Development and validation of a nomogram for predicting a forgotten joint in patients one year after robotic-assisted total knee arthroplasty

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This study aimed to identify the individual predictors and develop a nomogram to predict a forgotten joint in patients 1 year after robotic-assisted total knee arthroplasty (RA-TKA). Methods This retrospective study involved 199 patients with knee osteoarthritis who underwent RA-TKA. All participants completed the FJS-12 questionnaire at 1-year follow-up, with scores above 77.1 considered indicative of a forgotten joint. The demographic data, surgical data, preoperative and postoperative imaging data were collected for analysis. Univariate and multivariate logistic regression analyses were conducted to determine predictors and establish a predictive model. The receiver operating characteristic curve, calibration curve analysis and decision curve analysis (DCA) were used to evaluate the discriminatory ability, calibration and clinical usefulness of the model. Results Overall, 44.22% (88/199) of knees achieved a forgotten joint 1 year after RA-TKA. Five variables were identified as independent predictors, including age, sex, prothesis type, operative time and changes in the arithmetic hip-knee-ankle angle (aHKA). The area under the curve (AUC) of the nomogram was 0.726 and 0.725 (95% CI 0.660–0.788) using 500 bootstrap resampling. The Hosmer–Lemeshow test showed that the model was of goodness-of-fit (p = 0.886). And the DCA showed net benefits when the threshold probability was between 20%-75%. Conclusions A nomogram was developed for predicting a forgotten joint 1 year after RA-TKA. This model showed good discrimination and calibration, which could assist surgeons in optimizing patient selection, preoperative planning and intraoperative decisions, ultimately improving outcomes of RA-TKA. Robotic-assisted total knee arthroplasty Forgotten Joint Score Clinical prediction model Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Total knee arthroplasty (TKA) is an effective treatment to alleviate pain and improve function for patients with end-stage knee osteoarthritis [ 1 ]. Despite significant advances in prosthesis design, implantation method and perioperative management, approximately 20% of patients remain complaining about dissatisfied postoperative outcomes [ 2 ]. Robotic-assisted TKA (RA-TKA) has gained increasing attention as it enhances precision and reduces errors in alignment and soft-tissue balance [ 3 – 5 ]. Nevertheless, the superiority of RA-TKA over traditional TKA remains debated, with mixed evidence in both short-term and long-term outcomes [ 6 – 10 ]. The Forgotten Joint Score (FJS-12), developed by Behrend et al, has been used to assess patients’ ability to forget their artificial joints during activities of daily life [ 11 ]. The FJS-12 is valued for its high discrimination and low ceiling effect. Achieving a forgotten joint is considered as the ultimate goal after TKA [ 12 , 13 ]. While some studies have explored variables associated with FJS-12, such as age, sex, BMI, Kellgren–Lawrence (K–L) grades and mental health, findings have been inconsistent [ 13 – 15 ]. Recent research suggested that alterations of preoperative and postoperative coronal plane alignment of knee (CPAK) classification also significantly impacted the FJS-12 [ 16 ]. Nomograms are commonly used in prognostic and diagnostic research to visualize statistical models. By incorporating multiple predictors into an easy-to-read graph, they allow surgeons and patients to intuitively understand how factors influence outcomes and calculate individualized probabilities [ 17 ]. While several prediction models have been established for TKA, focusing on outcomes like postoperative pain and dissatisfaction and length of stay, nomograms specific to RA-TKA are limited, mainly targeting short-term functional outcomes [ 18 – 21 ]. To our knowledge, there are no published nomograms available for the prediction of a forgotten joint after TKA or RA-TKA. Based on previously reported predictors, this study retrospectively collected patient demographic data, preoperative and postoperative imaging and surgical details to determine factors associated with achieving a forgotten joint after RA-TKA. Our objective was to establish and internally validate an individualized prediction nomogram for predicting the probability of a forgotten joint 1 year after RA-TKA. Ultimately, we hoped to increase the probability of patients achieving the ultimate goal of a forgotten joint by optimizing modifiable factors. Materials and patients Patients With the approval of the institutional review board of our hospital, we retrospectively reviewed 231 primary RA-TKA procedures performed between May 2021 and October 2023. The inclusion criteria were: (1) diagnosis of knee osteoarthritis; (2) finished a minimal 1-year follow-up; (3) complete preoperative and postoperative imaging data. The exclusion criteria were: (1) inflammatory arthritis; (2) complications related to RA-TKA or underwent revision surgery within 1 year; (3) other diseases that affected knee function or symptoms, such as neurological or musculoskeletal disease; (4) incomplete clinical data. Finally, a total of 199 knees were included in the study after excluding 32 knees due to lost follow-up or other exclusion criteria (Fig. 1 ). Surgical protocol Surgical protocol A preoperative computed tomography (CT) scan of the affected limb was conducted to generate a personalized surgical plan using the Mako System (Stryker Corp, Mahwah, NJ, USA). All surgical procedures were performed by a professional team under a combined spinal–epidural anesthesia. Pneumatic tourniquets were routinely used. A medial parapatellar approach was employed in cases, with minimal soft-tissue envelope stripping. Tracker arrays were affixed to the femur and tibia for further registration. Accessible osteophytes were thoroughly removed, and soft-tissue tension was evaluated using varus and valgus stress tests in extension, as well as by inserting a spoon at 90° of flexion. Real-time data from the robotic system guided surgeons in fine-tuning implant positioning, achieving balanced flexion and extension gaps with a tolerance of 1-2mm lateral laxity. Lower limb alignment was generally maintained within a 