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Prospective Comparison of Available Primary Total Knee Arthroplasty Sizing Equations
Journal Pre-proof Prospective Comparison of Available Primary Total Knee Arthroplasty Sizing Equations Michael Murphy, M.D., Stephen Wallace, M.D., Nicholas Brown, M.D., Assistant Professor PII:
S0883-5403(19)31113-1
DOI:
https://doi.org/10.1016/j.arth.2019.11.041
Reference:
YARTH 57658
To appear in:
The Journal of Arthroplasty
Received Date: 3 October 2019 Revised Date:
3 November 2019
Accepted Date: 27 November 2019
Please cite this article as: Murphy M, Wallace S, Brown N, Prospective Comparison of Available Primary Total Knee Arthroplasty Sizing Equations, The Journal of Arthroplasty (2020), doi: https:// doi.org/10.1016/j.arth.2019.11.041. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier Inc. All rights reserved.
Title: Prospective Comparison of Available Primary Total Knee Arthroplasty Sizing Equations Authors: Michael Murphy, M.D. Corresponding Author Loyola University Medical Center Department of Orthopaedic Surgery and Rehabilitation 2160 S. First Avenue Maguire Suite 1700 Maywood, IL 60153 Fax: 708-216-9348 Phone: 708-216-4992 [email protected] Stephen Wallace, M.D. Loyola University Medical Center Department of Orthopaedic Surgery and Rehabilitation 2160 S. First Avenue Maguire Suite 1700 Maywood, IL 60153 Fax: 708-216-9348 Phone: 708-216-4992 [email protected] Nicholas Brown, M.D. Assistant Professor, Loyola University Medical Center Department of Orthopaedic Surgery and Rehabilitation 2160 S. First Avenue Maguire Suite 1700 Maywood, IL 60153 Fax: 708-216-9348 Phone: 708-216-4992 [email protected]
1
Title: Prospective Comparison of Available Primary Total Knee Arthroplasty Sizing Equations
2 3
Abstract
4
Background: Several studies have proposed regression equations that can increase the accuracy
5
of predicting femur and tibia component sizes for total knee arthroplasty (TKA). This study
6
compared available regression equations in their ability to prospectively predict component size
7
in a unique patient series.
8 9
Methods: Demographic data, implanted femur and tibia TKA component sizes were collected
10
on a consecutive 382 patients undergoing index TKA. Equations by Bhowmik-Stoker et al. [1],
11
Ren et al. [2], Sershon et al. [3], and Miller et al. [4] were identified that used age, race,
12
ethnicity, gender, height, weight, or body mass index. Equation outputs were converted to
13
implant-corrected sizes and compared to the implanted component.
14 15
Results: Femur and tibia sizes were accurately predicted within 1 size 88% and 92%, 84% and
16
86%, and 79% and 92% for Bhowmik-Stoker et al., Sershon et al., and Miller et al., respectively.
17
Ren et al. was within 1 tibia size 88% of the time. Adding one more common implant size
18
improved this accuracy by an average of 9.1% and 6.6% for the femur and tibia, respectively.
19
For femur components, Bhowmik-Stoker et al. outperformed Sershon et al. by 0.14 sizes
20
(p<0.001) and Miller et al. by 0.21 sizes (p<0.001) on average. For tibia components, Bhowmik-
21
Stoker et al. outperformed Sershon et al. by 0.09 sizes (p=0.028) and Ren et al. by 0.11 sizes
22
(p=0.005) on average.
23
24
Conclusion: Equations by Bhowmik-Stoker et al. more accurately predicted implanted TKA
25
size. In cases of greater uncertainty, the practicing surgeon may err on having more common
26
TKA sizes available.
27 28
Keywords: Implant size, Regression, Total knee arthroplasty
29 30
Introduction
31 32
Accurate templating in total knee arthroplasty (TKA) provides surgeons the ability to
33
streamline operating room efficiency, decrease economic burden with fewer intraoperative trays,
34
and prepare the surgical team for potential difficulties [5-8]. Variable accuracy, cost, and the
35
time commitment associated with TKA templating methods have led to the search for a simpler
36
model [9-12].
37
Today, many surgeons estimate prosthesis size by importing digital radiographs into a
38
commercially available templating program that allows the surgeon to overlay product-specific
39
implant outlines. This frequently involves manually evaluating numerous components and sizes
40
until a final best fit option is selected. This process is naturally subject to obtaining true
41
anteroposterior (AP) and lateral radiographs without rotation or knee flexion [13]. Several
42
studies have explored the accuracy of templating from radiographs with accuracy ranging about
43
50-75% [14-18]. Inaccurate or limited preoperative planning can increase patient morbidity and
44
cost, which has led to a growth of interest in methods that may improve efficiency and
45
affordability of patient care [19-23].
46
Previous articles have explored the accuracy of templating from demographic data.
47
Factors such as gender, height, weight, and body mass index (BMI) are readily available and
48
may be used to reasonably predict component size [1-4, 10-12]. In addition, characteristics
49
including patient ethnicity and bone morphology have also shown to have an effect on
50
component size [24-26]. The purpose of this study was to assess the accuracy and precision
51
among published equations for predicting knee replacement prosthesis size through a
52
prospective, consecutive series of patients.
53 54
Material and Methods
55 56
After obtaining approval from the hospital’s institutional review board, a consecutive
57
series of patients undergoing elective primary TKA were prospectively enrolled from January to
58
December 2018. A total of 427 TKAs were performed by four fellowship-trained arthroplasty
59
surgeons at a single institution. The surgeons chose implants based upon personal preference,
60
intra-operative measurements, and preoperative planning. Patients with a prior contralateral TKA
61
were excluded due to potential sizing bias. In addition, patients undergoing bilateral
62
simultaneous TKA were limited to using only a single side toward this study. Finally, component
63
models that were infrequently used (n = 13, 3.0%) were excluded. Each patient record was
64
reviewed twice, documenting laterality, gender, race/ethnicity, height, weight, BMI, TKA model,
65
and component sizes. Discrepancies in data were reviewed a third time.
66
A consecutive series of 382 patients undergoing elective primary TKA were
67
prospectively enrolled. The mean age of the subjects was 66+10 years (range: 42 to 89 years).
68
One hundred and thirty-four subjects were male and 248 female. Fifty-one percent of the
69
operations were on the right side and 49% were on the left. Exactly half were Stryker Triathlon
70
(Stryker Corp, Kalamazoo, MI) and half were DePuy PFC Sigma (DePuy Synthes, Warsaw, IN).
71
The majority of patients identified as White (74.3%), followed by Black (13.6%), Hispanic
72
(8.9%), Asian (1.8%), Middle Eastern (1.0%) and Indian (0.8%). The mean age was 65.9 years
73
(standard deviation: 9.8, range: 42 to 89), body mass index was 33.9 kg/m2 (standard deviation:
74
7.1, range 20.2 to 62.2), height was 167.9 cm (standard deviation: 10.7, range: 134.6 to 198.1),
75
and weight was 95.7 kg (standard deviation: 22.9, range: 49.4 to 181.4). See Table 1 for
76
demographic data.
77
A total of five studies were identified that proposed a formula using patient demographic
78
data to predict TKA component size without the use of preoperative radiographs. One study was
79
excluded due to its use of an infrequently collected variable: shoe size [10]. A final four studies
80
were selected to be included (variables: gender, age, height, weight, BMI, ethnicity/race) [1-4].
81
Sizing data was obtained using publicly available data for each total knee arthroplasty
82
systems. The documented femoral and tibial component sizes were converted to millimeters in
83
both the AP and mediolateral (ML) directions.
84
To compare results between equations, all equations were first converted from their
85
implant-specific output to the AP femur and ML tibia dimensions. The equations offered by
86
Miller et al., Bhowmik-Stoker et al., and Ren et al. involve predicting component sizes for
87
specific commercial implants [1,2,4]. Meanwhile, the equation offered by Sershon et al. directly
88
predicted AP femur and ML tibia dimensions [3]. Once converted to AP femur and ML tibia
89
dimensions, these dimensions were used to identify the best fitting size for the TKA system
90
implanted in the tested patient. As an example, one patient’s demographic data predicted femur
91
and tibia sizes 5.3 and 5.9 for Bhowmik-Stoker et al. (Triathlon, Stryker Corp, Kalamazoo, MI),
92
4.2 and 4.0 for Miller et al. (PFC Sigma, DePuy Synthes, Warsaw, IN), and size G tibia for Ren
93
et al. (Persona Knee system, Zimmer Biomet, Warsaw, IN). This converted to AP femur and ML
94
tibia dimensions of 69.0mm and 76.8mm for Bhowmik-Stoker et al., 65.8mm and 75.9mm for
95
Miller et al., 79.0mm tibia for Ren et al., and Sershon et al. directly output dimensions at
96
64.3mm and 71.6mm, respectively. This patient had a Stryker Triathlon system knee implanted.
