A Clinical Prediction Rule for Histological Chorioamnionitis in Preterm Newborns

Background

Histological chorioamnionitis (HC) is an intrauterine inflammatory process highly associated with preterm birth and adverse neonatal outcome. HC is often clinically silent and diagnosed postnatally by placental histology. Earlier identification could facilitate treatment individualisation to improve outcome in preterm newborns.

Aim

Develop a clinical prediction rule at birth for HC and HC with fetal involvement (HCF) in preterm newborns.

Methods

Clinical data and placental pathology were obtained from singleton preterm newborns (gestational age ≤32.0 weeks) born at Erasmus UMC Rotterdam from 2001 to 2003 (derivation cohort; n = 216) or Máxima MC Veldhoven from 2009 to 2010 (validation cohort; n = 206). HC and HCF prediction rules were developed with preference for high sensitivity using clinical variables available at birth.

Results

HC and HCF were present in 39% and 24% in the derivation cohort and in 44% and 22% in the validation cohort, respectively. HC was predicted with 87% accuracy, yielding an area under ROC curve of 0.95 (95%CI = 0.92–0.98), a positive predictive value of 80% (95%CI = 74–84%), and a negative predictive value of 93% (95%CI = 88–96%). Corresponding figures for HCF were: accuracy 83%, area under ROC curve 0.92 (95%CI = 0.88–0.96), positive predictive value 59% (95%CI = 52–62%), and negative predictive value 97% (95%CI = 93–99%). External validation expectedly resulted in some loss of test performance, preferentially affecting positive predictive rather than negative predictive values.

Conclusion

Using a clinical prediction rule composed of clinical variables available at birth, HC and HCF could be predicted with good test characteristics in preterm newborns. Further studies should evaluate the clinical value of these rules to guide early treatment individualisation.     

Consuming More of Daily Caloric Intake at Dinner Predisposes to Obesity. A 6-Year Population-Based Prospective Cohort Study

Background/Objectives

It has been hypothesized that assuming most of the caloric intake later in the day leads to metabolic disadvantages, but few studies are available on this topic. Aim of our study was to prospectively examine whether eating more of the daily caloric intake at dinner leads to an increased risk of obesity, hyperglycemia, metabolic syndrome, and non-alcoholic fatty liver disease (NAFLD).

Subjects/Methods

1245 non-obese, non-diabetic middle-aged adults from a population-based cohort underwent a 3-day food record questionnaire at enrollment. Anthropometric values, blood pressure, blood metabolic variables, and estimated liver fat were measured at baseline and at 6-year follow-up.

Design

Prospective cohort study.

Results

Subjects were divided according to tertiles of percent daily caloric intake at dinner. A significant increase in the incidence rate of obesity (from 4.7 to 11.4%), metabolic syndrome (from 11.1 to 16.1%), and estimated NAFLD (from 16.5 to 23.8%) was observed from the lower to higher tertile. In a multiple logistic regression model adjusted for multiple covariates, subjects in the highest tertile showed an increased risk of developing obesity (OR = 2.33; 95% CI 1.17–4.65; p = 0.02), metabolic syndrome (OR = 1.52; 95% CI 1.01–2.30; p = 0.04), and NAFLD (OR = 1.56; 95% CI 1.10–2.22; p = 0.01).

Conclusions

Consuming more of the daily energy intake at dinner is associated with an increased risk of obesity, metabolic syndrome, and NAFLD.     

Statistical Cluster Analysis of the British Thoracic Society Severe Refractory Asthma Registry: Clinical Outcomes and Phenotype Stability

Background

Severe refractory asthma is a heterogeneous disease. We sought to determine statistical clusters from the British Thoracic Society Severe refractory Asthma Registry and to examine cluster-specific outcomes and stability.

Methods

Factor analysis and statistical cluster modelling was undertaken to determine the number of clusters and their membership (N = 349). Cluster-specific outcomes were assessed after a median follow-up of 3 years. A classifier was programmed to determine cluster stability and was validated in an independent cohort of new patients recruited to the registry (n = 245).

