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.

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 Establishment of Risk Prediction Model of Postoperative Pancreatic Fistula after Pancreatoduodenectomy: 2016 Edition of Definition and Grading System of Pancreatic Fistula

2021 ◽  
Author(s):  
Jun Yu ◽  
Chao-yi Ren ◽  
Jun Wang ◽  
Wei Cui ◽  
Jin-juan Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Pancreatic Fistula ◽  
Pancreatic Duct ◽  
Risk Prediction ◽  
Main Pancreatic Duct ◽  
Postoperative Pancreatic Fistula ◽  
Risk Prediction Model ◽  
Validation Set

Abstract ObjectiveTo establish a risk prediction model for pancreatic fistula according to the pancreatic fistula standards of the 2016 edition.MethodsClinical data from 182 patients with PD admitted to Tianjin Third Central Hospital from January 2016 to February 2020 were retrospectively analyzed. Patients were divided into modeling (01/2016 to 12/2018) and validation (01/2019 to 02/2020) sets according to the time of admission. The risk factors for postoperative pancreatic fistula (POPF) were screened by univariate and multivariate logistic regression analyses, and a risk prediction model for POPF was established in the modeling set. This score was tested in the validation set.ResultsLogistic regression analysis showed that the main pancreatic duct index and CT value were independent risk factors according to the 2016 pancreatic fistula grading standard, based on which a risk prediction model for POPF was established. Receiver operating characteristic curve analysis showed that the area under the curve was 0.788 in the modeling set and 0.824 in the validation set.ConclusionThe main pancreatic duct index and CT value of the pancreas are closely related to the occurrence of pancreatic fistula after PD, and the established risk prediction model for pancreatic fistula has good prediction accuracy.

scholarly journals Comparison of traditional versus artificial intelligence based coronary artery disease risk prediction scores in young patients with acute coronary syndrome

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
S Vohra ◽  
R Sethi ◽  
P Sharma ◽  
A Pradhan ◽  
P Vishwakarma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Risk Factors ◽  
Acute Coronary Syndrome ◽  
High Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Risk Prediction Model ◽  
Coronary Syndrome ◽  
Indian Patients

Abstract Background Ever since the concept of preventive cardiology has come into vogue, several risk identification models have come up which combine several risk factors to create a risk prediction score for occurrence of cardiovascular (CV) event. While carrying a proven validation in Western population, none of the risk prediction model has been satisfactorily evaluated in Indians especially young <40 years old. Objectives To compare Artificial Intelligence based novel risk score with traditional risk scores in young (less than 40 years age) patients presenting with acute coronary syndrome (ACS) and to estimate the relative efficacy of different coronary artery disease (CAD) risk scores in young Indian Patients. Design Single center, Observational, Non-interventional study. Participants Cohort of Patients more than 20 but less than 40 years old with ACS in the department of Cardiology from 1st January 2019 to 31st October 2019. Methods 314 young patients [mean age 36.14±4.17 years] presenting with acute coronary syndrome (ACS) were enrolled. The three clinically most pertinent risk assessment models [Framingham Risk score (FRS), World Health Organization risk prediction charts (WHO/ISH), and QRISK3 scores] and Artificial Intelligence based novel risk score (AICVD) were applied on day 1 of presentation, and tried to see whether one risk score versus other risk score could have predicted the event earlier had we applied it before the occurrence of ACS. Risk factors considered included those already in traditional scoring systems and new risk factors (diet, alcohol, tobacco, dyslipidemia, physical activity, family history of heart disease, history of heart disease, heart rate, respiratory rate, chronic heart symptoms and psychological stress). Results WHO/ISH provided the lowest high risk estimate with only 1 (0.9%) patient estimated to be having >20% 10-year risk. The FRS estimated high risk (>20% 10-year risk) in 3 (1%) patients. The QRISK3 estimated high risk (>10% 10-year risk) in 20 (6.5%) patient. In comparison, AICVD risk prediction model stood tall by identifying 73 (23.2%) patients as high risk and 62.74% patients as more than moderate risk for having CV events at 7 years (p<0.001). Conclusion Perhaps, this is the first study which has compared artificial intelligence based novel risk prediction model with the three most commonly applied models, in the young Indian patients. We found that a cohort of young Indian patients presenting with ACS, when studied retrospectively, was identified as “high risk” most likely by AICVD risk prediction model rather than the traditional counterparts. The WHO/ISH risk prediction charts and FRS were the poorest predictors. Performance of QRISK3 score also remained less than satisfactory. These findings suggested that AICVD risk prediction model is a promising tool to assess for CV risk in Indian population. FUNDunding Acknowledgement Type of funding sources: None. Predictability of risk prediction models

scholarly journals Establishment of risk prediction model of postoperative pancreatic fistula after pancreatoduodenectomy: 2016 edition of definition and grading system of pancreatic fistula: a single center experience with 223 cases