97
When converting these dimensions to the Stryker Triathlon system, this equated sizes 6 and 6 for
98
Bhowmik-Stoker et al., 5 and 6 for Miller et al., size 6 tibia for Ren et al., and 5 and 4 for
99
Sershon et al. The sizes implanted for this patient was a Stryker Triathlon size 8 femur and 7
100
tibia. Thus, the equations were off by -2 and -1 for Bhowmik-Stoker et al., -3 and -1 for Miller et
101
al., -1 for the tibia for Ren et al., and -3 and -3 for Sershon et al. for the femur and tibia,
102
respectively.
103 104
Statistical Analysis
105 106
A power analysis was first performed to determine the appropriate study sample size. To
107
generate a small effect size in component dimensions between equations (d = 0.20), one would
108
need to enroll a total of 310 patients to achieve a power of 0.80 with a type I error rate of 0.05.
109
Predicted component dimensions in millimeters were performed for each model and
110
subsequently rounded to the closest available size for the implanted TKA model. The predicted
111
femur and tibia component size for each model was compared to the final implanted model.
112
A linear mixed-effects model was used to estimate correlation each model had with final
113
intraoperative component size meanwhile accounting for the influence of the other models [27].
114
Given each patient’s demographic data was used to generate four TKA size estimations, one for
115
each of the four published regression models, a random factor uj was included in the statistical
116
model to account for the repeated use of a patient’s demographic variables. Statistical
117
significance was set at p < 0.05. Statistical analysis was performed with IBM SPSS, version 26
118
(Armonk, NY).
119 120
Results
121 122
The equation published by Bhowmik-Stoker et al. best predicted implanted femur and
123
tibia component size for this patient series. Figure 1 shows the frequency each equation predicted
124
a specific size relative to the implanted size. Bhowmik-Stoker et al. averaged within 0.72+0.69
125
and 0.70+0.66 sizes, Sershon et al. averaged within 0.85+0.75 and 0.78+0.74 sizes, and Miller et
126
al. averaged within 0.92+0.75 and 0.70+0.66 sizes from the implanted femur and tibia TKA
127
components, respectively. Ren et al. averaged within 0.81+0.66 sizes from the implanted tibia
128
TKA components. For femur component size, Bhowmik-Stoker et al. statistically outperformed
129
Sershon et al. by an average of 0.14 sizes (p < 0.001) and Miller et al. by 0.21 sizes (p < 0.001).
130
For tibia component size, Bhowmik-Stoker et al. statistically outperformed Sershon et al. by an
131
average of 0.09 sizes (p = 0.028) and Ren et al. by an average of 0.11 sizes (p = 0.005).
132
Bhowmik-Stoker et al. and Miller et al. resulted in the same variations from implanted tibia size,
133
although having unique predictions, see Tables 2 and 3 with Figures 2 and 3 for details.
134
All published equations showed decreased accuracy at larger and smaller sizes, see
135
Figures 2-7 for details. For femur sizes, the equations by Bhowmik-Stoker et al. was within 1
136
size 87.7% (95% CI: 80.7 to 94.7%) of cases, Sershon et al. 84.0% (95% CI: 76.5 to 91.5%), and
137
Miller et al. 79.3% (95% CI: 71.8 to 86.9%) of cases, see Table 2 for details. For tibia sizes, the
138
equations by Bhowmik-Stoker et al. was within 1 size 91.6% (95% CI: 85.0 to 98.2%) of cases,
139
Sershon et al. 85.6% (95% CI: 78.2 to 93.0%), Miller et al. 91.6% (95% CI: 85.0 to 98.2%), and
140
Ren et al. 88.0% (95% CI: 81.4 to 94.5%) of cases, see Table 3 for details.
141
The coefficient of determination (R2) of each equation relative to the implanted femur
142
size was: 0.542, 0.482, and 0.467 for Bhowmik-Stoker et al., Sershon et al., and Miller et al.,
143
respectively. The coefficient of determination of each equation relative to the implanted tibia size
144
was: 0.628, 0.583, 0.536, and 0.528 for Ren et al., Miller et al., Bhowmik-Stoker et al., and
145
Sershon et al., respectively. All coefficient of determination values had p values of p < 0.001.
146 147
Discussion
148 149
Several studies have explored the reliability of templating TKA sizes from demographic
150
data. All authors report R2 values ranging 0.50 to 0.79 and accuracy within 1 size ranging 85 to
151
100% of cases, making it difficult for the practicing surgeon to discern a singular equation they
152
may employ in practice [1-4]. Further, this study marks the first assessment of available
153
equations on a population of patients unique to that which was used to model the equation. This
154
study identified the femur and tibia equations presented by Bhowmik-Stoker et al. to be
155
statistically most accurate.
156
The results demonstrate the applications of any author’s equations in predicting
157
component size was universally more accurate in predicting tibia component sizes compared to
158
that of the femoral component. This may be a result of the larger range seen between modern
159
tibial sizes, commonly differing by 3-6mm compared to 3-4mm in femoral implant sizes.
160
The data presented here does not suggest that predicting sizing in TKA based on
161
demographic variables is superior to preoperative templating. TKA templating based on
162
demographic variables alone will not account for deformity correction, anatomic variants, bone
163
less, or other factors that may be accounted for with traditional methods. Rather, for surgeons
164
that frequently use templating resources, this study identified a statistically superior model to
165
validate their results. The ability to accurately predict TKA size can have several benefits to the
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practicing surgeon in the operating room. With reliable predictions of a small range of implant
167
sizes based on readily identifiable patient characteristics, trays of trials may be consolidated,
168
operating room efficacy may be streamlined, and the surgical team may be better prepared for
169
potential difficulties.
170
This study showed that each of the equations tended to over-predict sizes when
171
implanting a smaller size, and under predict when implanting a larger size. This finding may
172
reflect that the relationship between demographic data and implant sizes are not linear,
173
presenting the possibility for improved accuracy with nonlinear modeling methods. This finding
174
may present concern to the practicing surgeon when preparing for sizes that are larger or smaller
175
than average, that employing these equations for these cases may be less accurate. However,
176
further analysis when larger size errors occurred revealed the majority of these cases were of
177
common sizes between sizes 2 to 6 for the femur and tibia. See Table 4 for details. In the case of
178
having implants available during the procedure, this may suggest the practicing surgeon may err
179
on having more common sizes available for more uncertain cases. For example, if the equation
180
were to predict a size 3, the surgeon having size 5 available may be more frequently beneficial
181
over having size 1 available. For the equations supplied by Bhowmik-Stoker et al., this
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additional size would improve the surgeon having correct sizes available from 87.7 to 94.2% for
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the femur and from 91.6% to 95.8% of cases for the tibia component sizes.
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This study has several limitations. The study involved the implantation of Triathlon and
185
PFC Sigma knees. In an effort to eliminate to both standardize the results and eliminate the
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implant related nature of these equations, all results were first converted to AP femur and ML
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tibia dimensions before finally fitting to the most appropriate implant size. However, there exists
188
a potential bias given the implant-specific data each equation was first modeled with. Although
189
Sershon et al. used several implants to model their equation, Bhowmik-Stoker et al., Ren et al.,
190
and Miller et al. used a singular component system for their studies: Triathlon, Persona Knee
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System, and PFC Sigma, respectively. The use of a similar implant system in the present study of
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PFC Sigma and Triathlon may put Bhowmik-Stoker et al. and Miller et al. at an advantage.
193
However, it may also be worth noting that Sershon et al. and Ren et al. did average slightly lower
194
accuracy (82-92%) when compared to that reported by Miller et al. (100%) and Bhowmik-Stoker
195
et al. (94%) [1-4].
196
Additionally, this study only evaluated the predicted component size compared to the
197
actual size implanted during surgery. This assumes that the implanted size was the ideal fit for
198
the patient. No post-operative radiographs or clinical follow up data was assessed to gauge if
199
implanted components were undersized or oversized.
200
Another limitation involves the use of patients from a single institution. The patient
201
population from a single institution should not be misconstrued to fully encompass that of
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another institution, practice, or a country in general. Rather, the reader may find strength in the
203
results of this study due to its use of a population unique from the original studies and meanwhile
204
demonstrating a diverse component size and demographic distribution.
205 206
Conclusion
207 208
This study presents a method for which templating equations may be applied across knee
209
systems. Using demographic variables, the authors identified the equations of Bhowmik-Stoker
210
et al. to be statistically superior to others in predicting final implant TKA size, reliably predicting
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within 1 size. In cases of greater uncertainty, the practicing surgeon may error on having more
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common TKA sizes readily available to streamline operating room efficiency and decrease costs.