Findings

Five clusters were identified. Cluster 1 (34%) were atopic with early onset disease, cluster 2 (21%) were obese with late onset disease, cluster 3 (15%) had the least severe disease, cluster 4 (15%) were the eosinophilic with late onset disease and cluster 5 (15%) had significant fixed airflow obstruction. At follow-up, the proportion of subjects treated with oral corticosteroids increased in all groups with an increase in body mass index. Exacerbation frequency decreased significantly in clusters 1, 2 and 4 and was associated with a significant fall in the peripheral blood eosinophil count in clusters 2 and 4. Stability of cluster membership at follow-up was 52% for the whole group with stability being best in cluster 2 (71%) and worst in cluster 4 (25%). In an independent validation cohort, the classifier identified the same 5 clusters with similar patient distribution and characteristics.

Interpretation

Statistical cluster analysis can identify distinct phenotypes with specific outcomes. Cluster membership can be determined using a classifier, but when treatment is optimised, cluster stability is poor.     

A New Equation to Estimate Glomerular Filtration Rate in Chinese Elderly Population

Background

We sought to develop a new equation to estimate glomerular filtration rate (GFR) in Chinese elderly population.

Methods

A total of 668 Chinese elderly participants, including the development cohort (n = 433), the validation cohort (n = 235) were enrolled. The new equation using the generalized additive model, and age, gender, serum creatinine as predictor variables was developed and the performances was compared with the CKD-EPI equation.

Results

In the validation data set, both bias and precision were improved with the new equation, as compared with the CKD-EPI equation (median difference, −1.5 ml/min/1.73 m² vs. 7.4 ml/min/1.73 m² for the new equation and the CKD-EPI equation, [P<0.001]; interquartile range [IQR] for the difference, 16.2 ml/min/1.73 m² vs. 19.0 ml/min/1.73 m² [P<0.001]), as were accuracies (15% accuracy, 40.4% vs. 30.6% [P = 0.02]; 30% accuracy, 71.1% vs. 47.2%, [P<0.001]; 50% accuracy, 90.2% vs. 75.7%, [P<0.001]), allowing improvement in GFR categorization (GFR category misclassification rate, 37.4% vs. 53.2% [P = <0.001]).

Conclusions

A new equation was developed in Chinese elderly population. In the validation data set, the new equation performed better than the original CKD-EPI equation. The new equation needs further external validations. Calibration of the GFR referent standard to a more accurate one should be an useful way to improve the performance of GFR estimating equations.     

A Random Forest Based Risk Model for Reliable and Accurate Prediction of Receipt of Transfusion in Patients Undergoing Percutaneous Coronary Intervention

Background

Transfusion is a common complication of Percutaneous Coronary Intervention (PCI) and is associated with adverse short and long term outcomes. There is no risk model for identifying patients most likely to receive transfusion after PCI. The objective of our study was to develop and validate a tool for predicting receipt of blood transfusion in patients undergoing contemporary PCI.

Methods

Random forest models were developed utilizing 45 pre-procedural clinical and laboratory variables to estimate the receipt of transfusion in patients undergoing PCI. The most influential variables were selected for inclusion in an abbreviated model. Model performance estimating transfusion was evaluated in an independent validation dataset using area under the ROC curve (AUC), with net reclassification improvement (NRI) used to compare full and reduced model prediction after grouping in low, intermediate, and high risk categories. The impact of procedural anticoagulation on observed versus predicted transfusion rates were assessed for the different risk categories.

Results

Our study cohort was comprised of 103,294 PCI procedures performed at 46 hospitals between July 2009 through December 2012 in Michigan of which 72,328 (70%) were randomly selected for training the models, and 30,966 (30%) for validation. The models demonstrated excellent calibration and discrimination (AUC: full model  = 0.888 (95% CI 0.877–0.899), reduced model AUC = 0.880 (95% CI, 0.868–0.892), p for difference 0.003, NRI = 2.77%, p = 0.007). Procedural anticoagulation and radial access significantly influenced transfusion rates in the intermediate and high risk patients but no clinically relevant impact was noted in low risk patients, who made up 70% of the total cohort.

Conclusions

The risk of transfusion among patients undergoing PCI can be reliably calculated using a novel easy to use computational tool (https://bmc2.org/calculators/transfusion). This risk prediction algorithm may prove useful for both bed side clinical decision making and risk adjustment for assessment of quality.     

Prospective Validation of a Prognostic Model for Respiratory Syncytial Virus Bronchiolitis in Late Preterm Infants: A Multicenter Birth Cohort Study

Objectives

This study aimed to update and validate a prediction rule for respiratory syncytial virus (RSV) hospitalization in preterm infants 33–35 weeks gestational age (WGA).

