AIM:To determine the impact of different characteristics on postoperative outcomes for patients in a joint arthroplasty Perioperative Surgical Home(PSH) program.METHODS:A retrospective review was performed for patient...AIM:To determine the impact of different characteristics on postoperative outcomes for patients in a joint arthroplasty Perioperative Surgical Home(PSH) program.METHODS:A retrospective review was performed for patients enrolled in a joint arthroplasty PSH program who had undergone primary total hip arthroplasty(THA) and total knee arthroplasty(TKA).Patients were preoperatively stratified based on specific procedure performed,age,gender,body mass index(BMI),American Society of Anesthesiologists Physical Classification System(ASA) score,and Charleston Comorbidity Index(CCI) score.The primary outcome criterion was hospital length of stay(LOS).Secondary criteria including operative room(OR) duration,trans-fusion rate,Post-Anesthesia Care Unit(PACU) stay,readmission rate,post-operative complications,and discharge disposition.For each outcome,the predictor variables were entered into a generalized linear model with appropriate response and assessed for predictive relationship to the dependent variable.Significance level was set to 0.05.RESULTS:A total of 337 patients,200 in the TKA cohort and 137 in the THA cohort,were eligible for the study.Nearly two-third of patients were female.Patient age averaged 64 years and preoperative BMI averaged 29 kg/m2.The majority of patients were ASA score Ⅲ and CCI score 0.After analysis,ASA score was the only variable predictive for LOS(P = 0.0011) and each increase in ASA score above 2 increased LOS by approximately 0.5 d.ASA score was also the only variable predictive for readmission rate(P = 0.0332).BMI was the only variable predictive for PACU duration(P = 0.0136).Specific procedure performed,age,gender,and CCI score were not predictive for any of the outcome criteria.OR duration,transfusion rate,postoperative complications or discharge disposition were not significantly associated with any of the predictor variables.CONCLUSION:The joint arthroplasty PSH model reduces postoperative outcome variability for patients with different preoperative characteristics and medical comorbidities.展开更多
美国麻醉医师协会全身状态(American Society of Anesthesiologists Physical Status,ASA-PS)分级系统作为术前评估工具被广泛应用于成年人,但是否适用于小儿仍存在疑问。文章回顾了ASA-PS分级系统的历史沿革及其在成年人中的应用情况,...美国麻醉医师协会全身状态(American Society of Anesthesiologists Physical Status,ASA-PS)分级系统作为术前评估工具被广泛应用于成年人,但是否适用于小儿仍存在疑问。文章回顾了ASA-PS分级系统的历史沿革及其在成年人中的应用情况,介绍了ASA-PS分级系统在儿科应用中信度的研究,并分析其在儿科应用中信度不高的原因,还介绍了ASA-PS分级系统以外适用于儿科的术前风险评估系统。希望未来能建立针对儿科的术前评估系统或者对现有ASA-PS分级系统进行修订以适应儿科需要。展开更多
目的 建立病情-麻醉-手术三位一体预测术中心血管并发症风险的新方法.方法 分别以美国麻醉学协会的患者体质分级标准(American Society of Anesthesiologists Physical Status Classification,ASA-PS)、Carrillo's方法作为病情、手...目的 建立病情-麻醉-手术三位一体预测术中心血管并发症风险的新方法.方法 分别以美国麻醉学协会的患者体质分级标准(American Society of Anesthesiologists Physical Status Classification,ASA-PS)、Carrillo's方法作为病情、手术、麻醉风险分级标准,制作风险评估量表.在2016年1月-2016年12月期间对3 543例各科手术患者进行术前风险评估和分级,记录术中心血管并发症.将病情-麻醉-手术的风险等级与术中心血管并发症进行二元逻辑分析,获得回归系数.利用Logistic回归方程,建立病情-麻醉-手术三位一体风险评估数学模型,用三位一体模型对术中心血管并发症进行预测并与直接用ASA-PS建模的模型比较.结果 3 543例患者术中共发生心血管并发症311例(8.78%).三位一体方法中3种元素对术中并发症的贡献大小依次为病情、麻醉和手术元素,回归系数分别为0.886、0.508、0.268;ASA-pS的回归系数为1.089.三位一体方法术中并发症预测公式为logit (P) =-6.298+0.886×ASA-PS等级+0.508×麻醉等级+0.268×手术等级;ASA-PS方法公式为logit(P)=-4.758+ 1.089×ASA-PS.三位一体方法的受试者工作特征曲线(receiver operating characteristic curve,ROC)和ROC曲线下面积(area under the ROC curve,AUROC)为0.809,ASA-PS的AUROC为0.732.结论 与ASA-PS比较,病情-麻醉-手术三位一体风险评估新方法预测术中心血管并发症的效力和拟合度较好.展开更多
