Progress in revascularization strategy of ST-segment elevation myocardial infarction with multiple vessel disease / 天津医药

ST-segment elevation myocardial infarction (STEMI) is the most serious type of coronary heart disease, accounting for 25%to 40%of acute myocardial infarction (AMI). The key to treat STEMI is to restore myocardial perfusion in the infarct area, to rescue the ischemic myocardium, and to reduce the size of infarction. About 41%to 67%of patients with STEMI have multiple vascular disease (MVD). Compared with single vessel disease, the clinical outcome of MVD is worse. In these patients, it still remains a controversial topic in emergency interventions for STEMI patients, the infarct-related artery only revascularization or multi-vessel revascularization, and the timing of revascularization. The clinical studies of revascularization strategy for MVD in STEMI patients have been ongoing, and the results have also led to the continuous updating of guidelines and treatment strategies.

Early diagnosis and optimal treatment of acute myocardial infarction / 天津医药

In recent years, benefited from the progress of clinical studies of early diagnosis and early revascularization in acute myocardial infarction (AMI), the guidelines for Europe and the United States and the Chinese academic continue to be updated to guide our clinical practice. However, AMI still remains one of the major causes of death in the worldwide. Over the past 10 years, the incidence of AMI increased rapidly in China, and the mortality kept at a high level. There is still plenty of room to improve in the early diagnosis of cardiac troponin, revascularization strategy optimization of non-infarct-related vascular, optimized new antiplatelet therapy, development of regional synergies network and chest pain center. There is still a long way from the standardized treatment of AMI. It is very important to complete early diagnosis and optimum treatment of AMI.

Study on the influence of landscape elements regarding on the transmission of hemorrhagic fever with renal syndrome in Changsha / 中华预防医学杂志

<p><b>OBJECTIVE</b>To explore the influence of landscape elements on the transmission of hemorrhagic fever with renal syndrome (HFRS) in Changsha.</p><p><b>METHODS</b>A total of 327 cases of HFRS diagnosed between year 2005 - 2009 were recruited in the study. Based on the demographic data, meteorological data and the data of second national land survey during the same period, a GIS landscape elements database of HFRS at the township scale of Changsha was established. Spatial-temporal cluster analysis methods were adopted to explore the influence of landscape elements on the spatial-temporal distribution of HFRS in Changsha during the year of 2005 - 2009.</p><p><b>RESULTS</b>The annual incidences of HFRS in Changsha between year 2005 - 2009 were 1.16/100 000 (70 cases), 0.95/100 000 (58 cases), 1.40/100 000(87 cases), 0.75/100 000(47 cases) and 1.02/100 000(65 cases) respectively. The results of poisson regression model analysis of principal component showed that the incidence of HFRS was positively correlated with farmland area (M = 29.00 km2) and urban and rural area (M = 6.12 km2; incidence rate ratios (IRR) = 1.34, 95% CI: 1.27 - 1.41); but negatively correlated with forestland area (M = 39.00 km2; IRR = 0.67, 95% CI: 0.55 - 0.81) and garden plot area (M = 0.99 km2; IRR = 0.74, 95% CI: 0.63 - 0.86). A significant cluster of the spatial-temporal distribution of HFRS cases was found in the study. The primary cluster (28.9 N, 113.37 E, radius at 22.22 km, RR = 5.23, log likelihood ratio (LLR) = 51.61, P <0.01, 67 cases of HFRS and incidence at 4.4/100 000) was found between year 2006 and 2007; and the secondary cluster (28.2 N, 113.6 E, RR = 10.77, LLR = 16.01, P < 0.01, 11 cases of HFRS and the incidence at 10.6/100 000) was found between year 2008 and 2009.</p><p><b>CONCLUSION</b>The landscape elements were found to be closely related to the prevalence and transmission of HFRS.</p>

Study on sensitivity of climatic factors on influenza A (H1N1) based on classification and regression tree and wavelet analysis / 中华预防医学杂志

