Improved intra-operative decision making in RA-TKR with the help of pre-operative CT scan

Abstract

Background

Robotic-assisted total knee replacement (RA-TKR) is a significant advancement in orthopedic surgery, but intra-operative decision-making remains challenging. Pre-operative imaging techniques, particularly CT scans, have gained momentum, providing insights into the patient's anatomy, improving implant positioning and alignment. However, further research is needed to explore their influence on RA-TKR planning and execution.

Materials and methods

The hospital based cross-sectional study was conducted in Orthopedics department of Sparsh Speciality Hospital, Bangalore & Sunshine Hospital, Hyderabad. A total of 1020 participants in the age group over 50 years during the study period were included based on convenient sampling. The axial CT images were taken preoperatively and RA-TKA was done for all the patients.

Results

The study participant's average age was 64.01 ± 7.13. Out of 1020 patients 259 (24.4%) were males and 761 (74.6%) were females. The median femoral, tibia and Polyethylene predicted and the actual component were same with the side of surgery and BMI. The median femoral predicted actual component was significantly higher among the age category of more than 80 years when compared to other age groups. The median femoral, tibia and Polyethylene predicted was higher in males when compared to females.

Conclusion

Pre-operative CT scans enhance RA-TKR procedures by providing precise anatomical insights, enhancing implant placement, and identifying potential issues, improving surgical outcomes and patient satisfaction.

1. Introduction

Robotic-assisted total knee replacement (RA-TKR) has emerged as a groundbreaking advancement in orthopedic surgery, promising remarkable precision and accuracy in implant positioning and alignment. While the advantages of RA-TKR are evident, the real challenge lies in making crucial intra-operative decisions, particularly concerning the selection of the optimal implant size and the prediction of necessary bony resection. To address this challenge, the integration of pre-operative imaging techniques, such as computed tomography (CT) scans, has gained momentum in the field, offering a more comprehensive understanding of the patient's anatomy prior to surgery and thereby enhancing preoperative planning and overall surgical outcomes.¹ (see Table 1, Table 2, Table 3, Table 4, Table 5, Table 6)

Table 1.

Basic characteristic of study population.

	Number	Percentage
Age Category
50–60 years	355	34.8%
>60–70 years	474	46.5%
>70–80 years	178	17.5%
>80 years	13	1.3%
Mean age in years ±SD	64.01 ± 7.13
Gender
Male	259	25.4 %
Female	761	74.6 %
Side
Right	512	50.2%
Left	508	49.8%
Anthropometric Measurement
Mean Height in cms± SD	161.10 ± 8.47
Mean weight in kgs ±SD	73.83 ± 10.47
Mean BMI in kg/m2 ± SD	28.58 ± 4.45
Total	1020	100%

Table 2.

Implant size Actual component compared to the predicted RA-TKA.

Site of surgery	Predicted	Actual component	P value
Femoral Median (IQR)	3 (2–4)	3 (2–3)	0.257
Proportion of Femoral prediction	1013/1020 (99.3%)
Tibia Median (IQR)	2 (1–3)	2 (1–3)	0.134
Proportion of Tibia prediction	1007/1020 (98.7%)
Polyethylene Mean ± SD	9.48 ± 0.91	9.49 ± 0.94	*0.346
Proportion of Polyethylene prediction	1010/1020 (99.0%)
Total	1020	1020

Note: p value based on Wilcoxon signed rank test, *P value based on paired t-test.

Table 3.

Gender wise comparison between Implant sizes Actual component compared to the predicted RA-TKA.

Site of surgery	Male	Female	P value
Femoral Predicted Median (IQR)	4 (4–5)	2 (2–3)	0.001
Femoral actual component Median (IQR)	4 (4–5)	2 (2–3)	0.001
Tibia Predicted Median (IQR)	4 (4–5)	2 (1–3)	0.001
Tibia actual component Median (IQR)	4 (4–5)	2 (1–3)	0.001
Polyethylene Predicted Mean ± SD in mm	9.35 ± 0.78	9.53 ± 0.94	*0.003
Polyethylene actual component Mean ± SD in mm	9.35 ± 0.78	9.54 ± 0.98	*0.002
Total	259	761

Note: p value based on Wilcoxon signed rank test, *P value based on independent sample t-test.

Table 4.

Age category wise comparison between Implant sizes Actual component compared to the predicted RA-TKA.

