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= <0.001), age (59.4 ± 13.7 versus 48.1 ± 13.6, p= <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 >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 >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.

Establishment of Prediction Models for venous thromboembolism in non-oncological urological inpatients--a single center experience

2020 ◽  
Author(s):  
Kaixuan Li ◽  
Haozhen Li ◽  
Quan Zhu ◽  
Ziqiang Wu ◽  
Zhao Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Venous Thromboembolism ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Predictive Ability ◽  
Multivariate Logistic Regression Model ◽  
Single Center Experience

Abstract Background To establish prediction models for venous thromboembolism (VTE) in non-oncological urological inpatients. Methods A retrospective analysis of 1453 inpatients was carried out and the risk factors for VTE had been clarified our previous studies. Results Risk factors included the following 5 factors: presence of previous VTE (X1), presence of anticoagulants or anti-platelet agents treatment before admission (X2), D-dimer value (≥ 0.89 µg/ml, X3), presence of lower extremity swelling (X4), presence of chest symptoms (X5). The logistic regression model is Logit (P) = − 5.970 + 2.882 * X1 + 2.588 * X2 + 3.141 * X3 + 1.794 * X4 + 3.553 * X5. When widened the p value to not exceeding 0.1 in multivariate logistic regression model, two addition risk factors were enrolled: Caprini score (≥ 5, X6), presence of complications (X7). The prediction model turns into Logit (P) = − 6.433 + 2.696 * X1 + 2.507 * X2 + 2.817 * X3 + 1.597 * X4 + 3.524 * X5 + 0.886 * X6 + 0.963 * X7. Internal verification results suggest both two models have a good predictive ability, but the prediction accuracy turns to be both only 43.0% when taking the additional 291 inpatients’ data in the two models. Conclusion We built two similar novel prediction models to predict VTE in non-oncological urological inpatients. Trial registration: This trial was retrospectively registered at http://www.chictr.org.cn/index.aspx under the public title“The incidence, risk factors and establishment of prediction model for VTE n urological inpatients” with a code ChiCTR1900027180 on November 3, 2019. (Specific URL to the registration web page: http://www.chictr.org.cn/showproj.aspx?proj=44677).

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 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 1244. Evaluation of Intraoperative Topical Vancomycin and the Incidence of Acute Kidney Injury

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S448-S448
Author(s):  
Alison L Blackman ◽  
Sabeen Ali ◽  
Xin Gao ◽  
Rosina Mesumbe ◽  
Carly Cheng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Serum Creatinine ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Kidney Injury ◽  
Topical Formulation ◽  
Multivariable Logistic Regression Model ◽  
Multivariable Logistic Regression

Abstract Background The use of intraoperative topical vancomycin (VAN) is a strategy aimed to prevent surgical site infections (SSI). Although there is evidence to support its efficacy in SSI prevention following orthopedic spine surgeries, data describing its safety, specifically acute kidney injury (AKI) risk, is limited. The purpose of this study was to determine the AKI incidence associated with intraoperative topical VAN. Methods This is a retrospective cohort study reviewing patient encounters where intraoperative topical VAN was administered from February 2018 to July 2018. All adult patients ( ≥18 years) that received topical VAN in the form of powder, beads, rods, paste, cement spacers, or unspecified topical routes were included. Patient encounters were excluded for AKI or renal replacement therapy (RRT) at baseline, ≤ 2 serum creatinine values drawn after surgery, and/or if irrigation was the only topical formulation given. The primary outcome was the percentage of patients who developed AKI after intraoperative topical VAN administration. AKI was defined as an increase in serum creatinine (SCr) ≥50% from baseline, an increase in SCr >0.5 from baseline, or0 if RRT was initiated after topical VAN was given. Secondary outcomes included analysis of AKI risk factors and SSI incidence. AKI risk factors were analyzed using a multivariable logistic regression model. Results A total of 589 patient encounters met study criteria. VAN powder was the most common formulation (40.9%), followed by unspecified topical routes (30.7%) and beads (9.9%%). Nonspinal orthopedic surgeries were the most common procedure performed 46.7%. The incidence of AKI was 8.7%. In a multivariable logistic regression model, AKI was associated with concomitant systemic VAN (OR 3.39, [3.39–6.22]) and total topical VAN dose. Each doubling of the topical dose was associated with increased odds of developing AKI (OR = 1.42, [1.08–1.86]). The incidence of SSI was 5.3%. Conclusion AKI rates associated with intraoperative topical VAN are comparable to that of systemic VAN. Total topical vancomycin dose and concomitant systemic VAN was associated with an increased AKI risk. Additional analysis is warranted to compare these patients to a similar population that did not receive topical VAN. Disclosures All authors: No reported disclosures.

scholarly journals Sparse estimation for case–control studies with multiple disease subtypes

Biostatistics ◽  
2020 ◽  
Author(s):  
Nadim Ballout ◽  
Cedric Garcia ◽  
Vivian Viallon
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Conditional Logistic Regression ◽  
Case Control ◽  
Case Control Studies ◽  
Conditional Logistic Regression Model ◽  
Symmetric Formulation ◽  
Disease Subtypes

Summary The analysis of case–control studies with several disease subtypes is increasingly common, e.g. in cancer epidemiology. For matched designs, a natural strategy is based on a stratified conditional logistic regression model. Then, to account for the potential homogeneity among disease subtypes, we adapt the ideas of data shared lasso, which has been recently proposed for the estimation of stratified regression models. For unmatched designs, we compare two standard methods based on $L_1$-norm penalized multinomial logistic regression. We describe formal connections between these two approaches, from which practical guidance can be derived. We show that one of these approaches, which is based on a symmetric formulation of the multinomial logistic regression model, actually reduces to a data shared lasso version of the other. Consequently, the relative performance of the two approaches critically depends on the level of homogeneity that exists among disease subtypes: more precisely, when homogeneity is moderate to high, the non-symmetric formulation with controls as the reference is not recommended. Empirical results obtained from synthetic data are presented, which confirm the benefit of properly accounting for potential homogeneity under both matched and unmatched designs, in terms of estimation and prediction accuracy, variable selection and identification of heterogeneities. We also present preliminary results from the analysis of a case–control study nested within the EPIC (European Prospective Investigation into Cancer and nutrition) cohort, where the objective is to identify metabolites associated with the occurrence of subtypes of breast cancer.

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.

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