3° deviation from neutral mechanical alignment. Surgeons performed bone cuttings as planned with the assistance of a semi-active robotic arm providing haptic feedback. Only when a balance cannot be achieved by adjusting bone cuttings within the acceptable alignment range, sequential soft-tissue releases will be performed. Soft-tissue balance was reassessed after trial component placement. Cemented cruciate-retaining (CR) or posterior-stabilized (PS) implants (Triathlon Tritanium, Stryker, Mahwah, NJ) were selected depending on the quality of posterior-cruciate ligament. No patients underwent patella replacement. All patients followed a standardized rehabilitation plan after surgery. Data collection Patients’ baseline demographic data and potential factors were collected, including age, sex, body mass index (BMI), American Society of Anesthesiologists (ASA) classification, comorbidities, history of ipsilateral joint surgery and operative time. Radiological assessment of knee osteoarthritis severity was performed using Kellgren–Lawrence (K–L) grades. Additionally, preoperative and postoperative weight-bearing full-length radiographs of the lower extremity were analyzed to measure coronal alignment. Hip-knee-ankle angle (HKA), lateral distal femoral angle (LDFA) and medial proximal tibial angle (MPTA) were measured on preoperative and postoperative images using Mimics 19.0 software (Materialise, Leuven, Belgium). According to the CPAK classification, the arithmetic hip-knee-ankle angle (aHKA = MPTA – LDFA) and joint line obliquity (JLO = MPTA + LDFA) were calculated [ 22 ]. Furthermore, we calculated the absolute changes in preoperative and postoperative HKA, LDFA, MPTA, aHKA, and JLO. Outcome measure The FJS-12 was used as an outcome measure to assess patients' ability to forget their artificial joints. It consisted of 12 questions scored on a 5-point Likert scale, with total scores ranging from 0 to 100 [ 11 ]. Patients were required to complete the FJS-12 questionnaire at 1-year follow-up. According to the study of Singh et al, we chose 77.1 points as the threshold for achieving a forgotten joint [ 23 ]. Statistical analysis Continuous variables were expressed as mean ± standard deviation and categorical variables were expressed as frequencies and percentages (%). Univariate and multivariate logistic regression analyses were performed to identify the independent predictors of a forgotten joint, with p < 0.05 considered statistically significant. To prevent the loss of important predictors, variables with p < 0.1 in univariate analysis were included in the subsequent multivariate logistic regression. A backward stepwise method was used in to develop a predictive model. The discriminatory ability of the model was determined using the concordance index (C-index) and receiver operating characteristic curve (ROC) analysis. To better evaluate the predictive performance, internal validation was performed using 500 bootstrap resampling. Calibration curves that assessed the agreement between actual probability and predicted probability of achieving a forgotten joint, were also conducted using 500 bootstrap resampling. A Hosmer-Lemeshow test was employed, with p > 0.05 indicating a high goodness of fit. Finally, a decision curve analysis (DCA) was performed to evaluate the clinical usefulness of the nomogram. Statistical analysis was conducted using R 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). Results A total of 199 patients were included in this study, with a mean age of 67.32 ± 6.36 years. According to the FJS-12 at 1-year follow-up, 44.22% (88/199) of knees achieved a forgotten joint (FJS-12 > 77.1). Baseline demographic data, radiographic data and operative time of the patients are shown in Table 1 . Of the 25 variables collected, eight variables were selected based on a p-value < 0.1 from univariate logistic regression (Table 2 ), including age, sex, ASA, preoperative aHKA, prosthesis type, operative time, HKA changes (ΔHKA) and aHKA changes (ΔaHKA). These variables were subsequently incorporated into the multivariate logistic regression. Table 1 Demographic and clinical characteristics of all patients. Characteristics Overall (n = 199) Non-forgotten joint group (n = 112) Forgotten joint group (n = 87) Age(years) 67.32 ± 6.36 67.86 ± 6.31 66.62 ± 6.40 BMI(kg/m2) 27.44 ± 3.45 27.32 ± 3.16 27.61 ± 3.81 Sex Woman 153(76.9%) 95(84.8%) 58(66.7%) Man 46(23.1%) 17(15.2%) 29(33.3%) Side Right 103(51.8%) 59(52.7%) 44(50.6%) Left 96(48.2%) 53(47.3%) 43(49.4%) ASA I 63(31.7%) 30(26.8%) 33(37.9%) II 135(67.8%) 81(72.3%) 54(62.1%) III 1(0.5%) 1(0.9%) 0(0.0%) Cardiovascular disease No 91(45.7%) 49(43.8%) 42(48.3%) Yes 108(54.3%) 63(56.3%) 45(51.7%) Diabetes No 162(81.4%) 89(79.5%) 73(83.9%) Yes 37(18.6%) 23(20.5%) 14(16.1%) History of ipsilateral joint surgery No 187(94.0%) 106(94.6%) 81(93.1%) Yes 12(6.0%) 6(5.4%) 6(6.9%) K–L grades 3 118(59.3%) 62(55.9%) 56(63.6%) 4 81(40.7%) 49(44.1%) 32(36.4&) preoperative HKA(°) 172.73 ± 6.00 172.15 ± 6.50 173.47 ± 5.24 preoperative LDFA(°) 88.73 ± 2.86 88.97 ± 2.95 88.43 ± 2.72 preoperative MPTA(°) 84.87 ± 3.33 84.55 ± 3.57 85.28 ± 2.96 preoperative aHKA(°) -3.86 ± 5.03 -4.42 ± 5.51 -3.14 ± 4.27 preoperative JLO(°) 173.60 ± 3.63 173.52 ± 3.55 173.71 ± 3.75 prosthesis type PS 41(20.6%) 29(25.9%) 12(13.8%) CR 158(79.4%) 83(74.1%) 75(86.2%) operative time(min) 98.61 ± 21.40 102.87 ± 22.67 93.13 ± 18.36 postoperative HKA(°) 178.73 ± 3.04 178.66 ± 3.02 178.81 ± 3.08 postoperative LDFA(°) 90.32 ± 1.81 90.39 ± 1.88 90.22 ± 1.72 postoperative MPTA(°) 89.33 ± 1.72 89.25 ± 1.72 89.42 ± 1.72 postoperative aHKA(°) -0.99 ± 2.42 -1.14 ± 2.51 -0.80 ± 2.31 postoperative JLO(°) 179.65 ± 2.58 179.65 ± 2.59 179.65 ± 2.57 ΔHKA(°) 6.63 ± 3.78 7.33 ± 4.03 5.74 ± 3.23 ΔLDFA(°) 2.41 ± 1.88 2.35 ± 1.88 2.48 ± 1.89 ΔMPTA(°) 4.54 ± 2.86 4.82 ± 3.12 4.19 ± 2.46 ΔaHKA(°) 3.82 ± 3.02 4.35 ± 3.27 3.13 ± 2.53 ΔJLO(°) 6.19 ± 3.77 6.22 ± 3.96 6.16 ± 3.53 FJS-12 69.12 ± 22.07 55.30 ± 19.77 86.92 ± 6.88 The data were showed as mean ± standard deviation or as frequencies and percentages (%). BMI, body mass index; ASA, American Society of Anesthesiologists; K-L, Kellgren–Lawrence; HKA, hip-knee-ankle angle; HKA, hip-knee-ankle angle; LDFA, lateral distal femoral angle; MPTA, medial proximal tibial angle; aHKA, arithmetic hip-knee-ankle angle; JLO, joint line obliquity; FJS-12, Forgotten