Feasibility of Predicting the Risk of Atrial Fibrillation after Coronary Artery Bypass Surgery with Logistic Regression Model

2002 ◽  
Vol 91 (4) ◽  
pp. 339-344 ◽  
Author(s):  
T. Hakala ◽  
O. Pitkänen ◽  
M. Hippeläinen
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Coronary Artery ◽  
Regression Model ◽  
Hospital Stay ◽  
Predictive Model ◽  
Logistic Regression Model ◽  
Artery Bypass ◽  
Gastrointestinal Complications

Background and Aims: The aim of this study was to determine the risk factors of postoperative atrial fibrillation (AF) after coronary artery bypass grafting and to create predictive model and to evaluate the effects of AF on patients outcome. Material and Methods: Data of 3,676 consecutive patients were analysed to identify the predictors of AF. Multivariate logistic regression model was validated prospectively in 1,107 patients. Results: Increasing age (p < 0.001), preoperative use of digoxin (p = 003), need of intra-aortic balloon pump or inotropic medication in the weaning off cardiopulmonary by pass or during the first 24 hours postoperatively (p = 0.013), increasing body surface area (p = 0.006) and lower ejection fraction (p = 0.048) were independent risk factors for postoperative AF. The predictive model gave area under the receiver-operating characteristic (ROC) curve 0.682, 95 % confidence interval 0.663–0.701, and p < 0.001. The patients with AF incidence had more postoperative stroke (p = 0.008), confusion (p < 0.001) severe gastrointestinal complications (p = 0.005), readmission to ICU (p < 0.001), longer ICU (p < 0.001) and hospital stay (p < 0.001) when compared with the patients who remained in sinus rhythm. Conclusion: Logistic regression model with the parameters used was not accurate enough for clinical purposes. Postoperative AF is associated with postoperative stroke, severe gastrointestinal complications, readmission to ICU, and longer ICU and hospital stay.

scholarly journals A comprehensive, contemporary assessment of the association between hepatosteatosis and coronary artery calcium scoring

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
T Heseltine ◽  
SW Murray ◽  
RL Jones ◽  
M Fisher ◽  
B Ruzsics
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Coronary Artery ◽  
Regression Model ◽  
Coronary Artery Calcium ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Multinomial Logistic Regression ◽  
Cvd Risk ◽  
Male Sex

Abstract Funding Acknowledgements Type of funding sources: None. onbehalf Liverpool Multiparametric Imaging Collaboration Background Coronary artery calcium (CAC) score is a well-established technique for stratifying an individual’s cardiovascular disease (CVD) risk. Several well-established registries have incorporated CAC scoring into CVD risk prediction models to enhance accuracy. Hepatosteatosis (HS) has been shown to be an independent predictor of CVD events and can be measured on non-contrast computed tomography (CT). We sought to undertake a contemporary, comprehensive assessment of the influence of HS on CAC score alongside traditional CVD risk factors. In patients with HS it may be beneficial to offer routine CAC screening to evaluate CVD risk to enhance opportunities for earlier primary prevention strategies. Methods We performed a retrospective, observational analysis at a high-volume cardiac CT centre analysing consecutive CT coronary angiography (CTCA) studies. All patients referred for investigation of chest pain over a 28-month period (June 2014 to November 2016) were included. Patients with established CVD were excluded. The cardiac findings were reported by a cardiologist and retrospectively analysed by two independent radiologists for the presence of HS. Those with CAC of zero and those with CAC greater than zero were compared for demographic and cardiac risks. A multivariate analysis comparing the risk factors was performed to adjust for the presence of established risk factors. A binomial logistic regression model was developed to assess the association between the presence of HS and increasing strata of CAC. Results In total there were 1499 patients referred for CTCA without prior evidence of CVD. The assessment of HS was completed in 1195 (79.7%) and CAC score was performed in 1103 (92.3%). There were 466 with CVD and 637 without CVD. The prevalence of HS was significantly higher in those with CVD versus those without CVD on CTCA (51.3% versus 39.9%, p = 0.007). Male sex (50.7% versus 36.1% p= &lt;0.001), age (59.4 ± 13.7 versus 48.1 ± 13.6, p= &lt;0.001) and diabetes (12.4% versus 6.9%, p = 0.04) were also significantly higher in the CAC group compared to the CAC score of zero. HS was associated with increasing strata of CAC score compared with CAC of zero (CAC score 1-100 OR1.47, p = 0.01, CAC score 101-400 OR:1.68, p = 0.02, CAC score &gt;400 OR 1.42, p = 0.14). This association became non-significant in the highest strata of CAC score. Conclusion We found a significant association between the increasing age, male sex, diabetes and HS with the presence of CAC. HS was also associated with a more severe phenotype of CVD based on the multinomial logistic regression model. Although the association reduced for the highest strata of CAC (CAC score &gt;400) this likely reflects the overall low numbers of patients within this group and is likely a type II error. Based on these findings it may be appropriate to offer routine CVD risk stratification techniques in all those diagnosed with HS.