Site of surgery	50–60 years	>60–70 years	>70–80 years	>80 years	P value
Femoral Predicted Median (IQR)	3 (2–4)	3 (2–4)	3 (2–4)	4 (2.5–5)	0.038
Femoral actual component Median (IQR)	3 (2–4)	3 (2–4)	2.5 (2–4)	4 (2.5–5)	0.031
Tibia Predicted Median (IQR)	2 (2–3)	2 (1–3)	2 (1–3)	3 (2–5)	0.127
Tibia actual component Median (IQR)	2 (2–3)	2 (1–3)	2 (1–3)	3 (2–5)	0.152
Polyethylene Predicted Mean ± SD in mm	9.53 ± 0.94	9.48 ± 0.89	9.38 ± 0.90	9.62 ± 0.96	*0.305
Polyethylene actual component Mean ± SD in mm	9.52 ± 1.01	9.49 ± 0.89	9.42 ± 0.93	9.62 ± 0.96	*0.651
Total	355	474	178	13

Note: p value based on Kruskal-Wallis H test, *P value based on One way ANOVA.

Table 5.

Side wise comparison between study groups.

Side	Right	Left	P value
Femoral Predicted Median (IQR)	3 (2–4)	3 (2–4)	0.395
Femoral actual component Median (IQR)	3 (2–4)	3(2–4)	0.498
Tibia Predicted Median (IQR)	2 (2–3)	2 (1–3)	0.326
Tibia actual component Median (IQR)	2 (1.25–3)	2 (1–3)	0.291
Polyethylene Predicted Mean ± SD in mm	9.49 ± 0.94	9.47 ± 0.89	0.679
Polyethylene actual component Mean ± SD in mm	9.49 ± 0.98	9.49 ± 0.90	0.999
Total	512	508

Note: p value based on Wilcoxon signed rank test, *P value based on independent sample t-test.

Table 6.

BMI comparison between study groups.

Site of surgery	Normal weight	Overweight	Obesity class I	Obesity class II	P value
Femoral Predicted Median (IQR)	3 (2–4)	3 (2–4)	2(2–3)	3(2–4)	0.395
Femoral actual component Median (IQR)	3(2–4)	3(2–4)	2(2–3)	3(2–4)	0.498
Tibia Predicted Median (IQR)	2(2–3)	2(2–3)	2(1–3)	3(2–4)	0.326
Tibia actual component Median (IQR)	2(2–3)	2(2–3)	2(1–3)	3(2–4)	0.291
Polyethylene Predicted Mean ± SD in mm	9.53 ± 0.88	9.47 ± 0.95	9.50 ± 0.92	9.32 ± 0.74	0.679
Polyethylene actual component Mean ± SD in mm	9.54 ± 0.89	9.48 ± 9.56	9.50 ± 1.01	9.35 ± 0.76	0.999
Total	245	407	293	75

Note: p value based on Kruskal-Wallis H test, *P value based on One way ANOVA.

The importance of pre-operative imaging, especially the utilization of CT scans, has become increasingly evident in the preoperative planning of orthopedic surgeries, including RA-TKR. CT scans provide superior visualization of the patient's bony anatomy, offering critical insights that aid in predicting the extent of bony resection required and in selecting the most suitable implant size. The incorporation of pre-operative CT scans has been found to elevate the precision of preoperative planning, resulting in improved implant positioning and alignment. Additionally, patients who undergo pre-operative CT scans exhibit a lower incidence of mispositioned implants, experience fewer complications, and enjoy improved functional outcomes.²

While the impact of pre-operative CT scans on preoperative planning and implant positioning is well-documented, there is still a need to explore their influence on intra-operative decision-making during RA-TKR.³ This gap in knowledge relates to the specific role of pre-operative CT scans in guiding real-time surgical decisions, reducing the risk of implant malposition, and minimizing the need for intra-operative adjustments. Furthermore, the potential impact of pre-operative CT scans on the assessment of physical characteristics in relation to implant sizes remains an area requiring further investigation.^4,5

Controversy may arise regarding the necessity and cost-effectiveness of pre-operative CT scans for RA-TKR. While the advantages of pre-operative CT scans in enhancing surgical precision are evident, some may argue that the associated costs and radiation exposure should be carefully considered. Therefore, it is crucial to address these concerns and establish the effectiveness of pre-operative CT scans in RA-TKR, particularly in improving intra-operative decision-making.^6,7

This study aims to comprehensively examine the role of pre-operative CT scans in the planning and execution of RA-TKR. Specifically, it seeks to determine how pre-operative CT scans aid in planning for implant sizes and assess their influence on physical characteristics that impact implant sizing. By utilizing pre-operative CT scans in conjunction with the RA-TKR procedure, the study aims to improve intra-operative decision-making, resulting in enhanced surgical outcomes and increased patient satisfaction.

