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).

scholarly journals Nomogram for Predicting the Postoperative Venous Thromboembolism in Spinal Metastasis Tumor: A Multicenter Retrospective Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao-ran Zhang ◽  
Ming-you Xu ◽  
Xiong-gang Yang ◽  
Feng Wang ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Venous Thromboembolism ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

IntroductionVenous thromboembolism can be divided into deep vein thrombosis and pulmonary embolism. These diseases are a major factor affecting the clinical prognosis of patients and can lead to the death of these patients. Unfortunately, the literature on the risk factors of venous thromboembolism after surgery for spine metastatic bone lesions are rare, and no predictive model has been established.MethodsWe retrospectively analyzed 411 cancer patients who underwent metastatic spinal tumor surgery at our institution between 2009 and 2019. The outcome variable of the current study is venous thromboembolism that occurred within 90 days of surgery. In order to identify the risk factors for venous thromboembolism, a univariate logistic regression analysis was performed first, and then variables significant at the P value less than 0.2 were included in a multivariate logistic regression analysis. Finally, a nomogram model was established using the independent risk factors.ResultsIn the multivariate logistic regression model, four independent risk factors for venous thromboembolism were further screened out, including preoperative Frankel score (OR=2.68, 95% CI 1.78-4.04, P=0.001), blood transfusion (OR=3.11, 95% CI 1.61-6.02, P=0.041), Charlson comorbidity index (OR=2.01, 95% CI 1.27-3.17, P=0.013; OR=2.29, 95% CI 1.25-4.20, P=0.017), and operative time (OR=1.36, 95% CI 1.14-1.63, P=0.001). On the basis of the four independent influencing factors screened out by multivariate logistic regression model, a nomogram prediction model was established. Both training sample and validation sample showed that the predicted probability of the nomogram had a strong correlation with the actual situation.ConclusionThe prediction model for postoperative VTE developed by our team provides clinicians with a simple method that can be used to calculate the VTE risk of patients at the bedside, and can help clinicians make evidence-based judgments on when to use intervention measures. In clinical practice, the simplicity of this predictive model has great practical value.

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.

scholarly journals 282. Risk Factors for Mortality in Severe COVID-19 Patients Admitted to the Intensive Care Unit: A Retrospective Single-Center Study in Saudi Arabia

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S246-S247
Author(s):  
Sherif Khattab ◽  
Souad AlMuthree ◽  
Mohamed Bakry ◽  
Noha Ibraheem ◽  
Omar Alghamdi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Logistic Regression ◽  
Regression Model ◽  
Sofa Score ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors ◽  
Cox Proportional Hazard Regression

Abstract Background The first case of COVID-19 in the Kingdom of Saudi Arabia (KSA) was reported in March 2020. This study aims to describe the overall mortality in the ICU during the COVID-19 pandemic and to determine independent risk factors for overall survival & 29 days mortality. Methods This is a retrospective single-center study; data for adult patients admitted to the ICU with COVID-19 between 1st March 2020 to 31st December 2020 were extracted and reviewed. Overall survival was described using Kaplan-Meier curves with reporting of median overall survival and 29 days survival estimates. Multivariate analysis was performed using Cox proportional hazards model and multivariate logistic regression model. Figure 1. Study flow chart Table 1. Demographic characteristics categorized by Gender Results Eligible subjects were 209 (Figure 1) and subjects demographics are summarized in (Table1). Observed death events were 82 (39.2% of the total cohort), 61% of deaths reached at 2 weeks of ICU stay (n.= 50), median overall survival (OS) was reached at day 23, as shown in (Figure 2). The multivariate Cox proportional hazard regression analysis (Figure 3) showed elevated SOFA score [aHR= 1.10, P < 0.001] and Vasopressors [aHR= 3.23, P= 0.002] as independent risk factors for overall ICU mortality. Independent protective factors were: Systemic corticosteroids use (P= 0.019), Insulin use (P= 0.026) and Liposomal Amphotericin B (LAMB) use (P= 0.019). For mortality at day 29, the multivariate logistic regression model (Figure. 4) showed elevated SOFA score (P= 0.005), any need for ventilation escalation after ICU admission (P= 0.014), Ribavirin use (P=0.016) and Vasopressors use ( P< 0.001) as independent risk factors. Angiotensin-Converting Enzyme inhibitors (ACEi) use was a protective factor (P=0.025). Figure 2. Overall Survival (OS) for patients admitted to the ICU due to COVID-19 - Kaplan Meier (KM) Figure 3. Multivariate Cox proportional hazard regression model for factors associated with overall mortality in patients admitted to the ICU due to COVID-19 Figure 4. Multivariate logistic regression model for factors associated with 29 days mortality in patients admitted to the ICU due to COVID-19 Conclusion SOFA score and vasopressors are independent predictors for overall survival and 29-day mortality in the ICU. The need for ventilation escalation after ICU admission appeared to lead to poor prognosis in regard to 29-day mortality only. Systemic corticosteroids are lifesaving, further studies are required to confirm the observed clinical benefits with insulin, LAMB and ACEi use in the ICU and to investigate any hazardous impact of ribavirin on COVID-19 outcomes. Study limitations Residual confounding of other measured and/or unobserved factors cannot be ruled out. Disclosures Sherif Khattab, BPharm, Gilead Sciences (Employee, Shareholder) Mohamed Bakry, MBBCh, Gilead Sciences (Employee)Roche Pharma (Employee)

