BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection...BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection also significantly increases the risk of disease and death.Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke(AIS)is of great significance.It can guide clinical practice to perform corresponding prevention and control work early,minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected.Baseline data and post-stroke infection status of all study subjects were assessed,and the risk factors for poststroke infection in patients with AIS were analyzed.RESULTS Totally,48 patients with AIS developed stroke,with an infection rate of 23.3%.Age,diabetes,disturbance of consciousness,high National Institutes of Health Stroke Scale(NIHSS)score at admission,invasive operation,and chronic obstructive pulmonary disease(COPD)were risk factors for post-stroke infection in patients with AIS(P<0.05).A nomogram prediction model was constructed with a C-index of 0.891,reflecting the good potential clinical efficacy of the nomogram prediction model.The calibration curve also showed good consistency between the actual observations and nomogram predictions.The area under the receiver operating characteristic curve was 0.891(95%confidence interval:0.839–0.942),showing predictive value for post-stroke infection.When the optimal cutoff value was selected,the sensitivity and specificity were 87.5%and 79.7%,respectively.CONCLUSION Age,diabetes,disturbance of consciousness,NIHSS score at admission,invasive surgery,and COPD are risk factors for post-stroke infection following AIS.The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.展开更多
BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challengin...BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors.AIM To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively.METHODS We retrospectively analysed the inpatient records of Shaanxi Provincial People’s Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002(NRS 2002)scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance(NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model.RESULTS A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus(42.2%), the liver(27.6%), the gastrointestinal tract(19.1%), the appendix(5.9%), the kidney(3.7%), and the groin area(1.4%). SSI occurred in 5% of the patients(n = 150). The risk factors associated with SSI were as follows: Age;gender;marital status;place of residence;history of diabetes;surgical season;surgical site;NRS 2002 score;preoperative white blood cell, procalcitonin(PCT), albumin, and low-density lipoprotein cholesterol(LDL) levels;preoperative antibiotic use;anaesthesia method;incision grade;NNIS score;intraoperative blood loss;intraoperative drainage tube placement;surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio(OR) = 5.698, 95% confidence interval(CI): 3.305-9.825, P = 0.001], antibiotic use(OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3(OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia(OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2(OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 μg/L(OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L(OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL(OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season(P < 0.05), surgical site(P < 0.05), and incision grade I or Ⅲ(P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score(0.662).CONCLUSION The patient’s condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.展开更多
AIM To undertook a systematic review to determine factors that increase a patient's risk of developing lower limb periprosthetic joint infections(PJI).METHODS This systematic review included full-text studies that...AIM To undertook a systematic review to determine factors that increase a patient's risk of developing lower limb periprosthetic joint infections(PJI).METHODS This systematic review included full-text studies that reviewed risk factors of developing either a hip or knee PJI following a primary arthroplasty published from January 1998 to November 2016. A variety of keywords were used to identify studies through international databases referencing hip arthroplasty, knee arthroplasty, infection, and risk factors. Studies were only included if they included greater than 20 patients in their study cohort, and there was clear documentation of the statistical parameter used; specifically P-value, hazard ratio, relative risk, or/and odds ratio(OR). Furthermore a quality assessment criteria for the individual studies was undertaken to evaluate the presence of record and reporting bias. RESULTS Twenty-seven original studies reviewing risk factors relating to primary total hip and knee arthroplasty infections were included. Four studies(14.8%) reviewed PJI of the hip, 3(11.21%) of the knee, and 20(74.1%) reviewed both