Nomogram prediction model for renal anaemia in IgA nephropathy patients

Abstract In this study, we focused on the influencing factors of renal anaemia in patients with IgA nephropathy and constructed a nomogram model. We divided 462 patients with IgA nephropathy diagnosed by renal biopsy into anaemic and non-anaemic groups. Then, the influencing factors of renal anaemia in patients with IgA nephropathy were analysed by least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression, and a nomogram model for predicting renal anaemia was established. Eventually, nine variables were obtained, which are easy to apply clinically. The areas under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve reached 0.835 and 0.676, respectively, and the C-index reached 0.848. The calibration plot showed that the model had good discrimination, accuracy, and diagnostic efficacy. In addition, the C-index of the model following internal validation reached 0.823. Decision curve analysis suggested that the model had a certain degree of clinical significance. This new nomogram model of renal anaemia combines the basic information, laboratory findings, and renal biopsy results of patients with IgA nephropathy, providing important guidance for predicting and clinically intervening in renal anaemia.

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Development and Validation of a Nomogram for Predicting Bladder Calculi Risk in Patients With Benign Prostatic Hyperplasia

10.21203/rs.3.rs-934816/v1 ◽

2021 ◽

Author(s):

Euxu Xie ◽

Xuelian Gu ◽

Chen Ma ◽

Li Guo ◽

Man Li ◽

...

Keyword(s):

Risk Factors ◽

Benign Prostatic Hyperplasia ◽

Characteristic Curve ◽

Prostatic Hyperplasia ◽

Calibration Plot ◽

Lasso Regression ◽

Net Benefit ◽

Bladder Calculi ◽

Validation Set ◽

Selection Operator

Abstract Objective To develop and validate a nomogram for predicting bladder calculi risk in patients with benign prostatic hyperplasia (BPH).Methods A total of 368 patients who underwent transurethral resection of the prostate (TURP) and had histologically proven BPH from January 2018 to January 2021 were retrospectively collected. Eligible patients were randomly assigned to the training and validation datasets. Least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal risk factors. A prediction model was established based on the selected characteristics. The performance of the nomogram was assessed by calibration plots and the area under the receiver operating characteristic curve (AUROC). Furthermore, decision curve analysis (DCA) was used to determine the net benefit rate of of the nomogram. Results Among 368 patients who met the inclusion criteria, older age, a history of diabetes and hyperuricemia, longer intravesical prostatic protrusion (IPP)and larger prostatic urethral angulation (PUA) were independent risk factors for bladder calculi in patients with BPH. These factors were used to develop a nomogram, which had a good identification ability in predicting the risk of bladder calculi in patients, with AUROCs of 0.911 (95% CI: 0.876–0.945) in the training set and 0.884 (95% CI: 0.820–0.948) in the validation set. The calibration plot showed that the model had good calibration. Moreover, DCA indicated that the model had a goodclinical benefit. Conclusion We developed and internally validated the first nomogram to date to help physicians assess the risk of bladder calculi in patients with BPH, which may help physicians improve individual interventions and make better clinical decisions.

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Clinical and pathological factors of renal anaemia in patients with IgA nephropathy in Chinese adults: a cross-sectional study

BMJ Open ◽

10.1136/bmjopen-2018-023479 ◽

2019 ◽

Vol 9 (1) ◽

pp. e023479 ◽

Cited By ~ 2

Author(s):

Yang Wang ◽

Ri-bao Wei ◽

Ting-yu Su ◽

Meng-jie Huang ◽

Ping Li ◽

...

Keyword(s):