By systematically addressing these aspects, this research contributes to our understanding of the value of pre-operative imaging, particularly in RA-TKR procedures, and helps bridge the existing gap in knowledge surrounding the specific benefits of CT scans in guiding intra-operative decisions, ultimately leading to more successful surgical outcomes.

2. Aims and objectives

• •To determine the help of pre-operative CT scan in planning for implant sizes in RA-TKR.
• •To assess the accuracy of the predictive models for femoral, tibial, and polyethylene components in joint surgery.
• •To assess the effect of physical characteristics on implant sizes.
• •To investigate age-related variations in predicted and actual joint component sizes.
• •To assess the relationship between body mass index (BMI) and the accuracy of predicted joint component sizes.

2.1. Material and methods

The hospital-based cross-sectional study was conducted in the orthopedics department of Sparsh Speciality Hospital, Bangalore, and Sunshine Hospital, Hyderabad. The study was conducted over a period of six months. As per convenient sampling, a total of 1020 participants in the age group of more than 50 years with a study duration were included in the study. The study participants included patients attending an orthopedics department with a history of osteoarthritis and undergoing RA-TKR.

2.1.1. Sample size calculation

“According to Xufeng Wan et al. et al. study,⁷ considering the implant size of the tibial component was successfully predicted in 25/28 cases (89%). as 89% with a precision of 5% and 95% confidence interval, the sample size is calculated as” 150.

Formula used for sample size calculation

N = Z21-α/2 * p * (1 - p) / d2

Z1-α/2 - two tailed proabability for 95% confidence interval = 1.96.

“p (%) - prevalence of The implant size of the tibial component was successfully predicted in 25/28 cases (89%). = 0.89″

“d (%) - precision or allowable error for The implant size of the tibial component

was successfully predicted in 25/28 cases (89%). = 0.05″

N = 1.96^2 * 0.89 * (1 - 0.89) / 0.05^2

N = 150.43

Thus the total sample size required for the study is 150 but the present study was conducted over six month period and we have selected 1020 study participants those who fulfilled the inclusion criteria during the study period.

2.1.2. Inclusion criteria

• •All patients undergoing RA-TKR
• •Age more than 50 years
• •Osteoarthritis
• •Varus/Valgus knee

2.1.3. Exclusion criteria

• •Rheumatoid arthritis & other inflammatory arthritis
• •Fixed flexion deformity
• •Bone defects

2.1.4. Preoperative planning

As per the manufacturer's policy, a standard CT scan of the entire lower limb was performed before to the surgery for preoperative planning.

2.1.5. Robotic arm-assisted surgery

The RA-TKA system enables tracking of the femur and tibia's location intraoperatively and eliminates the need for cutting blocks or alignment guides. The robotic probe was used intraoperatively to register specific bone features. The surgical plan was then updated in real time by using the patient-specific model created by the robotic software. Following that, bone cuts were made utilizing the RA-TKA system on the proximal tibia, anterior condyle, posterior condyle, posterior chamfer, distal femur, and anterior chamfer, in the order specified per the preoperative plan. For the purpose of kinematic tracking of soft tissue balancing, tracker arrays are positioned; the femoral tracker array is fixed in the distal femur using an arthrotomy, and the tibial tracker array is fixed percutaneously in the proximal tibia.

2.1.6. Statistical analysis

The date was entered in Microsoft excel and analysed using SPSS software version 21. shapiro wilk test is used to find the normality of the data. If the continuous data follows normal distribution, it will express as mean and Standard deviation, if the data not follows normal distribution median and inter quartile range were used. Description of categorical variables was expressed as frequency and proportion. Wilcoxon signed rank test was used for comparing two or more independent samples of equal or different sample sizes. All the tests were two-tailed, with results considered statistically significant if the p-value was less than 0.05.

2.1.6.1. Ethical consideration

Strict adherence was made to ethical precepts such as beneficence, justice, and regard for the patient. The “Institutional Ethics Committee” granted permission to carry out the current investigation (IEC).

2.1.7. Results

The mean Age of the study participant was 64.01 ± 7.13 years and majority of the study participants belongs to the age category of >60–70 years which is 474 (46.5%). Out of 1020 patients 259 (24.4%) were males and 761 (74.6%) were females. Among the 1020 participants, 512 had right side surgery and 508 (49.8%) had left side surgery. The mean height of the study participants was 161.10 ± 8.47 cms, mean weight was 73.83 ± 10.47 kgs and the mean BMI was 28.58 ± 4.45 kg/m2.

