Establishing an imaging prediction model to predict the remaining life of knee joint for varus knee osteoarthritis patients

doi:10.21203/rs.3.rs-2219034/v1

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Research Article

Establishing an imaging prediction model to predict the remaining life of knee joint for varus knee osteoarthritis patients

https://doi.org/10.21203/rs.3.rs-2219034/v1

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Purpose

This study aimed to establish a prediction model basing on onset age, gender, body mass index (BMI) and imaging measurements to predict the remaining life of knee joint for varus knee osteoarthritis patients, and to aid in patient selection and decision-making efforts regarding knee joint preservation and total knee arthroplasty (TKA).

Methods

Using data collected from our retrospective TKA database, 132 patients with varus knee osteoarthritis (153 knees) were included in the study. We focused on patients with a diagnosis of Kellgren–Lawrence (K-L) grade II in the initial stage of knee osteoarthritis (OA) accompanied by mild symptoms and a diagnosis of K-L grade IV with severe symptoms and TKA records at the endpoint. The interval between baseline and endpoint was defined as the remaining life of knee joint. The following imaging indexes of knee joints were assessed using standard anteroposterior (AP) radiography, medial proximal tibial angle (MPTA) and joint line convergence angle (JLCA) at baseline (MPTA₀ and JLCA₀) and at TKA period (MPTA_T and JLCA_T); changes in MPTA and JLCA between baseline and TKA period (ΔMPTA and ΔJLCA); and rates of change in MPTA and JLCA between baseline and TKA period (kMPTA and kJLCA). Meanwhile, basic information of patients was collected, including onset age, gender, body mass index (BMI). Multivariate linear regression analysis was performed for the prediction model about the remaining life of knee joint using statistically significant indexes.

Results

Multivariate linear regression analysis showed that, onset age, BMI, JLCA₀, kMPTA and kJLCA were significantly negatively correlated with the remaining life of knee joint, which showed effectiveness in prediction model for TKA. Prediction model: remaining life of knee joint = 195.3–0.6×Onset age-2.0×BMI-4.4×JLCA₀-159.9×kMPTA-205.5×kJLCA.

Conclusions

Onset age, BMI, JLCA₀ and rates of change in MPTA and JLCA robustly predict the risk of requiring TKA.

Level of Evidence: Retrospective Study Level III

correlation of data

genu varum

osteoarthritis

prediction model

total knee arthroplasty

Knee osteoarthritis (OA) is the most common cause of aging-related disability characterized by degenerative pathological changes of knee joints. No pharmaceutical intervention is available to halt or reverse its progression and total knee arthroplasty (TKA) is the major surgical treatment for end-stage osteoarthritis [5, 18]. The demand for primary TKA surgeries performed annually in the USA is expected to increase nearly sevenfold, from 450,000 in 2005 to 3.48 million by 2030 [13]. High costs and societal burden require reduced need for TKA, which necessitates comprehensive evaluation and surveillance of knee joint degeneration.

Knee joint preservation is the goal of all preoperative treatments for knee OA. Early identification of individuals at risk of needing TKA is essential for effective prevention strategy implementation. Weight loss, knee injury prevention, and personalized exercise regimens are important and efficient[3, 7, 15, 16, 21]. But it is common that, despite full explanations are told from their surgeons, many patients with mild arthritis do not aware the severity of arthritis or the end status of knee joints in the future; this may be the main reason for poor compliance in patients and high incidence of TKA.

Generally, providing patients with an expective maximum life span is expected to focus their attention on disease and improve clinical compliance. However, an objective prediction of knee arthroplasty time for a particular individual is complex. Osteoarthritis Research Society International[1] and Outcome Measures In Rheumatoid Arthritis Clinical Trials[2] have published ‘Recommendations’ about evaluating symptoms, morphological seriousness and progression of the disease, however, the evaluation of OA progression has not been successfully addressed. Basic research has found that some biochemical OA markers are closely related to the progression of knee OA. It has been published that, cartilage oligomeric matrix protein [6], hyaluronic acid [19] and serum osteocalcin [4] et al., are biochemical markers having predictive values for knee OA. However, invasive methods are difficult to be widely accepted.

