Predicting Neovascular Glaucoma Risk in a Chinese Proliferative Diabetic Retinopathy Population: Development and Assessment of a New Predictive Nomogram

Abstract Background: The aim of this study was to develop and internally validate a postoperative NVG risk nomogram in a Chinese population of patients with PDR.Methods: We developed a prediction model based on a training dataset of 107 PDR patients who underwent vitrectomy from March,2017 to March,2018 in Shenyang Fourth People’s Hospital, and they were followed up for at least 12 months. The presence or absence of NVG were observed. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the postoperative NVG risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.Results: Predictors contained in the prediction nomogram included HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy. The model displayed good discrimination with a C-index of 0.852 (95% CI: 0.740–0.964) and good calibration. High C-index value of 0.849 could still be reached in the interval validation. Decision curve analysis showed that the NVG nomogram was clinically useful when intervention was decided at the NVG possibility threshold of 2%.Conclusion: This novel NVG nomogram incorporating HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy could be conveniently used to facilitate the postoperative NVG risk prediction in PDR patients.

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Predicting Neovascular Glaucoma risk in a Chinese Proliferative Diabetic Retinopathy population: development and assessment of a new predictive nomogram

10.21203/rs.3.rs-30623/v2 ◽

2020 ◽

Author(s):

Xinyue Zhang ◽

li xu ◽

Xiaolong Chen

Keyword(s):

Diabetic Nephropathy ◽

Regression Model ◽

Clinical Data ◽

Risk Model ◽

Curve Analysis ◽

Calibration Plot ◽

Multivariable Logistic Regression Analysis ◽

Decision Curve Analysis ◽

Anti Vegf ◽

Selection Operator

Abstract Background: The aim of this study was to develop and evaluate a postoperative NVG risk nomogram based on the clinical data of a Chinese population of patients with PDR.Methods: A prediction model has been established based on the clinical data of 107 PDR patients who underwent vitrectomy from March,2017 to March,2018 in Shenyang Fourth People’s Hospital, and they were followed up for at least 12 months.The presence or absence of NVG were observed.The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the postoperative NVG risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. C-index, calibration plot, and decision curve analysis were also introduced to evaluate the model. The bootstrapping validation was also utilized to accomplish internal validation.Results: Risk factors screened out by the model included HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy. The model was testified with a satisfying C-index of 0.852 (95% CI: 0.740–0.964).Decision curve analysis showed that the NVG nomogram was clinically useful when intervention was adopted with the NVG possibility threshold of 2%.Conclusion: This novel nomogram revealed that a good control of HbAlc level, absence of diabetic nephropathy and anti-VEGF therapy are prophylactic factors of postoperative NVG in PDR patients.

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Dosimetry of Submandibular Glands on Xerostomia for Nasopharyngeal Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2020.601403 ◽

2021 ◽

Vol 10 ◽

Author(s):

Xin-Bin Pan ◽

Yang Liu ◽

Shi-Ting Huang ◽

Su Pei ◽

Kai-Hua Chen ◽

...

Keyword(s):

Nasopharyngeal Carcinoma ◽

Regression Model ◽

Curve Analysis ◽

Calibration Plot ◽

Calibration Model ◽

Predicting Model ◽

Decision Curve Analysis ◽

Submandibular Glands ◽

The Mean ◽

Selection Operator

PurposeTo investigate dosimetry of submandibular glands on xerostomia after intensity-modulated radiotherapy for nasopharyngeal carcinoma (NPC).MethodsFrom September 2015 to March 2016, 195 NPC patients were investigated. Xerostomia was evaluated at 12 months after treatment via the RTOG/EORTC system. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for grades 2–3 xerostomia. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis.ResultsThe V30 of the parotid glands was selected based on the least absolute shrinkage and selection operator regression. The nomogram displayed good discrimination with a C-index of 0.698 (95% confidence interval [CI]: 0.626–0.771) and good calibration (model 1). Addition of the dosimetric parameters including the mean dose to the submandibular glands, V50 of the submandibular glands, and volume of the submandibular glands to the model 1 failed to show incremental prognostic value (model 2). The model 2 showed a C-index of 0.704 (95% CI: 0.632–0.776). Decision curve analysis demonstrated that the model 1 was clinically useful when intervention was decided at the possibility threshold of > 20%. Within this range, net benefit was comparable between the model 1 and model 2.ConclusionPGv30 was a major predictive factor of grades 2–3 xerostomia for NPC. In contrast, the mean dose to the submandibular glands, V50 of the submandibular glands, and volume of the submandibular glands were not independent predictive factors.

