The Use of a Nomogram to Visually Interpret a Logistic Regression Prediction Model for Giant Cell Arteritis

Ing, Edsel; Ing, Royce

doi:10.1080/01658107.2018.1425728

Cited by 16 publications

(9 citation statements)

References 13 publications

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“…Besides, the nomogram established in this study is an efficient tool to help clinical workers' decision-making owing to its user-friendly interface and optimal predictive ability. Similarly, the nomograms based on logistic regression were used to visually interpret the prediction model in several studies ( 19 , 30 , 31 ). However, the present predictive model lacked internal and external validations due to the limited sample size.…”

Section: Discussionmentioning

confidence: 99%

Incidence and Predictors of Synchronous Bone Metastasis in Newly Diagnosed Differentiated Thyroid Cancer: A Real-World Population-Based Study

Zhang

Ren

et al. 2022

Front. Surg.

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BackgroundClinical and sociodemographic characteristics of differentiated thyroid cancer (DTC) patients with synchronous bone metastasis (SBM) remain unclear. This real-world study aimed to elucidate the incidence and prognosis of DTC patients with SBM using population-based data.MethodsData of patients with newly diagnosed DTC from 2010 to 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariable logistic regression analysis was utilized to identify predictors of developing SBM in patients with DTC and was further evaluated by receiver operator characteristics (ROC) analysis. Multivariable Cox regression was applied to identify prognostic factors associated with overall survival (OS) and cancer-specific survival (CSS).ResultsA total of 67,176 patients with DTC were screened from the database, with 0.36% (244/67,176) developed SBM. The age-adjusted incidence of SBM in patients with DTC was relatively stable during the study period with an average annual percentage change (AAPC) of 2.52. Multivariable logistic regression analysis recognized seven factors (older age, male gender, black race, other races, follicular histology, the American Joint Committee on Cancer (AJCC) T2, T3, T4 staging, and N1 staging) as predictors of developing SBM among the entire cohort, with the value of area under the curve (AUC) of 0.931 (95% CI: 0.915–0.947). The median survival time of DTC patients with SBM was 22 months (interquartile range, 7–47 months). The multivariable Cox regression analysis indicated multiple metastatic sites, surgical procedures, and chemotherapy as predictors for the survival of patients.ConclusionsPredictors and prognostic factors of SBM in patients with DTC were identified in this study. Patients with risk factors should be given more attention in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Incidence and Predictors of Synchronous Bone Metastasis in Newly Diagnosed Differentiated Thyroid Cancer: A Real-World Population-Based Study

Zhang

Ren

et al. 2022

Front. Surg.

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“…Popular in fields such as oncology and transplantation, nomograms are a powerful way to distill group data to determine the probability of a clinical event occurring in an individual. 15 , 38 , 39 They have been used in ophthalmology to predict outcomes after refractive surgery, [40][41][42][43][44] Descemet stripping automated EK graft preparation, 45 giant cell arteritis management, 46 and uveal melanoma treatment. 47 Given the popularity of PK worldwide, however, it is surprising that few nomograms predicting corneal graft failure have been studied.…”

Section: Discussionmentioning

confidence: 99%

Development of a Nomogram to Predict Graft Survival After Penetrating Keratoplasty

