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.

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 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.

scholarly journals Can C-Reactive Protein to Albumin Ratio Predict In-Hospital Death Rate Due to COVID-19 in Patients With Hypertension?

Angiology ◽  
2021 ◽  
pp. 000331972110121
Author(s):  
Faysal Saylik ◽  
Tayyar Akbulut ◽  
Safak Kaya
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Hospital Mortality ◽  
Independent Predictor ◽  
Hospital Death ◽  
C Reactive Protein ◽  
Albumin Ratio ◽  
Cutoff Value ◽  
Reactive Protein ◽  
Kaplan Meier

Hypertension is one of the main morbidity and mortality risk factors in patients with coronavirus disease 2019 (COVID-19). We investigated the association between the C-reactive protein (CRP) to albumin ratio (CAR) and in-hospital mortality in patients with hypertensive COVID-19. A total of 176 patients with hypertension diagnosed with COVID-19 were included in this study. The CAR was compared between survivors and nonsurvivors. Logistic regression analysis was used to detect independent predictors of mortality due to COVID-19 in patients with hypertension. A cutoff value of CAR was obtained for predicting in-hospital death in patients with hypertensive COVID-19. Kaplan-Meier analysis was performed for survival analysis in the study population. The CAR values were significantly higher in nonsurvivors than in survivors with hypertension. Moreover, the CAR was an independent predictor of in-hospital death in patients with hypertensive COVID-19, as shown in multivariable logistic regression analysis. Receiver operating characteristic analysis yielded a cutoff value of 20.75 for the CAR for predicting in-hospital death in patients with hypertension. Kaplan-Meier curve analysis showed that patients with hypertensive COVID-19 with a CAR value of ≥20.75 had a higher incidence of in-hospital death. The CAR might be used as an independent predictor of in-hospital mortality in patients with hypertensive COVID-19.

scholarly journals An increased pretreatment neutrophil-to-lymphocyte ratio predicts severe novel coronavirus-infected pneumonia

2020 ◽  
Author(s):  
Xiaoyue Wang ◽  
Desheng Jiang ◽  
Huang Huang ◽  
Xiaofeng Chen ◽  
Chunlei Zhou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Early Warning ◽  
Roc Curve ◽  
Neutrophil To Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Independent Risk Factors ◽  
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 at the 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. Logistic regression analysis was performed to identify the independent risk factors that predicted severe COVID-19. The receiver-operating characteristic (ROC) curve of independent risk factors was calculated, and the area under the curve (AUC) was used to evaluate the efficiency of the prediction of 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), C-reactive protein (CRP) and the serum albumin (ALB) level (P<0.05). The severity of COVID-19 was correlated positively with the comorbidity of cancer, age, NLR, and CRP but was negatively correlated with the ALB level (P<0.05). Multivariate logistic regression analysis showed that the NLR and ALB level were independent risk factors for severe COVID-19 (OR=1.319, 95% CI: 1.043-1.669, P=0.021; OR=0.739, 95% CI: 0.616-0.886, P=0.001), with AUCs of 0.851 and 0.128, respectively. An NLR of 4.939 corresponded to the maximum joint sensitivity and specificity according to the ROC curve (0.700 and 0.917, respectively).Conclusion An increased NLR can serve as an early warning sign of severe COVID-19.

ANALISIS HUBUNGAN SARANA, PENGETAHUAN, DAN SIKAP BIDAN PRAKTIK MANDIRI DENGAN KEPATUHAN MENJALANKAN STANDAR PELAYANAN ANTENATAL CARE DI KOTA PADANGSIDIMPUAN

2019 ◽  
Vol 2 (1) ◽  
pp. 27-33
Author(s):  
Megawati Sinambela ◽  
Evi Erianty Hasibuan
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Antenatal Care ◽  
Pregnant Women ◽  
Logistic Regression Analysis ◽  
Care Service ◽  
Multivariate Logistic Regression Analysis ◽  
Bivariate Analysis ◽  
Cross Sectional ◽  
The North