Non-invasive imaging markers have been used as indirect surrogate measures of disease status and progression with variable success. A previous study used magnetic resonance imaging to identify knees at risk of requiring joint replacement in the following year [22]. But, because of its high costs, magnetic resonance imaging is not widely used for this purpose; such a model for predicting arthritis outcome after 1 year may lead to missed opportunities for joint preservation. Arthritis severity indexes, such as medial and lateral knee joint space narrowing and K-L grade, could be used to predict the risk of needing joint arthroplasty [14]. In addition, changes in proximal tibiofibular joint morphology have been shown to predict the risk of TKA [23].

At present, imaging examination is the most commonly used, convenient and intuitive method for the diagnosis and evaluation of bone and joint diseases. The purpose of this preliminary study is to build a predictive model regarding the remaining life of knee joint basing on basic information and several effective imaging indexes. Finding a better and easier monitoring method for varus knee OA to strive for knee joint preservation is the main idea of this study.

Study population

The inclusion criteria were as follows: mild symptomatic varus knee OA diagnosed as K-L grade II in at least one knee at baseline, and TKA record accompanied by diagnosis of K-L grade IV at the end of review (i.e., endpoint); knee symptoms including severe pain, severely limited activity, and poor quality of life, determined by review of medical records or telephone interviews; and standard anteroposterior (AP) radiography obtained at baseline and the endpoint. The exclusion criteria were as follows: previous knee trauma or surgery; congenital deformity or skeletal dysplasia of the knee. The interval between baseline and endpoint was defined as the remaining life of the knee joint. In total, the study population consisted of 132 patients treated between January 2014 and July 2021 [153 knees; 33 men, 99 women; mean age at onset of knee OA, 63.38 (range 51–76) years; mean body mass index, 26.19 (range 19.96–32.23) kg/m²; mean remaining life of the knee joint, 73.28 (range 24–190) months]. 7635 knees were excluded because of incomplete radiological data, valgus deformity, poor image quality, skeletal dysplasia, rheumatoid arthritis, or history of lower limb trauma or surgery.

This study was approved by the Research Ethics Committee of the Affiliated Hospital of Qingdao University (approval No. QYFYWZLL 26088). Written informed consent for data analysis was retrospectively obtained from all patients.

Radiological measurements

Standard AP radiographs of the knee were obtained in all patients at baseline and during TKA periods. The measurements were made independently by one radiologist with 3 years of experience in postprocessing procedures and by a second radiologist with 2 years of experience in orthopedic radiology; all measurements were performed using a Picture Archiving and Communication System. Each patient’s position was adjusted such that the toes were pointing straight forward with the feet separated sufficiently for balance.

All measurements were made on standard AP radiographs. The medial proximal tibial angle (MPTA) was measured as the angle between the tibial plateau and the mechanical axis of the tibia. The joint line convergence angle (JLCA) was measured as the angle between the line connecting the distal femur and proximal tibial articular surfaces on AP radiographs. Intraarticular varus deformity caused by narrowing of the medial joint space often manifests as a positive JLCA value (Fig. 1). TKA was confirmed by radiography.

Consistent magnification of the two images is the most important factor to ensure measurement accuracy. We measured the width of the horizontal position of the upper edge, which had the highest density on X-rays and was named the fibula width; we judged the consistency of the magnification by comparison of data that were obtained both times (Fig. 2).

The radiographic severity of knee OA was assessed with the K-L grading system: grade 0, definite absence of X-ray changes in osteoarthrosis; grade I, suspected narrowing of joint clearance and possible osteophytes; grade II, obvious osteophytes and slight narrowing of the joint space; grade III, moderate osteophytes, narrowing of joint space, mild sclerosis of subchondral bone, and small range of motion; and grade IV, formation of a large number of osteophytes, which can spread to the chondroid surface, with a narrower joint gap, extremely obvious hardening changes, joint hypertrophy, and obvious deformity [12, 20].

