目的建立脓毒症患者发生脓毒症休克的临床评分预测模型,从而早期识别和积极治疗脓毒症休克的高危患者。方法回顾性分析东莞市人民医院2018年1月1日至2021年12月31日以脓毒症入院的部分脓毒症患者82例,根据患者是否进展为脓毒症休克将其...目的建立脓毒症患者发生脓毒症休克的临床评分预测模型,从而早期识别和积极治疗脓毒症休克的高危患者。方法回顾性分析东莞市人民医院2018年1月1日至2021年12月31日以脓毒症入院的部分脓毒症患者82例,根据患者是否进展为脓毒症休克将其分为脓毒症组及脓毒症休克组。对患者的一般临床资料进行单因素分析,组间有统计学意义的连续变量指标应用受试者操作特征曲线寻找最佳截断值并分析其诊断价值;根据截断值对连续变量进行二分类资料转换,运用多因素二分类Logistic回归分析进一步筛选对脓毒症休克有预测价值的指标,根据各变量的β回归系数设立相应分值建立脓毒症休克预测模型。最后将2022年1月1日至2023年12月31日期间以脓毒症入院的64例患者对模型进行验证。结果单因素分析显示年龄、性别在两组间无统计学意义,其余观察指标均有统计学意义。将降钙素原(procalcitonin,PCT)≥12μg/L、C反应蛋白(C-reactive protein,CRP)≥181mg/L、中性粒细胞与淋巴细胞比值(neutrophil to lymphocyte ratio,NLR)≥17三项指标纳入多因素Logistic回归模型。预测模型方程:Y=2.471×PCT+1.76×CRP+1.009×NLR,截断值为2.62,即Y≥2.62时预示脓毒症进展为脓毒症休克可能性大,模型敏感度、特异度、准确度分别为89.5%、63.6%、85.9%。验证队列的验证结果为:敏感度88.2%、特异度83.3%、准确度85.9%。结论本研究建立的脓毒症休克临床评分预测模型简单易行,对于早期识别脓毒症患者是否进展为脓毒症休克有一定的价值,为临床及时救治脓毒症休克的高危患者提供理论依据。展开更多
Background The vasovagal reflex syndrome （VVRS） is common in the patiems undergoing percutaneous coronary intervemion （PCI） However, prediction and prevention of the risk for the VVRS have not been completely fulf...Background The vasovagal reflex syndrome （VVRS） is common in the patiems undergoing percutaneous coronary intervemion （PCI） However, prediction and prevention of the risk for the VVRS have not been completely fulfilled. This study was conducted to develop a Risk Prediction Score Model to identify the determinants of VVRS in a large Chinese population cohort receiving PCI. Methods From the hos- pital electronic medical database, we idemified 3550 patients who received PCI （78.0% males, mean age 60 years） in Chinese PLA General Hospital from January 1, 2000 to August 30, 2016. The multivariate analysis and receiver operating characteristic 01OC） analysis were performed. Results The adverse events of VVRS in the patients were significantly increased after PCI procedure than before the operation （all P 〈 0.001）. The rate of VVRS [95% confidence interval （CI）] in patients receiving PCI was 4.5% （4.1%-5.6%）. Compared to the patients suffering no VVRS, incidence of VVRS involved the following factors, namely female gender, primary PCI, hypertension, over two stems im- plantation in the left anterior descending （LAD）, and the femoral puncture site. The multivariate analysis suggested that they were independ- ent risk factors for predicting the incidence of VVRS （all P 〈 0.001）. We developed a risk prediction score model for VVRS. ROC analysis showed that the risk prediction score model was effectively predictive of the incidence of VVRS in patients receiving PCI （c-statistic 0.76, 95% CI： 0.72-0.79, P 〈 0.001）. There were decreased evems of VVRS in the patients receiving PCI whose diastolic blood pressure dropped by more than 30 mmHg and heart rate reduced by 10 times per minute （AUC： 0.84, 95% CI： 0.81-0.87, P 〈 0.001）. Conclusion The risk prediction score is quite efficient in predicting the incidence of VVRS in patients receiving PCI. In which, the following factors may be in- volved, the femoral puncture site, female gender, hypertension, primary PCI, and over 2 stents implanted in LAD.展开更多
This paper presents results from a statistical validation of the hindcasts of surface wind by a high-reso-ution-mesoscale atmospheric numerical model Advanced Research WRF （ARW3.3）, which is set up to force the oper...This paper presents results from a statistical validation of the hindcasts of surface wind by a high-reso-ution-mesoscale atmospheric numerical model Advanced Research WRF （ARW3.3）, which is set up to force the operational coastal ocean forecast system at Indian Na- tional Centre for Ocean Information Services （INCOIS）. Evaluation is carried out based on comparisons of day-3 forecasts of surface wind with in situ and remote-sensing data. The results show that the model predicts the surface wind fields fairly accurately over the west coast of India, with high skill in predicting the surface wind during the pre-monsoon season. The model predicts the diurnal variability of the surface wind with reasonable accuracy. The model simulates the land-sea breeze cycle in the coastal region realistically, which is very clearly observed during the northeast monsoon and pre-monsoon season and is less prominent during the southwest monsoon season.展开更多
