Frailty Index to Measure Health Status in People with Systemic Sclerosis

Objective.To develop and validate, as a measure of overall health status, a Frailty Index (FI) for patients (n = 1372) in the Canadian Scleroderma Research Group (CSRG) Registry.Methods.Forty-four items were selected from the CSRG database as health deficits and recoded using FI criteria. To test construct validity, we compared measurement properties of the CSRG-FI to other FI, and related it to measures of damage, age, and time since diagnosis. To test criterion validity, we compared the baseline FI to that at last recorded visit and to mortality.Results.The mean CSRG-FI was 0.33 with a sub-maximal limit of 0.67. In patients with diffuse disease, the mean was 0.38(SD 0.14); in patients with limited disease, the mean was 0.31(SD 0.13). The CSRG-FI was weakly (but significantly) correlated with the Rodnan Skin Score (r = 0.28 in people with diffuse disease; 0.18 with limited) and moderately with the Physician Assessment of Damage (r = 0.51 for both limited and diffuse). The risk of death increased with higher FI scores and with higher physician ratings of damage. The area under the receiver operating characteristic curve for the baseline FI in relation to death was 0.75, higher than for other measures (range: 0.57–0.67).Conclusion.The FI quantifies overall health status in people with scleroderma and predicts mortality. Whether the FI might help with decisions about who might best be served by more aggressive treatment, such as bone marrow transplantation, needs to be evaluated.

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Sernbo score predicts survival after intracapsular hip fracture in the elderly

Annals of The Royal College of Surgeons of England ◽

10.1308/003588413x13511609954653 ◽

2013 ◽

Vol 95 (1) ◽

pp. 29-33 ◽

Cited By ~ 20

Author(s):

EJC Dawe ◽

E Lindisfarne ◽

T Singh ◽

I McFadyen ◽

P Stott

Keyword(s):

Hip Fracture ◽

High Risk ◽

Risk Group ◽

Characteristic Curve ◽

Social Situation ◽

High Risk Group ◽

Low Risk ◽

Risk Of Death ◽

Intracapsular Hip Fracture ◽

The Mean

Introduction The Sernbo score uses four factors (age, social situation, mobility and mental state) to divide patients into a high-risk and a low-risk group. This study sought to assess the use of the Sernbo score in predicting mortality after an intracapsular hip fracture. Methods A total of 259 patients with displaced intracapsular hip fractures were included in the study. Data from prospectively generated databases provided 22 descriptive variables for each patient. These included operative management, blood tests and co-mobidities. Multivariate analysis was used to identify significant predictors of mortality. Results The mean patient age was 85 years and the mean follow-up duration was 1.5 years. The one-year survival rate was 92% (±0.03) in the low-risk group and 65% (±0.046) in the high-risk group. Four variables predicted mortality: Sernbo score >15 (p=0.0023), blood creatinine (p=0.0026), ASA (American Society of Anaesthesiologists) grade >3 (p=0.0038) and non-operative treatment (p=0.0377). Receiver operating characteristic curve analysis showed the Sernbo score as the only predictor of 30-day mortality (area under curve 0.71 [0.65–0.76]). The score had a sensitivity of 92% and a specificity of 51% for prediction of death at 30 days. Conclusions The Sernbo score identifies patients at high risk of death in the 30 days following injury. This very simple score could be used to direct extra early multidisciplinary input to high-risk patients on admission with an intracapsular hip fracture.

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83 A Deficit Accumulation Frailty Index Predicts Mortality in Older South Africans: Findings from the HAALSI Study

Age and Ageing ◽

10.1093/ageing/afz192.01 ◽

2020 ◽

Vol 49 (Supplement_1) ◽

pp. i27-i27

Author(s):

F J Barker ◽

J I Davies ◽

F X Gomez-Olive ◽

K Kahn ◽

F E Matthews ◽

...

