Development and validation of risk stratification trees for incident slow gait speed in persons at high risk for knee osteoarthritis

The development of a clinical nomogram to predict medication nonadherence in patients with knee osteoarthritis

Knee osteoarthritis (KOA) is a common bone disease in older patients. Medication adherence is of great significance in the prognosis of this disease. Therefore, this study analyzed the high-risk factors that lead to medication nonadherence in patients with KOA and constructed a nomogram risk prediction model. The basic information and clinical characteristics of inpatients diagnosed with KOA at the Department of Orthopedics, The Affiliated Hospital of Chengde Medical University, were collected from January 2020 to January 2022. The Chinese version of the eight-item Morisky scale was used to evaluate medication adherence. The Kellgren-Lawrence (KL) classification was performed in combination with the imaging data of patients. Least absolute shrinkage and selection operator regression analysis and logistic multivariate regression analysis were used to analyze high-risk factors leading to medication nonadherence, and a prediction model of the nomogram was constructed. The model was internally verified using bootstrap self-sampling. The index of concordance (C-index), area under the operating characteristic curve (AUC), decision curve, correction curve, and clinical impact curve were used to evaluate the model. A total of 236 patients with KOA were included in this study, and the non-adherence rate to medication was 55.08%. Seven influencing factors were included in the nomogram prediction: age, underlying diseases, diabetes, age-adjusted Charlson comorbidity index (aCCI), payment method, painkillers, and use of traditional Chinese medicine. The C-index and AUC was 0.935. The threshold probability of the decision curve analysis was 0.02-0.98. The nomogram model can be effectively applied to predict the risk of medication adherence in patients with KOA, which is helpful for medical workers to identify and predict the risk of individualized medication adherence in patients with KOA at an early stage of treatment, and then carry out early intervention.

Development of a nomogram prediction model for gait speed trajectories in persons with knee osteoarthritis

To examine heterogeneous trajectories of 8-year gait speed among patients with symptomatic knee osteoarthritis (KOA) and to develop a nomogram prediction model. We analyzed data from the Osteoarthritis Initiative (OAI) assessed at baseline and follow-up over 8 years (n = 1289). Gait speed was measured by the 20-m walk test. The gait speed trajectories among patients with KOA were explored by latent class growth analysis. A nomogram prediction model was created based on multivariable logistic regression. Three gait speed trajectories were identified: the fast gait speed group (30.4%), moderate gait speed group (50.5%) and slow gait speed group (19.1%). Age ≥ 60 years, female, non-white, nonmarried, annual income < $50,000, obesity, depressive symptoms, comorbidity and WOMAC pain score ≥ 5 were risk factors for the slow gait trajectory. The area under the ROC curve of the prediction model was 0.775 (95% CI 0.742-0.808). In the external validation cohort, the AUC was 0.773 (95% CI 0.697-0.848). Heterogeneous trajectories existed in the gait speed of patients with KOA and could be predicted by multiple factors. Risk factors should be earlier identified, and targeted intervention should be carried out to improve physical function of KOA patients.

Knee Confidence Trajectories Over Eight Years and Factors Associated With Poor Trajectories in Individuals With or at Risk for Knee Osteoarthritis

OBJECTIVE

To identify distinct trajectories of lack of knee confidence over an 8-year follow-up period and to examine baseline factors associated with poor trajectories in individuals with or at risk for knee osteoarthritis (OA).

METHODS

The Osteoarthritis Initiative is a prospective cohort study of individuals with or at high risk for knee OA. Confidence in the knees was assessed within the Knee Injury and Osteoarthritis Outcome Score instrument querying how much the individual is troubled by lack of confidence in his/her knee(s), rated as not-at-all (score = 0), mildly (score = 1), moderately (score = 2), severely (score = 3), and extremely (score = 4) troubled, reported annually from baseline to 96 months. Lack of knee confidence was defined as a score of ≥2. We used latent class models to identify subgroups that share similar underlying knee confidence trajectories over an 8-year period and multivariable multinomial logistic regression models to examine baseline factors associated with poor trajectories.

RESULTS

Among 4,515 participants (mean ± SD age 61.2 ± 9.2 years, mean ± SD BMI 28.6 ± 4.8 kg/m2 ; 2,640 [58.5%] women), 4 distinct knee confidence trajectories were identified: persistently good (65.6%); declining (9.1%); poor, improving (13.9%); and persistently poor (11.4%). Baseline predictors associated with persistently poor confidence (reference: persistently good) were younger age, male sex, higher body mass index (BMI), depressive symptoms, more advanced radiographic disease, worse knee pain, weaker knee extensors, history of knee injury and surgery, and reported hip and/or ankle pain.

CONCLUSION

Findings suggest the dynamic nature of self-reported knee confidence and that addressing modifiable factors (e.g., BMI, knee strength, depressive symptoms, and lower extremity pain) may improve its long-term course.

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

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.

West Lake staging: A new staging system orchestrated by X-ray and MRI on knee osteoarthritis

Purpose: To investigate the differences on X-ray and MRI among each stage of knee osteoarthritis (KOA) and further propose a new staging system called West Lake (WL) staging. Methods: A cross-sectional study was conducted on patients with KOA. Stage I, II, III, and IV were divided based on stepwise treatment strategy of Knee osteoarthritis (KOA). Joint space widths (JSW) were measured on X-rays, whereas cartilage injuries (CI) and bone marrow lesions (BML) were evaluated on MRI. The differences of them across the groups were calculated by T-test. Receiver operating characteristic (ROC) curves were rendered to obtain the areas under the curves (AUC), Youden index and corresponding cut-off points. Results: Eventually, there were significant differences on JSW, CI, and BML between stage II/III and III/IV, while no significant differences between stage I/II. In stage II/III, the AUC of JSW, CI, BML was 0.99, 0.76, 0.71 and the Youden index was 0.94, 0.38, 0.45, meanwhile the cut-off points were ≤5.1 mm, >1, >2. In stage III/IV, the AUC of JSW, CI, BML was 0.96, 0.79, 0.74 and the Youden index was 0.84, 0.58, 0.38, meanwhile the cut-off points were ≤3.2 mm, >3, >4. Conclusion: The WL staging was described as follows: Stage I, X-ray shows no joint space narrow, normal MRI or MRI shows cartilage degeneration and only 1 or 2 sections are involved in BML. Stage II, X-ray shows joint space narrow, MRI shows cartilage defect but no full-thickness cartilage defect, meanwhile 3 or 4 sections are involved in BML. Stage III, X-ray shows serious joint space narrow even JSW disappeared, MRI shows full-thickness cartilage defect, more than 4 sections are involved in BML.