Acute heart failure: is ACTION-ICU useful?

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

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Patients (pts) with acute heart failure (AHF) are a heterogeneous population. Risk stratification at admission may help predict in-hospital complications and needs. 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 as predictor of in-hospital M (IHM), post discharge early M [1-month mortality (1mM)] and 1-month readmission (1mRA), in our center population, using real-life data. Methods Based on a single-center retrospective study, data collected from pts admitted in the Cardiology department with AHF between 2010 and 2017. Pts without data on previous cardiovascular history or uncompleted clinical data were excluded. Statistical analysis used non-parametric tests, logistic regression analysis and ROC curve analysis. Results We included 300 pts admitted with AHF. Mean age was 67.4 ± 12.6 years old and 72.7% were male. 37.7% had previous history of revascularization procedures, 66.9% had hypertension, 41% were diabetic and 38% had dyslipidaemia. Mean heart rate was 95.5 ± 27.5bpm, mean systolic blood pressure (SBP) was 131.2 ± 37.0mmHg, mean urea level at admission was 68.8 ± 40.7mg/dL, mean sodium was 137.6 ± 4.7mmol/L, mean glomerular filtration rate (GFR) was 57.1 ± 23.5ml/min. 35.3% were admitted in Killip-Kimball class (KKC) 4. Mean ACTION-ICU score was 10.4 ± 2.3. Inotropes’ usage was necessary in 32.7% of the pts, 11.3% of the pts needed non-invasive ventilation (NIV), 8% needed invasive ventilation (IV). IHM rate was 5% and 1mM was 8%. 6.3% of the pts were readmitted 1 month after discharge. Older age (p < 0.001), lower SBP (p = 0,035), presenting in KKC 4 (p < 0.001, OR 8.13) and need of inotropes (p < 0.001) were predictors of IHM in our population. 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 < 0.001, CI 1.03-1.09) were predictors of need of NIV. None of the studied variables were predictive of need of IV. LVEF (OR 0.924, p < 0.001, CI 0.899-0.949), lower SBP (OR 0.80, p < 0.001, CI 0.971-0.988), higher urea (OR 1.01, p < 0.001, CI 1.005-1.018) and lower sodium (OR 0.92, p = 0.002, CI 0.873-0.971) were predictors inotropes’ usage. ACTION-ICU was able to predict 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 ACTION-ICU performs well when predicting IHM (Area under curve (AUC) 0.729, confidence interval (CI) 0.59-0.87), inotropes’ usage (AUC 0.619, CI 0.54-0.70) and 1mM (AUC 0.705, CI 0.58-0.84). Conclusion In our population, ACTION-ICU score was able to predict IHM, 1mM and inotropes’s usage.

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 < 0.001), lower SBP (p = 0,035) and need of inotropes (p < 0.001) were predictors of IHM in our population. As expected, patients presenting in KKC 4 had higher IHM (OR 8.13, p < 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 < 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 < 0.001, CI 0.899-0.949), lower SBP (OR 0.80, p < 0.001, CI 0.971-0.988), higher urea (OR 1.01, p < 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 < 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 < 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.

scholarly journals GRACE score vs GWTG-HF score: risk stratification in acute heart failure

2021 ◽  
Vol 10 (Supplement_1) ◽  
Author(s):  
M Santos ◽  
S Paula ◽  
I Almeida ◽  
H Santos ◽  
H Miranda ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Risk Stratification ◽  
Acute Heart Failure ◽  
Roc Curve ◽  
Curve Analysis ◽  
Invasive Ventilation ◽  
Roc Curve Analysis ◽  
Post Discharge ◽  
Grace Score

