• Communications for Statistical Applications and Methods
• /
• v.28 no.4
• /
• pp.339-350
• /
• 2021

In medical research, the risk factors associated with human diseases need to be identified to predict the incidence rate and determine the treatment plan. Logistic regression analysis is primarily used in order to select risk factors. However, individuals who are unfamiliar with statistics outcomes have trouble using these methods. In this study, we develop a nomogram that graphically represents the numerical association between the disease and risk factors in order to identify the risk factors for delirium and to interpret and use the results more effectively. By using the logistic regression model, we identify risk factors related to delirium, construct a nomogram and predict incidence rates. Additionally, we verify the developed nomogram using a receiver operation characteristics (ROC) curve and calibration plot. Nursing home, stroke/epilepsy, metabolic abnormality, hemodynamic instability, and analgesics were selected as risk factors. The validation results of the nomogram, built with the factors of training set and the test set of the AUC showed a statistically significant determination of 0.893 and 0.717, respectively. As a result of drawing the calibration plot, the coefficient of determination was 0.820. By using the nomogram developed in this paper, health professionals can easily predict the incidence rate of delirium for individual patients. Based on this information, the nomogram could be used as a useful tool to establish an individual's treatment plan.

• Korean Journal of Radiology
• /
• v.25 no.10
• /
• pp.876-886
• /
• 2024

Objective: To develop a nomogram that integrates clinical-pathologic and imaging variables to predict ipsilateral breast tumor recurrence (IBTR) in women with ductal carcinoma in situ (DCIS) treated with breast-conserving surgery (BCS). Materials and Methods: This retrospective study included consecutive women with DCIS who underwent BCS at two hospitals. Patients who underwent BCS between 2003 and 2016 in one hospital and between 2005 and 2013 in another were classified into development and validation cohorts, respectively. Twelve clinical-pathologic variables (age, family history, initial presentation, nuclear grade, necrosis, margin width, number of excisions, DCIS size, estrogen receptor, progesterone receptor, radiation therapy, and endocrine therapy) and six mammography and ultrasound variables (breast density, detection modality, mammography and ultrasound patterns, morphology and distribution of calcifications) were analyzed. A nomogram for predicting 10-year IBTR probabilities was constructed using the variables associated with IBTR identified from the Cox proportional hazard regression analysis in the development cohort. The performance of the developed nomogram was evaluated in the external validation cohort using a calibration plot and 10-year area under the receiver operating characteristic curve (AUROC) and compared with the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram. Results: The development cohort included 702 women (median age [interquartile range], 50 [44-56] years), of whom 30 (4%) women experienced IBTR. The validation cohort included 182 women (48 [43-54] years), 18 (10%) of whom developed IBTR. A nomogram was constructed using three clinical-pathologic variables (age, margin, and use of adjuvant radiation therapy) and two mammographic variables (breast density and calcification morphology). The nomogram was appropriately calibrated and demonstrated a comparable 10-year AUROC to the MSKCC nomogram (0.73 vs. 0.66, P = 0.534) in the validation cohort. Conclusion: Our nomogram provided individualized risk estimates for women with DCIS treated with BCS, demonstrating a discriminative ability comparable to that of the MSKCC nomogram.

• Communications for Statistical Applications and Methods
• /
• v.30 no.1
• /
• pp.21-35
• /
• 2023

Metabolic syndrome is a serious disease that can eventually lead to various complications, such as stroke and cardiovascular disease. In this study, we aimed to identify the risk factors related to metabolic syndrome for its prevention and recognition and propose a nomogram that visualizes and predicts the probability of the incidence of metabolic syndrome. We conducted an analysis using data from the Korea National Health and Nutrition Survey (KNHANES VII) and identified 10 risk factors affecting metabolic syndrome by using the Rao-Scott chi-squared test, considering the characteristics of the complex sample. A naïve Bayesian classifier was used to build a nomogram for metabolic syndrome. We then predicted the incidence of metabolic syndrome using the nomogram. Finally, we verified the nomogram using a receiver operating characteristic curve and a calibration plot.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.3
• /
• pp.1481-1488
• /
• 2014

Background: To avoid performing axillary lymph node dissection (ALND) for non-sentinel lymph node (SLN)-negative patients with-SLN positive axilla, nomograms for predicting the status have been developed in many centers. We created a new nomogram predicting non-SLN metastasis in SLN-positive patients with invasive breast cancer and evaluated 14 existing breast cancer models in our patient group. Materials and Methods: Two hundred and thirty seven invasive breast cancer patients with SLN metastases who underwent ALND were included in the study. Based on independent predictive factors for non-SLN metastasis identified by logistic regression analysis, we developed a new nomogram. Receiver operating characteristics (ROC) curves for the models were created and the areas under the curves (AUC) were computed. Results: In a multivariate analysis, tumor size, presence of lymphovascular invasion, extranodal extension of SLN, large size of metastatic SLN, the number of negative SLNs, and multifocality were found to be independent predictive factors for non-SLN metastasis. The AUC was found to be 0.87, and calibration was good for the present Ondokuz Mayis nomogram. Among the 14 validated models, the MSKCC, Stanford, Turkish, MD Anderson, MOU (Masaryk), Ljubljana, and DEU models yielded excellent AUC values of > 0.80. Conclusions: We present a new model to predict the likelihood of non-SLN metastasis. Each clinic should determine and use the most suitable nomogram or should create their own nomograms for the prediction of non- SLN metastasis.