Antenatal care is a service provided to pregnant women to monitor, support maternal health and detect mothers whether normal or problematic pregnant women. According to the WHO, globally more than 70% of maternal deaths are caused by complications of pregnancy and childbirth such as hemorrhage, hypertension, sepsis, and abortion. Based on data obtained from the profile of the North Sumatra provincial health office in 2017, in the city of Padangsidimpuan in 2017 the coverage of ANC visits reached (76.58%) and had not reached the target in accordance with the 2017 Provincial Health Office strategy plan (95%). This type of research was an observational analytic study with a cross sectional design. The population in this study were independent practice midwives who were in the Padangsidimpuan, the sample in this study amounted to 102 respondents. The technique of collecting data used questionnaires and data analysis used univariate, bivariate and multivariate analysis with logistic regression analysis. Based on bivariate analysis showed that there was a relationship between facilities, knowledge and attitudes of independent midwives with compliance with the standards of antenatal care services with a value of p <0.05. The results of the study with multivariate logistic regression analysis showed that the factors associated with the compliance of independent midwives in carrying out antenatal care service standards were attitudes with values (p = 0.026).

scholarly journals Efficacy of flurbiprofen axetil for preventing postanesthetic shivering in patients undergoing gynecologic laparotomy surgeries

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Atsushi Kotera
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Postoperative Pain ◽  
Logistic Regression Analysis ◽  
Pain Control ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Postoperative Pain Control ◽  
Flurbiprofen Axetil ◽  
Postanesthetic Shivering

Abstract Background Postanesthetic shivering is an unpleasant adverse event in surgical patients. A nonsteroidal anti-inflammatory drug has been reported to be useful in preventing postanesthetic shivering in several previous studies. The aim of this study was to evaluate the efficacy of flurbiprofen axetil being a prodrug of a nonsteroidal anti-inflammatory drug for preventing postanesthetic shivering in patients undergoing gynecologic laparotomy surgeries. Method This study is a retrospective observational study. I collected data from patients undergoing gynecologic laparotomy surgeries performed between October 1, 2019, and September 30, 2020, at Kumamoto City Hospital. All the patients were managed with general anesthesia with or without epidural analgesia. The administration of intravenous 50 mg flurbiprofen axetil for postoperative pain control at the end of the surgery was left to the individual anesthesiologist. The patients were divided into two groups: those who had received intravenous flurbiprofen axetil (flurbiprofen group) and those who had not received intravenous flurbiprofen axetil (non-flurbiprofen group), and I compared the frequency of postanesthetic shivering between the two groups. Additionally, the factors presumably associated with postanesthetic shivering were collected from the medical charts. Intergroup differences were assessed with the χ2 test with Yates’ correlation for continuity category variables. The Student’s t test was used to test for differences in continuous variables. Furthermore, a multivariate logistic regression analysis was performed to elucidate the relationship between the administration of flurbiprofen axetil and the incidence of PAS. Results I retrospectively examined the cases of 141 patients aged 49 ± 13 (range 21-84) years old. The overall postanesthetic shivering rate was 21.3% (30 of the 141 patients). The frequency of postanesthetic shivering in the flurbiprofen group (n = 31) was 6.5%, which was significantly lower than that in the non-flurbiprofen group (n = 110), 25.5% (p value = 0.022). A multivariate logistic regression analysis showed that administration of flurbiprofen axetil was independently associated with a reduced incidence of postanesthetic shivering (odds ratio 0.12; 95% confidence interval, 0.02-0.66, p value = 0.015). Conclusions My result suggests that intraoperative 50 mg flurbiprofen axetil administration for postoperative pain control is useful to prevent postanesthetic shivering in patients undergoing gynecologic laparotomy surgeries.

scholarly journals Risk factors for tissue expander infection in scar reconstruction: a retrospective cohort study of 2374 consecutive cases