Shiuey

Zhang

Rapuano

et al. 2021

American Journal of Ophthalmology

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To develop a nomogram to predict the 3-and 5-year likelihood of graft survival after penetrating keratoplasty (PK) based on preoperative assessment and intraoperative plan. • DESIGN: Retrospective clinical case-control study. • METHODS: Data from 1,029 consecutive PKs in 903 eyes of 835 patients performed at a single tertiary center from May 2007 to September 2018 were extracted from electronic medical records and evaluated for corneal graft failure, defined as irreversible and visually significant graft edema, haze, or scarring. Thirty-seven variables were assessed by multivariable Cox models. A nomogram to predict the probability of graft survival was created. • RESULTS: Mean recipient age was 57.1 ± 22.0 years and mean follow-up was 4.22 ± 3.05 years. Overall, 37.4% of grafts failed during follow-up. Eleven variables were significantly associated with graft failure, including active microbial infection at the time of PK (hazard ratio [HR] = 5.10, 95% confidence interval [CI] 3.53-7.37), intraocular silicone oil at the conclusion of the PK (HR = 4.28, 95% CI 2.38-7.71), history of systemic autoimmune disease (HR = 2.83, 95% CI 1.63-4.90), 4 quadrants of corneal neovascularization (HR = 2.76, 95% CI 1.56-4.86), any prior anterior segment surgery (HR = 2.41, 95% CI 1.55-3.75), and lens status as anterior chamber intraocular lens at the conclusion of surgery (HR = 2.35, 95% CI 1.30-4.26). The nomogram exhibited a concordance index of 0.76 (95% CI 0.74-0.78); internal calibration plots depicted strong correlation between prediction and observation of graft survival. • CONCLUSIONS: PK graft prognosis may be predicted relatively accurately based on 11 variables. Although established from retrospective data, this nomogram would be valuable for data-driven patient counseling prior to corneal transplantation.

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“…For every one-unit increase in age, there is a 6% increase in the odds of a positive TABx; for each unit increase in platelets, the odds of a positive TABx increases 0.5%. This concept is better visualized on a nomogram 28…”

Section: Discussionmentioning

confidence: 99%

“…This concept is better visualized on a nomogram. 28 Some prediction models for TABx require extensive calculations, eg, multiple likelihood ratios. 11 Our user-friendly model has an online spreadsheet to calculate the risk score for the LR and NN models (https://goo.gl/THCnuU).…”

Section: Discussionmentioning

confidence: 99%

<p>Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation</p>

Ing

Miller

Nguyen

et al. 2019

OPTH

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Purpose To develop and validate neural network (NN) vs logistic regression (LR) diagnostic prediction models in patients with suspected giant cell arteritis (GCA). Design: Multicenter retrospective chart review. Methods An audit of consecutive patients undergoing temporal artery biopsy (TABx) for suspected GCA was conducted at 14 international medical centers. The outcome variable was biopsy-proven GCA. The predictor variables were age, gender, headache, clinical temporal artery abnormality, jaw claudication, vision loss, diplopia, erythrocyte sedimentation rate, C-reactive protein, and platelet level. The data were divided into three groups to train, validate, and test the models. The NN model with the lowest false-negative rate was chosen. Internal and external validations were performed. Results Of 1,833 patients who underwent TABx, there was complete information on 1,201 patients, 300 (25%) of whom had a positive TABx. On multivariable LR age, platelets, jaw claudication, vision loss, log C-reactive protein, log erythrocyte sedimentation rate, headache, and clinical temporal artery abnormality were statistically significant predictors of a positive TABx ( P ≤0.05). The area under the receiver operating characteristic curve/Hosmer–Lemeshow P for LR was 0.867 (95% CI, 0.794, 0.917)/0.119 vs NN 0.860 (95% CI, 0.786, 0.911)/0.805, with no statistically significant difference of the area under the curves ( P =0.316). The misclassification rate/false-negative rate of LR was 20.6%/47.5% vs 18.1%/30.5% for NN. Missing data analysis did not change the results. Conclusion Statistical models can aid in the triage of patients with suspected GCA. Misclassification remains a concern, but cutoff values for 95% and 99% sensitivities are provided ( https://goo.gl/THCnuU ).

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The Use of a Nomogram to Visually Interpret a Logistic Regression Prediction Model for Giant Cell Arteritis

Cited by 16 publications

References 13 publications

Incidence and Predictors of Synchronous Bone Metastasis in Newly Diagnosed Differentiated Thyroid Cancer: A Real-World Population-Based Study

Incidence and Predictors of Synchronous Bone Metastasis in Newly Diagnosed Differentiated Thyroid Cancer: A Real-World Population-Based Study

Development of a Nomogram to Predict Graft Survival After Penetrating Keratoplasty

<p>Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation</p>

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