文摘AIM:To determine the impact of different characteristics on postoperative outcomes for patients in a joint arthroplasty Perioperative Surgical Home(PSH) program.METHODS:A retrospective review was performed for patients enrolled in a joint arthroplasty PSH program who had undergone primary total hip arthroplasty(THA) and total knee arthroplasty(TKA).Patients were preoperatively stratified based on specific procedure performed,age,gender,body mass index(BMI),American Society of Anesthesiologists Physical Classification System(ASA) score,and Charleston Comorbidity Index(CCI) score.The primary outcome criterion was hospital length of stay(LOS).Secondary criteria including operative room(OR) duration,trans-fusion rate,Post-Anesthesia Care Unit(PACU) stay,readmission rate,post-operative complications,and discharge disposition.For each outcome,the predictor variables were entered into a generalized linear model with appropriate response and assessed for predictive relationship to the dependent variable.Significance level was set to 0.05.RESULTS:A total of 337 patients,200 in the TKA cohort and 137 in the THA cohort,were eligible for the study.Nearly two-third of patients were female.Patient age averaged 64 years and preoperative BMI averaged 29 kg/m2.The majority of patients were ASA score Ⅲ and CCI score 0.After analysis,ASA score was the only variable predictive for LOS(P = 0.0011) and each increase in ASA score above 2 increased LOS by approximately 0.5 d.ASA score was also the only variable predictive for readmission rate(P = 0.0332).BMI was the only variable predictive for PACU duration(P = 0.0136).Specific procedure performed,age,gender,and CCI score were not predictive for any of the outcome criteria.OR duration,transfusion rate,postoperative complications or discharge disposition were not significantly associated with any of the predictor variables.CONCLUSION:The joint arthroplasty PSH model reduces postoperative outcome variability for patients with different preoperative characteristics and medical comorbidities.
文摘美国麻醉医师协会全身状态(American Society of Anesthesiologists Physical Status,ASA-PS)分级系统作为术前评估工具被广泛应用于成年人,但是否适用于小儿仍存在疑问。文章回顾了ASA-PS分级系统的历史沿革及其在成年人中的应用情况,介绍了ASA-PS分级系统在儿科应用中信度的研究,并分析其在儿科应用中信度不高的原因,还介绍了ASA-PS分级系统以外适用于儿科的术前风险评估系统。希望未来能建立针对儿科的术前评估系统或者对现有ASA-PS分级系统进行修订以适应儿科需要。
文摘目的 建立病情-麻醉-手术三位一体预测术中心血管并发症风险的新方法.方法 分别以美国麻醉学协会的患者体质分级标准(American Society of Anesthesiologists Physical Status Classification,ASA-PS)、Carrillo's方法作为病情、手术、麻醉风险分级标准,制作风险评估量表.在2016年1月-2016年12月期间对3 543例各科手术患者进行术前风险评估和分级,记录术中心血管并发症.将病情-麻醉-手术的风险等级与术中心血管并发症进行二元逻辑分析,获得回归系数.利用Logistic回归方程,建立病情-麻醉-手术三位一体风险评估数学模型,用三位一体模型对术中心血管并发症进行预测并与直接用ASA-PS建模的模型比较.结果 3 543例患者术中共发生心血管并发症311例(8.78%).三位一体方法中3种元素对术中并发症的贡献大小依次为病情、麻醉和手术元素,回归系数分别为0.886、0.508、0.268;ASA-pS的回归系数为1.089.三位一体方法术中并发症预测公式为logit (P) =-6.298+0.886×ASA-PS等级+0.508×麻醉等级+0.268×手术等级;ASA-PS方法公式为logit(P)=-4.758+ 1.089×ASA-PS.三位一体方法的受试者工作特征曲线(receiver operating characteristic curve,ROC)和ROC曲线下面积(area under the ROC curve,AUROC)为0.809,ASA-PS的AUROC为0.732.结论 与ASA-PS比较,病情-麻醉-手术三位一体风险评估新方法预测术中心血管并发症的效力和拟合度较好.