213 214
References
215 216
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Acknowledgements The authors thank William Adams for his statistical support and guidance.
Table 1. Distribution of gender, laterality, ethnicity/race, age, body mass index (BMI), height, and weight among the patient population. Demographics Gender Laterality
Ethnicity/Race
Male Female Right Left White Black Hispanic Asian Middle Eastern Indian Mean
Age (years) BMI (kg/m2) Height (cm) Weight (kg)
65.9 33.9 167.9 95.7
Number
Percent
134 248 195 187 284 52 34 7 4 3
35.1% 64.9% 51.0% 49.0% 74.3% 13.6% 8.9% 1.8% 1.0% 0.8%
Standard Deviation 9.8 7.1 10.7 22.9
Range 42 - 89 20.2 – 62.2 134.6 – 198.1 49.4 – 181.4
Table 2. Number of times each model estimated an exact match or within 1, 2, or 3 sizes from the implanted femoral component. Femur
Exact 1 2 158 177 44 Bhowmik-Stoker et al. (41.4%) (46.3%) (11.5%) 128 193 50 Sershon et al. (33.5%) (50.5%) (13.1%) 116 187 71 Miller et al. (30.4%) (49.0%) (18.6)
3 3 (0.8%) 11 (2.9%) 8 (2.1%)
Table 3. Number of times each model estimated an exact match or within 1, 2, or 3 sizes from the implanted tibia component. Tibia Bhowmik-Stoker et al. Sershon et al. Miller et al. Ren et al.
Exact 153 (40.0%) 146 (38.2%) 153 (40.1%) 123 (32.2%)
1 2 3 197 27 5 (51.6%) (7.1%) (1.3%) 181 47 8 (47.2%) (12.3%) (2.1%) 197 27 5 (51.6%) (7.1%) (1.3%) 213 43 3 (55.8%) (11.3%) (0.8%)
Table 4. Frequency each equation predicted sizes +3, +2, +1, 0, -1, -2, and -3 sizes (first column) from that implanted with respect to the predicted size (first row). Cell contents show number of patients each equation predicted the respective size (first row) followed by the number of times that size was predicted, finally the percent between these two. The last column shows how frequently the equation was off by +3, +2, +1, 0, -1, -2, and -3 sizes. PREDICTED SIZE: 1 3 2 1 BhowmikStoker et al.
0 -1 -2
FEMUR
-3 3 2 Sershon et al.
1 0 -1 -2
1.5
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA NA
NA NA
2 0 of 5 (0%) 0 of 5 (0%) 0 of 5 (0%) 3 of 5 (60%) 2 of 5 (40%) 0 of 5 (0%) 0 of 5 (0%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 1 of 1 (100%) 0 of 1 (0%) 0 of 1
2.5 0 of 18 (0%) 0 of 18 (0%) 1 of 18 (5.6%) 5 of 18 (27.8%) 9 of 18 (50%) 3 of 18 (16.7%) 0 of 18 (0%) 0 of 9 (0%) 0 of 9 (0%) 2 of 9 (22.2%) 4 of 9 (44.4%) 2 of 9 (22.2%) 1 of 9
3 0 of 88 (0%) 0 of 88 (0%) 19 of 88 (21.6%) 34 of 88 (38.6%) 27 of 88 (30.7%) 7 of 88 (8%) 1 of 88 (1.1%) 0 of 142 (0%) 0 of 142 (0%) 32 of 142 (22.5%) 50 of 142 (35.2%) 51 of 142 (35.9%) 9 of 142
4 0 of 126 (0%) 12 of 126 (9.5%) 28 of 126 (22.2%) 57 of 126 (45.2%) 24 of 126 (19%) 5 of 126 (4%) 0 of 126 (0%) 0 of 159 (0%) 4 of 159 (2.5%) 27 of 159 (17%) 47 of 159 (29.6%) 53 of 159 (33.3%) 24 of 159
5 2 of 94 (2.1%) 8 of 94 (8.5%) 19 of 94 (20.2%) 44 of 94 (46.8%) 16 of 94 (17%) 5 of 94 (5.3%) 0 of 94 (0%) 0 of 49 (0%) 1 of 49 (2%) 8 of 49 (16.3%) 19 of 49 (38.8%) 6 of 49 (12.2%) 8 of 49
6 0 of 43 (0%) 2 of 43 (4.7%) 10 of 43 (23.3%) 15 of 43 (34.9%) 14 of 43 (32.6%) 2 of 43 (4.7%) 0 of 43 (0%) 0 of 21 (0%) 1 of 21 (4.8%) 4 of 21 (19%) 7 of 21 (33.3%) 7 of 21 (33.3%) 2 of 21
7 0 of 8 (0%) 0 of 8 (0%) 0 of 8 (0%) 0 of 8 (0%) 8 of 8 (100%) 0 of 8 (0%) 0 of 8 (0%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 1 of 1 (100%) 0 of 1
TOTAL 2 of 382 (0.5%) 22 of 382 (5.8%) 77 of 382 (20.2%) 158 of 382 (41.4%) 100 of 382 (26.2%) 22 of 382 (5.8%) 1 of 382 (0.3%) 0 of 382 (0%) 6 of 382 (1.6%) 73 of 382 (19.1%) 128 of 382 (33.5%) 120 of 382 (31.4%) 44 of 382
-3 3 2 1 Miller et al.
0
NA NA NA NA NA
-1 NA -2 -3 3 2 1
TIBIA
BhowmikStoker et al.
0 -1 -2 -3
Sershon et al.
3
NA NA 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 1 of 1 (100%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) NA
NA 0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%) 2 of 2 (100%) 0 of 2 (0%) 0 of 2 (0%) NA NA NA NA NA NA NA NA
(0%) 0 of 1 (0%) 0 of 20 (0%) 0 of 20 (0%) 0 of 20 (0%) 4 of 20 (20%) 11 of 20 (55%) 4 of 20 (20%) 1 of 20 (5%) 0 of 11 (0%) 0 of 11 (0%) 2 of 11 (18.2%) 5 of 11 (45.5%) 3 of 11 (27.3%) 1 of 11 (9.1%) 0 of 11 (0%) 0 of 3 (0%)
(11.1%) 0 of 9 (0%) 0 of 37 (0%) 0 of 37 (0%) 0 of 37 (0%) 12 of 37 (32.4%)
(6.3%) 0 of 142 (0%) 0 of 128 (0%) 0 of 128 (0%) 19 of 128 (14.8%) 43 of 128 (33.6%)
(15.1%) 4 of 159 (2.5%) 0 of 128 (0%) 4 of 128 (3.1%) 11 of 128 (8.6%) 40 of 128 (31.3%)
(16.3%) 7 of 49 (14.3%) 0 of 59 (0%) 1 of 59 (1.7%) 4 of 59 (6.8%) 17 of 59 (28.8%)
(9.5%) 0 of 21 (0%) 0 of 8 (0%) 0 of 8 (0%) 0 of 8 (0%) 0 of 8 (0%)
16 of 37 (43.2%) 9 of 37 (24.3%) 0 of 37 (0%) 0 of 48 (0%) 0 of 48 (0%) 5 of 48 (10.4%) 27 of 48 (56.3%) 16 of 48 (33.3%) 0 of 48 (0%) 0 of 48 (0%) 0 of 100 (0%)
51 of 128 (39.8%) 13 of 128 (10.2%) 2 of 128 (1.6%) 0 of 119 (0%) 1 of 119 (0.8%) 45 of 119 (37.8%) 45 of 119 (37.8%) 23 of 119 (19.3%) 5 of 119 (4.2%) 0 of 119 (0%) 0 of 143 (0%)
54 of 128 (42.2%) 16 of 128 (12.5%) 3 of 128 (2.3%) 1 of 117 (0.9%) 7 of 117 (6%) 34 of 117 (29.1%) 49 of 117 (41.9%) 24 of 117 (20.5%) 2 of 117 (1.7%) 0 of 117 (0%) 0 of 106 (0%)
19 of 59 (32.2%) 16 of 59 (27.1%) 2 of 59 (3.4%) 4 of 57 (7%) 6 of 57 (10.5%) 19 of 57 (33.3%) 17 of 57 (29.8%) 8 of 57 (14%) 3 of 57 (5.3%) 0 of 57 (0%) 0 of 8 (0%)
0 of 8 (0%) 8 of 8 (100%) 0 of 8 (0%) 0 of 27 (0%) 2 of 27 (7.4%) 8 of 27 (29.6%) 7 of 27 (25.9%) 10 of 27 (37%) 0 of 27 (0%) 0 of 27 (0%) 0 of 20 (0%)
(0%) 0 of 1 (0%) NA NA NA NA
(11.5%) 11 of 382 (2.9%) 0 of 382 (0%) 6 of 382 (1.6%) 73 of 382 (19.1%) 128 of 382 (33.5%) 120 of 382 (31.4%)
NA NA NA 0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%) 2 of 2 (100%) 0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%)
44 of 382 (11.5%) 11 of 382 (2.9%) 0 of 382 (0%) 6 of 382 (1.6%) 73 of 382 (19.1%) 128 of 382 (33.5%) 120 of 382 (31.4%) 44 of 382 (11.5%) 11 of 382 (2.9%) 0 of 382 (0%)
2
NA
NA
0 of 3 (0%)
NA
2 of 3 (66.7%)
NA
0 of 3 (0%)