• YAKHAK HOEJI
• /
• v.45 no.2
• /
• pp.153-160
• /
• 2001

This research developed an intravenous (IV) vancomycin dosing nomogram based on the clinical pharmacokinetic data of Korean adult patients. Total 99 pairs of steady-state peak and trough serum concentrations of vancomycin were obtained from 73 adult patients in a tertiary general hospital. Serum vancomycin concentrations were determined to assess the appropriateness of initial vancomycin dosing. Only 47.2% of the cases were within therapeutic range. To characterize the clinical pharmacokinetics (PK) of vancomycin, PK parameters including elimination rate constant ( $K_{e}$) half-life( $T_{1}$2/), clearance (C $l_{van}$), volume of distribution ( $V_{d}$) were calculated by using one-compartment, first order pharmacokinetic equations. PK parameters were evaluated based on the differences of patients'renal function and age. Regression analysis showed a significant correlation between C $l_{van}$ and $C_{cr}$ (C $l_{van}$ = -1.89+0.914 $C_{cr}$ , r=0.763) and between $K_{e}$ and $C_{cr}$ , ( $K_{e}$=-0.0037+0.00139 $C_{cr}$ =0.724). The relationship between $K_{e}$ and $C_{cr}$ , and the mean $V_{d}$ were utilized for developing the nomogram to individualize the initial dosing regimen of vancomycin for the patients with various degrees of renal functions. The nomogram may be used as an efficient tool to determine safe and effective doses of vancomycin for the Korean adult patients.nts.nts.nts.s.nts.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.23 no.3
• /
• pp.276-285
• /
• 2020

Purpose: Non-alcoholic fatty liver disease (NAFLD) ranges in severity from simple steatosis to steatohepatitis. Early detection of NAFLD is important for preventing the disease from progressing to become an irreversible end-stage liver disease. We developed a nomogram that allows for non-invasive screening for NAFLD in obese children. Methods: Anthropometric and laboratory data of 180 patients from our pediatric obesity clinic were collected. Diagnoses of NAFLD were based on abdominal ultrasonographic findings. The nomogram was constructed using predictors from a multivariate analysis of NAFLD risk factors. Results: The subjects were divided into non-NAFLD (n=67) and NAFLD groups (n=113). Factors, including sex, body mass index, abdominal circumference, blood pressure, insulin resistance, and levels of aspartate aminotransferase, alanine aminotransferase (ALT), γ-glutamyl transpeptidase (γGT), uric acid, triglycerides, and insulin, were significantly different between the two groups (all p<0.05) as determined using homeostatis model assessment of insulin resistance (HOMA-IR). In our multivariate logistic regression analysis, elevated serum ALT, γGT, and triglyceride levels were significantly related to NAFLD development. The nomogram was established using γGT, uric acid, triglycerides, HOMA-IR, and ALT as predictors of NAFLD probability. Conclusion: The newly developed nomogram may help predict NAFLD risk in obese children. The nomogram may also allow for early NAFLD diagnosis without the need for invasive liver biopsy or expensive liver imaging, and may also allow clinicians to intervene early to prevent the progression of NAFLD to become a more advanced liver disease.

• Journal of Home Health Care Nursing
• /
• v.30 no.1
• /
• pp.48-58
• /
• 2023

Purpose: The purpose of this study was to construct a nomogram for predicting difficulty in peripheral intravenous cannulation (DPIVC) for adult inpatients. Methods: This study conducted a secondary analysis of data from the intravenous cannulation cohort by intravenous specialist nurses at a tertiary hospital in Seoul. Overall, 504 patients were included; of these, 166 (32.9%) patients with failed cannulation in the first intravenous cannulation attempt were included in the case group, while the remaining 338 patients were included in the control group. The nomogram was built with the identified risk factors using a multiple logistic regression analysis. The model performance was analyzed using the Hosmer-Lemeshow test, area under the curve (AUC), and calibration plot. Results: Five factors, including vein diameter, vein visibility, chronic kidney disease, diabetes, and chemotherapy, were risk factors of DPIVC. The nomogram showed good discrimination with an AUC of 0.81 (95% confidence interval: 0.80-0.82) by the sample data and 0.79 (95% confidence interval: 0.74-0.84) by bootstrapping validation. The Hosmer-Lemeshow goodness-of-fit test showed a p-value of 0.694, and the calibration curve of the nomogram showed high coherence between the predicted and actual probabilities of DPIVC. Conclusion: This nomogram can be used in clinical practice by nurses to predict DPIVC probability. Future studies are required, including those on factors possibly affecting intravenous cannulation.

• Journal of Korean Academy of Nursing
• /
• v.51 no.3
• /
• pp.280-293
• /
• 2021

Purpose: This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). Methods: This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients' electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow-Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. Results: The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ² = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. Conclusion: The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.

• The Korean Journal of Applied Statistics
• /
• v.30 no.4
• /
• pp.591-602
• /
• 2017

The concentration of fine dust has increased in Korea and people have become more concerned with respiratory diseases. This study selected risk factors for chronic obstructive pulmonary disease (COPD) through demographic and clinical features and constructed a nomogram. First, logistic regression analysis was performed using demographic and clinical feature and the pulmonary function test results of the Korean National Health and Nutrition Examination Survey (KNHANES) $6^{th}$ (2013-2015) and the nomogram was constructed to visualize the risk factors of chronic obstructive pulmonary disease in order to facilitate the interpretation of the analysis results. The ROC curve and calibration plot were also used to verify the nomogram of chronic obstructive pulmonary disease.

• Journal of Korea Multimedia Society
• /
• v.13 no.5
• /
• pp.722-729
• /
• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.