Indexes

(1) MPTA and JLCA at baseline (MPTA₀ and JLCA₀); (2) MPTA and JLCA in the TKA period (MPTA_T and JLCA_T); (3) changes in MPTA and JLCA between baseline and TKA period (ΔMPTA and ΔJLCA); and (4) rates of change in MPTA and JLCA between baseline and TKA periods (kMPTA and kJLCA).

The equations used to calculate indexes (3) and (4) were as follows:

ΔMPTA = MPTA₀ − MPTA_T

ΔJLCA = JLCA_T−JLCA₀

kMPTA=(MPTA₀ − MPTA_T)/ interval time

kJLCA=(JLCA_T−JLCA₀)/ interval time

Statistical analysis

All statistical evaluations were performed using PASW Statistics 25.0 (SPSS, Chicago, IL, USA). The Kolmogorov–Smirnov normality test was conducted before statistical analyses to determine whether the use of a parametric test was appropriate. Continuous variables conforming to normal distribution were expressed as means and standard deviations. Categorical variables were expressed as frequencies (%). Paired t-tests were used to compare the two measurements of fibular width, which can reflect consistent magnification of the images. Pearson correlation analysis and Kendall tau-b correlation analysis were performed to determine whether each index was associated with the remaining life of the knee joint. The absolute correlation coefficient (r) was used to define very strong (r = 0.80–1.00), strong (r = 0.60–0.79), moderate (r = 0.40–0.59), weak (r = 0.20–0.39), and no (r < 0.20) correlations. If there were pairwise correlations between several indexes and the remaining life of the knee joint, multivariate linear regression analysis was performed for further validation. Intra- and interclass correlation coefficients with 95% confidence intervals were used to assess intra- and interrater variabilities. An interclass correlation coefficient > 0.75 was considered to indicate excellent agreement. In all analyses, P < 0.05 was considered to indicate statistical significance.

Basic information

In total, 153 knees in 132 patients were included in this study. The mean remaining life of knee joint was 73.28 (range 24–190) months. Good to excellent intra- and interobserver variabilities were achieved for all measurements with intra- and interrater interclass correlation coefficients of 0.90–0.96 and 0.88–0.93, respectively.

Comparison of fibular width measurements at baseline and during TKA periods

The difference in measurement of fibula width at baseline and during TKA periods reflects the consistent magnification of images taken at the two time points. Paired t-test analysis showed no significant difference in fibula width between the two periods (t=−0.36, P=0.72) (Table 1).

Table 1. Difference in fibula width at baseline and TKA periods

			95% CI of the difference
	`X	S	Lower	Upper			t value	P value
Fibula width difference: baseline period -TKA period	-0.07	1.04	-0.48		0.34	-0.36		0.72
Table 1.`X, mean value; S, standard deviation; CI, confidence interval

Correlations between indexes and remaining life of the knee joint

Pearson and Kendall tau-b correlation analyses showed that BMI, JLCA₀, kMPTA and kJLCA were significantly correlated with the remaining life of the knee joint (all P<0.05). In contrast, onset age, gender, MPTA₀, MPTA_T, JLCA_T, ΔMPTA and ΔJLCA were not significantly correlated with the remaining life of knee joint (all P>0.05) (Tables 2–3).

Table 2. Correlations between basic information and remaining life of knee joints

		Onset age	Gender	BMI
remaining life of knee joints	r	-0.06	0.05	-0.35
	P	0.44	0.43	<0.01
Table 2. BMI, Body mass index

Table 3. Correlations between imaging indexes and remaining life of knee joints

		MPTA₀	JLCA₀	MPTA_T	JLCA_T	ΔMPTA	ΔJLCA	kMPTA	kJLCA
remaining life of knee joints	r	-0.05	-0.26	0.09	0.05	-0.12	0.06	-0.56	-0.57
	P	0.52	0.01	0.29	0.51	0.13	0.43	<0.01	<0.01
Table 3. BMI, Body mass index

Model for prediction of the remaining life of the knee joint

We used JLCA₀, kMPTA, kJLCA and all basic information to develop a model for prediction of the remaining life of knee joint. Multivariate linear regression analysis showed that onset age, BMI, JLCA₀, kMPTA and kJLCA were significantly negatively correlated with the remaining life of knee joint (Table 4).

