embase databases and Google Scholar. Original studies reporting the performance of neutrophil CD64 for sepsis diagnosis in adult patients were retained. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchical summary receiver operating characteristic (SROC) curve were calculated. RESULTS: We included 14 studies (2471 patients) from 2006 to 2017 in the meta-analysis. The pooled sensitivity and specificity of neutrophil CD64 for diagnosing infection in adult patients with septic syndrome were 0.87 (95% CI 0.80-0.92) and 0.89 (95% CI 0.82-0.93), respectively. The area under the SROC curve and the DOR were 0.94 (95% CI 0.92-0.96) and 53 (95% CI 22-128), respectively. There was significant heterogeneity between the studies included. Subgroup analyses showed that this heterogeneity was due to differences in sample size and the proportions of patients with sepsis included in the studies. Six studies (927 patients) compared neutrophil CD64 and CRP determinations, and six studies (744 patients) compared neutrophil CD64 and PCT determinations. The area under the SROC curve was larger for neutrophil CD64 than for CRP (0.89 [95% CI 0.87-0.92] vs. 0.84 [95% CI 0.80-0.88], P < 0.05) or PCT (0.89 [95% CI 0.84-0.95] vs. 0.84 [95% CI 0.79-0.89], P < 0.05). CONCLUSIONS: In adult patients with septic syndrome, neutrophil CD64 levels are an excellent biomarker with moderate accuracy outperforming both CRP and PCT determinations.

Using weighted regression model for estimating cohort effect in age-period contingency table data.

BACKGROUND: Recently, the multiphase method was proposed to estimate cohort effects after removing the effects of age and period in age-period contingency table data. Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and is strongly associated with cirrhosis, due to both alcohol and viral etiologies. In epidemiology, age-period-cohort (APC) model can be used to describe (or predict) the secular trend in HCC mortality. RESULTS: The confidence interval (CI) of the weighted estimates was found to be relatively narrow (compared to unweighted estimates). Moreover, for males, the mortality trend reverses itself during 2006-2010 was found from an increasing trend into a slightly deceasing trend. For females, the increasing trend reverses (earlier than males) itself during 2001-2005. CONCLUSIONS: The weighted estimation of the regression model is recommended for the multiphase method in estimating the cohort effects in age-period contingency table data. IMPACT: The regression model can be modified through the weighted average estimate of the effects with narrower CI of each cohort. METHODS: After isolating the residuals during the median polish phase, the final phase is to estimate the magnitude of the cohort effects using the regression model of these residuals on the cohort category with the weight equal to the occupied proportion according to the number of death of HCC in each cohort.

Transfusion of Red Blood Cells to Patients with Sepsis.

Sepsis is one of the major causes of death worldwide, and is the host response to infection which renders our organs malfunctioning. Insufficient tissue perfusion and oxygen delivery have been implicated in the pathogenesis of sepsis-related organ dysfunction, making transfusion of packed red blood cells (pRBCs) a reasonable treatment modality. However, clinical trials have generated controversial results. Even the notion that transfused pRBCs increase the oxygen-carrying capacity of blood has been challenged. Meanwhile, during sepsis, the ability of our tissues to utilize oxygen may also be reduced, and the increased blood concentrations of lactate may be the results of strong inflammation and excessive catecholamine release, rather than impaired cell respiration. Leukodepleted pRBCs more consistently demonstrated improvement in microcirculation, and the increase in blood viscosity brought about by pRBC transfusion helps maintain functional capillary density. A restrictive strategy of pRBC transfusion is recommended in treating septic patients.

Comparison of diagnostic accuracy in sepsis between presepsin, procalcitonin, and C-reactive protein: a systematic review and meta-analysis.