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Patients (P) with acute heart failure (AHF) are a heterogeneous population. Therefore, early risk stratification at admission is essential. The Get With The Guidelines Heart Failure score (GWTG-HF) predicts in-hospital mortality (IHM) of patients admitted with AHF. GRACE score estimates risk of death, including IHM and long-term mortality (M), in non-ST elevation acute coronary syndromes. Objective To validate GRACE score in AHF and to compare GRACE and GWTG-HF scores as predictors of IHM, post discharge early and late M [1-month mortality (1mM) and 1-year M (1yM)], 1-month readmission (1mRA) and 1-year readmission (1yRA), in our center population, 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 35.3% were admitted in Killip-Kimball class (KKC) 4. Mean GRACE was 147.9 ± 30.2 and mean GWTG-HF was 41.7 ± 9.6. Inotropes’ usage was necessary in 32.7% of the P, 11.3% of the patients needed non-invasive ventilation, 8% needed invasive ventilation. IHM rate was 5%, 1mM was 8% and 1yM 27%. 6.3% of the patients were readmitted 1 month after discharge and 52.7% had at least one more admission in the year following discharge. Older age (p < 0.001), lower SBP (p = 0,005), higher urea (p = 0,001), lower sodium (p = 0.005), previous history of percutaneous coronary intervention (p = 0,017), lower GFR (p < 0.001) and need of inotropes (0.001) were predictors of 1yM after discharge in our population. As expected, patients presenting in KKC 4 had higher IHM (OR 8.13, p < 0.001), higher 1mM (OR 4.13, p = 0.001) and higher 1yM (OR 1.96, p = 0.011). On the other hand, KKC at admission did not predict readmission (either 1mRA or 1yRA, respectively p = 0.887 and p = 0.695). Logistic regression confirmed that GWTG-HF was a good predictor of IHM (OR 1.12, p < 0.001, CI 1.05-1.19) but also 1mM (OR 1.1, p = 0.001, CI 1.04-1.16) and 1yM (OR 1.08, p < 0.001, CI 1.04-1.11). GRACE also showed the ability to predict IHM (OR 1.06, p < 0.001, CI 1.03-1.10), 1mM (OR 1.04, p < 0.001, CI 1.02-1.06) and 1yM (OR 1.03, p < 0.001, CI 1.01-1.03). ROC curve analysis revealed that GRACE and GWTG-HF were accurate at predicting IHM (AUC 0.866 and 0.774, respectively), 1mM (AUC 0.779 and 0.727, respectively) and 1yM (AUC 0.676 and 0.672, respectively). Both scores failed at predicting 1mRA (GRACE p = 0.463; GWTG-HF p = 0.841) and 1yRA (GRACE p = 0.244; GWTG-HF p = 0.806). Conclusion This study confirms that, in our population, both scores were excellent at predicting IHM, with GRACE performing better. Although both scores were able to predict post-discharge mortality outcomes, their performance was poorer.

scholarly journals Acute heart failure: does etiology matter?

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
M Santos ◽  
H Santos ◽  
I Almeida ◽  
S Paula ◽  
H Miranda ◽  
...  
Keyword(s):  
Heart Failure ◽  
Left Ventricle ◽  
Acute Heart Failure ◽  
Previous History ◽  
Real Life ◽  
Study Data ◽  
Post Discharge ◽  
Funding Sources ◽  
Kaplan Meier ◽  
Revascularization Procedures