The median femoral predicted was 3 (IQR = 2–4) and the actual component was 3 (IQR = 2–4). The median femoral predicted and actual component was equal distributed and it is not statistically significant with the p value shows more than 0.05. The size of the femoral component was successfully predicted using the RA-TKA in 1013/1020 (99.3%) cases. The median tibia predicted was 2 (IQR = 1–3) and the actual component was 2 (IQR = 1–3). The median tibia predicted and actual component was equal distributed and it is not statistically significant with the p value shows more than 0.05. The size of the tibia component was successfully predicted using the RA-TKA in 1007/1020 (98.7%) cases. The mean Polyethylene predicted was 9.48 ± 0.91 mm and the actual component was 9.49 ± 0.94 and it is equally distributed with the p value shows more than 0.05. The size of the Polyethylene was successfully predicted using the RA-TKA in 1010/1020 (99.0%) cases.

The male median femoral predicted was 4 (IQR = 4–5) and the female median predicted was 2 (IQR = 2–3). The median male prediction was higher when compared to females and the difference is statistically significant with the p value shows less than 0.05. The male median femoral actual component was 4 (IQR = 4–5) and the female median actual component was 2 (IQR = 2–3). The median male actual component was higher when compared to females and the difference is statistically significant with the p value shows less than 0.05. The male median tibia predicted was 4 (IQR = 4–5) and the female median predicted was 2 (IQR = 1–3). The median male tibia prediction was higher when compared to females and the difference is statistically significant with the p value shows less than 0.05. The male median tibial actual component was 4 (IQR = 4–5) and the female median actual component was 2 (IQR = 1–3). The median male tibia actual component was higher when compared to females and the difference is statistically significant with the p value shows less than 0.05. The mean male polyethylene predicted was 9.35 ± 0.78 mm and the female's polyethylene predicted was 9.53 ± 0.94 mm. The mean polyethylene female predicted was significantly higher when compared to males and the difference is statistically significant with the p value shows less than 0.05. The mean male polyethylene actual component was 9.35 ± 0.78 mm and the female's polyethylene actual component was 9.54 ± 0.98 mm. The mean polyethylene female actual component was significantly higher when compared to males and the difference is statistically significant with the p value shows less than 0.05.

The median femoral predicted actual component was significantly higher among the age category of more than 80 years when compared to other age groups and the p value shows less than 0.05. The median tibia predicted and actual component was equally distributed in all the age group with the p value shows more than 0.05. The mean polyethylene predicted and actual component was equally distributed in all the age category and the p value shows more than 0.05.

The median femoral predicted actual component was equally distributed in both the side and the p value shows more than 0.05. The median tibia predicted and actual component was equally distributed in both side with the p value shows more than 0.05. The mean polyethylene predicted and actual component was equally distributed in both the side and the p value shows more than 0.05.

The median femoral predicted actual component was equally distributed in all categories of BMI and the p value shows more than 0.05. The median tibia predicted and actual component was equally distributed in all categories of BMI with the p value shows more than 0.05. The mean polyethylene predicted and actual component was equally distributed in in all categories of BMI and the p value shows more than 0.05.

3. Discussion

The utilization of pre-operative CT scans to enhance intra-operative decision-making in Robotic Assisted Total Knee Replacement (RA-TKR) is a significant development in orthopedic surgery.³ The findings of this article confirm the substantial benefits associated with incorporating pre-operative CT scans into the RA-TKR procedure.

The study included 1020 RA-TKR patients who underwent pre-operative CT scan and subsequent surgery. The mean age among 1020 RA-TKR study participants was 64.01 ± 7.13 years. Most of them were females with right sided surgery and the mean BMI was 28.58 ± 4.45 kg/m2. These findings are consistent with the study findings of Wan et al.⁷ with mean age of 65.2 ± 6.4 years, 71% female participants with mean BMI of 27.4 ± 3.0 kg/m2.

The results of the study indicated that pre-operative CT scan significantly improved intra-operative decision making in RA-TKR patients. The findings of the study by Marchand et al.⁸ demonstrated the benefits of using a robotic arm-assisted tool to support a surgeon in the operating theatre. The RA-TKA device produced intraoperatively balanced knees by minimizing variations in medial vs lateral flexion and extension gaps. Additionally, the RA-TKA program was able to properly anticipate and forecast the implications of even little alterations in bone cuts, which led to the surgeon reporting significant intraoperative comfort.

While the present study showcases the advantages of pre-operative CT scans, it's essential to consider the reliability and accuracy of digital templating, as addressed in the work of Trickett et al.⁹ Digital templating, which aims to predict implant sizes, has limitations in predicting the correct component size consistently. This underlines the significance of more advanced technologies like pre-operative CT scans in improving the precision of implant sizing.

