Predicting medication nonadherence risk in a Chinese inflammatory rheumatic disease population: development and assessment of a new predictive nomogram

Wang, Huijing; Zhang, Le; Liu, Zhe; Wang, Xiaodong; Geng, Shikai; Li, Jiaoyu; Li, Ting; Ye, Shuang

doi:10.2147/ppa.s159293

Cited by 83 publications

(80 citation statements)

References 37 publications

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“…3C . The decision curve revealed that when the threshold probability of a patient and doctor is 20% and 100%, respectively, in the entire cohort, using this nomogram to predict the efficacy of endoscopic nasal septoplasty provide additional benefits as reported previously ( Wang et al, 2018 ). Within this range, net benefit was comparable with several overlaps, on the basis of the prediction nomogram.…”

Section: Resultssupporting

confidence: 63%

A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study

Kang

Chen

et al. 2020

PeerJ

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Background To create a nomogram prediction model for the efficacy of endoscopic nasal septoplasty, and the likelihood of patient benefiting from the operation. Methods A retrospective analysis of 155 patients with nasal septum deviation (NSD) was performed to develop a predictive model for the efficacy of endoscopic nasal septoplasty. Quality of life (QoL) data was collected before and after surgery using Sinonasal Outcome Test-22 (SNOT-22) scores to evaluate the surgical outcome. An effective surgical outcome was defined as a SNOT-22 score change ≥ 9 points after surgery. Multivariate logistic regression analysis was then used to establish a predictive model for the NSD treatment. The predictive quality and clinical utility of the predictive model were assessed by C-index, calibration plots, and decision curve analysis. Results The identified risk factors for inclusion in the predictive model were included. The model had a good predictive power, with a AUC of 0.920 in the training group and a C index of 0.911 in the overall sample. Decision curve analysis revealed that the prediction model had a good clinical applicability. Conclusions Our prediction model is efficient in predicting the efficacy of endoscopic surgery for NSD through evaluation of factors including: history of nasal surgery, preoperative SNOT-22 score, sinusitis, middle turbinate plasty, BMI, smoking, follow-up time, seasonal allergies, and advanced age. Therefore, it can be cost-effective for individualized preoperative assessment.

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Section: Resultssupporting

confidence: 63%

A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study

Kang

Chen

et al. 2020

PeerJ

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show abstract

“…The decision curve analysis of the efficacy prediction model of endoscopic NSD surgery is shown in Figure 3C. The decision curve revealed that when the threshold probability of a patient and doctor is 20% and 100%, respectively, in the entire cohort, using this nomogram to predict the efficacy of endoscopic nasal septoplasty provide additional benefits as reported previously (Wang et al, 2018).Within this range, net benefit was comparable with several overlaps, on the basis of the prediction nomogram. Therefore, the decision curve showed that clinical decisions based on the nomogram prediction model yielded better returns.…”

Section: Manuscript To Be Reviewedsupporting

confidence: 56%

Peer Review #1 of "A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study (v0.1)"

2020

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Background: To create a nomogram prediction model for the efficacy of endoscopic nasal septoplasty , and the likelihood of patient benefiting from the operation. Methods: A retrospective analysis of 155 patients with nasal septum deviation (NSD) was performed to develop a predictive model for the efficacy of endoscopic nasal septoplasty. Quality of life (QoL) data was collected before and after surgery using Sinonasal Outcome Test-22 (SNOT-22) scores to evaluate the surgical outcome. An effective surgical outcome was defined as a SNOT-22 score change≥9 points after surgery. Multivariate logistic regression analysis was then used to establish a predictive model for the NSD treatment. The predictive quality and clinical utility of the predictive model were assessed by C-index, calibration plots, and decision curve analysis. Results: The identified risk factors for inclusion in the predictive model were included. The model had a good predictive power, with a AUC of 0.920 in the training group and a C index of 0.911 in the overall sample. Decision curve analysis revealed that the prediction model had a good clinical applicability. Conclusions: Our prediction model is efficient in predicting the efficacy of endoscopic surgery for NSD through evaluation of factors including: history of nasal surgery, preoperative SNOT-22 score, sinusitis, middle turbinate plasty, BMI, smoking, follow-up time, seasonal allergies, and advanced age. Therefore, it can be cost-effective for individualized preoperative assessment.

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“…This method is suitable for high dimensional data compression and can be used to analyse the optimal predicted risk factors of the disease. 22 After features with nonzero coefficients in the LASSO regression model were selected, multivariable logistic regression analysis was performed on patients with DN and DR to screen out all significant risk factors. Multivariable logistic regression analysis was usually used to develop the predicting model 23 Due to the potential connection between DN and DR, to ensure the accuracy of the results, we carried out multivariable logistic regression analysis of the above significant risk factors on the sample data of patients with both DN and DR.…”

Section: Discussionmentioning

confidence: 99%

<p>Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study</p>

Shi¹,

Niu²,

Wu³

et al. 2020

DMSO

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Purpose: This study aimed to develop a diabetic nephropathy (DN) or diabetic retinopathy (DR) incidence risk nomogram in China's population with type 2 diabetes mellitus (T2DM) based on a community-based sample. Methods: We carried out questionnaire evaluations, physical examinations and biochemical tests among 4219 T2DM patients in Shanghai. According to the incidence of DN and DR, 4219 patients in our study were divided into groups of T2DM patients with DN or DR, patients with both, and patients without any complications. We successively used least absolute shrinkage and selection operator regression analysis and logistic regression analysis to optimize the feature selection for DN and DR. To ensure the accuracy of the results, we carried out multivariable logistic regression analysis of the above significant risk factors on the sample data for both DN and DR. The selected features were included to establish a prediction model. The C-index, calibration plot, curve analysis and internal validation were used to validate the distinction, calibration, and clinical practicality of the model. Results: The predictors in the prediction model included disease course, body mass index (BMI), total triglycerides (TGs), systolic blood pressure (SBP), postprandial blood glucose (PBG), haemoglobin A1C (HbA1c) and blood urea nitrogen (BUN). The model displayed moderate predictive power with a C-index of 0.807 and an area under the receiver operating characteristic curve of 0.807. In internal verification, the C-index reached 0.804. The risk threshold was 16-75% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice. Conclusion: This DN or DR incidence risk nomogram incorporating disease course, BMI, TGs, SBP, PBG, HbA1c and BUN can be used to predict DN or DR incidence risk in T2DM patients. The research team has developed an online app based on a clinical prediction model incorporating risk factors for rapid and simple prediction.

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Predicting medication nonadherence risk in a Chinese inflammatory rheumatic disease population: development and assessment of a new predictive nomogram

Cited by 83 publications

References 37 publications

A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study

A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study

Peer Review #1 of "A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study (v0.1)"

<p>Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study</p>

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