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Development of a Novel Nomogram for Predicting Premature Rupture of Membrane in Pregnant Women With Vulvovaginal Candidiasis

Frontiers in Medicine ◽

10.3389/fmed.2021.717978 ◽

2021 ◽

Vol 8 ◽

Author(s):

Lilin Yang ◽

Haikuan Wang ◽

Yanfang Li ◽

Cheng Zeng ◽

Xi Lin ◽

...

Keyword(s):

Prediction Model ◽

Regression Model ◽

Pregnant Women ◽

Curve Analysis ◽

Vulvovaginal Candidiasis ◽

Training Dataset ◽

Premature Rupture ◽

Decision Curve Analysis ◽

History Of ◽

Selection Operator

Objective: The aim of this study was to develop a nomogram to predict the risk of premature rupture of membrane (PROM) in pregnant women with vulvovaginal candidiasis (VVC).Patients and methods: We developed a prediction model based on a training dataset of 417 gravidas with VVC, the data were collected from January 2013 to December 2020. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using bootstrapping validation.Results: Predictors contained in the prediction nomogram included age, regular perinatal visits, history of VVC before pregnancy, symptoms with VVC, cured of VVC during pregnancy, and bacterial vaginitis. The model displayed discrimination with a C-index of 0.684 (95% confidence interval: 0.631–0.737). Decision curve analysis showed that the PROM nomogram was clinically useful when intervention was decided at a PROM possibility threshold of 13%.Conclusion: This novel PROM nomogram incorporating age, regular perinatal visits, history of VVC before pregnancy, symptoms with VVC, cured of VVC during pregnancy, and bacterial vaginitis could be conveniently used to facilitate PROM risk prediction in gravidas.

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Novel Model Predicts Diabetic Nephropathy in Type 2 Diabetes

American Journal of Nephrology ◽

10.1159/000505145 ◽

2019 ◽

Vol 51 (2) ◽

pp. 130-138 ◽

Cited By ~ 2

Author(s):

Shimin Jiang ◽

Jinying Fang ◽

Tianyu Yu ◽

Lin Liu ◽

Guming Zou ◽

...

Keyword(s):

Type 2 Diabetes ◽

Diabetic Nephropathy ◽

Kidney Disease ◽

External Validation ◽

Curve Analysis ◽

Calibration Plot ◽

The Novel ◽

Decision Curve Analysis ◽

Novel Model

Background: Clinical indicators for accurately distinguishing diabetic nephropathy (DN) from non-diabetic renal disease in type 2 diabetes (T2D) are lacking. This study aimed to develop and validate a nomogram for predicting DN in T2D patients with kidney disease. Methods: A total of 302 consecutive patients with T2D who underwent renal biopsy at China-Japan Friendship Hospital between January 2014 and June 2019 were included in the study. The data were randomly split into a training set containing 70% of the patients (n = 214) and a validation set containing the remaining 30% of patients (n = 88). Multivariable logistic regression analyses were applied to develop a prediction nomogram incorporating the candidates selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using a concordance index (C-index), calibration plot, and decision curve analysis. Both internal and external validations were assessed. Results: A multivariable model that included gender, diabetes duration, diabetic retinopathy, hematuria, glycated hemoglobin A1c, anemia, blood pressure, urinary protein excretion, and estimated glomerular filtration rate was represented as the nomogram. The model demonstrated very good discrimination with a C-index of 0.934 (95% CI 0.904–0.964). The calibration plot diagram of predicted probabilities against observed DN rates indicated excellent concordance. The C-index value was 0.91 for internal validation and 0.875 for external validation. Decision curve analysis demonstrated that the novel nomogram was clinically useful. Conclusion: The novel model was very useful for predicting DN in patients with T2D and kidney disease, and thereby could be used by clinicians either in triage or as a replacement for biopsy.

