Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study

Objectives. This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). Methods. A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation. Results. Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation. Conclusion. Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals.

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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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A Simple-To-Use Nomogram to Predict Survival After Acute Respiratory Distress Syndrome

10.21203/rs.3.rs-115208/v1 ◽

2020 ◽

Author(s):

Yong Liu ◽

Jiang Liu ◽

Liang Huang

Keyword(s):

Regression Analysis ◽

Acute Respiratory Distress Syndrome ◽

Respiratory Distress Syndrome ◽

Respiratory Distress ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Distress Syndrome ◽

Curve Analysis ◽

Lasso Regression ◽

Decision Curve Analysis

Abstract Background: The aim of this study to construct and validate a simple-to-use nomogram to predict the survival of patients with acute respiratory distress syndrome.Methods: A total of 197 patients with acute respiratory distress syndrome were selected from the Dryad Digital Repository. All eligible individuals were randomly stratified into the training set (n=133) and the testing set (n=64) as 2: 1 ratio. LASSO regression analysis was used to select the optimal predictors, and receiver operating characteristic and calibration curves were used to evaluate accuracy and discrimination of the model. Clinical usefulness of the nomogram was also assessed using decision curve analysis and Kaplan–Meier analysis.Results: Age, albumin, platelet count, Acute Physiology and Chronic Health Evaluation II score, PaO2/FiO2, lactate dehydrogenase, high-resolution computed tomography score, and syndrome etiology were identified as independent prognostic factors on LASSO regression analysis; these factors were integrated for the construction of the nomogram. Results of calibration plots, decision curve analysis, and receiver operating characteristic analysis showed that this model has good predictive ability of patient survival in acute respiratory distress syndrome. Moreover, a significant difference in the 28-day survival was shown between the patients stratified into different risk groups (P < 0.001).Conclusions: We satisfactorily constructed a simple-to-use nomogram based on eight relevant factors to predict survival and prognosis of patients with acute respiratory distress syndrome. This model can aid personalized treatment and clinical decision-making.

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A Simple-to-Use Web-Based Calculator for Survival Prediction in Acute Respiratory Distress Syndrome

Frontiers in Medicine ◽

10.3389/fmed.2021.604694 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yong Liu ◽

Jian Liu ◽

Liang Huang

Keyword(s):

Regression Analysis ◽

Acute Respiratory Distress Syndrome ◽

Respiratory Distress Syndrome ◽

Respiratory Distress ◽

Operating Characteristic ◽

Distress Syndrome ◽

Curve Analysis ◽

Lasso Regression ◽

Web Based ◽

Decision Curve Analysis

Background: The aim of this study was to construct and validate a simple-to-use model to predict the survival of patients with acute respiratory distress syndrome.Methods: A total of 197 patients with acute respiratory distress syndrome were selected from the Dryad Digital Repository. All eligible individuals were randomly stratified into the training set (n=133) and the validation set (n=64) as 2: 1 ratio. LASSO regression analysis was used to select the optimal predictors, and receiver operating characteristic and calibration curves were used to evaluate accuracy and discrimination of the model. Clinical usefulness of the model was also assessed using decision curve analysis and Kaplan-Meier analysis.Results: Age, albumin, platelet count, PaO2/FiO2, lactate dehydrogenase, high-resolution computed tomography score, and etiology were identified as independent prognostic factors based on LASSO regression analysis; these factors were integrated for the construction of the nomogram. Results of calibration plots, decision curve analysis, and receiver operating characteristic analysis showed that this model has good predictive ability of patient survival in acute respiratory distress syndrome. Moreover, a significant difference in the 28-day survival was shown between the patients stratified into different risk groups (P < 0.001). For convenient application, we also established a web-based calculator (https://huangl.shinyapps.io/ARDSprognosis/).Conclusions: We satisfactorily constructed a simple-to-use model based on seven relevant factors to predict survival and prognosis of patients with acute respiratory distress syndrome. This model can aid personalized treatment and clinical decision-making.

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Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study

BMC Cancer ◽

10.1186/s12885-021-08410-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Suyu Wang ◽

Yue Yu ◽

Wenting Xu ◽

Xin Lv ◽

Yufeng Zhang ◽

...