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jun Yu ◽  
Chao-yi Ren ◽  
Jun Wang ◽  
Wei Cui ◽  
Jin-juan Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Pancreatic Fistula ◽  
Pancreatic Duct ◽  
Risk Prediction ◽  
Main Pancreatic Duct ◽  
Postoperative Pancreatic Fistula ◽  
Risk Prediction Model ◽  
Validation Set

Abstract Objective To establish a risk prediction model for pancreatic fistula according to the pancreatic fistula standards of the 2016 edition. Methods Clinical data from 223 patients with PD admitted to Tianjin Third Central Hospital from January 2016 to December 2020 were retrospectively analyzed. Patients were divided into modeling (January 2016 to December 2018) and validation (January 2019 to December 2020) sets according to the time of admission. The risk factors for postoperative pancreatic fistula (POPF) were screened by univariate and multivariate logistic regression analyses, and a risk prediction model for POPF was established in the modeling set. This score was tested in the validation set. Results Logistic regression analysis showed that the main pancreatic duct index and CT value were independent risk factors according to the 2016 pancreatic fistula grading standard, based on which a risk prediction model for POPF was established. Receiver operating characteristic curve analysis showed that the area under the curve was 0.775 in the modeling set and 0.848 in the validation set. Conclusion The main pancreatic duct index and CT value of the pancreas are closely related to the occurrence of pancreatic fistula after PD, and the established risk prediction model for pancreatic fistula has good prediction accuracy.

scholarly journals A Risk Prediction Model for Acute Kidney Injury in Pulmonary Tuberculosis Patients during Anti-tuberculosis Treatment

2021 ◽  
Author(s):  
Zhi xiang Du ◽  
Fang Chang ◽  
Zi jian Wang ◽  
Da ming Zhou ◽  
Yang Li ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Pulmonary Tuberculosis ◽  
Prediction Model ◽  
Risk Prediction ◽  
Logistic Regression Analysis ◽  
Predictive Factors ◽  
Kidney Injury ◽  
Ca 125

Abstract Background Acute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some pulmonary tuberculosis (PTB) patients. We aimed to develop a risk prediction model to early recognize PTB patients at high risk of AKI during anti-TB treatment.Methods In this retrospective cohort study, clinical baseline, and laboratory test data of 315 inpatients with active PTB from January 2019 and June 2020 were screened for predictive factors. The factors were analyzed by logistic regression analysis. A nomogram was established by the results of the logistic regression analysis. The prediction model discrimination and calibration were evaluated by the concordance index (C-Index), ROC Curve, and Hosmer-Lemeshow analysis.Results 7 factors (Microalbuminuria, Hematuria, CYS-C, Albumin, eGFR, BMI and CA-125) are acquired to develop the predictive model. According to the logistic regression, Microalbuminuria (OR=3.038, 95% CI 1.168-7.904), Hematuria (OR=3.656, 95% CI 1.325-10.083), CYS-C (OR=4.416, 95% CI 2.296-8.491), CA-125 (OR=3.93, 95% CI 1.436-10.756) were risk parameter and ALB (OR=0.741, 95% CI 0.650-0.844) was protective parameter. The nomogram demonstrated a good prediction in estimating AKI. C-Index= 0.967, AUC=0.967, 95% CI (0.941-0.984) Sensitivity=91.04%, Specificity=93.95%, Hosmer-Lemeshow analysis SD=0.00054, Quantile of absolute error=0.049. Conclusion Microalbuminuria, Hematuria, Albumin reduction, elevated CYS-C, and CA125 are predictive factors for AKI in PTB patients during anti-tuberculosis treatments. The predictive nomogram based on five predictive factors is achieved a good risk prediction of AKI during anti-tuberculosis treatments.

scholarly journals Constructing a Predictive Model of Depression in Chemotherapy Patients with Non-Hodgkin’s Lymphoma to Improve Medical Staffs’ Psychiatric Care

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Cheng Hu ◽  
Qian Li ◽  
Ji Shou ◽  
Feng-xian Zhang ◽  
Xia Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Risk Prediction ◽  
Logistic Regression Analysis ◽  
Clinical Utility ◽  
Predictive Power ◽  
Depressive Symptomatology ◽  
Predictive Ability ◽  
Chemotherapy Patients

Objectives. Depression is highly prevalent in non-Hodgkin’s lymphoma (NHL) patients undergoing chemotherapy. The social stress associated with malignancy induces neurovascular pathology promoting clinical levels of depressive symptomatology. The purpose of this study was to establish an effective depressive symptomatology risk prediction model to those patients. Methods. This study included 238 NHL patients receiving chemotherapy, 80 of whom developed depressive symptomatology. Different types of variables (sociodemographic, medical, and psychosocial) were entered in the models. Three prediction models (support vector machine-recursive feature elimination model, random forest model, and nomogram prediction model based on logistic regression analysis) were compared in order to select the one with the best predictive power. The selected model was then evaluated using calibration plots, ROC curves, and C -index. The clinical utility of the nomogram was assessed by the decision curve analysis (DCA). Results. The nomogram prediction has the most efficient predictive ability when 10 predictors are included ( AUC = 0.938 ). A nomogram prediction model was constructed based on the logistic regression analysis with the best predictive accuracy. Sex, age, medical insurance, marital status, education level, per capita monthly household income, pathological stage, SSRS, PSQI, and QLQ-C30 were included in the nomogram. The C -index was 0.944, the AUC value was 0.972, and the calibration curve also showed the good predictive ability of the nomogram. The DCA curve suggested that the nomogram had a strong clinical utility. Conclusions. We constructed a depressive symptomatology risk prediction model for NHL chemotherapy patients with good predictive power and clinical utility.

