scholarly journals Could IL-6 predict the clinical severity of COVID-19?

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Guzin Aykal ◽  
Hatice Esen ◽  
Derya Seyman ◽  
Tuğba Çalışkan
Keyword(s):  
Intensive Care Unit ◽  
Prognostic Value ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Clinical Severity ◽  
Longitudinal Analyses ◽  
Significant Difference ◽  
Severe Group ◽  
Operating Characteristic Curve ◽  
Excessive Inflammatory Response

Abstract Objectives An excessive inflammatory response to SARS-CoV-2 is thought to be a major cause of disease severity in COVID-19. The aim herein was to determine the prognostic value of IL-6, and demonstrate the comparison between IL-6 and related parameters in COVID-19. Methods Data were collected from 115 COVID-19 patients. Results The median age was 46.04 years in the mild group, 56.42 years in the moderate group, and 62.92 years in the severe group (p=0.001). There was a significant difference in the hospitalized clinic to intensive care unit ratio among the patients (p<0.001). The IL-6 values were significantly higher in the severe group than those in the mild (p=0.04) and moderate groups (p=0.043). The area under the receiver operating characteristic curve for IL-6, as predictor of severe clinical condition, was 0.864 (95% CI 0.765–0.963 p=0.000). The longitudinal analyses showed that the severe group presented with significantly increased IL-6 levels during hospitalization. Conclusions IL‐6 seemed to be a guide in the early diagnosis of severe COVID-19 and an ideal marker for monitoring negative outcome.

Does Age Itself Have an Adverse Effect on Survivorship of Meniscal Allograft Transplantation? A Cartilage Status and Time From Previous Meniscectomy–Matched Cohort Study

2020 ◽  
Vol 48 (7) ◽  
pp. 1696-1701 ◽  
Author(s):  
Ju-Ho Song ◽  
Seong-Il Bin ◽  
Jong-Min Kim ◽  
Bum-Sik Lee ◽  
Dong-Wook Son
Keyword(s):  
Clinical Outcomes ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Rank Test ◽  
Meniscal Allograft Transplantation ◽  
Log Rank Test ◽  
Meniscal Allograft ◽  
Age Related ◽  
Significant Difference ◽  
Operating Characteristic Curve

Background: The aging process is accompanied by several conditions that could affect the outcome of meniscal allograft transplantation (MAT). These conditions have made it difficult for clinicians to determine the effect of chronologic age on survivorship after MAT. Hypothesis: Advanced age does not have an adverse effect on survivorship of MAT when controlling for age-related factors, such as cartilage status and time from previous meniscectomy. Study Design: Cohort study; Level of evidence, 3. Methods: The records of 264 consecutive patients who underwent primary medial or lateral MAT were reviewed. To check whether there was a difference in MAT survivorship according to age, a cutoff value was calculated from a time-dependent receiver operating characteristic curve. Survival rates, as well as clinical improvement as determined using the Lysholm score, were compared between groups divided by the cutoff value. Patients were matched for cartilage status and elapsed time from previous meniscectomy. Differences in survivorship and clinical outcomes were assessed between the matched groups. Results: A time-dependent receiver operating characteristic curve showed that the difference in MAT survivorship was maximized with a cutoff age of 43 years. Kaplan-Meier analysis showed a significant difference in MAT survivorship between the older and younger groups (log-rank test, P = .01). However, after matching for cartilage status and time from previous meniscectomy, which left 56 patients per group, there was no significant difference in MAT survivorship (log-rank test, P = .10) between the groups. Regarding clinical outcomes, the mean Lysholm scores were not significantly different between the older and younger groups ( P = .19, before matching; P = .39, after matching). Conclusion: MAT survivorship was more affected by age-related prognostic factors, such as cartilage status and time from previous meniscectomy, than age itself. Clinical outcomes did not show differences according to age, either.

scholarly journals Evaluation of Nottingham Hip Fracture Score, Age-Adjusted Charlson Comorbidity Index and the Physiological and Operative Severity Score for the enumeration of Mortality and morbidity as predictors of mortality in elderly neck of femur fracture patients