Joint Score. Table 2 Univariable and multivariable logistic regression analyses of predictive factors for achieving a forgotten joint. Variables Univariable logistic regression Multivariable logistic regression (Backward Stepwise) OR(95% CI) p-value OR(95% CI) p-value Age ∗ 0.64(0.42–0.97) 0.04 0.6(0.38–0.94) 0.03 BMI 1.02(0.94–1.11) 0.55 - - Sex 2.79(1.41–5.53) < 0.001 2.73(1.30–5.73) 0.01 side 1.09(0.62–1.91) 0.77 - - ASA 0.59(0.32–1.06) 0.08 - - cardiovascular disease 0.83(0.47–1.46) 0.53 - - diabetes 0.74(0.36–1.54) 0.43 - - history of ipsilateral joint surgery 1.31(0.41–4.21) 0.65 - - K–L grades 0.72(0.41–1.28) 0.268 preoperative HKA 1.04(0.99–1.09) 0.13 - - preoperative LDFA 0.93(0.85–1.03) 0.18 - - preoperative MPTA 1.07(0.98–1.17) 0.13 - - preoperative aHKA 1.05(0.99–1.12) 0.08 - - preoperative JLO 1.01(0.94–1.10) 0.72 - - prosthesis type 2.18(1.01–4.58) 0.04 2.52(1.10–5.76) 0.03 operative time 0.98(0.96–0.99) < 0.001 0.98(0.96–0.99) 0.01 postoperative HKA 1.02(0.93–1.12) 0.72 - - postoperative LDFA 0.95(0.81–1.11) 0.51 - - postoperative MPTA 1.06(0.90–1.25) 0.49 - - postoperative aHKA 1.06(0.94–1.19) 0.33 - - postoperative JLO 1.00(0.90–1.12) 1.00 - - ΔHKA 0.89(0.82–0.96) < 0.001 - - ΔLDFA 1.04(0.89–1.20) 0.64 - - ΔMPTA 0.92(0.83–1.02) 0.13 - - ΔaHKA 0.86(0.77–0.96) 0.01 0.88(0.79–0.99) 0.03 ΔJLO 1.00(0.92–1.07) 0.92 - - *The age was divided into three age groups (≤ 60, 60–70, ≥ 70). OR: Odds ratio; CI: Confidence interval. BMI, body mass index; ASA, American Society of Anesthesiologists; K-L, Kellgren–Lawrence; HKA, hip-knee-ankle angle; HKA, hip-knee-ankle angle; LDFA, lateral distal femoral angle; MPTA, medial proximal tibial angle; aHKA, arithmetic hip-knee-ankle angle; JLO, joint line obliquity; FJS-12, Forgotten Joint Score; Development of an individualized prediction model Multivariable logistic regression was performed based on previously determined variables. The results showed that age, sex, prosthesis type, operative time and ΔaHKA were independent predictors of achieving a forgotten joint after RA-TKA (Table 2 ). Thus, these independent predictors were then used to develop a predictive model and displayed as a nomogram (Fig. 2 ). The corresponding score can be determined based on the variable values. This total score can be calculated and then be used to estimate the probability of achieving a forgotten joint. Performance and validation of the model The C-index for this nomogram was 0.726 and was internally validated to be 0.725 (95% CI: 0.660–0.788) by 500 bootstrap resampling, indicating good discriminatory ability of the prediction model (Fig. 3 ). The model also showed good calibration, as evidenced by calibration curves generated from 500 bootstrap resampling (Fig. 4 ). Furthermore, the Hosmer–Lemeshow test (p = 0.886) confirmed a goodness-of-fit between the predicted probability and observed probability. Clinical usefulness of the model The DCA was conducted to evaluate the clinical usefulness of this prediction model (Fig. 5 ). The decision curve showed that if the threshold probability ranged from 20–75%, applying this nomogram to predict a forgotten joint could acquire greater net benefits compared to both strategies of predicting that all patients would achieve a forgotten joint or that none would. Discussion In this study, an individualized nomogram for predicting a forgotten joint 1 year after RA-TKA was developed and internally validated. We identified five variables including age, sex, prosthesis type, operative time and ΔaHKA, that had a significant importance in predicting the joint awareness. This model demonstrated good discriminative ability and calibration, which have been validated through C-index and calibration curves. Additionally, the wide threshold probability range displayed by the DCA curve confirmed its clinical usefulness. Only few prediction models have been established for patients undergoing RA-TKA. Motesharei et al designed a prediction model to estimate the operative time for RA-TKA based on demographic and anatomic data [ 24 ]. Duan et al developed a nomogram to predict the early functional outcome in patients after RA-TKA, which demonstrated excellent prediction performance and clinical application potential [ 21 ]. Another nomogram model based on computed tomography radiomics was also developed to predict the satisfaction of patients 3 months after RA-TKA [ 19 ]. To our knowledge, this is the first nomogram that has been developed for predicting a forgotten joint after RA-TKA. The forgotten joint, as the ultimate goal of TKA, has a low ceiling effect, allowing it to discriminate patients with good to excellent joint function [ 13 ]. In our study, the average 1-year FJS-12 was 69.21 ± 22.11, with 44.22% (88/199) of knees achieving a forgotten joint. Our result was comparable to that of another multicenter study on RA-TKA, which reported an average FJS-12 of 70.2 ± 27.8 [ 25 ]. Although there is still controversy over the differences in postoperative outcomes between RA-TKA and conventional TKA, RA-TKA offers the advantage of reducing errors associated with manual procedures [ 4 , 5 ]. Furthermore, surgeons can better customize an individualized alignment based on the patient's specific anatomy and native soft-tissue envelope [ 26 ]. Our research found that the changes in aHKA had a negative impact on achieving a forgotten joint after RA-TKA. Before the concept of constitutional alignment emerged, some studies have explored the relationship between mechanical HKA and FJS-12. It has been found that patients with more than ± 1 changes in functional HKA and femoral phenotype had significantly lower FJS-12 [ 27 ]. A systematic review and meta-analysis reported that residual mild varus alignment after TKA can lead to a higher FJS-12 than neutral alignment in patients with preoperative varus knees [ 28 ]. These views potentially suggested that pursuing a one-size-fits-all neutral alignment may not be the optimal approach. Instead, respecting and restoring a patient’s specific native alignment could lead to better outcomes, reinforcing the importance of personalized surgical strategies. To enhance patient satisfaction, the philosophy of restoring a patient’s prearthritic alignment has gained popularity, as it was believed to promote a more natural knee kinematic model and improve soft-tissue balance [ 29 , 30 ]. In 