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Patients (pts) with acute heart failure (AHF) are a heterogeneous population. The etiology of the heart disfunction may play a role in prognosis. Risk stratification at admission may help predict in-hospital complications and needs. Objective To explore predictors of in-hospital mortality (IHM), post discharge early mortality [1-month mortality (1mM)] and late mortality [1-year mortality (1yM)] and early and late readmission, respectively 1-month readmission (1mRA) and 1-year readmission (1yRA), in our center population, using real-life data. Methods Based on a single-center retrospective study, data collected from patients (pts) admitted in the Cardiology department with AHF between 2010 and 2017. Pts without data on previous cardiovascular history or uncompleted clinical data were excluded. The pts were divided in 3 groups: ischemic etiology (IE), valvular etiology (VE) and other etiologies (OE), which included hypertensive and idiopathic cardiomyopathies). Statistical analysis used non-parametric tests and Kaplan-Meyer survival analysis. Results We included 300 pts admitted with AHF. Mean age was 67.4 ± 12.6 years old and 72.7% were male. 37.7% had previous history of revascularization procedures, 66.9% had hypertension, 41% were diabetic and 38% had dyslipidaemia. The heart failure was of IE in 45%, VE in 22.7% and of OE in 32.3% of the cases. There were no significant differences between groups regarding body mass index, Killip-Kimball class, systolic blood pressure at admission, blood tests aspects at admission (namely, creatinine, sodium or urea), inotropes’ usage or need of non-invasive or invasive ventilation. However, IE group had higher percentage of males comparing to VE e OE (83.0% vs 55.9% vs 70.1%, respectively, p < 0.001), higher rates of prior revascularization procedures (68.9%, vs 19.1%, vs 7.2%, p < 0.001) and higher rates of traditional cardiovascular risk factors, namely hypertension (74.1% vs 55.9% vs 57.7%, p = 0.014), diabetes mellitus (48.1% vs 27.9% vs 27.8%, p = 0.002) and dyslipidaemia (48.9% vs 30.9% vs 40.2%, p = 0.022). OE group was younger compared to IE and VE (63.9 ± 13.5 vs 68.9 ± 11.1 vs 69.5 ± 13.0 years old, respectively, p = 0.003). VE group had less left ventricle disfunction comparing to IE and VE groups (left ventricle ejection fraction 40.8 ± 14.1 vs 32.2 ± 9.8 vs 31.6 ± 12.8%, respectively, p < 0.001). The groups showed no significant differences regarding IHM (IE 5.2% vs VE 8.8% vs OE 2.1%, p = 0.146), 1mRA (IE 8.1&, VE 7.4%, OE 3.1%, p = 0.276) or 1yRA (IE 55.6%, VE 54.4%, OE 47.4%, p = 0.449). However, VE group had higher rates of 1mM (VE 13.2% vs IE 8.9% vs OE 3.1%, p = 0.05) and 1yM compared to IE and OE (33.8% vs 30.4% vs 17.5%, respectively, p = 0.34). These aspects are represented in Kaplan Meier survival curves. Conclusion In our population, the etiology of heart failure was predictor of early and late post-discharge mortality but not readmission.

scholarly journals Creatinine Levels at Admission as Predictors of In-Hospital Major Adverse Cardiovascular Events in Acute Heart Failure Patient

2020 ◽  
Vol 6 (1) ◽  
pp. 16-22
Author(s):  
Farida Hanum Margolang ◽  
Refli Hasan ◽  
Abdul Halim Raynaldo ◽  
Harris Hasan ◽  
Ali Nafiah ◽  
...  
Keyword(s):  
Heart Failure ◽  
Acute Heart Failure ◽  
Roc Curve ◽  
Cardiovascular Events ◽  
Major Adverse Cardiovascular Events ◽  
High Morbidity ◽  
Roc Curve Analysis ◽  
Heart Failure Patients ◽  
Long Term Prognosis

Background: Acute heart failure is a global health problem with high morbidity and mortality. Short term and long term prognosis of these patients is poor. Therefore, early identification of patients at high risk for major adverse cardiovascular events (MACEs) during hospitalization was needed to improve outcome. Creatinine levels at admission could be used as predictors of major adverse cardiovascular events in acute heart failure patients because creatinine is a simple and routine biomarker of renal function examined in patients with acute heart failure. This study aimed to determine whether creatinine can be used as a predictor of major adverse adverse cardiovascular events in patients with acute heart failure.Methods: This study is a prospective cohort study of 108 acute heart failure patients treated at H. Adam Malik Hospital from July 2018 to January 2019. Creatinine cut-off points were determined using the ROC curve, then bivariate and multivariate analyzes were performed to determine predictors of major adverse cardiovascular events during hospitalization.Results: From 108 study subjects, 24 (22.2%) subjects experienced major adverse cardiovascular events during hospitalization. The subjects who died were 20 people (83.4%), subjects with arrhythmia were 2 people (8.3%), and those who had stroke were 2 people (8.3 %). Through the ROC curve analysis, we found creatinine cut-off values of ≥1.7 mg / dl (AUC 0.899, 95% CI 0.840- 0.957, p <0.05). Creatinine ≥1.7 mg/dl could predict major adverse cardiovascular events with a sensitivity of 87.5% and specificity of 79.5%. Multivariate analysis showed that creatinine ≥1.7 mg / dl was an independent factor to predict MACEs during hospitalization in this study (OR 18,310, p 0.001) as well as creatinine clearance and heart rate.Conclusion: Creatinine levels at admission is an independent predictor for major adverse cardiovascular events during hospitalization in acute heart failure patients.