1 NA 0 NA -1 NA -2 -3 3 2 1 Miller et al.
0 -1 -2 -3 3
NA
1 of 3 (33.3%) 0 of 3 (0%) 0 of 3 (0%) 0 of 16 (0%) 0 of 16 (0%) 2 of 16 (12.5%) 4 of 16 (25%) 9 of 16 (56.3%) 1 of 16 (6.3%) 0 of 16 (0%) 0 of 60 (0%)
NA
NA
NA 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 1 of 1 (100%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) NA
NA 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 1 of 1 (100%) 0 of 1 (0%) 0 of 1 (0%) 0 of 11 (0%)
NA
3 of 11 1 of 60 (27.3%) (1.7%)
Ren et al. 2
0 of 100 (0%) 14 of 100 (14%) 49 of 100 (49%) 30 of 100 (30%) 7 of 100 (7%) 0 of 100 (0%) 0 of 40 (0%) 0 of 40 (0%) 4 of 40 (10%) 23 of 40 (57.5%) 13 of 40 (32.5%) 0 of 40 (0%) 0 of 40 (0%) 2 of 115 (1.7%) 37 of 115 (32.2%)
3 of 143 (2.1%)
3 of 106 (2.8%)
0 of 8 (0%)
4 of 20 (20%)
0 of 2 (0%)
29 of 143 (20.3%)
22 of 106 (20.8%)
2 of 8 (25%)
7 of 20 (35%)
0 of 2 (0%)
49 of 143 (34.3%)
37 of 106 (34.9%)
2 of 8 (25%)
7 of 20 (35%)
2 of 2 (100%)
44 of 143 (30.8%) 18 of 143 (12.6%) 0 of 143 (0%) 0 of 115 (0%) 1 of 115 (0.9%) 45 of 115 (39.1%) 43 of 115 (37.4%) 23 of 115 (20%) 3 of 115 (2.6%) 0 of 115 (0%)
1 of 8 (12.5%) 3 of 8 (37.5%) 0 of 8 (0%) 4 of 70 (5.7%) 9 of 70 (12.9%) 21 of 70 (30%) 24 of 70 (34.3%) 9 of 70 (12.9%) 3 of 70 (4.3%) 0 of 70 (0%) 1 of 27 (3.7%)
2 of 20 (10%) 0 of 20 (0%) 0 of 20 (0%) 0 of 23 (0%) 2 of 23 (8.7%) 4 of 23 (17.4%) 8 of 23 (34.8%) 9 of 23 (39.1%) 0 of 23 (0%) 0 of 23 (0%) 0 of 44 (0%)
0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%) 0 of 3 (0%) 0 of 3 (0%) 0 of 3 (0%) 3 of 3 (100%) 0 of 3 (0%) 0 of 3 (0%) 0 of 3 (0%)
NA
27 of 106 (25.5%) 9 of 106 (8.5%) 8 of 106 (7.5%) 1 of 113 (0.9%) 8 of 113 (7.1%) 34 of 113 (30.1%) 47 of 113 (41.6%) 23 of 113 (20.4%) 0 of 113 (0%) 0 of 113 (0%) 8 of 125 (6.4%)
NA
30 of 125 (24%)
7 of 27 (25.9%)
6 of 44 (13.6%)
NA
NA
6 of 382 (1.6%) 73 of 382 (19.1%) 128 of 382 (33.5%) 120 of 382 (31.4%) 44 of 382 (11.5%) 11 of 382 (2.9%) 0 of 382 (0%) 6 of 382 (1.6%) 73 of 382 (19.1%) 128 of 382 (33.5%) 120 of 382 (31.4%) 44 of 382 (11.5%) 11 of 382 (2.9%) 0 of 382 (0%) 6 of 382 (1.6%)
1 NA 0 NA -1 -2 -3
NA NA NA
25 of 6 of 11 60 (54.5%) (41.7%) 34 of 2 of 11 60 (18.2%) (56.7%) 0 of 11 0 of 60 (0%) (0%) 0 of 11 0 of 60 (0%) (0%) 0 of 11 0 of 60 (0%) (0%)
57 of 115 (49.6%) 19 of 115 (16.5%) 0 of 115 (0%) 0 of 115 (0%) 0 of 115 (0%)
NA
NA NA NA NA
62 of 125 (49.6%) 25 of 125 (20%) 0 of 125 (0%) 0 of 125 (0%) 0 of 125 (0%)
15 of 27 (55.6%) 4 of 27 (14.8%) 0 of 27 (0%) 0 of 27 (0%) 0 of 27 (0%)
26 of 44 (59.1%) 12 of 44 (27.3%) 0 of 44 (0%) 0 of 44 (0%) 0 of 44 (0%)
73 of 382 (19.1%) NA 128 of 382 (33.5%) NA NA NA NA
120 of 382 (31.4%) 44 of 382 (11.5%) 11 of 382 (2.9%)
Figure 1. Distribution of Femur and Tibia implanted sizes for all implants and predicted size for both Triathlon (sizes: 1-8) and PFC Sigma (sizes: 1, 1.5, 2, 2.5, 3-6).
Figure 2. Correlation of each model with the implanted Femur component size.
Figure 3. Correlation of each model with the implanted Tibia component size.
Figure 4. Correlation of the model described by Sershon et al. for the implanted femur and tibia component.
Figure 5. Correlation of the model described by Miller et al. for the implanted femur and tibia component.
Figure 6. Correlation of the model described by Bhowmik-Stoker et al. for the implanted femur and tibia component.
Figure 7. Correlation of the model described by Ren et al. for the implanted tibia component.
Supplemental Table 1. Frequency of sizes implanted and predicted with each equation and component, respectively. IMPLANT SIZE FEMUR Implanted Bhowmik-Stoker et al. Sershon et al. Miller et al. TIBIA Implanted Bhowmik-Stoker et al. Sershon et al. Miller et al. Ren et al.
1 0 0 0 0 5 1 0 1
1.5 0 0 0 2 0 0 0 1
2 15 5 1 20 37 11 3 16
2.5 28 18 9 37 58 48 100 40
3 80 88 142 128 102 119 143 115
4 108 126 159 128 94 117 106 113
5 85 94 49 59 54 57 8 70
6 37 43 21 8 17 27 20 23
7 19 8 1 0 15 2 2 3
8 10 0 0 0 0 0 0 0
0
11
60
115
0
125
27
44
0
0
S0883-5403(19)31113-1
DOI:
https://doi.org/10.1016/j.arth.2019.11.041
Reference:
YARTH 57658
To appear in:
The Journal of Arthroplasty
Received Date: 3 October 2019 Revised Date:
3 November 2019
Accepted Date: 27 November 2019
Please cite this article as: Murphy M, Wallace S, Brown N, Prospective Comparison of Available Primary Total Knee Arthroplasty Sizing Equations, The Journal of Arthroplasty (2020), doi: https:// doi.org/10.1016/j.arth.2019.11.041. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier Inc. All rights reserved.
Title: Prospective Comparison of Available Primary Total Knee Arthroplasty Sizing Equations Authors: Michael Murphy, M.D. Corresponding Author Loyola University Medical Center Department of Orthopaedic Surgery and Rehabilitation 2160 S. First Avenue Maguire Suite 1700 Maywood, IL 60153 Fax: 708-216-9348 Phone: 708-216-4992 [email protected] Stephen Wallace, M.D. Loyola University Medical Center Department of Orthopaedic Surgery and Rehabilitation 2160 S. First Avenue Maguire Suite 1700 Maywood, IL 60153 Fax: 708-216-9348 Phone: 708-216-4992 [email protected] Nicholas Brown, M.D. Assistant Professor, Loyola University Medical Center Department of Orthopaedic Surgery and Rehabilitation 2160 S. First Avenue Maguire Suite 1700 Maywood, IL 60153 Fax: 708-216-9348 Phone: 708-216-4992 [email protected]
1
Title: Prospective Comparison of Available Primary Total Knee Arthroplasty Sizing Equations
2 3
Abstract
4
Background: Several studies have proposed regression equations that can increase the accuracy
5
of predicting femur and tibia component sizes for total knee arthroplasty (TKA). This study
6
compared available regression equations in their ability to prospectively predict component size
7
in a unique patient series.
