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Three-dimensional-based native alignment phenotype classification system: Description for use in planning for deformities during total knee arthroplasty
, Daniel Hameedc, Jeremy Dubinc, Martin Roched, Mike Bowese, Laura Scholle, Todd Gutowskie, Michael A. Montc,
⁎Corresponding author: Michael A. Mont. mmont@lifebridghealth.org
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Received: ,
Accepted: ,
This article was originally published by Reed Elsevier India Pvt. Ltd. and was migrated to Scientific Scholar after the change of Publisher.
Abstract
Abstract
Total knee arthroplasty (TKA) is a complex surgical procedure that traditionally relies on two-dimensional radiographs for pre-operative planning. These radiographs may not capture the intricate details of individual knee anatomy, potentially limiting the precision of surgical interventions. With advancements in imaging technology, there is an opportunity to refine TKA outcomes. This study introduces the Native Alignment Phenotype classification system that is based on pre-operative 3-dimensional computed tomography (CT) scans, aiming to provide a more detailed understanding of knee deformities and their influence on characterizing knee osteoarthritis and planning for TKA procedures.
There were 1406 pre-operative non-weight-bearing CT scans analyzed by a single surgeon experienced with robotically-assisted total knee arthroplasties. These scans were converted into three-dimensional models, focusing on the coronal and sagittal planes. Intraoperatively, the robotic system was used to capture native coronal and sagittal deformities for each patient. These values were captured with the patient's leg held in a non-stress, extension pose. A new classification system, ‘The Native Alignment Phenotype’, was developed to categorize the specific differences between individual knees.
There were four primary knee malalignments identified: varus deformity; valgus deformity; and two deformities in the sagittal plane. These malalignments were further categorized based on the degrees of deviation, creating groups with 5° coronal and sagittal ranges. A total of 77 phenotypic alignment patterns were found based on the analyzed cohort. In the coronal plane, varus HKA deformity between 6 and 10° was the most common, with 36.9% of the cases, followed by varus HKA alignment, which was between 0 and 5°, representing 34.3% of the cases. In the sagittal plane, neutral and flexion contracture deformities between 0 and 5° were the most common, with 32.6% of the cases, followed by a fixed flexion contracture alignment, which was between 6 and 10°, representing 28.7% of the cases. When combining coronal and sagittal planes, the most common alignment was the varus between 0 and 5° with a flexion contracture between 0 and 5° (12.5% of cases), closely followed by the varus between 6 and 10° with a flexion contracture between 6 and 10° (12.4% of cases).
The Native Alignment Phenotype classification system offers a nuanced understanding of knee deformities based on three-dimensional (CT scan) assessments, potentially leading to improved surgical outcomes in TKA. By leveraging the detailed data from the CT scans, this system provides a more comprehensive view of the knee's anatomy, emphasizing the importance of individualized, data-driven approaches in knee surgery.
1 Introduction
Implanting a total knee arthroplasty (TKA) requires major thought, planning, and analysis of individual radiographs. There have been numerous alignment schemes utilized in performing a TKA based on radiographs, most notably mechanical alignment (MA) or anatomic alignment, as well as kinematic alignment (KA). More recently, some investigators have focused on individualized alignment. The MA schema places an emphasis on a neutral mechanical axis and a joint line that is parallel to the floor; however, it overlooks individual variability. Kinematic alignment, in contrast, examines the native knee prior to surgical intervention and attempts to restore that anatomy. A recent study described a new system for classifying various knee phenotypes, referred to as the coronal plane alignment of the knee (CPAK) classification.1 This system was created with constitutional or pre-arthritic knee alignment and joint line obliquity in mind. Investigators examined 500 healthy and 500 arthritic knees, as well as comparing the balance of the individual knees postoperatively. They divided the knee into nine classes, which would potentially enable improved pre-operative planning, easier surgery, and hopefully improved outcomes.
