CC BY 4.0 · J Knee Surg 2024; 37(12): 864-872
DOI: 10.1055/a-2343-2444
Original Article

Early Clinical and Economic Outcomes for the VELYS Robotic-Assisted Solution Compared with Manual Instrumentation for Total Knee Arthroplasty

Philip Huang
1   OrthoIndy, Indianapolis, Indiana
,
Michael Cross
1   OrthoIndy, Indianapolis, Indiana
,
2   Epidemiology and Real-World Data Sciences, Johnson and Johnson MedTech, New Brunswick, New Jersey
,
Dhara Intwala
3   DePuy Synthes Digital, Robotics and Emerging Channels, Raynham, Massachusetts
,
Jill Ruppenkamp
2   Epidemiology and Real-World Data Sciences, Johnson and Johnson MedTech, New Brunswick, New Jersey
,
Daniel Hoeffel
4   DePuy Synthes, Medical Affairs, Palm Beach Gardens, Florida
› Author Affiliations
Funding This study was supported by the Johnson and Johnson MedTech.
 

Abstract

Robotic-assisted total knee arthroplasty (TKA) has been developed to improve functional outcomes after TKA by increasing surgical precision of bone cuts and soft tissue balancing, thereby reducing outliers. The DePuy Synthes VELYS robotic-assisted solution (VRAS) is one of the latest entrants in the robotic TKA market. Currently, there is limited evidence investigating early patient and economic outcomes associated with the use of VRAS. The Premier Healthcare Database was analyzed to identify patients undergoing manual TKA with any implant system compared with a cohort of robotic-assisted TKAs using VRAS between September 1, 2021 and February 28, 2023. The primary outcome was all-cause and knee-related all-setting revisits within 90-day post-TKA. Secondary outcomes included number of inpatient revisits (readmission), operating room time, discharge status, and hospital costs. Baseline covariate differences between the two cohorts were balanced using fine stratification methodology and analyzed using generalized linear models. The cohorts included 866 VRAS and 128,643 manual TKAs that had 90-day follow-up data. The rates of both all-cause and knee-related all-setting follow-up visits (revisits) were significantly lower in the VRAS TKA cohort compared with the manual TKA cohort (13.86 vs. 17.19%; mean difference [MD]: −3.34 [95% confidence interval: −5.65 to −1.03] and 2.66 vs. 4.81%; MD: −2.15 [−3.23 to −1.08], respectively, p-value < 0.01) at 90-day follow-up. The incidence of knee-related inpatient readmission was also significantly lower (53%) for VRAS compared with manual TKA. There was no significant difference between total cost of care at 90-day follow-up between VRAS and manual TKA cases. On average, the operating room time was higher for VRAS compared with manual TKA (138 vs. 134 minutes). In addition, the discharge status and revision rates were similar between the cohorts. The use of VRAS for TKA is associated with lower follow-up visits and knee-related readmission rates in the first 90-day postoperatively. The total hospital cost was similar for both VRAS and manual TKA cohort while not accounting for the purchase of the robot.


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Total knee arthroplasty (TKA) is a well-established and cost-effective procedure for the treatment of end-stage knee osteoarthritis. To further improve surgical outcomes, robotic-assisted solutions have been developed to increase surgical precision and reduce surgical variability. Robotic technology in TKA has been shown to improve patient outcomes, especially range of motion, patient satisfaction, and facilitate a shorter recovery time.[1] [2] [3] [4] [5] [6] [7] Current literature also suggests that use of robotic-assisted TKA can reduce soft tissue trauma leading to decreased pain and expedited recovery.[8] [9] However, some studies have suggested that the benefits of robotic-assisted TKA may be only apparent in the early postoperative period[10] [11] and have highlighted concerns regarding the learning curve[12] [13] and increased costs associated with the use of robotic surgery.[14] [15]

The DePuy Synthes VELYS robotic-assisted solution (VRAS) is one of the latest entrants in the rapidly evolving field of robotic technology for TKA. VRAS is an imageless system designed to eliminate the need for preoperative CT scans, which can lower preoperative preparation time, cost, and radiation exposure. It is only compatible with the ATTUNE Knee System (DePuy Synthes), a widely used knee implant, and has the ability to facilitate precise, accurate, and informed decision-making during surgery.[16] Early results from recent studies have shown promising results for the use of VRAS in TKA.[17] [18]

Most of the current literature is focused on evaluating robotic technology for TKA as a class or related to one of the more established robotic systems. Current VRAS-specific evidence is generally focused around single sites[17] [18] or cadaveric studies.[19] [20] This retrospective comparative study is designed to evaluate early postoperative clinical and economic outcomes with the use of VRAS in TKA compared with a large cohort of manual TKAs, utilizing a large hospital billing database.

Methods

Data Source

Data from the Premier Healthcare Database were used to identify patients undergoing manual TKA with any implant system compared with a cohort of robotic-assisted TKA using VRAS. The Premier Healthcare Database is nationally representative and encompasses extensive clinical coding information, including diagnoses, procedures, and hospital-administered medications.[21] It draws data from more than 1,000 hospitals and healthcare systems, covering over 20% of all hospital admissions in the United States. Additionally, the database includes a chargemaster, which includes device-specific details. The database was reviewed by the New England Institutional Review Board (IRB) and was determined to be exempt from IRB approval.


