Objective:To assess the predictive value of systemic immune inflammation index(SII)for sepsis in low-and medium-risk community-acquired pneumonia.Methods:A total of 589 elderly patients with low-and medium-risk commun...Objective:To assess the predictive value of systemic immune inflammation index(SII)for sepsis in low-and medium-risk community-acquired pneumonia.Methods:A total of 589 elderly patients with low-and medium-risk community-acquired pneumonia admitted to the Emergency Department of the Second Affiliated Hospital of Hainan Medical University from January 2020 to January 2023 were included as the research subjects,and the general information and laboratory test results of the patients were collected,and the optimal cut-off value of continuous variables for predicting sepsis in elderly patients with low-and medium-risk community-acquired pneumonia was determined by plotting the receiver work characteristic(ROC)curve,which was converted into dichotomous variables and univariate and multivariate logistic Regression analysis of the influencing factors of sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.Based on this,a nomogram model is constructed to predict the risk of sepsis.The differentiation,consistency and accuracy of the model were verified by calibration curve and subject operating characteristic(ROC)curve,and the clinical utility of the model was determined by decision curve analysis.Results:A total of 589 elderly patients with low-and intermediate-risk community-acquired pneumonia were included in this study,of which 96(16.30%)developed sepsis.There were significant differences in age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,PCT,SII and other indexes between sepsis and non-sepsis groups(P<0.05).Logistics regression analysis showed that age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,and SII were independent risk factors for sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.The nomogram prediction model was used to verify the results,and the AUC was 0.826(95%CI:0.780-0.872),and the calibration curve tended to the ideal curve with good accuracy.The decision curve shows that when the threshold of the model is between 0.10~0.78,the model has the advantage of clinical benefit.Conclusion:The nomogram prediction model constructed based on SII to predict sepsis in elderly patients with low-and medium-risk community-acquired pneumonia has good accuracy,which can predict the occurrence of sepsis early,help early identification of high-risk groups and timely intervention,and thus improve the prognosis of patients.展开更多
Objective:To investigate the predictive value of controlling nutritional status(CONUT)score for progression to chronic critical illness sepsis in elderly patients,and to construct a predictive model based on CONUT sco...Objective:To investigate the predictive value of controlling nutritional status(CONUT)score for progression to chronic critical illness sepsis in elderly patients,and to construct a predictive model based on CONUT score histogram.Methods:739 elderly patients with sepsis admitted from January 2020 to December 2022 were selected as the study objects,and were divided into chronic critical illness group(n=188)and non-chronic critical illness group(n=551)according to whether chronic critical illness disease occurred.Clinical data of the patients were collected and compared.The predictive value of CONUT score,PNI and NLR in the progression of senile sepsis to chronic severe disease was compared,and the optimal threshold value was determined,which was used to convert the numerical variables into binary variables.Through univariate analysis and multivariate Logistic regression analysis,the risk factors affecting the progression of elderly sepsis patients to chronic critical illness disease were screened out,and the prediction model was built based on the nomogram.The efficacy and clinical utility of the prediction model were evaluated by the area under the ROC curve(AUC),calibration curve and decision curve analysis(DCA).Results:The best cut-off value for CONUT score in predicting elderly sepsis progressing to chronic critical illness was 4 points.The predictive performance of CONUT score(AUC=0.739)was better than that of PNI(AUC=0.609)and NLR(AUC=0.582)in elderly sepsis progressing to chronic critical illness(CONUT score vs PNI:Z=5.960,P<0.001;CONUT score vs NLR:Z=6.119,P<0.001).Univariate analysis showed that age,CCI score,SOFA score,sepsis shock,serum Lac,CONUT score,mechanical ventilation(MV),and continuous renal replacement therapy(CRRT)treatment were related to elderly sepsis progressing to chronic critical illness(P<0.05).Multivariate logistic regression analysis showed that CONUT score≥4 points,age≥75 years,CCI score≥3 points,SOFA score>5 points,sepsis shock,and serum Lac≥4 mmol/L were independent risk factors for elderly sepsis progressing to chronic critical illness(P<0.05).The nomogram showed that CONUT score had the greatest contribution value in predicting elderly sepsis progressing to chronic critical illness.Based on this,the nomogram prediction model had an AUC of 0.846[95%CI(0.812~0.879)],with a sensitivity of 75.5%and specificity of 81.3%,indicating good predictive performance.The calibration curve was close to the ideal curve,and the DCA threshold had clinical utility advantages when ranging from 0.1 to 0.9.Conclusion:The nomographic prediction model based on CONUT score can effectively predict the risk probability of senile sepsis progressing to chronic critical illness,helpful for clinicians to identify the high risk group of chronic and severe senile sepsis,which is helpful for clinicians to identify high-risk populations of elderly sepsis with chronic critical illness.展开更多
基金Natural Science Foundation of Hainan Province(No.819MS128)。
文摘Objective:To assess the predictive value of systemic immune inflammation index(SII)for sepsis in low-and medium-risk community-acquired pneumonia.Methods:A total of 589 elderly patients with low-and medium-risk community-acquired pneumonia admitted to the Emergency Department of the Second Affiliated Hospital of Hainan Medical University from January 2020 to January 2023 were included as the research subjects,and the general information and laboratory test results of the patients were collected,and the optimal cut-off value of continuous variables for predicting sepsis in elderly patients with low-and medium-risk community-acquired pneumonia was determined by plotting the receiver work characteristic(ROC)curve,which was converted into dichotomous variables and univariate and multivariate logistic Regression analysis of the influencing factors of sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.Based on this,a nomogram model is constructed to predict the risk of sepsis.The differentiation,consistency and accuracy of the model were verified by calibration curve and subject operating characteristic(ROC)curve,and the clinical utility of the model was determined by decision curve analysis.Results:A total of 589 elderly patients with low-and intermediate-risk community-acquired pneumonia were included in this study,of which 96(16.30%)developed sepsis.There were significant differences in age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,PCT,SII and other indexes between sepsis and non-sepsis groups(P<0.05).Logistics regression analysis showed that age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,and SII were independent risk factors for sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.The nomogram prediction model was used to verify the results,and the AUC was 0.826(95%CI:0.780-0.872),and the calibration curve tended to the ideal curve with good accuracy.The decision curve shows that when the threshold of the model is between 0.10~0.78,the model has the advantage of clinical benefit.Conclusion:The nomogram prediction model constructed based on SII to predict sepsis in elderly patients with low-and medium-risk community-acquired pneumonia has good accuracy,which can predict the occurrence of sepsis early,help early identification of high-risk groups and timely intervention,and thus improve the prognosis of patients.