Nevertheless, all of these systems, up to this point in time, have been based on radiographs. Simply put, radiographs are two-dimensional representations of three-dimensional objects. They can be misleading for several reasons, including: observer errors; technical mistakes (when collecting the radiograph); subtle differences in individual anatomy; as well as rotational or projectional differences that can skew the results and interpretation of these films. With advanced 3-dimensional (3D) imaging technology, such as magnetic resonance imaging (MRI) and computed tomography (CT) scans, we have an improved ability to evaluate the anatomy and deformities of the knee before performing total knee arthroplasties.2
In the last fifteen years, robotic systems for total knee arthroplasty have developed and gained popularity. Often, CT scans are used as a planning tool for the navigation of these systems. This has allowed the field of arthroplasty to accumulate a multitude of pre-operative CT scans available for scrutiny.
This paper will evaluate the native three-dimensional phenotypes of the knee joint of patients in the pre-operative setting for CT-scan-based robotic-assisted TKA (RA-TKA) using both coronal and sagittal planes based upon knee data from over 1400 patients. We will use this information to describe a new classification system, the Native Alignment Phenotype, that is based on the aforementioned CT scans. It is expected that these various phenotypes will influence the overall workflow of TKA as well as the positioning of the implant. It is also the precursor for further schemas that will provide an intraoperative assessment of the soft-tissue envelope for balancing. Therefore, this paper will propose a precise method for TKA based on three-dimensional modeling obtained from these CT scans, and we are hopeful that in the future, this systematic approach may yield improved patient-based outcomes.
2 Methods
The goal of this paper was to define the spectrum of pre-operative osteoarthritic knee alignments based on three-dimensional reconstructions of CT scans and alignment data of the knee collected intra-operatively using robotic technology. Ideally, when implemented, this will allow for a more individualized and efficient surgery with decreased soft-tissue dissection and a balanced knee, which may lead to better outcomes with shorter recovery times.
2.1 Data collection
A retrospective study was conducted on 1406 RATKA cases performed by a single, high-volume joint arthroplasty surgeon. These cases were meticulously chosen to represent a broad spectrum of patients who had RATKA surgery. The objective was to ensure a diverse representation of knee deformities, thus enabling a comprehensive analysis.
For each RATKA, a file specific to the patient was stored on the robotic system. These files included preoperative information based on the patient's CT, such as preoperative deformity. To access this data, files were collected from the robotic system and then decrypted using a programming script. All patient health identifiers were removed, and data specific to the native alignment phenotype was extracted for analysis.
The native alignment phenotype for each patient was defined as pre-operative coronal and sagittal alignments. These values were gathered after bone registration was performed, but prior to any bone cuts. The surgeon held the leg in extension without applying corrective stresses and captured pre-operative coronal deformity and the presence of flexion contracture or hyper-extension.
2.2 Phenotypic classification system
Given the limitations of radiographs in representing the three-dimensional complexity of the knee, our approach was based on the CT scans. These scans, when converted into 3D models, allowed for an in-depth exploration of the knee's anatomy, specifically focusing on the coronal and sagittal planes.
Using these detailed models, we developed the Native Alignment Phenotype classification system. This system was designed to catalog the intricate differences between individual knees and provide a framework for understanding how these differences might influence the TKA workflow and the implant's positioning.
3 Results
3.1 Phenotypic definitions
3.1.1 Phenotypic definitions
After analysis of the CT scans, we concluded that there are four main malalignments to consider: Varus, valgus, and two deformities in the sagittal plane. Within these main malalignments are sub-categories in the coronal plane created by 5° segments (i.e., 0 to 5° varus/valgus; 6 to 10° varus/valgus; and 11 to 15° varus/valgus). This segmenting was continued until all varus and valgus deformities were included, maximum varus of 19° and a maximum valgus of 12.° These sub-categories were then combined with deformities in the sagittal plane (i.e., recurvatum versus procurvatum (flexion contracture)) in the same manner of sub-categories, where the maximum flexion contracture was 41° and the maximum recurvatum deformity was 15° (Fig. 1). In an effort to condense the above and to better define the phenotypes, we have devised the following phenotype definitions for a total of 77 phenotypes (Fig. 1).