2021, MacDessi et al proposed the coronal plane alignment of the knee (CPAK) classification, which suggested that aHKA can be used to predict the constitutional alignment in cases of unicompartmental joint space narrowing without bone loss [ 22 ]. Franceschetti et al observed that patients with preoperative varus aHKA had the worst FJS-12 at 1-year follow-up compared to other aHKA classifications [ 31 ]. A recent study revealed that alternation in varus/valgus alignment before and after TKA was a negative predictive factor for FJS-12 at a mean 55-month follow-up. But they did not find any correlation between postoperative apex proximal and the FJS-12 [ 16 ]. Our findings were similar to theirs, showing that greater changes in aHKA significantly reduced the probability of achieving a forgotten joint, while JLO changes were not relevant. Altering a patient’s constitutional alignment to a certain extent could disrupt their original kinematics and lose respect for their native soft-tissue envelope. Moreover, these changes in constitutional alignment often required increased manipulation and trauma to the soft-tissue, raising the risk of soft-tissue imbalance. This may be the potential cause of postoperative pain and discomfort, thereby reducing the probability of achieving a forgotten joint. In our model, the prosthesis type was another variable for predicting a forgotten joint. Although both PS and CR implants have been widely used for years, their impact on outcomes remains inconclusive. Some studies reported findings contrary to ours. For example, Daffara et al found that there was no significant difference in FJS-12 between CR and PS implants in RA-TKA patients at a minimum 2-year follow-up [ 32 ]. Similarly, no differences in FJS-12 were observed between CR and PS groups in a RA-TKA cohort [ 33 ]. Consistent with our results, Thuysbaert et al reported significantly higher FJS-12 for patients with CR implants compared to PS implants at 2-year follow-up [ 34 ]. This could be attributed to the preservation of posterior-cruciate ligament in CR designs, which enhanced proprioceptive function and maintained normal femoral rollback, resulting in a more physiological knee kinematic model [ 35 , 36 ]. Variables such as age, sex, and BMI have been widely debated as predictive factors for outcomes in TKA. Nielsen et al found that FJS-12 increased with age for women, whereas for men, scores peaked at age 67 and declined in both younger and older groups [ 15 ]. Additionally, Rissolio et al also reported poorer FJS-12 in women, regardless of age [ 37 ]. A cluster analysis demonstrated that men around the age of 63 with lower BMI were most likely to achieve better FJS-12 at 1-year follow-up [ 13 ]. Similar to their results, our study identified older age and woman as negative predictors for achieving a forgotten joint. To enhance clinical relevance, we categorized age into three groups (≤ 60, 60–70, ≥ 70). However, no correlation between BMI and joint awareness was observed in our cohort. This could be attributed to differences in patient characteristics, women often underwent TKA at more advanced stages of arthritis compared to men and tended to experience more residual pain and functional limitations postoperatively [ 38 ]. Additionally, advanced age can negatively affect overall physical health, which may indirectly reduce surgical satisfaction [ 39 ]. Previous studies linking BMI to poorer outcomes often focused on patients with high BMI (more than 30–40 kg/m²), which was rare in our cohort, where the mean BMI was 27.44 ± 3.45 kg/m² [ 40 – 42 ]. This may explain why BMI did not emerge as a significant factor in predicting joint awareness in our analysis. Operative time was a novel variable to predict joint awareness in our study which has not been reported before. Most previous researches have focused on the correlation between operative time and short-term periprosthetic joint infection [ 43 , 44 ]. In our cohort, patients who were readmitted were due to flexion restriction or soft-tissue imbalance rather than infection. Our team possessed extensive expertise in performing RA-TKA, having transcended the learning curve. We believed that prolonged operative time was often due to severe joint deformities that required multiple adjustments to achieve target alignment and soft-tissue balance. This interpretation has also been confirmed by Motesharei et al, revealing that the presence of osteophytes required additional operative time. Furthermore, the removal of posterior osteophytes also impacted the gap balance, potentially resulting in extra bone cuttings or soft-tissue releases [ 24 ]. These could also increase the risk of soft-tissue imbalance and postoperative discomfort, which may ultimately reduce the likelihood of achieving a forgotten joint. This predictive model has clinical guidance significance, as some of the predictive factors are modifiable. This allows for the identification of patients with a lower probability of achieving a forgotten joint, enabling targeted clinical interventions to improve outcomes. For example, implementing a robotic surgical plan aimed at restoring constitutional alignment and prioritizing the use of CR implants, may help optimize alignment while also effectively reducing operative time. Additionally, this model can enhance patient selection by providing evidence-based preoperative consultations and setting realistic postoperative expectations, ultimately contributing to improved surgical satisfaction. However, this study still has several limitations. First, our research was based on a retrospective study with a limited sample size, which was inevitable to selection bias. Thus, a multicenter and prospective study is needed to expand the sample size in the future. Second, the predictive model which was validated through 500 bootstrap resampling may lead to overfitting. Third, other variables with potential relevance to the outcome need to be further included in future research. Last, multicenter external validation is still necessary to further determine the efficacy and reliability of this predictive model. Conclusion In this study, we developed a nomogram for predicting a forgotten joint of patients 1 year after RA-TKA. This model has been verified to have good discrimination and calibration, with potential clinical application value. By using this