8 9
Methods: Demographic data, implanted femur and tibia TKA component sizes were collected
10
on a consecutive 382 patients undergoing index TKA. Equations by Bhowmik-Stoker et al. [1],
11
Ren et al. [2], Sershon et al. [3], and Miller et al. [4] were identified that used age, race,
12
ethnicity, gender, height, weight, or body mass index. Equation outputs were converted to
13
implant-corrected sizes and compared to the implanted component.
14 15
Results: Femur and tibia sizes were accurately predicted within 1 size 88% and 92%, 84% and
16
86%, and 79% and 92% for Bhowmik-Stoker et al., Sershon et al., and Miller et al., respectively.
17
Ren et al. was within 1 tibia size 88% of the time. Adding one more common implant size
18
improved this accuracy by an average of 9.1% and 6.6% for the femur and tibia, respectively.
19
For femur components, Bhowmik-Stoker et al. outperformed Sershon et al. by 0.14 sizes
20
(p<0.001) and Miller et al. by 0.21 sizes (p<0.001) on average. For tibia components, Bhowmik-
21
Stoker et al. outperformed Sershon et al. by 0.09 sizes (p=0.028) and Ren et al. by 0.11 sizes
22
(p=0.005) on average.
23
24
Conclusion: Equations by Bhowmik-Stoker et al. more accurately predicted implanted TKA
25
size. In cases of greater uncertainty, the practicing surgeon may err on having more common
26
TKA sizes available.
27 28
Keywords: Implant size, Regression, Total knee arthroplasty
29 30
Introduction
31 32
Accurate templating in total knee arthroplasty (TKA) provides surgeons the ability to
33
streamline operating room efficiency, decrease economic burden with fewer intraoperative trays,
34
and prepare the surgical team for potential difficulties [5-8]. Variable accuracy, cost, and the
35
time commitment associated with TKA templating methods have led to the search for a simpler
36
model [9-12].
37
Today, many surgeons estimate prosthesis size by importing digital radiographs into a
38
commercially available templating program that allows the surgeon to overlay product-specific
39
implant outlines. This frequently involves manually evaluating numerous components and sizes
40
until a final best fit option is selected. This process is naturally subject to obtaining true
41
anteroposterior (AP) and lateral radiographs without rotation or knee flexion [13]. Several
42
studies have explored the accuracy of templating from radiographs with accuracy ranging about
43
50-75% [14-18]. Inaccurate or limited preoperative planning can increase patient morbidity and
44
cost, which has led to a growth of interest in methods that may improve efficiency and
45
affordability of patient care [19-23].
46
Previous articles have explored the accuracy of templating from demographic data.
47
Factors such as gender, height, weight, and body mass index (BMI) are readily available and
48
may be used to reasonably predict component size [1-4, 10-12]. In addition, characteristics
49
including patient ethnicity and bone morphology have also shown to have an effect on
50
component size [24-26]. The purpose of this study was to assess the accuracy and precision
51
among published equations for predicting knee replacement prosthesis size through a
52
prospective, consecutive series of patients.
53 54
Material and Methods
55 56
After obtaining approval from the hospital’s institutional review board, a consecutive
57
series of patients undergoing elective primary TKA were prospectively enrolled from January to
58
December 2018. A total of 427 TKAs were performed by four fellowship-trained arthroplasty
59
surgeons at a single institution. The surgeons chose implants based upon personal preference,
60
intra-operative measurements, and preoperative planning. Patients with a prior contralateral TKA
61
were excluded due to potential sizing bias. In addition, patients undergoing bilateral
62
simultaneous TKA were limited to using only a single side toward this study. Finally, component
63
models that were infrequently used (n = 13, 3.0%) were excluded. Each patient record was
64
reviewed twice, documenting laterality, gender, race/ethnicity, height, weight, BMI, TKA model,
65
and component sizes. Discrepancies in data were reviewed a third time.
66
A consecutive series of 382 patients undergoing elective primary TKA were
67
prospectively enrolled. The mean age of the subjects was 66+10 years (range: 42 to 89 years).
68
One hundred and thirty-four subjects were male and 248 female. Fifty-one percent of the
69
operations were on the right side and 49% were on the left. Exactly half were Stryker Triathlon
70
(Stryker Corp, Kalamazoo, MI) and half were DePuy PFC Sigma (DePuy Synthes, Warsaw, IN).
71
The majority of patients identified as White (74.3%), followed by Black (13.6%), Hispanic
72
(8.9%), Asian (1.8%), Middle Eastern (1.0%) and Indian (0.8%). The mean age was 65.9 years
73
(standard deviation: 9.8, range: 42 to 89), body mass index was 33.9 kg/m2 (standard deviation:
74
7.1, range 20.2 to 62.2), height was 167.9 cm (standard deviation: 10.7, range: 134.6 to 198.1),
75
and weight was 95.7 kg (standard deviation: 22.9, range: 49.4 to 181.4). See Table 1 for
76
demographic data.
77
A total of five studies were identified that proposed a formula using patient demographic
78
data to predict TKA component size without the use of preoperative radiographs. One study was
79
excluded due to its use of an infrequently collected variable: shoe size [10]. A final four studies
80
were selected to be included (variables: gender, age, height, weight, BMI, ethnicity/race) [1-4].
81
Sizing data was obtained using publicly available data for each total knee arthroplasty
82
systems. The documented femoral and tibial component sizes were converted to millimeters in
83
both the AP and mediolateral (ML) directions.
84
To compare results between equations, all equations were first converted from their
85
implant-specific output to the AP femur and ML tibia dimensions. The equations offered by
86
Miller et al., Bhowmik-Stoker et al., and Ren et al. involve predicting component sizes for
87
specific commercial implants [1,2,4]. Meanwhile, the equation offered by Sershon et al. directly
88
predicted AP femur and ML tibia dimensions [3]. Once converted to AP femur and ML tibia
89
dimensions, these dimensions were used to identify the best fitting size for the TKA system
90
implanted in the tested patient. As an example, one patient’s demographic data predicted femur
91
and tibia sizes 5.3 and 5.9 for Bhowmik-Stoker et al. (Triathlon, Stryker Corp, Kalamazoo, MI),
92
4.2 and 4.0 for Miller et al. (PFC Sigma, DePuy Synthes, Warsaw, IN), and size G tibia for Ren
93
et al. (Persona Knee system, Zimmer Biomet, Warsaw, IN). This converted to AP femur and ML
94
tibia dimensions of 69.0mm and 76.8mm for Bhowmik-Stoker et al., 65.8mm and 75.9mm for
95
Miller et al., 79.0mm tibia for Ren et al., and Sershon et al. directly output dimensions at
96
64.3mm and 71.6mm, respectively. This patient had a Stryker Triathlon system knee implanted.
97
When converting these dimensions to the Stryker Triathlon system, this equated sizes 6 and 6 for
98
Bhowmik-Stoker et al., 5 and 6 for Miller et al., size 6 tibia for Ren et al., and 5 and 4 for
99
Sershon et al. The sizes implanted for this patient was a Stryker Triathlon size 8 femur and 7
100
tibia. Thus, the equations were off by -2 and -1 for Bhowmik-Stoker et al., -3 and -1 for Miller et
101
al., -1 for the tibia for Ren et al., and -3 and -3 for Sershon et al. for the femur and tibia,
102
respectively.
103 104
Statistical Analysis
105 106
A power analysis was first performed to determine the appropriate study sample size. To
107
generate a small effect size in component dimensions between equations (d = 0.20), one would
108
need to enroll a total of 310 patients to achieve a power of 0.80 with a type I error rate of 0.05.
109
Predicted component dimensions in millimeters were performed for each model and
110
subsequently rounded to the closest available size for the implanted TKA model. The predicted
111
femur and tibia component size for each model was compared to the final implanted model.
112
A linear mixed-effects model was used to estimate correlation each model had with final
113
intraoperative component size meanwhile accounting for the influence of the other models [27].
114
Given each patient’s demographic data was used to generate four TKA size estimations, one for
115
each of the four published regression models, a random factor uj was included in the statistical
116
model to account for the repeated use of a patient’s demographic variables. Statistical
117
significance was set at p < 0.05. Statistical analysis was performed with IBM SPSS, version 26
118
(Armonk, NY).
