scholarly journals Development of a Risk Prediction Model for Central-Line–Associated Bloodstream Infection (CLABSI) in Patients With Continuous Renal Replacement Therapy

2020 ◽  
Vol 41 (S1) ◽  
pp. s515-s515
Author(s):  
Hui Zhang
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Renal Replacement Therapy ◽  
Prediction Model ◽  
Prediction Models ◽  
Total Bilirubin ◽  
Cutoff Point ◽  
Dialysis Catheter ◽  
Spss Software

Background: The number of patients with end-stage renal disease and acute kidney injury in China is large and increasing year by year. Continuous renal replacement therapy (CRRT) is one of the important treatment methods. However, long-time CRRT would inevitably lead to CLABSI, which would seriously affect the treatment and prognosis of the patient. Although CLABSIs can be prevented and controlled, the rate of CLABSI in China is still higher than in other countries. Therefore, it is urgent to find new intervention methods on the basis of existing methods. Surveillance is the prerequisite of infection prevention and control. We sought to develop a risk prediction model for CLABSI in patients with CRRT according to uncontrollable risk factors, which could be used for early assessment and screening of high-risk infection groups. Such a tool would bring the supervision and infection control to the forefront in addressing these issues. Methods: We selected 3,103 CRRT patients in the West China Hospital of Sichuan University from January 2013 to December 2018 using the hospital infection and infectious disease monitoring module of electronic medical records (EMR) system with the integration and elimination criteria. Data mining and feature selection were performed using Weka software. Separately, prediction models developed by Weka software and SPSS software were compared with each other using the area under the curve (AUC) method to assess the performance of the forecasting models. Result: The incidence of CLABSI in CRRT patients was 8.01 per 1,000 catheter days (238 of 29,711). According to the multifactor regression analysis by SPSS software, the retaining time of dialysis catheter, C-reactive protein levels, total bilirubin, acute pancreatitis, and systemic inflammation reaction syndrome were the risk factors. According to the Youden’s index, the cutoff point of the retaining time of dialysis catheter was 5.5 days; the cutoff point of CRP was 112.5mg/L; and the cutoff point of total bilirubin was 14.15 μmol/L. The prediction models of CLABSI for CRRT patients were developed: The AUC of the prediction model developed by SPSS software was 0.763 (95% CI, 0.717–0.809). The receiver operating characteristic (ROC) curve analysis showed that the AUCs of the prediction models developed separately by Weka software using Bayes, logistic regression analysis, multiple layer Perceptron and J48, and SPSS software through logistic regression analysis were between 0.6 and 0.8. Using the down-sampling technique, the AUC ranged between 0.7 and 0.9, and the sensitivity, precision, and κ value increased. Thus, these models had definite clinical significance. Conclusion: The prediction models of CLABSI for CRRT patients, developed based on the big healthcare data, not only had good judgment ability, but also had good application value for individual evaluations and the target population.Funding: This study was supported by the Health Commission of Sichuan Province.Disclosures: None

scholarly journals A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liu ◽  
Jian Zhang ◽  
Haodong Huang ◽  
Yunting Wang ◽  
Zuyue Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Retrospective Study ◽  
Kawasaki Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Ivig Resistance ◽  
Independent Risk Factors

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.

MO733RISK FACTORS FOR INFECTIVE ENDOCARDITIS IN PATIENTS RECEIVING HAEMODIALYSIS- A PROPENSITY MATCHED COHORT STUDY

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Saif Al-Chalabi ◽  
Tricia Tay ◽  
Rajkumar Chinnadurai ◽  
Philip A Kalra
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Infective Endocarditis ◽  
Logistic Regression Analysis ◽  
Heart Diseases ◽  
Dialysis Access ◽  
Dialysis Catheter ◽  
Valvular Heart Diseases ◽  
Dialysis Vintage