model, clinicians can improve patient selection, guide the development of preoperative robotic plans, and optimize intraoperative decisions, ultimately leading to better clinical outcomes. Abbreviations FJS-12 Forgotten Joint Score RA-TKA robotic-assisted total knee arthroplasty DCA decision curve analysis aHKA arithmetic hip-knee-ankle angle AUC area under the curve TKA total knee arthroplasty K-L Kellgren–Lawrence CPAK coronal plane alignment of knee CT computed tomography CR cruciate-retaining PS posterior-stabilized BMI body mass index ASA American Society of Anesthesiologists HKA hip-knee-ankle angle LDFA lateral distal femoral angle MPTA medial proximal tibial angle JLO joint line obliquity C-index concordance index ROC receiver operating characteristic curve HKA changes ΔHKA aHKA changes ΔaHKA Declarations Acknowledgments We thank the staff in the Department of Orthopedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China. Funding This work was supported in part by National Key R&D Program of China (Grant No. 2023YFC2411000 to Y. Z.), Beijing Hospitals Authority Clinical Medicine Development of special funding support (ZLRK202505 to Y. Z.) Competing interests The authors declare that they have no conflict of interest. Availability of Data and Materials Data are available from the first author (Chengshuai Zhang, [email protected] ) on reasonable request and with permission of the hospital’s ethics institutional review board. Clinical trial number : not applicable Ethics approval statement This study was approved by the Institutional Review Board of Beijing Jishuitan Hospital (approval No. K2022-092-01). All methods were performed in line with the Declaration of Helsinki. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent for publication Not applicable. References Scott CEH, Turnbull GS, MacDonald D, Breusch SJ. Activity levels and return to work following total knee arthroplasty in patients under 65 years of age. Bone Joint J. 2017;99–B:1037–46. https://doi.org/10.1302/0301-620X.99B8.BJJ-2016-1364.R1 . 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Does Robotic-assisted TKA Result in Better Outcome Scores or Long-Term Survivorship Than Conventional TKA? A Randomized, Controlled Trial. Clin Orthop Relat Res. 2020;478:266–75. https://doi.org/10.1097/CORR.0000000000000916 . Liow MHL, Goh GS, Wong MK, Chin PL, Tay DK, Yeo SJ. Robotic-assisted total knee arthroplasty may lead to improvement in quality-of-life measures: a 2-year follow-up of a prospective randomized trial. Knee Surg Sports Traumatol Arthrosc. 2017;25:2942–51. https://doi.org/10.1007/s00167-016-4076-3 . Behrend H, Giesinger K, Giesinger JM, Kuster MS. The forgotten joint as the ultimate goal in joint arthroplasty: validation of a new patient-reported outcome measure. J Arthroplasty. 2012;27:430–e436431. https://doi.org/10.1016/j.arth.2011.06.035 . Eckhard L, Munir S, Wood D, Talbot S, Brighton R, Walter B, Bare J. The ceiling effects of patient reported outcome measures for total knee arthroplasty. Orthop Traumatol Surg Res. 2021;107:102758. https://doi.org/10.1016/j.otsr.2020.102758 . Behrend H, Zdravkovic V, Giesinger J, Giesinger K. Factors Predicting the Forgotten Joint Score After Total Knee Arthroplasty. J Arthroplasty. 2016;31:1927–32. https://doi.org/10.1016/j.arth.2016.02.035 . Eymard F, Charles-Nelson A, Katsahian S, Chevalier X, Bercovy M. Predictive Factors of Forgotten Knee Acquisition After Total Knee Arthroplasty: Long-Term Follow-Up of a Large Prospective Cohort. J Arthroplasty. 2017;32:413–e418411. https://doi.org/10.1016/j.arth.2016.06.020 . Nielsen KA, Thomsen MG, Latifi R, Kallemose T, Husted H, Troelsen A. Does post-operative knee awareness differ between knees in bilateral simultaneous total knee arthroplasty? Predictors of high or low knee awareness. Knee Surg Sports Traumatol Arthrosc. 2016;24:3352–8. https://doi.org/10.1007/s00167-016-4013-5 . Konishi T, Hamai S, Tsushima H, Kawahara S, Akasaki Y, Yamate S, Ayukawa S, Nakashima Y. Pre- and postoperative Coronal Plane Alignment of the Knee classification and its impact on clinical outcomes in total knee arthroplasty. Bone Joint J. 2024;106–B:1059–66. https://doi.org/10.1302/0301-620X.106B10.BJJ-2023-1425.R1 . Park SY. Nomogram: An analogue tool to deliver digital knowledge. J Thorac Cardiovasc Surg. 2018;155:1793. https://doi.org/10.1016/j.jtcvs.2017.12.107 . Zhu B, Zhang D, Sang M, Zhao L, Wang C, Xu Y. Establishment and evaluation of a predictive model for length of hospital stay after total knee arthroplasty: A single-center retrospective study in China. Front Surg. 2023;10:1102371. https://doi.org/10.3389/fsurg.2023.1102371 . Tian R, Duan X, Xing F, Zhao Y, Liu C, Li H, Kong N, Cao R, Guan H, Li Y, Li X, Zhang J, Wang K, Yang P, Wang C. Computed tomography radiomics in predicting patient satisfaction after robotic-assisted total knee arthroplasty. Int J Comput Assist Radiol Surg. 2024. https://doi.org/10.1007/s11548-024-03192-1 . Muertizha M, Cai X, Ji B, Aimaiti A, Cao L. Factors contributing to 1-year dissatisfaction after total knee arthroplasty: a nomogram prediction model. J Orthop Surg Res. 2022;17:367. https://doi.org/10.1186/s13018-022-03205-2 . Duan X, Zhao Y, Zhang J, Kong N, Cao R, Guan H, Li Y, Wang K, Yang P, Tian R. Prediction of early functional outcomes in patients after robotic-assisted total knee arthroplasty: a nomogram prediction model. Int J Surg. 2023;109:3107–16. https://doi.org/10.1097/JS9.0000000000000563 . MacDessi SJ, Griffiths-Jones W, Harris IA, Bellemans J, Chen DB. Coronal Plane Alignment of the Knee (CPAK) classification. Bone Joint J. 2021;103–B:329–37. https://doi.org/10.1302/0301-620X.103B2.BJJ-2020-1050.R1 . Singh V, Fiedler B, Huang S, Oh C, Karia RJ, Schwarzkopf R. Patient Acceptable Symptom State for the Forgotten Joint Score in Primary Total Knee Arthroplasty. J Arthroplasty. 2022;37:1557–61. https://doi.org/10.1016/j.arth.2022.03.069 . Motesharei A, Batailler C, De Massari D, Vincent G, Chen AF, Lustig S. Predicting robotic-assisted total knee arthroplasty operating time: benefits of machine-learning and 3D patient-specific data. Bone Jt Open. 2022;3:383–9. https://doi.org/10.1302/2633-1462.35.BJO-2022-0014.R1 . Joo PY, Chen AF, Richards J, Law TY, Taylor K, Marchand K, Clark G, Collopy D, Marchand RC, Roche M, Mont MA, Malkani AL. Clinical results and patient-reported outcomes following robotic-assisted primary total knee arthroplasty: a multicentre study. Bone Jt Open. 2022;3:589–95. https://doi.org/10.1302/2633-1462.37.BJO-2022-0076.R1 . Kafelov M, Batailler C, Shatrov J, Al-Jufaili J, Farhat J, Servien E, Lustig S. Functional positioning principles for image-based robotic-assisted TKA achieved a higher Forgotten Joint Score at 1 year compared to conventional TKA with restricted kinematic alignment. Knee Surg Sports Traumatol Arthrosc. 