Modern robotics platform associated with reduced 90-day complications in hospital setting

Abstract

BACKGROUND:

Early complications in total knee arthroplasty (TKA) associated with modern robotics platforms integrated with digital balancing technology have not been investigated.

OBJECTIVE:

The objective was to compare 90-day complication rates between a manual technique and a modern robotic-assisted ligament balancing TKA platform.

METHODS:

895 primary TKA procedures from a single surgeon were retrospectively reviewed (614 manual TKA, 281 using a modern robotics platform with an integrated digitally controlled ligament balancing device). Post-operative complications within the 90-day episode of care were recorded by the Michigan Arthroplasty Registry Collaborative Quality Initiative. Differences in complication rates between techniques were further divided into inpatient hospital, outpatient hospital, and ambulatory surgery center (ASC) cohorts.

RESULTS:

In the pooled hospital cohort and inpatient hospital cohort, ‘Return to OR’ was significantly lower for the robotic assisted group (1.3% vs 5.2% and 0% vs 4.9%, respectively, $p<$ 0.0370). Specifically, the need for manipulation under anesthesia (MUA) was reduced for the robotics group (0.0% vs 2.5%, $p=$ 0.0352). No differences in overall complication rate or cause of complication was found between manual and robotics cases for the hospital outpatient and ASC cohorts ( $p\geqslant$ 0.68).

CONCLUSION:

Within the hospital setting, robotic assisted ligament balancing technology was associated with reduced 90-day postoperative complications for ‘Return to OR’ and MUA.

Keywords

Total knee arthroplasty robotics soft tissue complications hospital ambulatory surgical center ASC

1. Introduction

Primary total knee arthroplasty (TKA) is a highly successful and routine surgical procedure. Between 2000–2019, the volume of TKAs performed increased by 156% and is expected to grow by another 139% by 2040 [1]. More than half of this growing population of TKA procedures is expected to occur in an outpatient setting by 2026 [2]. Although TKA is the current state of the art treatment for patients with end stage osteoarthritis, there remain significant concerns about early complication rates in patients receiving primary TKA procedures. 90-day complication rates have been documented to occur in 2.5% of cases [3, 4], while patient dissatisfaction at one year after the procedure has been reported between 10–18% [5, 6, 35]. The cause of early complications and dissatisfaction are multifactorial; among the most common causes are instability, stiffness, persistent pain and infection [7].

In an attempt to improve outcomes and reduce complications in TKA, computer-navigated and robotic assisted platforms have been developed to assist orthopedic surgeons. The goal of most computer-navigated and robotics platforms is to improve the accuracy of implant positioning, bone cuts, soft-tissue balancing, and limb alignment [8]. In a study comparing utilization trends and 90-day complication rates for computer and robotic-assisted to conventional TKA in the United States from 2010 to 2018, Bendich et al. [3] reported a $+$ 2204% increase in utilization of robotics, and lower 90-day readmissions associated with the use of both technologies. Still, patients continue to experience postoperative complications with the use of robotic technology. In a systematic review, robotic assisted TKAs have been associated with femoral or tibial shaft fractures at pin-hole sites and iatrogenic soft tissue and bony injuries [9].

Recent advancements in robotic assisted TKA have included more sophisticated methods of tensioning and balancing the soft tissues throughout the range of knee motion to avoid having to perform soft tissue releases. The addition of soft tissue releases during TKA have been associated with worse two-year postoperative patient reported outcomes [10]. This is supported by other studies showing soft tissue protection is associated with improved patient outcomes and reduced postoperative pain [11, 12, 13]. The combination of balancing ligaments and avoiding soft tissue release, may lead to fewer soft tissue related complications such as manipulation under anesthesia, and revisions due to instability, or inadequate range of motion [14]. However, the complication rates associated with these modern robotic assisted surgery methods have not been well investigated.

As more TKAs are being performed in an out-patient setting and funded under value-based care programs such as the Centers for Medicare and Medicaid Services (CMS) Comprehensive Care for Joint Replacement (CJR) Model, as well as private insurer based 90-day bundled payment structures [15, 16, 17], a better understanding of the early complication rates associated with modern technologies relative to conventional approaches will be of value to physicians and administrators to justify the costs of such technologies. Therefore, the purpose of this study was to compare early complication rates of a modern robotic system with soft-tissue balancing capabilities to conventional instrumentation in both the in- and out-patient settings. We hypothesize that the combination of a modern robotics platform, with an integrated digital joint tensioning device will allow surgeons to better balance the knee and reduce short term complications associated with poor joint balance.