Prediction model: remaining life of the knee joint = 195.3-0.6×Onset age-2.0×BMI-4.4×JLCA₀-159.9×kMPTA-205.5×kJLCA

Table 4. Multivariate linear regression analysis of remaining life of knee joints

	Unstandardized coefficient	Standardized coefficient B	t-value	P-value	B 95%CI
Intercept	195.3		8.81	<0.01	151.5	239.1
Onset age	-0.6	-0.2	-2.82	<0.01	-1.1	-0.2
Gender	-2.9	-0.1	-0.75	0.45	-10.3	4.6
BMI	-2.0	-0.2	-2.91	<0.01	-3.4	-0.6
JLCA₀	-4.4	-0.1	-2.27	0.03	-8.2	-0.6
kMPTA	-159.9	-0.3	-3.70	<0.01	-245.3	-74.5
kJLCA	-205.5	-0.3	-3.62	<0.01	-317.8	-93.2
Table 4. B, regression coefficient; CI, confidence interval

To our knowledge, there are few studies focusing on the prediction of the progression of knee OA. In this study, the most important finding is that, a larger onset age, BMI, JLCA₀, kMPTA and kJLCA is associated with a shorter remaining life of knee joint and greater risk of requiring TKA. Changes of MPTA and JLCA indicate knee varus deformity and narrowing of knee compartments. The rates of change in MPTA and JLCA calculated in this clinical study are the average value between the periods of arthritis onset and TKA; and the clinical importance of this prediction model is to provide early detection and health guidance for patients with knee OA, which is of great significance in reducing utilization rate of TKA. When patients are regularly followed up, X-rays are routinely used and corresponding values could be measured; meanwhile, we register their basic information. The values of two times follow-up are completely obtained and substituted into the prediction model in this topic, so as to distinguish between patients with rapid and slow progression of knee OA; then, individualized and targeted guidance and treatment could be implemented.

Here, we proposed the use of several indexes for prediction of disease progression and remaining life of knee joint; our model was confirmed to be effective and reliable. From the statistical verification results of standardized coefficient, kMPTA and kJLCA play the main roles in the prediction model (standardized coefficient B is 0.3 and 0.3, respectively). Besides, X-rays were used to calculate the rates of change in the medial proximal tibial angle and the joint line convergence angle; this procedure is economical, convenient and time-saving, which could be related to a better patients’ compliance.

Patients’ compliance is determined by many factors. Previous studies have shown that willingness to consider joint replacement, socioeconomic status, general health status, arthritis severity, educational level, age, and sex are important factors determining the utilization rate of TKA[9–11]. Willingness to consider joint replacement appears to be the most dominant factor associated with utilization of TKA; low socioeconomic status, poor general health status, low educational level, and female sex are associated with low TKA utilization rates. Therefore, delaying OA progression and reducing the need for TKA are important but challenging issues. Defining the maximum remaining life of knee joint (i.e., the risk of TKA and residual life of knee joints) will help to improve patient awareness and compliance, and prevent the increased need for TKA via early medical intervention, which helps to reduce the social costs of severe knee OA. From this point, calculating the exact remaining life of the knee joint is not the most important, but through this way to make patients aware of the severity of OA and actively cooperate with individual preventive measures to slow down the progression of OA and reduce the demand for TKA. When patients with high risk of requiring TKA are differentiated from patients with mild knee OA by onset age, BMI, JLCA₀, kMPTA and kJLCA in this prediction model, early individualized medical intervention (e.g., weight loss, personalized exercise, and knee-preserving surgery) could enable postponement or prevention of TKA.