In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology consi...In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.展开更多
文摘目的建立脓毒症患者发生脓毒症休克的临床评分预测模型,从而早期识别和积极治疗脓毒症休克的高危患者。方法回顾性分析东莞市人民医院2018年1月1日至2021年12月31日以脓毒症入院的部分脓毒症患者82例,根据患者是否进展为脓毒症休克将其分为脓毒症组及脓毒症休克组。对患者的一般临床资料进行单因素分析,组间有统计学意义的连续变量指标应用受试者操作特征曲线寻找最佳截断值并分析其诊断价值;根据截断值对连续变量进行二分类资料转换,运用多因素二分类Logistic回归分析进一步筛选对脓毒症休克有预测价值的指标,根据各变量的β回归系数设立相应分值建立脓毒症休克预测模型。最后将2022年1月1日至2023年12月31日期间以脓毒症入院的64例患者对模型进行验证。结果单因素分析显示年龄、性别在两组间无统计学意义,其余观察指标均有统计学意义。将降钙素原(procalcitonin,PCT)≥12μg/L、C反应蛋白(C-reactive protein,CRP)≥181mg/L、中性粒细胞与淋巴细胞比值(neutrophil to lymphocyte ratio,NLR)≥17三项指标纳入多因素Logistic回归模型。预测模型方程:Y=2.471×PCT+1.76×CRP+1.009×NLR,截断值为2.62,即Y≥2.62时预示脓毒症进展为脓毒症休克可能性大,模型敏感度、特异度、准确度分别为89.5%、63.6%、85.9%。验证队列的验证结果为:敏感度88.2%、特异度83.3%、准确度85.9%。结论本研究建立的脓毒症休克临床评分预测模型简单易行,对于早期识别脓毒症患者是否进展为脓毒症休克有一定的价值,为临床及时救治脓毒症休克的高危患者提供理论依据。
文摘Background The vasovagal reflex syndrome （VVRS） is common in the patiems undergoing percutaneous coronary intervemion （PCI） However, prediction and prevention of the risk for the VVRS have not been completely fulfilled. This study was conducted to develop a Risk Prediction Score Model to identify the determinants of VVRS in a large Chinese population cohort receiving PCI. Methods From the hos- pital electronic medical database, we idemified 3550 patients who received PCI （78.0% males, mean age 60 years） in Chinese PLA General Hospital from January 1, 2000 to August 30, 2016. The multivariate analysis and receiver operating characteristic 01OC） analysis were performed. Results The adverse events of VVRS in the patients were significantly increased after PCI procedure than before the operation （all P 〈 0.001）. The rate of VVRS [95% confidence interval （CI）] in patients receiving PCI was 4.5% （4.1%-5.6%）. Compared to the patients suffering no VVRS, incidence of VVRS involved the following factors, namely female gender, primary PCI, hypertension, over two stems im- plantation in the left anterior descending （LAD）, and the femoral puncture site. The multivariate analysis suggested that they were independ- ent risk factors for predicting the incidence of VVRS （all P 〈 0.001）. We developed a risk prediction score model for VVRS. ROC analysis showed that the risk prediction score model was effectively predictive of the incidence of VVRS in patients receiving PCI （c-statistic 0.76, 95% CI： 0.72-0.79, P 〈 0.001）. There were decreased evems of VVRS in the patients receiving PCI whose diastolic blood pressure dropped by more than 30 mmHg and heart rate reduced by 10 times per minute （AUC： 0.84, 95% CI： 0.81-0.87, P 〈 0.001）. Conclusion The risk prediction score is quite efficient in predicting the incidence of VVRS in patients receiving PCI. In which, the following factors may be in- volved, the femoral puncture site, female gender, hypertension, primary PCI, and over 2 stents implanted in LAD.
基金University Grants Commission (UGC) for funding to pursue this work
文摘This paper presents results from a statistical validation of the hindcasts of surface wind by a high-reso-ution-mesoscale atmospheric numerical model Advanced Research WRF （ARW3.3）, which is set up to force the operational coastal ocean forecast system at Indian Na- tional Centre for Ocean Information Services （INCOIS）. Evaluation is carried out based on comparisons of day-3 forecasts of surface wind with in situ and remote-sensing data. The results show that the model predicts the surface wind fields fairly accurately over the west coast of India, with high skill in predicting the surface wind during the pre-monsoon season. The model predicts the diurnal variability of the surface wind with reasonable accuracy. The model simulates the land-sea breeze cycle in the coastal region realistically, which is very clearly observed during the northeast monsoon and pre-monsoon season and is less prominent during the southwest monsoon season.
文摘In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.