3.2 Phenotypic distributions
Fig. 2 depicts the distribution of the phenotypes for the 1406 cases by one-plane analyses. In the coronal plane, varus deformity between 6 and 10° was the most common, with 36.9% of the cases, followed by varus alignment, which was between 0 and 5°, representing 34.3% of the cases (Fig. 2). In the sagittal plane, neutral and flexion contracture deformities between 0 and 5° were the most common, with 32.5% of the cases, followed by flexion contracture alignment, which was between 6 and 10°, representing 28.6% of the cases (Fig. 3). When combining coronal and sagittal planes (Fig. 4), the most common alignment was the varus between 0 and 5° with a flexion contracture between 0 and 5° (12.5% of cases), closely followed by the varus between 6 and 10° group with a flexion contracture of 6–10° (12.4% of cases).
4 Discussion
The knee, being one of the most complex joints in the human body, has always posed challenges in terms of surgical interventions, especially in the realm of total knee arthroplasty (TKA). The traditional reliance on radiographs for pre-operative planning, while useful, has its limitations due to its two-dimensional nature. This study, by leveraging the power of three-dimensional knee evaluations with CT scans, has provided a more comprehensive understanding of the knee's three-dimensional phenotypes, which we believe may markedly influence the planning, balancing, and eventually positively impact the outcomes of TKAs.
The Native Alignment Phenotype classification system, as proposed in this study, offers a more nuanced understanding of knee deformities. By identifying four primary malalignments and their sub-categories, surgeons can now have a clearer roadmap for surgical planning. The fact that over 80% of the cases displayed a varus alignment in the coronal plane underscores the importance of recognizing and addressing this common deformity. Furthermore, the combined phenotypes, which integrate findings from both the coronal and sagittal planes, are particularly noteworthy. This combined approach ensures that surgeons are not just focusing on one plane of deformity but are considering the knee's alignment in a more holistic manner. Such an approach may lead to better surgical outcomes and fewer post-operative complications.
The data on the influence of knee position on implant alignment is also of importance. Implant misalignment can lead to a host of complications, including pain, reduced range of motion, and even implant failure. By understanding how the knee's position, especially in terms of flexion or hyperextension, affects implant alignment, surgeons can make more informed decisions during the procedure. We observed that 12% of cases required a soft-tissue release, which, when necessary, can lead to longer recovery times and potential complications. By understanding which specific phenotypic alignments most frequently require these ancillary procedures, surgeons can potentially anticipate and mitigate these challenges.
While this study offers valuable insights, it is essential to recognize its potential limitations. Even though CT scans provide a detailed 3D view, they might not capture every detail of the knee's structure. Also, the study mainly focused on the anatomy of the knee, but individual patient factors like age, activity level, or overall health can also influence surgical outcomes. Also, while the new classification method is promising, it is based on a single study, and more research is needed to validate it and confirm its effectiveness across different patient groups and settings. Various systems may use a different coordinate system to define values.
It should also be noted that the phenotypes presented here have been determined without reference to the variation of the coronal and sagittal planes in repeat measurements; additionally, we have not yet determined the range of phenotypes that would be expected to be found in knees that do not have osteoarthritis and are of apparently neutral alignment. Future work will address these issues.
5 Conclusion
The Native Alignment Phenotype classification system, based on CT scans, offers a groundbreaking approach to understanding knee deformities in three dimensions. This system not only provides a more detailed view of the knee's anatomy, but also offers actionable insights that may directly influence surgical planning and outcomes. By moving away from the traditional reliance on radiographs and embracing the more comprehensive data provided by CT scans, the field of orthopaedics stands to make major advances in improving patient outcomes in total knee arthroplasty. The future of TKA lies in individualized, data-driven approaches, and this study is a substantial step in that direction.
Funding
None.
Data availability
Available in a respository upon request.
Patient consent
No patient consent needed due to retrospective nature and public database.
Ethical approval
IRB exemption due to retrospective nature and public database.
Authors’ contribution
RM- Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; and Writing - review & editing.
JD- Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; and Writing - review & editing.
LS-Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; and Writing - review & editing.
DH- Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; and Writing - review & editing.
CS- Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; and Writing - review & editing.
TG- Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; and Writing - review & editing.
MM- Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; and Writing - review & editing.
MB- Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; and Writing - review & editing.
RD- Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; and Writing - review & editing.
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