Study Design

The RISK study consisted of 2 multicenter prospective birth cohorts in 41 hospitals. Risk factors were assessed at birth among healthy preterm infants 33–35 WGA. All hospitalizations for respiratory tract infection were screened for proven RSV infection by immunofluorescence or polymerase chain reaction. Multivariate logistic regression analysis was used to update an existing prediction model in the derivation cohort (n = 1,227). In the validation cohort (n = 1,194), predicted versus actual RSV hospitalization rates were compared to determine validity of the model.

Results

RSV hospitalization risk in both cohorts was comparable (5.7% versus 4.9%). In the derivation cohort, a prediction rule to determine probability of RSV hospitalization was developed using 4 predictors: family atopy (OR 1.9; 95%CI, 1.1–3.2), birth period (OR 2.6; 1.6–4.2), breastfeeding (OR 1.7; 1.0–2.7) and siblings or daycare attendance (OR 4.7; 1.7–13.1). The model showed good discrimination (c-statistic 0.703; 0.64–0.76, 0.702 after bootstrapping). External validation showed good discrimination and calibration (c-statistic 0.678; 0.61–0.74).

Conclusions

Our prospectively validated prediction rule identifies infants at increased RSV hospitalization risk, who may benefit from targeted preventive interventions. This prediction rule can facilitate country-specific, cost-effective use of RSV prophylaxis in late preterm infants.     

Development and Validation of a Bedside Score to Predict Early Death in Cancer of Unknown Primary Patients

Background

We have investigated predictors of 90-day-mortality in a large cohort of non-specific cancer of unknown primary patients.

Methods

Predictors have been identified by univariate and then logistic regression analysis in a single-center cohort comprising 429 patients (development cohort). We identified four predictors that produced a predictive score that has been applied to an independent multi-institutional cohort of 409 patients (validation cohort). The score was the sum of predictors for each patient (0 to 4).

Results

The 90-day-mortality-rate was 33 and 26% in both cohorts. Multivariate analysis has identified 4 predictors for 90-day-mortality: performance status>1 (OR = 3.03, p = 0.001), at least one co-morbidity requiring treatment (OR = 2.68, p = 0.004), LDH>1.5×the upper limit of normal (OR = 2.88, p = 0.007) and low albumin or protein levels (OR = 3.05, p = 0.007). In the development cohort, 90-day-mortality-rates were 12.5%, 32% and 64% when the score was [0–1], 2 and [3]–[4], respectively. In the validation cohort, risks were 13%, 25% and 62% according to the same score values.

Conclusions

We have validated a score that is easily calculated at the beside that estimates the 90-days mortality rate in non-specific CUP patients. This could be helpful to identify patients who would be better served with palliative care rather than aggressive chemotherapy.     

A Molecular Prognostic Model Predicts Esophageal Squamous Cell Carcinoma Prognosis

Background

Esophageal squamous cell carcinoma (ESCC) has the highest mortality rates in China. The 5-year survival rate of ESCC remains dismal despite improvements in treatments such as surgical resection and adjuvant chemoradiation, and current clinical staging approaches are limited in their ability to effectively stratify patients for treatment options. The aim of the present study, therefore, was to develop an immunohistochemistry-based prognostic model to improve clinical risk assessment for patients with ESCC.

Methods

We developed a molecular prognostic model based on the combined expression of axis of epidermal growth factor receptor (EGFR), phosphorylated Specificity protein 1 (p-Sp1), and Fascin proteins. The presence of this prognostic model and associated clinical outcomes were analyzed for 130 formalin-fixed, paraffin-embedded esophageal curative resection specimens (generation dataset) and validated using an independent cohort of 185 specimens (validation dataset).

Results

The expression of these three genes at the protein level was used to build a molecular prognostic model that was highly predictive of ESCC survival in both generation and validation datasets (P = 0.001). Regression analysis showed that this molecular prognostic model was strongly and independently predictive of overall survival (hazard ratio = 2.358 [95% CI, 1.391–3.996], P = 0.001 in generation dataset; hazard ratio = 1.990 [95% CI, 1.256–3.154], P = 0.003 in validation dataset). Furthermore, the predictive ability of these 3 biomarkers in combination was more robust than that of each individual biomarker.

Conclusions

This technically simple immunohistochemistry-based molecular model accurately predicts ESCC patient survival and thus could serve as a complement to current clinical risk stratification approaches.     