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CT-based Radiomic Nomogram for Predicting the Severity of Patients With COVID-19

10.21203/rs.3.rs-875790/v1 ◽

2021 ◽

Author(s):

Hengfeng Shi ◽

Zhihua Xu ◽

Guohua Cheng ◽

Hongli Ji ◽

Linyang He ◽

...

Keyword(s):

Validation Cohort ◽

Chest Ct ◽

Curve Analysis ◽

Multivariable Logistic Regression Analysis ◽

Good Discrimination ◽

Decision Curve Analysis ◽

Selection Operator ◽

Lasso Method ◽

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. We aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19. 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.Conclusion: 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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Predicting Risk of Insulin Resistance in a Chinese Population with Polycystic Ovary Syndrome: Designing and Testing a New Predictive Nomogram

BioMed Research International ◽

10.1155/2020/8031497 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Feng Jiang ◽

Ke Wei ◽

Wenjun Lyu ◽

Chuyan Wu

Keyword(s):

Insulin Resistance ◽

Polycystic Ovary Syndrome ◽

Regression Model ◽

Chinese Population ◽

Risk Model ◽

Polycystic Ovary ◽

Training Dataset ◽

Multivariable Logistic Regression Analysis ◽

Ovary Syndrome ◽

Selection Operator

Background. This research is aimed at establishing and internally validating the risk nomogram of insulin resistance (IR) in a Chinese population of patients with polycystic ovary syndrome (PCOS). Methods. We developed a predictive model based on a training dataset of 145 PCOS patients, and data were collected between March 2018 and May 2019. The least absolute shrinkage and selection operator regression model was used to optimize function selection for the insulin resistance risk model. Multivariable logistic regression analysis was used to construct a prediction model integrating the function selected in the regression model of the least absolute shrinkage and selection operator. The predicting model’s characteristics of prejudice, disease, and lifestyle were analyzed using the C-index, the calibration diagram, and the study of the decision curve. External validity was assessed using the validation of bootstrapping. Results. Predictors contained in the prediction nomogram included occupation, disease durations (years), BMI, current use of metformin, and activities. With a C-index of 0.739 (95 percent confidence interval: 0.644–0.830), the model showed good differentiation and proper calibration. In the interval validation, a high C-index value of 0.681 could still be achieved. Examination of the decision curve found that the IR nomogram was clinically useful when the intervention was determined at the 11 percent IR potential threshold. Conclusion. This novel IR nomogram incorporates occupation, disease durations (years), BMI, current use of metformin, and activities. This nomogram could be used to promote the estimation of individual IR risk in patients with PCOS.

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Development and Validation of Nomogram to Predict Very Early Recurrence of Combined Hepatocellular-Cholangiocarcinoma After Hepatic Resection: A Multi-Institutional Study

10.21203/rs.3.rs-1094632/v1 ◽

2021 ◽

Author(s):

Yijun Wu ◽

Hongzhi Liu ◽

Jianxing Zeng ◽

Yifan Chen ◽

Guoxu Fang ◽

...

Keyword(s):