BACKGROUND: The soluble cluster of differentiation 14 (or presepsin) is a free fragment of glycoprotein expressed on monocytes and macrophages. Although many studies have been conducted recently, the diagnostic performance of presepsin for sepsis remains debated. We performed a systematic review and meta-analysis of the available literature to assess the accuracy of presepsin for the diagnosis of sepsis in adult patients and compared the performance between presepsin, C-reactive protein (CRP), and procalcitonin (PCT). METHODS: A comprehensive systemic search was conducted in PubMed, EMBASE, and Google Scholar for studies that evaluated the diagnostic accuracy of presepsin for sepsis until January 2017. The hierarchical summary receiver operating characteristic method was used to pool individual sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the receiver operating characteristic curve (AUC). RESULTS: Eighteen studies, comprising 3470 patients, met our inclusion criteria. The pooled diagnosis sensitivity and specificity of presepsin for sepsis were 0.84 (95% CI 0.80-0.87) and 0.76 (95% CI 0.67-0.82), respectively. Furthermore, the pooled DOR, PLR, NLR, and AUC were 16 (95% CI 10-25), 3.4 (95% CI 2.5-4.6), 0.22 (95% CI 0.17-0.27), and 0.88 (95% CI 0.85-0.90), respectively. Significant heterogeneity was found in both sensitivities (Cochrane Q = 137.43, p < 0.001, I 2 = 87.63%) and specificities (Cochrane Q = 180.76, p < 0.001, I 2 = 90.60%). Additionally, we found no significant difference between presepsin and PCT (AUC 0.87 vs. 0.86) or CRP (AUC 0.85 vs. 0.85). However, for studies conducted in ICU, the pooled sensitivity of presepsin was found to be higher than PCT (0.88, 95% CI 0.82-0.92 vs. 0.75, 95% CI 0.68-0.81), while the pooled specificity of presepsin was lower than PCT (0.58, 95% CI 0.42-0.73 vs. 0.75, 95% CI 0.65-0.83). CONCLUSION: Based on the results of our meta-analysis, presepsin is a promising marker for diagnosis of sepsis as PCT or CRP, but its results should be interpreted more carefully and cautiously since too few studies were included and those studies had high heterogeneity between them. In addition, continuing re-evaluation during the course of sepsis is advisable.

Association of In-Hospital Mortality and Dysglycemia in Septic Patients.

BACKGROUND: The associations between dysglycemia and mortality in septic patients with and without diabetes are yet to be confirmed. Our aim was to analyze the association of diabetes and sepsis mortality, and to examine how dysglycemia (hyperglycemia, hypoglycemia and glucose variability) affects in-hospital mortality of patients with suspected sepsis in emergency department (ED) and intensive care units. METHODS: Clinically suspected septic patients admitted to ED were included, and stratified into subgroups according to in-hospital mortality and the presence of diabetes. We analyzed patients' demographics, comorbidities, clinical and laboratory parameters, admission glucose levels and severity of sepsis. Odds ratio of mortality was assessed after adjusting for possible confounders. The correlations of admission glucose and CoV (blood glucose coefficients of variation) and mortality in diabetes and non-diabetes were also tested. RESULTS: Diabetes was present in 58.3% of the patients. Diabetic patients were older, more likely to have end-stage renal disease and undergoing hemodialysis, but had fewer malignancies, less sepsis severity (lower Mortality in Emergency Department Sepsis Score), less steroid usage in emergency department, and lower in-hospital mortality rate (aOR:0.83, 95% CI 0.65-0.99, p = 0.044). Hyperglycemia at admission (glucose≥200 mg/dL) was associated with higher risks of in-hospital mortality among the non-diabetes patients (OR:1.83 vs. diabetes, 95% CI 1.20-2.80, p = 0.005) with the same elevated glucose levels at admission. In addition, CoV>30% resulted in higher risk of death as well (aOR:1.88 vs. CoV between 10 and 30, 95%CI 1.24-2.86 p = 0.003). CONCLUSIONS: This study indicates that while diabetes mellitus seems to be a protective factor in sepsis patients, hyper- or hypoglycemia status on admission, and increased blood glucose variation during hospital stays, were independently associated with increased odds ratio of mortality.