Logistic Regression ◽

Iga Nephropathy ◽

Influencing Factors ◽

Cross Sectional Study ◽

General Information ◽

Sectional Study ◽

Renal Anaemia ◽

Cross Sectional ◽

Hospitalised Patients ◽

Tubulointerstitial Lesions

ObjectiveFew studies with large sample populations concerning renal anaemia and IgA nephropathy have been reported worldwide. The purpose of this cross-sectional study was to examine the clinical and pathological characteristics and influencing factors associated with renal anaemia in patients with IgA nephropathy, which is the most common aetiology of chronic kidney disease.MethodsA total of 462 hospitalised patients with IgA nephropathy confirmed by renal biopsy who met the inclusion criteria were consecutively recruited from January 2014 to January 2016. Their general information, routine blood test results, blood chemistries, estimated glomerular filtration rates (eGFRs) and renal pathologies were collected. The Oxford classification was used to characterise the renal pathologies. Univariable and multivariate logistic regression models were used to analyse the influencing factors of anaemia associated with IgA nephropathy.ResultsThe incidence of renal anaemia was 28.5% (132/462 patients) in our study (21.3% in males and 38.9% in females). The anaemia type was primarily normocytic and normochromic. The rate of anaemia in patients with eGFR values of 30–59 mL/min/1.73 m2was higher than that in patients with an eGFR >60 mL/min/1.73 m2(42.9% vs 17.8%, p<0.001). Notably, in the group with eGFR values <15 mL/min/1.73 m2, the anaemia rate was 100%. Logistic regression analysis showed that factors affecting anaemia in patients with IgA nephropathy included being female (OR 3.02, 95% CI 1.76 to 5.17), low albumin levels (OR 0.87, 95% CI 0.82 to 0.93), reduced eGFR values (OR 0.98, 95% CI 0.97 to 0.99) and renal tubulointerstitial lesions >50% (OR 2.57, 95% CI 1.22 to 5.40).ConclusionsThe female sex, hypoalbuminaemia, reduced eGFR levels and severe renal tubulointerstitial lesions were correlated with renal anaemia in patients with IgA nephropathy. These results provide new insight into our understanding of anaemia in IgA nephropathy and may improve the management and treatment of clinical renal anaemia.

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Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head

10.21203/rs.2.23333/v1 ◽

2020 ◽

Author(s):

Qiang Xu ◽

Hangjun Chen ◽

Sihai Chen ◽

Jing Shan ◽

Guoming Xia ◽

...

Keyword(s):

Risk Factors ◽

Femoral Head ◽

Demographic Data ◽

Characteristic Curve ◽

Model Development ◽

Lasso Regression ◽

Early Screening ◽

Good Discrimination ◽

Internal Validation ◽

Selection Operator

Abstract Background Although corticosteroids and alcohol are two major risk factors for nontraumatic osteonecrosis of the femoral head (NONFH), the effects of other factors have rarely been studied, thereby making early diagnosis and treatment of NONFH difficult. This study aimed to develop and validate a nomogram to estimate the probability of NONFH using clinical risk factors other than corticosteroids and alcohol consumption. Methods A training cohort of 790 patients (n=434, NONFH; n=356, femoral neck fractures [non-NONFH]) diagnosed in our hospital from January 2011 to December 2016 was used for model development. A least absolute shrinkage and selection operator (lasso) regression model was used for date dimension reduction and optimal predictor selection. A predictive model was developed from univariate and multivariate logistic regression analyses. Performance characterisation of the resulting nomogram included calibration, discriminatory ability, and clinical usefulness. After internal validation, the nomogram was further evaluated in a separate cohort of 300 consecutive patients included between January 2017 and December 2018. Results The simple prediction nomogram included five predictors from univariate and multivariate analyses, including gender, total cholesterol levels, triglyceride levels, white blood cell count, and platelet count. Internal validation showed that the model had good discrimination (area under the receiver operating characteristic curve [AUC]=0.80) and calibration. Good discrimination (AUC=0.81) and calibration were preserved in the validation cohort. Decision curve analysis showed that the predictive nomogram was clinically useful. Conclusions The simple diagnostic nomogram, which combines demographic data and laboratory blood test results, was able to quantify the probability of NONFH in cases of early screening and diagnosis.

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Development and Validation of a Nomogram to Predict the 180-Day Readmission Risk for Chronic Heart Failure: A Multicenter Prospective Study

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.731730 ◽

2021 ◽

Vol 8 ◽

Author(s):

Shanshan Gao ◽

Gang Yin ◽

Qing Xia ◽

Guihai Wu ◽

Jinxiu Zhu ◽

...

Keyword(s):

Heart Failure ◽

Chronic Heart Failure ◽

Stepwise Regression ◽

Prediction Models ◽

Cox Regression ◽

Univariate Analysis ◽

External Validation ◽

Calibration Plot ◽

Lasso Regression ◽

Internal Validation

Background: The existing prediction models lack the generalized applicability for chronic heart failure (CHF) readmission. We aimed to develop and validate a widely applicable nomogram for the prediction of 180-day readmission to the patients.Methods: We prospectively enrolled 2,980 consecutive patients with CHF from two hospitals. A nomogram was created to predict 180-day readmission based on the selected variables. The patients were divided into three datasets for development, internal validation, and external validation (mean age: 74.2 ± 14.1, 73.8 ± 14.2, and 71.0 ± 11.7 years, respectively; sex: 50.2, 48.8, and 55.2% male, respectively). At baseline, 102 variables were submitted to the least absolute shrinkage and selection operator (Lasso) regression algorithm for variable selection. The selected variables were processed by the multivariable Cox proportional hazards regression modeling combined with univariate analysis and stepwise regression. The model was evaluated by the concordance index (C-index) and calibration plot. Finally, the nomogram was provided to visualize the results. The improvement in the regression model was calculated by the net reclassification index (NRI) (with tenfold cross-validation and 200 bootstraps).Results: Among the selected 2,980 patients, 1,696 (56.9%) were readmitted within 180 days, and 1,502 (50.4%) were men. A nomogram was established by the results of Lasso regression, univariate analysis, stepwise regression and multivariate Cox regression, as well as variables with clinical significance. The values of the C-index were 0.75 [95% confidence interval (CI): 0.72–0.79], 0.75 [95% CI: 0.69–0.81], and 0.73 [95% CI: 0.64–0.83] for the development, internal validation, and external validation datasets, respectively. Calibration plots were provided for both the internal and external validation sets. Five variables including history of acute heart failure, emergency department visit, age, blood urea nitrogen level, and beta blocker usage were considered in the final prediction model. When adding variables involving hospital discharge way, alcohol taken and left bundle branch block, the calculated values of NRI demonstrated no significant improvements.Conclusions: A nomogram for the prediction of 180-day readmission of patients with CHF was developed and validated based on five variables. The proposed methodology can improve the accurate prediction of patient readmission and have the wide applications for CHF.