Abstract Background and Aims Infective endocarditis (IE) is a serious infective complication that usually results in prolonged hospitalisation and is associated with high morbidity and mortality. It is sometimes difficult to promptly diagnose infective endocarditis when a patient receiving hemodialysis presents with signs and symptoms of bacteremia, a delay which can lead to worse outcomes. In this study, we aimed to identify the risk factors that can predict infective endocarditis in haemodialysis patients with bacteremia. Method This retrospective observational study was conducted on all patients diagnosed with infective endocarditis (IE) and receiving maintenance hemodialysis between 2005 and 2018 in Salford Royal Hospital and its satellite dialysis units (catchment population of 1.5 million). The IE patients were propensity score matched in a 1:2 ratio with similar hemodialysis patients without IE but with bacteremia between 2011 and 2015. Propensity scores were generated by using binary logistic regression analysis incorporating age, gender, diabetes status, and dialysis vintage as variables. Logistic regression analysis was used to predict the risk factors associated with developing IE. Statistics were performed using SPSS version-24. Results We had a sample of 105 patients (35 IE vs 70 bacteremia). The median age of the patients was 65 years with a predominance of males (60%). 43% were diabetic, 11.5% were receiving immunosuppression and 72% had a catheter for dialysis access. IE patients had higher peak C-reactive protein (CRP) during admission compared to patients with bacteremia and no IE (253 mg/l vs 152 mg/l, p=0.001). Patients who developed IE had a longer duration of dialysis catheter use than the bacteremia group (150 vs 19 days; p<0.001) (table 1). There was no significant difference between causative microorganisms in both groups. Staphylococcus aureus caused most cases (54% in IE and 47% in bacteremia). Our study showed clearly that patients who had IE had longer hospital stay (45 vs 18 days, p=0.001) with a far higher 30-day mortality rate (54.3% vs 17.1%, p<0.001). Logistic regression analysis showed previous valvular heart diseases (OR: 20.1; p<0.001), a higher peak CRP (OR:1.01; p=0.001), and a longer duration of catheter use (OR: 1.01; p=0.035) as significant predictors for infective endocarditis (table 2). Conclusion Bacteremia in patients receiving hemodialysis through a catheter as access should be actively investigated with a high index of suspicion for IE particularly those having valvular heart diseases, hypertension, higher peak CRP, and those with a longer duration of dialysis catheter usage. Work up may need to include invasive investigations such as transesophageal echocardiogram to confirm or reliably rule out this devastating condition.

scholarly journals Time to Appropriate Antibiotic Therapy is an Independent Indicator of Poor Outcome in Children with Nosocomial Klebsiella Pneumoniae Bloodstream Infection

2020 ◽  
Author(s):  
Jie Cheng ◽  
Qinyuan Li ◽  
Guangli Zhang ◽  
Huiting Xu ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Septic Shock ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Antibiotic Therapy ◽  
Bloodstream Infection ◽  
Logistic Regression Analysis ◽  
Cutoff Point ◽  
Cart Analysis ◽  
Poor Outcome

Abstract Objectives: To evaluate the effects of time to appropriate therapy (TTAT) on outcomes in children with nosocomial K. pneumoniae bloodstream infection, and to find an optimal time window for empiric antibiotics administration. Methods: Children with nosocomial K. pneumoniae bloodstream infection hospitalized in Children’s Hospital of Chongqing Medical University from April 2014 to December 2019 were enrolled retrospectively. TTAT cutoff point and risk factors were determined and analyzed by Classification and Regression Tree (CART) analysis and Logistic Regression analysis. Results: Overall, sixty-seven patients were enrolled. The incidence of septic shock and mortality was 17.91% (12/67) and 13.43% (9/67), respectively. The CART-derived TTAT cutoff point was 10.7 hours. The multivariate logistic regression analysis indicated delayed therapy (TTAT ≥ 10.7 h), PRISM III scores ≥ 10, early TTP (TTP ≤ 13 h), and need for invasive mechanical ventilation were independent risk factors of septic shock (OR 9.87, 95% CI 1.46-66.59, P = 0.019; OR 9.69, 95% CI 1.15-81.39, P = 0.036; OR 8.28, 95% CI 1.37-50.10, P = 0.021; OR 6.52, 95% CI 1.08-39.51, P = 0.042; respectively) and in-hospital mortality (OR 22.19, 95% CI 1.25-393.94, P = 0.035; OR 40.06, 95% CI 2.32-691.35, P = 0.011; OR 22.60, 95% CI 1.78-287.27, P = 0.016; OR 12.21, 95% CI 1.06-140.67, P = 0.045; respectively). Conclusions: TTAT is an independent predictor of poor outcome in children with nosocomial K. pneumoniae bloodstream infection. Initial appropriate antibiotic therapy should begin within 10.7 hours from the onset of bloodstream infection.