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Study Population

Patients with a Current Procedural Terminology code or International Classification of Diseases, Tenth Revision (ICD-10) code indicative of primary TKA from September 1, 2021 to February 28, 2023 were included in the study. The start date of data collection was based on the first available VRAS TKA in the database. The date of the admission for the TKA procedure was defined as the index date. Utilizing ICD-10 data and demographic information, patients with any of the following criteria were excluded from the study: age < 18 years, diagnosis for aseptic loosening, infection, osteomyelitis, knee fracture at the time of index, or had a partial-knee procedure. Additionally, the following patients were excluded from 90-day follow-up analysis: patients that underwent a second primary procedure within 90 days of index or had continuous enrollment for less than 90-day postindex.


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Variables

Patient demographics included age, gender, race, marital status, payer type, procedure setting (inpatient or outpatient), smoking status, and comorbidities. Baseline comorbidities were assessed using the Elixhauser Comorbidity Index and Functional Comorbidity Index (FCI). The overall Elixhauser score reflects the overall comorbidity level by assessing 31 dimensions related to chronic diseases. Additionally, this score has demonstrated an association with the risk of mortality and healthcare resource utilization.[22] [23] The FCI includes 18 medical conditions and holds relevance in orthopaedic care as it was created as a measure of patient functional capacity.[24] [25] Provider characteristics included hospital bed size, annual volume of TKA procedures (per hospital/physician), geographic location, hospital location (urban or rural), and teaching status. Procedural characteristics included admission year and fixation type (cemented or uncemented).


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Outcomes

The primary outcome was all-setting follow-up visits (revisits) within 90-day post-TKA. Secondary outcomes included readmission rates within 90-day, operating room time, discharge status (home vs. skilled nursing facility), and hospital costs including index and 90-day total cost of care (index + follow-up cost). Hospital cost at index was further subcategorized into supply and operation room cost.


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Data Analysis

All variables and outcomes listed above were analyzed using standard descriptive statistics. Continuous variables were presented in terms of means and standard deviations (along with 95% confidence intervals [CIs]), binary outcomes were presented as proportions with 95% CIs. To control the differences between the VRAS and manual TKA cohorts' fine stratification and weighing (FSW) methodology was used.[26] [27] A total of 200 strata were created, and no individual patient information was discarded using this method. A love-plot was generated to show changes in standardized mean difference (SMD) between pre- and postbalancing of the covariates. Absolute SMD of 0.2 was used to assess good covariate balance.[28] Subsequently, weighted generalized linear regression models were utilized to calculate the adjusted effect of the exposure after stratification. All costs were inflated to 2023 U.S. dollars using the Bureau of Labor Statistics consumer price index.[29]


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Results

[Table 1] provides the distribution of the population for each cohort. A total of 1,180 VRAS TKA and 161,866 manual TKA cases were included in the study, with 866 VRAS and 128,643 manual TKA cases having 90-day follow-up data.

Table 1

Patient attrition using the inclusion and exclusion criteria

Description

VRAS TKA

Manual TKA

1

Include patients with primary TKA surgery from Q4 2015 to April 2023

1,814

1,388,591

2

Include patients with data Publish_type = CP

1,665

1,264,423

3

Excluding patients (and all of their episodes) with at least 2 or more pat_keys with same admission dates

1,665

1,263,860

4

Take Index admission episode

1,413

1,097,711

5

Include patients with age ≥ 18

1,413

1,097,584

6

Include elective patients only

1,343

1,036,615

7

Exclude patients with fracture of knee at index

1,340

1,032,147

8

Exclude patients with diagnosis of aseptic loosening at index

1,336

1,009,651

9

Exclude patients with cancer diagnosis at index

1,332

1,005,842

10

Exclude patients with diagnosis of infection/osteomyelitis at index

1,330

997,841

11

Exclude patients without unknown gender

1,330

997,670

12

Exclude patients 0 costs at index

1,330

992,895

13

Exclude partial knee patients

1,330

981,711

14

Exclude manual TKA patients before September 1, 2021 (VELYS data availability)

1,330

161,866

15

Exclude VRAS patients that have indication of any other robotic technology usage

1,180

161,866

A[a]

Exclude patients that have less than 90-day follow-up data

885

133,892

B[a]

Exclude patients that have bilateral procedures within 90 days

866

128,643

Abbreviations: TKA, total knee arthroplasty; VRAS, VELYS robotic-assisted solution.


a A and B criteria only applied for 90-day follow-up analysis.


Patient and Provider Baseline Characteristics

The patient and provider baseline characteristics of the study cohorts are presented in [Tables 2] and [3], respectively. The VRAS and manual TKA patients exhibited overall similarity (SMD < 0.2) in terms of demographics and comorbidities. The majority of patients were married, Caucasian women of similar age, and with Medicare as the primary payor. Approximately half of the patients in both cohorts had one to two comorbidities. The only significant baseline difference observed was in patients with existing knee pain indication, where the VRAS cohort (31%) had a higher prevalence compared with the manual TKA cohort (8%), with an SMD of 0.62. The majority of the cases were outpatient cases with almost 97% of VRAS TKA and 90% of manual TKA cases being outpatient.

