Development and validation of a nomogram for predicting stroke risk in rheumatoid arthritis patients.
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ABSTRACT:
SUBMITTER: Xin F
PROVIDER: S-EPMC8221354 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The clinical features of rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) (RA-ILD) usually manifest to an advanced stage of lung disease, which leads the challenge of early diagnosis and the difficulty in guiding treatments for patients with RA-ILD in clinical settings. The aim of this study was to construct a nomogram for identifying ILD in RA patients. Through the incorporation of the level of matrix metalloproteinase-3 (MMP-3) in plasma, demographics, clinical feature, and laboratory parameters of 223 RA patients (85 RA-ILD) which were grouped as training cohorts and validation cohorts, an identifying nomogram of RA-ILD was built. Candidate variables for the nomogram were screened using univariable analysis and multivariable logistic regression analysis. The accuracy of the diagnostic nomogram was measured via concordance index (C-index), calibration plots, and decision curve analysis (DCA). Results showed that plasma MMP-3 protein was elevated in RA-ILD patients compared with non-ILD RA patients in both training cohorts (p = 0.0475) and validation cohorts (p = 0.0006). Following a final regression analysis, the gender of male, current smoking state, levels of circulating rheumatoid factor (RF), C-reactive protein (CRP), and MMP-3 were identified as risk factors for the construction of the nomogram. The calibration plots further showed a favorable consistency between the identifying nomogram and actual clinical findings. In consistence, the C-index (0.826 for both training cohorts and validation cohorts) indicated the satisfactory discriminative ability of the nomogram. Although the incorporation of MMP-3 failed to significantly improve identified outcomes of the nomogram as determined by DCA, including the level of circulating MMP-3 increased the diagnostic accuracy of the nomogram for ILD in RA patients. Thus, our proposed model can serve as a non-invasive tool to identify ILD in RA patients, which may assist physicians to make treatment decisions for RA patients.
| S-EPMC9245420 | biostudies-literature
Project description:We developed and validated a nomogram to predict coronary heart disease (CHD) in patients with rheumatoid arthritis (RA) in northern China. We analyzed a cohort of RA patients admitted to the Department of Rheumatology and Immunology of the First Affiliated Hospital of China Medical University from 2011 to 2017. To select a high-performance model for clinical data prediction, we evaluated the F1-scores of six machine learning models. Based on the results, we selected multivariable logistic regression analysis for the development of a prediction model. We then generated an individualized prediction nomogram that included age, sex, hypertension, anti-cyclic citrullinated peptide antibody positivity, the erythrocyte sedimentation rate, and serum LDL-cholesterol, triglyceride and HDL-cholesterol levels. The prediction model exhibited better discrimination than the Framingham Risk Score in predicting CHD in RA patients. The area under the curve of the prediction model was 0.77, with a sensitivity of 63.9% and a specificity of 77.2%. The nomogram exhibited good calibration and clinical usefulness. In conclusion, our prediction model was more accurate than the Framingham Risk Score in predicting CHD in RA patients. Our nomogram combining various risk factors can be used for the individualized preoperative prediction of CHD in patients with RA.
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Project description:ObjectiveThis study aimed to develop a risk of psoriatic arthritis (PsA) predictive model for plaque psoriasis patients based on the available features.MethodsPatients with plaque psoriasis or PsA were recruited. The characteristics, skin lesions, and nail clinical manifestations of the patients have been collected. The least absolute shrinkage was used to optimize feature selection, and logistic regression analysis was applied to further select features and build a PsA risk predictive model. Calibration, discrimination, and clinical utility of the prediction model were evaluated by using the calibration plot, C-index, the area under the curve (AUC), and decision curve analysis. Internal validation was performed using bootstrapping validation. The model was subjected to external validation with two separate cohorts.ResultsAge at onset, duration, nail involvement, erythematous lunula, onychorrhexis, oil drop, and subungual hyperkeratosis were presented as predictors to perform the prediction nomogram. The predictive model showed good calibration and discrimination (C-index: 0.759; 95% CI: 0.707-0.811). The AUC of this prediction model was 0.7578092. Excellent performances of the C-index were reached in the internal validation and external cohort validation (0.741, 0.844, and 0.845). The decision curve indicated good effect of the PsA nomogram in guiding clinical practice.ConclusionThis novel PsA nomogram could assess the risk of PsA in plaque psoriasis patients with good efficiency.