2. Methods

2.1 Patients

90-day complication data for a consecutive cohort was queried from the Michigan Arthroplasty Registry Collaborative Quality Initiative (MARCQI). MARCQI is a Michigan based registry for hip and knee replacements in partnership with Blue Cross Blue Shield of Michigan Value Partnership program. Any hospital readmission, return to emergency or operating room occurring within 90 days post-TKA is documented by trained clinical data abstractors and file based uploads, and recorded to the MARCQI database as an early complication. All cases performed by the lead author (JHD) from the date of first use of the robotic assisted TKA platform over a period of 5 years were analyzed (Nov 2017–Oct 2022). All patients received identical pre- and post-operative case management independent of TKA technique. Allocation of patients to manual versus robotic instrumentation was based on the presence of an existing implant in the patient’s contralateral knee, and excessive BMI leading to the inability to properly position the robotic femoral guidance tool. Inclusion criteria were cases performed by the lead author during the time period analyzed. Exclusion criteria were uni-compartment knee arthroplasty, complex primary TKA cases requiring greater stabilization than a standard CR or PS TKA components, and revision total knee arthroplasty. A total of 895 TKA cases were identified. TKA’s were performed in a hospital (both inpatient and outpatient) and ambulatory surgical center (ASC) setting. The breakdown of cases across settings is shown in Table 1. Indications for hospital or ASC setting were primarily determined by insurance authorization.

Table 1
Summary of number of TKA cases investigated and the surgical setting in which the TKAs were performed

TKA method	Hospital inpatient	Hospital outpatient	ASC
Manual	305	158	151
Robot assisted	91	69	121
Total	396	227	272

The occurrence and type of complication was recorded by MARCQI. Significant differences in complications were further investigated to determine the underlying reason for the complication by reviewing patient charts. In addition to complications, demographics data including age, side, American Society of Anaesthesiologists (ASA) score, body mass index (BMI) and length of stay (LOS) were extracted from MARCQI. Institutional Review Board approval was obtained prior to the start of study (WCG IRB no. 120190312 and Ascension Providence IRB no. RMI20220048).

2.2 Conventional technique

Standard instrumentation was used for all non-robotic patients, and all knees were performed with a medial parapatellar approach and aimed to generate a neutral coronal alignment. The femur was prepared first using an intramedullary system. Anatomic landmarks were utilized for accurate angular and rotational alignment of the femoral component. Traditional bony landmarks of the tibia were utilized to achieve accurate angular alignment, tibial slope and depth resection. Spacer block technique was used in all patients to confirm accurate balance of the flexion and extension gaps, as well as the medial and lateral compartments. Appropriate modifications were made with ligament releases if there was any ligament imbalance of the flexion and extension gaps. Provisional trialing was performed in all patients confirming restoration of full range of motion as well as accurate balance of the medial and lateral compartments. The patella was resurfaced, and final implants were fixed with bone cement.

2.3 Robotic technique

Figure 1.

Corin OMNIBot (left), BalanceBot (center) and OMNIBotics control tower (right).