A Non-Invasive Laboratory Panel as a Diagnostic and Prognostic Biomarker for Thrombotic Microangiopathy: Development and Application in a Chinese Cohort Study

Background

Thrombotic microangiopathy (TMA) in the kidney is a histopathologic lesion that occurs in a number of clinical settings and is often associated with poor renal prognosis. The standard test for the diagnosis of TMA is the renal biopsy; noninvasive parameters such as potential biomarkers have not been developed.

Methods

We analyzed routine parameters in a cohort of 220 patients with suspected TMA and developed a diagnostic laboratory panel by logistic regression. The levels of candidate markers were validated using an independent cohort (n = 46), a cohort of systemic lupus erythematosus (SLE) (n = 157) and an expanded cohort (n = 113), as well as 9 patients with repeat biopsies.

Results

Of the 220 patients in the derivation cohort, 51 patients with biopsy-proven TMA presented with a worse renal prognosis than those with no TMA (P = 0.002). Platelet and L-lactate dehydrogenase (LDH) levels showed an acceptable diagnostic value of TMA (AUC = 0.739 and 0.756, respectively). A panel of 4 variables - creatinine, platelets, ADAMTS13 (a disintegrin and metalloprotease with thrombospondin type 1 repeats 13) activity and LDH - can effectively discriminate patients with TMA (AUC = 0.800). In the validation cohort, the platelet and LDH levels and the 4-variable panel signature robustly distinguished patients with TMA. The discrimination effects of these three markers were confirmed in patients with SLE. Moreover, LDH levels and the 4-variable panel signature also showed discrimination values in an expanded set. Among patients undergoing repeat biopsy, increased LDH levels and panel signatures were associated with TMA status when paired evaluations were performed. Importantly, only the 4-variable panel was an independent prognostic marker for renal outcome (hazard ratio = 3.549; P<0.001).

Conclusions

The noninvasive laboratory diagnostic panel is better for the early detection and prognosis of TMA compared with a single parameter, and may provide a promising biomarker for clinical application.     

Individualizing Life Expectancy Estimates for Older Adults Using the Gompertz Law of Human Mortality

Background

Guidelines recommend incorporating life expectancy (LE) into clinical decision-making for preventive interventions such as cancer screening. Previous research focused on mortality risk (e.g. 28% at 4 years) which is more difficult to interpret than LE (e.g. 7.3 years) for both patients and clinicians. Our objective was to utilize the Gompertz Law of Human Mortality which states that mortality risk doubles in a fixed time interval to transform the Lee mortality index into a LE calculator.

Methods

We examined community-dwelling older adults age 50 and over enrolled in the nationally representative 1998 wave of the Health and Retirement Study or HRS (response rate 81%), dividing study respondents into development (n = 11701) and validation (n = 8009) cohorts. In the development cohort, we fit proportional hazards Gompertz survival functions for each of the risk groups defined by the Lee mortality index. We validated our LE estimates by comparing our predicted LE with observed survival in the HRS validation cohort and an external validation cohort from the 2004 wave of the English Longitudinal Study on Ageing or ELSA (n = 7042).

Results

The ELSA cohort had a lower 8-year mortality risk (14%) compared to our HRS development (23%) and validation cohorts (25%). Our model had good discrimination in the validation cohorts (Harrell’s c 0.78 in HRS and 0.80 in the ELSA). Our predicted LE’s were similar to observed survival in the HRS validation cohort without evidence of miscalibration (Hosmer-Lemeshow, p = 0.2 at 8 years). However, our predicted LE’s were longer than observed survival in the ELSA cohort with evidence of miscalibration (Hosmer-Lemeshow, p<0.001 at 8 years) reflecting the lower mortality rate in ELSA.

Conclusion

We transformed a previously validated mortality index into a LE calculator that incorporated patient-level risk factors. Our LE calculator may help clinicians determine which preventive interventions are most appropriate for older US adults.     

A Gene Signature to Determine Metastatic Behavior in Thymomas

Purpose

Thymoma represents one of the rarest of all malignancies. Stage and completeness of resection have been used to ascertain postoperative therapeutic strategies albeit with limited prognostic accuracy. A molecular classifier would be useful to improve the assessment of metastatic behaviour and optimize patient management.

Methods

qRT-PCR assay for 23 genes (19 test and four reference genes) was performed on multi-institutional archival primary thymomas (n = 36). Gene expression levels were used to compute a signature, classifying tumors into classes 1 and 2, corresponding to low or high likelihood for metastases. The signature was validated in an independent multi-institutional cohort of patients (n = 75).