Development and validation of a parsimonious and pragmatic CHARM score to predict mortality in patients with suspected sepsis.

BACKGROUND: We aimed to derive and validate a parsimonious and pragmatic clinical prediction rule using the concepts of Predisposition, Infection, Response, and Organ Dysfunction to predict in-hospital mortality; and to compare it with other prediction rules, as well as with conventional biomarkers for evaluating the mortality risk of patients with suspected sepsis in the emergency department (ED). METHODS: We conducted a pragmatic cohort study with consecutive ED patients aged 18 or older with documented diagnostic codes of infection and two sets of blood culture ordered by physicians between 2010 and 2012 in a tertiary teaching hospital. RESULTS: 7011 and 12,110 patients were included in the derivation cohort and the validation cohort for the final analysis. There were 479 deaths (7%) in the derivation cohort and 1145 deaths (9%) in the validation cohort. Independent predictors of death were absence of Chills (odds ratio: 2.28, 95% confidence interval: 1.75-2.97), Hypothermia (2.12, 1.57-2.85), Anemia (2.45, 1.97-3.04), wide Red cell Distribution Width (RDW) (3.27, 2.63-4.05) and history of Malignancy (2.00, 1.63-2.46). This novel clinical prediction rule (CHARM) performed well for stratifying patients into mortality risk groups (sensitivity: 99.4%, negative predictive value 99.7%, receiver operating characteristic area 0.77). The CHARM score also outperformed the other scores or biomarkers such as PIRO, SIRS, MEDS, CURB-65, C-reactive protein, procalcitonin and lactate (all p<.05). CONCLUSIONS: In patients with suspected sepsis, this parsimonious and pragmatic model could be utilized to stratify the mortality risk of patients in the early stage of sepsis.

Systemic inflammatory response syndrome is more associated with bacteremia in elderly patients with suspected sepsis in emergency departments.

Early diagnosis of bacteremia for patients with suspected sepsis is 1 way to improve prognosis of sepsis. Systemic inflammatory response syndrome (SIRS) has long been utilized as a screening tool to detect bacteremia by front-line healthcare providers. The value of SIRS to predict bacteremia in elderly patients (≥65 years) with suspected sepsis has not yet been examined in emergency departments (EDs).We aimed to evaluate the performance of SIRS components in predicting bacteremia among elderly patients in EDs.We retrospectively evaluated patients with suspected sepsis and 2 sets of blood culture collected within 4 hours after admitting to ED in a tertiary teaching hospital between 2010 and 2012. Patients were categorized into 3-year age groups: young (18-64 years), young-old (65-74 years), and old patients (≥75 years). Vital signs and Glasgow Coma Scale with verbal response obtained at the triage, comorbidities, sites of infection, blood cultures, and laboratory results were retrieved via the electronic medical records.A total of 20,192 patients were included in our study. Among them, 9862 (48.9%) were the elderly patients (young-old and old patients), 2656 (13.2%) developed bacteremia. Among patients with bacteremia, we found the elderly patients had higher SIRS performance (adjusted odds ratio [aOR]: 2.40, 95% confidence interval [CI]: 1.90-3.03 in the young-old and aOR: 2.66, 95% CI: 2.19-3.23 in the old). Fever at the triage was most predictive of bacteremia, especially in the elderly patients (aOR: 2.19, 95% CI: 1.81-2.65 in the young-old and aOR: 2.27, 95% CI: 1.95-2.63 in the old), and tachypnea was not predictive of bacteremia among the elderly patients (all P > 0.2).The performance of SIRS to predict bacteremia was more suitable for elderly patients in EDs observed in this study. The elderly patients presented with more fever and less tachypnea when they had bacteremia.

Impact of performance grading on annual numbers of acute myocardial infarction-associated emergency department visits in Taiwan: Results of segmented regression analysis.