Abstract 12455: Evaluation of N-Terminal Pro-B-type Natriuretic Peptide as a Therapeutic Response Biomarker in Group I Pulmonary Arterial Hypertension

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Aaron M Wolfson ◽  
Micheal L Maitland ◽  
Vasiliki Thomeas ◽  
Cherylanne Glassner ◽  
Mardi Gomberg-Maitland
Keyword(s):  
Heart Failure ◽  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
Roc Curve ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Goal Directed Therapy ◽  
Group I ◽  
Target Values ◽  
Pulmonary Arterial

Purpose: Goal directed management of left heart failure with an NT-proBNP target-based approach has some evidence of providing a survival benefit. To evaluate the potential utility of serial NT-proBNP measurements for goal-directed therapy in right heart failure we retrospectively assessed NT-proBNP as a predictor for survival in Group I pulmonary arterial hypertension (PAH) patients. Methods: We identified 103 Group I PAH patients from a pulmonary hypertension registry who had baseline elevated NT-proBNP prior to either the initiation or escalation of therapy and at least two serial NT-proBNP measurements. In a two-step process, we (1) estimated baseline NT-proBNP and slope (rate of change of NT-proBNP) with a linear mixed-effects model using all patient data and then (2) compared the power of serial versus single measurements in predicting survival with measured and model-derived values of baseline NT-proBNP with a Receiver Operative Characteristic (ROC) curve analysis . Survival was determined using the Kaplan-Meier methodology. Results: ROC curve analysis revealed significantly higher AUC for model-derived NT-proBNP values compared to the measured values (AUC: for baseline 0.74 vs 0.66, p= 0.009; for slope 0.78 vs 0.66, p= 0.02). Optimal cutpoints for prediction of survival on baseline NT-proBNP were 2012 (measured) vs. 1810 (model-derived) pg/mL. The optimal cutpoint for model-derived change in NT-proBNP was -0.004 log10pg/mL/month. Sensitivity, specificity, and negative predictive values for the three predictor variables were: 64%, 67%, 80% (measured baseline NT-proBNP), 61%, 80%, 81% (model-derived baseline NT-proBNP) and 73%, 57%, 85% (model-derived slope). Conclusions: In PAH patients, serial NT-proBNP measurements better predict survival than single measurements. This retrospective finding reveals that changes in NT-proBNP are associated with overall survival in PAH patients, and set initial target values for a pilot prospective study of NT-proBNP goal-directed therapy.

Abstract 12872: Machine Learning on Dual-isotope Myocardial Semiconductor SPECT Can Predict Cardiac Prognosis in Patients With Congestive Heart Failure

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Masato Shimizu ◽  
shummo cho ◽  
Yoshiki Misu ◽  
Mari Ohmori ◽  
Ryo Tateishi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Congestive Heart Failure ◽  
Random Forest ◽  
Roc Curve ◽  
Cardiac Death ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Dual Isotope ◽  
Machine Learning Model