scholarly journals Analyzing and modeling thermal complaints in a commercial building in France

2021 ◽  
Vol 13 (1) ◽  
pp. 2416-2425
Author(s):  
Ezgi Kocaman ◽  
Merve Kuru ◽  
Gülben Çalış
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Environmental Conditions ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Multivariate Logistic Regression Model ◽  
Commercial Building ◽  
Multivariate Logistic Regression ◽  
Operative Temperature ◽  
Heating And Cooling

Abstract Buildings are interactive environments in which their operations and occupants are linked. Although buildings are operated according to the standards, occupant complaints may arise when there is a mismatch between indoor environmental conditions and actual user needs. Therefore, the accuracy of thermal comfort prediction models suggested by the standards and alternative prediction models need to be investigated. This study aims at assessing the performance of the predicted mean vote (PMV) model suggested by the ISO 7730 Standard to detect occupant thermal dissatisfaction. In addition, a multivariate logistic regression model was developed to predict thermal complaints with respect to “too warm” and “too cold.” This case study was conducted in a commercial building located in Paris, France, between January 2017 and May 2018. Indoor environmental conditions were monitored via sensors and an online tool was used to collect occupant thermal complaints. A total of 53 thermal complaints were analyzed. The results showed that all the operative temperature measurements in both the heating and cooling seasons were within the thresholds suggested by the standards. The PMV method suggested that only 4% of the occupants were dissatisfied with the indoor environment whereas the actual dissatisfaction ratio was 100% under these indoor environmental conditions. In addition, the multivariate logistic regression model showed that operative temperature and season have a significant effect on thermal complaints. Furthermore, the accuracy of the developed model was 90.6%.

scholarly journals Mediastinal/hilar lymphadenopathy: A possibly overlooked variable in the classification of lung nodules

2021 ◽  
Author(s):  
qingxia fan
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Lung Nodule ◽  
Added Value ◽  
Lung Nodules ◽  
Hilar Lymphadenopathy

Abstract Background Clinical prediction models to classify lung nodules often exclude patients with mediastinal/hilar lymphadenopathy, although the presence of mediastinal/hilar lymphadenopathy does not always indicate malignancy. Herein, we developed and validated a multimodal prediction model for lung nodules in which patients with mediastinal/hilar lymphadenopathy were included. Methods A total of 359 patients with pulmonary nodules were considered for enrollment in the study. We developed and validated a logistic regression model including patients with mediastinal/hilar lymphadenopathy. Discrimination of the model was assessed by area under the operating curve. Goodness of fit was performed via the Hosmer-Lemeshow test, and a nomogram of the logistic regression model was drawn. Results There were 311 cases included in the final analysis. A logistic regression model was developed and validated. There were nine independent variables included in the model. The AUC of the training and validation sets was 0.93 (95% CI, 0.90–0.97) and 0.91 (95% CI, 0.85–0.98), respectively. In the validation set with or without mediastinal/hilar lymphadenopathy, the AUC was 0.95 (95% CI, 0.90–0.99) and 0.91 (95%CI, 0.87–0.95), respectively. The Hosmer-Lemeshow goodness-of-fit statistic was 0.22. A nomogram was drawn to visualize the model. Conclusions We developed and validated a multimodal risk prediction model for lung nodules with excellent discrimination and calibration, regardless of the inclusion of mediastinal/hilar lymphadenopathy. This broadens the application of lung nodule prediction models. Furthermore, the presence of mediastinal/hilar lymphadenopathy added value for predicting lung nodule malignancy, highlighting the importance of this variable in clinical practice.

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 Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Cheng-Jian Cao ◽  
Cong Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Lasso Regression

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

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