Table 2

Patient characteristics of patients undergoing total knee arthroplasty using either manual approach or VELYS robotic-assisted solution, before and after fine stratification and weighting

Variable

Prefine stratification

Postfine stratification

Manual

VRAS

SMD

Manual

VRAS

SMD

N

128,643

866

128,643

866

Age, mean (SD)

68.00

(9.20)

67.73 (8.94)

0.03

67.59 (9.17)

67.73 (8.94)

0.015

Age category (%)

0.057

0.028

 18–34

0.08

0

0

0

 35–44

0.85

0.92

1.09

0.92

 45–54

7.07

7.62

7.46

7.62

 55–64

25.39

25.29

25.15

25.29

 65–74

41.49

42.61

43.52

42.61

 75 and above

25.11

23.56

22.78

23.56

Gender: men (%)

38.98

38.8

0.004

36.47

38.8

0.048

Marital status (%)

0.129

0.045

 Married

60.58

66.74

65.89

66.74

 Single

35.75

30.37

31.8

30.37

 Other

3.66

2.89

2.31

2.89

Race (%)

0.137

0.08

 Asian

1.47

1.62

1.66

1.62

 Black

9.71

7.62

8.72

7.62

 Other

6.04

3.7

2.46

3.7

 White

82.78

87.07

87.15

87.07

Payer (%)

0.069

0.065

 Commercial

27.04

30.14

28.64

30.14

 Medicaid

4.63

4.39

4.10

4.39

 Medicare

64.09

61.32

61.86

61.32

 Other

4.24

4.16

5.40

4.16

Functional Comorbidity Index, mean (SD)

3.23 (1.62)

3.13 (1.65)

0.059

2.98 (1.78)

3.13 (1.65)

0.088

Elixhauser Comorbidity Index, mean (SD)

2.03 (1.57)

1.83 (1.54)

0.128

1.77 (1.62)

1.83 (1.54)

0.041

Elixhauser categories (%)

0.184

0.094

 No comorbidities

16.86

24.02

27.9

24.02

 1–2

49.46

45.15

41.58

45.15

 3–4

26.47

25.17

24.71

25.17

 5 or greater

7.21

5.66

5.81

5.66

Additional condition (%)

 Knee pain

7.71

31.18

0.621

36.19

31.18

0.106

 Smoking

29.62

34.18

0.098

32.3

34.18

0.04

 Arthritis

97.93

99.65

0.158

99.48

99.65

0.02

 COPD

6.12

4.97

0.051

4.47

4.97

0.02

 Heart failure

63.53

55.77

0.159

52.9

55.77

0.06

 Diabetes

21.73

17.09

0.118

15.71

17.09

0.04

 Obesity

32.14

27.14

0.11

28.58

27.14

0.03

Abbreviations: COPD, chronic obstructive pulmonary disease; SD, standard deviation; SMD, standardized mean difference; TKA, total knee arthroplasty; VRAS, VELYS robotic-assisted solution.


Table 3

Provider characteristics of patients undergoing total knee arthroplasty using either manual approach or VELYS robotic-assisted solution, before and after fine stratification and weighting

Variable

Prefine stratification

Postfine stratification

Manual

VRAS

SMD

Manual

VRAS

SMD

Number of patients

128,643

866

128,643

866

Urban hospital (vs. rural) (%)

86.56

96.88

0.381

95.42

96.88

0.076

Region (%)

1.273

0.108

 Midwest

30.18

10.39

12.59

10.39

 Northeast

13.32

61.66

57.77

61.66

 South

42.8

27.94

29.43

27.94

 West

13.69

0

0.2

0

Hospital bed size (%)

0.886

0.227

 000–099

12.94

7.85

6.99

7.85

 100–199

19.6

28.64

27.47

28.64

 200–299

19.61

10.85

15.01

10.85

 300–399

17

28.52

33.26

28.52

 400–499

10.96

24.02

16.85

24.02

 500 and above

19.9

0.12

0.42

0.12

Teaching hospital (vs. community) (%)

41.57

81.76

0.908

75.14

81.76

0.161

Annual provider volume (%)

0.423

0.103

 000–138

18.07

25.98

26.69

25.98

 139–313

27.82

27.02

26.5

27.02

 314–576

27.57

12.12

15.22

12.12

 Above 576

26.53

34.87

31.59

34.87

Annual physician volume (%)

0.607

0.2

 0–20

17.54

6.58

8.71

6.58

 21–50

22.92

11.09

16.91

11.09

 51–100

25.02

21.13

20.41

21.13

 Above 100

34.53

61.2

53.97

61.2

Cemented fixation

(vs. uncemented) (%)

2.09

1.62

0.035

2.01

1.62

0.03

Inpatient procedures (%)

10.21

2.77

0.305

2.73

2.77

0.002

Abbreviations: SMD, standardized mean difference; TKA, total knee arthroplasty; VRAS, VELYS robotic-assisted solution.


There was a significant difference (SMD > 0.2) in some of the baseline provider characteristics between the VRAS and manual TKA cohorts. Most patients in both cohorts were admitted to urban hospitals, 97% of VRAS and 87% of manual TKA cases, with SMD of 0.38. However, there was a significant difference in the hospital location (SMD: 1.27) with VRAS cases predominantly performed in Northeast (62%) and a majority of manual cases being performed in the South (43%). In the VRAS cohort, most hospitals (82%) were teaching hospitals, whereas in the manual cohort they were primarily community hospitals (58%), with an SMD of 0.91. The difference in hospital bed size was significantly different, with an SMD of 0.89. Annual provider and physician TKA volumes exhibited significant differences, with SMDs of 0.42 and 0.61, respectively.