2020 ◽  
Vol 8 ◽  
pp. 205031212091826 ◽  
Author(s):  
Michael James Nelson ◽  
Justin Scott ◽  
Palvannan Sivalingam
Keyword(s):  
Hip Fracture ◽  
Charlson Comorbidity Index ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Mortality And Morbidity ◽  
Operative Severity Score ◽  
Significant Difference ◽  
Comorbidity Index ◽  
Operating Characteristic Curve

Background: This study evaluated the use of several risk prediction models in estimating short- and long-term mortality following hip fracture in an Australian population. Methods: Data from 195 patients were retrospectively analysed and applied to three models of interest: the Nottingham Hip Fracture Score, the Age-Adjusted Charlson Comorbidity Index and the Physiological and Operative Severity Score for enUmeration of Mortality and Morbidity. The performance of these models was assessed with receiver operating characteristic curve as well as logistic regression modelling. Results: The median age of participants was 83 years and 69% were women. Ten percent of patients were deceased by 30 days, 25% at 6 months and 31% at 12 months post-operatively. While there was no statistically significant difference between the models, the Age-Adjusted Charlson Comorbidity Index had the largest area under the receiver operating characteristic curve for within 30 day and 12 month mortality, while the Nottingham Hip Fracture Score was largest for 6-month mortality. There was no evidence to suggest that the models were selecting a specific subgroup of our population, therefore, no indication was present to suggest that using multiple models would improve mortality prediction. Conclusions: While there was no statistically significant difference in mortality prediction, the Nottingham Hip Fracture Score is perhaps the best suited clinically, due to its ease of implementation. Larger prospective data collection across a variety of sites and its role in guiding clinical management remains an area of interest.

scholarly journals Semiquantitative Cough Strength Score for Predicting Reintubation After Planned Extubation

2015 ◽  
Vol 24 (6) ◽  
pp. e86-e90 ◽  
Author(s):  
Jun Duan ◽  
Lintong Zhou ◽  
Meiling Xiao ◽  
Jinhua Liu ◽  
Xiangmei Yang
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Spontaneous Breathing ◽  
Operating Characteristic ◽  
Peak Flow ◽  
Characteristic Curve ◽  
Spontaneous Breathing Trial ◽  
Extubation Outcome ◽  
Cough Strength ◽  
Operating Characteristic Curve

Background Semiquantitative cough strength score (SCSS, graded 0–5) and cough peak flow (CPF) have been used to predict extubation outcome in patients in whom extubation is planned; however, the correlation of the 2 assessments is unclear. Methods In the intensive care unit of a university-affiliated hospital, 186 patients who were ready for extubation after a successful spontaneous breathing trial were enrolled in the study. Both SCSS and CPF were assessed before extubation. Reintubation was recorded 72 hours after extubation. Results Reintubation rate was 15.1% within 72 hours after planned extubation. Patients in whom extubation was successful had higher SCSSs than did reintubated patients (mean [SD], 3.2 [1.6] vs 2.2 [1.6], P = .002) and CPF (74.3 [40.0] vs 51.7 [29.4] L/min, P = .005). The SCSS showed a positive correlation with CPF (r = 0.69, P &lt; .001). Mean CPFs were 38.36 L/min, 39.51 L/min, 44.67 L/min, 57.54 L/min, 78.96 L/min, and 113.69 L/min in patients with SCSSs of 0, 1, 2, 3, 4, and 5, respectively. The discriminatory power for reintubation, evidenced by area under the receiver operating characteristic curve, was similar: 0.677 for SCSS and 0.678 for CPF (P = .97). As SCSS increased (from 0 to 1 to 2 to 3 to 4 to 5), the reintubation rate decreased (from 29.4% to 25.0% to 19.4% to 16.1% to 13.2% to 4.1%). Conclusions SCSS was convenient to measure at the bedside. It was positively correlated with CPF and had the same accuracy for predicting reintubation after planned extubation.

scholarly journals Cut-off value of serum homocysteine in relation to increase of coronary artery calcification

2020 ◽  
pp. jim-2020-001478
Author(s):  
Nam-Seok Joo ◽  
Susie Jung ◽  
Yu-Na Kim ◽  
Beom-Hee Choi
Keyword(s):  
Coronary Artery ◽  
Coronary Artery Calcification ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Homocysteine Concentration ◽  
Serum Homocysteine ◽  
Significant Difference ◽  
Operating Characteristic Curve ◽  
Age And Sex