2023;31:5591–602. https://doi.org/10.1007/s00167-023-07609-3 . Rak D, Klann L, Heinz T, Anderson P, Stratos I, Nedopil AJ, Rudert M. Influence of Mechanical Alignment on Functional Knee Phenotypes and Clinical Outcomes in Primary TKA: A 1-Year Prospective Analysis. J Pers Med. 2023;13. https://doi.org/10.3390/jpm13050778 . Wan XF, Yang Y, Wang D, Xu H, Huang C, Zhou ZK, Xu J. Comparison of Outcomes After Total Knee Arthroplasty Involving Postoperative Neutral or Residual Mild Varus Alignment: A Systematic Review and Meta-analysis. Orthop Surg. 2022;14:177–89. https://doi.org/10.1111/os.13155 . MacDessi SJ, Griffiths-Jones W, Chen DB, Griffiths-Jones S, Wood JA, Diwan AD, Harris IA. Restoring the constitutional alignment with a restrictive kinematic protocol improves quantitative soft-tissue balance in total knee arthroplasty: a randomized controlled trial. Bone Joint J. 2020;102–B:117–24. https://doi.org/10.1302/0301-620X.102B1.BJJ-2019-0674.R2 . Maderbacher G, Keshmiri A, Krieg B, Greimel F, Grifka J, Baier C. Kinematic component alignment in total knee arthroplasty leads to better restoration of natural tibiofemoral kinematics compared to mechanic alignment. Knee Surg Sports Traumatol Arthrosc. 2019;27:1427–33. https://doi.org/10.1007/s00167-018-5105-1 . Franceschetti E, Campi S, Giurazza G, Tanzilli A, Gregori P, Laudisio A, Hirschmann MT, Samuelsson K, Papalia R. (2024) Mechanically aligned total knee arthroplasty does not yield uniform outcomes across all coronal plane alignment of the knee (CPAK) phenotypes. Knee Surg Sports Traumatol Arthrosc. https://doi.org/10.1002/ksa.12349 Daffara V, Zambianchi F, Bazzan G, Matveitchouk N, Berni A, Piacentini L, Cuoghi Costantini R, Catani F. No difference in clinical outcomes between functionally aligned cruciate-retaining and posterior-stabilized robotic-assisted total knee arthroplasty. Int Orthop. 2023;47:711–7. https://doi.org/10.1007/s00264-023-05693-1 . Richards JA, Williams MD, Gupta NA, Kitchen JM, Whitaker JE, Smith LS, Malkani AL. No difference in PROMs between robotic-assisted CR versus PS total knee arthroplasty: a preliminary study. J Robot Surg. 2022;16:1209–17. https://doi.org/10.1007/s11701-021-01352-y . Thuysbaert G, Luyckx T, Ryckaert A, Gunst P, Noyez J, Winnock De Grave P. Reduced joint awareness after total knee arthroplasty with a cruciate retaining design. Acta Orthop Belg. 2020;86:482–8. Li N, Tan Y, Deng Y, Chen L. Posterior cruciate-retaining versus posterior stabilized total knee arthroplasty: a meta-analysis of randomized controlled trials. Knee Surg Sports Traumatol Arthrosc. 2014;22:556–64. https://doi.org/10.1007/s00167-012-2275-0 . Wodowski AJ, Swigler CW, Liu H, Nord KM, Toy PC, Mihalko WM. Proprioception and Knee Arthroplasty: A Literature Review. Orthop Clin North Am. 2016;47:301–9. https://doi.org/10.1016/j.ocl.2015.09.005 . Rissolio L, Sabatini L, Risitano S, Bistolfi A, Galluzzo U, Masse A, Indelli PF. Is It the Surgeon, the Patient, or the Device? A Comprehensive Clinical and Radiological Evaluation of Factors Influencing Patient Satisfaction in 648 Total Knee Arthroplasties. J Clin Med. 2021;10. https://doi.org/10.3390/jcm10122599 . Parsley BS, Bertolusso R, Harrington M, Brekke A, Noble PC. Influence of gender on age of treatment with TKA and functional outcome. Clin Orthop Relat Res. 2010;468:1759–64. https://doi.org/10.1007/s11999-010-1348-y . Clement ND, Burnett R. Patient satisfaction after total knee arthroplasty is affected by their general physical well-being. Knee Surg Sports Traumatol Arthrosc. 2013;21:2638–46. https://doi.org/10.1007/s00167-013-2523-y . D'Apuzzo MR, Novicoff WM, Browne JA. The John Insall Award: Morbid obesity independently impacts complications, mortality, and resource use after TKA. Clin Orthop Relat Res. 2015;473:57–63. https://doi.org/10.1007/s11999-014-3668-9 . Mahomed N, Gandhi R, Daltroy L, Katz JN. The self-administered patient satisfaction scale for primary hip and knee arthroplasty. Arthritis. 2011;2011:591253. https://doi.org/10.1155/2011/591253 . Si HB, Zeng Y, Shen B, Yang J, Zhou ZK, Kang PD, Pei FX. The influence of body mass index on the outcomes of primary total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc. 2015;23:1824–32. https://doi.org/10.1007/s00167-014-3301-1 . Ravi B, Jenkinson R, O'Heireamhoin S, Austin PC, Aktar S, Leroux TS, Paterson M, Redelmeier DA. Surgical duration is associated with an increased risk of periprosthetic infection following total knee arthroplasty: A population-based retrospective cohort study. EClinicalMedicine. 2019;16:74–80. https://doi.org/10.1016/j.eclinm.2019.09.015 . Duchman KR, Pugely AJ, Martin CT, Gao Y, Bedard NA, Callaghan JJ. Operative Time Affects Short-Term Complications in Total Joint Arthroplasty. J Arthroplasty. 2017;32:1285–91. https://doi.org/10.1016/j.arth.2016.12.003 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 May, 2025 Read the published version in BMC Musculoskeletal Disorders → Version 1 posted Editorial decision: Revision requested 08 May, 2025 Reviews received at journal 02 May, 2025 Reviews received at journal 21 Apr, 2025 Reviews received at journal 20 Apr, 2025 Reviewers agreed at journal 20 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviews received at journal 11 Apr, 2025 Reviews received at journal 31 Mar, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers agreed at journal 23 Mar, 2025 Reviewers agreed at journal 21 Mar, 2025 Reviewers agreed at journal 21 Mar, 2025 Reviewers invited by journal 21 Mar, 2025 Editor assigned by journal 21 Mar, 2025 Editor invited by journal 21 Mar, 2025 Submission checks completed at journal 21 Mar, 2025 First submitted to journal 21 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6163272","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432776848,"identity":"086b9b92-5298-4497-82c4-a893f38562b2","order_by":0,"name":"Chengshuai