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 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 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 Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

scholarly journals A Nomogram for Predicting In-Hospital Mortality in Patients with Takotsubo Syndrome

2021 ◽  
Author(s):  
Jun Chen ◽  
Yimin Wang ◽  
Xinyang Shou ◽  
Qiang Liu ◽  
Ziwei Mei
Keyword(s):  
Logistic Regression ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Risk Prediction ◽  
Clinical Utility ◽  
Risk Prediction Model ◽  
Multivariate Logistic Regression Model ◽  
Calibration Plot ◽  
Multivariable Logistic Regression Analysis ◽  
Takotsubo Syndrome

Abstract BACKGROUND Despite the large number of studies focus on the prognosis and in-hospital outcomes risk factors of patients with takotsubo syndrome, there was still lack of utility and visual risk prediction model for predicting the in-hospital mortality of patients with takotsubo syndrome. OBJECTIVES Our study aimed to establish a utility risk prediction model for the prognosis of in-hospital patients with takotsubo syndrome (TTS). METHODS The study is a retrospective cohort study. Model of in-hospital mortality of TTS patients was developed by multivariable logistic regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. The clinical utility of the model was evaluated by decision curve analysis (DCA). RESULTS Overall, 368 TTS patients (320 Survivals and 48 deaths) were included in our research from MIMIC-IV database. The incidence of in-hospital mortality with TTS is 13.04%. Lasso regression and multivariate logistic regression model verified that potassium, pt, age, myocardial infarction, WBC, hematocrit, anion gap and SOFA score were significantly associated with in-hospital mortality of TTS patients. The nomogram demonstrated a good discrimination with a AUC of ROC 0.811(95%Cl: 0.746-0.876) in training set and 0.793(95%Cl: 0.724-0.862) in test set. The calibration plot of risk prediction model showed predicted probabilities against observed death rates indicated excellent concordance. DCA showed that the nomogram has good clinical benefits. Conclusion We developed a nomogram that predict hospital mortality in patients with TTS according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.

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 A Prediction Model for Cognitive Impairment Risk in Colorectal Cancer after Chemotherapy Treatment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shu-Ping Zhou ◽  
Su-Ding Fei ◽  
Hui-Hui Han ◽  
Jing-Jing Li ◽  
Shuang Yang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Cognitive Impairment ◽  
Prediction Model ◽  
Clinical Utility ◽  
Prediction Models ◽  
Predictive Ability ◽  
Support Vector ◽  
Colorectal Cancer Patients

Background. A prediction model can be developed to predict the risk of cancer-related cognitive impairment in colorectal cancer patients after chemotherapy. Methods. A regression analysis was performed on 386 colorectal cancer patients who had undergone chemotherapy. Three prediction models (random forest, logistic regression, and support vector machine models) were constructed using collected clinical and pathological data of the patients. Calibration and ROC curves and C -indexes were used to evaluate the selected models. A decision curve analysis (DCA) was used to determine the clinical utility of the line graph. Results. Three prediction models including a random forest, a logistic regression, and a support vector machine were constructed. The logistic regression model had the strongest predictive power with an area under the curve (AUC) of 0.799. Age, BMI, colostomy, complications, CRA, depression, diabetes, QLQ-C30 score, exercise, hypercholesterolemia, diet, marital status, education level, and pathological stage were included in the nomogram. The C -index (0.826) and calibration curve showed that the nomogram had good predictive ability and the DCA curves indicated that the model had strong clinical utility. Conclusions. A prediction model with good predictive ability and practical clinical value can be developed for predicting the risk of cognitive impairment in colorectal cancer after chemotherapy.

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 Development and validation of a model to estimate the risk of acute ischemic stroke in geriatric patients with primary hypertension

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xifeng Zheng ◽  
Fang Fang ◽  
Weidong Nong ◽  
Dehui Feng ◽  
Yu Yang
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Prediction Model ◽  
Acute Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Geriatric Patients ◽  
Primary Hypertension ◽  
Multivariable Logistic Regression Analysis ◽  
Training Set

Abstract Objectives This study aimed to construct and validate a prediction model of acute ischemic stroke in geriatric patients with primary hypertension. Methods This retrospective file review collected information on 1367 geriatric patients diagnosed with primary hypertension and with and without acute ischemic stroke between October 2018 and May 2020. The study cohort was randomly divided into a training set and a testing set at a ratio of 70 to 30%. A total of 15 clinical indicators were assessed using the chi-square test and then multivariable logistic regression analysis to develop the prediction model. We employed the area under the curve (AUC) and calibration curves to assess the performance of the model and a nomogram for visualization. Internal verification by bootstrap resampling (1000 times) and external verification with the independent testing set determined the accuracy of the model. Finally, this model was compared with four machine learning algorithms to identify the most effective method for predicting the risk of stroke. Results The prediction model identified six variables (smoking, alcohol abuse, blood pressure management, stroke history, diabetes, and carotid artery stenosis). The AUC was 0.736 in the training set and 0.730 and 0.725 after resampling and in the external verification, respectively. The calibration curve illustrated a close overlap between the predicted and actual diagnosis of stroke in both the training set and testing validation. The multivariable logistic regression analysis and support vector machine with radial basis function kernel were the best models with an AUC of 0.710. Conclusion The prediction model using multiple logistic regression analysis has considerable accuracy and can be visualized in a nomogram, which is convenient for its clinical application.

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