Validation Cohort ◽

Risk Groups ◽

Early Recurrence ◽

Curve Analysis ◽

Multivariable Logistic Regression Analysis ◽

Good Discrimination ◽

Decision Curve Analysis ◽

High Incidence ◽

Combined Hepatocellular Cholangiocarcinoma ◽

Development And Validation

Abstract Background and Objectives Combined hepatocellular cholangiocarcinoma (cHCC) has a high incidence of early recurrence. The objective of this study is to construct a model predicting very early recurrence (VER)(ie, recurrence within 6 months after surgery) of cHCC. Methods 131 consecutive patients from Eastern Hepatobiliary Surgery Hospital served as a development cohort to construct a nomogram predicting VER by using multivariable logistic regression analysis. The model was internally and externally validated in an validation cohort of 90 patients from Mengchao Hepatobiliary Hospital using the C concordance statistic, calibration analysis and decision curve analysis (DCA). Results The VER nomogram contains microvascular invasion(MiVI), macrovascular invasion(MaVI) and CA19-9>25mAU/mL. The model shows good discrimination with C-indexes of 0.77 (95%CI: 0.69 - 0.85 ) and 0.76 (95%CI:0.66 - 0.86) in the development cohort and validation cohort respectively. Decision curve analysis demonstrated that the model are clinically useful and the calibration of our model was favorable. Our model stratified patients into two different risk groups, which exhibited significantly different VER. Conclusions Our model demonstrated favorable performance in predicting VER in cHCC patients.

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Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

10.21203/rs.3.rs-123528/v1 ◽

2020 ◽

Author(s):

Ruyi Zhang ◽

Mei Xu ◽

Xiangxiang Liu ◽

Miao Wang ◽

Qiang Jia ◽

...

Keyword(s):

Thyroid Carcinoma ◽

External Validation ◽

Cancer Staging ◽

Curve Analysis ◽

Calibration Plot ◽

Net Benefit ◽

Training Set ◽

Decision Curve Analysis ◽

Validation Set ◽

Good Calibration

Abstract Objectives To develop a clinically predictive nomogram model which can maximize patients’ net benefit in terms of predicting the prognosis of patients with thyroid carcinoma based on the 8th edition of the AJCC Cancer Staging method. MethodsWe selected 134,962 thyroid carcinoma patients diagnosed between 2004 and 2015 from SEER database with details of the 8th edition of the AJCC Cancer Staging Manual and separated those patients into two datasets randomly. The first dataset, training set, was used to build the nomogram model accounting for 80% (94,474 cases) and the second dataset, validation set, was used for external validation accounting for 20% (40,488 cases). Then we evaluated its clinical availability by analyzing DCA (Decision Curve Analysis) performance and evaluated its accuracy by calculating AUC, C-index as well as calibration plot.ResultsDecision curve analysis showed the final prediction model could maximize patients’ net benefit. In training set and validation set, Harrell’s Concordance Indexes were 0.9450 and 0.9421 respectively. Both sensitivity and specificity of three predicted time points (12 Months,36 Months and 60 Months) of two datasets were all above 0.80 except sensitivity of 60-month time point of validation set was 0.7662. AUCs of three predicted timepoints were 0.9562, 0.9273 and 0.9009 respectively for training set. Similarly, those numbers were 0.9645, 0.9329, and 0.8894 respectively for validation set. Calibration plot also showed that the nomogram model had a good calibration.ConclusionThe final nomogram model provided with both excellent accuracy and clinical availability and should be able to predict patients’ survival probability visually and accurately.

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Development and validation of models to predict cesarean delivery among low-risk nulliparous women at term: a retrospective study in China

10.21203/rs.3.rs-44296/v2 ◽

2020 ◽

Author(s):

Fangcan Sun ◽

Bing Han ◽

Fangfang Wu ◽

Qianqian Shen ◽

Minhong Shen ◽

...

Keyword(s):