joints. Nineteen studies(70.4%) were retrospective and 8(29.6%) prospective. Record bias was identified in the majority of studies(66.7%). The definition of PJI varied amongst the studies but there was a general consensus to define infection by previously validated methods. The most significant risks were the use of preoperative high dose steroids(OR = 21.0, 95%CI: 3.5-127.2, P < 0.001), a BMI above 50(OR = 18.3, P < 0.001), tobacco use(OR = 12.76, 95%CI: 2.47-66.16, P= 0.017), body mass index below 20(OR = 6.00, 95%CI: 1.2-30.9, P = 0.033), diabetes(OR = 5.47, 95%CI: 1.77-16.97, P = 0.003), and coronary artery disease(OR = 5.10, 95%CI: 1.3-19.8, P = 0.017).CONCLUSION We have highlighted the need for the provider to optimise modifiable risk factors, and develop strategies to limit the impact of non-modifiable factors.展开更多
Since the coronavirus disease 2019,the extended time indoors makes people more concerned about indoor air quality,while the increased ventilation in seeks of reducing infection probability has increased the energy usa...Since the coronavirus disease 2019,the extended time indoors makes people more concerned about indoor air quality,while the increased ventilation in seeks of reducing infection probability has increased the energy usage from heating,ventilation,and air-conditioning systems.In this study,to represent the dynamics of indoor temperature and air quality,a coupled grey-box model is developed.The model is identified and validated using a data-driven approach and real-time measured data of a campus office.To manage building energy usage and indoor air quality,a model predictive control strategy is proposed and developed.The simulation study demonstrated 18.92%energy saving while maintaining good indoor air quality at the testing site.Two nationwide simulation studies assessed the overall energy saving potential and the impact on the infection probability of the proposed strategy in different climate zones.The results showed 20%–40%energy saving in general while maintaining a predetermined indoor air quality setpoint.Although the infection risk is increased due to the reduced ventilation rate,it is still less than the suggested threshold(2%)in general.展开更多
目的:系统评价心脏植入式电子设备(CIED)植入术后设备感染(DRI)的风险预测模型。方法:通过计算机检索PubMed、Embase、Web of Science、Cochrane图书馆、CINAHL、中国生物医学文献数据库、中国知网、维普网、万方数据库中与CIED植入术后...目的:系统评价心脏植入式电子设备(CIED)植入术后设备感染(DRI)的风险预测模型。方法:通过计算机检索PubMed、Embase、Web of Science、Cochrane图书馆、CINAHL、中国生物医学文献数据库、中国知网、维普网、万方数据库中与CIED植入术后DRI风险预测模型相关的文献,检索时间为从建库至2023年12月2日。由2名研究者独立筛选文献、提取资料并完成纳入文献的偏倚风险与适用性评价。结果:共纳入16项研究,模型总体适用性较好,但偏倚风险较高,ROC曲线的AUC为0.67~0.96。11项研究完成了内部验证,5项研究进行了外部验证。囊袋和(或)电极重置/装置升级、肾功能不全或肾功能衰竭、年龄、植入埋藏式心脏复律除颤器或心脏再同步化治疗、使用抗凝药是DRI的预测因子。结论:目前CIED植入术后DRI风险预测模型整体性能较好,适用性较好,但偏倚风险较高。需在数据来源、变量筛选、模型评价等方面提高研究质量,开展前瞻性队列研究,完善现有模型的外部验证,并积极研发适用于我国人群的预测模型。展开更多
基金Shandong Province Grassroots Health Technology Innovation Program Project,No.JCK22007.
文摘BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection also significantly increases the risk of disease and death.Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke(AIS)is of great significance.It can guide clinical practice to perform corresponding prevention and control work early,minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected.Baseline data and post-stroke infection status of all study subjects were assessed,and the risk factors for poststroke infection in patients with AIS were analyzed.RESULTS Totally,48 patients with AIS developed stroke,with an infection rate of 23.3%.Age,diabetes,disturbance of consciousness,high National Institutes of Health Stroke Scale(NIHSS)score at admission,invasive operation,and chronic obstructive pulmonary disease(COPD)were risk factors for post-stroke infection in patients with AIS(P<0.05).A nomogram prediction model was constructed with a C-index of 0.891,reflecting the good potential clinical efficacy of the nomogram prediction model.The calibration curve also showed good consistency between the actual observations and nomogram predictions.The area under the receiver operating characteristic curve was 0.891(95%confidence interval:0.839–0.942),showing predictive value for post-stroke infection.When the optimal cutoff value was selected,the sensitivity and specificity were 87.5%and 79.7%,respectively.CONCLUSION Age,diabetes,disturbance of consciousness,NIHSS score at admission,invasive surgery,and COPD are risk factors for post-stroke infection following AIS.The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.
基金Supported by Key Research and Development Program of Shaanxi,No.2020GXLH-Y-019 and 2022KXJ-141Innovation Capability Support Program of Shaanxi,No.2019GHJD-14 and 2021TD-40+1 种基金Science and Technology Talent Support Program of Shaanxi Provincial People's Hospital,No.2021LJ-052023 Natural Science Basic Research Foundation of Shaanxi Province,No.2023-JC-YB-739.