The OMNIBotics robot assisted platform with the BalanceBot digital joint tensioner was used in all robotic assisted cases (Corin, Cirencester, UK, Fig. 1). All knees were performed with a medial parapatellar approach with a tibial first gap balancing workflow. The initial exposure includes excision of the medial and lateral menisci and resection of the ACL and PCL. Optical tracking arrays were then attached to the femur and tibia using two cancellous bone screws in the distal femur (intra-incision) and two cortical bone screws in the proximal tibia (extra-incision). The hip center, knee joint and ankle center were digitized using the OMNIBotics robotics platform. The 3D anatomy of the knee joint was mapped using imageless BoneMorphing shape models. Pre-operative kinematics were then captured through the range of motion reporting maximum varus and valgus angle as well as pre-operative deformity and maximum flexion and extension. An anatomic tibial resection was then planned by accounting for cartilage wear in the worn compartment with further limitations of 0–3 deg varus coronal alignment. A standard tibial slope of 3 deg was selected. The tibial resection was then navigated and validated by the robotics system. All distal, anterior and posterior femoral osteophytes were removed. The BalanceBot was then inserted into the joint on the tibial resection plane and set to 80 Newtons (N) force per side in extension and flexion (Fig. 2). The knee was taken through a range of flexion from 90 to 0 deg and the medial and lateral gaps under 80 N load were recorded by the robotics platform. This data was then used to plan the femoral component position to achieve gaps within 1 mm of the planned tibial insert thickness in the medial and lateral compartment in flexion (90 deg) and extension (10 deg), and with minimal laxity medially in mid-flexion (Fig. 3). The distal femoral resection was modified to balance the extension gap by adjusting the depth of distal femoral resection, as well as the varus/valgus angle. The medial and lateral compartment flexion gap was balanced by adjusting the rotational position of the femoral component, as well as flexion/extension and anterior/posterior positioning of the component. Limits of femoral coronal alignment were $\pm$ 4 deg varus and 3 deg internal rotation to 8 deg external rotation. Ligament tension can be measured within 0.5 mm increments with the desired goal to achieve equal tension in the medial and lateral compartments. If balance was not possible within these limits, 1 mm of lateral laxity was allowed in extension and midflexion, and up to 2 mm of lateral laxity in flexion. Once the femoral plan was verified the femoral resections are performed and validated using a miniature bone mounted robotic cutting guide. After all resections are validated, a Corin Apex trial implant was placed on the femur and the BalanceBot was re-inserted to capture final post-operative joint balance. All patellae were resurfaced. A deep dish ultra-congruent tibial insert was used in all cases and components were fixed using bone cement.

Figure 2.

The BalanceBot is inserted into the knee pre-femoral resection with a standardized load applied to the soft tissue envelope on the medial and lateral sides (left). The knee is taken through a range of motion with the robotics system records the medial and lateral gaps (right) under standardized tension.

Figure 3.

The femoral component size and position is planned to have balanced gaps in flexion and extension, with a stable medial compartment throughout the flexion range.

2.4 Perioperative care

All patients in the hospital and ASC group underwent the same pre-operative and post-operative total knee arthroplasty protocol. All patients participated in a combined pre-operative educational and exercise program. A standardized pre-emptive, multimodal pain management protocol was used in addition to a post-operative rehabilitation program designed to focus on early recovery of motion and early return of knee function.

2.5 Analysis

All hospital data was initially pooled to compare the demographics and complication rate between manual TKA and robot assisted TKA. Data was then grouped into Hospital Inpatient and Outpatient cohorts and the ASC cohort. Any significant differences in complications were further analyzed by comparing the rate of underlying causes for the complications.

Continuous data (BMI, LOS) were compared using Student’s $t$ -tests and categorical data (side, ASA score, complication occurrence, underlying cause of complication) were compared using Fischer Exact tests. To determine the impact of demographic variables on complication rate, a logistic regression model was performed in which demographic variables, TKA technique, hospital length of stay and hospital inpatient/outpatient status were inputs to predict the occurrence of any complication.

2.6 Power analysis

Two power analyses were performed to determine the minimum difference in complication rate in the Hospital and ASC setting. In the Hospital setting, a 2 sample 1 sided proportion power analysis was performed with a power of 0.8, alpha of 0.05, and sampling ratio of 1 robotic assisted case to 4 manual

