scholarly journals Development and validation of a nomogram for predicting severity in patients with hemorrhagic fever with renal syndrome: A retrospective study

Open Medicine ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. 944-954
Author(s):  
Zheng Yang ◽  
Qinming Hu ◽  
Zhipeng Feng ◽  
Yi Sun
Keyword(s):  
Retrospective Study ◽  
Risk Model ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Public Health Problem ◽  
Hemorrhagic Fever ◽  
Multiple Organ ◽  
Hantavirus Infection ◽  
Lasso Regression ◽  
Selection Operator

Abstract Background Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by hantavirus infection. Patients with severe HFRS may develop multiple organ failure or even death, which makes HFRS a serious public health problem. Methods In this retrospective study, we included a total of 155 consecutive patients who were diagnosed with HFRS, of whom 109 patients served as a training cohort and 46 patients as an independent verification cohort. In the training set, the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables of the risk model. Multivariate logistic regression analysis was used to construct a nomogram containing the characteristic variables selected in the LASSO regression model. Results The area under the receiver operating characteristic curve (AUC) of the nomogram indicated that the model had good discrimination. The calibration curve exhibited that the nomogram was in good agreement between the prediction and the actual observation. Decision curve analysis and clinical impact curve suggested that the predictive nomogram had clinical utility. Conclusion In this study, we established a simple and feasible model to predict severity in patients with HFRS, with which HFRS would be better identified and patients can be treated early.

scholarly journals Development and Validation of a Nomogram for Predicting Severity in Patients With Hemorrhagic Fever With Renal Syndrome: a Retrospective Study

2021 ◽  
Author(s):  
Zheng Yang ◽  
Qinming Hu ◽  
Zhipeng Feng ◽  
Yi Sun
Keyword(s):  
Retrospective Study ◽  
Risk Model ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Public Health Problem ◽  
Hemorrhagic Fever ◽  
Multiple Organ ◽  
Hantavirus Infection ◽  
Lasso Regression ◽  
Training Set

Abstract Background: Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by hantavirus infection. China is one of the most endemic countries of HFRS in the world. Patients with severe HFRS may develop multiple organ failure or even death, which makes HFRS a serious public health problem in China. Therefore, we constructed and verified a reliable nomogram to predict the severity in patients with HFRS and provide guidance for medical practice.Methods: In this retrospective study, we included a total of 155 consecutive patients with HFRS who were diagnosed from January 1, 2015 to December 31, 2019, of which 109 patients served as a training cohort and 46 patients as an independent verification cohort. 54 laboratory and clinical indicators were applied to assess the severity of HFRS patients. In the training set, the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables of the risk model. Multivariate logistic regression analysis was used to construct a nomogram containing the characteristic variables selected in the LASSO regression model. The nomogram's performance was evaluated by the discrimination, calibration, and clinical applicability in the training set and validation set.Results: The prediction nomogram included six predictors such as neutrophils, hemoglobin (Hb), platelets, creatinine, calcium (Ca) and dyspnea, which were screened by LASSO regression. The area under the receiver operating characteristic curve (AUC) of the nomogram in the training and validation cohorts was 0.969 (95%CI:0.935-1.000) and 0.934(95%CI: 0.847-1.000), respectively, indicating that the model has good discrimination. The calibration curve exhibited that the nomogram was in good agreement between the prediction and the actual observation. Decision curve analysis and clinical impact curve indicated that the predictive nomogram had clinical utility.Conclusion: In this study, we established a simple and feasible model to predict severity in patients with HFRS, with which HFRS will be better identified and patients can be treated early.

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

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.

scholarly journals Construction and Validation of an Autophagy-Related Prognostic Model for Osteosarcoma Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hu Qian ◽  
Ting Lei ◽  
Pengfei Lei ◽  
Yihe Hu
Keyword(s):  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Bone Development ◽  
Treatment Strategies ◽  
Risk Groups ◽  
Low Risk ◽  
Lasso Regression ◽  
Prognostic Accuracy ◽  
Selection Operator

While the prognostic value of autophagy-related genes (ARGs) in OS patients remains scarcely known, increasing evidence is indicating that autophagy is closely associated with the development and progression of osteosarcoma (OS). Therefore, we explored the prognostic value of ARGs in OS patients and illuminate associated mechanisms in this study. When the OS patients in the training/validation cohort were stratified into high- and low-risk groups according to the risk model established using least absolute shrinkage and selection operator (LASSO) regression analysis, we observed that patients in the low-risk group possessed better prognosis ( P < 0.0001 ). Univariate/Multivariate COX regression and subgroup analysis demonstrated that the ARGs-based risk model was an independent survival indicator for OS patients. The nomogram incorporating the risk model and clinical features exhibited excellent prognostic accuracy. GO, KEGG, and GSVA analyses collectively indicated that bone development-associated pathway mediated the contribution of ARGs to the malignance of OS. Immune infiltration analysis suggested the potential pivotal role of macrophage in OS. In summary, the risk model based on 12 ARGs possessed potent capacity in predicting the prognosis of OS patients. Our work may assist clinicians to map out more reasonable treatment strategies and facilitate individual-targeted therapy in osteosarcoma.