scholarly journals Time to Appropriate Antibiotic Therapy is an Independent Indicator of Poor Outcome in Children with Nosocomial Klebsiella Pneumoniae Bloodstream Infection

2021 ◽  
Author(s):  
Jie Cheng ◽  
Qinyuan Li ◽  
Guangli Zhang ◽  
Huiting Xu ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Septic Shock ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Antibiotic Therapy ◽  
Bloodstream Infection ◽  
Logistic Regression Analysis ◽  
Cutoff Point ◽  
Cart Analysis ◽  
Poor Outcome

Abstract We aim to evaluate the effects of time to appropriate therapy (TTAT) on outcomes in children with nosocomial K. pneumoniae bloodstream infection, and to find an optimal time window for empiric antibiotics administration. Children with nosocomial K. pneumoniae bloodstream infection hospitalized in Children’s Hospital of Chongqing Medical University from April 2014 to December 2019 were enrolled retrospectively. TTAT cutoff point and risk factors were determined and analyzed by Classification and Regression Tree (CART) analysis and Logistic Regression analysis. Overall, sixty-seven patients were enrolled. The incidence of septic shock and mortality was 17.91% (12/67) and 13.43% (9/67), respectively. The CART-derived TTAT cutoff point was 10.7 hours. The multivariate logistic regression analysis indicated delayed therapy (TTAT ≥ 10.7 h), PRISM III scores ≥ 10, early TTP (TTP ≤ 13 h), and need for invasive mechanical ventilation were independent risk factors of septic shock (OR 9.87, 95% CI 1.46-66.59, P = 0.019; OR 9.69, 95% CI 1.15-81.39, P = 0.036; OR 8.28, 95% CI 1.37-50.10, P = 0.021; OR 6.52, 95% CI 1.08-39.51, P = 0.042; respectively) and in-hospital mortality (OR 22.19, 95% CI 1.25-393.94, P = 0.035; OR 40.06, 95% CI 2.32-691.35, P = 0.011; OR 22.60, 95% CI 1.78-287.27, P = 0.016; OR 12.21, 95% CI 1.06-140.67, P = 0.045; respectively). Conclusions: TTAT is an independent predictor of poor outcome in children with nosocomial K. pneumoniae bloodstream infection. Initial appropriate antibiotic therapy should begin within 10.7 hours from the onset of bloodstream infection.

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 Identification of risk factors for daptomycin-associated creatine phosphokinase elevation and development of a risk prediction model for incidence probability

2021 ◽  
Author(s):  
Masaru Samura ◽  
Naoki Hirose ◽  
Takenori Kurata ◽  
Keisuke Takada ◽  
Fumio Nagumo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Logistic Regression Analysis ◽  
Creatine Phosphokinase ◽  
Risk Prediction Model ◽  
Risk Scores

Abstract Background In this study, we investigated the risk factors for daptomycin-associated creatine phosphokinase (CPK) elevation and established a risk score for CPK elevation. Methods Patients who received daptomycin at our hospital were classified into the normal or elevated CPK group based on their peak CPK levels during daptomycin therapy. Univariable and multivariable analyses were performed, and a risk score and prediction model for the incidence probability of CPK elevation were calculated based on logistic regression analysis. Results The normal and elevated CPK groups included 181 and 17 patients, respectively. Logistic regression analysis revealed that concomitant statin use (odds ratio [OR] 4.45, 95% confidence interval [CI] 1.40–14.47, risk score 4), concomitant antihistamine use (OR 5.66, 95% CI 1.58–20.75, risk score 4), and trough concentration (Cmin) between 20 and <30 µg/mL (OR 14.48, 95% CI 2.90–87.13, risk score 5) and ≥30.0 µg/mL (OR 24.64, 95% CI 3.21–204.53, risk score 5) were risk factors for daptomycin-associated CPK elevation. The predicted incidence probabilities of CPK elevation were <10% (low risk), 10%–<25% (moderate risk), and ≥25% (high risk) with the total risk scores of ≤4, 5–6, and ≥8, respectively. The risk prediction model exhibited a good fit (area under the receiving-operating characteristic curve 0.85, 95% CI 0.74–0.95). Conclusions These results suggested that concomitant use of statins with antihistamines and Cmin ≥20 µg/mL were risk factors for daptomycin-associated CPK elevation. Our prediction model might aid in reducing the incidence of daptomycin-associated CPK elevation.