Keyword(s):

South Africa ◽

Older People ◽

Frailty Index ◽

Cox Proportional Hazards ◽

Sub Saharan Africa ◽

Deficit Accumulation ◽

South Africans ◽

Risk Of Death ◽

The Mean ◽

Cumulative Deficit

Abstract Introduction Few studies have investigated frailty in older people in sub-Saharan Africa, yet such information is vital to prepare responses to rapid population ageing. We aimed to derive and test a cumulative deficit frailty index in a population of older people from rural South Africa. Methods We analysed data from the Health and Ageing in Africa: Longitudinal Studies of an INDEPTH Community (HAALSI) study, which enrolled participants aged 40 years and older nested within the Agincourt Health and Demographic Survey Site, South Africa. We created a 32-variable cumulative deficit frailty index using questionnaire (illnesses, symptoms and activities of daily living), physical performance and physiological indices, and blood test results. Each variable was dichotomised to 1 (deficit) or 0 (no deficit). The frailty index for each individual was calculated as the mean of all frailty variables. Frailty categories were defined using cut-offs from the UK electronic frailty index: 0-0.12 (non-frail), >0.12-0.24 (mild frailty), >0.24-0.36 (moderate frailty) and >0.36 (severe frailty). Cox proportional hazards models, both unadjusted and adjusted for age and sex, were fitted to test the association between frailty status and all-cause mortality. Results We analysed data from 3989 participants, mean age 61 years (SD 13); 2175 (54.5%) were female. The mean follow-up period was 17 months; 1464 (36.7%) were non-frail, 2059 (51.6%) had mild frailty, 402 (10.1%) had moderate frailty and 64 (1.6%) had severe frailty. A total of 135 (3.4%) died. Adjusted Cox models showed worse frailty category was associated with higher risk of death compared with non-frail individuals: hazard ratios 1.94 (95% CI 1.23, 3.07) for mild frailty, 3.25 (95% CI 1.86, 5.68) for moderate frailty, and 5.50 (95% CI 2.44, 12.40) for severe frailty. Conclusions Frailty measured by a cumulative deficits index is common and predicts mortality in a rural population of older South Africans.

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Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning

Journal of Personalized Medicine ◽

10.3390/jpm10040211 ◽

2020 ◽

Vol 10 (4) ◽

pp. 211 ◽

Cited By ~ 1

Author(s):

Yong Joon Suh ◽

Jaewon Jung ◽

Bum-Joo Cho

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Operating Characteristic ◽

Meta Analysis ◽

Characteristic Curve ◽

Malignant Lesion ◽

Model Performance ◽

Mean Values ◽

The Mean ◽

Deep Learning Model

Mammography plays an important role in screening breast cancer among females, and artificial intelligence has enabled the automated detection of diseases on medical images. This study aimed to develop a deep learning model detecting breast cancer in digital mammograms of various densities and to evaluate the model performance compared to previous studies. From 1501 subjects who underwent digital mammography between February 2007 and May 2015, craniocaudal and mediolateral view mammograms were included and concatenated for each breast, ultimately producing 3002 merged images. Two convolutional neural networks were trained to detect any malignant lesion on the merged images. The performances were tested using 301 merged images from 284 subjects and compared to a meta-analysis including 12 previous deep learning studies. The mean area under the receiver-operating characteristic curve (AUC) for detecting breast cancer in each merged mammogram was 0.952 ± 0.005 by DenseNet-169 and 0.954 ± 0.020 by EfficientNet-B5, respectively. The performance for malignancy detection decreased as breast density increased (density A, mean AUC = 0.984 vs. density D, mean AUC = 0.902 by DenseNet-169). When patients’ age was used as a covariate for malignancy detection, the performance showed little change (mean AUC, 0.953 ± 0.005). The mean sensitivity and specificity of the DenseNet-169 (87 and 88%, respectively) surpassed the mean values (81 and 82%, respectively) obtained in a meta-analysis. Deep learning would work efficiently in screening breast cancer in digital mammograms of various densities, which could be maximized in breasts with lower parenchyma density.

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The Utility of Pleural Fluid Lactate Dehydrogenase to Adenosine Deaminase Ratio in Pleural Tuberculosis

Respiration ◽

10.1159/000509555 ◽

2020 ◽

pp. 1-5

Author(s):

Amanda Beukes ◽

Jane Alexandra Shaw ◽

Andreas H. Diacon ◽

Elvis M. Irusen ◽

Coenraad F.N. Koegelenberg

Keyword(s):

