Use of percent positive biopsy cores to predict prostate cancer–specific death in patients treated with dose-escalated radiotherapy.

2011 ◽  
Vol 29 (7_suppl) ◽  
pp. 35-35
Author(s):  
Y. Qian ◽  
F. Y. Feng ◽  
S. Halverson ◽  
K. Blas ◽  
H. M. Sandler ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Roc Curve ◽  
Risk Group ◽  
Curve Analysis ◽  
External Beam Radiation ◽  
Rank Test ◽  
Roc Curve Analysis ◽  
Cut Point ◽  
Log Rank Test ◽  
Positive Biopsy

35 Background: The percent of positive biopsy cores (PPC)-considered a surrogate of local disease burden-has been shown to predict biochemical failure (BF) after external beam radiation therapy (EBRT), but most series have used conventional dose RT. Dose-escalated RT has been demonstrated to improve prostate cancer outcomes, but the value of PPC is unclear in the setting of RT doses high enough to decrease local failure. Methods: A retrospective evaluation was performed of 651 patients treated to ≥75 Gy with biopsy core information available. Patients were stratified for PPC by quartile, and differences by quartile in BF, freedom from metastasis (FFM), cause specific survival (CSS), and overall survival (OS) were assessed using the log-rank test. Receiver operated characteristic (ROC) curve analysis was utilized to determine an optimal cut-point for PPC. Cox proportional hazards multivariate regression was utilized to assess the impact of PPC on clinical outcome when adjusting for risk group. Results: With median follow-up of 62 months the median number of cores sampled was 7 (IQR: 6–12) with median PPC in 38% (IQR: 17%-67%). On log-rank test, BF, FFM, and CSS were all associated with PPC (p < 0.005 for all), with worse outcomes only for the highest PPC quartile (>67%). There was no observed difference in OS based upon PPC. ROC curve analysis confirmed a cut-point of 67% as most closely associated with CSS (p<0.001, AUC=0.71). On multivariate analysis after adjusting for NCCN risk group and ADT use, PPC>67% increased the risk for BF (p<0.0001, HR:2.1 [1.4–3.0]), FFM (p<0.05, HR:1.7 [1.1 to 2.9]), and CSS (p<0.06 (HR:2.1 [1.0–4.6]). When analyzed as a continuous variable controlling for risk group and ADT use, increasing PPC increased the risk for BF (p < 0.002), metastasis (p < 0.05), and CSS (p < 0.02), with a 1–2% increase in relative risk of recurrence for each 1% increase in the PPC. Conclusions: For patients treated with dose-escalated RT, the PPC adds prognostic value but at a higher cut-point then previously utilized. Patients with PPC >67% remain at increased risk for failure even with dose-escalated EBRT and may receive benefit from further intensification of therapy. No significant financial relationships to disclose.

scholarly journals Presepsin values and prognostic nutritional index predict mortality in intensive care unit patients with sepsis: a pilot study

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yuichiro Shimoyama ◽  
Osamu Umegaki ◽  
Noriko Kadono ◽  
Toshiaki Minami
Keyword(s):  
Multivariate Analysis ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Prognostic Nutritional Index ◽  
Early Mortality ◽  
Curve Analysis ◽  
Rank Test ◽  
Roc Curve Analysis ◽  
Log Rank Test ◽  
Nutritional Index

Abstract Objective Sepsis is a major cause of mortality for critically ill patients. This study aimed to determine whether presepsin values can predict mortality in patients with sepsis. Results Receiver operating characteristic (ROC) curve analysis, Log-rank test, and multivariate analysis identified presepsin values and Prognostic Nutritional Index as predictors of mortality in sepsis patients. Presepsin value on Day 1 was a predictor of early mortality, i.e., death within 7 days of ICU admission; ROC curve analysis revealed an AUC of 0.84, sensitivity of 89%, and specificity of 77%; and multivariate analysis showed an OR of 1.0007, with a 95%CI of 1.0001–1.0013 (p = 0.0320).

