scholarly journals C-Reactive Protein-to-Albumin Ratio Predicts Sepsis and Prognosis in Patients with Severe Burn Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yaohua Yu ◽  
Weiwei Wu ◽  
Yanyan Dong ◽  
Jiliang Li
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Multivariate Logistic Regression Analysis ◽  
Severe Burn ◽  
C Reactive Protein ◽  
Multivariate Logistic Regression ◽  
Albumin Ratio ◽  
Reactive Protein ◽  
Severe Burns ◽  
Independent Risk Factors

Background. Sepsis is a leading cause of mortality among severe burns. This study was conducted to investigate the predictive role of C-reactive protein-to-albumin ratio (CAR) for sepsis and prognosis in severe burns. Methods. Patients with severe burn injuries from 2013 to 2017 were enrolled and divided into septic and nonseptic groups based on the presence of sepsis within 30 days postburn. Independent risk factors for sepsis were performed by the univariate and multivariate logistic regression analyses. The association between CAR level at admission and postburn 30-day mortality was designed via the Kaplan–Meier method. Results. Of all the 196 enrolled patients, 83 patients developed sepsis within 30 days postburn injury, with an incidence of 42.3%. TBSA percentage (OR: 1.65, 95% CI: 1.17-2.32, P = 0.014 ) and CAR at admission (OR: 2.25, 95% CI: 1.33-3.56, P = 0.009 ) were the two independent risk factors for sepsis in severe burns by the multivariate logistic regression analysis. A higher CAR level (≥1.66) at admission was associated with a lower postburn 30-day survival rate ( P = 0.005 ). Conclusions. The CAR level at admission was an independent risk factor for sepsis and prognosis in severe burns.

Increased pretreatment C-reactive protein-to-albumin ratio predicts severe coronavirus disease 2019

2020 ◽  
Author(s):  
Xiaoyue Wang ◽  
Yan Xu ◽  
Huang Huang ◽  
Desheng Jiang ◽  
Chunlei Zhou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Early Warning ◽  
Warning Signs ◽  
C Reactive Protein ◽  
Albumin Ratio ◽  
Reactive Protein ◽  
Independent Risk Factors

Abstract Objective The aim of this study was to identify early warning signs for severe coronavirus disease 2019 (COVID-19). Methods We retrospectively analysed the clinical data of 90 patients with COVID-19 from Guanggu District of Hubei Women and Children Medical and Healthcare Center, comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. The cutoff values were determined by receiver operating characteristic curve analysis. Logistic regression analysis was performed to identify the independent risk factors for severe COVID-19. Results The patients with mild and severe COVID-19 had significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), and pretreatment C-reactive protein-to-albumin ratio (CAR) ( P =0.000; P =0.008; P=0.000; P =0.000). The severity of COVID-19 was positively correlated with comorbid cancer, age, NLR, and CAR ( P <0.005). Multivariate logistic regression analysis showed that age, the NLR and the CAR were independent risk factors for severe COVID-19 (OR=1.086, P =0.008; OR=1.512, P =0.007; OR=17.652, P =0.001). Conclusion An increased CAR can serve as an early warning sign of severe COVID-19 in conjunction with the NLR and age.

scholarly journals A novel procalcitonin-based score for detecting sepsis among critically ill patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245748
Author(s):  
Tung-Lin Tsui ◽  
Ya-Ting Huang ◽  
Wei-Chih Kan ◽  
Mao-Sheng Huang ◽  
Min-Yu Lai ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Validation Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Sepsis Group ◽  
C Reactive Protein ◽  
Multivariate Logistic Regression ◽  
Reactive Protein