A recent study reported that coronary artery calcification (CAC) and serum homocysteine were well associated; however, no report is available for the cut-off value of serum homocysteine according to increase of coronary-artery calcification volume score (CVS). The data of 469 out of 777 subjects in 1 health promotion center located in Seoul were selected after exclusion of the missing data of serum homocysteine and CVS. CVS was categorized into 2 groups: CVS=0 and CVS>0. Serum homocysteine according to the CVS groups was compared, and the cut-off value of serum homocysteine according to the increase of CVS (>0) was calculated using the receiver operating characteristic curve. Mean age was 54.5 years and the proportion of females was 22.2%. Mean serum homocysteine concentration and CVS were 11.2 μmol/L and 50.4, respectively. After adjustments for age and sex, serum homocysteine was associated with CVS (r=0.167, p=0.001), and Log(Homocysteine) also showed a significant difference according to the CVS groups. The cut-off value of serum homocysteine according to the increase of CVS (>0) was 9.45 μmol/L (area under the curve=0.569 (95% CI 0.512 to 0.625), p=0.015). The cut-off value of serum homocysteine was 9.45 μmol/L according to the increase of coronary-artery CVS.

Predicting outcomes in organophosphate poisoning based on APACHE II and modified APACHE II scores

2007 ◽  
Vol 26 (7) ◽  
pp. 573-578 ◽  
Author(s):  
N. Eizadi-Mood ◽  
M. Saghaei ◽  
M. Jabalameli
Keyword(s):  
Confidence Interval ◽  
Prognostic Value ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Emergency Situation ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Organophosphate Poisoning ◽  
Biochemical Tests ◽  
Operating Characteristic Curve

The aim of this study was to evaluate the scores of the Acute Physiology and Chronic Health Evaluation (APACHE) II and a modified APACHE II system (MAS), without parameters of biochemical tests; and to find prognostic value of individual elements of the APACHE II and MAS in predicting outcomes in organophosphate (OP) poisoning. Data were collected from 131 patients. The median (25th—75th percentiles) of APACHE II score for survivors without intubation were found to be lower than those of non survivors or survivors with intubation and ventilation, [4 (1—7); versus 17.5 (7.8—29), and 13.5 (7.8—16.3)]. Logistic regression analysis identified white blood cell (WBC), potassium, Glasgow coma scale (GCS), age and sodium in APACHE II; GCS and mean arterial pressure in MAS system as prognostically valuable. There was no statistically significance difference between APACHE II and MAS scores in terms of area under Receiver Operating Characteristic Curve [(0.902, 95% confidence interval: (0.837—0.947) for APACHE II), and (0.892, 95% confidence interval: (0.826—0.940) for MAS); P = 0.74) to predict need for intubation. It is concluded usage of MAS facilitates the prognostication of the OP poisoned patients due to simplicity, less time-consuming and effectiveness in an emergency situation. Human & Experimental Toxicology (2007) 26: 573—578.

scholarly journals Predicting 30-Day and 180-Day Mortality in Elderly Proximal Hip Fracture Patients: Evaluation of 4 Risk Prediction Scores at a Level I Trauma Center

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 497
Author(s):  
Arastoo Nia ◽  
Domenik Popp ◽  
Georg Thalmann ◽  
Fabian Greiner ◽  
Natasa Jeremic ◽  
...  
Keyword(s):  
Hip Fracture ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Risk Prediction ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Acs Nsqip ◽  
Significant Difference ◽  
Operating Characteristic Curve ◽  
Receiver Operating

This study evaluated the use of risk prediction models in estimating short- and mid-term mortality following proximal hip fracture in an elderly Austrian population. Data from 1101 patients who sustained a proximal hip fracture were retrospectively analyzed and applied to four models of interest: Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM), Charlson Comorbidity Index, Portsmouth-POSSUM and the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP®) Risk Score. The performance of these models according to the risk prediction of short- and mid-term mortality was assessed with a receiver operating characteristic curve (ROC). The median age of participants was 83 years, and 69% were women. Six point one percent of patients were deceased by 30 days and 15.2% by 180 days postoperatively. There was no significant difference between the models; the ACS-NSQIP had the largest area under the receiver operating characteristic curve for within 30-day and 180-day mortality. Age, male gender, and hemoglobin (Hb) levels at admission <12.0 g/dL were identified as significant risk factors associated with a shorter time to death at 30 and 180 days postoperative (p < 0.001). Among the four scores, the ACS-NSQIP score could be best-suited clinically and showed the highest discriminative performance, although it was not specifically designed for the hip fracture population.