Correlation of vertebral trabecular attenuation in Hounsfield units and the upper instrumented vertebra with proximal junctional failure after surgical treatment of degenerative lumbar disease

2020 ◽  
pp. 1-8
Author(s):  
Qi Wang ◽  
Chi Wang ◽  
Xiaobo Zhang ◽  
Fanqi Hu ◽  
Wenhao Hu ◽  
...  
Keyword(s):  
Roc Curve ◽  
Curve Analysis ◽  
Rank Test ◽  
Hounsfield Units ◽  
Roc Curve Analysis ◽  
Free Survival ◽  
Log Rank Test ◽  
Degenerative Lumbar Disease ◽  
Proximal Junctional Failure ◽  
Upper Instrumented Vertebra

OBJECTIVEThe aim of this study was to investigate whether bone mineral density (BMD) measured in Hounsfield units (HUs) is correlated with proximal junctional failure (PJF).METHODSA retrospective study of 104 patients with adult degenerative lumbar disease was performed. All patients underwent posterior instrumented fusion of 4 or more segments and were followed up for at least 2 years. Patients were divided into two groups on the basis of whether they had mechanical complications of PJF. Age, sex ratio, BMI, follow-up time, upper instrumented vertebra (UIV), lower instrumented vertebra, and vertebral body osteotomy were recorded. The spinopelvic parameters were measured on early postoperative radiographs. The HU value of L1 trabecular attenuation was measured on axial and sagittal CT scans. Statistical analysis was performed to compare the difference of continuous and categorical variables. Receiver operating characteristic (ROC) curve analysis was used to obtain attenuation thresholds. A Kaplan-Meier curve and log-rank test were used to analyze the differences in PJF-free survival. Multivariate analysis via a Cox proportional hazards model was used to analyze the risk factors.RESULTSThe HU value of L1 trabecular attenuation in the PJF group was lower than that in the control group (p < 0.001). The spinopelvic parameter L4–S1 lordosis was significantly different between the groups (p = 0.033). ROC curve analysis determined an optimal threshold of 89.25 HUs (sensitivity = 78.3%, specificity = 80.2%, area under the ROC curve = 0.799). PJF-free survival significantly decreased in patients with L1 attenuation ≤ 89.25 HUs (p < 0.001, log-rank test). When L1 trabecular attenuation was ≤ 89.25 HUs, PJF-free survival in patients with the UIV at L2 was the lowest, compared with patients with their UIV at the thoracolumbar junction or above (p = 0.028, log-rank test).CONCLUSIONSHUs could provide important information for surgeons to make a treatment plan to prevent PJF. L1 trabecular attenuation ≤ 89.25 HUs measured by spinal CT scanning could predict the incidence of PJF. Under this condition, the UIV at L2 significantly increases the incidence of PJF.

scholarly journals Presepsin values and Prognostic Nutritional Index predict mortality in intensive care unit patients with sepsis: a pilot study

2021 ◽  
Author(s):  
Yuichiro Shimoyama ◽  
Osamu Umegaki ◽  
Noriko Kadono ◽  
Toshiaki Minami
Keyword(s):  
Multivariate Analysis ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Prognostic Nutritional Index ◽  
Early Mortality ◽  
Curve Analysis ◽  
Rank Test ◽  
Roc Curve Analysis ◽  
Log Rank Test ◽  
Nutritional Index

Abstract Objective Sepsis is a major cause of mortality for critically ill patients. This study aimed to determine whether presepsin values can predict mortality in patients with sepsis. Results Receiver operating characteristic (ROC) curve analysis, Log-rank test, and multivariate analysis identified presepsin values and Prognostic Nutritional Index as predictors of mortality in sepsis patients. Presepsin value on Day 1 was a predictor of early mortality, i.e., death within 7 days of ICU admission; ROC curve analysis revealed an AUC of 0.84, sensitivity of 89%, and specificity of 77%; and multivariate analysis showed an OR of 1.0007, with a 95%CI of 1.0001–1.0013 (p = 0.0320).

scholarly journals Is There a Role for Prostate Tumour Overexpressed-1 in the Diagnosis of HGPIN and of Prostatic Adenocarcinoma? A Comparison with α-Methylacyl CoA Racemase