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Patient and Provider Postcovariate Balancing Characteristics and Outcome Results

[Tables 2] and [3] display the patient and provider characteristics following covariate balancing using the FSW method. In general, a satisfactory balance was achieved, with most SMDs below 0.20 ([Fig. 1]). Patient characteristics stayed consistent, all having SMDs below 0.2. The provider characteristics were well-balanced across cohorts, with the majority having SMDs below 0.20. The one exception was hospital bed size, which had slightly higher SMD of 0.23, reduced from 0.89. This variable was included in the regression analysis to account for any remaining imbalance. Patients comorbidities were balanced across both cohorts and details are included in [supplemental Table A] and [B] (available online).

Zoom Image
Fig. 1 Covariate balance before and after fine stratification—90-day follow-up.

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Primary Outcomes

The primary outcomes of the study populations within 90-day follow-up are presented in [Table 4]. The rates of both all-cause and knee-related all-setting follow-up visits (revisits) were significantly lower in the VRAS TKA cohort compared with the manual TKA cohort (13.86 vs. 17.19%; mean difference [MD]: −3.34 [95% CI: −5.65 to −1.03] and 2.66 vs. 4.81%; MD: −2.15 [−3.23 to −1.08], respectively, p-value < 0.01). Similarly, the rate of knee-related readmission was significantly lower in the VRAS TKA cohort (0.69 vs. 1.46%; MD: −0.77 [−1.32 to −0.21]). Although the rate of all-cause readmission was also lower in the VRAS TKA cohort, the difference did not reach statistical significance (1.73 vs. 2.25%; MD: −0.52 [−1.4 to 0.35]).

Table 4

Revisit and readmission rates of patients undergoing total knee arthroplasty using either manual approach or VELYS robotic-assisted solution

VRAS TKA

(95% CI)

Manual TKA (95% CI)

Mean difference

(95% CI)

Revisit (%)

 Number of patients

866

128,643

 All-cause

13.86 (11.56 to 16.16)

17.19 (16.99–17.4)

−3.34 (−5.65 to −1.03)

 Knee-related

2.66 (1.58–3.73)

4.81 (4.69–4.93)

−2.15 (−3.23 to −1.08)

Readmission (%)

 Number of patients

866

128,643

 All-cause

1.73 (0.86–2.6)

2.25 (2.17–2.34)

−0.52 (−1.4 to 0.35)

 Knee-related

0.69 (0.14–1.25)

1.46 (1.39–1.53)

−0.77 (−1.32 to −0.21)

Abbreviations: CI, confidence interval; TKA, total knee arthroplasty; VRAS, VELYS robotic-assisted solution.



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Secondary Outcomes

Secondary outcomes of the study population including discharge status, operating room time, length of stay, cost of care, and revision rate are presented in [Table 5]. The vast majority of patients (96%) in both the VRAS and manual TKA cohorts were discharged to home or home health services. The proportion of patients discharged to skilled nursing facility was also similar in both cohorts (2.1 vs. 2.7%). The VRAS procedures had statistically significant longer operating room time (MD: 4 [2–7] minutes) than manual TKA procedures (138 vs. 134 minutes). The VRAS TKA cohort exhibited a slightly shorter length of stay (3.1 vs. 3.6 days) compared with the manual TKA cohort, although there were only 44 VRAS inpatient cases to begin with. The 90-day revision rate was low and similar for both the VRAS and manual cohorts (0.09 vs. 0.18%).

Table 5

Outcomes and resource utilization of patients undergoing total knee arthroplasty using either manual approach or VELYS robotic-assisted solution

VRAS TKA

(95% CI)

Manual TKA (95% CI)

Mean difference

(95% CI)

Discharge status

 Number of patients

1,180

161,866

 Home or home health discharge (%)

96.61 (95.58–97.64)

95.99 (95.89–96.08)

0.62 (−0.41 to 1.66)

 Skilled nursing facility discharge (%)

2.12 (1.3–2.94)

2.67 (2.59 to 2.75)

−0.55 (−1.38 to 0.27)

Operating room time

137.96 (135.5–140.42)

133.67 (133.47–133.88)

4.28 (1.81–6.75)

Length of stay (LOS)

 Number of patients

44

16,792

 Avg. hospital LOS (d)

3.11 (2.73–3.50)

3.63 (3.59–3.68)

−0.52 (−0.91 to −0.13)

90-day cost of care ($)

 Number of patients

866

128,643

 All-cause ($)

15,357 (14,833–15881)

14944 (14,902–14985)

413 (−112 to 938)

 Knee-related ($)

14,955 (14,478–15,433)

14,547 (14,509–14,585)

408 (−69 to 887)

90-day revision rate (%)

 Number of patients

866

128,643

 Revision rate (%)

0.09 (0.01–0.19)

0.18 (0.09–0.27)

−0.09 (−0.23 to 0.05)

Abbreviations: CI, confidence interval; TKA, total knee arthroplasty; VRAS, VELYS robotic-assisted solution.


Cost of Care

The overall 90-day all-cause cost of care was similar for both the VRAS and manual TKA cohorts ($15,357 vs. $14,944; MD: $413 [−$112 to $938]). Similarly, the knee-related 90-day costs were also similar for both cohorts ($14,956 vs. $14,547; MD: $409 [−$69 to $887]). The index costs (i.e., surgical procedure costs) were also similar for both cohorts with supply and operating room costs making about 85% of total cost. On average, VRAS had $6,661 in supply cost compared with $6,459 for manual TKA cases. The operating room costs on average were $6,420 and $5,998 for VRAS and manual TKA cases, respectively.