119 120
Results
121 122
The equation published by Bhowmik-Stoker et al. best predicted implanted femur and
123
tibia component size for this patient series. Figure 1 shows the frequency each equation predicted
124
a specific size relative to the implanted size. Bhowmik-Stoker et al. averaged within 0.72+0.69
125
and 0.70+0.66 sizes, Sershon et al. averaged within 0.85+0.75 and 0.78+0.74 sizes, and Miller et
126
al. averaged within 0.92+0.75 and 0.70+0.66 sizes from the implanted femur and tibia TKA
127
components, respectively. Ren et al. averaged within 0.81+0.66 sizes from the implanted tibia
128
TKA components. For femur component size, Bhowmik-Stoker et al. statistically outperformed
129
Sershon et al. by an average of 0.14 sizes (p < 0.001) and Miller et al. by 0.21 sizes (p < 0.001).
130
For tibia component size, Bhowmik-Stoker et al. statistically outperformed Sershon et al. by an
131
average of 0.09 sizes (p = 0.028) and Ren et al. by an average of 0.11 sizes (p = 0.005).
132
Bhowmik-Stoker et al. and Miller et al. resulted in the same variations from implanted tibia size,
133
although having unique predictions, see Tables 2 and 3 with Figures 2 and 3 for details.
134
All published equations showed decreased accuracy at larger and smaller sizes, see
135
Figures 2-7 for details. For femur sizes, the equations by Bhowmik-Stoker et al. was within 1
136
size 87.7% (95% CI: 80.7 to 94.7%) of cases, Sershon et al. 84.0% (95% CI: 76.5 to 91.5%), and
137
Miller et al. 79.3% (95% CI: 71.8 to 86.9%) of cases, see Table 2 for details. For tibia sizes, the
138
equations by Bhowmik-Stoker et al. was within 1 size 91.6% (95% CI: 85.0 to 98.2%) of cases,
139
Sershon et al. 85.6% (95% CI: 78.2 to 93.0%), Miller et al. 91.6% (95% CI: 85.0 to 98.2%), and
140
Ren et al. 88.0% (95% CI: 81.4 to 94.5%) of cases, see Table 3 for details.
141
The coefficient of determination (R2) of each equation relative to the implanted femur
142
size was: 0.542, 0.482, and 0.467 for Bhowmik-Stoker et al., Sershon et al., and Miller et al.,
143
respectively. The coefficient of determination of each equation relative to the implanted tibia size
144
was: 0.628, 0.583, 0.536, and 0.528 for Ren et al., Miller et al., Bhowmik-Stoker et al., and
145
Sershon et al., respectively. All coefficient of determination values had p values of p < 0.001.
146 147
Discussion
148 149
Several studies have explored the reliability of templating TKA sizes from demographic
150
data. All authors report R2 values ranging 0.50 to 0.79 and accuracy within 1 size ranging 85 to
151
100% of cases, making it difficult for the practicing surgeon to discern a singular equation they
152
may employ in practice [1-4]. Further, this study marks the first assessment of available
153
equations on a population of patients unique to that which was used to model the equation. This
154
study identified the femur and tibia equations presented by Bhowmik-Stoker et al. to be
155
statistically most accurate.
156
The results demonstrate the applications of any author’s equations in predicting
157
component size was universally more accurate in predicting tibia component sizes compared to
158
that of the femoral component. This may be a result of the larger range seen between modern
159
tibial sizes, commonly differing by 3-6mm compared to 3-4mm in femoral implant sizes.
160
The data presented here does not suggest that predicting sizing in TKA based on
161
demographic variables is superior to preoperative templating. TKA templating based on
162
demographic variables alone will not account for deformity correction, anatomic variants, bone
163
less, or other factors that may be accounted for with traditional methods. Rather, for surgeons
164
that frequently use templating resources, this study identified a statistically superior model to
165
validate their results. The ability to accurately predict TKA size can have several benefits to the
166
practicing surgeon in the operating room. With reliable predictions of a small range of implant
167
sizes based on readily identifiable patient characteristics, trays of trials may be consolidated,
168
operating room efficacy may be streamlined, and the surgical team may be better prepared for
169
potential difficulties.
170
This study showed that each of the equations tended to over-predict sizes when
171
implanting a smaller size, and under predict when implanting a larger size. This finding may
172
reflect that the relationship between demographic data and implant sizes are not linear,
173
presenting the possibility for improved accuracy with nonlinear modeling methods. This finding
174
may present concern to the practicing surgeon when preparing for sizes that are larger or smaller
175
than average, that employing these equations for these cases may be less accurate. However,
176
further analysis when larger size errors occurred revealed the majority of these cases were of
177
common sizes between sizes 2 to 6 for the femur and tibia. See Table 4 for details. In the case of
178
having implants available during the procedure, this may suggest the practicing surgeon may err
179
on having more common sizes available for more uncertain cases. For example, if the equation
180
were to predict a size 3, the surgeon having size 5 available may be more frequently beneficial
181
over having size 1 available. For the equations supplied by Bhowmik-Stoker et al., this
182
additional size would improve the surgeon having correct sizes available from 87.7 to 94.2% for
183
the femur and from 91.6% to 95.8% of cases for the tibia component sizes.
184
This study has several limitations. The study involved the implantation of Triathlon and
185
PFC Sigma knees. In an effort to eliminate to both standardize the results and eliminate the
186
implant related nature of these equations, all results were first converted to AP femur and ML
187
tibia dimensions before finally fitting to the most appropriate implant size. However, there exists
188
a potential bias given the implant-specific data each equation was first modeled with. Although
189
Sershon et al. used several implants to model their equation, Bhowmik-Stoker et al., Ren et al.,
190
and Miller et al. used a singular component system for their studies: Triathlon, Persona Knee
191
System, and PFC Sigma, respectively. The use of a similar implant system in the present study of
192
PFC Sigma and Triathlon may put Bhowmik-Stoker et al. and Miller et al. at an advantage.
193
However, it may also be worth noting that Sershon et al. and Ren et al. did average slightly lower
194
accuracy (82-92%) when compared to that reported by Miller et al. (100%) and Bhowmik-Stoker
195
et al. (94%) [1-4].
196
Additionally, this study only evaluated the predicted component size compared to the
197
actual size implanted during surgery. This assumes that the implanted size was the ideal fit for
198
the patient. No post-operative radiographs or clinical follow up data was assessed to gauge if
199
implanted components were undersized or oversized.
200
Another limitation involves the use of patients from a single institution. The patient
201
population from a single institution should not be misconstrued to fully encompass that of
202
another institution, practice, or a country in general. Rather, the reader may find strength in the
203
results of this study due to its use of a population unique from the original studies and meanwhile
204
demonstrating a diverse component size and demographic distribution.
205 206
Conclusion
207 208
This study presents a method for which templating equations may be applied across knee
209
systems. Using demographic variables, the authors identified the equations of Bhowmik-Stoker
210
et al. to be statistically superior to others in predicting final implant TKA size, reliably predicting
211
within 1 size. In cases of greater uncertainty, the practicing surgeon may error on having more
212
common TKA sizes readily available to streamline operating room efficiency and decrease costs.
213 214
References
215 216
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Technol Int 2018;33:337e42.
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3. Sershon RA, Courtney PM, Rosenthal BD, Sporer SM, Levine BR. Can demographic
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12. Trainor S, Collins J, Mulvey H, Fitz W. Total knee replacement sizing: Shoe size is a
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better predictor for implant size than body height. Arch Bone Jt Surg 2018;6:100e4.
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13. Trickett R, Hodgson P, Forster M, Robertson A. The reliability and accuracy of digital
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templating in total knee replacement. Bone Joint J 2009;91-B:903-6.
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14. Levine B, Fabi D, Deirmenigan C. Digital templating in primary total hip and knee arthroplasty. Orthopedics 2010;33:187-9. 15. Peek A, Bloch B, Auld J. How useful is templating for total knee replacement component sizing? Knee 2012;19:266-9. 16. Aslam N, Lo S, Nagarajah K, Pasapula C, Akmal M. Reliability of preoperative templating in total knee arthroplasty. Acta Orthop Belg 2004;70:560-4. 17. Del Gaizo D, Soileau E, Lachiewicz P. Value of preoperative templating for primary total knee arthroplasty. J Knee Surg 2009;22:284-93. 18. Howcroft D, Fehily M, Peck C, Fox A, Dillon B, Johnson DS. The role of preoperative
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19. Nichols CI, Vose JK. Clinical outcomes and costs within 90 days of primary or revision total joint arthroplasty. J Arthroplasty 2016;31:1400-6.e3. 20. Bozic KJ, Kamath AF, Ong K, et al. Comparative epidemiology of revision arthroplasty:
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failed THA poses greater clinical and economic burdens than failed TKA. Clin Orthop
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Relat Res 2015;473:2131-8.
