Purpose – The purpose of this paper is to present a bibliometric analysis of scientific output of the knowledge management (KM), the aim being to offer an overview of research activity in this field and characterize its most significant aspects. In addition, this study aims to quantitatively analyze KM research trends, forecasts, and citations from 1993 to 2012 in Web of Science (WOS). Design/methodology/approach – A total of 12,925 documents related to KM research were collected from following databases: Science Citation Index Expanded, Social Sciences Citation Index, Arts & Humanities Citation Index, Conference Proceedings Citation Index-Science, and Conference Proceedings Citation Index-Social Science & Humanities. These documents were carefully reviewed and subjected to bibliometric data analysis techniques. Findings – A number of research questions pertaining to patterns in scientific outputs, subject categories and major journals, author keywords frequencies, characteristics of the international collaboration, most cited papers and significant papers distribution of KM research were proposed and answered. In addition, there are five research sights on KM research are as follows: management science, computer science, information science, business, and engineering. Based on these findings, many implications emerged that improve one's understanding of the identity of KM as a distinct multi-discipline scientific field. Research limitations/implications – Comprehensiveness and inclusiveness of the analyzed KM-related data set in WOS because of some KM-centric journals are not indexed by Thomson Reuters. Originality/value – The paper offers an overview and evaluation of research activity into the KM viewed through the WOS during 1993-2012.

BackgroundGlycosylated hemoglobin A1C (HbA1c) has been widely recognized as a marker for predicting the severity of diabetes mellitus (DM) and several cardiovascular diseases. However, whether HbA1c could predict the severity and clinical outcomes in patients with stable coronary artery disease (CAD) remains largely unknown. We determine relationship of HbA1c with severity and outcome in patients with stable CAD.MethodsWe enrolled 1433 patients with stable angina who underwent coronary angiography and were followed up for an average 12 months. The patients were classified into three groups by tertiles of baseline HbA1c level (low group <5.7%, n = 483; intermediate group 5.7 - 6.3%, n = 512; high group >6.3%, n = 438). The relationships between the plasma HbA1c and severity of CAD and early clinical outcomes were evaluated.ResultsHigh HbA1c was associated with three-vessel disease. Area under the receivers operating characteristic curve (AUC = 0.67, 95% CI: 0.63-0.71, P < 0.001) and multivariate logistic regression analysis suggested that HbA1C was an independent predictor of severity of CAD (OR = 1.60, 95% CI: 1.29-1.99, P < 0.001) even after adjusting for gender, age, risk factor of CAD, lipid profile and fasting blood glucose. During follow-up, 133 patients underwent pre-specified outcomes. After adjusting for multiple variables in the Cox regression model, HbA1C remained to be an independent predictor of poor prognosis (HR = 1.28, 95% CI: 1.12-1.45, P < 0.001).ConclusionsWe concluded that high level of baseline HbA1c appeared to be an independent predictor for the severity of CAD and poor outcome in patients with stable CAD.

ObjectiveWhether lipoprotein(a) (Lp(a)) is a predictor for recurrent cardiovascular events (RCVEs) in patients with coronary artery disease (CAD) has not been established. This study, hence, aimed to examine the potential impact of Lp(a) on RCVEs in a real-world, large cohort of patients with the first cardiovascular event (CVE).MethodsIn this multicentre, prospective study, 7562 patients with angiography-diagnosed CAD who had experienced a first CVE were consecutively enrolled. Lp(a) concentrations of all subjects were measured at admission and the participants were categorised according to Lp(a) tertiles. All patients were followed-up for the occurrence of RCVEs including cardiovascular death, non-fatal myocardial infarction and stroke.ResultsDuring a mean follow-up of 61.45±19.57 months, 680 (9.0%) RCVEs occurred. The results showed that events group had significantly higher Lp(a) levels than non-events group (20.58 vs 14.95 mg/dL, p<0.001). Kaplan-Meier analysis indicated that Lp(a) tertile 2 (p=0.001) and tertile 3 (p<0.001) groups had significantly lower cumulative event-free survival rates compared with tertile 1 group. Moreover, multivariate Cox regression analysis further revealed that Lp(a) was independently associated with RCVEs risk (HR: 2.01, 95% CI: 1.44 to 2.80, p<0.001). Moreover, adding Lp(a) to the SMART risk score model led to a slight but significant improvement in C-statistic (∆C-statistic: 0.018 (95% CI: 0.011 to 0.034), p=0.002), net reclassification (6.8%, 95% CI: 0.5% to 10.9%, p=0.040) and integrated discrimination (0.3%, 95% CI: 0.1% to 0.7%, p<0.001).ConclusionsCirculating Lp(a) concentration was indeed a useful predictor for the risk of RCVEs in real-world treated patients with CAD, providing additional information concerning the future clinical application of Lp(a).