2012 ◽  
Vol 25 (1) ◽  
pp. 67-74 ◽  
Author(s):  
M. Scarpelli ◽  
R. Mazzucchelli ◽  
F. Barbisan ◽  
A. Santinelli ◽  
A. Lopez-Beltran ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Accuracy ◽  
Differential Display ◽  
Roc Curve ◽  
Intraepithelial Neoplasia ◽  
Prostatic Intraepithelial Neoplasia ◽  
Curve Analysis ◽  
High Grade ◽  
Roc Curve Analysis ◽  
Prostate Tumour

Prostate Tumour Overexpressed-1 (PTOV1) was recently identified as a novel gene and protein during a differential display screening for genes overexpressed in prostate cancer (PCa). α-Methyl-CoA racemose (AMACR) mRNA was identified as being overexpressed in PCa. PTOV1 and racemase were immunohistochemically evaluated in PCa, high-grade prostatic intraepithelial neoplasia (HGPIN), atrophy and normal-looking epithelium (NEp) in 20 radical prostatectomies (RPs) with pT2a Gleason score 6 prostate cancer with the aim of analyzing the differences in marker expression between PTOV1 and AMACR. The level of expression of PTOV1 and AMACR increased from NEp and atrophy through HGPIN, away from and adjacent to prostate cancer, to PCa. With the ROC curve analysis the overall accuracy in distinguishing PCa vs HGPIN away from and adjacent to cancer was higher for AMACR than for PTOV1. In conclusion, AMACR can be considered a more accurate marker than PTOV1 in the identification of HGPIN and of PCa. However, PTOV1 may aid in the diagnosis of PCa, at least to supplement AMACR as another positive marker of carcinoma and to potentially increase diagnostic accuracy.

scholarly journals A Novel Ferroptosis-Related Long Non-coding RNA Prognostic Risk Model for Gastric Cancer

2021 ◽  
Author(s):  
Shenglan Huang ◽  
Dan Li ◽  
Lingling Zhuang ◽  
Liying Sun ◽  
Jianbing Wu
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Clinical Stage ◽  
Curve Analysis ◽  
Clinicopathological Features ◽  
Roc Curve Analysis ◽  
Kaplan Meier

Abstract Background：Gastric cancer (GC) is one of the most common malignant tumors with a poor prognosis. Ferroptosis is a novel and distinct type of non-apoptotic cell death that is closely associated with metabolism, redox biology, and tumor prognosis. Recently, ferroptosis-related long non-coding RNAs (lncRNAs) have received increasing attention in predicting cancer prognosis. Thus, we aimed to construct an ferroptosis-related lncRNAs signature for predicting the prognosis of patients with gastric cancer.Methods：We built an ferroptosis-related lncRNA risk signature by using Cox regression based on TCGA database. Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) in different risk groups. Cox regression was performed to explore whether the signature could be used as an independent factor. A nomogram was built involving the risk score and clinicopathological features. Furthermore, we explored the biological functions and immune states in two groups.Results：Eight ferroptosis-related lncRNAs were obtained for constructing the prognosis model in gastric cancer. Kaplan–Meier curve analysis revealed that patients in the high-risk group had worse survival than those in the low-risk group. The survival outcome was also appropriate for subgroup analysis, including age, sex, grade, and clinical stage. Multivariate Cox regression analysis and receiver operating characteristic (ROC) curve analysis demonstrated that the risk score was an independent prognostic factor and superior to traditional clinicopathological features in predicting GC prognosis. Next, we established a nomogram according to clinical parameters (age, sex, grade, and clinical stage) and risk score. All the verified results, including ROC curve analysis, calibration curve, and decision curve analysis, demonstrated that the nomogram could accurately predict the survival of patients with gastric cancer. Gene set enrichment analysis revealed that these lncRNAs were mainly involved in cell adhesion, cancer pathways, and immune function regulation.Conclusion: We established a novel ferroptosis-related prognostic risk signature including eight lncRNAs and constructed a nomogram to predict the prognosis of gastric cancer patients, which may improve prognostic predictive accuracy and guide individualized treatment for patients with GC.

scholarly journals Construction on of a Ferroptosis-Related lncRNA-Based Model to Improve the Prognostic Evaluation of Gastric Cancer Patients Based on Bioinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiahui Pan ◽  
Xinyue Zhang ◽  
Xuedong Fang ◽  
Zhuoyuan Xin
Keyword(s):  
Gastric Cancer ◽  
Cancer Patients ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Gastric Cancer Patients