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Discussion

The objective of the study was to understand the early patient and economic outcomes associated with the use of VRAS in TKA in comparison to manual TKAs. The study identified that both follow-up visits (revisits) and readmission rates were lower for VRAS compared with the manual TKA cohort. All-cause and knee-related revisits occurred at significantly lower rates in the VRAS TKA cohort compared with the manual TKA cohort (19 and 45%, respectively). Furthermore, the VRAS TKA cohort exhibited a statistically significant decrease in knee-related readmissions (53%). These findings are consistent with those found by of Clatworthy,[17] who reported improvements in knee function and pain at early stages with the use of the VRAS technology.

The VRAS TKA cohort exhibited an increase in operating room time (4 minutes, 138 vs. 134 minutes), most likely associated with the integration of robotic instrumentation. Previous studies have reported a learning curve of 5 to 20 cases to achieve surgery times equivalent to the traditional manual approach.[30] [31] While the adoption of all new technology requires new skills to be learned and practice to become proficient, this study helps to alleviate the surgeons' concern that adoption of VRAS will be associated with a prolonged learning curve, which will impact their procedure efficiency in the long term as 4-minute difference is not clinically significant.

The economic analysis did not identify significant cost differences between the VRAS and manual TKA cohorts both at index and 90-day cost of care. However, the economic analysis did not account for initial purchase cost for the robotic system. Past studies have reported conflicting findings on cost analysis associated with the use of robotic technologies in TKA.[32] [33] [34] While increased intraoperative costs were linked to robotic TKA,[33] [34] these costs were subsequently compensated by more significant savings in postoperative costs within the 90-day episode of care compared with manual TKA.[34] [35] [36] The most common reasons for savings included reduced length of stay, decreased opioid prescription, and reduced postdischarge utilization of services associated with the use of robotic TKA.[34] [36] [37] [38]

This is the first study to investigate the impact of the VRAS on patient healthcare outcomes and associated costs in a large database. Using a relatively large population across a large geographic area makes the results of this study not only relevant to surgeons, but also to healthcare policy decision-makers and health systems in their effort to provide optimal outcomes and reduced costs. In this regard, our study provides valuable information on the potential benefits and drawbacks of using VRAS compared with manual TKA. Additionally, the use of FSW methodology preserves all patient data, allowing for the inclusion of outlier patients and yielding more representative outcomes and effectively control for confounders between the VRAS and manual TKA cohorts.

The study has several limitations. The Premier Healthcare Database is not specifically designed for research purposes and could answer only limited research questions. It is also prone to issues such as incorrect coding and missing information. Hence, both knee- and all-cause-related outcome numbers were reported. While knee-related outcomes hold greater clinical significance, they may be somewhat underrepresented due to coding errors. All-cause related outcomes comprise all care and potentially can encompass unrelated episodes. The actual rates likely fall between the rates of knee-related and all-cause outcomes. Moreover, the study only included patients from the Premier Healthcare hospitals in the United States and hence may not be reflective of the experience of patients from other hospitals or countries. Additionally, although FSW methodology was used to control confounders between cohorts, unmeasurable variables such as socioeconomic status, surgeon technique, and other factors could still contribute to residual confounding after adjusted analyses. Another limitation is the relatively small cohort of VRAS cases compared with the manual group. Finally, all limitations associated with retrospective observational studies also apply herein.


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Conclusion

Our study presents compelling evidence supporting the benefits of VRAS in TKA, particularly with respect to reduced follow-up revisits and knee-related readmissions. While economic considerations warrant careful examination, our findings suggest that the VRAS has similar hospital costs as manual TKA while not accounting for purchasing fee for the robot.


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Conflict of Interest

P.H. and M.C. are consultants for Depuy Synthes. A.G., D.I., J.R. and D.H. are employees of Johnson & Johnson.