<p><b>OBJECTIVE</b>To analyze the periodicity of pandemic influenza A (H1N1) in Changsha in year 2009 and its correlation with sensitive climatic factors.</p><p><b>METHODS</b>The information of 5439 cases of influenza A (H1N1) and synchronous meteorological data during the period between May 22th and December 31st in year 2009 (223 days in total) in Changsha city were collected. The classification and regression tree (CART) was employed to screen the sensitive climatic factors on influenza A (H1N1); meanwhile, cross wavelet transform and wavelet coherence analysis were applied to assess and compare the periodicity of the pandemic disease and its association with the time-lag phase features of the sensitive climatic factors.</p><p><b>RESULTS</b>The results of CART indicated that the daily minimum temperature and daily absolute humidity were the sensitive climatic factors for the popularity of influenza A (H1N1) in Changsha. The peak of the incidence of influenza A (H1N1) was in the period between October and December (Median (M) = 44.00 cases per day), simultaneously the daily minimum temperature (M = 13°C) and daily absolute humidity (M = 6.69 g/m(3)) were relatively low. The results of wavelet analysis demonstrated that a period of 16 days was found in the epidemic threshold in Changsha, while the daily minimum temperature and daily absolute humidity were the relatively sensitive climatic factors. The number of daily reported patients was statistically relevant to the daily minimum temperature and daily absolute humidity. The frequency domain was mostly in the period of (16 ± 2) days. In the initial stage of the disease (from August 9th and September 8th), a 6-day lag was found between the incidence and the daily minimum temperature. In the peak period of the disease, the daily minimum temperature and daily absolute humidity were negatively relevant to the incidence of the disease.</p><p><b>CONCLUSION</b>In the pandemic period, the incidence of influenza A (H1N1) showed periodic features; and the sensitive climatic factors did have a "driving effect" on the incidence of influenza A (H1N1).</p>

The warning model and influence of climatic changes on hemorrhagic fever with renal syndrome in Changsha city / 中华预防医学杂志

<p><b>OBJECTIVE</b>To realize the influence of climatic changes on the transmission of hemorrhagic fever with renal syndrome (HFRS), and to explore the adoption of climatic factors in warning HFRS.</p><p><b>METHODS</b>A total of 2171 cases of HFRS and the synchronous climatic data in Changsha from 2000 to 2009 were collected to a climate-based forecasting model for HFRS transmission. The Cochran-Armitage trend test was employed to explore the variation trend of the annual incidence of HFRS. Cross-correlations analysis was then adopted to assess the time-lag period between the climatic factors, including monthly average temperature, relative humidity, rainfall and Multivariate Elño-Southern Oscillation Index (MEI) and the monthly HFRS cases. Finally the time-series Poisson regression model was constructed to analyze the influence of different climatic factors on the HFRS transmission.</p><p><b>RESULTS</b>The annual incidence of HFRS in Changsha between 2000 - 2009 was 13.09/100 000 (755 cases), 9.92/100 000 (578 cases), 5.02/100 000 (294 cases), 2.55/100 000 (150 cases), 1.13/100 000 (67 cases), 1.16/100 000 (70 cases), 0.95/100 000 (58 cases), 1.40/100 000 (87 cases), 0.75/100 000 (47 cases) and 1.02/100 000 (65 cases), respectively. The incidence showed a decline during these years (Z = -5.78, P < 0.01). The results of Poisson regression model indicated that the monthly average temperature (18.00°C, r = 0.26, P < 0.01, 1-month lag period; IRR = 1.02, 95%CI: 1.00 - 1.03, P < 0.01), relative humidity (75.50%, r = 0.62, P < 0.01, 3-month lag period; IRR = 1.03, 95%CI: 1.02 - 1.04, P < 0.01), rainfall (112.40 mm, r = 0.25, P < 0.01, 6-month lag period; IRR = 1.01, 95CI: 1.01 - 1.02, P = 0.02), and MEI (r = 0.31, P < 0.01, 3-month lag period; IRR = 0.77, 95CI: 0.67 - 0.88, P < 0.01) were closely associated with monthly HFRS cases (18.10 cases).</p><p><b>CONCLUSION</b>Climate factors significantly influence the incidence of HFRS. If the influence of variable-autocorrelation, seasonality, and long-term trend were controlled, the accuracy of forecasting by the time-series Poisson regression model in Changsha would be comparatively high, and we could forecast the incidence of HFRS in advance.</p>