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 Failure prediction of government funded start-up firms

2017 ◽  
Vol 14 (2) ◽  
pp. 296-306 ◽  
Author(s):  
Oliver Lukason ◽  
Kaspar Käsper
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Financial Development ◽  
Logistic Regression Analysis ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Development Patterns ◽  
Start Up

This study aims to create a prediction model that would forecast the bankruptcy of government funded start-up firms (GFSUs). Also, the financial development patterns of GFSUs are outlined. The dataset consists of 417 Estonian GFSUs, of which 75 have bankrupted before becoming five years old and 312 have survived for five years. Six financial ratios have been calculated for one (t+1) and two (t+2) years after firms have become active. Weighted logistic regression analysis is applied to create the bankruptcy prediction models and consecutive factor and cluster analyses are applied to outline the financial patterns. Bankruptcy prediction models obtain average classification accuracies, namely 63.8% for t+1 and 67.8% for t+2. The bankrupt firms are distinguished with a higher accuracy than the survived firms, with liquidity and equity ratios being the useful predictors of bankruptcy. Five financial patterns are detected for GFSUs, but bankrupt GFSUs do not follow any distinct patterns that would be characteristic only to them.

scholarly journals Risk factors analysis of hyperbilirubinemia after off-pump coronary artery bypass grafting: a retrospective observational study

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Yingdi Gao ◽  
Dongjie Li ◽  
Honghong Dong ◽  
Yulin Guo ◽  
Yuanshu Peng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Blood Transfusion ◽  
Logistic Regression Analysis ◽  
Total Bilirubin ◽  
Serum Total Bilirubin ◽  
Preoperative Serum ◽  
Independent Risk Factors ◽  
Perioperative Blood Transfusion

Abstract Background Hyperbilirubinemia is a common complication after off-pump coronary artery bypass grafting (OPCAB), but the incidence and the risk factors are unclear. This study aimed to analyze the incidence and risk factors of postoperative hyperbilirubinemia in patients undergoing OPCAB. Methods From December 2016 to March 2019, a total of 416 consecutive patients undergoing OPCAB were enrolled in this single-center retrospective study. Patients were divided into the normal serum total bilirubin group and the hyperbilirubinemia group based on the serum total bilirubin levels. Perioperative variables between the two groups were compared by univariate logistic regression analysis. Then, multivariate binary logistic regression analysis was used to analyze the independent risk factors of developing hyperbilirubinemia in patients underwent OPCAB. P < 0.05 was considered as statistically significant. Results Thirty two of 416 (7.7%) patients developed postoperative hyperbilirubinemia. Univariate regression analysis showed significant differences in gender (73.96% vs. 93.75%, P = 0.012), preoperative total bilirubin levels (11.92 ± 4.52 vs. 18.28 ± 7.57, P < 0.001), perioperative IABP implantation (22.66% vs. 43.75%, P = 0.008), perioperative blood transfusion (37.50% vs. 56.25%, P = 0.037) between the two groups. Multivariate logistic regression analysis revealed that elevated preoperative serum total bilirubin levels (OR = 1.225, 95% CI 1.145–1.310, P < 0.001), perioperative blood transfusion (OR = 4.488, 95% CI 1.876–10.737, P = 0.001) and perioperative IABP implantation (OR = 4.016, 95% CI 1.709–9.439, P = 0.001) were independent risk factors for hyperbilirubinemia after OPCAB. Conclusions Hyperbilirubinemia is also a common complication after OPCAB. Elevated preoperative serum total bilirubin levels, perioperative blood transfusion, and perioperative IABP implantation were independent risk factors for patients developing hyperbilirubinemia after OPCAB. Further studies need to be conducted to confirm the risk factors of hyperbilirubinemia after OPCAB procedure.

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