Introduction: Dual-isotope (201TlCl and 123I-β-methyl-P-iodophenyl-pentadecanoic acid (BMIPP) ) single photon emission computed tomography (SPECT) is utilized to estimate not only in patients with ischemic heart disease but with congestive heart failure (CHF). We tried to construct predictive model for cardiac prognosis on the SPECT for cardiac death by machine learning. Hypothesis: Machine learning is a powerful tool to predict cardiac prognosis in patients with CHF Methods: Consecutive 310 patients who admitted with CHF (77.1±3.1 years, 164 males) were enrolled. After initial treatment, they underwent electrocardiography gated SPECT and observed in median 507 days [IQR: 165, 1032]. Multivariate Cox regression analysis for cardiac death was performed, and predictive model was constructed by ROC curve analysis and machine learning (Random Forest and Deep Learning). The accuracies (= [True positive + True negative] / Total) of the prediction models were compared with ROC curve model. Results: Thirty-six patients fell into cardiac death. Cox analysis showed Age, left ventricular ejection fraction (LVEF), summed rest score (SRS) of BMIPP, and mismatch score were significant predictors (Hazard ratio: 1.068, 0.970, 1.032, 1.092, P value: <0.001, 0.014, 0.002, <0.001, respectively). ROC curve analysis of them revealed the accuracy of the cut-off value was 0.479-0.773. Conversely, machine learning model demonstrated higher accuracy for cardiac death (Random Forest: 0.895, Deep Learning: 0.935). The top 4 feature importance of the random forest were LVEF (0.299), SRS BMIPP (0.263), Age (0.262), and mismatch score (0.160). Conclusions: Machine learning model on SPECT had powerful predictive value for predicting cardiac death in patients with CHF.

scholarly journals Prognostic value of baseline versus 6-month follow infarct size in patients with reperfused STEMI

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
F Valente ◽  
J Gavara ◽  
M Calvo ◽  
P Rello ◽  
M Maymi ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ventricular Arrhythmia ◽  
Infarct Size ◽  
Roc Curve ◽  
Prognostic Value ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Prognostic Information ◽  
Kaplan Meier

Abstract Background Acute infarct size is a predictor of clinical outcomes in acute ST segment elevation myocardial infarction (STEMI) patients, although its prognostic value has differed between studies. In acute STEMI, infarct size is often overestimated due to the presence of extensive myocardial oedema, a confounder that is no longer present at a 6-month follow-up study. It was our purpose to assess whether infarct size in the acute phase or at 6-months follow-up provided superior prognostic information in STEMI patients. Methods STEMI patients who underwent successful primary percutaneous revascularization were included and a cardiac magnetic resonance (CMR) was performed between 5–7 days after STEMI and at 6 months to study infarct size (as a % of myocardial mass). The primary endpoint was a composite of cardiovascular mortality, hospitalization for heart failure and ventricular arrhythmia. Results A total of 796 patients were included (mean age 58.3±11.5 years, 82.4% male, 52.3% anterior infarction). During a mean follow-up of 59 months, 59 patients (7.4%) presented with the primary end-point (cardiovascular death n=7, hospitalization for heart failure n=52, ventricular arrhythmia n=1). ROC curve analysis (figure 1) showed a non-significant difference between baseline and 6-month infarct size for the prediction of the primary endpoint (baseline AUC 0.685 95% CI 0.610–0.760, 6-month AUC 0.713 95% CI 0.643–0.782, p=0.60). Optimal cut-off values for baseline and 6-months follow-up infarct size for prediction of outcomes, respectively 22% and 17.5%, were used for Kaplan-Meier curve analysis (figure 2). Conclusion Infarct size estimated during the first week after STEMI and at 6-months follow-up showed similar predictive value and with similar cut-off values. Therefore, the prognostic information provided by infarct size can be obtained during initial STEMI admission and does not require a waiting period for infarct size stabilization. FUNDunding Acknowledgement Type of funding sources: None. ROC curve analysis Kaplan-Meier analysis

scholarly journals Downregulation of lncRNA-PVT1 participates in the development of progressive chronic kidney disease among patients with congestive heart failure

