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.

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.

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 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 <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 < 0.001), BUN (p = 0.003) and LV function (p < 0.001) were predictors of 1mM. Age (p < 0.001), HR (p = 0.009), KKC (p = 0.032), BUN (p < 0.001), sodium (p < 0.001), creatinine (p < 0.001), Hb (p < 0.001), LV function (p < 0.001), de novo AF (p < 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 < 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 Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaohua Ban ◽  
Xinping Shen ◽  
Huijun Hu ◽  
Rong Zhang ◽  
Chuanmiao Xie ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Roc Curve ◽  
Multinomial Logistic Regression ◽  
Univariate Analysis ◽  
Ct Imaging ◽  
Squamous Cell Carcinomas ◽  
Curve Analysis ◽  
Imaging Features ◽  
Roc Curve Analysis ◽  
Ct Features

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

scholarly journals Right choice of a method for determination of cut-off values: A statistical tool for a diagnostic test

2014 ◽  
Vol 5 (3) ◽  
pp. 30-34 ◽  
Author(s):  
Balkishan Sharma ◽  
Ravikant Jain
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Diagnostic Test ◽  
Roc Curve ◽  
Traditional Method ◽  
Statistical Technique ◽  
Curve Analysis ◽  
Operating Characteristics ◽  
Roc Curve Analysis

Objective: The clinical diagnostic tests are generally used to identify the presence of a disease. The cutoff value of a diagnostic test should be chosen to maximize the advantage that accrues from testing a population of human and others. When a diagnostic test is to be used in a clinical condition, there may be an opportunity to improve the test by changing the cutoff value. To enhance the accuracy of diagnosis is to develop new tests by using a proper statistical technique with optimum sensitivity and specificity. Method: Mean±2SD method, Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been discussed with their respective applications. Results: The study highlighted some important methods to determine the cutoff points for a diagnostic test. The traditional method is to identify the cut-off values is Mean±2SD method. Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been proved to be beneficial statistical tools for determination of cut-off points.Conclusion: There may be an opportunity to improve the test by changing the cut-off value with the help of a correctly identified statistical technique in a clinical condition when a diagnostic test is to be used. The traditional method is to identify the cut-off values is Mean ± 2SD method. It was evidenced in certain conditions that logistic regression is found to be a good predictor and the validity of the same can be confirmed by identifying the area under the ROC curve. Abbreviations: ROC-Receiver operating characteristics and DA-Discriminant Analysis. Asian Journal of Medical Science, Volume-5(3) 2014: 30-34 http://dx.doi.org/10.3126/ajms.v5i3.9296      

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.

scholarly journals Title: Preoperative albumin-to-fibrinogen ratio predicts postoperative pulmonary complications in patients after minimally invasive esophagectomy

2021 ◽  
Author(s):  
Zhang Peng ◽  
Zhao Song
Keyword(s):  
Risk Factors ◽  
Esophageal Cancer ◽  
Logistic Regression ◽  
Roc Curve ◽  
Training Group ◽  
Pulmonary Complications ◽  
Curve Analysis ◽  
Postoperative Pulmonary Complications ◽  
Multivariate Logistic Regression ◽  
Roc Curve Analysis

Abstract Background Postoperative pulmonary complications (PPCs) are the most common postoperative complications in patients with esophageal cancer. Prediction of PPCs by establishing a preoperative physiological function parameter model can help patients make adequate preoperative preparation, reduce treatment costs, and improve prognosis and quality of life. The purpose of this study was to investigate the relationship between albumin-to-fibrinogen ratio (AFR), prognostic nutritional index (PNI), albumin-to-globulin ratio (AGR), neutrophils-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), and monocyte-to -lymphocyte ratio (MLR) and other preoperative laboratory tests and PPCs in patients after esophagectomy. Methods Retrospective analysis was performed on total 712 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from July 2018 to December 2020. Patients were divided into training (535 patients) and validation (177) groups for comparison of baseline data, perioperative indicators, and laboratory examination data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the efficacy, sensitivity and specificity of AFR, and Youden’s index was used to calculate the cut-off values of AFR. Univariate and multivariate logistic regression analyses were used to assess the risk factors for PPCs in training group. Results 112 (20.9%) in training group and 36 (20.3%) in validation group developed PPCs. The AUC value predicted by AFR using ROC curve analysis was 0.817, sensitivity 76.2% and specificity 78.7% in training group while AUC 0.803, sensitivity 69.4% and specificity 85.8%. Multivariate logistic regression analysis showed that smoking index, American Society of Anesthesiologists (ASA), AFR, and recurrent laryngeal nerve palsy were independent risk factors for PPCs. Conclusion Preoperative AFR can effectively predict the occurrence of PPCs in patients with esophageal cancer

scholarly journals The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhichuang Lian ◽  
Yafang Li ◽  
Wenyi Wang ◽  
Wei Ding ◽  
Zongxin Niu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Underlying Diseases

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

scholarly journals Association Between D-Dimer Level and In-Hospital Death of Pulmonary Embolism Patients

Dose-Response ◽  
2020 ◽  
Vol 18 (4) ◽  
pp. 155932582096843
Author(s):  
Zi-Kai Song ◽  
Haidi Wu ◽  
Xiaoyan Xu ◽  
Hongyan Cao ◽  
Qi Wei ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Hospital Death ◽  
Roc Curve Analysis ◽  
Survival Group ◽  
Death Group ◽  
D Dimer

To investigate whether D-dimer level could predict pulmonary embolism (PE) severity and in-hospital death, a total of 272 patients with PE were divided into a survival group (n = 249) and a death group (n = 23). Comparisons of patient characteristics between the 2 groups were performed using Mann-Whitney U test. Significant variables in univariate analysis were entered into multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed to determine the predictive value of D-dimer level alone or together with the simplified Pulmonary Embolism Severity Index (sPESI) for in-hospital death. Results showed that patients in the death group were significantly more likely to have hypotension (P = 0.008), tachycardia (P = 0.000), elevated D-dimer level (P = 0.003), and a higher sPESI (P = 0.002) than those in the survival group. Multivariable logistic regression analysis showed that D-dimer level was an independent predictor of in-hospital death (OR = 1.07; 95% CI, 1.003-1.143; P = 0.041). ROC curve analysis showed that when D-dimer level was 3.175 ng/ml, predicted death sensitivity and specificity were 0.913 and 0.357, respectively; and when combined with sPESI, specificity (0.838) and area under the curve (0.740) were increased. Thus, D-dimer level is associated with in-hospital death due to PE; and the combination with sPESI can improve the prediction level.

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