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

External Validation ◽

Predictive Performance ◽

Information Criterion ◽

Curve Analysis ◽

Tnm Stage ◽

Concordance Index ◽

Cancer Specific Survival ◽

Decision Curve Analysis

Abstract Background The prognostic roles of three lymph node classifications, number of positive lymph nodes (NPLN), log odds of positive lymph nodes (LODDS), and lymph node ratio (LNR) in lung adenocarcinoma are unclear. We aim to find the classification with the strongest predictive power and combine it with the American Joint Committee on Cancer (AJCC) 8th TNM stage to establish an optimal prognostic nomogram. Methods 25,005 patients with T1-4N0–2M0 lung adenocarcinoma after surgery between 2004 to 2016 from the Surveillance, Epidemiology, and End Results database were included. The study cohort was divided into training cohort (13,551 patients) and external validation cohort (11,454 patients) according to different geographic region. Univariate and multivariate Cox regression analyses were performed on the training cohort to evaluate the predictive performance of NPLN (Model 1), LODDS (Model 2), LNR (Model 3) or LODDS+LNR (Model 4) respectively for cancer-specific survival and overall survival. Likelihood-ratio χ2 test, Akaike Information Criterion, Harrell concordance index, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to evaluate the predictive performance of the models. Nomograms were established according to the optimal models. They’re put into internal validation using bootstrapping technique and external validation using calibration curves. Nomograms were compared with AJCC 8th TNM stage using decision curve analysis. Results NPLN, LODDS and LNR were independent prognostic factors for cancer-specific survival and overall survival. LODDS+LNR (Model 4) demonstrated the highest Likelihood-ratio χ2 test, highest Harrell concordance index, and lowest Akaike Information Criterion, and IDI and NRI values suggested Model 4 had better prediction accuracy than other models. Internal and external validations showed that the nomograms combining TNM stage with LODDS+LNR were convincingly precise. Decision curve analysis suggested the nomograms performed better than AJCC 8th TNM stage in clinical practicability. Conclusions We constructed online nomograms for cancer-specific survival and overall survival of lung adenocarcinoma patients after surgery, which may facilitate doctors to provide highly individualized therapy.

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Development of a Survival Model Based on Autophagy-Associated Genes for Predicting Prognosis of Gastric Cancer

10.21203/rs.3.rs-30248/v1 ◽

2020 ◽

Author(s):

Wanli Yang ◽

Lili Duan ◽

Xinhui Zhao ◽

Liaoran Niu ◽

Yiding Li ◽

...

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Prediction Model ◽

Roc Curve ◽

Survival Model ◽

Differentially Expressed ◽

Lasso Regression ◽

Prognosis Prediction ◽

Cellular Processes ◽

Model Based

Abstract Background: Gastric cancer (GC) is one of lethal diseases worldwide. Autophagy-associated genes play a crucial role in the cellular processes of GC. Our study aimed to investigate and identify the prognostic potential of autophagy-associated genes signature in GC. Methods: RNA-seq and clinical information of GC and normal controls were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the Wilcoxon signed-rank test was used to pick out the differentially expressed autophagy-associated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the potential roles and mechanisms of autophagy-associated genes in GC. Cox proportional hazard regression analysis and Lasso regression analysis were carried out to identify the overall survival (OS) related autophagy-associated genes, which were then collected to construct a predictive model. Kaplan-Meier method and receiver operating characteristic (ROC) curve were utilized to validate the accuracy of this model. Finally, a clinical nomogram was established by combining the clinical factors and autophagy-associated genes signature. Results: A total of 28 differentially expressed autophagy-associated genes were identified. GO and KEGG analyses revealed that several important cellular processes and signaling pathways were correlated with these genes. Through Cox regression and Lasso regression analyses, we identified 4 OS-related autophagy-associated genes (GRID2, ATG4D, GABARAPL2, and CXCR4) and constructed a prognosis prediction model. GC Patients with high-risk had a worse OS than those in low-risk group (5-year OS, 27.7% vs 38.3%; P=9.524e-07). The area under the ROC curve (AUC) of the prediction model was 0.67. The nomogram was demonstrated to perform better for predicting 3-year and 5-year survival possibility for GC patients with a concordance index (C-index) of 0.70 (95% CI: 0.65-0.72). The calibration curves also presented good concordance between nomogram-predicted survival and actual survival. Conclusions: We constructed and evaluated a survival model based on the autophagy-associated genes for GC patients, which may improve the prognosis prediction in GC.

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Established the first clinical prediction model regarding the risk of hyperuricemia in IgA nephropathy

10.21203/rs.3.rs-888732/v1 ◽

2021 ◽

Author(s):

Yin-Hong Geng ◽

Zhe Zhang ◽

Jun-Jun Zhang ◽

Bo Huang ◽

Zui-Shuang Guo ◽

...

Keyword(s):

Risk Factors ◽

Iga Nephropathy ◽

Interstitial Fibrosis ◽

Characteristic Curve ◽

Univariate Analysis ◽

Curve Analysis ◽

University Hospital ◽

Prediction Ability ◽

Decision Curve Analysis ◽

Incidence Risk

Abstract Objective. To construct a novel nomogram model that predicts the risk of hyperuricemia incidence in IgA nephropathy (IgAN) . Methods. Demographic and clinicopathological characteristics of 1184 IgAN patients in the First Affiliated Hospital of Zhengzhou University Hospital were collected. Univariate analysis and multivariate logistic regression were used to screen out hyperuricemia risk factors. The risk factors were used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Results. Independent predictors for hyperuricemia incidence risk included sex, hypoalbuminemia, hypertriglyceridemia, blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), 24-hour urinaryprotein (24h TP), Gross and tubular atrophy/interstitial fibrosis (T). The nomogram model exhibited moderate prediction ability with an AUC of 0.834 ((95% CI 0.804–0.864)). The AUC from validation reached 0.787 (95% CI 0.736-0.839). The decision curve analysis displayed that the hyperuricemia risk nomogram was clinically applicable.Conclusion. Our novel and simple nomogram containing 8 factors may be useful in predicting hyperuricemia incidence risk in IgAN.