Lactate Dehydrogenase ◽

Adenosine Deaminase ◽

Pleural Fluid ◽

Operating Characteristic ◽

Retrospective Studies ◽

Characteristic Curve ◽

Diagnostic Efficiency ◽

Pleural Tuberculosis ◽

High Incidence ◽

The Mean

In high-burden settings, the diagnosis of pleural tuberculosis (TB) is frequently inferred in patients who present with lymphocyte predominant exudative effusions and high adenosine deaminase (ADA) levels. Two recent small retrospective studies suggested that the lactate dehydrogenase (LDH)/ADA ratio is significantly lower in TB than in non-TB pleural effusions and that the LDH/ADA ratio may be useful in differentiating pleural TB from other pleural exudates. We compared the pleural LDH/ADA ratios, ADA levels, and lymphocyte predominance of a prospectively collected cohort of patients with proven pleural TB (n = 160) to those with a definitive alternative diagnosis (n = 68). The mean pleural fluid LDH/ADA ratio was lower in patients with pleural TB than alternative diagnoses (6.2 vs. 34.3, p < 0.001). The area under the receiver operating characteristic curve was 0.92 (p < 0.001) for LDH/ADA ratio and 0.88 (p < 0.001) for an ADA ≥40 U/L alone. A ratio of ≤12.5 had the best overall diagnostic efficiency, while a ratio of ≤10 had a specificity of 90% and a positive predictive value of 95%, with a sensitivity of 78%, making it a clinically useful “rule in” value for pleural TB in high incidence settings. When comparing the LDH/ADA ratio to an ADA level ≥40 U/L in the presence of a lymphocyte predominant effusion, the latter performed better. When lymphocyte values are unavailable, our data suggest that the LDH/ADA ratio is valuable in distinguishing TB effusions from other pleural exudates.

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Utility of minimum clinically important difference in assessing pain, disability, and health state after transforaminal lumbar interbody fusion for degenerative lumbar spondylolisthesis

Journal of Neurosurgery Spine ◽

10.3171/2010.12.spine10472 ◽

2011 ◽

Vol 14 (5) ◽

pp. 598-604 ◽

Cited By ~ 146

Author(s):

Scott L. Parker ◽

Owoicho Adogwa ◽

Alexandra R. Paul ◽

William N. Anderson ◽

Oran Aaronson ◽

...

Keyword(s):

Operating Characteristic ◽

Interbody Fusion ◽

Characteristic Curve ◽

Transforaminal Lumbar Interbody Fusion ◽

Minimum Clinically Important Difference ◽

Leg Pain ◽

Sf 36 ◽

Degenerative Lumbar Spondylolisthesis ◽

The Mean ◽

Lumbar Spondylolisthesis

Object Outcome studies for spine surgery rely on patient-reported outcomes (PROs) to assess treatment effects. Commonly used health-related quality-of-life questionnaires include the following scales: back pain and leg pain visual analog scale (BP-VAS and LP-VAS); the Oswestry Disability Index (ODI); and the EuroQol-5D health survey (EQ-5D). A shortcoming of these questionnaires is that their numerical scores lack a direct meaning or clinical significance. Because of this, the concept of the minimum clinically important difference (MCID) has been put forth as a measure for the critical threshold needed to achieve treatment effectiveness. By this measure, treatment effects reaching the MCID threshold value imply clinical significance and justification for implementation into clinical practice. Methods In 45 consecutive patients undergoing transforaminal lumbar interbody fusion (TLIF) for low-grade degenerative lumbar spondylolisthesis-associated back and leg pain, PRO questionnaires measuring BP-VAS, LPVAS, ODI, and EQ-5D were administered preoperatively and at 2 years postoperatively, and 2-year change scores were calculated. Four established anchor-based MCID calculation methods were used to calculate MCID, as follows: 1) average change; 2) minimum detectable change (MDC); 3) change difference; and 4) receiver operating characteristic curve analysis for two separate anchors (the health transition index [HTI] of the 36-Item Short Form Health Survey [SF-36], and the satisfaction index). Results All patients were available at the 2-year follow-up. The 2-year improvements in BP-VAS, LP-VAS, ODI, and EQ-5D scores were 4.3 ± 2.9, 3.8 ± 3.4, 19.5 ± 11.3, and 0.43 ± 0.44, respectively (mean ± SD). The 4 MCID calculation methods generated a range of MCID values for each of the PROs (BP-VAS, 2.1–5.3; LP-VAS, 2.1–4.7; ODI, 11–22.9; and EQ-5D, 0.15–0.54). The mean area under the curve (AUC) for the receiver operating characteristic curve from the 4 PRO-specific calculations was greater for the HTI versus satisfaction anchor (HTI [AUC 0.73] vs satisfaction [AUC 0.69]), suggesting HTI as a more accurate anchor. Conclusions The TLIF-specific MCID is highly variable based on calculation technique. The MDC approach with the SF-36 HTI anchor appears to be most appropriate for calculating MCID because it provided a threshold above the 95% CI of the unimproved cohort (greater than the measurement error), was closest to the mean change score reported by improved and satisfied patients, and was least affected by the choice of anchor. Based on the MDC method with HTI anchor, MCID scores following TLIF are 2.1 points for BP-VAS, 2.8 points for LP-VAS, 14.9 points for ODI, and 0.46 quality-adjusted life years for EQ-5D.