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Yingwei Chang ◽  
Chunmei Liu ◽  
Jing Wang ◽  
Jing Feng ◽  
Yulan Chen ◽  
...  
Keyword(s):  
Heart Failure ◽  
Chronic Kidney Disease ◽  
Congestive Heart Failure ◽  
Kidney Disease ◽  
Roc Curve ◽  
Kidney Injury ◽  
Diagnostic Value ◽  
Curve Analysis ◽  
Roc Curve Analysis

Abstract Background Congestive heart failure (CHF) is a major cause of the development of progressive chronic kidney disease (CKD), while the mechanism is still unknown. LncRNA PVT1 contributes to kidney injury. This study aimed to explore the role of PVT1 in the development of CKD in CHF patients. Methods Expression of PVT1 in plasma samples of CHF patients with and without CKD was determined by RT-qPCR. The diagnostic value of plasma PVT1 for CKD was evaluated by ROC curve analysis. The predictive value of PVT1 for the development of CKD in CHF patients was analyzed by a 2-year follow-up study. Changes in PVT1 expression in CKD patients during treatment were analyzed by RT-qPCR and reflected by heatmaps. Results Plasma PVT1 was downregulated in CHF and further downregulated in CHF patients complicated with progressive CKD. ROC curve analysis showed that plasma PVT1 levels could be used to distinguish CHF patients complicated with CKD from CHF patients without CKD and healthy controls. During a 2-year follow-up, patients with high CHF levels had a low incidence of progressive CKD among CHF patients. Moreover, with the treatment of progressive CKD, plasma PVT1 was upregulated. Conclusions LncRNA-PVT1 downregulation may participate in the development of progressive CKD among patients with CHF.

scholarly journals GWTG-HF score and acute coronary syndrome

2021 ◽  
Vol 10 (Supplement_1) ◽  
Author(s):  
M Santos ◽  
H Santos ◽  
I Almeida ◽  
H Miranda ◽  
S Paula ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Logistic Regression ◽  
Roc Curve ◽  
Curve Analysis ◽  
St Elevation Myocardial Infarction ◽  
Lv Function ◽  
Roc Curve Analysis ◽  
St Elevation ◽  
Excellent Accuracy

Abstract Funding Acknowledgements Type of funding sources: None. OnBehalf on behalf of the Investigators of " Portuguese Registry of ACS " Introduction Regarding prognosis, acute coronary syndromes (ACS) are heterogeneous. Post-hospitalization (PH) risk stratification is crucial. The Get With The Guidelines Heart Failure score (GWTG-HFS) predicts in-hospital mortality (M) of patients (P) admitted with acute heart failure. Objective To validate GWTG-HFS as predictor of PH early and late M and readmission (RA) rates, in our center population, using real-life data. Methods Based on a single-center retrospective study, data collected from admissions between 1/01/20168 and 11/12/2019. Patients who survived the ACS and were discharged from the hospital were included. Concerning prognosis, we assessed 1-month M and RA (1mM and 1mRA), 6-month M and RA (6mM and 6mRA), 1-year M and RA (1yM and 1yRA). Statistical analysis used non-parametric tests, logistic regression and ROC curve analysis. Results 268 patients with ACS, mean age was 66.4 ± 12.5 years old and 59.7% were male. The diagnosis was unstable angina in 2.6%, non-ST elevation myocardial infarction (NSTEMI) in 66.4% and ST elevation myocardial infarction (STEMI) in 31%. 41.8% of the P were or had been smokers, 68.5% had hypertension, 34.5% were diabetic and 50.9% had dyslipidaemia. Concerning coronary artery disease, 250 were submitted to coronary angiography – 18.8% had no lesions or non-significant lesions (stenosis &lt;50%), 34.8% had one significant lesion, 23.2% had 2 significant lesions and 23.2% had 3 or more. Regarding left ventricle (LV) function, 70.5% of the P had no LV dysfunction, 15.7% had mild LV impairment (LVI), 9.3% moderate LVI and 4.5% had severe LVI. 1mM rate was 1.9% and 1yM rate was 7.8%. Age (p = 0.034), diabetes (p = 0.031), KKC (p &lt; 0.001), BUN (p = 0.003) and LV function (p &lt; 0.001) were predictors of 1mM. Age (p &lt; 0.001), HR (p = 0.009), KKC (p = 0.032), BUN (p &lt; 0.001), sodium (p &lt; 0.001), creatinine (p &lt; 0.001), Hb (p &lt; 0.001), LV function (p &lt; 0.001), de novo AF (p &lt; 0.001) and number of arteries with significant disease (p = 0.044) were predictors of 1yM. Logistic regression and ROC curve analysis showed that GWTG-HFS was able to predict 1mM (Odds ratio (OR) 1.18, p = 0.005, confidence interval (CI) 1.05-1.33; area under curve (AUC) 0.872) and 1yM (OR 1.16, p = 0.001, CI 1.09-1.24, AUC 0.838) with excellent accuracy, and 1mRA (OR 1.10, p = 0.006, CI 1.03-1.18, AUC 0.677) and 1yRA (OR 1.04, p = 0.024, CI 1.01-1.08, AUC 0.580) with poor accuracy. A sub-analysis regarding NSTEMI P showed that GWTG-HFS was able to predict 1mM (OR 1.20, p = 0.010, CI 1.05-1.39, AUC 0.902) and 1yM (OR 1.15, p &lt; 0.001, CI 1.07-1.23, AUC 0.817) with excellent accuracy. On the other hand, sub-analysis regarding STEMI showed that GWTG-HFS was not able to predict 1mM (p = 0.495) but was accurate at predicting 1yM (OR 1.18, p = 0.048, CI 1.00-1.39, AUC 0.881). Conclusion This study confirms that, in our population, GWTG-HFS is a valuable tool in PH risk score stratification in ACS, particularly NSTEMI.