Background Procalcitonin (PCT) has been widely investigated as an infection biomarker. The study aimed to prove that serum PCT, combining with other relevant variables, has an even better sepsis-detecting ability in critically ill patients. Methods We conducted a retrospective cohort study in a regional teaching hospital enrolling eligible patients admitted to intensive care units (ICU) between July 1, 2016, and December 31, 2016, and followed them until March 31, 2017. The primary outcome measurement was the occurrence of sepsis. We used multivariate logistic regression analysis to determine the independent factors for sepsis and constructed a novel PCT-based score containing these factors. The area under the receiver operating characteristics curve (AUROC) was applied to evaluate sepsis-detecting abilities. Finally, we validated the score using a validation cohort. Results A total of 258 critically ill patients (70.9±16.3 years; 55.4% man) were enrolled in the derivation cohort and further subgrouped into the sepsis group (n = 115) and the non-sepsis group (n = 143). By using the multivariate logistic regression analysis, we disclosed five independent factors for detecting sepsis, namely, “serum PCT level,” “albumin level” and “neutrophil-lymphocyte ratio” at ICU admission, along with “diabetes mellitus,” and “with vasopressor.” We subsequently constructed a PCT-based score containing the five weighted factors. The PCT-based score performed well in detecting sepsis with the cut-points of 8 points (AUROC 0.80; 95% confidence interval (CI) 0.74–0.85; sensitivity 0.70; specificity 0.76), which was better than PCT alone, C-reactive protein and infection probability score. The findings were confirmed using an independent validation cohort (n = 72, 69.2±16.7 years, 62.5% men) (cut-point: 8 points; AUROC, 0.79; 95% CI 0.69–0.90; sensitivity 0.64; specificity 0.87). Conclusions We proposed a novel PCT-based score that performs better in detecting sepsis than serum PCT levels alone, C-reactive protein, and infection probability score.

scholarly journals Characteristics of Lymphocyte Subsets and Cytokine Profiles of Patients with Coronavirus Disease 2019

2020 ◽  
Author(s):  
Peng-Fei Pan ◽  
Xin-Xin Du ◽  
Wei-Hua Shi ◽  
Li Li ◽  
Qi-Long Zhou ◽  
...  
Keyword(s):  
T Cells ◽  
Lymphocyte Subsets ◽  
Multivariate Logistic Regression Analysis ◽  
Absolute Number ◽  
C Reactive Protein ◽  
Multivariate Logistic Regression ◽  
Severe Condition ◽  
Reactive Protein ◽  
Cytokine Profiles ◽  
Independent Risk Factors

Abstract Background: To explore the changes in lymphocyte subsets and cytokine profiles in patients with coronavirus disease 2019 (COVID-19) and their relationship with disease severity. Methods: This study included 228 patients with COVID-19 who were treated at Chongqing University Three Gorges Hospital from January 1, 2020 to February 20, 2020. The characteristics of lymphocyte subsets and cytokine profiles of severe and mild COVID-19 patients were compared. Of the 228 patients enrolled, 48 were severe patients and 180 were mild patients. Results: Lymphocyte counts, absolute number of total T lymphocytes, CD4+T cells, CD8+T cells, and total B lymphocytes were significantly lower in severe patients (0.8×109/L, 424.5×106/L, 266×106/L, 145.5×106/L, 109.5×106/L, respectively) than in mild patients (1.2×109/L, 721×106/L, 439.5×106/L, 281.5×106/L, 135×106/L, respectively). A multivariate logistic regression analysis showed that age, C-reactive protein (CRP) and the neutrophil-to-lymphocyte ratio (NLR) were independent risk factors for developing into severe condition. The lymphocyte subsets decreased and cytokine profiles increased more significantly in severe patients than in mild patients. Conclusions: CRP, NLR, and age may serve as powerful factors for early identification of severe patients.

scholarly journals Nomogram for Predicting Persistent Organ Failure with Acute Pancreatitis in Pregnancy

2022 ◽  
Author(s):  
Chengcheng Sheng ◽  
Zongxu Xu ◽  
Jun Wang
Keyword(s):  
Risk Factors ◽  
Acute Pancreatitis ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Organ Failure ◽  
Multivariate Logistic Regression Analysis ◽  
Concordance Index ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors ◽  
In Pregnancy