Surgical Site Infection in a Tertiary Neonatal Surgery Centre

2018 ◽  
Vol 29 (03) ◽  
pp. 260-265 ◽  
Author(s):  
Adiam Woldemicael ◽  
Sarah Bradley ◽  
Caroline Pardy ◽  
Justin Richards ◽  
Paolo Trerotoli ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Surgical Site Infection ◽  
Hospital Stay ◽  
Logistic Regression Model ◽  
Length Of Hospital Stay ◽  
Neonatal Surgery ◽  
Site Infection ◽  
Increased Risk

Introduction Surgical site infection (SSI) is a key performance indicator to assess the quality of surgical care. Incidence and risk factors for SSI in neonatal surgery are lacking in the literature. Aim To define the incidence of SSI and possible risk factors in a tertiary neonatal surgery centre. Materials and Methods This is a prospective cohort study of all the neonates who underwent abdominal and thoracic surgery between March 2012 and October 2016. The variables analyzed were gender, gestational age, birth weight, age at surgery, preoperative stay in neonatal intensive care unit, type of surgery, length of stay, and microorganisms isolated from the wounds. Statistical analysis was done with chi-square, Student's t- or Mann–Whitney U-tests. A logistic regression model was used to evaluate determinants of risk for SSI; variables were analyzed both with univariate and multivariate models. For the length of hospital stay, a logistic regression model was performed with independent variables. Results A total of 244 neonates underwent 319 surgical procedures. The overall incidence of SSIs was 43/319 (13.5%). The only statistical differences between neonates with and without SSI were preoperative stay (<4 days vs. ≥4 days, p < 0.01) and length of hospital stay (<30 days vs. ≥30 days, p < 0.01). A pre-operative stay longer than 4 days was associated with almost three times increased risk of SSI (odds ratio [OR] 2.96, 95% confidence interval [CI] 1.05–8.34, p = 0.0407). Gastrointestinal procedures were associated with more than ten times the risk of SSI compared with other procedures (OR 10.17, 95% CI 3.82–27.10, p < 0.0001). Gastroschisis closure and necrotizing enterocolitis (NEC) laparotomies had the highest incidence SSI (54% and 62%, respectively). The risk of longer length of hospital stay after SSI was more than three times higher (OR = 3.36, 95%CI 1.63–6.94, p = 0.001). Conclusion This is the first article benchmarking the incidence of SSI in neonatal surgery in the United Kingdom. A preoperative stay ≥4 days and gastrointestinal procedures were independent risk factors for SSI. More research is needed to develop strategies to reduce SSI in selected neonatal procedures.

scholarly journals Etiology, Incidence, and Risk Factors for Meningitis after Ventriculoperitoneal Shunt Procedures: A Multicenter Study

2020 ◽  
Vol 41 (S1) ◽  
pp. s221-s222
Author(s):  
Danilo Silva ◽  
Henrique Couto ◽  
Hoberdan Pereira ◽  
Gregory Lauar Souza ◽  
Andressa Silveira ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Hospital Admission ◽  
Regression Model ◽  
Hospital Stay ◽  
Ventriculoperitoneal Shunt ◽  
Logistic Regression Model ◽  
Hospital Death ◽  
Communicating Hydrocephalus ◽  
Increased Risk

Background: The ventriculoperitoneal shunt is the main procedure used for to treat communicating hydrocephalus. Surgical site infection associated with the shunt device is the most common complication and a cause of morbidity and mortality of related to the treatment. We sought to answer 3 questions: (1) What is the risk of meningitis after ventricular shunt operations? (2) What are the risk factors for meningitis? (3) What are the main microorganisms causing meningitis? Methods: We conducted a retrospective cohort study of patients undergoing ventricular shunt operations between July 2015 and June 2018 from 12 hospitals at Belo Horizonte, Brazil. Data were gathered by standardized methods defined by the CDC NHSN. Our sample size was 926, and we evaluated 26 preoperative and operative variables by univariate and multivariate analysis. Our outcome variables of interest were meningitis and hospital death. Results: In total, 71 cases of meningitis were diagnosed (risk, 7.7%; 95% CI, 6.1%–9.6%). The mortality rate among patients without infection was 10%, whereas hospital mortality of infected patients was 13% (P = .544). The 3 main risk factors for meningitis after ventricular shunt were identified by logistic regression model: age <2 years (OR, 3.20; P < .001), preoperative hospital stay >4 days (OR, 2.02; P = .007) and >1 surgical procedure, in addition to ventricular shunt (OR, 3.23; P = .043). Almost 1 of 3 of all patients was <2 years old (290, 31%). Also, 430 patients had >4 preoperative days (46%). Patients aged ≥2 years who underwent surgery 4 days after hospital admission had an increased risk of meningitis, from 4% to 6% (P = .140). If a patient <2 years old underwent surgery 4 or more days after hospital admission, the risk of meningitis increased from 9% to 18% (P = .026; Fig. 1). We built a risk index using the number of main risk factors based on a logistic regression model (0, 1, 2 or 3; Fig. 2). Conclusions:We identified 2 intrinsic risk factors for meningitis after ventricular shunt, age <2 years and multiple surgical procedures, and 1 extrinsic risk factor, the preoperative length of hospital stay.Funding: NoneDisclosures: None