Zhang","email":"","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chengshuai","middleName":"","lastName":"Zhang","suffix":""},{"id":432776851,"identity":"62fd0085-9dd3-453e-956f-a8d26857e15a","order_by":1,"name":"Zhaolun Wang","email":"","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhaolun","middleName":"","lastName":"Wang","suffix":""},{"id":432776852,"identity":"b9409a6e-f733-439f-b327-e2f20e89cac3","order_by":2,"name":"Jianzeng Zhang","email":"","orcid":"","institution":"First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jianzeng","middleName":"","lastName":"Zhang","suffix":""},{"id":432776853,"identity":"fc1937ea-22b7-4267-8ece-2f5cd62d6a19","order_by":3,"name":"Qi Wang","email":"","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Wang","suffix":""},{"id":432776854,"identity":"e13c9143-27d6-4e4c-a197-ca177d1b5ee3","order_by":4,"name":"Dejin Yang","email":"","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dejin","middleName":"","lastName":"Yang","suffix":""},{"id":432776855,"identity":"6ffaa0fe-80b2-4594-b47e-cbdd5c19f6b7","order_by":5,"name":"Yixin Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYHACNiCWAJLMBxgYG4jSwQzTwpZAkhYQ4DEgTgu/9PljDz62WST2Sfd8k/i5w0aOgf3w0Q34tEj2JbMbzmyTMGaTObtNsvdMmjEDT1raDXxaDM4ws0nztknIsUnkbpPgbTuc2CDBY0aUFh42iZxnkn9J0QK0JQfEIEKLZA+zmeSMc0C/SKQZW8u2pRmzEfILPw/jM4kPZXWJ82ckP7z5ts1Gjp/98DG8WsCAERI1LBIgkg2vUjj4AyaZPxCnehSMglEwCkYaAADkbj6BSQ1YOgAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yixin","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-03-05 13:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6163272/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6163272/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12891-025-08789-4","type":"published","date":"2025-05-31T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79174388,"identity":"d52aaa28-2085-45b7-849d-a2f71ee6c677","added_by":"auto","created_at":"2025-03-25 09:51:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55125,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of patient exclusion.\u003c/p\u003e","description":"","filename":"floatimage122.png","url":"https://assets-eu.researchsquare.com/files/rs-6163272/v1/6c747cc76c12f15a0656e044.png"},{"id":79176326,"identity":"0cf2e41a-1323-49cc-b9c7-47721dfc5eb9","added_by":"auto","created_at":"2025-03-25 09:59:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10214,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the probability of achieving a forgotten joint 1-year after RA-TKA. Find the corresponding score upwards based on the individual variable value of the patient. Then add up the scores of all variables to obtain the total score. Finally, draw a vertical line downwards based on the position of the total score to find the corresponding prediction probability.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6163272/v1/12582f43bec452e2d3a84888.png"},{"id":79174390,"identity":"7a91d87a-f34f-4c33-ab34-228f48b09e28","added_by":"auto","created_at":"2025-03-25 09:51:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":162700,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic curve (ROC) of the model and internal validation. (A) represents the ROC of the predictive model. (B) represents the ROC of internal validation using 500 bootstrap resampling, and the gray area represents the 95% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage313.png","url":"https://assets-eu.researchsquare.com/files/rs-6163272/v1/3ce8a013f297d9f02b6b1f0b.png"},{"id":79176327,"identity":"01a90cf7-2dc9-40b5-af20-72e223f4fb3c","added_by":"auto","created_at":"2025-03-25 09:59:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8264,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the predictive model using 500 bootstrap resampling. The diagonal line represents the ideal state that the predicted probability is consistent with the actual probability. The closer fit to the diagonal line represents a better prediction performance.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6163272/v1/19d858eca115732cfcd81f58.png"},{"id":79174392,"identity":"88f970e1-7323-46bb-b2a3-9015b9c3d686","added_by":"auto","created_at":"2025-03-25 09:51:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6606,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the nomogram. The dashed line represents the predictive model. The gray line represents predicting all patients as forgotten joints, and the black horizontal line represents predicting all patients as non-forgotten joints. The decision curve indicates that the threshold probability of a forgotten joint is between 20% and 75%, applying this model can achieve net benefits.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6163272/v1/a4accbd29abcfe7fd7121de0.png"},{"id":83783028,"identity":"d39495c7-f25d-4d44-964e-8fb5ddcf3541","added_by":"auto","created_at":"2025-06-02 16:10:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1241365,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6163272/v1/080da63d-8d43-4f7a-b0c9-8575b26afc57.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a nomogram for predicting a forgotten joint in patients one year after robotic-assisted total knee arthroplasty","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTotal knee arthroplasty (TKA) is an effective treatment to alleviate pain and improve function for patients with end-stage knee osteoarthritis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite significant advances in prosthesis design, implantation method and perioperative management, approximately 20% of patients remain complaining about dissatisfied postoperative outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Robotic-assisted TKA (RA-TKA) has gained increasing attention as it enhances precision and reduces errors in alignment and soft-tissue balance [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nevertheless, the superiority of RA-TKA over traditional TKA remains debated, with mixed evidence in both short-term and long-term outcomes [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Forgotten Joint Score (FJS-12), developed by Behrend et al, has been used to assess patients\u0026rsquo; ability to forget their artificial joints during activities of daily life [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The FJS-12 is valued for its high discrimination and low ceiling effect. Achieving