Logistic Regression ◽

Cesarean Delivery ◽

Prediction Models ◽

External Validation ◽

Curve Analysis ◽

Low Risk ◽

Calibration Plot ◽

Fetal Sex ◽

Decision Curve Analysis ◽

Nulliparous Women

Abstract Background A prediction algorithm to identify women with high risk of an emergency cesarean could help reduce morbidity and mortality associated with labor. The objective of the present study was to derive and validate a simple model to predict intrapartum cesarean delivery for low-risk nulliparous women in Chinese population.Methods We conducted a retrospective cohort study of low-risk nulliparous women with singleton, term, cephalic pregnancies. A predictive model for cesarean delivery was derived using univariate and multivariable logistic regression from the hospital of the First Affiliated Hospital of Soochow University. External validation of the prediction model was then performed using the data from Sihong county People’s Hospital. A new nomogram was established based on the development cohort to predict the cesarean. The ROC curve, calibration plot and decision curve analysis were used to assess the predictive performance.Results The intrapartum cesarean delivery rates in the development cohort and the external validation cohort were 8.79% (576/6,551) and 7.82% (599/7,657). Multivariable logistic regression analysis showed that maternal age, height, BMI, weight gained during pregnancy, gestational age, induction method, meconium-stained amniotic fluid and neonatal sex were independent factors affecting cesarean outcome. We had established two prediction models according to fetal sex was involved or not. The AUC was 0.782 and 0.774, respectively. The two prediction models were well-calibrated with Hosmer-Lemeshow test P=0.263 and P=0.817, respectively. Decision curve analysis demonstrated that two models had clinical application value, and they provided greatest net benefit between threshold probabilities of 4% to 60%. And internal validation using Bootstrap method demonstrated similar discriminatory ability. We external validated the model involving fetal sex, for which the AUC was 0.775, while the slope and intercept of the calibration plot were 0.979 and 0.004, respectively. On the external validation set, another model had an AUC of 0.775 and a calibration slope of 1.007. The online web server was constructed based on the nomogram for convenient clinical use.Conclusions Both two models established by these factors have good prediction efficiency and high accuracy, which can provide the reference for clinicians to guide pregnant women to choose an appropriate delivery mode.

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Development And Validation Of Models To Predict Cesarean Delivery Among Low-Risk Nulliparous Women At Term: A Retrospective Study In China

10.21203/rs.3.rs-44296/v1 ◽

2020 ◽

Author(s):

Fangcan Sun ◽

Bing Han ◽

Fangfang Wu ◽

Qianqian Shen ◽

Minhong Shen ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Cesarean Delivery ◽

Prediction Models ◽

External Validation ◽

Curve Analysis ◽

Low Risk ◽

Multivariable Logistic Regression Analysis ◽

Decision Curve Analysis ◽

Nulliparous Women

Abstract Background: Cesarean delivery after failure of trial of labor is associated with adverse maternal and perinatal outcomes. A prediction algorithm to identify women with high risk of an emergency cesarean could help reduce morbidity and mortality associated with labor. The objective of the present study was to derive and validate a simple model to predict cesarean delivery for low-risk nulliparous women in Chinese population.Methods: This retrospective study analyzed the low-risk nulliparous women with singleton cephalic full-term fetus delivered in two medical centers. After the clinical data of the women who delivered at the tertiary referral center (n=6 551) was collected and was used univariate and multivariable logistic regression analysis, the prediction model was fitted. We performed external validation using data from nulliparous who delivered from another hospital(secondary referral center, n=7 657). A new nomogram was established based on the development cohort to predict the cesarean. The ROC curve, calibration plot and decision curve analysis were used to assess the predictive performance. Results: The cesarean delivery rates in the development cohort and the external validation cohort were 8.79% (576/6 551) and 7.82% (599/7 657). Multivariable logistic regression analysis showed that maternal age, height, BMI, weight gained during pregnancy, gestational age, induction method, meconium-stained amniotic fluid and neonatal sex were independent factors affecting cesarean outcome. Because sex of the fetuses were unknown until they born(China's Fertility Policy), we established two prediction models according to fetal sex was involved or not. The AUC was 0.782 and 0.774, respectively. The Hosmer-Lemeshow goodness-of-fit test showed that these two models fitted well. Decision curve analysis demonstrated that the models were clinically useful. And internal validation using Bootstrap method showed that these prediction models perform well. On the external validation set, the AUC were 0.775 and 0.775, respectively. The calibration plots for the probability of cesarean showed a good correlation. The online web server was constructed based on the nomogram for convenient clinical use.Conclusions: Both two models established by these factors have good prediction efficiency and high accuracy, which can provide the reference for clinicians to guide pregnant women to choose an appropriate delivery mode.

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