Table 2
Comparison of demographics

Demographic	Hospital all			Hosp. Inp.			Hosp. Outp.			ASC
	Robotic	Manual	$p$ -value	Robotic	Manual	$p$ -value	Robotic	Manual	$p$ -value	Robotic	Manual	$p$ -value
$n$	160	463	–	91	305	–	69	158	–	121	151	–
Age (years)	70.0 $\pm$ 8.9	68.2 $\pm$ 9.5	0.0327	70.7 $\pm$ 9.4	68.4 $\pm$ 9.8	0.0459	69.1 $\pm$ 8.1	67.9 $\pm$ 8.9	0.3120	66.7 $\pm$ 8.3	62.8 $\pm$ 9.6	0.0061
% Left	45	47	0.6207	42	47	0.3960	49	48	0.8753	–	–	–
% Inpatient	57	66	0.0420	–	–	–	–	–	–	–	–	–
% ASA I	4	5	0.9670	2	4	0.4169	6	6	0.3460	0	1	0.2865
% ASA II	61	59		67	58		52	62		93	62
% ASA III	34	35		29	37		42	32		7	36
% ASA IV	1	1		1	1		0	0		0	0
BMI (kg/m²)	31.8 $\pm$ 6.7	34.1 $\pm$ 8.6	0.0004	31.2 $\pm$ 6.5	34.5 $\pm$ 8.9	0.0002	32.5 $\pm$ 6.9	33.4 $\pm$ 7.9	0.4023	30.7 $\pm$ 5.5	31.5 $\pm$ 5.7	0.2725
LOS (days)	1.3 $\pm$ 1.3	1.5 $\pm$ 1.2	0.0877	1.8 $\pm$ 1.0	1.9 $\pm$ 1.2	0.2541	0.7 $\pm$ 0.5	0.6 $\pm$ 0.5	0.2309	0	0	–

Table 3

Comparison of cause of complication between robotic assisted and manual TKA in all healthcare settings. All values given as $n$ (%)

Demographic	Hospital all					Hosp. Inp.					Hosp. Outp.					ASC
	Robotic		Manual		$p$ -value	Robotic		Manual		$p$ -value	Robotic		Manual		$p$ -value	Robotic		Manual		$p$ -value
$n$	160		463		–	91		305		–	69		158		–	121		151		–
DVT	0		1	(0.2)	0.9999	0	(0)	1	(0.3)	0.9999	0	(0)	0	(0)	0.9999	1	(0.8)	1	(0.7)	0.9999
COVID-19	0		2	(0.4)	0.9999	0	(0)	2	(0.6)	0.9999	0	(0)	0	(0)	0.9999	1	(0.8)	2	(1.3)	0.9999
ER	7	(4.4)	24	(5.2)	0.8339	3	(3.3)	16	(5.2)	0.9999	4	(5.8)	8	(5.1)	0.7584	3	(2.5)	1	(0.7)	0.2160
Fracture	0		3	(0.6)	0.9999	0	(0)	3	(1.0)	0.9999	0	(0)	0	(0)	0.9999	0	(0)	0	(0)	0.9999
Hematoma	5	(3.1)	23	(5.0)	0.3852	1	(1.1)	16	(5.2)	0.3337	4	(5.8)	7	(4.4)	0.7390	0	(0)	0	(0)	0.9999
Return to OR	2	(1.3)	22	(5.2)	0.0473	0	(0)	15	(4.9)	0.0273	2	(2.9)	7	(5.7)	0.5864	8	(6.6)	16	(10.6)	0.2496
Periprosthetic infection	3	(1.9)	4	(0.9)	0.3808	2	(2.2)	2	(0.7)	0.2275	1	(1.4)	2	(1.3)	0.8950	0	(0)	0	(0)	0.9999
PE	0		1	(0.2)	0.9999	0		1	(0.3)	0.9999	0	(0)	0	(0)	0.9999	0	(0)	0	(0)	0.9999
Readmission	5	(3.1)	16	(3.5)	0.9999	5	(5.5)	16	(5.2)	0.9999	0	(0)	0	(0)	0.9999	2	(1.7)	0	(0)	0.1128
Unplanned admission	2	(1.3)	6	(1.3)	0.9876	0		3	(1.0)	0.3478	2	(2.9)	3	(1.9)	0.6168	0	(0)	0	(0)	0.9999
All (removing duplicates)	18	(11)	76	(16)	0.1254	8	(9)	50	(17)	0.0903	10	(14)	26	(17)	0.8440	15	(12)	20	(13)	0.8355

Table 4

List of all reasons for return to OR broken down by inpatient hospital and ASC setting

Reason for return to OR	Hosp. Inp.		Hosp. Outp.		ASC
	Robotic	Manual	Robotic	Manual	Robotic	Manual
MUA	0	6	0	6	7	15
Medial retinaculum repair	0	5	0	0	0	0
Lateral retinaculum repair	0	1	0	0	0	1
Quadriceps tendon repair	0	8	2	1	1	1
Patellar tendon repair	0	1	0	0	0	0
Incision and drainage	0	4	0	0	0	0
Revision (1 component)	0	1	0	0	0	0
Achilles tendon allograft reconstruction	0	0	1	1	0	0

