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

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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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 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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The Stunting Tool for Early Prevention: development and external validation of a novel tool to predict risk of stunting in children at 3 years of age

BMJ Global Health ◽

10.1136/bmjgh-2019-001801 ◽

2019 ◽

Vol 4 (6) ◽

pp. e001801

Author(s):

Sarah Hanieh ◽

Sabine Braat ◽

Julie A Simpson ◽

Tran Thi Thu Ha ◽

Thach D Tran ◽

...

Keyword(s):

Characteristic Curve ◽

External Validation ◽

Model Development ◽

Validation Data ◽

Data Set ◽

Growth Faltering ◽

Development Data ◽

Gestational Age At Birth ◽

Development And Validation ◽

The Impact

IntroductionGlobally, an estimated 151 million children under 5 years of age still suffer from the adverse effects of stunting. We sought to develop and externally validate an early life predictive model that could be applied in infancy to accurately predict risk of stunting in preschool children.MethodsWe conducted two separate prospective cohort studies in Vietnam that intensively monitored children from early pregnancy until 3 years of age. They included 1168 and 475 live-born infants for model development and validation, respectively. Logistic regression on child stunting at 3 years of age was performed for model development, and the predicted probabilities for stunting were used to evaluate the performance of this model in the validation data set.ResultsStunting prevalence was 16.9% (172 of 1015) in the development data set and 16.4% (70 of 426) in the validation data set. Key predictors included in the final model were paternal and maternal height, maternal weekly weight gain during pregnancy, infant sex, gestational age at birth, and infant weight and length at 6 months of age. The area under the receiver operating characteristic curve in the validation data set was 0.85 (95% Confidence Interval, 0.80–0.90).ConclusionThis tool applied to infants at 6 months of age provided valid prediction of risk of stunting at 3 years of age using a readily available set of parental and infant measures. Further research is required to examine the impact of preventive measures introduced at 6 months of age on those identified as being at risk of growth faltering at 3 years of age.

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Establishment of a Nomogram for Predicting Early Death in Viral Myocarditis

Cardiology Research and Practice ◽

10.1155/2021/9947034 ◽

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.

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Predicting Sex from Retinal Fundus Photographs Using Automated Deep Learning

10.21203/rs.3.rs-402433/v1 ◽

2021 ◽

Author(s):

Edward Korot ◽

Nikolas Pontikos ◽

Xiaoxuan Liu ◽

Siegfried K Wagner ◽

Livia Faes ◽

...

Keyword(s):

Deep Learning ◽

Characteristic Curve ◽

External Validation ◽

Model Development ◽

Model Performance ◽

Validation Dataset ◽

Internal Validation ◽

Fundus Photographs ◽

The Uk ◽

Retinal Fundus

Abstract Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.

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Development and validation of a radiomics nomogram for identification of severity of patients with COVID-19

10.21203/rs.3.rs-54606/v1 ◽

2020 ◽

Author(s):

Jian Wang ◽

Zhihua Xu ◽

Guohua Cheng ◽

Qiuxiang Hu ◽

Linyang He ◽

...

Keyword(s):

Validation Cohort ◽

Chest Ct ◽

Multivariable Logistic Regression Analysis ◽

Good Discrimination ◽

Decision Curve Analysis ◽

Selection Operator ◽

Lasso Method ◽

Development And Validation ◽

Radiomics Signature ◽

Good Calibration

Abstract Background The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severe COVID-19 determines the management and treatment, even prognosis. Thus, we aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19.Methods There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness.Results The radiomics signature consisting of 4 selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P < 0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts, showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19.Conclusions We present an easy-to-use radiomics nomogram to identify the severe patients with COVID-19 for better guiding a prompt management and treatment.

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Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study

BMJ Open ◽

10.1136/bmjopen-2018-023912 ◽

2018 ◽

Vol 8 (11) ◽

pp. e023912 ◽

Cited By ~ 3

Author(s):

Marleen Hamoen ◽

Yvonne Vergouwe ◽

Alet H Wijga ◽

Martijn W Heymans ◽

Vincent W V Jaddoe ◽

...

Keyword(s):

Blood Pressure ◽

Prediction Model ◽

High Blood Pressure ◽

Characteristic Curve ◽

Model Development ◽

Population Based ◽

The Body ◽

Dynamic Prediction ◽

Internal Validation ◽

Primordial Prevention

ObjectivesTo develop a dynamic prediction model for high blood pressure at the age of 9–10 years that could be applied at any age between birth and the age of 6 years in community-based child healthcare.Design, setting and participantsData were used from 5359 children in a population-based prospective cohort study in Rotterdam, the Netherlands.Outcome measureHigh blood pressure was defined as systolic and/or diastolic blood pressure ≥95th percentile for gender, age and height. Using multivariable pooled logistic regression, the predictive value of characteristics at birth, and of longitudinal information on the body mass index (BMI) of the child until the age of 6 years, was assessed. Internal validation was performed using bootstrapping.Results227 children (4.2%) had high blood pressure at the age of 9–10 years. Final predictors were maternal hypertensive disease during pregnancy, maternal educational level, maternal prepregnancy BMI, child ethnicity, birth weight SD score (SDS) and the most recent BMI SDS. After internal validation, the area under the receiver operating characteristic curve ranged from 0.65 (prediction at age 3 years) to 0.73 (prediction at age 5–6 years).ConclusionsThis prediction model may help to monitor the risk of developing high blood pressure in childhood which may allow for early targeted primordial prevention of cardiovascular disease.

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

Open Medicine ◽

10.1515/med-2021-0307 ◽

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.

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A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China

10.1101/2020.03.24.20042119 ◽

2020 ◽

Cited By ~ 4

Author(s):

Ying Zhou ◽

Zhen Yang ◽

Yanan Guo ◽

Shuang Geng ◽

Shan Gao ◽

...

Keyword(s):

Risk Factors ◽

Disease Severity ◽

Predictive Value ◽

Demographic Data ◽

Characteristic Curve ◽

Severe Pneumonia ◽

Internal Validation ◽

Predictive Values ◽

Radiological Data ◽

D Dimer

AbstractBackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2.MethodsPatients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared using the Statistical Package for the Social Sciences (IBM Corp., Armonk, NY, USA) to explore clinical characteristics and risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation.ResultsA total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age [odds ratio (OR): 1.059, 95% confidence interval (CI): 1.036–1.082; p < 0.001], N/L (OR: 1.322, 95% CI: 1.180–1.481; p < 0.001), CRP (OR: 1.231, 95% CI: 1.129–1.341; p = 0.002), and D-dimer (OR: 1.059, 95% CI: 1.013–1.107; p = 0.011). We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. The sensitivity and specificity were 89.47% and 67.42%. In the training sets, the negative and positive predictive values were 93.80% and 41.32%, respectively, with a specificity of 70.76% and a sensitivity of 89.87%.ConclusionsA product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.

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A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China

10.21203/rs.3.rs-29566/v1 ◽

2020 ◽

Cited By ~ 4

Author(s):

Ying Zhou ◽

Zhen Yang ◽

Yanan Guo ◽

Shuang Geng ◽

Shan Gao ◽

...

Keyword(s):

Risk Factors ◽

Disease Severity ◽

Predictive Value ◽

Demographic Data ◽

Characteristic Curve ◽

Severe Pneumonia ◽

Internal Validation ◽

Predictive Values ◽

Radiological Data ◽

D Dimer

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2.Methods Patients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared to explore risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation.Results A total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age, N/L, CRP and D-dimer. We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. In the training sets, the negative and positive predictive values were 93.80% and 41.32%.Conclusions A product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.

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