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

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.

scholarly journals Establishment of a Nomogram for Predicting Early Death in Viral Myocarditis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xuejun Sun ◽  
Naxin Xie ◽  
Mengling Guo ◽  
Xuelian Qiu ◽  
Hongwei Chen ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
Viral Myocarditis ◽  
Early Death ◽  
Clinical Situation ◽  
Good Prediction ◽  
Lasso Regression ◽  
Training Cohort ◽  
Selection Operator ◽  
Medical University ◽  
Electrocardiogram Ecg

Objective. This research aimed to establish a nomogram for predicting early death in viral myocarditis (VMC) patients. Method. A total of 362 consecutive VMC patients in Fujian Medical University Affiliated First Quanzhou Hospital between January 1, 2009, and December 31, 2019, were included. A least absolute shrinkage and selection operator (LASSO) regression model was used to detect the risk factors that most consistently and correctly predicted early death in VMC. The performance of the nomogram was assessed by calibration, discrimination, and clinical utility. Result. 9 factors were screened by LASSO regression analysis for predicting the early death of VMC. Combined with the actual clinical situation, the heart failure (HF) (OR: 2.13, 95% CI: 2.76–5.95), electrocardiogram (ECG) (OR: 6.11, 95% CI: 1.05–8.66), pneumonia (OR: 3.62, 95% CI: 1.43–9.85), brain natriuretic peptide (BNP) (OR: 4.66, 95% CI: 3.07–24.06), and lactate dehydrogenase (LDH) (OR: 1.90, 95% CI: 0.19–9.39) were finally used to construct the nomogram. The nomogram’s C-index was 0.908 in the training cohort and 0.924 in the validation cohort. And the area under the receiver operating characteristic curve of the nomogram was 0.91 in the training cohort and 0.924 in the validating cohort. Decision curve analysis (DCA) also showed that the nomogram was clinically useful. Conclusion. This nomogram achieved an good prediction of the risk of early death in VMC patients.

scholarly journals Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability

Vaccines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1221
Author(s):  
Fan Hu ◽  
Ruijie Gong ◽  
Yexin Chen ◽  
Jinxin Zhang ◽  
Tian Hu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
External Validation ◽  
Multivariate Logistic Regression Analysis ◽  
Stability Index ◽  
Lasso Regression ◽  
Mobile Population ◽  
Leading Position ◽  
The Government ◽  
Selection Operator

Since China’s launch of the COVID-19 vaccination, the situation of the public, especially the mobile population, has not been optimistic. We investigated 782 factory workers for whether they would get a COVID-19 vaccine within the next 6 months. The participants were divided into a training set and a testing set for external validation conformed to a ratio of 3:1 with R software. The variables were screened by the Lead Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Then, the prediction model, including important variables, used a multivariate logistic regression analysis and presented as a nomogram. The Receiver Operating Characteristic (ROC) curve, Kolmogorov–Smirnov (K-S) test, Lift test and Population Stability Index (PSI) were performed to test the validity and stability of the model and summarize the validation results. Only 45.54% of the participants had vaccination intentions, while 339 (43.35%) were unsure. Four of the 16 screened variables—self-efficacy, risk perception, perceived support and capability—were included in the prediction model. The results indicated that the model has a high predictive power and is highly stable. The government should be in the leading position, and the whole society should be mobilized and also make full use of peer education during vaccination initiatives.