Table 5

Result of logistic regression model on impact of demographics and hospital status on likelihood of any complication

Variable	Hospital pooled regression value	Hospital pooled $p$ -value	Inpatient regression value	Inpatient $p$ -value
BMI	0.0144	0.3627	0.0127	0.5146
Side	0.2396	0.2848	0.2240	0.4344
Inpatient/outpatient	$-$ 0.2992	0.2683	–	–
ASA	$-$ 0.5323	0.0111	$-$ 0.6687	0.0117
LOS	$-$ 0.1391	0.1970	$-$ 0.1222	0.2816
Age	0.0079	0.5592	0.0104	0.5366

cases and a difference in complication rate of 10%, resulting in 113 robotic assisted hospital cases and 452 manual hospital cases. For the ASC setting the same power analysis was performed, however due to the difference in the number of robotic to non-robotic procedures, a sampling ratio of 1:1 was selected and a complication rate difference of 13%, resulting in 129 cases required per group.

3. Results

3.1 Hospital data pooled

LOS, ASA score and side were not different between the robot assisted and manual cohorts (Table 3), however, the manual cohort reported younger age (68.2 $\pm$ 9.5 vs 70.0 $\pm$ 8.9, $p=$ 0.0327), a higher BMI of 2.3 points (31.8 $\pm$ 6.7 vs 34.1 $\pm$ 8.6, $p=$ 0.0004) and greater proportion as an inpatient (57% vs 66%, $p=$ 0.042). Total complications were not different between these groups ( $p=$ 0.0998), however, when the causes of the complications were separately compared (Table 3), ‘Return to OR’ was significantly reduced in the robot cohort (1.3% vs 5.2%, $p=$ 0.0368). The reasons for ‘Return to OR’ broken down by healthcare setting is shown in Table 4. The impact of age, BMI, side, ASA score and inpatient percentage on the likelihood of complication are investigated using a logistic regression model in Table 5. Age, inpatient percentage and BMI are not significantly associated with increased complications indicating the differences in robotic and manual cohorts do not impact the analysis. ASA score is a significant predictor of complication, however this score is not significantly different between groups.

3.2 Hospital inpatient

Similar to the pooled hospital cohort, age and BMI are the only significant differences in demographics between groups (Table 3). ‘Return to OR’ remains significantly reduced in the robotic cohort (0.0% vs 4.9%, $p=$ 0.0273, Table 3). The impact of age and BMI on the likelihood of complication is also investigated using a logistic regression model in Table 5. Age and BMI are not significantly associated with increased complications indicating the differences in robotic and manual cohorts do not impact the analysis. ASA score remains a significant predictor of complication, however this score is not significantly different between groups.

Of the 15 patients who recorded a ‘Return to OR’ complication in the manual cohort, 40% of these (6) were due to poor joint balance and required manipulation under anesthesia (MUA). Other reasons for ‘Return to OR’ were 8x quadriceps tendon repair, 5x medial retinaculum repair, 4x incision and drainage, 1x patellar tendon repair, 1x lateral retinaculum repair, and 1 revision (Table 4). Five knees returned for multiple reasons. When comparing the rate of the cause of ‘Return to OR’s, a reduced rate of MUA was identified (0.0% vs 2.5%, $p=$ 0.0352).

3.3 Hospital outpatient and ASC

No differences in demographics, overall complication or cause of complication was observed in the hospital outpatient group or ASC group (Tables 3 and 3), however a similar trend was observed in a reduction in ‘Return to OR’ in both robotic groups.

4. Discussion

Robotic assisted TKA with predictive ligament balancing is associated with a reduced rate of postoperative complications related to ‘Return to OR’ in a hospital setting. By using objective measurements to intraoperatively balance the knee, the rate of MUA’s necessitating a return to the OR following a primary TKA procedure was significantly decreased. Specifically we found a 2.5% reduction of MUAs for the hospital inpatient cohort. No increase in robotics specific complications were observed, such as pin site fractures, as a result of using the robotics system.