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Radiomic Analysis of Contrast-Enhanced Mammography With Different Image Types: Classification of Breast Lesions

Frontiers in Oncology ◽

10.3389/fonc.2021.600546 ◽

2021 ◽

Vol 11 ◽

Author(s):

Simin Wang ◽

Ning Mao ◽

Shaofeng Duan ◽

Qin Li ◽

Ruimin Li ◽

...

Keyword(s):

Operating Characteristic ◽

Characteristic Curve ◽

High Energy ◽

Breast Lesions ◽

Wilcoxon Rank Sum Test ◽

Contrast Enhanced ◽

Malignant Breast ◽

The Mean ◽

Selection Operator

Objective: A limited number of studies have focused on the radiomic analysis of contrast-enhanced mammography (CEM). We aimed to construct several radiomics-based models of CEM for classifying benign and malignant breast lesions.Materials and Methods: The retrospective, double-center study included women who underwent CEM between November 2013 and February 2020. Radiomic analysis was performed using high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images from CEM. Datasets were randomly divided into the training and testing sets at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) method and least absolute shrinkage and selection operator (LASSO) logistic regression were used to select the radiomic features and construct the best classification models. The performances of the models were assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). Leave-group-out cross-validation (LGOCV) for 100 rounds was performed to obtain the mean AUCs, which were compared by the Wilcoxon rank-sum test and the Kruskal–Wallis rank-sum test.Results: A total of 192 women with 226 breast lesions (101 benign; 125 malignant) were enrolled. The median age was 48 years (range, 22–70 years). For the classification of breast lesions, the AUCs of the best models were 0.931 (95% CI: 0.873–0.989) for HE, 0.897 (95% CI: 0.807–0.981) for LE, 0.882 (95% CI: 0.825–0.987) for DES images and 0.960 (95% CI: 0.910–0.998) for all of the CEM images in the testing set. According to LGOCV, the models constructed with the HE images and all of the CEM images showed the highest mean AUCs for the training (0.931 and 0.938, respectively; P < 0.05 for both) and testing sets (0.892 and 0.889, respectively; P = 0.55 for both), which were significantly higher than those of the two models constructed with the LE and DES images in the training (0.912 and 0.899, respectively; all P < 0.05) and testing sets (0.866 and 0.862, respectively; all P < 0.05).Conclusions: Radiomic analysis of CEM images was valuable for classifying benign and malignant breast lesions. The use of HE images or all three types of CEM images can achieve the best performance.

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A Simple and Novel Noninvasive Method of Estimating Markedly Elevated Pulmonary Vascular Resistance in Patients with Pre-capillary Pulmonary Hypertension

10.21203/rs.3.rs-660018/v1 ◽

2021 ◽

Author(s):

Ya-Nan Zhai ◽

Ai-Li Li ◽

Xin-Cao Tao ◽

Wan-Mu Xie ◽

Qian Gao ◽

...