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

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.

scholarly journals Predicting bleeding risk in a Chinese immune thrombocytopenia (ITP) population: development and assessment of a new predictive nomogram

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mingjing Wang ◽  
Weiyi Liu ◽  
Yonggang Xu ◽  
Hongzhi Wang ◽  
Xiaoqing Guo ◽  
...  
Keyword(s):  
Feature Selection ◽  
Predictive Model ◽  
Risk Model ◽  
Validation Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Bleeding Risk ◽  
Inflammatory Factors ◽  
Calibration Plot ◽  
Ifn Γ ◽  
Selection Operator

Abstract The aim of this study was to develop a model that could be used to forecast the bleeding risk of ITP based on proinflammatory and anti-inflammatory factors. One hundred ITP patients were recruited to build a new predictive nomogram, another eighty-eight ITP patients were enrolled as validation cohort, and data were collected from January 2016 to January 2019. Four demographic characteristics and fifteen clinical characteristics were taken into account. Eleven cytokines (IFN-γ, IL-1, IL-4, IL-6, IL-8, IL-10, IL-17A, IL-22, IL-23, TNF-α and TGF-β) were used to study and the levels of them were detected by using a cytometric bead array (CBA) human inflammation kit. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariate logistic regression analysis was applied to build a new predictive nomogram based on the results of the least absolute shrinkage and selection operator regress ion model. The application of C-index, ROC curve, calibration plot, and decision curve analyses were used to assess the discrimination, calibration, and clinical practicability of the predictive model. Bootstrapping validation was used for testing and verifying the predictive model. After feature selection, cytokines IL-1, IL-6, IL-8, IL-23 and TGF-β were excluded, cytokines IFN-γ, IL-4, IL-10, IL-17A, IL-22, TGF-β, the count of PLT and the length of time of ITP were used as predictive factors in the predictive nomogram. The model showed good discrimination with a C-index of 0.82 (95% confidence interval 0.73376–0.90 624) in training cohortn and 0.89 (95% CI 0.868, 0.902) in validation cohort, an AUC of 0.795 in training cohort, 0.94 in validation cohort and good calibration. A high C-index value of 0.66 was reached in the interval validation assessment. Decision curve analysis showed that the bleeding risk nomogram was clinically useful when intervention was decided at the possibility threshold of 16–84%. The bleeding risk model based on IFN-γ, IL-4, IL-10, IL-17A, IL-22, TGF-β, the count of PLT and the length of time of ITP could be conveniently used to predict the bleeding risk of ITP.

scholarly journals HFRS with Severe Heart Liver and Renal Failure: a Case Report

2012 ◽  
Vol 1 (3) ◽  
pp. 146-148
Author(s):  
Qing Zhou ◽  
Meng-Hou Lu ◽  
Lei Fu ◽  
De-Ming Tan
Keyword(s):  
Renal Damage ◽  
Hemorrhagic Fever ◽  
Organ Damage ◽  
Multiple Organ ◽  
Hantavirus Infection ◽  
System Failure ◽  
Organ System Failure ◽  
Multiple Organ System Failure ◽  
Multiple System Organ ◽  
System Organ

Abstract Hemorrhagic fever with renal syndrome (HFRS) is caused by hantavirus infection, which was characterized by abrupt high fever, systemic hemorrhage, hypotension and renal damage. Although multiple system organ damage was not uncommon, but multiple organ system failure were rare. Hereafter we report one case with simultaneous renal, heart and liver failure. In this case, we received some experience and lessons.

Difusi dan Pola Spasial Sebaran Penyakit Demam Berdarah Dengue (DBD) Di Kota Bandar Lampung

2018 ◽  
Vol 1 (1) ◽  
pp. 87-95
Author(s):  
Nurul Qamila ◽  
Agel Vidian Krama
Keyword(s):  
Public Health ◽  
Spatial Analysis ◽  
Aedes Aegypti ◽  
Dengue Virus ◽  
Spatial Pattern ◽  
Dengue Hemorrhagic Fever ◽  
Health Problem ◽  
Public Health Problem ◽  
Hemorrhagic Fever ◽  
Descriptive Method

Dengue hemorrhagic fever (DHF) is a contagious disease caused by the dengue virus and is transmitted by the mosquito Aedes aegypti (Aa.aegypti). The population is still a public health problem that increases the number of sufferers and also widespread, with population and education. This study aims to reveal the spatial pattern and distribution of Dengue Hemorrhagic Fever (DHF) with the spatial pattern and the spread of Dengue Hemorrhagic Fever (DHF) can result in different locations of these allegations. From the map that can be used for the prevention of Dengue Hemorrhagic Fever (DBD) in Bandar Lampung City. This study aims to reveal the spatial pattern and distribution of Dengue Hemorrhagic Fever (DHF) with the descriptive method and spatial pattern of Dengue Hemorrhagic Fever (DHF) can result in different locations of these allegations. From the map that can be used for the prevention of Dengue Hemorrhagic Fever (DBD) in Bandar Lampung City. Keywords: DHF, Spatial Analysis

Sign in / Sign up

Export Citation Format

Share Document