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Project description:BackgroundSarcopenia is linked to an unfavorable prognosis in individuals with rheumatoid arthritis (RA). Early identification and treatment of sarcopenia are clinically significant. This study aimed to create and validate a nomogram for predicting sarcopenia risk in RA patients, providing clinicians with a reliable tool for the early identification of high-risk patients.MethodsPatients with RA diagnosed between August 2022 and January 2024 were included and randomized into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and multifactorial logistic regression analysis were used to screen the risk variables for RA-associated muscle loss and to create an RA sarcopenia risk score. The predictive performance and clinical utility of the risk model were evaluated by plotting the receiver operating characteristic curve and calculating the area under the curve (AUC), along with the calibration curve and clinical decision curve (DCA).ResultsA total of 480 patients with RA were included in the study (90% female, with the largest number in the 45-59 age group, about 50%). In this study, four variables (body mass index, disease duration, hemoglobin, and grip strength) were included to construct a nomogram for predicting RA sarcopenia. The training and validation set AUCs were 0.915 (95% CI: 0.8795-0.9498) and 0.907 (95% CI: 0.8552-0.9597), respectively, proving that the predictive model was well discriminated. The calibration curve showed that the predicted values of the model were basically in line with the actual values, demonstrating good calibration. The DCA indicated that almost the entire range of patients with RA can benefit from this novel prediction model, suggesting good clinical utility.ConclusionThis study developed and validated a nomogram prediction model to predict the risk of sarcopenia in RA patients. The model can assist clinicians in enhancing their ability to screen for RA sarcopenia, assess patient prognosis, make early decisions, and improve the quality of life for RA patients.
| S-EPMC11317408 | biostudies-literature
Project description:BACKGROUND: The Obermair nomogram was recently developed to predict the risk of relapse in patients with borderline ovarian tumours (BOTs) based on five readily available clinical, biological, and pathological characteristics. We set out to externally validate and assess its robustness using a multi-institutional BOT database. METHODS: All consecutive patients treated for BOTs in the two participating centres between January 1980 and December 2008 and who had all the nomogram variables documented were identified for analysis. RESULTS: Three hundred and fourteen eligible patients were identified and used for external validation analysis. The median follow-up and initial relapse time were 46.43 (range: 0.1-360) and 66.64 (range: 8-77) months, respectively. The nomogram concordance index was 0.54 (95% CI, 0.52-0.56). The correspondence between the actual relapse and the nomogram predictions suggests a limited calibration of the nomogram in the validation cohort. CONCLUSION: This external validation study of the Obermair nomogram showed limitations in its generalisability to a new and independent patient population.
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Project description:ObjectivesThis study aimed to develop a nomogram for predicting the risk of pressure injury (PI) in adult patients undergoing abdominal surgery and validate its effectiveness among these patients.MethodsThis study retrospectively included 11,247 adult patients, who underwent abdominal surgery and postoperative supervision in ICU, in a tertiary care hospital in western China between January 2017 and December 2020. All datasets were extracted from the patient's medical records and randomly divided into the training cohort (8,997) and the validation cohort (2,250) by 8:2. The univariable logistic regression was used to select potentially relevant features. Then, multivariable logistic regression was also conducted and utilized to establish the nomogram. The nomogram was compared with the Braden scale for predicting PI in the validation cohort through the area under the curve (AUC) of the receiver operator characteristic (ROC) curve, Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA).Results873 (7.8%) patients suffered PIs. Logistic regression analysis showed that time of operation, weight, type of operation, albumin, and Braden scale score were independent risk factors for PI. A nomogram integrating five selected characteristics was constructed. The AUC of the ROC curve for the nomogram was 0.831, with a specificity of 85.2% and sensitivity of 63.7%. The AUC of the ROC curve for the Braden scale was 0.567, with a specificity of only 33.0%. The P-values of the H-L test were 0.45 (nomogram) and 0.22 (Braden scale), both indicating good calibration. The DCA also displayed that the nomogram had better predictive validity.ConclusionCompared with the Braden scale, the nomogram showed a better predictive performance. This nomogram is informative and has the potential to better guide caregivers for risk stratification and prevention of PI, although it requires further validation.
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Project description:BackgroundRisk perception is a multidimensional phenomenon that describes the individual's judgment of the likelihood of experiencing something unpleasant. Risk perception helps to understand how rheumatoid arthritis patients perceive disease-related-risks. We developed and validated a risk perception questionnaire for Spanish speaking rheumatoid arthritis patients.MethodsThe questionnaire development and validation was performed in 3 steps, using respective convenience samples. Step-1 included the conceptual model construction, 20 patient's interviews to identify components from the conceptual model-dimensions and 11 healthcare provider´s consultations who identified RA related manifestations/complications (network and frequencies analysis). Step-2 consisted of item generation and reduction and questionnaire feasibility (n = 100). Step-3 consisted of the questionnaire psychometric validation (n = 270), which included content, face, construct (exploratory factor analysis) and criterion validity (logistic regression analysis) and consistency and stability (Cronbach's α and test-retest).ResultsSamples were representative of typical RA outpatients. Initial conceptual model included 7 dimensions, 3 for probability and 1 each, for responsibility, prevention, control and for severity (Step-1). The final version was considered feasible by the patients and included 27 items (Step-2). A five-factor model was most appropriated and resulted in 68.8% of the variance explained: Cronbach's α = 0.90, intraclass-correlation-coefficient = 0.93 (95% CI = 0.90-0.95). A positive relation between number of external criteria from the charts and risk perception was found; all items had ≥80% agreement from experts; patients agreed about item´s semantic clarity (89%) and format adequacy (97%), (Step-3).ConclusionsThe risk perception questionnaire was valid and reliable to evaluate risk perception construct in RA outpatients; it can be incorporated to routine care and clinical research, and guide interventions to improve patient's health behaviors.