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21. Keswani A, Koenig KM, Bozic KJ. Value-based healthcare: Part 1-designing and
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implementing integrated practice units for the management of musculoskeletal disease.
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Clin Orthop Relat Res 2016;474:2100-3.
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22. Keswani A, Koenig KM, Ward L, Bozig KJ. Value-based healthcare: Part 2-addressing
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23. Attarian DE, Wahl JE, Wellman SS, Bolognesi MP. Developing a high-efficiency
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operating room for total joint arthroplasty in an academic setting. Clin Orthop Relat Res.
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24. Dai Y, Scuderi GR, Penninger C, Bischoff JE, Rosenberg A. Increased shape and size
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offerings of femoral components improve fit during total knee arthroplasty. Knee Surgery
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Sports Traumatol Arthroplasty 2014;22:2931e40.
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25. Bellemans J, Carpentier K, Vandenneucker H, Vanlauwe J, Victor J. Both morphotype
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and gender influence the shape of the knee in patients undergoing TKA. Clin Orthop
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Relat Res 2010;468:29e36.
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26. Mahfouz M, Fatah E, Bowers L, Scuderi G. Three-dimensional morphology of the knee reveals ethnic differences. Clin Orthop Relat Res 2012;470:172e85. 27. Kwon SS, Lee KM, Chung CY, Lee SY, Park MS. An introduction to the linear mixed model for orthopaedic research. JBJS Rev 2014;2:1-7.
Acknowledgements The authors thank William Adams for his statistical support and guidance.
Table 1. Distribution of gender, laterality, ethnicity/race, age, body mass index (BMI), height, and weight among the patient population. Demographics Gender Laterality
Ethnicity/Race
Male Female Right Left White Black Hispanic Asian Middle Eastern Indian Mean
Age (years) BMI (kg/m2) Height (cm) Weight (kg)
65.9 33.9 167.9 95.7
Number
Percent
134 248 195 187 284 52 34 7 4 3
35.1% 64.9% 51.0% 49.0% 74.3% 13.6% 8.9% 1.8% 1.0% 0.8%
Standard Deviation 9.8 7.1 10.7 22.9
Range 42 - 89 20.2 – 62.2 134.6 – 198.1 49.4 – 181.4
Table 2. Number of times each model estimated an exact match or within 1, 2, or 3 sizes from the implanted femoral component. Femur
Exact 1 2 158 177 44 Bhowmik-Stoker et al. (41.4%) (46.3%) (11.5%) 128 193 50 Sershon et al. (33.5%) (50.5%) (13.1%) 116 187 71 Miller et al. (30.4%) (49.0%) (18.6)
3 3 (0.8%) 11 (2.9%) 8 (2.1%)
Table 3. Number of times each model estimated an exact match or within 1, 2, or 3 sizes from the implanted tibia component. Tibia Bhowmik-Stoker et al. Sershon et al. Miller et al. Ren et al.
Exact 153 (40.0%) 146 (38.2%) 153 (40.1%) 123 (32.2%)
1 2 3 197 27 5 (51.6%) (7.1%) (1.3%) 181 47 8 (47.2%) (12.3%) (2.1%) 197 27 5 (51.6%) (7.1%) (1.3%) 213 43 3 (55.8%) (11.3%) (0.8%)
Table 4. Frequency each equation predicted sizes +3, +2, +1, 0, -1, -2, and -3 sizes (first column) from that implanted with respect to the predicted size (first row). Cell contents show number of patients each equation predicted the respective size (first row) followed by the number of times that size was predicted, finally the percent between these two. The last column shows how frequently the equation was off by +3, +2, +1, 0, -1, -2, and -3 sizes. PREDICTED SIZE: 1 3 2 1 BhowmikStoker et al.
0 -1 -2
FEMUR
-3 3 2 Sershon et al.
1 0 -1 -2
1.5
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA NA
NA NA
2 0 of 5 (0%) 0 of 5 (0%) 0 of 5 (0%) 3 of 5 (60%) 2 of 5 (40%) 0 of 5 (0%) 0 of 5 (0%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 1 of 1 (100%) 0 of 1 (0%) 0 of 1
2.5 0 of 18 (0%) 0 of 18 (0%) 1 of 18 (5.6%) 5 of 18 (27.8%) 9 of 18 (50%) 3 of 18 (16.7%) 0 of 18 (0%) 0 of 9 (0%) 0 of 9 (0%) 2 of 9 (22.2%) 4 of 9 (44.4%) 2 of 9 (22.2%) 1 of 9
3 0 of 88 (0%) 0 of 88 (0%) 19 of 88 (21.6%) 34 of 88 (38.6%) 27 of 88 (30.7%) 7 of 88 (8%) 1 of 88 (1.1%) 0 of 142 (0%) 0 of 142 (0%) 32 of 142 (22.5%) 50 of 142 (35.2%) 51 of 142 (35.9%) 9 of 142
4 0 of 126 (0%) 12 of 126 (9.5%) 28 of 126 (22.2%) 57 of 126 (45.2%) 24 of 126 (19%) 5 of 126 (4%) 0 of 126 (0%) 0 of 159 (0%) 4 of 159 (2.5%) 27 of 159 (17%) 47 of 159 (29.6%) 53 of 159 (33.3%) 24 of 159
5 2 of 94 (2.1%) 8 of 94 (8.5%) 19 of 94 (20.2%) 44 of 94 (46.8%) 16 of 94 (17%) 5 of 94 (5.3%) 0 of 94 (0%) 0 of 49 (0%) 1 of 49 (2%) 8 of 49 (16.3%) 19 of 49 (38.8%) 6 of 49 (12.2%) 8 of 49
6 0 of 43 (0%) 2 of 43 (4.7%) 10 of 43 (23.3%) 15 of 43 (34.9%) 14 of 43 (32.6%) 2 of 43 (4.7%) 0 of 43 (0%) 0 of 21 (0%) 1 of 21 (4.8%) 4 of 21 (19%) 7 of 21 (33.3%) 7 of 21 (33.3%) 2 of 21
7 0 of 8 (0%) 0 of 8 (0%) 0 of 8 (0%) 0 of 8 (0%) 8 of 8 (100%) 0 of 8 (0%) 0 of 8 (0%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 1 of 1 (100%) 0 of 1
TOTAL 2 of 382 (0.5%) 22 of 382 (5.8%) 77 of 382 (20.2%) 158 of 382 (41.4%) 100 of 382 (26.2%) 22 of 382 (5.8%) 1 of 382 (0.3%) 0 of 382 (0%) 6 of 382 (1.6%) 73 of 382 (19.1%) 128 of 382 (33.5%) 120 of 382 (31.4%) 44 of 382
-3 3 2 1 Miller et al.
0
NA NA NA NA NA
-1 NA -2 -3 3 2 1
TIBIA
BhowmikStoker et al.
0 -1 -2 -3
Sershon et al.