Supplementary Material

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  • 6 Zhang J, Ndou WS, Ng N. et al. Robotic-arm assisted total knee arthroplasty is associated with improved accuracy and patient reported outcomes: a systematic review and meta-analysis. Knee Surg Sports Traumatol Arthrosc 2022; 30 (08) 2677-2695
  • 7 Mulpur P, Masilamani ABS, Prakash M, Annapareddy A, Hippalgaonkar K, Reddy AVG. Comparison of patient reported outcomes after robotic versus manual total knee arthroplasty in the same patient undergoing staged bilateral knee arthroplasty. J Orthop 2022; 34: 111-115
  • 8 Hampp EL, Sodhi N, Scholl L. et al. Less iatrogenic soft-tissue damage utilizing robotic-assisted total knee arthroplasty when compared with a manual approach: a blinded assessment. Bone Joint Res 2019; 8 (10) 495-501
  • 9 Kayani B, Konan S, Pietrzak JRT, Haddad FS. Iatrogenic bone and soft tissue trauma in robotic-arm assisted total knee arthroplasty compared with conventional jig-based total knee arthroplasty: a prospective cohort study and validation of a new classification system. J Arthroplasty 2018; 33 (08) 2496-2501
  • 10 Jeon SW, Kim KI, Song SJ. Robot-assisted total knee arthroplasty does not improve long-term clinical and radiologic outcomes. J Arthroplasty 2019; 34 (08) 1656-1661
  • 11 Kayani B, Fontalis A, Haddad IC, Donovan C, Rajput V, Haddad FS. Robotic-arm assisted total knee arthroplasty is associated with comparable functional outcomes but improved forgotten joint scores compared with conventional manual total knee arthroplasty at five-year follow-up. Knee Surg Sports Traumatol Arthrosc 2023; 31 (12) 5453-5462
  • 12 Kayani B, Konan S, Huq SS, Tahmassebi J, Haddad FS. Robotic-arm assisted total knee arthroplasty has a learning curve of seven cases for integration into the surgical workflow but no learning curve effect for accuracy of implant positioning. Knee Surg Sports Traumatol Arthrosc 2019; 27 (04) 1132-1141
  • 13 Song SJ, Park CH. Learning curve for robot-assisted knee arthroplasty; optimizing the learning curve to improve efficiency. Biomed Eng Lett 2023; 13 (04) 515-521
  • 14 Tompkins GS, Sypher KS, Griffin TM, Duwelius PD. Can a reduction in revision rates make robotic total knee arthroplasty cost neutral with manual total knee arthroplasty at ten-year follow-up? an episode cost analysis. J Arthroplasty 2022; 37 (8S): S777-S781 , 781.e3
  • 15 Mancino F, Jones CW, Benazzo F, Singlitico A, Giuliani A, De Martino I. Where are we now and what are we hoping to achieve with robotic total knee arthroplasty? A critical analysis of the current knowledge and future perspectives. Orthop Res Rev 2022; 14: 339-349
  • 16 Johnson & Johnson MedTech. VELYS™ robotics designed for digital precision in knee replacement surgery. Accessed November 2, 2023 at: https://www.jnjmedtech.com/en-US/patient/velys/robotic-assisted-solution
  • 17 Clatworthy M. Patient-specific TKA with the VELYS™ robotic-assisted solution. Surg Technol Int 2022; 40: 315-320
  • 18 Morrisey ZS, Barra MF, Guirguis PG, Drinkwater CJ. Transition to robotic total knee arthroplasty with kinematic alignment is associated with a short learning curve and similar acute-period functional recoveries. Cureus 2023; 15 (05) e38872
  • 19 Doan GW, Courtis RP, Wyss JG, Green EW, Clary CW. Image-free robotic-assisted total knee arthroplasty improves implant alignment accuracy: a cadaveric study. J Arthroplasty 2022; 37 (04) 795-801
  • 20 Singh V, Teo GM, Long WJ. Versatility and accuracy of a novel image-free robotic-assisted system for total knee arthroplasty. Arch Orthop Trauma Surg 2021; 141 (12) 2077-2086
  • 21 PINC AI Applied Sciences. PINC AI™ healthcare database: data that informs and performs. July 2023. https://offers.premierinc.com/rs/381-NBB-525/images/PINC_AI_Healthcare_Data_White_Paper.pdf
  • 22 Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998; 36 (01) 8-27
  • 23 Menendez ME, Neuhaus V, van Dijk CN, Ring D. The Elixhauser comorbidity method outperforms the Charlson index in predicting inpatient death after orthopaedic surgery. Clin Orthop Relat Res 2014; 472 (09) 2878-2886
  • 24 Groll DL, To T, Bombardier C, Wright JG. The development of a comorbidity index with physical function as the outcome. J Clin Epidemiol 2005; 58 (06) 595-602
  • 25 Groll DL, Heyland DK, Caeser M, Wright JG. Assessment of long-term physical function in acute respiratory distress syndrome (ARDS) patients: comparison of the Charlson Comorbidity Index and the Functional Comorbidity Index. Am J Phys Med Rehabil 2006; 85 (07) 574-581
  • 26 Desai RJ, Rothman KJ, Bateman BT, Hernandez-Diaz S, Huybrechts KF. A propensity-score-based fine stratification approach for confounding adjustment when exposure is infrequent. Epidemiology 2017; 28 (02) 249-257
  • 27 Hong G. Marginal mean weighting through stratification: a generalized method for evaluating multivalued and multiple treatments with nonexperimental data. Psychol Methods 2012; 17 (01) 44-60
  • 28 Zhang Z, Kim HJ, Lonjon G, Zhu Y. written on behalf of AME Big-Data Clinical Trial Collaborative Group. Balance diagnostics after propensity score matching. Ann Transl Med 2019; 7 (01) 16
  • 29 U. S. Bureau of Labor Statistics. Consumer Price Index. Accessed February 13, 2024 at: https://www.bls.gov/cpi/
  • 30 Mahure SA, Teo GM, Kissin YD, Stulberg BN, Kreuzer S, Long WJ. Learning curve for active robotic total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc 2022; 30 (08) 2666-2676
  • 31 Tay ML, Carter M, Zeng N, Walker ML, Young SW. Robotic-arm assisted total knee arthroplasty has a learning curve of 16 cases and increased operative time of 12 min. ANZ J Surg 2022; 92 (11) 2974-2979
  • 32 Ong KL, Coppolecchia A, Chen Z, Watson HN, Jacofsky D, Mont MA. Robotic-arm assisted total knee arthroplasty: cost savings demonstrated at one year. Clinicoecon Outcomes Res 2022; 14: 309-318
  • 33 Kolessar DJ, Hayes DS, Harding JL, Rudraraju RT, Graham JH. Robotic-arm assisted technology's impact on knee arthroplasty and associated healthcare costs. J Health Econ Outcomes Res 2022; 9 (02) 57-66
  • 34 Cotter EJ, Wang J, Illgen RL. Comparative cost analysis of robotic-assisted and jig-based manual primary total knee arthroplasty. J Knee Surg 2022; 35 (02) 176-184
  • 35 Alton TB, Chitnis AS, Goldstein L. et al. Resource utilization and costs for robotic-assisted and manual total knee arthroplasty - a premier healthcare database study. Expert Rev Med Devices 2023; 20 (04) 303-311
  • 36 Pierce J, Needham K, Adams C, Coppolecchia A, Lavernia C. Robotic arm-assisted knee surgery: an economic analysis. Am J Manag Care 2020; 26 (07) e205-e210
  • 37 Mitchell J, Wang J, Bukowski B. et al. Relative clinical outcomes comparing manual and robotic-assisted total knee arthroplasty at minimum 1-year follow-up. HSS J 2021; 17 (03) 267-273
  • 38 Severson ELT, Fleming M. et al. Resource utilization with robotic and manual total knee arthroplasty in the critical access hospital setting. Presented at: Mid-America Orthopaedic Association (MAOA) 2023 ; April 19–23, 2023; Miramar Beach, FL.