2020 ◽  
Vol 8 ◽  
Author(s):  
Chen Dong ◽  
Minhui Zhu ◽  
Luguang Huang ◽  
Wei Liu ◽  
Hengxin Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Treatment Failure ◽  
Logistic Regression Analysis ◽  
Disease Duration ◽  
Multivariate Logistic Regression Analysis ◽  
Tissue Expansion ◽  
Multivariate Logistic Regression ◽  
Hematoma Evacuation

Abstract Background Tissue expansion is used for scar reconstruction owing to its excellent clinical outcomes; however, the complications that emerge from tissue expansion hinder repair. Infection is considered a major complication of tissue expansion. This study aimed to analyze the perioperative risk factors for expander infection. Methods A large, retrospective, single-institution observational study was carried out over a 10-year period. The study enrolled consecutive patients who had undergone tissue expansion for scar reconstruction. Demographics, etiological data, expander-related characteristics and postoperative infection were assessed. Univariate and multivariate logistic regression analysis were performed to identify risk factors for expander infection. In addition, we conducted a sensitivity analysis for treatment failure caused by infection as an outcome. Results A total of 2374 expanders and 148 cases of expander infection were assessed. Treatment failure caused by infection occurred in 14 expanders. Multivariate logistic regression analysis identified that disease duration of ≤1 year (odds ratio (OR), 2.07; p &lt; 0.001), larger volume of expander (200–400 ml vs &lt;200 ml; OR, 1.74; p = 0.032; &gt;400 ml vs &lt;200 ml; OR, 1.76; p = 0.049), limb location (OR, 2.22; p = 0.023) and hematoma evacuation (OR, 2.17; p = 0.049) were associated with a high likelihood of expander infection. Disease duration of ≤1 year (OR, 3.88; p = 0.015) and hematoma evacuation (OR, 10.35; p = 0.001) were so related to high risk of treatment failure. Conclusions The rate of expander infection in patients undergoing scar reconstruction was 6.2%. Disease duration of &lt;1 year, expander volume of &gt;200 ml, limb location and postoperative hematoma evacuation were independent risk factors for expander infection.

Cross-Sectional Examination of Homocysteine Levels with Sarcopenia and Its Components in Memory Clinic Outpatients

2021 ◽  
pp. 1-10
Author(s):  
Yosuke Yamada ◽  
Hiroyuki Umegaki ◽  
Fumie Kinoshita ◽  
Chi Hsien Huang ◽  
Taiki Sugimoto ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Cognitive Impairment ◽  
Cognitive Function ◽  
Linear Regression ◽  
Logistic Regression Analysis ◽  
Linear Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Memory Clinic ◽  
Cross Sectional

Background: Homocysteine is a common risk factor for cognitive impairment and sarcopenia. However, very few studies have shown an association between sarcopenia and serum homocysteine levels after adjustment for cognitive function. Objective: The purpose of this study was to investigate the relationship between homocysteine and sarcopenia in memory clinic patients. Methods: This cross-sectional study investigated outpatients in a memory clinic. We enrolled 1,774 participants (≥65 years old) with measured skeletal muscle mass index (SMI), hand grip strength (HGS), and homocysteine. All participants had undergone cognitive assessments and were diagnosed with dementia, mild cognitive impairment, or normal cognition. Patient characteristics were compared according to sarcopenia presence, SMI level, or HGS. Multivariate logistic regression analysis was performed to determine the association of homocysteine with sarcopenia, low SMI, or low HGS. Next, linear regression analysis was performed using HGS as a continuous variable. Results: Logistic regression analysis showed that low HGS was significantly associated with homocysteine levels (p = 0.002), but sarcopenia and low SMI were not. In linear regression analysis, HGS was negatively associated with homocysteine levels after adjustment for Mini-Mental State Examination score (β= –2.790, p <  0.001) or clinical diagnosis of dementia (β= –3.145, p <  0.001). These results were similar for men and women. Conclusion: Our results showed a negative association between homocysteine and HGS after adjustment for cognitive function. Our findings strengthen the assumed association between homocysteine and HGS. Further research is needed to determine whether lower homocysteine levels lead to prevent muscle weakness.

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