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Retrospective Study on the Influencing Factors and Prediction of Hospitalization Expenses for Chronic Renal Failure in China Based on Random Forest and LASSO Regression

Frontiers in Public Health ◽

10.3389/fpubh.2021.678276 ◽

2021 ◽

Vol 9 ◽

Author(s):

Pingping Dai ◽

Weifu Chang ◽

Zirui Xin ◽

Haiwei Cheng ◽

Wei Ouyang ◽

...

Keyword(s):

Renal Failure ◽

Chronic Renal Failure ◽

Random Forest ◽

Length Of Stay ◽

Prediction Model ◽

Regression Model ◽

Influencing Factors ◽

Lasso Regression ◽

Health Administration ◽

Selection Operator

Aim: With the improvement in people's living standards, the incidence of chronic renal failure (CRF) is increasing annually. The increase in the number of patients with CRF has significantly increased pressure on China's medical budget. Predicting hospitalization expenses for CRF can provide guidance for effective allocation and control of medical costs. The purpose of this study was to use the random forest (RF) method and least absolute shrinkage and selection operator (LASSO) regression to predict personal hospitalization expenses of hospitalized patients with CRF and to evaluate related influencing factors.Methods: The data set was collected from the first page of data of the medical records of three tertiary first-class hospitals for the whole year of 2016. Factors influencing hospitalization expenses for CRF were analyzed. Random forest and least absolute shrinkage and selection operator regression models were used to establish a prediction model for the hospitalization expenses of patients with CRF, and comparisons and evaluations were carried out.Results: For CRF inpatients, statistically significant differences in hospitalization expenses were found for major procedures, medical payment method, hospitalization frequency, length of stay, number of other diagnoses, and number of procedures. The R2 of LASSO regression model and RF regression model are 0.6992 and 0.7946, respectively. The mean absolute error (MAE) and root mean square error (RMSE) of the LASSO regression model were 0.0268 and 0.043, respectively, and the MAE and RMSE of the RF prediction model were 0.0171 and 0.0355, respectively. In the RF model, and the weight of length of stay was the highest (0.730).Conclusions: The hospitalization expenses of patients with CRF are most affected by length of stay. The RF prediction model is superior to the LASSO regression model and can be used to predict the hospitalization expenses of patients with CRF. Health administration departments may consider formulating accurate individualized hospitalization expense reimbursement mechanisms accordingly.

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Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population

Scientific Reports ◽

10.1038/s41598-020-77693-9 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Qiang Xu ◽

Hangjun Chen ◽

Sihai Chen ◽

Jing Shan ◽

Guoming Xia ◽

...

Keyword(s):

Femoral Head ◽

Demographic Data ◽

Characteristic Curve ◽

Model Development ◽

Lasso Regression ◽

Early Screening ◽

Good Discrimination ◽

Internal Validation ◽

Selection Operator ◽

Development And Validation

AbstractAlthough corticosteroids and alcohol are two major risk factors for nontraumatic osteonecrosis of the femoral head (NONFH), the effects of other factors have rarely been studied, thereby making early diagnosis and treatment of NONFH difficult. This study aimed to develop and validate a nomogram to NONFH, but patients with alcohol- and steroid-related NONFH are not at all taken into account in this study. A training cohort of 790 patients (n = 434, NONFH; n = 356, femoral neck fractures [non-NONFH]) diagnosed in our hospital from January 2011 to December 2016 was used for model development. A least absolute shrinkage and selection operator (lasso) regression model was used for date dimension reduction and optimal predictor selection. A predictive model was developed from univariate and multivariate logistic regression analyses. Performance characterisation of the resulting nomogram included calibration, discriminatory ability, and clinical usefulness. After internal validation, the nomogram was further evaluated in a separate cohort of 300 consecutive patients included between January 2017 and December 2018. The simple prediction nomogram included five predictors from univariate and multivariate analyses, including gender, total cholesterol levels, triglyceride levels, white blood cell count, and platelet count. Internal validation showed that the model had good discrimination [area under the receiver operating characteristic curve (AUC) = 0.80] and calibration. Good discrimination (AUC = 0.81) and calibration were preserved in the validation cohort. Decision curve analysis showed that the predictive nomogram was clinically useful. The simple diagnostic nomogram, which combines demographic data and laboratory blood test results, was able to quantify the probability of NONFH in cases of early screening and diagnosis.