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Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development (Preprint)

10.2196/preprints.19588 ◽

2020 ◽

Author(s):

Tao Fan ◽

Bo Hao ◽

Shuo Yang ◽

Bo Shen ◽

Zhixin Huang ◽

...

Keyword(s):

Risk Factors ◽

Regression Analysis ◽

Clinical Characteristics ◽

Cox Regression ◽

Disease Onset ◽

Lasso Regression ◽

Prediction Ability ◽

Calibration Curves ◽

Independent Risk Factors ◽

Critical Patients

BACKGROUND In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. OBJECTIVE The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. METHODS In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. RESULTS A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P<.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; P<.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; P=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; P=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, P<.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; P<.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; P=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. CONCLUSIONS This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

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Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis

Frontiers in Medicine ◽

10.3389/fmed.2020.590460 ◽

2020 ◽

Vol 7 ◽

Author(s):

Bin Zhang ◽

Qin Liu ◽

Xiao Zhang ◽

Shuyi Liu ◽

Weiqi Chen ◽

...

Keyword(s):

Prognostic Factors ◽

Clinical Utility ◽

Predictive Accuracy ◽

Fasting Blood Glucose ◽

External Validation ◽

Predictive Performance ◽

Curve Analysis ◽

Discriminative Ability ◽

Decision Curve Analysis ◽

Poor Outcome

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19.Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness.Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram.Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

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P0560POST-DISCHARGE FOLLOW UP AFTER AKI. AN EXTERNAL VALIDATION AND DECISION CURVE ANALYSIS OF TWO RISK MODELS

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa142.p0560 ◽

2020 ◽

Vol 35 (Supplement_3) ◽

Author(s):

Simon Sawhney ◽

Zhi Tan ◽

Corri Black ◽

Brenda Hemmelgarn ◽

Angharad Marks ◽

...

Keyword(s):

Risk Model ◽

Discharge Planning ◽

External Validation ◽

Curve Analysis ◽

Net Benefit ◽

Post Discharge ◽

Decision Curve Analysis ◽

One Year ◽

Unplanned Admissions

Abstract Background and Aims There is limited evidence to inform which people should receive follow up after AKI and for what reasons. Here we report the external validation (geographical and temporal) and potential clinical utility of two complementary models for predicting different post-discharge outcomes after AKI. We used decision curve analysis, a technique that enables visualisation of the trade-off (net benefit) between identifying true positives and avoiding false positives across a range of potential risk thresholds for a risk model. Based on decision curve analysis we compared model guided approaches to follow up after AKI with alternative strategies of standardised follow up – e.g. follow up of all people with AKI, severe AKI, or a discharge eGFR<30. Method The Alberta AKI risk model predicts the risk of stage G4 CKD at one year after AKI among those with a baseline GFR>=45 and at least 90 days survival (2004-2014, n=9973). A trial is now underway using this tool at a 10% threshold to identify high risk people who may benefit from specialist nephrology follow up. The Aberdeen AKI risk model provides complementary predictions of early mortality or unplanned readmissions within 90 days of discharge (2003, n=16453), aimed at supporting non-specialists in discharge planning, with a threshold of 20-40% considered clinically appropriate in the study. For the Alberta model we externally validated using Grampian residents with hospital AKI in 2011-2013 (n=9382). For the Aberdeen model we externally validated using all people admitted to hospital in Grampian in 2012 (n=26575). Analysis code was shared between the sites to maximise reproducibility. Results Both models discriminated well in the external validation cohorts (AUC 0.855 for CKD G4, and AUC 0.774 for death and readmissions model), but as both models overpredicted risks, recalibration was performed. For both models, decision curve analysis showed that prioritisation of patients based on the presence or severity of AKI would be inferior to a model guided approach. For predicting CKD G4 progression at one year, a strategy guided by discharge eGFR<30 was similar to a model guided approach at the prespecified 10% threshold (figure 1). In contrast for early unplanned admissions and mortality, model guided approaches were superior at the prespecified 20-40% threshold (figure 2). Conclusion In conclusion, prioritising AKI follow up is complex and standardised recommendations for all people may be an inefficient and inadequate way of guiding clinical follow-up. Guidelines for AKI follow up should consider suggesting an individualised approach both with respect to purpose and prioritisation.

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