Development and validation of a simple clinical nomogram for predicting obstructive sleep apnea

Sun, Xishi; Zheng, Zhenzhen; Liang, Jin‐Hua; Chen, Riken; Huang, Hongbo; Yao, Xiaoyan; Lei, Wei; Peng, Min; Cheng, Junfen; Zhang, Nuofu

doi:10.1111/jsr.13546

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…The long dashed line (Ideal) of the calibration curve expresses the ideal one of the nomogram model, and the predicted one is identical to the actual one. Logistic calibration represents the bootstrap-corrected performance of the column chart model, while Nonparametric represents the apparent accuracy of the column chart model [21]. If the prediction value is equal to the true result, the logistic calibration and the nonparametric curve are completely coincident; if the estimated figure is higher than the true one, the nonparametric curve is below the logistic calibration; otherwise, the nonparametric curve is above the logistic calibration.…”

Section: The Evaluation Of the Accuracy And Stability Of The Modelmentioning

confidence: 99%

A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea

Zhang,

Yang,

Sheng

et al. 2023

Foods

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To investigate different contents of pu-erh tea polyphenol affected by abiotic stress, this research determined the contents of tea polyphenol in teas produced by Yuecheng, a Xishuangbanna-based tea producer in Yunnan Province. The study drew a preliminary conclusion that eight factors, namely, altitude, nickel, available cadmium, organic matter, N, P, K, and alkaline hydrolysis nitrogen, had a considerable influence on tea polyphenol content with a combined analysis of specific altitudes and soil composition. The nomogram model constructed with three variables, altitude, organic matter, and P, screened by LASSO regression showed that the AUC of the training group and the validation group were respectively 0.839 and 0.750, and calibration curves were consistent. A visualized prediction system for the content of pu-erh tea polyphenol based on the nomogram model was developed and its accuracy rate, supported by measured data, reached 80.95%. This research explored the change of tea polyphenol content under abiotic stress, laying a solid foundation for further predictions for and studies on the quality of pu-erh tea and providing some theoretical scientific basis.

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Section: The Evaluation Of the Accuracy And Stability Of The Modelmentioning

confidence: 99%

A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea

Zhang,

Yang,

Sheng

et al. 2023

Foods

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Les multiples enjeux de la sémiologie du syndrome d’apnées obstructives du sommeil chez l’adulte

Micoulaud-Franchi,

Gauld,

Martin

et al. 2024

Médecine du Sommeil

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Analysis of factors influencing bronchiectasis patients with active pulmonary tuberculosis and development of a nomogram prediction model

Yang,

Du,

et al. 2024

Front. Med.

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BackgroundTo identify the risk factors for bronchiectasis patients with active pulmonary tuberculosis (APTB) and to develop a predictive nomogram model for estimating the risk of APTB in bronchiectasis patients.MethodsA retrospective cohort study was conducted on 16,750 bronchiectasis patients hospitalized at the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between January 2019 and December 2023. The 390 patients with APTB were classified as the case group, while 818 patients were randomly sampled by computer at a 1:20 ratio from the 16,360 patients with other infections to serve as the control group. Relevant indicators potentially leading to APTB in bronchiectasis patients were collected. Patients were categorized into APTB and inactive pulmonary tuberculosis (IPTB) groups based on the presence of tuberculosis. The general characteristics of both groups were compared. Variables were screened using the least absolute shrinkage and selection operator (LASSO) analysis, followed by multivariate logistic regression analysis. A nomogram model was established based on the analysis results. The model’s predictive performance was evaluated using calibration curves, C-index, and ROC curves, and internal validation was performed using the bootstrap method.ResultsLASSO analysis identified 28 potential risk factors. Multivariate analysis showed that age, gender, TC, ALB, MCV, FIB, PDW, LYM, hemoptysis, and hypertension are independent risk factors for bronchiectasis patients with APTB (p < 0.05). The nomogram demonstrated strong calibration and discrimination, with a C-index of 0.745 (95% CI: 0.715–0.775) and an AUC of 0.744 for the ROC curve. Internal validation using the bootstrap method produced a C-index of 0.738, further confirming the model’s robustness.ConclusionThe nomogram model, developed using common clinical serological characteristics, holds significant clinical value for assessing the risk of APTB in bronchiectasis patients.

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Development and validation of a simple clinical nomogram for predicting obstructive sleep apnea

Abstract: Obstructive sleep apnea is the most common type of sleep breathing disorder.

Cited by 4 publications

References 29 publications

A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea

A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea

Les multiples enjeux de la sémiologie du syndrome d’apnées obstructives du sommeil chez l’adulte

Analysis of factors influencing bronchiectasis patients with active pulmonary tuberculosis and development of a nomogram prediction model

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