Dataset Information

Development and validation of a nomogram risk prediction model for malignancy in dermatomyositis patients: a retrospective study.

ABSTRACT:

Background

Dermatomyositis accompanied with malignancy is a common poor prognostic factor of dermatomyositis. Thus, the early prediction of the risk of malignancy in patients with dermatomyositis can significantly improve the prognosis of patients. However, the identification of antibodies related to malignancy in dermatomyositis patients has not been widely implemented in clinical practice. Herein, we established a predictive nomogram model for the diagnosis of dermatomyositis associated with malignancy.

Methods

We retrospectively analyzed 240 cases of dermatomyositis patients admitted to Sun Yat-sen Memorial Hospital, Sun Yat-sen University from January 2002 to December 2019. According to the year of admission, the first 70% of the patients were used to establish a training cohort, and the remaining 30% were assigned to the validation cohort. Univariate analysis was performed on all variables, and statistically relevant variables were further included in a multivariate logistic regression analysis to screen for independent predictors. Finally, a nomogram was constructed based on these independent predictors. Bootstrap repeated sampling calculation C-index was used to evaluate the model's calibration, and area under the curve (AUC) was used to evaluate the model discrimination ability.

Results

Multivariate logistic analysis showed that patients older than 50-year-old, dysphagia, refractory itching, and elevated creatine kinase were independent risk factors for dermatomyositis associated with malignancy, while interstitial lung disease was a protective factor. Based on this, we constructed a nomogram using the above-mentioned five factors. The C-index was 0.780 (95% CI [0.690-0.870]) in the training cohort and 0.756 (95% CI [0.618-0.893]) in the validation cohort, while the AUC value was 0.756 (95% CI [0.600-0.833]). Taken together, our nomogram showed good calibration and was effective in predicting which dermatomyositis patients were at a higher risk of developing malignant tumors.

SUBMITTER: Zhong J

PROVIDER: S-EPMC8667746 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesTo develop and validate an individualized nomogram to predict probability of patients with ureteral calculi developing into urosepsis.MethodsThe clinical data of 747 patients with ureteral calculi who were admitted from June 2013 to December 2015 in Affiliated Nanhai Hospital of Southern Medical University were selected and included in the development group, while 317 ureteral calculi patients who were admitted from January 2016 to December 2016 were included in the validation group. The independent risk factors of ureteral calculi associated with urosepsis were screened using univariate and multivariate logistic regression analyses. The corresponding nomogram prediction model was drawn according to the regression coefficients. The area under the receiver operating characteristic curves and the GiViTI calibration belts were used to estimate the discrimination and calibration of the prediction model, respectively.ResultsMultivariate logistic regression analysis showed that the five risk factors of gender, mean computed tomography(CT) attenuation value of hydronephrosis, functional solitary kidney, urine white blood cell(WBC) count and urine nitrite were independent risk factors of ureteral calculi associated with urosepsis. The areas under the receiver operating characteristic curve of the development group and validation group were 0.913 and 0.874 respectively, suggesting that the new prediction model had good discrimination capacity. P-values of the GiViTI calibration test of the two groups were 0.247 and 0.176 respectively, and the 95% CIs of GiViTI calibration belt in both groups did not cross the diagonal bisector line. Therefore the predicted probability of the model was consistent with the actual probability which suggested that the calibration of the prediction model in both groups were perfect and prediction model had strong concordance performance.ConclusionThe individualized prediction model for patients with ureteral calculi can facilitate improved screening and early identification of patients having higher risk of urosepsis.

Project description:ObjectiveThis research aims to construct and authenticate a comprehensive predictive model for all-cause mortality, based on a multifaceted array of risk factors.MethodsThe derivation cohort for this study was the Chinese Longitudinal Healthy Longevity Survey (CLHLS), while the Healthy Ageing and Biomarkers Cohort Study (HABCS) and the China Health and Retirement Longitudinal Study (CHARLS) were used as validation cohorts. Risk factors were filtered using lasso regression, and predictive factors were determined using net reclassification improvement. Cox proportional hazards models were employed to establish the mortality risk prediction equations, and the model's fit was evaluated using a discrimination concordance index (C-index). To evaluate the internal consistency of discrimination and calibration, a 10x10 cross-validation technique was employed. Calibration plots were generated to compare predicted probabilities with observed probabilities. The prediction ability of the equations was demonstrated using nomogram.ResultsThe CLHLS (mean age 88.08, n = 37074) recorded 28158 deaths (179683 person-years) throughout the course of an 8-20 year follow-up period. Additionally, there were 1384 deaths in the HABCS (mean age 86.74, n = 2552), and 1221 deaths in the CHARLS (mean age 72.48, n = 4794). The final all-cause mortality model incorporated demographic characteristics like age, sex, and current marital status, as well as functional status indicators including cognitive function and activities of daily living. Additionally, lifestyle factors like past smoking condition and leisure activities including housework, television viewing or radio listening, and gardening work were included. The C-index for the derivation cohort was 0.728 (95% CI: 0.724-0.732), while the external validation results for the CHARS and HABCS cohorts were 0.761 (95% CI: 0.749-0.773) and 0.713 (95% CI: 0.697-0.729), respectively.ConclusionThis study introduces a reliable, validated, and acceptable mortality risk predictor for older adults in China. These predictive factors have potential applications in public health policy and clinical practice.

Project description:BackgroundThis study aimed to develop and validate a nomogram to predict probability of in-stent restenosis (ISR) in patients undergoing percutaneous coronary intervention (PCI).MethodsPatients undergoing PCI with drug-eluting stents between July 2009 and August 2011 were retrieved from a cohort study in a high-volume PCI center, and further randomly assigned to training and validation sets. The least absolute shrinkage and selection operator (LASSO) regression model was used to screen out significant features for construction of nomogram. Multivariable logistic regression analysis was applied to build a nomogram-based predicting model incorporating the variables selected in the LASSO regression model. The area under the curve (AUC) of the receiver operating characteristics (ROC), calibration plot and decision curve analysis (DCA) were performed to estimate the discrimination, calibration and utility of the nomogram model respectively.ResultsA total of 463 patients with DES implantation were enrolled and randomized in the development and validation sets. The predication nomogram was constructed with five risk factors including prior PCI, hyperglycemia, stents in left anterior descending artery (LAD), stent type, and absence of clopidogrel, which proved reliable for quantifying risks of ISR for patients with stent implantation. The AUC of development and validation set were 0.706 and 0.662, respectively, indicating that the prediction model displayed moderate discrimination capacity to predict restenosis. The high quality of calibration plots in both datasets demonstrated strong concordance performance of the nomogram model. Moreover, DCA showed that the nomogram was clinically useful when intervention was decided at the possibility threshold of 9%, indicating good utility for clinical decision-making.ConclusionsThe individualized prediction nomogram incorporating 5 commonly clinical and angiographic characteristics for patients undergoing PCI can be conveniently used to facilitate early identification and improved screening of patients at higher risk of ISR.