Keyword(s):

Pulmonary Hypertension ◽

Pulmonary Vascular Resistance ◽

Vascular Resistance ◽

Operating Characteristic ◽

Characteristic Curve ◽

Curve Analysis ◽

Internal Diameter ◽

Noninvasive Method ◽

Simple Method ◽

The Mean

Abstract Background: Several echocardiographic methods to estimate pulmonary vascular resistance (PVR) in patients with pulmonary hypertension (PH) have been proposed. So far, most studies have focused on relatively low PVR. We aimed to clarify the clinical usefulness of our new echocardiographic index of evaluating markedly elevated PVR in pre-capillary PH patients. Methods: We studied 129 consecutive patients with pre-capillary PH. We estimated the mean pulmonary artery pressure using echocardiography (mPAPEcho) and measured LV internal diameter at end diastole (LVIDd). The ratio of mPAPEcho / LVIDd was then correlated with invasive PVR. Using receiver operating characteristic curve analysis, a cutoff value for the index was generated to identify patients with PVR > 15 Wood units (WU). Results: mPAPEcho / LVIDd correlated well with PVR (r = 0.70, P < 0.0001). There was a better correlation between PVR and mPAPEcho / LVIDd in patients with PVR > 15 WU compared with TRV2 /TVIRVOT and sPAPEcho / LVIDd. A cut-off value of 1.14 had an 80.0% sensitivity and 74.7% specificity to determine PVR > 15 WU (AUC=0.840, p<0.0001). Conclusions: The index of mPAPEcho / LVIDd could be a valuable noninvasive and simple method of estimating markedly elevated PVR in pre-capillary PH patients.

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Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms

Journal of NeuroInterventional Surgery ◽

10.1136/neurintsurg-2019-015214 ◽

2019 ◽

Vol 12 (4) ◽

pp. 417-421 ◽

Cited By ~ 5

Author(s):

Alexander R Podgorsak ◽

Ryan A Rava ◽

Mohammad Mahdi Shiraz Bhurwani ◽

Anusha R Chandra ◽

Jason M Davies ◽

...

Keyword(s):

Operating Characteristic ◽

Characteristic Curve ◽

Region Of Interest ◽

Semantic Segmentation ◽

Parametric Imaging ◽

Dice Similarity Coefficient ◽

Imaging Method ◽

Digital Subtraction ◽

Imaging Procedures ◽

The Mean

BackgroundAngiographic parametric imaging (API) is an imaging method that uses digital subtraction angiography (DSA) to characterize contrast media dynamics throughout the vasculature. This requires manual placement of a region of interest over a lesion (eg, an aneurysm sac) by an operator.ObjectiveThe purpose of our work was to determine if a convolutional neural network (CNN) was able to identify and segment the intracranial aneurysm (IA) sac in a DSA and extract API radiomic features with minimal errors compared with human user results.MethodsThree hundred and fifty angiographic images of IAs were retrospectively collected. The IAs and surrounding vasculature were manually contoured and the masks put to a CNN tasked with semantic segmentation. The CNN segmentations were assessed for accuracy using the Dice similarity coefficient (DSC) and Jaccard index (JI). Area under the receiver operating characteristic curve (AUROC) was computed. API features based on the CNN segmentation were compared with the human user results.ResultsThe mean JI was 0.823 (95% CI 0.783 to 0.863) for the IA and 0.737 (95% CI 0.682 to 0.792) for the vasculature. The mean DSC was 0.903 (95% CI 0.867 to 0.937) for the IA and 0.849 (95% CI 0.811 to 0.887) for the vasculature. The mean AUROC was 0.791 (95% CI 0.740 to 0.817) for the IA and 0.715 (95% CI 0.678 to 0.733) for the vasculature. All five API features measured inside the predicted masks were within 18% of those measured inside manually contoured masks.ConclusionsCNN segmentation of IAs and surrounding vasculature from DSA images is non-inferior to manual contours of aneurysms and can be used in parametric imaging procedures.

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Patient-Doctor Contact Interval and Clinical Outcomes in Continuous Ambulatory Peritoneal Dialysis Patients

American Journal of Nephrology ◽

10.1159/000464258 ◽

2017 ◽

Vol 45 (4) ◽

pp. 346-352 ◽

Cited By ~ 2

Author(s):

Chunyan Yi ◽

Qunying Guo ◽

Jianxiong Lin ◽

Jianying Li ◽

Xueqing Yu ◽

...