Address for correspondence

Anshu Gupta, PhD
Medical Device Epidemiology, Johnson and Johnson
410 George Street, New Brunswick, NJ 8901

Publication History

Received: 23 April 2024

Accepted: 11 June 2024

Accepted Manuscript online:
12 June 2024

Article published online:
28 June 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

  • References

  • 1 Kayani B, Konan S, Tahmassebi J, Pietrzak JRT, Haddad FS. Robotic-arm assisted total knee arthroplasty is associated with improved early functional recovery and reduced time to hospital discharge compared with conventional jig-based total knee arthroplasty: a prospective cohort study. Bone Joint J 2018; 100-B (07) 930-937
  • 2 Khlopas A, Sodhi N, Hozack WJ. et al. Patient-reported functional and satisfaction outcomes after robotic-arm-assisted total knee arthroplasty: early results of a prospective multicenter investigation. J Knee Surg 2020; 33 (07) 685-690
  • 3 Liow MHL, Goh GS, Wong MK, Chin PL, Tay DK, Yeo SJ. Robotic-assisted total knee arthroplasty may lead to improvement in quality-of-life measures: a 2-year follow-up of a prospective randomized trial. Knee Surg Sports Traumatol Arthrosc 2017; 25 (09) 2942-2951
  • 4 Marchand RC, Sodhi N, Anis HK. et al. One-year patient outcomes for robotic-arm-assisted versus manual total knee arthroplasty. J Knee Surg 2019; 32 (11) 1063-1068
  • 5 Blum CL, Lepkowsky E, Hussein A, Wakelin EA, Plaskos C, Koenig JA. Patient expectations and satisfaction in robotic-assisted total knee arthroplasty: a prospective two-year outcome study. Arch Orthop Trauma Surg 2021; 141 (12) 2155-2164
  • 6 Zhang J, Ndou WS, Ng N. et al. Robotic-arm assisted total knee arthroplasty is associated with improved accuracy and patient reported outcomes: a systematic review and meta-analysis. Knee Surg Sports Traumatol Arthrosc 2022; 30 (08) 2677-2695
  • 7 Mulpur P, Masilamani ABS, Prakash M, Annapareddy A, Hippalgaonkar K, Reddy AVG. Comparison of patient reported outcomes after robotic versus manual total knee arthroplasty in the same patient undergoing staged bilateral knee arthroplasty. J Orthop 2022; 34: 111-115
  • 8 Hampp EL, Sodhi N, Scholl L. et al. Less iatrogenic soft-tissue damage utilizing robotic-assisted total knee arthroplasty when compared with a manual approach: a blinded assessment. Bone Joint Res 2019; 8 (10) 495-501
  • 9 Kayani B, Konan S, Pietrzak JRT, Haddad FS. Iatrogenic bone and soft tissue trauma in robotic-arm assisted total knee arthroplasty compared with conventional jig-based total knee arthroplasty: a prospective cohort study and validation of a new classification system. J Arthroplasty 2018; 33 (08) 2496-2501
  • 10 Jeon SW, Kim KI, Song SJ. Robot-assisted total knee arthroplasty does not improve long-term clinical and radiologic outcomes. J Arthroplasty 2019; 34 (08) 1656-1661
  • 11 Kayani B, Fontalis A, Haddad IC, Donovan C, Rajput V, Haddad FS. Robotic-arm assisted total knee arthroplasty is associated with comparable functional outcomes but improved forgotten joint scores compared with conventional manual total knee arthroplasty at five-year follow-up. Knee Surg Sports Traumatol Arthrosc 2023; 31 (12) 5453-5462
  • 12 Kayani B, Konan S, Huq SS, Tahmassebi J, Haddad FS. Robotic-arm assisted total knee arthroplasty has a learning curve of seven cases for integration into the surgical workflow but no learning curve effect for accuracy of implant positioning. Knee Surg Sports Traumatol Arthrosc 2019; 27 (04) 1132-1141
  • 13 Song SJ, Park CH. Learning curve for robot-assisted knee arthroplasty; optimizing the learning curve to improve efficiency. Biomed Eng Lett 2023; 13 (04) 515-521
  • 14 Tompkins GS, Sypher KS, Griffin TM, Duwelius PD. Can a reduction in revision rates make robotic total knee arthroplasty cost neutral with manual total knee arthroplasty at ten-year follow-up? an episode cost analysis. J Arthroplasty 2022; 37 (8S): S777-S781 , 781.e3
  • 15 Mancino F, Jones CW, Benazzo F, Singlitico A, Giuliani A, De Martino I. Where are we now and what are we hoping to achieve with robotic total knee arthroplasty? A critical analysis of the current knowledge and future perspectives. Orthop Res Rev 2022; 14: 339-349
  • 16 Johnson & Johnson MedTech. VELYS™ robotics designed for digital precision in knee replacement surgery. Accessed November 2, 2023 at: https://www.jnjmedtech.com/en-US/patient/velys/robotic-assisted-solution