Results

A nine-gene signature that can predict metastatic behavior of thymomas was developed and validated. Using radial basis machine modeling in the training set, 5-year and 10-year metastasis-free survival rates were 77% and 26% for predicted low (class 1) and high (class 2) risk of metastasis (P = 0.0047, log-rank), respectively. For the validation set, 5-year metastasis-free survival rates were 97% and 30% for predicted low- and high-risk patients (P = 0.0004, log-rank), respectively. The 5-year metastasis-free survival rates for the validation set were 49% and 41% for Masaoka stages I/II and III/IV (P = 0.0537, log-rank), respectively. In univariate and multivariate Cox models evaluating common prognostic factors for thymoma metastasis, the nine-gene signature was the only independent indicator of metastases (P = 0.036).

Conclusion

A nine-gene signature was established and validated which predicts the likelihood of metastasis more accurately than traditional staging. This further underscores the biologic determinants of the clinical course of thymoma and may improve patient management.     

LDL-Migration Index (LDL-MI), an Indicator of Small Dense Low-Density Lipoprotein (sdLDL), Is Higher in Non-Alcoholic Steatohepatitis than in Non-Alcoholic Fatty Liver: A Multicenter Cross-Sectional Study

Background

Non-alcoholic fatty liver disease (NAFLD) is associated with increased risks of atherosclerotic diseases, including cardiovascular disease. However, the difference in risk between patients with non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH) has not yet been determined. Accumulating evidence has shown that high amounts of small dense low-density lipoprotein (sdLDL) are closely associated with atherosclerotic diseases. This study investigated differences in risk factors for atherosclerotic diseases, especially LDL-migration index (LDL-MI), an indicator of sdLDL, between patients with NAFL and NASH.

Methods

LDL-MI was analyzed in a primary cohort of 156 patients with NAFLD, including 53 with NAFL and 103 with NASH, and a validation cohort of 69 patients with NAFLD, including 25 with NAFL and 44 with NASH.

Results

In the primary cohort, NASH was associated with elevated LDL-MI (p = 0.039). Multiple regression analysis showed that NASH and the non-use of lipid lowering medications were independently correlated with higher LDL-MI in all patients with NAFLD. Among patients not on lipid lowering medications, those with NASH had significantly higher LDL-MI than those with NAFL (p = 0.001). These findings were confirmed in a validation cohort, in that LDL-MI was significantly higher in patients with NASH than with NAFL (p = 0.043).

Conclusion

This study is the first to show that LDL-MI, an indicator of sdLDL, was higher in patients with NASH than with NAFL, suggesting that the risk of atherosclerotic diseases may be higher in NASH than NAFL. Patients with NASH should be followed closely, especially for the progression of liver pathology and atherosclerotic diseases.

Trial Registration

UMIN000009614     

Is There an Association between Work Stress and Diurnal Cortisol Patterns? Findings from the Whitehall II Study

Objective

The evidence on whether there is work stress related dysregulation of the hypothalamic-pituitary-adrenal axis is equivocal. This study assessed the relation between work stress and diurnal cortisol rhythm in a large-scale occupational cohort, the Whitehall II study.

Methods

Work stress was assessed in two ways, using the job-demand-control (JDC) and the effort-reward-imbalance (ERI) models. Salivary cortisol samples were collected six times over a normal day in 2002–2004. The cortisol awakening response (CAR) and diurnal cortisol decline (slope) were calculated.

Results

In this large occupational cohort (N = 2,126, mean age 57.1), modest differences in cortisol patterns were found for ERI models only, showing lower reward (β = −0.001, P-value = 0.04) and higher ERI (β = 0.002, P-value = 0.05) were related to a flatter slope in cortisol across the day. Meanwhile, moderate gender interactions were observed regarding CAR and JDC model.

Conclusions

We conclude that the associations of work stress with cortisol are modest, with associations apparent for ERI model rather than JDC model.     

A Model to Discriminate Malignant from Benign Thyroid Nodules Using Artificial Neural Network

Objective

This study aimed to construct a model for using in differentiating benign and malignant nodules with the artificial neural network and to increase the objective diagnostic accuracy of US.

