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. 2022 Sep 2:2022:3605369.
doi: 10.1155/2022/3605369. eCollection 2022.

A Simple Nomogram for Predicting Osteoarthritis Severity in Patients with Knee Osteoarthritis

Qingzhu Zhang  1   2 , Yinhui Yao  3 , Jinzhu Wang  2 , Yufeng Chen  1 , Dong Ren  1 , Pengcheng Wang  1
Affiliations

A Simple Nomogram for Predicting Osteoarthritis Severity in Patients with Knee Osteoarthritis

Qingzhu Zhang et al. Comput Math Methods Med. .

Abstract

Objective: To explore the influencing factors of knee osteoarthritis (KOA) severity and establish a KOA nomogram model.

Methods: Inpatient data collected in the Department of Joint Surgery, Chengde Medical University Affiliated Hospital from January 2020 to January 2022 were used as the training cohort. Patients with knee osteoarthritis who were admitted to the Third Hospital of Hebei Medical University from February 2022 to May 2022 were taken as the external validation group of the model. In the training group, the least absolute shrinkage and selection operator (LASSO) method was used to screen the factors of KOA severity to determine the best prediction index. Then, after combining the significant factors from the LASSO and multivariate logistic regressions, a prediction model was established. All potential prediction factors were included in the KOA severity prediction model, and the corresponding nomogram was drawn. The consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), GiViTi calibration band, net classification improvement (NRI) index, and integrated discrimination improvement (IDI) index evaluation of a model predicted KOA severity. Decision curve analysis (DCA) and clinical influence curves were used to study the model's potential clinical value. The validation group also used the above evaluation indexes to measure the diagnostic efficiency of the model. Spearman correlation was used to investigate the relationship between nomogram-related markers and osteoarthritis severity.

Results: The total sample included 572 patients with knee osteoarthritis, including 400 patients in the training cohort and 172 patients in the validation cohort. The nomogram's predictive factors were age, pulse, absolute value of lymphocytes, mean corpuscular haemoglobin concentration (MCHC), and blood urea nitrogen (BUN). The C-index and AUC of the model were 0.802. The GiViTi calibration band (P = 0.065), NRI (0.091), and IDI (0.033) showed that the modified model can distinguish between severe KOA and nonsevere KOA. DCA showed that the KOA severity nomogram has clinical application value with threshold probabilities between 0.01 and 0.78. The external verification results also show the stability and diagnosis of the model. Age, pulse, MCHC, and BUN are correlated with osteoarthritis severity.

Conclusions: A nomogram model for predicting KOA severity was established for the first time that can visually identify patients with severe KOA and is novel for indirectly evaluating KOA severity by nonimaging means.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Prediction factors for osteoarthritis severity were selected, and an osteoarthritis severity nomogram was developed in patients with knee osteoarthritis in the training cohort. (a, b) Least absolute shrinkage and selection operator (LASSO) coefficient profiles of the 14 prediction factors. (c) Logistic regression analyses of the 5 prediction factors in patients with knee osteoarthritis. (d) Nomogram prediction of osteoarthritis severity in patients with knee osteoarthritis.
Figure 2
Figure 2
Evaluation of the KOA nomogram and its clinical use in patients with KOA in the training cohort. (a) ROC curve based on the predictive nomogram for osteoarthritis severity. (b) Calibration plots for predicting osteoarthritis severity. (c) Decision curve analysis for the osteoarthritis severity nomogram in patients with knee osteoarthritis. (d) Clinical impact plot for predicting osteoarthritis severity.
Figure 3
Figure 3
Evaluation of the KOA nomogram and its clinical use in patients with KOA in the validation cohort. (a) Nomogram prediction of osteoarthritis severity in patients with knee osteoarthritis. (b) ROC curve based on the predictive nomogram for osteoarthritis severity. (c) Calibration plots for predicting osteoarthritis severity. (d) Decision curve analysis for the osteoarthritis severity nomogram in patients with knee osteoarthritis. (e) Clinical impact plot for predicting osteoarthritis severity.
Figure 4
Figure 4
Correlation analysis of nomogram-related markers and osteoarthritis severity.
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