Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population
| Autor: | Min Dai, Qiang Xu, Sihai Chen, Hangjun Chen, Xuqiang Liu, Jing Shan, Zhiyou Cao, Guoming Xia |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Blood Platelets
Male medicine.medical_specialty Multivariate analysis lcsh:Medicine 030204 cardiovascular system & hematology Predictive markers Logistic regression Trauma Article Cohort Studies 03 medical and health sciences Femoral head 0302 clinical medicine Asian People medicine Humans Blood test lcsh:Science Triglycerides Probability 030222 orthopedics Multidisciplinary Receiver operating characteristic medicine.diagnostic_test business.industry lcsh:R Osteonecrosis Univariate Diagnostic markers Femur Head Middle Aged Nomogram Nomograms Cholesterol Logistic Models medicine.anatomical_structure ROC Curve Cohort Female lcsh:Q Radiology business |
| Zdroj: | Scientific Reports, Vol 10, Iss 1, Pp 1-9 (2020) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | Although corticosteroids and alcohol are two major risk factors for nontraumatic osteonecrosis of the femoral head (NONFH), the effects of other factors have rarely been studied, thereby making early diagnosis and treatment of NONFH difficult. This study aimed to develop and validate a nomogram to NONFH, but patients with alcohol- and steroid-related NONFH are not at all taken into account in this study. A training cohort of 790 patients (n = 434, NONFH; n = 356, femoral neck fractures [non-NONFH]) diagnosed in our hospital from January 2011 to December 2016 was used for model development. A least absolute shrinkage and selection operator (lasso) regression model was used for date dimension reduction and optimal predictor selection. A predictive model was developed from univariate and multivariate logistic regression analyses. Performance characterisation of the resulting nomogram included calibration, discriminatory ability, and clinical usefulness. After internal validation, the nomogram was further evaluated in a separate cohort of 300 consecutive patients included between January 2017 and December 2018. The simple prediction nomogram included five predictors from univariate and multivariate analyses, including gender, total cholesterol levels, triglyceride levels, white blood cell count, and platelet count. Internal validation showed that the model had good discrimination [area under the receiver operating characteristic curve (AUC) = 0.80] and calibration. Good discrimination (AUC = 0.81) and calibration were preserved in the validation cohort. Decision curve analysis showed that the predictive nomogram was clinically useful. The simple diagnostic nomogram, which combines demographic data and laboratory blood test results, was able to quantify the probability of NONFH in cases of early screening and diagnosis. |
| Databáze: | OpenAIRE |
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