A similar reduction (1.6%) in MUAs has been reported by Geller et al. [18] with the use of an electronic sensor device to augment ligament balancing. In conventional TKA procedures, the surgeon must manually assess the soft-tissue balance, using prior knowledge and experience. The subjective nature of this assessment leaves the measurement vulnerable to imprecision and residual imbalance [19]. The inability to proactively balance ligaments may result in stiffness and limited range of motion. Without successful physical therapy, these symptoms can result in an MUA [20].

Failure to balance ligaments accurately with boney resections can be addressed with soft-tissue releases. These releases cause trauma to the tissue such that the subsequent healing may increase the risk for MUA after a primary TKA procedure [21]. This study, along with previous literature showing a reduction in soft tissue release rates using similar technology [10], adds evidence that the robotic assisted ligament balancing system assists the orthopedic surgeon to predict and achieve accurate post-operative joint balance and decrease the risk of MUA and need for return to the OR.

With the continuing increase in TKAs performed in ASCs, it is important to determine safety of robotic assisted TKAs in this setting. Eason et al. [22], showed no difference in postoperative complication rates between hospital and ASC patient populations indicating robotic assisted TKA can be safe in an ASC setting. Similar results have been found for conventional TKAs [2, 23, 24, 25]. There remains concern however, of an increased risk when performing surgery at ASCs due to the inability to transfer patients to high care settings offered in hospitals quickly and efficiently [26]. Our study agrees with those mentioned above, showing no increase in total complications or robotics related complications in the hospital outpatient or ASC setting.

We also found no difference in length of stay between the robotic and conventional cohort across all TKA settings. This result is consistent with prior literature in which conflicting reports of both longer and shorter lengths of stay have been reported for robotic versus conventional TKA [9]. Further, our results indicate a 3.1% readmission rate within the robotic-hospital cohort, and is comparable to the 3.8% readmission rate Charpentier et al. [27] found in their retrospective review of early complications of TKAs within the MARCQI database over a 5-year period.

A difference in BMI was found between the robotics and conventional cohorts in the hospital pooled data and hospital inpatient data, however, when using a logistic regression model to control for differences in demographics BMI was not associated with increased complications. Although, traditionally high BMI has been associated with higher complications [38], recent literature is consistent with our results showing no difference in both short [36] and long term [37] complications.

Quadriceps tendon repair was the second most common cause for return to OR after MUA. The cause of the repairs were due to a deficient medial retinaculum in patients with high BMI and excessive physical therapy during early recovery. The impact of these tears on mid to long term extensor mechanism performance is unknown.

A major concern of robotic assisted TKA procedures is the additional capital costs and cost of care. Studies showing longer operative times in addition to the learning curve for the surgical team, have raised concern for increased costs associated with robotic TKAs [11, 28]. However, current literature demonstrates compensation for these costs, based on the decrease in complications post-operatively. Using a Markov model, Lee et al. [29] reported added value of robotic assisted surgery to be up to $8750USD per TKA when the joint was quantitatively balanced over a 5 year period. The main drivers of value in Lee’s study were reduced revisions and improved patient outcomes over the midterm.

Our study focused on early complications, which holds significance to the 90-day global period for bundled payments. Since the introduction of bundled payments, the medical center which performed the procedure is financially responsible for complications or readmissions that occur during this 90-day window. Therefore, demonstrating a reduction of these costs has implications of value to hospitals and ASCs looking to invest in modern robotic technology. Analysis of a large national database determined 90-day episode-of-care costs for robotic assisted TKAs were lower than manual TKAs arising from reduced episode of care costs during recovery [30]. Furthermore, Cool et al. [31] also demonstrated lower 90-day episode-of-care costs associated with robotic TKAs, reporting overall savings of $2391USD for the robotic cohort. These savings were attributed to lower readmission rate and economically favorable discharge destinations. In 2014, CMS reported 62% of MUAs occurred within the 90-day window with an average cost of $1200 per case [18]. Furthermore, a large database study found a 1.5 to 7.5 times higher cost of care for patients that underwent a MUA [32]. The financial impact of MUAs may include hospital charges, additional medication costs, surgeon and anesthesia fees, further follow-up appointments, added physical therapy, and in some cases revision surgery. These costs, along with the burden to the lifestyle of the patient and inability to return to work, can place heavy financial hardships onto patients in addition to the financial strain on the surgical centers that are responsible for the cost of care during the 90-day postoperative period. Our reporting of no difference in length of stay or short term robotic-related complications, in combination with the observed reduction of MUA, indicate significant potential savings when ligaments are qualitatively balanced. In addition to the results presented here, previous studies have found improved patient reported outcome scores in a robotic assisted ligament balancing cohort when balance targets are achieved and soft tissue releases are avoided [10, 33]. The reduction in complications presented in the current study, combined with those from Wakelin et al. [33] and Vigdorchik et al. [10], indicate improved patient outcomes may be realized throughout the entire TKA journey when balanced accurately with a modern robotics platform.