Knee OA therapy demands a strategy that specialists agree with in considering the clinical symptoms and the disease severity. Conservative strategy is divided into pharmacological and non‑pharmacological approaches. Studies showed that, losing weight means a decrease of the pressure on the knee joint, improving physical function and biomechanics; a decrease in weight of 1 kilogram could produce quadruple reduction of the forces acting on the knee[17]. For subjects with OA symptoms, relief of symptoms has often been observed through weight loss and physical strengthening exercises only[8]. Non-steroidal anti-inflammatory drugs (NSAIDs), proprotracyclines, steroids, hyaluronic acid and platelet-rich plasma et al. could be chosen in pharmacologic strategy. Conservative and noninvasive strategy can be discussed in patients with mild knee symptoms or a slow progression. If patients in the follow-up are in a stage of rapid progress from mild OA, an invasive medical knee joint preservation strategy should be considered. Arthroscopic lavage and debridement and osteotomy around the knee are often identified as a treatment option. This phased treatment strategy, which relies on identifying people with rapid progress, is not clear; a complete strategy needs to be refined by further research, and the results of this study can play an important role in the identification and classification of at-risk populations.

The data presented here are clinically advanced in the following three respects. First, X-ray examination is economical and convenient, greatly improving the rates of patient examination and re-examination; this facilitates knee OA evaluation based on dynamic changes. Second, all patients enrolled in this study had TKA indications, such as K-L grade IV, severe pain, severe limitation of activity, and poor quality of life. However, they had been treated with TKA, thus avoiding some bias (e.g., low rate of TKA utilization because of low socioeconomic status, poor general health status, and/or low educational level). Third, risk of requiring TKA can be shown intuitively by this mathematical model which helps us to identify patients with rapidly aggravated knee OA and intuitive and pellucid result is helpful to improve patients' compliance by improving their understanding of knee OA and TKA. Early medical intervention will be carried out strictly for these patients to delay the progress of OA.

This study had several limitations. First, patients without records of TKA after the longest follow-up period in this study were excluded; this ensured that the study focused only on TKA risk, rather than the threshold for determining whether TKA surgery will be performed in the future. Therefore, additional systematic studies are required. Second, retrospective research can explore correlations in data and build a correlation model based on random sampling. However, to make the predictive model more convincing, additional prospective studies are needed to verify its sensitivity and reliability by comparing differences between theoretical and actual values. Third, although the total amount of data was large, a small amount of data met our study criteria, reflecting incomplete follow-up because of patient concerns regarding X-rays or a lack of appropriate understanding concerning the seriousness of knee OA.

The prediction model in this topic could reliably predict the risk of requiring TKA. According to patients’ basic information, JLCA₀, kMPTA and kJLCA findings obtained from radiography of knee joints, patients with high risk of requiring TKA can be differentiated from all patients with knee OA. Early medical intervention may enable postponement or prevention of TKA, thus reducing the burdens on healthcare and medical insurance systems.

Authorship declaration

All authors listed meet the authorship criteria according to the latest guidelines of the International Committee of Medical Journal Editors, and all authors are in agreement with the manuscript.

Funding

This study was funded by National Natural Science Foundation of China (NO. 31802022).

Conflicts of interest

The authors declare that they have no conflict of interest.

Author contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by Wenru Ma. Material preparation, data collection and analysis were performed by Chunpu Li and Guangzhen Liu. Tengbo Yu and Yi Zhang were responsible for the overall direction and review. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Acknowledgments

We are appreciated it for the technical support provided by the Radiology Department of the affiliated Hospital of Qingdao University.

Ethics approval and consent to participate

Approval was obtained from the research ethics committee of the Affiliated Hospital of QingDao University. (Ethics number: QYFYWZLL 26499) All methods were carried out in accordance with relevant guidelines and regulations. Verbal informed consent was obtained from all individual participants included in the study and this was approved by the ethics committee.

Availability of data and materials

The datasets used during the current study available from the corresponding author on reasonable request.

Consent for publication

Not applicable.

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No competing interests reported.

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Establishing an imaging prediction model to predict the remaining life of knee joint for varus knee osteoarthritis patients

Status:

Version 1

Abstract

Purpose

Methods

Results

Conclusions

Figures

Introduction

Methods

Statistical analysis

Results

Basic information

Comparison of fibular width measurements at baseline and during TKA periods

Correlations between indexes and remaining life of the knee joint

Model for prediction of the remaining life of the knee joint

Discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1