BackgroundGastric cancer is one of the most serious gastrointestinal malignancies with bad prognosis. Ferroptosis is an iron-dependent form of programmed cell death, which may affect the prognosis of gastric cancer patients. Long non-coding RNAs (lncRNAs) can affect the prognosis of cancer through regulating the ferroptosis process, which could be potential overall survival (OS) prediction factors for gastric cancer.MethodsFerroptosis-related lncRNA expression profiles and the clinicopathological and OS information were collected from The Cancer Genome Atlas (TCGA) and the FerrDb database. The differentially expressed ferroptosis-related lncRNAs were screened with the DESeq2 method. Through co-expression analysis and functional annotation, we then identified the associations between ferroptosis-related lncRNAs and the OS rates for gastric cancer patients. Using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a prognostic model based on 17 ferroptosis-related lncRNAs. We also evaluated the prognostic power of this model using Kaplan–Meier (K-M) survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA).ResultsA ferroptosis-related “lncRNA–mRNA” co-expression network was constructed. Functional annotation revealed that the FOXO and HIF-1 signaling pathways were dysregulated, which might control the prognosis of gastric cancer patients. Then, a ferroptosis-related gastric cancer prognostic signature model including 17 lncRNAs was constructed. Based on the RiskScore calculated using this model, the patients were divided into a High-Risk group and a low-risk group. The K-M survival curve analysis revealed that the higher the RiskScore, the worse is the obtained prognosis. The ROC curve analysis showed that the area under the ROC curve (AUC) of our model is 0.751, which was better than those of other published models. The multivariate Cox regression analysis results showed that the lncRNA signature is an independent risk factor for the OS rates. Finally, using nomogram and DCA, we also observed a preferable clinical practicality potential for prognosis prediction of gastric cancer patients.ConclusionOur prognostic signature model based on 17 ferroptosis-related lncRNAs may improve the overall survival prediction in gastric cancer.

scholarly journals Preoperative albumin-to-fibrinogen ratio predicts chemotherapy resistance and prognosis in patients with advanced epithelial ovarian cancer

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Wen Yu ◽  
Zhongxue Ye ◽  
Xi Fang ◽  
Xingzhi Jiang ◽  
Yafen Jiang
Keyword(s):  
Risk Factors ◽  
Ovarian Cancer ◽  
Prognostic Factors ◽  
Chemotherapy Resistance ◽  
Curve Analysis ◽  
Rank Test ◽  
Debulking Surgery ◽  
Roc Curve Analysis ◽  
Log Rank Test ◽  
Kaplan Meier

Abstract Background Epithelial ovarian cancer (EOC) is the majority ovarian cancer (OC) type with a poor prognosis. This present study aimed to investigate potential prognostic factors including albumin-to-fibrinogen ratio (AFR) for advanced EOC patients with neoadjuvant chemotherapy (NAC) followed by debulking surgery. Methods A total of 313 advanced EOC patients with NAC followed by debulking surgery from 2010 to 2017 were enrolled. The predictive value of AFR for the overall survival (OS) was evaluated by receiver operating characteristic (ROC) curve analysis. The univariate and multivariate Cox proportional hazards regression analyses were applied to investigate prognostic factors for advanced EOC patients. The association between preoperative AFR and progression free survival (PFS) or OS was determined via the Kaplan–Meier method using log-rank test. Results The ROC curve analysis showed that the cutoff value of preoperative AFR in predicting OS was determined to be 7.78 with an area under the curve (AUC) of 0.773 (P < 0.001). Chemotherapy resistance, preoperative CA125 and AFR were independent risk factors for PFS in advanced EOC patients. Furthermore, chemotherapy resistance, residual tumor and AFR were significant risk factors for OS by multivariate Cox analysis. A low preoperative AFR (≤7.78) was significantly associated with a worse PFS and OS via the Kaplan–Meier method by log-rank test (P < 0.001). Conclusions A low preoperative AFR was an independent risk factor for PFS and OS in advanced EOC patients with NAC followed by debulking surgery.