scholarly journals Validation of Three Postoperative Risk Prediction Models for Intensive Care Unit Mortality after Cardiac Surgery

2018 ◽  
Vol 66 (08) ◽  
pp. 651-660 ◽  
Author(s):  
Camila Caiado ◽  
Charles McCollum ◽  
Michael Goldstein ◽  
Ignacio Malagon ◽  
Rajamiyer Venkateswaran ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cardiac Surgery ◽  
Risk Prediction ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Icu Mortality ◽  
Risk Prediction Models ◽  
Postoperative Risk ◽  
Operating Characteristic Curve

Background Several cardiac surgery risk prediction models based on postoperative data have been developed. However, unlike preoperative cardiac surgery risk prediction models, postoperative models are rarely externally validated or utilized by clinicians. The objective of this study was to externally validate three postoperative risk prediction models for intensive care unit (ICU) mortality after cardiac surgery. Methods The logistic Cardiac Surgery Scores (logCASUS), Rapid Clinical Evaluation (RACE), and Sequential Organ Failure Assessment (SOFA) scores were calculated over the first 7 postoperative days for consecutive adult cardiac surgery patients between January 2013 and May 2015. Model discrimination was assessed using receiver operating characteristic curve analyses. Calibration was assessed using the Hosmer–Lemeshow (HL) test, calibration plots, and observed to expected ratios. Recalibration of the models was performed. Results A total of 2255 patients were included with an ICU mortality rate of 1.8%. Discrimination for all three models on each postoperative day was good with areas under the receiver operating characteristic curve of >0.8. Generally, RACE and logCASUS had better discrimination than SOFA. Calibration of the RACE score was better than logCASUS, but ratios of observed to expected mortality for both were generally <0.65. Locally recalibrated SOFA, logCASUS and RACE models all performed well. Conclusion All three models demonstrated good discrimination for the first 7 days after cardiac surgery. After recalibration, logCASUS and RACE scores appear to be most useful for daily risk prediction after cardiac surgery. If appropriately calibrated, postoperative cardiac surgery risk prediction models have the potential to be useful tools after cardiac surgery.

scholarly journals Artificial neural network approach for acute poisoning mortality prediction in emergency departments

2021 ◽  
Vol 8 (3) ◽  
pp. 229-236
Author(s):  
Seon Yeong Park ◽  
Kisung Kim ◽  
Seon Hee Woo ◽  
Jung Taek Park ◽  
Sikyoung Jeong ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Acute Poisoning ◽  
Mortality Prediction ◽  
Ann Model ◽  
Operating Characteristic Curve

Objective The number of deaths due to acute poisoning (AP) is on the increase. It is crucial to predict AP patient mortality to identify those requiring intensive care for providing appropriate patient care as well as preserving medical resources. The aim of this study is to predict the risk of in-hospital mortality associated with AP using an artificial neural network (ANN) model.Methods In this multicenter retrospective study, ANN and logistic regression models were constructed using the clinical and laboratory data of 1,304 patients seeking emergency treatment for AP. The ANN model was first trained on 912/1,304 (70%) randomly selected patients and then tested on the remaining 392/1,304 (30%). Receiver operating characteristic curve analysis was used to evaluate the mortality prediction of the two models.Results Age, endotracheal intubation status, and intensive care unit admission were significant predictors of mortality in patients with AP in the multivariate logistic regression model. The ANN model indicated age, Glasgow Coma Scale, intensive care unit admission, and endotracheal intubation status were critical factors among the 12 independent variables related to in-hospital mortality. The area under the receiver operating characteristic curve for mortality prediction was significantly higher in the ANN model compared to the logistic regression model.Conclusion This study establishes that the ANN model could be a valuable tool for predicting the risk of death following AP. Thus, it may facilitate effective patient triage and improve the outcomes.

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