Keyword(s):

Peritoneal Dialysis ◽

Clinical Outcomes ◽

Operating Characteristic ◽

Patient Survival ◽

Characteristic Curve ◽

Survival Rates ◽

Guangzhou City ◽

Clinical Events ◽

The Mean

Background: The optimal patient-doctor contact (PDC) interval remains unknown in peritoneal dialysis (PD) patients. The aim was to investigate the association between PDC interval and clinical outcomes in continuous ambulatory PD (CAPD) patients. Methods: In this retrospective cohort study, CAPD patients who resided in Guangzhou city between January 2006 and December 2012 were included. According to receiver operating characteristic curve analysis, all patients were classified as high (PDC interval ≤2 months) and low (PDC interval >2 months) PDC frequency groups. Biochemical data, clinical events, and clinical outcomes during the follow-up period were compared. Results: Of 433 CAPD patients, the mean age was 51.3 ± 15.7 years, 54.3% of patients were male, and 29.1% with diabetes. The median vintage of PD was 45.8 (26.3-69.1) months. Patients with high PDC frequency (n = 233) had better patient-survival rates (99.6, 87.7, and 76.5% vs. 92.7, 76.5, and 58.7% at 1, 3, and 5 years; p < 0.001), lower peritonitis rate (0.17 vs. 0.23 episodes per patient-year; p < 0.001), and hospitalization rate (0.49 vs. 0.67 episodes per patient-year; p < 0.001) than those in the low PDC frequency group (n = 200). After adjustment for confounders, PDC interval of no more than 2 months was independently associated with better patient survival (hazard ratio 0.60, 95% CI 0.42-0.86, p = 0.006). Conclusion: A PDC interval of 2 months or less was associated with better clinical outcomes in CAPD patients. This indicates that a shorter PDC interval should be encouraged for them to achieve better clinical outcomes.

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Protein Carbonyl, But Not Malondialdehyde, Is Associated With ICU Mortality in Patients With Septic Shock

Journal of Intensive Care Medicine ◽

10.1177/0885066617710218 ◽

2017 ◽

Vol 34 (8) ◽

pp. 669-673 ◽

Cited By ~ 3

Author(s):

Nara Aline Costa ◽

Ana Lucia Gut ◽

Paula Schmidt Azevedo ◽

Ana Angelica Henrique Fernandes ◽

Bertha Furlan Polegato ◽

...

Keyword(s):

Septic Shock ◽

Operating Characteristic ◽

Characteristic Curve ◽

Area Under The Curve ◽

Protein Carbonyl ◽

Apache Ii ◽

Apache Ii Score ◽

Icu Mortality ◽

Logistic Regression Models ◽

The Mean

Background:The objective of our study was to evaluate the association of serum malondialdehyde (MDA) and protein carbonyl concentration with intensive care unit (ICU) mortality in patients with septic shock.Methods:We prospectively evaluated 175 patients aged over 18 years with septic shock upon ICU admission. However, 16 patients were excluded. Thus, 159 patients were enrolled in the study. In addition, we evaluated 16 control patients. At the time of the patients’ enrollment, demographic information was recorded. Blood samples were taken within the first 24 hours of the patient’s admission to determine serum MDA and protein carbonyl concentrations.Results:The mean age was 67.3 ± 15.9 years, 44% were males, and the ICU mortality rate was 67.9%. Median MDA concentration was 1.53 (0.83-2.22) µmol/L, and median protein carbonyl concentration was 24.0 (12.7-32.8) nmol/mL. Patients who died during ICU stay had higher protein carbonyl concentration. However, there was no difference in MDA levels between these patients. Receiver operating characteristic curve analysis showed that higher levels of protein carbonyl were associated with ICU mortality (area under the curve: 0.955; 95% confidence interval [CI]: 0.918-0.992; P < .001) at the cutoff of >22.83 nmol/mL (sensibility: 80.4% and specificity: 98.1%). In the logistic regression models, protein carbonyl concentrations (odds ratio [OR]: 1.424; 95% CI: 1.268-1.600; P < .001), but not MDA concentrations (OR: 1.087; 95% CI: 0.805-1.467; P = .59), were associated with ICU mortality when adjusted for age, gender, and Acute Physiology and Chronic Health Evaluation (APACHE) II score; and when adjusted by APACHE II score, lactate, and urea; protein carbonyl concentrations (OR: 1.394; 95% CI: 1.242-1.564; P < .001); and MDA (OR: 1.054; 95% CI: 0.776-1.432; P = .73).Conclusion:In conclusion, protein carbonyl, but not MDA, concentration is associated with ICU mortality in patients with septic shock.

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