Materials and methods

618 consecutive patients (528 women, 161 men) with 689 thyroid nodules (425 malignant and 264 benign nodules) were enrolled in the present study. The presence and absence of each sonographic feature was assessed for each nodule - shape, margin, echogenicity, internal composition, presence of calcifications, peripheral halo and vascularity on color Doppler. The variables meet the following criteria: important sonographic features and statistically significant difference were selected as the input layer to build the ANN for predicting the malignancy of nodules.

Results

Six sonographic features including shape (Taller than wide, p<0.001), margin (Not Well-circumscribed, p<0.001), echogenicity (Hypoechogenicity, p<0.001), internal composition (Solid, p<0.001), presence of calcifications (Microcalcification, p<0.001) and peripheral halo (Absent, p<0.001) were significantly associated with malignant nodules. A three-layer 6-8-1 feed-forward ANN model was built. In the training cohort, the accuracy of the ANN in predicting malignancy of thyroid nodules was 82.3% (AUROC = 0.818), the sensitivity and specificity was 84.5% and 79.1%, respectively. In the validation cohort, the accuracy, sensitivity and specificity was 83.1%, 83.8% and 81.8%, respectively. The AUROC was 0.828.

Conclusion

ANN constructed by sonographic features can discriminate benign and malignant thyroid nodules with high diagnostic accuracy.     

MTHFR Glu429Ala and ERCC5 His46His Polymorphisms Are Associated with Prognosis in Colorectal Cancer Patients: Analysis of Two Independent Cohorts from Newfoundland

Introduction

In this study, 27 genetic polymorphisms that were previously reported to be associated with clinical outcomes in colorectal cancer patients were investigated in relation to overall survival (OS) and disease free survival (DFS) in colorectal cancer patients from Newfoundland.

Methods

The discovery and validation cohorts comprised of 532 and 252 patients, respectively. Genotypes of 27 polymorphisms were first obtained in the discovery cohort and survival analyses were performed assuming the co-dominant genetic model. Polymorphisms associated with disease outcomes in the discovery cohort were then investigated in the validation cohort.

Results

When adjusted for sex, age, tumor stage and microsatellite instability (MSI) status, four polymorphisms were independent predictors of OS in the discovery cohort MTHFR Glu429Ala (HR: 1.72, 95%CI: 1.04–2.84, p = 0.036), ERCC5 His46His (HR: 1.78, 95%CI: 1.15–2.76, p = 0.01), SERPINE1 −675indelG (HR: 0.52, 95%CI: 0.32–0.84, p = 0.008), and the homozygous deletion of GSTM1 gene (HR: 1.4, 95%CI: 1.03–1.92, p = 0.033). In the validation cohort, the MTHFR Glu429Ala polymorphism was associated with shorter OS (HR: 1.71, 95%CI: 1.18–2.49, p = 0.005), although with a different genotype than the discovery cohort (CC genotype in the discovery cohort and AC genotype in the validation cohort). When stratified based on treatment with 5-Fluorouracil (5-FU)-based regimens, this polymorphism was associated with reduced OS only in patients not treated with 5-FU. In the DFS analysis, when adjusted for other variables, the TT genotype of the ERCC5 His46His polymorphism was associated with shorter DFS in both cohorts (discovery cohort: HR: 1.54, 95%CI: 1.04–2.29, p = 0.032 and replication cohort: HR: 1.81, 95%CI: 1.11–2.94, p = 0.018).

Conclusions

In this study, associations of the MTHFR Glu429Ala polymorphism with OS and the ERCC5 His46His polymorphism with DFS were identified in two colorectal cancer patient cohorts. Our results also suggest that the MTHFR Glu429Ala polymorphism may be an adverse prognostic marker in patients not treated with 5-FU.     

Circulating microRNAs as a Fingerprint for Liver Cirrhosis

Background

Sensitive and specific detection of liver cirrhosis is an urgent need for optimal individualized management of disease activity. Substantial studies have identified circulation miRNAs as biomarkers for diverse diseases including chronic liver diseases. In this study, we investigated the plasma miRNA signature to serve as a potential diagnostic biomarker for silent liver cirrhosis.

Methods

A genome-wide miRNA microarray was first performed in 80 plasma specimens. Six candidate miRNAs were selected and then trained in CHB-related cirrhosis and controls by qPCR. A classifier, miR-106b and miR-181b, was validated finally in two independent cohorts including CHB-related silent cirrhosis and controls, as well as non−CHB-related cirrhosis and controls as validation sets, respectively.