文摘自改革开放以来,中国经济持续快速发展,与此同时环境也面临着挑战。在过去一段时间,全国城市普遍受到空气污染的严重影响,尤其是颗粒物(particulate matter,PM)污染和近期的臭氧(O_(3))污染。京津冀地区作为中国发展的关键板块,其环境问题更是引起了广泛的关注。在本文中,我们系统地研究了京津冀地区六种空气污染物(CO、NO_(2)、O_(3)、PM_(10)、PM_(2.5)和SO_(2))的时空变化特征。在2015—2021年,京津冀地区的CO、PM_(10)、PM_(2.5)和SO_(2)年浓度整体呈下降趋势,平均浓度分别降低0.11 mg/m^(3)、7.7 mg/m^(3)、5.4 mg/m^(3)和4.2 mg/m^(3),而NO_(2)和O_(3)-8h年平均浓度呈现先增后减的趋势。其中京津冀中南部地区SO_(2)、CO和NO_(2)的浓度较高,而京津冀北部地区的CO、NO_(2)和PM浓度较低;然而O_(3)受到空间变化的影响较小。在3月,京津冀地区几乎所有城市的PM_(10)呈上升趋势,特别是靠近内蒙古的张家口市。这可能是受到来自中国西北地区沙尘的影响。O_(3)的季节特征显示,O_(3)浓度在夏季到达高峰;其昼夜变化特征表明,O_(3)浓度在下午到达高峰。虽然京津冀地区的PM污染得到了有效改善,但污染物浓度仍高于中国环境空气质量(China national ambient air quality,CNAAQ)标准,并且该地区的O_(3)污染正不断恶化,未来需协同控制PM和O_(3)污染,以改善该地区的环境空气质量。
基金Natural Science Foundation of Hainan Provincial(No.819MS128)。
文摘Objective:To investigate the predictive value of controlling nutritional status(CONUT)score for progression to chronic critical illness sepsis in elderly patients,and to construct a predictive model based on CONUT score histogram.Methods:739 elderly patients with sepsis admitted from January 2020 to December 2022 were selected as the study objects,and were divided into chronic critical illness group(n=188)and non-chronic critical illness group(n=551)according to whether chronic critical illness disease occurred.Clinical data of the patients were collected and compared.The predictive value of CONUT score,PNI and NLR in the progression of senile sepsis to chronic severe disease was compared,and the optimal threshold value was determined,which was used to convert the numerical variables into binary variables.Through univariate analysis and multivariate Logistic regression analysis,the risk factors affecting the progression of elderly sepsis patients to chronic critical illness disease were screened out,and the prediction model was built based on the nomogram.The efficacy and clinical utility of the prediction model were evaluated by the area under the ROC curve(AUC),calibration curve and decision curve analysis(DCA).Results:The best cut-off value for CONUT score in predicting elderly sepsis progressing to chronic critical illness was 4 points.The predictive performance of CONUT score(AUC=0.739)was better than that of PNI(AUC=0.609)and NLR(AUC=0.582)in elderly sepsis progressing to chronic critical illness(CONUT score vs PNI:Z=5.960,P<0.001;CONUT score vs NLR:Z=6.119,P<0.001).Univariate analysis showed that age,CCI score,SOFA score,sepsis shock,serum Lac,CONUT score,mechanical ventilation(MV),and continuous renal replacement therapy(CRRT)treatment were related to elderly sepsis progressing to chronic critical illness(P<0.05).Multivariate logistic regression analysis showed that CONUT score≥4 points,age≥75 years,CCI score≥3 points,SOFA score>5 points,sepsis shock,and serum Lac≥4 mmol/L were independent risk factors for elderly sepsis progressing to chronic critical illness(P<0.05).The nomogram showed that CONUT score had the greatest contribution value in predicting elderly sepsis progressing to chronic critical illness.Based on this,the nomogram prediction model had an AUC of 0.846[95%CI(0.812~0.879)],with a sensitivity of 75.5%and specificity of 81.3%,indicating good predictive performance.The calibration curve was close to the ideal curve,and the DCA threshold had clinical utility advantages when ranging from 0.1 to 0.9.Conclusion:The nomographic prediction model based on CONUT score can effectively predict the risk probability of senile sepsis progressing to chronic critical illness,helpful for clinicians to identify the high risk group of chronic and severe senile sepsis,which is helpful for clinicians to identify high-risk populations of elderly sepsis with chronic critical illness.