The limitations of this study include use of a single robotics system, as well as a single surgeon. However by limiting the study to this specific cohort, the investigation excluded many potential confounding variables. In order to determine the widespread applications of this technique, further research should be performed with the robotic assisted ligament balancing system among other patient populations with additional orthopedic surgeons. Additionally, the retrospective nature of this study may present a limitation. As such, there could have been some selection bias as patients were not randomized. Allocation of patients to manual versus robotic was based on the presence of an existing implant in the patient’s contralateral knee, and excessive BMI leading to the inability to properly position the robotic femoral guidance tool. Although this led to a difference in age and BMI between the two groups, we investigated the impact of demographics and found age and BMI were not a significant factor in the regression model. Nevertheless, as the power analysis was initially defined to compare the robotics and manual groups, our study may be under powered for the demographics analysis and it is possible the demographics did impact the complication rate. A possible limitation to this study is the defined 90-day follow up window, while excluding complications immediately outside the window. However, due to the nature of the bundled payment model, patients who experience complications within 90 days will return to the original surgeon for treatment. This model results in a very high data collection compliance rate within the 90 day window, but less controlled data collection outside this window. Previous literature found that robotic TKAs did not show any significant differences in outcomes or complications when compared to manual TKA procedures [34]. Therefore a comparison between this specific robotic assisted ligament balancing system and other robotic systems is warranted. Investigating the robotic assisted ligament balancing system in contrast to other robotic or computer navigated systems lacking a ligament balancing sensor, could better indicate the impact of a modern robotics system without predictive ligament balancing and one with predictive ligament balancing technology. The robotics group used a single implant design with a PCL sacrificing technique, a multi radius femoral component and deep dish ultra-congruent tibial insert. The manual cohort, however, utilized both PCL retaining and sacrificing techniques and multiple femoral component and tibial insert geometries and stability. The impact of component selection on short term complications may represent a significant confounder.

Indications for MUA were limited or non-progressing range of motion, however, range of motion data was not available for all patients in this study. We therefore cannot determine if the robotics cohort reported higher range of motion than manual, or whether knee requiring MUA reported significantly worse ROM. Finally, although this study has almost 900 patients, it may not encompass the whole range of patients who receive TKA and may not report rare but significant complications. Additional research among a larger population of patients would be beneficial to further understanding the applications of this technique.

5. Conclusion

Within a hospital setting, a modern robotic assisted ligament balancing platform was associated with reduced postoperative complications, namely return to OR and MUA. No difference in complications were found in an outpatient hospital or ASC setting. Additional investigation among a larger cohort is needed to further understand the applications of this technology to rare complications.

Funding

The authors report no funding.

Ethical approval

The study was approved by the relevant institutional ethics committee (WCG IRB no. 120190312 and Ascension Providence IRB no. RMI20220048).

Informed consent

In accordance with the institutional ethics committee, informed consent was not necessary.

Footnotes

Acknowledgments

The authors would like to acknowledge the data collation and collection efforts by Katie Mabee, without her contribution this study would not be possible.

Conflict of interest

EW and CP are paid employees of Corin Ltd. JHD is a paid consultant of Corin Ltd.

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Modern robotics platform associated with reduced 90-day complications in hospital setting

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Methods

2.1 Patients

Table 1 Summary of number of TKA cases investigated and the surgical setting in which the TKAs were performed

2.3 Robotic technique

2.5 Analysis

2.6 Power analysis

Table 2 Comparison of demographics

3.1 Hospital data pooled

3.2 Hospital inpatient

3.3 Hospital outpatient and ASC

4. Discussion

5. Conclusion

Funding

Ethical approval

Informed consent

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Summary of number of TKA cases investigated and the surgical setting in which the TKAs were performed

Table 2
Comparison of demographics