  • 17 Clatworthy M. Patient-specific TKA with the VELYS™ robotic-assisted solution. Surg Technol Int 2022; 40: 315-320
  • 18 Morrisey ZS, Barra MF, Guirguis PG, Drinkwater CJ. Transition to robotic total knee arthroplasty with kinematic alignment is associated with a short learning curve and similar acute-period functional recoveries. Cureus 2023; 15 (05) e38872
  • 19 Doan GW, Courtis RP, Wyss JG, Green EW, Clary CW. Image-free robotic-assisted total knee arthroplasty improves implant alignment accuracy: a cadaveric study. J Arthroplasty 2022; 37 (04) 795-801
  • 20 Singh V, Teo GM, Long WJ. Versatility and accuracy of a novel image-free robotic-assisted system for total knee arthroplasty. Arch Orthop Trauma Surg 2021; 141 (12) 2077-2086
  • 21 PINC AI Applied Sciences. PINC AI™ healthcare database: data that informs and performs. July 2023. https://offers.premierinc.com/rs/381-NBB-525/images/PINC_AI_Healthcare_Data_White_Paper.pdf
  • 22 Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998; 36 (01) 8-27
  • 23 Menendez ME, Neuhaus V, van Dijk CN, Ring D. The Elixhauser comorbidity method outperforms the Charlson index in predicting inpatient death after orthopaedic surgery. Clin Orthop Relat Res 2014; 472 (09) 2878-2886
  • 24 Groll DL, To T, Bombardier C, Wright JG. The development of a comorbidity index with physical function as the outcome. J Clin Epidemiol 2005; 58 (06) 595-602
  • 25 Groll DL, Heyland DK, Caeser M, Wright JG. Assessment of long-term physical function in acute respiratory distress syndrome (ARDS) patients: comparison of the Charlson Comorbidity Index and the Functional Comorbidity Index. Am J Phys Med Rehabil 2006; 85 (07) 574-581
  • 26 Desai RJ, Rothman KJ, Bateman BT, Hernandez-Diaz S, Huybrechts KF. A propensity-score-based fine stratification approach for confounding adjustment when exposure is infrequent. Epidemiology 2017; 28 (02) 249-257
  • 27 Hong G. Marginal mean weighting through stratification: a generalized method for evaluating multivalued and multiple treatments with nonexperimental data. Psychol Methods 2012; 17 (01) 44-60
  • 28 Zhang Z, Kim HJ, Lonjon G, Zhu Y. written on behalf of AME Big-Data Clinical Trial Collaborative Group. Balance diagnostics after propensity score matching. Ann Transl Med 2019; 7 (01) 16
  • 29 U. S. Bureau of Labor Statistics. Consumer Price Index. Accessed February 13, 2024 at: https://www.bls.gov/cpi/
  • 30 Mahure SA, Teo GM, Kissin YD, Stulberg BN, Kreuzer S, Long WJ. Learning curve for active robotic total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc 2022; 30 (08) 2666-2676
  • 31 Tay ML, Carter M, Zeng N, Walker ML, Young SW. Robotic-arm assisted total knee arthroplasty has a learning curve of 16 cases and increased operative time of 12 min. ANZ J Surg 2022; 92 (11) 2974-2979
  • 32 Ong KL, Coppolecchia A, Chen Z, Watson HN, Jacofsky D, Mont MA. Robotic-arm assisted total knee arthroplasty: cost savings demonstrated at one year. Clinicoecon Outcomes Res 2022; 14: 309-318
  • 33 Kolessar DJ, Hayes DS, Harding JL, Rudraraju RT, Graham JH. Robotic-arm assisted technology's impact on knee arthroplasty and associated healthcare costs. J Health Econ Outcomes Res 2022; 9 (02) 57-66
  • 34 Cotter EJ, Wang J, Illgen RL. Comparative cost analysis of robotic-assisted and jig-based manual primary total knee arthroplasty. J Knee Surg 2022; 35 (02) 176-184
  • 35 Alton TB, Chitnis AS, Goldstein L. et al. Resource utilization and costs for robotic-assisted and manual total knee arthroplasty - a premier healthcare database study. Expert Rev Med Devices 2023; 20 (04) 303-311
  • 36 Pierce J, Needham K, Adams C, Coppolecchia A, Lavernia C. Robotic arm-assisted knee surgery: an economic analysis. Am J Manag Care 2020; 26 (07) e205-e210
  • 37 Mitchell J, Wang J, Bukowski B. et al. Relative clinical outcomes comparing manual and robotic-assisted total knee arthroplasty at minimum 1-year follow-up. HSS J 2021; 17 (03) 267-273
  • 38 Severson ELT, Fleming M. et al. Resource utilization with robotic and manual total knee arthroplasty in the critical access hospital setting. Presented at: Mid-America Orthopaedic Association (MAOA) 2023 ; April 19–23, 2023; Miramar Beach, FL.

Zoom Image
Fig. 1 Covariate balance before and after fine stratification—90-day follow-up.