A dynamic nomogram for predicting the risk of asthma: Development and validation in a database study

Yang, Lei; Li, Meihua; Zheng, Qinling; Ren, Chaofeng; Ma, Wei; Yang, Yumei

doi:10.1002/jcla.23820

Cited by 7 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The measurement method is simple, and the data are easily obtained with no additional cost. Moreover, a user‐friendly interface makes it easier for clinical nurses to tailor clinical decisions to individual patients 42 . Clinical nurses can automatically generate the occurrence probability of mental disorders when they input the collected information into the web page.…”

Section: Discussionmentioning

confidence: 99%

Construction of a risk prediction model for detecting postintensive care syndrome—mental disorders

Wang,

Li,

Fan

et al. 2023

Nursing in Critical Care

View full text Add to dashboard Cite

BackgroundPostintensive care syndrome (PICS) has adverse multidimensional effects on nearly half of the patients discharged from ICU. Mental disorders such as anxiety, depression and post‐traumatic stress disorder (PTSD) are the most common psychological problems for patients with PICS with harmful complications. However, developing prediction models for mental disorders in post‐ICU patients is an understudied problem.AimsTo explore the risk factors of PICS mental disorders, establish the prediction model and verify its prediction efficiency.Study DesignIn this cohort study, data were collected from 393 patients hospitalized in the ICU of a tertiary hospital from April to September 2022. Participants were randomly assigned to modelling and validation groups using a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to select the predictors, multiple logistic regression analysis was used to establish the risk prediction model, and a dynamic nomogram was developed. The Hosmer–Lemeshow (HL) test was performed to determine the model's goodness of fit. The area under the receiver operating characteristic (ROC) curve was used to evaluate the model's prediction efficiency.ResultsThe risk factors of mental disorders were Sepsis‐related organ failure assessment (SOFA) score, Charlson comorbidity index (CCI), delirium duration, ICU depression score and ICU sleep score. The HL test revealed that p = .249, the area under the ROC curve = 0.860, and the corresponding sensitivity and specificity were 84.8% and 71.0%, respectively. The area under the ROC curve of the verification group was 0.848. A mental disorders dynamic nomogram for post‐ICU patients was developed based on the regression model.ConclusionsThe prediction model provides a reference for clinically screening patients at high risk of developing post‐ICU mental disorders, to enable the implementation of timely preventive management measures.Relevance to Clinical PracticeThe dynamic nomogram can be used to systematically monitor various factors associated with mental disorders. Furthermore, nurses need to develop and apply accurate nursing interventions that consider all relevant variables.

show abstract

Section: Discussionmentioning

confidence: 99%

Construction of a risk prediction model for detecting postintensive care syndrome—mental disorders

Wang,

Li,

Fan

et al. 2023

Nursing in Critical Care

View full text Add to dashboard Cite

show abstract

“…Nomograms are graph-based tools used to calculate the predicted probability of an event of interest using the input parameters. Compared to traditional forms, web-based dynamic nomograms are gaining widespread use in prognosis and diagnosis of pulmonology and other disciplines thanks to their user-friendliness and accessibility [ 11 , 12 ]. The application simplifies the process by integrating the underlying regression formula of the prediction model into its calculation, thereby enabling rapid results for predicted risk.…”

Section: Introductionmentioning

confidence: 99%

“…The application simplifies the process by integrating the underlying regression formula of the prediction model into its calculation, thereby enabling rapid results for predicted risk. Its convenience may allow for tailored clinical decisions [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Web-Based Dynamic Nomogram to Predict the Risk of Methicillin-Resistant Staphylococcal Infection in Patients with Pneumonia

Duong-Thi-Thanh,

Truong-Quang,

Tran-Nguyen-Trong

et al. 2024

Diagnostics

View full text Add to dashboard Cite

The aim of this study was to create a dynamic web-based tool to predict the risks of methicillin-resistant Staphylococcus spp. (MRS) infection in patients with pneumonia. We conducted an observational study of patients with pneumonia at Cho Ray Hospital from March 2021 to March 2023. The Bayesian model averaging method and stepwise selection were applied to identify different sets of independent predictors. The final model was internally validated using the bootstrap method. We used receiver operator characteristic (ROC) curve, calibration, and decision curve analyses to assess the nomogram model’s predictive performance. Based on the American Thoracic Society, British Thoracic Society recommendations, and our data, we developed a model with significant risk factors, including tracheostomies or endotracheal tubes, skin infections, pleural effusions, and pneumatoceles, and used 0.3 as the optimal cut-off point. ROC curve analysis indicated an area under the curve of 0.7 (0.63–0.77) in the dataset and 0.71 (0.64–0.78) in 1000 bootstrap samples, with sensitivities of 92.39% and 91.11%, respectively. Calibration analysis demonstrated good agreement between the observed and predicted probability curves. When the threshold is above 0.3, we recommend empiric antibiotic therapy for MRS. The web-based dynamic interface also makes our model easier to use.

show abstract

“…In cancer and medicine, the nomogram is a helpful tool for determining prognosis. It enables estimations of the likelihood of a single endpoint event to be used in place of the usual prediction model formulas [ 19 ]. At present, few clinical predictive models exist for the risk of depression in hypertensive patients.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a depression risk prediction nomogram for US Adults with hypertension, based on NHANES 2007–2018

Wang

Zhang

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Depression is of increasing concern as its prevalence increases. Our study’s objective was to create and evaluate a nomogram to predict the likelihood that hypertension patients may experience depression. 13293 people with hypertension who were under 20 years old were chosen from the National Health and Nutrition Examination Survey (NHANES) database between 2007 and 2018 for this study. The training and validation sets were split up into the dataset at random in a 7:3 ratio. To find independent predictors, univariate and multivariate logistic regression were employed on the training set. Using information from the validation set, nomogram was subsequently created and internally validated. The effectiveness of the nomogram is assessed using calibration curve and receiver operator characteristic (ROC) curve. Combining univariate logistic regression analysis and multifactor logistic regression analysis, the results showed that age, sex, race, marital, education level, sleep time on workdays, poverty to income ratio, smoking, alcohol consumption, sedentary time and heart failure status were risk factors for hypertensive patients suffering from depression and were included in the nomogram model, and ROC analysis showed that the AUC of the training set was 0.757 (0.797–0.586), with a sensitivity of 0.586; the AUC of the test set was 0.724 (0.712–0.626), with a sensitivity of 0.626, which was a good fit. Decision curve analysis further confirms the value of nomogram for clinical application. In the civilian non-institutionalized population of the United States, our study suggests a nomogram that can aid in predicting the likelihood of depression in hypertension patients and aiding in the selection of the most effective treatments.

show abstract

A dynamic nomogram for predicting the risk of asthma: Development and validation in a database study

Cited by 7 publications

References 31 publications

Construction of a risk prediction model for detecting postintensive care syndrome—mental disorders

Construction of a risk prediction model for detecting postintensive care syndrome—mental disorders

A Web-Based Dynamic Nomogram to Predict the Risk of Methicillin-Resistant Staphylococcal Infection in Patients with Pneumonia

Development and validation of a depression risk prediction nomogram for US Adults with hypertension, based on NHANES 2007–2018

Contact Info

Product

Resources

About