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Background. Some studies have suggested a relation of plasma fibrinogen to the severity of coronary artery disease (CAD). However, whether plasma fibrinogen can predict the presence and severity of CAD in patients with diabetes mellitus has not been determined. Methods. A total of consecutive 373 diabetic patients with typical angina pectoris who received coronary angiography were enrolled and classified into three groups by tertiles of Gensini score (GS, low group <8; intermediate group 8~28; high group >28). The relationship between fibrinogen and GS was evaluated. Results. There were correlations of fibrinogen with hemoglobin A1c, C-reactive protein, and GS (r = 0.17, r = 0.52, and r = 0.21, resp.; all P < 0.001). Area under the receivers operating characteristic curve of fibrinogen was 0.62 (95% CI 0.56–0.68, P < 0.001) for predicting a high GS. Multivariate analysis suggested that plasma fibrinogen was an independent predictor of a high GS for diabetic patients (OR = 1.40, 95% CI 1.04–1.88, and P = 0.026) after adjusting for traditional risk factors of CAD. Conclusions. The present data indicated that plasma fibrinogen, a readily measurable systematic inflammatory marker, appeared to be an independent predictor for the severity of CAD in diabetic patients.

BackgroundBoth coronary artery disease (CAD) and diabetes mellitus (DM) are associated with inflammation. However, whether and which leukocytes can predict the presence and extent of CAD in patients with DM has not been investigated. The aim of the present study was to examine the association of leukocyte and its subsets counts with the severity of CAD in patients with DM.Methods and FindingsThree hundred and seventy-three diabetic patients who were scheduled for coronary angiography due to typical stable angina pectoris were enrolled in this study. They were classified into the three groups according to tertiles of Gensini score (GS, low group <8, n = 143; intermediate group 8∼28, n = 109; high group >28, n = 121). The relationship between the leukocyte and its subsets counts with the severity of CAD were evaluated. The data indicated that there were significant correlations between leukocyte and neutrophil counts with GS (r = 0.154 and 0.156, respectively, all P<0.003 for Pearson's correlation). Similarly, area under the receivers operating characteristic curve of leukocyte and neutrophil counts were 0.61 and 0.60 respectively (95%CI: 0.55–0.67, all P = 0.001) for predicting high GS. Multivariate logistic regression analysis demonstrated that leukocyte count was an independent predictor for high GS patients with DM (OR = 1.20, 95%CI 1.03–1.39, P = 0.023) after adjusting for conventional risk factors of CAD.ConclusionsCompared with its subsets, leukocyte count appeared to be an independent predictor for the severity of CAD and the optimal cut-off value for predicting high GS (>28 points) was 5.0×109 cells/L in diabetic patients.

BackgroundThe relationship between non-fasting remnant cholesterol and cardiovascular outcome in the era of potent statin therapy remained to be elucidated.MethodsA cohort study of three hundred and twenty eight diabetics diagnosed with new-onset stable coronary artery disease (CAD) by coronary angiography were enrolled. All cases were followed up for an average duration of twelve months. The association between baseline remnant cholesterol levels and major cardiovascular outcomes were evaluated using the receivers operating characteristic (ROC) curves and Cox proportional hazards regression analysis.ResultsDuring a period of 12-month’s follow-up, 14.3% patients (47/328) underwent pre-specified adverse outcomes. The remnant cholesterol associated with high sensitivity C-reactive protein, neutrophil count and fibrinogen (R 2 = 0.20, 0.12 and 0.14; P = 0.000, 0.036 and 0.010 respectively). Area under the ROC curves (AUC) indicated discriminatory power of the remnant cholesterol to predict the adverse outcomes for this population (AUC = 0.64, P < 0.005). Kaplan-Meier curve suggested that the lower levels of remnant cholesterol showed relatively lower cardiac events for diabetic patients with stable CAD (Log rank X 2 = 8.94, P = 0.04). However, according to multivariate Cox proportional hazards regression, apart from hemoglobin A1C (Hazard ratio [H.R.] =1.38, 95% CI: 1.14–1.66, P = 0.001) and Gensini scores (H.R. = 1.00, 95% CI: 1.00–1.02; P = 0.035), remnant cholesterol did not qualify as an independent predictor of adverse prognosis in these settings (H.R. = 1.05, 95% CI: 0.46–2.37, P = 0.909).ConclusionsNon-fasting remnant cholesterol was associated with inflammatory biomarkers and high incidence of revascularization, but not qualified as an independent predictor for short-term prognosis of diabetics with new-onset stable coronary artery disease.

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