文摘BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors.AIM To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively.METHODS We retrospectively analysed the inpatient records of Shaanxi Provincial People’s Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002(NRS 2002)scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance(NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model.RESULTS A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus(42.2%), the liver(27.6%), the gastrointestinal tract(19.1%), the appendix(5.9%), the kidney(3.7%), and the groin area(1.4%). SSI occurred in 5% of the patients(n = 150). The risk factors associated with SSI were as follows: Age;gender;marital status;place of residence;history of diabetes;surgical season;surgical site;NRS 2002 score;preoperative white blood cell, procalcitonin(PCT), albumin, and low-density lipoprotein cholesterol(LDL) levels;preoperative antibiotic use;anaesthesia method;incision grade;NNIS score;intraoperative blood loss;intraoperative drainage tube placement;surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio(OR) = 5.698, 95% confidence interval(CI): 3.305-9.825, P = 0.001], antibiotic use(OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3(OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia(OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2(OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 μg/L(OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L(OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL(OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season(P < 0.05), surgical site(P < 0.05), and incision grade I or Ⅲ(P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score(0.662).CONCLUSION The patient’s condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.
文摘AIM To undertook a systematic review to determine factors that increase a patient's risk of developing lower limb periprosthetic joint infections(PJI).METHODS This systematic review included full-text studies that reviewed risk factors of developing either a hip or knee PJI following a primary arthroplasty published from January 1998 to November 2016. A variety of keywords were used to identify studies through international databases referencing hip arthroplasty, knee arthroplasty, infection, and risk factors. Studies were only included if they included greater than 20 patients in their study cohort, and there was clear documentation of the statistical parameter used; specifically P-value, hazard ratio, relative risk, or/and odds ratio(OR). Furthermore a quality assessment criteria for the individual studies was undertaken to evaluate the presence of record and reporting bias. RESULTS Twenty-seven original studies reviewing risk factors relating to primary total hip and knee arthroplasty infections were included. Four studies(14.8%) reviewed PJI of the hip, 3(11.21%) of the knee, and 20(74.1%) reviewed both joints. Nineteen studies(70.4%) were retrospective and 8(29.6%) prospective. Record bias was identified in the majority of studies(66.7%). The definition of PJI varied amongst the studies but there was a general consensus to define infection by previously validated methods. The most significant risks were the use of preoperative high dose steroids(OR = 21.0, 95%CI: 3.5-127.2, P < 0.001), a BMI above 50(OR = 18.3, P < 0.001), tobacco use(OR = 12.76, 95%CI: 2.47-66.16, P= 0.017), body mass index below 20(OR = 6.00, 95%CI: 1.2-30.9, P = 0.033), diabetes(OR = 5.47, 95%CI: 1.77-16.97, P = 0.003), and coronary artery disease(OR = 5.10, 95%CI: 1.3-19.8, P = 0.017).CONCLUSION We have highlighted the need for the provider to optimise modifiable risk factors, and develop strategies to limit the impact of non-modifiable factors.
基金This research was jointly sponsored by Honeywell International Inc.and Syracuse University.
文摘Since the coronavirus disease 2019,the extended time indoors makes people more concerned about indoor air quality,while the increased ventilation in seeks of reducing infection probability has increased the energy usage from heating,ventilation,and air-conditioning systems.In this study,to represent the dynamics of indoor temperature and air quality,a coupled grey-box model is developed.The model is identified and validated using a data-driven approach and real-time measured data of a campus office.To manage building energy usage and indoor air quality,a model predictive control strategy is proposed and developed.The simulation study demonstrated 18.92%energy saving while maintaining good indoor air quality at the testing site.Two nationwide simulation studies assessed the overall energy saving potential and the impact on the infection probability of the proposed strategy in different climate zones.The results showed 20%–40%energy saving in general while maintaining a predetermined indoor air quality setpoint.Although the infection risk is increased due to the reduced ventilation rate,it is still less than the suggested threshold(2%)in general.
文摘目的:系统评价心脏植入式电子设备(CIED)植入术后设备感染(DRI)的风险预测模型。方法:通过计算机检索PubMed、Embase、Web of Science、Cochrane图书馆、CINAHL、中国生物医学文献数据库、中国知网、维普网、万方数据库中与CIED植入术后DRI风险预测模型相关的文献,检索时间为从建库至2023年12月2日。由2名研究者独立筛选文献、提取资料并完成纳入文献的偏倚风险与适用性评价。结果:共纳入16项研究,模型总体适用性较好,但偏倚风险较高,ROC曲线的AUC为0.67~0.96。11项研究完成了内部验证,5项研究进行了外部验证。囊袋和(或)电极重置/装置升级、肾功能不全或肾功能衰竭、年龄、植入埋藏式心脏复律除颤器或心脏再同步化治疗、使用抗凝药是DRI的预测因子。结论:目前CIED植入术后DRI风险预测模型整体性能较好,适用性较好,但偏倚风险较高。需在数据来源、变量筛选、模型评价等方面提高研究质量,开展前瞻性队列研究,完善现有模型的外部验证,并积极研发适用于我国人群的预测模型。