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A Historical Data based Ensemble System for Efficient Stock Price Prediction

Recent Patents on Computer Science ◽

10.2174/2213275912666190730161807 ◽

2019 ◽

Vol 12 ◽

Author(s):

Vijay Kumar Dwivedi ◽

Manoj Madhava Gore

Keyword(s):

Stock Price ◽

Prediction Models ◽

Historical Data ◽

Stock Exchange ◽

Primary Objective ◽

Lasso Regression ◽

Stock Price Prediction ◽

Price Prediction ◽

Consistent Manner ◽

Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

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A machine learning-based model for predicting intensive care unit length of stay in patients with cardiac surgery (Preprint)

10.2196/preprints.32887 ◽

2021 ◽

Author(s):

Nianyue Wu ◽

Siru Liu ◽

Haotian Zhang ◽

Xiaomin Hou ◽

Ping Zhang ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Length Of Stay ◽

Confidence Interval ◽

Medical Information ◽

Lasso Regression ◽

Icu Length Of Stay ◽

Data Source ◽

The Mean ◽

Selection Operator

BACKGROUND The intensive care unit (ICU) length of stay is significant to evaluate the effect of cardiac surgical treatment inpatient. OBJECTIVE This research aims to accurately predict the ICU length of stay in patients with cardiac surgery. Methods: We used machine learning methods to construct the model, and the medical information mart for intensive care (MIMIC IV) database was used as the data source. A total of 7,567 patients were enrolled and the mean length of stay in the ICU was 3.12 days. A total of 126 predictors were included, and 44 important predictors were screened by least absolute shrinkage and selection operator (Lasso) regression. METHODS We used machine learning methods to construct the model, and the medical information mart for intensive care (MIMIC IV) database was used as the data source. A total of 7,567 patients were enrolled and the mean length of stay in the ICU was 3.12 days. A total of 126 predictors were included, and 44 important predictors were screened by least absolute shrinkage and selection operator (Lasso) regression. RESULTS The mean accuracy are 0.603 (95% confidence interval (CI): [0.602-0.604]), 0.687 (95% confidence interval (CI): [0.687-0.688]) and 0.688 (95% confidence interval (CI): [0.687-0.689]) for the logistic regression (LR) with all variables, the gradient boosted decision tree (GBDT) with important variables and the GBDT with all variables respectively. CONCLUSIONS The GBDT model with important predictors partly overestimated patients whose length of stay was less than 3 days and underestimated patients whose length of stay was longer than 3 days. But the better prediction performance of GBDT facilitates early intervention of ICU patients with a long period of hospitalization.

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A Novel PM2.5 Concentrations Probability Density Prediction Model Combines The Least Absolute Shrinkage And Selection Operator With Quantile Regression

10.21203/rs.3.rs-982230/v1 ◽

2021 ◽

Author(s):

Shaomei Yang ◽

Haoyue Wu

Keyword(s):

Quantile Regression ◽

Probability Density ◽

Prediction Models ◽

Negative Impact ◽

Influential Factors ◽

Percentage Error ◽

Lasso Regression ◽

Density Prediction ◽

Significant Negative Impact ◽

Selection Operator

Abstract PM2.5 has a significant negative impact on human health and atmospheric quality, and accurate prediction of its concentration is necessary. PM2.5 concentration is influenced by a combination of factors from both meteorological conditions and air quality. It is essential to identify the significant factors influencing PM2.5 concentrations in the prediction process. To address this issue, this paper proposes the quantile regression (QR) model based on the least absolute shrinkage and selection operator (LASSO), combined with kernel density estimation (KDE) for probabilistic density prediction of PM2.5 concentrations. The model uses LASSO regression to select the influential factors, and then the quartiles of daily PM2.5 concentrations obtained using the QR model are imported into the KDE model to obtain the probability density curves of PM2.5 concentrations. In this paper, empirical analysis is performed with the data sets of Beijing, China, and Jinan, China, and the accuracy of the model is evaluated using the mean absolute percentage error(MAPE) and the relative mean square error (RMSE). The simulation results reveal that the LASSSO-QR-KDE model has a higher accuracy than the traditional prediction models and the currently used research models. The model provides a novel and excellent tool for policy makers to predict PM2.5 concentrations.

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