scholarly journals Re-Examination of Classic Risk Factors for Suicidal Behavior in the Psychiatric Population

Crisis ◽  
2015 ◽  
Vol 36 (4) ◽  
pp. 231-240
Author(s):  
Brittany B. Dennis ◽  
Pavel S. Roshanov ◽  
Monica Bawor ◽  
Wala ElSheikh ◽  
Sue Garton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Psychiatric Hospital ◽  
Risk Prediction ◽  
Risk Score ◽  
Suicidal Behavior ◽  
Psychiatric Patients ◽  
Risk Prediction Model ◽  
Predicted Probability ◽  
History Of

Abstract. Background: For decades we have understood the risk factors for suicide in the general population but have fallen short in understanding what distinguishes the risk for suicide among patients with serious psychiatric conditions. Aims: This prompted us to investigate risk factors for suicidal behavior among psychiatric inpatients. Method: We reviewed all psychiatric hospital admissions (2008–2011) to a centralized psychiatric hospital in Ontario, Canada. Using multivariable logistic regression we evaluated the association between potential risk factors and lifetime history of suicidal behavior, and constructed a model and clinical risk score to predict a history of this behavior. Results: The final risk prediction model for suicidal behavior among psychiatric patients (n = 2,597) included age (in three categories: 60–69 [OR = 0.74, 95% CI = 0.73–0.76], 70–79 [OR = 0.45, 95% CI = 0.44–0.46], 80+ [OR = 0.31, 95% CI = 0.30–.31]), substance use disorder (OR = 1.30, 95% CI = 1.27–1.32), mood disorder (OR = 1.49, 95% CI = 1.47–1.52), personality disorder (OR = 2.30, 95% CI = 2.25–2.36), psychiatric disorders due to general medical condition (OR = 0.52, 95% CI = 0.50–0.55), and schizophrenia (OR = 0.42, 95% CI = 0.41–0.43). The risk score constructed from the risk prediction model ranges from −9 (lowest risk, 0% predicted probability of suicidal behavior) to +5 (highest risk, 97% predicted probability). Conclusion: Risk estimation may help guide intensive screening and treatment efforts of psychiatric patients with high risk of suicidal behavior.

scholarly journals Risk factors for tissue expander infection in scar reconstruction: a retrospective cohort study of 2374 consecutive cases

2020 ◽  
Vol 8 ◽  
Author(s):  
Chen Dong ◽  
Minhui Zhu ◽  
Luguang Huang ◽  
Wei Liu ◽  
Hengxin Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Treatment Failure ◽  
Logistic Regression Analysis ◽  
Disease Duration ◽  
Multivariate Logistic Regression Analysis ◽  
Tissue Expansion ◽  
Multivariate Logistic Regression ◽  
Hematoma Evacuation

Abstract Background Tissue expansion is used for scar reconstruction owing to its excellent clinical outcomes; however, the complications that emerge from tissue expansion hinder repair. Infection is considered a major complication of tissue expansion. This study aimed to analyze the perioperative risk factors for expander infection. Methods A large, retrospective, single-institution observational study was carried out over a 10-year period. The study enrolled consecutive patients who had undergone tissue expansion for scar reconstruction. Demographics, etiological data, expander-related characteristics and postoperative infection were assessed. Univariate and multivariate logistic regression analysis were performed to identify risk factors for expander infection. In addition, we conducted a sensitivity analysis for treatment failure caused by infection as an outcome. Results A total of 2374 expanders and 148 cases of expander infection were assessed. Treatment failure caused by infection occurred in 14 expanders. Multivariate logistic regression analysis identified that disease duration of ≤1 year (odds ratio (OR), 2.07; p < 0.001), larger volume of expander (200–400 ml vs <200 ml; OR, 1.74; p = 0.032; >400 ml vs <200 ml; OR, 1.76; p = 0.049), limb location (OR, 2.22; p = 0.023) and hematoma evacuation (OR, 2.17; p = 0.049) were associated with a high likelihood of expander infection. Disease duration of ≤1 year (OR, 3.88; p = 0.015) and hematoma evacuation (OR, 10.35; p = 0.001) were so related to high risk of treatment failure. Conclusions The rate of expander infection in patients undergoing scar reconstruction was 6.2%. Disease duration of <1 year, expander volume of >200 ml, limb location and postoperative hematoma evacuation were independent risk factors for expander infection.

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