Abstract Background: Acute pancreatitis in pregnancy (APIP) with persistent organ failure (POF) poses a high risk of death for mother and fetus. This study sought to create a nomogram model for early prediction of POF with APIP patients.Methods: We conducted a cross-sectional study on APIP patients with organ failure (OF) between January 2012 and March 2021 in a university hospital. 131 patients were collected. Their clinical courses and pregnancy outcomes were obtained. Risk factors for POF were identified by univariate and multivariate logistic regression analysis. Prediction models with POF were built and nomogram was plotted. The performance of the nomogram was evaluated by using a bootstrapped-concordance index and calibration plots.Results: Hypertriglyceridemia was the most common etiology in this group of APIP patients, which accounted for 50% of transient organ failure (TOF) and 72.3% of POF. All in-hospital maternal death was in the POF group (P<0.05), which also had a significantly higher perinatal mortality rate than the TOF group (P<0.05). Univariate and multivariate logistic regression analysis determined that lactate dehydrogenase, triglycerides, serum creatinine, and procalcitonin were independent risk factors for predicting POF in APIP. A nomogram for POF was created by using the four indicators. The area under the curve was 0.875 (95% confidence interval 0.80–0.95). The nomogram had a bootstrapped-concordance index of 0.85 and was well-calibrated.Conclusions: Hypertriglyceridemia was the leading cause of organ failure-related APIP. Lactate dehydrogenase, triglycerides, serum creatinine, and procalcitonin were the independent risk factors of POF in APIP. Our nomogram model showed an effective prediction of POF with the four indicators in APIP patients.

scholarly journals Risk Factors for Maternal and Fetal Mortality in Acute Fatty Liver of Pregnancy and New Predictive Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhaoli Meng ◽  
Wei Fang ◽  
Mei Meng ◽  
Jicheng Zhang ◽  
Qizhi Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Maternal Mortality ◽  
Predictive Models ◽  
Multivariate Logistic Regression Analysis ◽  
Fetal Mortality ◽  
Multivariate Logistic Regression ◽  
Acute Fatty Liver ◽  
Independent Risk Factors

Acute fatty liver of pregnancy (AFLP) is a rare but potentially life-threatening hepatic disorder that leads to considerable maternal and fetal mortality. To explore the risk factors for maternal and fetal mortality in AFLP and develop new predictive models, through this retrospective study, we analyzed the demographic characteristics, clinical symptoms, and laboratory findings of 106 patients with AFLP who were admitted to Shandong Provincial Hospital. Risk factors for maternal and fetal mortality were analyzed by univariate and multivariate logistic regression analysis. The new models based on the multivariate logistic regression analysis and the model for end-stage liver disease (MELD) were tested in AFLP. The receiver operating characteristic curve (ROC) was applied to compare the predictive efficiency, sensitivity, and specificity of the two models. Prenatal nausea (p = 0.037), prolonged prothrombin time (p = 0.003), and elevated serum creatinine (p = 0.003) were independent risk factors for maternal mortality. The ROC curve showed that the area under the curve (AUC) of the MELD was 0.948, with a sensitivity of 100% and a specificity of 83.3%. The AUC of the new model for maternal mortality was 0.926, with a sensitivity of 90% and a specificity of 94.8%. Hepatic encephalopathy (p = 0.016) and thrombocytopenia (p = 0.001) were independent risk factors for fetal mortality. Using the ROC curve, the AUC of the MELD was 0.694, yielding a sensitivity of 68.8% and a specificity of 64.4%. The AUC of the new model for fetal mortality was 0.893, yielding a sensitivity of 100% and a specificity of 73.3%. Both the new predictive model for maternal mortality and the MELD showed good predictive efficacy for maternal mortality in patients with AFLP (AUC = 0.926 and 0.948, respectively), and the new predictive model for fetal mortality was superior to the MELD in predicting fetal mortality (AUC = 0.893 and 0.694, respectively). The two new predictive models were more readily available, less expensive, and easier to implement clinically, especially in low-income countries.

scholarly journals Hemodynamic and Morphological Parameters of Ruptured Mirror Posterior Communicating Artery Aneurysms

2021 ◽  
Vol 12 ◽  
Author(s):  
Jinlong Yuan ◽  
Chenlei Huang ◽  
Zhenbao Li ◽  
Xiaochun Jiang ◽  
Xintong Zhao ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Roc Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Posterior Communicating Artery ◽  
Morphological Parameters ◽  
Hemodynamic Parameters ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors ◽  
Wall Shear