Results

A profile of 2 miRNAs (miR-106b and miR-181b) was identified as liver cirrhosis biomarkers irrespective of etiology. The classifier constructed by the two miRNAs provided a high diagnostic accuracy for cirrhosis (AUC = 0.882 for CHB-related cirrhosis in the training set, 0.774 for CHB-related silent cirrhosis in one validation set, and 0.915 for non−CHB-related cirrhosis in another validation set).

Conclusion

Our study demonstrated that the combined detection of miR-106b and miR-181b has a considerable clinical value to diagnose patients with liver cirrhosis, especially those at early stage.     

The Effect of Ginseng (The Genus Panax) on Glycemic Control: A Systematic Review and Meta-Analysis of Randomized Controlled Clinical Trials

Importance

Despite the widespread use of ginseng in the management of diabetes, supporting evidence of its anti-hyperglycemic efficacy is limited, necessitating the need for evidence-based recommendations for the potential inclusion of ginseng in diabetes management.

Objective

To elucidate the effect of ginseng on glycemic control in a systematic review and meta-analysis of randomized controlled trials in people with and without diabetes.

Data sources

MEDLINE, EMBASE, CINAHL and the Cochrane Library (through July 3, 2013).

Study selection

Randomized controlled trials ≥30 days assessing the glycemic effects of ginseng in people with and without diabetes.

Data extraction

Relevant data were extracted by 2 independent reviewers. Discrepancies were resolved by consensus. The Heyland Methodological Quality Score and the Cochrane risk of bias tool were used to assess study quality and risk of bias respectively.

Data synthesis

Sixteen trials were included, in which 16 fasting blood glucose (n = 770), 10 fasting plasma insulin (n = 349), 9 glycated hemoglobin (n = 264), and 7 homeostasis model assessment of insulin resistance (n = 305) comparisons were reported. Ginseng significantly reduced fasting blood glucose compared to control (MD =  −0.31 mmol/L [95% CI: −0.59 to −0.03], P = 0.03). Although there was no significant effect on fasting plasma insulin, glycated hemoglobin, or homeostasis model assessment of insulin resistance, a priori subgroup analyses did show significant reductions in glycated hemoglobin in parallel compared to crossover trials (MD = 0.22% [95%CI: 0.06 to 0.37], P = 0.01).

Limitations

Most trials were of short duration (67% trials<12wks), and included participants with a relatively good glycemic control (median HbA1c non-diabetes = 5.4% [2 trials]; median HbA1c diabetes = 7.1% [7 trials]).

Conclusions

Ginseng modestly yet significantly improved fasting blood glucose in people with and without diabetes. In order to address the uncertainty in our effect estimates and provide better assessments of ginseng''s anti-diabetic efficacy, larger and longer randomized controlled trials using standardized ginseng preparations are warranted.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01841229","term_id":"NCT01841229"}}NCT01841229     

Zolpidem Use and the Risk of Injury: A Population-Based Follow-Up Study

Background

While an association between zolpidem use and fracture and road accident was previously proposed, this study aimed to further explore the frequency and risk of a wide spectrum of injuries in subjects prescribed with zolpidem in Taiwan.

Methods

We identified 77,036 subjects who received Zolpidem treatment between 2005 and 2007. We randomly selected 77,036 comparison subjects who were frequency-matched based-on their demographic profiles. We individually tracked each subject for a 90-day period to identify those who subsequently suffered an injury. Cox proportional hazards regressions were performed to calculate the hazard ratio of injury between the two groups.

Results

The incidence rate of injury during the 90-day follow-up period for the total subjects was 18.11 (95% CI = 17.69–18.54) per 100 person-years; this was 24.35 (95% CI = 23.66–25.05) and 11.86 (95% CI = 11.39–12.36) for the study and comparison cohort, respectively. After adjusting for demographic variables, the hazard ratio (HR) of injury during the 90-day follow-up period for study subjects was 1.83 (95% CI = 1.73–1.94) that of comparison subjects. Additionally, compared to comparison subjects, the adjusted HR of injury during the 90-day follow-up period for study subjects who were prescribed Zolpidem for >30 days was as high as 2.17 (95% CI = 2.05–2.32). The adjusted HR of injury to blood vessels for study subjects was particularly high when compared to comparison subjects (HR = 6.34; 95% CI = 1.37–29.38).

Conclusions

We found that patients prescribed with Zolpidem were at a higher risk for a wide range of injuries.