However, Ooka et al.¹⁰ had a differing view on the accuracy of pre-operative templating using X-rays, indicating that it is unreliable and inaccurate. This variance in findings emphasizes the need for advanced technologies like pre-operative CT scans that provide more detailed and precise information for the surgical team.

Interestingly, the present study found that the median tibia predicted and actual component was significantly distributed in all age groups equally. This finding suggests that pre-operative CT scans may not have a significant impact on tibial component positioning. The mean polyethylene predicted and actual component was also equally distributed in all age categories, and the p value was more than 0.05. This finding suggests that pre-operative CT scans may not have a significant impact on polyethylene component positioning.

Zhang et al.¹¹ conducted a systematic review and meta-analysis which showed a significantly lower difference between planned component position and implanted component position, and the spread was narrower for RA-TKA compared with the manual-TKA group among 16 studies.

In addition to its role in predicting implant sizes, pre-operative CT scans have a noteworthy impact on the precision of leg alignment during RA-TKR. The study findings indicate that the robotic arm-assisted system achieves more accurate leg alignment than manual TKA. This is consistent with several other studies that have demonstrated the superiority of robotic-assisted TKA in achieving precise alignment and implant placement.^3,4,12 Out of 261 RA-TKA instances, Sodhi et al.¹³ reported comparable results, stating that 100% of patients were corrected to neutral despite having varied degrees of varus deformity, some even more than 7°.

Another study by Marchand et al.¹⁴ reported a match-controlled comparison of 20 cemented RA-TKAs to 20 cemented manual TKAs and discovered that overall patient satisfaction and discomfort were considerably higher in patients who had RA-TKAs than in patients who had manual TKAs.

Another interesting finding from the study was the difference in predicted and actual polyethylene component sizes between male and female patients. The study found that the predicted polyethylene component size was significantly higher in females, but the actual component size was higher in males. This suggests that while pre-operative CT scans can provide accurate predictions for component size, there may be other factors such as the patient's build, genetics, or type of employment, that come into play during surgery that affect the final component size. Likewise the median tibial predicted and actual component was equally distributed in both the sides equally and also in all BMI categories, and it was not significant.

Despite gender, a considerable percentage of TKA patients experience mediolateral component overhang, which can lead to worse clinical outcomes. This overhang is associated with increased risk of knee discomfort, and robotic assistance during knee reconstruction surgeries has the potential to improve implant insertion accuracy, leading to better outcomes.^15,16 Robotic assistance during knee reconstruction surgeries has the potential to improve implant insertion accuracy as well as implant choice and subsequent fit.

4. Strengths and limitations

The current study on CT templating exhibits strengths in enhanced surgical precision. However, notable limitations include the absence of outcome scores, a lack of patient satisfaction score assessment, and an undisclosed approach to addressing potential biases. These shortcomings underscore the necessity for future research to delve deeper into these aspects, ensuring a more thorough evaluation of the approach's benefits and limitations in clinical practice.

5. Conclusion

The incorporation of pre-operative CT scans in RA-TKR procedures shows promise in enhancing implant placement and alignment through precise anatomical insights. While the current evidence suggests potential benefits, the conclusion cannot be deemed robust enough as it lacks support from Functional/Clinical Outcome, PROM, and patient satisfaction score assessments alongside the conducted CT study. The statement regarding improved surgical outcomes and patient satisfaction requires further validation through comprehensive evaluation. Consequently, there is a clear need for additional research to establish standardized guidelines and thoroughly explore the advantages and limitations of this approach, ensuring a more robust and evidence-based conclusion.

Patient/gaurdian consent

NOT APPLICABLE.

Funding

NIL.

CRediT authorship contribution statement

Ravikumar Mukartihal: Study Conception, Visualization, Methodology, Project administration. S.R. Arun: Writing – review & editing, Validation, and. Sharan S. Patil: Validation, and, Formal analysis. A.V. Gurava Reddy: Investigation, processed the experimental data, contributed to the design, and, implementation of the study, to the analysis of the results, and, to the writing of the manuscript. Adarsh Annapareddy: Investigation, processed the experimental data, contributed to the design, and, implementation of the study, to the analysis of the results, and, to the writing of the manuscript. V. Ratnakar: Investigation, processed the experimental data, contributed to the design, and, implementation of the study, to the analysis of the results, and, to the writing of the manuscript. Rajdeep das: carried out the implementation and designed the figures with help of. Shrishti Sharan Patil: Validation, and, Formal analysis.

Declaration of competing interest

NIL.