Objective: Morphological and hemodynamic parameters might predict rupture of intracranial aneurysms (IAs). A practical model for the study is patients with ruptured mirror IAs in which one is ruptured and the other is unruptured. Although there have been analyses of the morphology and hemodynamics of ruptured mirror posterior communicating artery aneurysms (PComAAs), the sample sizes in these studies were small and only considered hemodynamics or morphological characters. Therefore, this study aimed to investigate the morphological and hemodynamic parameters associated with ruptured mirror PComAAs.Methods: We considered 72 patients with ruptured mirror PComAAs using computational fluid dynamics (CFDs). Ruptured mirror PComAAs were divided into ruptured and unruptured groups. Fourteen morphological and eight hemodynamic parameters were calculated and compared. Significant parameters were analyzed by the multivariate logistic regression to identify independent risk factors. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for all independent risk factors to determine the predictability and identify the optimal threshold.Results: Four hemodynamic and three morphological parameters were significantly different between ruptured and unruptured groups: normalized wall shear stress (NWSS), mean WSS, low wall shear WSS area (LSA%), size, aspect ratio (AR), size ratio (SR), and inflow angle (IA). Multivariate logistic regression analysis showed that AR, SR, NWSS, mean WSS, and LSA% were all independent factors significantly associated with PComAAs rupture. The ROC analysis for independent risk factors indicated that AR (0.751), NWSS (0.755), mean WSS (0.69), and LSA (0.778) had merely acceptable AUC values. Only SR (0.803) had a high acceptable AUC value. The threshold value of SR was 1.96.Conclusions: SR (&gt;1.96) was the most significant parameter associated with IA rupture, whereas AR, NWSS, mean WSS, and LSA independently characterized the status of IA rupture.

scholarly journals Analysis of Risk Factors For Sepsis And Indicators of Prognosis In Patients With Sepsis: A Retrospective Observational Study

2021 ◽  
Author(s):  
Huifeng Wang ◽  
Zhiling Zhao ◽  
Zhao-hui Tong
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Albumin Level ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

Abstract Background: To investigate the independent risk factors for sepsis and the prognostic indicators of sepsis-related mortality to guide clinical practice.Methods: Adult patients diagnosed with sepsis in the respiratory intensive care unit (RICU), emergency ICU (EICU), and surgical ICU (SICU) of Beijing Chao-Yang Hospital, Capital Medical University, from January 2016 to April 2021 were enrolled. Comorbidities, complications, and laboratory indicators were retrospectively analyzed. Variables with a p value < 0.05 in the univariate analysis were entered into multivariate logistic regression analysis to identify the independent risk factors for sepsis. Receiver operating characteristic curve (ROC) analysis was used for those variables with P < 0.05 in multivariate regression to evaluate the fit of the predictive model and its prognostic efficacy. Results: A total of 123 adult patients with sepsis were enrolled, with 80 males and 43 females and a mean age of 61.56 ± 17.12 years. Acute respiratory distress syndrome (ARDS) occurred in 84 patients (68.3%), acute kidney injury (AKI) occurred in 28 patients (22.8%), acute myocardial injury (AMI) occurred in 6 patients (4.9%), disseminated intravascular coagulation (DIC) occurred in 14 patients (11.4%), septic shock occurred in 40 patients (32.5%), and 41 patients (33.3%) died. Multivariate logistic regression analysis showed that mean arterial pressure (MAP), acute physiology and chronic health evaluation II (APACHE II) score, albumin level, and the presence of DIC were independent risk factors for sepsis (P < 0.05). The area under the ROC curve for the model including MAP, albumin, and APACHE II score was the highest at 0.890.Conclusion: The MAP, APACHE II score, albumin level, and DIC were independent risk factors for sepsis. The inclusion of the MAP, albumin level, and APACHE II score in the model yielded the most accurate prediction of the risk of mortality.

An increased pretreatment C-reactive protein-to-albumin ratio predicts severe novel coronavirus-infected pneumonia

2020 ◽  
Author(s):  
Xiaoyue Wang ◽  
Yan Xu ◽  
Huang Huang ◽  
Desheng Jiang ◽  
Chunlei Zhou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Early Warning ◽  
Multivariate Logistic Regression Analysis ◽  
Warning Signs ◽  
C Reactive Protein ◽  
Albumin Ratio ◽  
Reactive Protein ◽  
Novel Coronavirus

Abstract Objective The aim of this study was to identify early warning signs for severe novel coronavirus-infected pneumonia (COVID-19). Methods We retrospectively analyzed the clinical data of 90 patients with COVID-19 from Guanggu District of Hubei Women and Children Medical and Healthcare Center comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. The cutoff values was determined by receiver operating characteristic curve analysis. Logistic regression analysis was performed to identify the independent risk factor that predicted the severe COVID-19. Results The patients with mild and severe COVID-19 showed significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), and pretreatment C-reactive protein-to-albumin ratio (CAR) (P < 0.05). The severity of COVID-19 was correlated positively with the comorbidity of cancer, age, NLR, and CAR (P < 0.05). Multivariate logistic regression analysis showed that age, NLR and CAR were independent risk factors for severe COVID-19 (OR = 1.086, P = 0.008; OR = 1.512, P = 0.007; OR = 17.652, P = 0.001, respectively). Conclusion An increased CAR can serve as an early warning sign of severe COVID-19 in conjunction with the NLR and age.