scholarly journals Identification of a 5-Gene-Based Scoring System by WGCNA and LASSO to Predict Prognosis for Rectal Cancer Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
He Huang ◽  
Shilei Xu ◽  
Aidong Chen ◽  
Fen Li ◽  
Jiezhong Wu ◽  
...  
Keyword(s):  
Risk Score ◽  
Roc Curve ◽  
Predictive Accuracy ◽  
Risk Group ◽  
Survival Curve ◽  
Curve Analysis ◽  
The Cancer Genome Atlas ◽  
Molecular Signature ◽  
Prognostic Signature ◽  
Roc Curve Analysis

Background. Although accumulating evidence suggested that a molecular signature panel may be more effective for the prognosis prediction than routine clinical characteristics, current studies mainly focused on colorectal or colon cancers. No reports specifically focused on the signature panel for rectal cancers (RC). Our present study was aimed at developing a novel prognostic signature panel for RC. Methods. Sequencing (or microarray) data and clinicopathological details of patients with RC were retrieved from The Cancer Genome Atlas (TCGA-READ) or the Gene Expression Omnibus (GSE123390, GSE56699) database. A weighted gene coexpression network was used to identify RC-related modules. The least absolute shrinkage and selection operator analysis was performed to screen the prognostic signature panel. The prognostic performance of the risk score was evaluated by survival curve analyses. Functions of prognostic genes were predicted based on the interaction proteins and the correlation with tumor-infiltrating immune cells. The Human Protein Atlas (HPA) tool was utilized to validate the protein expression levels. Results. A total of 247 differentially expressed genes (DEGs) were commonly identified using TCGA and GSE123390 datasets. Brown and yellow modules (including 77 DEGs) were identified to be preserved for RC. Five DEGs (ASB2, GPR15, PRPH, RNASE7, and TCL1A) in these two modules constituted the optimal prognosis signature panel. Kaplan-Meier curve analysis showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. Receiver operating characteristic (ROC) curve analysis demonstrated that this risk score had high predictive accuracy for unfavorable prognosis, with the area under the ROC curve of 0.915 and 0.827 for TCGA and GSE56699 datasets, respectively. This five-mRNA classifier was an independent prognostic factor. Its predictive accuracy was also higher than all clinical factor models. A prognostic nomogram was developed by integrating the risk score and clinical factors, which showed the highest prognostic power. ASB2, PRPH, and GPR15/TCL1A were predicted to function by interacting with CASQ2/PDK4/EPHA67, PTN, and CXCL12, respectively. TCL1A and GPR15 influenced the infiltration levels of B cells and dendritic cells, while the expression of PRPH was positively associated with the abundance of macrophages. HPA analysis supported the downregulation of PRPH, RNASE7, CASQ2, EPHA6, and PDK4 in RC compared with normal controls. Conclusion. Our immune-related signature panel may be a promising prognostic indicator for RC.

scholarly journals Evaluation of the impact of various definitions of rurality on the prediction of prostate cancer progression

2017 ◽  
Vol 1 ◽  
pp. 1-16
Author(s):  
Nancy Lightfoot ◽  
Bruce Oddson ◽  
Colin Berriault ◽  
Robert Lafrenie ◽  
Jacques Abourbih ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Progression ◽  
Cox Regression ◽  
External Beam Radiation ◽  
Rank Test ◽  
Prostate Cancer Progression ◽  
Beam Radiation ◽  
Cox Regression Analysis ◽  
Log Rank Test ◽  
The Impact

This study evaluates whether three definitions of rural and urban residence predict prostate cancer progression. People were classified as urban or rural using three definitions:  rural and small town (RST), Beale's rural-urban continuum codes, and the Rurality Index of Ontario (RIO) 2008 score. This was a chart-based cohort study of males with prostate cancer who underwent external beam radiation therapy (EBRT) in the Regional Cancer Program at Health Sciences North in Sudbury, Ontario from 1996 to 2003. Data indicative of each of the three definitions were used as predictors in Cox regression analysis for the period of 1,000 to 3,000 days after initial diagnosis and as the basis for dichotomous strata in a log rank test. Complete data were acquired from 629 charts. There was no significant association between any of the three definitions of rurality and prostate cancer progression. However, a Beale-based dichotomization led to survival differences using the log rank test. Beale stratification was potentially sensitive to relevant differences in populations that were not represented by the other two definitions. Given the moderate correlations between the different rurality scores, there may be merit to considering multiple rurality scores as they may lead to different cancer progression outcomes in some situations.