To reduce patient boarding time at the emergency department (ED) and to improve the overall quality of the emergent care system in Taiwan, the Minister of Health and Welfare of Taiwan (MOHW) piloted the Grading Responsible Hospitals for Acute Care (GRHAC) audit program in 2007-2009.The aim of the study was to evaluate the impact of the GRHAC audit program on the identification and management of acute myocardial infarction (AMI)-associated ED visits by describing and comparing the incidence of AMI-associated ED visits before (2003-2007), during (2007-2009), and after (2009-2012) the initial audit program implementation.Using aggregated data from the MOHW of Taiwan, we estimated the annual incidence of AMI-associated ED visits by Poisson regression models. We used segmented regression techniques to evaluate differences in the annual rates and in the year-to-year changes in AMI-associated ED visits between 2003 and 2012. Medical comorbidities such as diabetes mellitus, hyperlipidemia, and hypertensive disease were considered as potential confounders.Overall, the number of AMI-associated patient visits increased from 8130 visits in 2003 to 12,695 visits in 2012 (P-value for trend < 0.001), corresponding to an average annual growth rate of 5.3% (95%confidence interval [CI]: 0.5-10%). Although age was a major risk factor for AMI-associated ED visits, the statistical association was observed in middle-to-old (aged 40-64; P-value < 0.001) and older aged individuals (aged ≥65; P-value <0.001). As compared to 2003-2007, AMI-associated ED visits increased slightly during the intervention roll-in period (2007-2009, slope = 394.5, P-value = 0.117) followed by a dramatic uptake in the early post-intervention period (2010-2012, slope = 1037, P-value = 0.083).There was evidence suggesting for a significant intervention effect of the GRHAC program on identifying critically ill patients with AMI-associated diagnosis at the ED. As the program evaluation is still ongoing, we expect to observe a sustained program effect on hospitals' capacity for timely and correctly diagnosing and managing patients presenting with AMI-associated symptoms or signs at the ED.

STARD-compliant article: The utility of red cell distribution width to predict mortality for septic patients visiting the emergency department.

Sepsis is a common condition in the emergency department that is associated with high mortality. Red blood cell distribution width (RDW) has been used as a simple prognosis predictor for patients with community-acquired pneumonia, gram-negative bacteremia, and severe sepsis or septic shock. To evaluate the performance of RDW to predict in-hospital mortality among septic patients, we conducted a hospital-based retrospective cohort study in an emergency department of a tertiary teaching hospital. RDW was compared with other commonly used clinical prediction scores (Systemic Inflammatory Response Syndrome (SIRS), Mortality in Emergency Department Sepsis (MEDS) and the Confusion, Urea nitrogen, Respiratory rate, Blood pressure, 65 years of age and older (CURB65)). Of 6973 consecutive adult patients with a clinical diagnosis of sepsis and 2 sets of blood culture ordered by physicians, 477 (6.8%) died. The mortality group had higher RDW levels than the survival group (15.7% vs 13.8%). After dividing RDW into quartiles, the patients in the highest RDW quartile (RDW >15.6%; mortality, 16.7%) had more than twice the risk of in-hospital mortality compared with patients in the second highest quartile (RDW >14% and <15.6%; mortality, 7.3%), whereas the mortality rate in the lowest RDW quartile (<13.1%) was only 1.6%. The area under the receiver operating characteristic curve of RDW to predict mortality was 0.75 (95% confidence interval, 0.72-0.77), which is significantly higher than the areas under the curve of clinical prediction rules (SIRS, MEDS, and CURB65). After integrating RDW into these scores, all scores performed better in predicting mortality (0.73, 0.72, and 0.77, for SIRS, MEDS, and CURB65, respectively). RDW could be an independent predictor of mortality among septic patients. Clinicians could classify the septic patients into different risk groups according to RDW quartiles. For more accurate mortality prediction, RDW could be a potential parameter to be incorporated into clinical prediction rules.