Risk Factors for Wound Infection in Outpatients With Lower Extremity Burns

2020 ◽  
pp. 000313482095238
Author(s):  
Husayn A. Ladhani ◽  
Brian T. Young ◽  
Sarah E. Posillico ◽  
Charles J. Yowler ◽  
Christopher P. Brandt ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Lower Extremity ◽  
Wound Infection ◽  
Logistic Regression Analysis ◽  
Total Body Surface Area ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

Background We sought to evaluate risk factors for wound infection in patients with lower extremity (LE) burn. Methods Adults presenting with LE burn from January 2014 to July 2015 were included. Data regarding demographics, injury characteristics, and outcomes were obtained. The primary outcome was wound infection. Multivariate logistic regression analysis was performed to identify independent risk factors for wound infection. Results 317 patients were included with a mean age of 43 years and median total body surface area of .8%; 22 (7%) patients had a component of full-thickness (FT) burn; and 212 (67%) patients had below-the-knee (BTK) burn. The incidence of wound infection was 15%. The median time to infection was 5 days, and majority (61%) of the patients developed wound infection by day 5. Patients who developed wound infection were more likely to have an FT burn (22% vs. 5%, P < .001) and BTK burn (87% vs. 64%, P = .002), without a difference in other variables. Multivariate logistic regression analysis showed age (Odds ratio (OR) 1.02 and CI 1.00-1.04), presence of FT burn (OR 5.33 and CI 2.09-13.62), and BTK burn (OR 3.42 and CI 1.37-8.52) as independent risk factors for wound infection (area under the curve = .72). Conclusion Age, presence of FT burn, and BTK burn are independent risk factors for wound infection in outpatients with LE burns.

scholarly journals Predictive value of postoperative C-reactive protein-to-albumin ratio in anastomotic leakage after esophagectomy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Chi Zhang ◽  
Xiao Kun Li ◽  
Li Wen Hu ◽  
Chao Zheng ◽  
Zhuang Zhuang Cong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Anastomotic Leakage ◽  
Operation Time ◽  
C Reactive Protein ◽  
Roc Curve Analysis ◽  
Albumin Ratio ◽  
Reactive Protein ◽  
Potential Risk Factors ◽  
Independent Risk Factors ◽  
The Many

Abstract Introduction Among the many possible postoperative complications, anastomotic leakage (AL) is the most common and serious. Therefore, the purpose of this study was to explore the ability of various inflammatory and nutritional markers to predict postoperative AL in patients after esophagectomy. Methods A total of 273 patients were retrospectively evaluated and enrolled into this study. Perioperative, surgery-related, tumor-related and laboratory tests data were extracted and analyzed. The discriminatory ability and optimal cut-off value was evaluated according to the receiver operating characteristic (ROC) curve analysis. Univariate and multivariate analyses were performed to access the potential risk factors for AL. Results The overall incidence of AL was 12.5% (34/273). C-reactive protein-to-albumin ratio (CRP/ALB ratio) [AUC 0.943 (95% confidence interval (CI) = 0.911–0.976, p <  0.001)] and operation time [AUC 0.747 (95% CI = 0.679–0.815, p <  0.001)] had the greatest discrimination on AL prediction. Multivariate analysis demonstrated that CRP/ALB ratio and operation time were two independent risk factors for AL, and CRP/ALB ratio (OR = 102.909, p <  0.001) had an advantage over operation time (OR = 9.363, p = 0.020; Table 3). Conclusion Operation time and postoperative CRP/ALB ratio were two independent predictive indexes for AL. Postoperative CRP/ALB ratio greater than 3.00 indicated a high risk of AL. For patients with abnormal postoperative CRP/ALB ratio, early non-operative treatment or surgical intervention are needed to reduce the serious sequelae of AL.

Sign in / Sign up

Export Citation Format

Share Document