Acute heart failure: predicting early in-hospital outcomes

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
M Santos ◽  
S Paula ◽  
I Almeida ◽  
H Santos ◽  
H Miranda ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Acute Heart Failure ◽  
Roc Curve ◽  
Real Life ◽  
Study Data ◽  
Curve Analysis ◽  
Older Age ◽  
Invasive Ventilation ◽  
Roc Curve Analysis

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Patients (P) with acute heart failure (AHF) are a heterogeneous population. Risk stratification at admission may help predict in-hospital complications and needs. The Get With The Guidelines Heart Failure score (GWTG-HF) predicts in-hospital mortality (M) of P admitted with AHF. ACTION ICU score is validated to estimate the risk of complications requiring ICU care in non-ST elevation acute coronary syndromes. Objective To validate ACTION-ICU score in AHF and to compare ACTION-ICU to GWTG-HF as predictors of in-hospital M (IHM), early M [1-month mortality (1mM)] and 1-month readmission (1mRA), using real-life data. Methods Based on a single-center retrospective study, data collected from P admitted in the Cardiology department with AHF between 2010 and 2017. P without data on previous cardiovascular history or uncompleted clinical data were excluded. Statistical analysis used chi-square, non-parametric tests, logistic regression analysis and ROC curve analysis. Results Among the 300 P admitted with AHF included, mean age was 67.4 ± 12.6 years old and 72.7% were male. Systolic blood pressure (SBP) was 131.2 ± 37.0mmHg, glomerular filtration rate (GFR) was 57.1 ± 23.5ml/min. 35.3% were admitted in Killip-Kimball class (KKC) 4. ACTION-ICU score was 10.4 ± 2.3 and GWTG-HF was 41.7 ± 9.6. Inotropes’ usage was necessary in 32.7% of the P, 11.3% of the P needed non-invasive ventilation (NIV), 8% needed invasive ventilation (IV). IHM rate was 5% and 1mM was 8%. 6.3% of the P were readmitted 1 month after discharge. Older age (p &lt; 0.001), lower SBP (p = 0,035) and need of inotropes (p &lt; 0.001) were predictors of IHM in our population. As expected, patients presenting in KKC 4 had higher IHM (OR 8.13, p &lt; 0.001). Older age (OR 1.06, p = 0.002, CI 1.02-1.10), lower SBP (OR 1.01, p = 0.05, CI 1.00-1.02) and lower left ventricle ejection fraction (LVEF) (OR 1.06, p &lt; 0.001, CI 1.03-1.09) were predictors of need of NIV. None of the variables were predictive of IV. LVEF (OR 0.924, p &lt; 0.001, CI 0.899-0.949), lower SBP (OR 0.80, p &lt; 0.001, CI 0.971-0.988), higher urea (OR 1.01, p &lt; 0.001, CI 1.005-1.018) and lower sodium (OR 0.92, p = 0.002, CI 0.873-0.971) were predictors of inotropes’ usage. Logistic regression showed that GWTG-HF predicted IHM (OR 1.12, p &lt; 0.001, CI 1.05-1.19), 1mM (OR 1.10, p = 1.10, CI 1.04-1.16) and inotropes’s usage (OR 1.06, p &lt; 0.001, CI 1.03-1.10), however it was not predictive of 1mRA, need of IV or NIV. Similarly, ACTION-ICU predicted IHM (OR 1.51, p = 0.02, CI 1.158-1.977), 1mM (OR 1.45, p = 0.002, CI 1.15-1.81) and inotropes’ usage (OR 1.22, p = 0.002, CI 1.08-1.39), but not 1mRA, the need of IV or NIV. ROC curve analysis revealed that GWTG-HF score performed better than ACTION-ICU regarding IHM (AUC 0.774, CI 0.46-0-90 vs AUC 0.731, CI 0.59-0.88) and 1mM (AUC 0.727, CI 0.60-0.85 vs AUC 0.707, CI 0.58-0.84). Conclusion In our population, both scores were able to predict IHM, 1mM and inotropes’s usage.

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