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Project description:Coronary artery ectasia (CAE) is a rare finding and is associated with poor clinical outcomes. However, prognostic factors are not well studied and no prognostication tool is available. In a derivation set comprising 729 consecutive CAE patients between January 2009 and June 2014, a nomogram was developed using Cox regression. Total of 399 patients from July 2014 to December 2015 formed the validation set. The primary outcome was 5-year major adverse cardiovascular events (MACE), a component of cardiovascular death and nonfatal myocardial infarction. Besides the clinical factors, we used quantitative coronary angiography (QCA) and defined QCA classification of four types, according to max diameter (< or ≥5 mm) and max length ratio (ratio of lesion length to vessel length, < or ≥1/3) of the dilated lesion. A total of 27 cardiovascular deaths and 41 nonfatal myocardial infarctions occurred at 5-year follow-up. The nomogram effectively predicted 5-year MACE risk using predictors including age, prior PCI, high sensitivity C-reactive protein, N-terminal pro-brain natriuretic peptide, and QCA classification (area under curve [AUC] 0.75, 95% CI 0.68-0.82 in the derivation set; AUC 0.71, 95% CI 0.56-0.86 in the validation set). Patients were classified as high-risk if prognostic scores were ≥155 and the Kaplan-Meier curves were well separated (log-rank p < 0.001 in both sets). Calibration curve and Hosmer-Lemeshow test indicated similarity between predicted and actual 5-year MACE survival (p = 0.90 in the derivation and p = 0.47 in the validation set). This study developed and validated a simple-to-use method for assessing 5-year MACE risk in patients with CAE.
| S-EPMC8708195 | biostudies-literature
Project description:BackgroundCoronary artery stenosis (CAS) ≥50% often coexists in patients with ischemic stroke, which leads to a significant increase in the occurrence of major vascular events after stroke. This study aimed to develop a nomogram for diagnosing the presence of ≥50% asymptomatic CAS in patients with ischemic stroke.MethodsA primary cohort was established that included 275 non-cardioembolic ischemic stroke patients who were admitted from January 2011 to April 2013 to a teaching hospital in southern China. The preoperative data were used to construct two models by the best subset regression and the forward stepwise regression methods, and a nomogram between these models was established. The assessment of the nomogram was carried out by discrimination and calibration in an internal cohort.ResultsOut of the two models, model 1 contained eight clinical-related variables and exhibited the lowest Akaike Information Criterion value (322.26) and highest concordance index 0.716 (95% CI, 0.654-0.778). The nomogram showed good calibration and significant clinical benefit according to calibration curves and the decision curve analysis.ConclusionThe nomogram, composed of age, sex, NIHSS score on admission, hypertension history, fast glucose level, HDL cholesterol level, LDL cholesterol level, and presence of ≥50% cervicocephalic artery stenosis, can be used for prediction of ≥50% asymptomatic coronary artery disease (CAD). Further studies are needed to validate the effectiveness of this nomogram in other populations.
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Project description:BackgroundAsthma remains a serious health problem with increasing prevalence and incidence. This study was to develop and validate a dynamic nomogram for predicting asthma risk.MethodsTotally 597 subjects whose age ≥18 years old with asthma, an accurate age at first cigarette, and clear smoking status were selected from the National Health and Nutrition Examination Survey (NHANES) database (2013-2018). The dataset was randomly split into the training set and the testing set at a ratio of 4:6. Simple and multiple logistic regressions were used for identifying independent predictors. Then the nomogram was developed and internally validated using data from the testing set. The receiver operator characteristic (ROC) curve was used for assessing the performance of the nomogram.ResultsAccording to the simple and multiple logistic regressions, smoking ≥40 years, female gender, the age for the first smoking, having close relative with asthma were independently associated with the risk of an asthma attack. The nomogram was thereby developed with the link of https://yanglifen.shinyapps.io/Dynamic_Nomogram_for_Asthma/. The ROC analyses showed an AUC of 0.726 (0.724-0.728) with a sensitivity of 0.887 (0.847-0.928) in the training set, and an AUC of 0.702 (0.700-0.703) with a sensitivity of 0.860 (0.804-0.916) in the testing set, fitting well in calibration curves. Decision curve analysis further confirmed the clinical usefulness of the nomogram.ConclusionOur dynamic nomogram could help clinicians to assess the individual probability of asthma attack, which was helpful for improving the treatment and prognosis of asthma.
| S-EPMC8275008 | biostudies-literature