scholarly journals Prediction for cardiac prognosis in patients with congestive heart failure by machine learning on dual-isotope myocardial semiconductor SPECT

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Shimizu ◽  
S Cho ◽  
K Hara ◽  
M Ohmori ◽  
R Tateishi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Random Forest ◽  
Roc Curve ◽  
Cardiac Death ◽  
Learning Model ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Dual Isotope ◽  
Machine Learning Model

Abstract Background Dual-isotope (low doze 201TlCl and 123I-β-methyl-P-iodophenyl-pentadecanoic acid (BMIPP)) single photon emission computed tomography (SPECT) is utilized to estimate myocardial damage in patients with congestive heart failure (CHF). However, predictive model construction on the SPECT for cardiac death by machine learning was not studied. Purpose To elucidate predictive value of machine learning model on dual-isotope SPECT for CHF. Methods We enrolled consecutive 310 patients who admitted with CHF (77.1±3.1 years, 164 males). After initial treatment, they underwent electrocardiography gated SPECT and observed in median 507 days [IQR: 165, 1032]. Multivariate Cox regression analysis for cardiac death was performed, and predictive model was constructed by ROC curve analysis and machine learning (Random Forest and Deep Learning). The accuracies (= [True positive + True negative] / Total) of the prediction models were compared with ROC curve model. Results Thirty-six patients fell into cardiac death. Cox analysis showed Age, left ventricular ejection fraction (LVEF), summed rest score (SRS) of BMIPP, and mismatch score were significant predictors (Hazard ratio: 1.068, 0.970, 1.032, 1.092, P value: &lt;0.001, 0.014, 0.002, &lt;0.001, respectively). ROC curve analysis of them revealed the accuracy of the cut-off value was 0.479–0.773. Conversely, machine learning model demonstrated higher accuracy for cardiac death (Random Forest: 0.895, Deep Learning: 0.935). The top 4 feature importance of the random forest were LVEF (0.299), SRS BMIPP (0.263), Age (0.262), and mismatch score (0.160). Conclusion Machine learning model on SPECT was superior to conventional statistic model for predicting cardiac death in patients with CHF. Funding Acknowledgement Type of funding source: None

Sign in / Sign up

Export Citation Format

Share Document