3
NA NA 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 1 of 1 (100%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) NA
NA 0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%) 2 of 2 (100%) 0 of 2 (0%) 0 of 2 (0%) NA NA NA NA NA NA NA NA
(0%) 0 of 1 (0%) 0 of 20 (0%) 0 of 20 (0%) 0 of 20 (0%) 4 of 20 (20%) 11 of 20 (55%) 4 of 20 (20%) 1 of 20 (5%) 0 of 11 (0%) 0 of 11 (0%) 2 of 11 (18.2%) 5 of 11 (45.5%) 3 of 11 (27.3%) 1 of 11 (9.1%) 0 of 11 (0%) 0 of 3 (0%)
(11.1%) 0 of 9 (0%) 0 of 37 (0%) 0 of 37 (0%) 0 of 37 (0%) 12 of 37 (32.4%)
(6.3%) 0 of 142 (0%) 0 of 128 (0%) 0 of 128 (0%) 19 of 128 (14.8%) 43 of 128 (33.6%)
(15.1%) 4 of 159 (2.5%) 0 of 128 (0%) 4 of 128 (3.1%) 11 of 128 (8.6%) 40 of 128 (31.3%)
(16.3%) 7 of 49 (14.3%) 0 of 59 (0%) 1 of 59 (1.7%) 4 of 59 (6.8%) 17 of 59 (28.8%)
(9.5%) 0 of 21 (0%) 0 of 8 (0%) 0 of 8 (0%) 0 of 8 (0%) 0 of 8 (0%)
16 of 37 (43.2%) 9 of 37 (24.3%) 0 of 37 (0%) 0 of 48 (0%) 0 of 48 (0%) 5 of 48 (10.4%) 27 of 48 (56.3%) 16 of 48 (33.3%) 0 of 48 (0%) 0 of 48 (0%) 0 of 100 (0%)
51 of 128 (39.8%) 13 of 128 (10.2%) 2 of 128 (1.6%) 0 of 119 (0%) 1 of 119 (0.8%) 45 of 119 (37.8%) 45 of 119 (37.8%) 23 of 119 (19.3%) 5 of 119 (4.2%) 0 of 119 (0%) 0 of 143 (0%)
54 of 128 (42.2%) 16 of 128 (12.5%) 3 of 128 (2.3%) 1 of 117 (0.9%) 7 of 117 (6%) 34 of 117 (29.1%) 49 of 117 (41.9%) 24 of 117 (20.5%) 2 of 117 (1.7%) 0 of 117 (0%) 0 of 106 (0%)
19 of 59 (32.2%) 16 of 59 (27.1%) 2 of 59 (3.4%) 4 of 57 (7%) 6 of 57 (10.5%) 19 of 57 (33.3%) 17 of 57 (29.8%) 8 of 57 (14%) 3 of 57 (5.3%) 0 of 57 (0%) 0 of 8 (0%)
0 of 8 (0%) 8 of 8 (100%) 0 of 8 (0%) 0 of 27 (0%) 2 of 27 (7.4%) 8 of 27 (29.6%) 7 of 27 (25.9%) 10 of 27 (37%) 0 of 27 (0%) 0 of 27 (0%) 0 of 20 (0%)
(0%) 0 of 1 (0%) NA NA NA NA
(11.5%) 11 of 382 (2.9%) 0 of 382 (0%) 6 of 382 (1.6%) 73 of 382 (19.1%) 128 of 382 (33.5%) 120 of 382 (31.4%)
NA NA NA 0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%) 2 of 2 (100%) 0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%)
44 of 382 (11.5%) 11 of 382 (2.9%) 0 of 382 (0%) 6 of 382 (1.6%) 73 of 382 (19.1%) 128 of 382 (33.5%) 120 of 382 (31.4%) 44 of 382 (11.5%) 11 of 382 (2.9%) 0 of 382 (0%)
2
NA
NA
0 of 3 (0%)
NA
2 of 3 (66.7%)
NA
0 of 3 (0%)
1 NA 0 NA -1 NA -2 -3 3 2 1 Miller et al.
0 -1 -2 -3 3
NA
1 of 3 (33.3%) 0 of 3 (0%) 0 of 3 (0%) 0 of 16 (0%) 0 of 16 (0%) 2 of 16 (12.5%) 4 of 16 (25%) 9 of 16 (56.3%) 1 of 16 (6.3%) 0 of 16 (0%) 0 of 60 (0%)
NA
NA
NA 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 1 of 1 (100%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) NA
NA 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 0 of 1 (0%) 1 of 1 (100%) 0 of 1 (0%) 0 of 1 (0%) 0 of 11 (0%)
NA
3 of 11 1 of 60 (27.3%) (1.7%)
Ren et al. 2
0 of 100 (0%) 14 of 100 (14%) 49 of 100 (49%) 30 of 100 (30%) 7 of 100 (7%) 0 of 100 (0%) 0 of 40 (0%) 0 of 40 (0%) 4 of 40 (10%) 23 of 40 (57.5%) 13 of 40 (32.5%) 0 of 40 (0%) 0 of 40 (0%) 2 of 115 (1.7%) 37 of 115 (32.2%)
3 of 143 (2.1%)
3 of 106 (2.8%)
0 of 8 (0%)
4 of 20 (20%)
0 of 2 (0%)
29 of 143 (20.3%)
22 of 106 (20.8%)
2 of 8 (25%)
7 of 20 (35%)
0 of 2 (0%)
49 of 143 (34.3%)
37 of 106 (34.9%)
2 of 8 (25%)
7 of 20 (35%)
2 of 2 (100%)
44 of 143 (30.8%) 18 of 143 (12.6%) 0 of 143 (0%) 0 of 115 (0%) 1 of 115 (0.9%) 45 of 115 (39.1%) 43 of 115 (37.4%) 23 of 115 (20%) 3 of 115 (2.6%) 0 of 115 (0%)
1 of 8 (12.5%) 3 of 8 (37.5%) 0 of 8 (0%) 4 of 70 (5.7%) 9 of 70 (12.9%) 21 of 70 (30%) 24 of 70 (34.3%) 9 of 70 (12.9%) 3 of 70 (4.3%) 0 of 70 (0%) 1 of 27 (3.7%)
2 of 20 (10%) 0 of 20 (0%) 0 of 20 (0%) 0 of 23 (0%) 2 of 23 (8.7%) 4 of 23 (17.4%) 8 of 23 (34.8%) 9 of 23 (39.1%) 0 of 23 (0%) 0 of 23 (0%) 0 of 44 (0%)
0 of 2 (0%) 0 of 2 (0%) 0 of 2 (0%) 0 of 3 (0%) 0 of 3 (0%) 0 of 3 (0%) 3 of 3 (100%) 0 of 3 (0%) 0 of 3 (0%) 0 of 3 (0%)
NA
27 of 106 (25.5%) 9 of 106 (8.5%) 8 of 106 (7.5%) 1 of 113 (0.9%) 8 of 113 (7.1%) 34 of 113 (30.1%) 47 of 113 (41.6%) 23 of 113 (20.4%) 0 of 113 (0%) 0 of 113 (0%) 8 of 125 (6.4%)
NA
30 of 125 (24%)
7 of 27 (25.9%)
6 of 44 (13.6%)
NA
NA
6 of 382 (1.6%) 73 of 382 (19.1%) 128 of 382 (33.5%) 120 of 382 (31.4%) 44 of 382 (11.5%) 11 of 382 (2.9%) 0 of 382 (0%) 6 of 382 (1.6%) 73 of 382 (19.1%) 128 of 382 (33.5%) 120 of 382 (31.4%) 44 of 382 (11.5%) 11 of 382 (2.9%) 0 of 382 (0%) 6 of 382 (1.6%)
1 NA 0 NA -1 -2 -3
NA NA NA
25 of 6 of 11 60 (54.5%) (41.7%) 34 of 2 of 11 60 (18.2%) (56.7%) 0 of 11 0 of 60 (0%) (0%) 0 of 11 0 of 60 (0%) (0%) 0 of 11 0 of 60 (0%) (0%)
57 of 115 (49.6%) 19 of 115 (16.5%) 0 of 115 (0%) 0 of 115 (0%) 0 of 115 (0%)
NA
NA NA NA NA
62 of 125 (49.6%) 25 of 125 (20%) 0 of 125 (0%) 0 of 125 (0%) 0 of 125 (0%)
15 of 27 (55.6%) 4 of 27 (14.8%) 0 of 27 (0%) 0 of 27 (0%) 0 of 27 (0%)
26 of 44 (59.1%) 12 of 44 (27.3%) 0 of 44 (0%) 0 of 44 (0%) 0 of 44 (0%)
73 of 382 (19.1%) NA 128 of 382 (33.5%) NA NA NA NA
120 of 382 (31.4%) 44 of 382 (11.5%) 11 of 382 (2.9%)
Figure 1. Distribution of Femur and Tibia implanted sizes for all implants and predicted size for both Triathlon (sizes: 1-8) and PFC Sigma (sizes: 1, 1.5, 2, 2.5, 3-6).
Figure 2. Correlation of each model with the implanted Femur component size.
Figure 3. Correlation of each model with the implanted Tibia component size.
Figure 4. Correlation of the model described by Sershon et al. for the implanted femur and tibia component.
Figure 5. Correlation of the model described by Miller et al. for the implanted femur and tibia component.
Figure 6. Correlation of the model described by Bhowmik-Stoker et al. for the implanted femur and tibia component.
Figure 7. Correlation of the model described by Ren et al. for the implanted tibia component.
Supplemental Table 1. Frequency of sizes implanted and predicted with each equation and component, respectively. IMPLANT SIZE FEMUR Implanted Bhowmik-Stoker et al. Sershon et al. Miller et al. TIBIA Implanted Bhowmik-Stoker et al. Sershon et al. Miller et al. Ren et al.
1 0 0 0 0 5 1 0 1
1.5 0 0 0 2 0 0 0 1
2 15 5 1 20 37 11 3 16
2.5 28 18 9 37 58 48 100 40
3 80 88 142 128 102 119 143 115
4 108 126 159 128 94 117 106 113
5 85 94 49 59 54 57 8 70
6 37 43 21 8 17 27 20 23
7 19 8 1 0 15 2 2 3
8 10 0 0 0 0 0 0 0
0
11
60
115
0
125
27
44
0
0