Predictors of postoperative atrial fibrillation persisting beyond hospital discharge after coronary artery bypass grafting

Perfusion ◽  
2020 ◽  
pp. 026765912097864
Author(s):  
Aschraf El-Essawi ◽  
Ahmed Abdelhalim ◽  
Steffen Groeger ◽  
Ingo Breitenbach ◽  
Rene Brouwer ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Hospital Stay ◽  
Coronary Artery Bypass ◽  
Hospital Discharge ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Artery Bypass Grafting ◽  
Holter Monitor

Objective: Minimal invasive extracorporeal circuits (MiECC) have been associated with a significant reduction in the incidence of postoperative atrial fibrillation (AF). Nevertheless, AF remains one of the most common complications following elective primary coronary artery bypass grafting (CABG). The aim of this study was to identify the predictors of AF persisting beyond the hospital stay in elective primary CABG patients. Methods: We conducted a retrospective analysis for the predictors of AF that persisted beyond discharge between all patients who received an elective isolated CABG in our institution between 2009 and 2014. Patients with a positive history for intermittent or persistent AF were excluded from the analysis. Almost all patients were discharged to a rehabilitation facility where they stayed for 3 to 4 weeks postoperatively. At rehab approximately 91% of them received Holter monitoring at least once prior to their discharge. Results: A total of 770 patients were included in the analysis of which 763 patients survived the in-hospital stay. The incidence of AF at hospital discharge was 4.2% (32/763) while that on Holter monitor at Rehab was 1.5% (10/685). Age and the type of extracorporeal circuit (ECC) utilized were the only significant predictors for both AF at discharge ( p < 0.01 both) and on Holter monitor in rehab ( p < 0.01 and 0.02, respectively). This was also confirmed on multivariate analysis. Conclusion: Our findings show that the benefits of MiECC regarding the incidence of postoperative AF persist beyond hospital discharge. They may thus positively influence the outcomes of patients beyond the early postoperative period.

scholarly journals Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anping Guo ◽  
Jin Lu ◽  
Haizhu Tan ◽  
Zejian Kuang ◽  
Ying Luo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Liver Disease ◽  
Length Of Stay ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hospital Length ◽  
Clinical Treatment ◽  
Hospital Length Of Stay ◽  
Female Sex

AbstractTreating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13–22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05–0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47–72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95–145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9–0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

scholarly journals Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

2021 ◽  
Author(s):  
Li Lu Wei ◽  
Yu jian
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Bp Neural Network ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Overweight And Obesity ◽  
Tree Model ◽  
Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

scholarly journals Predictive potential assessment of preoperative risk factors for atrial fibrillation in patients with coronary artery disease after coronary artery bypass grafting

2020 ◽  
Vol 35 (4) ◽  
pp. 128-136
Author(s):  
K. I. Shakhgeldyan ◽  
V. Y. Rublev ◽  
B. I. Geltser ◽  
B. O. Shcheglov ◽  
V. G. Shirobokov ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Preoperative Risk ◽  
Preoperative Risk Factors ◽  
Artery Disease

Introduction. Postoperative atrial fibrillation (POAF) is one of the most common complications of coronary artery bypass grafting (CABG) and occurs in 25–65% of patients.Aim. The study aimed to assess the predictive potential of preoperative risk factors for POAF in patients with coronary artery disease (CAD) after CABG based on machine learning (ML) methods.Material and Methods. An observational retrospective study was carried out based on data from 866 electronic case histories of CAD patients with a median age of 63 years and a 95% confidence interval [63; 64], who underwent isolated CABG on cardiopulmonary bypass. Patients were assigned to two groups: group 1 comprised 147 (18%) patients with newly registered atrial fibrillation (AF) paroxysms; group 2 included 648 (81.3%) patients without cardiac arrhythmia. The preoperative clinical and functional status was assessed using 100 factors. We used statistical analysis methods (Chi-square, Fisher, Mann – Whitney, and univariate logistic regression (LR) tests) and ML tests (multivariate LR and stochastic gradient boosting (SGB)) for data processing and analysis. The models’ accuracy was assessed by three quality metrics: area under the ROC-curve (AUC), sensitivity, and specificity. The cross-validation procedure was performed at least 1000 times on randomly selected data.Results. The processing and analysis of preoperative patient status indicators using ML methods allowed to identify 10 predictors that were linearly and nonlinearly related to the development of POAF. The most significant predictors were the anteroposterior dimension of the left atrium, tricuspid valve insufficiency, ejection fraction <40%, duration of the P–R interval, and chronic heart failure of functional class III–IV. The accuracy of the best predictive multifactorial model of LR was 0.61 in AUC, 0.49 in specificity, and 0.72 in sensitivity. The values of similar quality metrics for the best model based on SGB were 0.64, 0.6, and 0.68, respectively.Conclusion. The use of SGB made it possible to verify the nonlinearly related predictors of POAF. The prospects for further research on this problem require the use of modern medical care methods that allow taking into account the individual characteristics of patients when developing predictive models.

Abstract 211: Association of Patient Characteristics With the Initiation of Dabigatran Versus Warfarin Among Anticoagulant Naive Patients With Non-valvular Atrial Fibrillation

2013 ◽  
Vol 6 (suppl_1) ◽  
Author(s):  
David R Walker ◽  
Jasmina Ivanova ◽  
Keith A Betts ◽  
Sapna Rao ◽  
Eric Q Wu
Keyword(s):  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Regression Model ◽  
Primary Care Physicians ◽  
Online Survey ◽  
Logistic Regression Model ◽  
Stroke Risk ◽  
Dabigatran Etexilate ◽  
Patient Characteristics ◽  
Categorical Variables

Background and Objective: Dabigatran etexilate (DE) and warfarin, both oral anticoagulants used for stroke risk reduction in patients with non-valvular atrial fibrillation (NVAF), have been or are being compared in several comparative effectiveness studies. Understanding patient characteristics of those prescribed DE vs. warfarin are important for interpreting such studies. The objective of this study is to identify the characteristics that differentiate NVAF patients prescribed DE versus warfarin as first-line anticoagulation. Methods: An online survey was administered in October 2012 to an established panel of cardiologists and primary care physicians (PCPs) in the US. Physicians were asked to identify medical charts of their patients diagnosed with NVAF and who had at least one prescription for DE or warfarin between 1/1/2011 and 6/30/2012. Patients were further required to be anticoagulant naïve prior to the first prescription of DE or warfarin. A computer generated random dice was applied to direct the random selection of the patients. Patient characteristics, comorbidities and clinical risk measures were compared between DE and warfarin patients using Chi-square tests for categorical variables and t-tests for continuous variables. A logistic regression model was utilized to evaluate patient characteristics associated with DE vs. warfarin use among anticoagulant naïve NVAF patients. Results: A total of 288 physicians (144 cardiologists and 144 PCPs) completed the survey. 262 medical records for DE patients and 247 for warfarin patients were randomly selected. The mean age of the DE and warfarin patients, respectively were 61.6 and 65.8 years (p < 0.01). The proportion of females was 20.6% and 41.7% in the DE and warfarin patients respectively (p<0.01). 86.3% of DE patients vs. 68.4% of warfarin patients were Caucasian (p<0.01). Other differences between DE and warfarin patients respectively included: previous myocardial infarction (3.8%, 9.3%; p<0.05), previous transient ischemic attack (8.4%, 16.2%; p <0.01), and CHA 2 DS 2 -VASc stroke risk score (2.21, 2.98; p<0.01). The logistic regression model found age (OR = 0.96; p=0.001), female gender (OR=0.46; p = 0.002), Hispanic/Latino (OR = 0.33; p=.007), Black (OR= 0.37; p = 0.006), and > 6 months and < 1 year for time from first NVAF diagnosis to first prescription date (OR = 0.38; p = 0.02) were associated with initiation of DE vs. warfarin. However, CHA 2 DS 2 -VASc was not found to be a significant predictor of anticoagulant prescription. Conclusions: Patients who are younger, male, Caucasian, and recently diagnosed with NVAF were significantly more likely to be initiated by their physician on DE vs. warfarin. These findings should be considered when doing comparative analyses of outcomes between patients on DE vs. warfarin.

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