a forgotten joint is considered as the ultimate goal after TKA [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While some studies have explored variables associated with FJS-12, such as age, sex, BMI, Kellgren\u0026ndash;Lawrence (K\u0026ndash;L) grades and mental health, findings have been inconsistent [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recent research suggested that alterations of preoperative and postoperative coronal plane alignment of knee (CPAK) classification also significantly impacted the FJS-12 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNomograms are commonly used in prognostic and diagnostic research to visualize statistical models. By incorporating multiple predictors into an easy-to-read graph, they allow surgeons and patients to intuitively understand how factors influence outcomes and calculate individualized probabilities [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While several prediction models have been established for TKA, focusing on outcomes like postoperative pain and dissatisfaction and length of stay, nomograms specific to RA-TKA are limited, mainly targeting short-term functional outcomes [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To our knowledge, there are no published nomograms available for the prediction of a forgotten joint after TKA or RA-TKA.\u003c/p\u003e \u003cp\u003eBased on previously reported predictors, this study retrospectively collected patient demographic data, preoperative and postoperative imaging and surgical details to determine factors associated with achieving a forgotten joint after RA-TKA. Our objective was to establish and internally validate an individualized prediction nomogram for predicting the probability of a forgotten joint 1 year after RA-TKA. Ultimately, we hoped to increase the probability of patients achieving the ultimate goal of a forgotten joint by optimizing modifiable factors.\u003c/p\u003e"},{"header":"Materials and patients","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e With the approval of the institutional review board of our hospital, we retrospectively reviewed 231 primary RA-TKA procedures performed between May 2021 and October 2023. The inclusion criteria were: (1) diagnosis of knee osteoarthritis; (2) finished a minimal 1-year follow-up; (3) complete preoperative and postoperative imaging data. The exclusion criteria were: (1) inflammatory arthritis; (2) complications related to RA-TKA or underwent revision surgery within 1 year; (3) other diseases that affected knee function or symptoms, such as neurological or musculoskeletal disease; (4) incomplete clinical data. Finally, a total of 199 knees were included in the study after excluding 32 knees due to lost follow-up or other exclusion criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSurgical protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eSurgical protocol\u003c/div\u003e \u003cp\u003eA preoperative computed tomography (CT) scan of the affected limb was conducted to generate a personalized surgical plan using the Mako System (Stryker Corp, Mahwah, NJ, USA). All surgical procedures were performed by a professional team under a combined spinal\u0026ndash;epidural anesthesia. Pneumatic tourniquets were routinely used. A medial parapatellar approach was employed in cases, with minimal soft-tissue envelope stripping. Tracker arrays were affixed to the femur and tibia for further registration. Accessible osteophytes were thoroughly removed, and soft-tissue tension was evaluated using varus and valgus stress tests in extension, as well as by inserting a spoon at 90\u0026deg; of flexion. Real-time data from the robotic system guided surgeons in fine-tuning implant positioning, achieving balanced flexion and extension gaps with a tolerance of 1-2mm lateral laxity. Lower limb alignment was generally maintained within a 3\u0026deg; deviation from neutral mechanical alignment. Surgeons performed bone cuttings as planned with the assistance of a semi-active robotic arm providing haptic feedback. Only when a balance cannot be achieved by adjusting bone cuttings within the acceptable alignment range, sequential soft-tissue releases will be performed. Soft-tissue balance was reassessed after trial component placement. Cemented cruciate-retaining (CR) or posterior-stabilized (PS) implants (Triathlon Tritanium, Stryker, Mahwah, NJ) were selected depending on the quality of posterior-cruciate ligament. No patients underwent patella replacement. All patients followed a standardized rehabilitation plan after surgery.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003ePatients\u0026rsquo; baseline demographic data and potential factors were collected, including age, sex, body mass index (BMI), American Society of Anesthesiologists (ASA) classification, comorbidities, history of ipsilateral joint surgery and operative time. Radiological assessment of knee osteoarthritis severity was performed using Kellgren\u0026ndash;Lawrence (K\u0026ndash;L) grades. Additionally, preoperative and postoperative weight-bearing full-length radiographs of the lower extremity were analyzed to measure coronal alignment. Hip-knee-ankle angle (HKA), lateral distal femoral angle (LDFA) and medial proximal tibial angle (MPTA) were measured on preoperative and postoperative images using Mimics 19.0 software (Materialise, Leuven, Belgium). According to the CPAK classification, the arithmetic hip-knee-ankle angle (aHKA\u0026thinsp;=\u0026thinsp;MPTA \u0026ndash; LDFA) and joint line obliquity (JLO\u0026thinsp;=\u0026thinsp;MPTA\u0026thinsp;+\u0026thinsp;LDFA) were calculated [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, we calculated the absolute changes in preoperative and postoperative HKA, LDFA, MPTA, aHKA, and JLO.\u003c/p\u003e\n\u003ch3\u003eOutcome measure\u003c/h3\u003e\n\u003cp\u003eThe FJS-12 was used as an outcome measure to assess patients' ability to forget their artificial joints. It consisted of 12 questions scored on a 5-point Likert scale, with total scores ranging from 0 to 100 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Patients were required to complete the FJS-12 questionnaire at 1-year follow-up. According to the study of Singh et al, we chose 77.1 points as the threshold for achieving a forgotten joint [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and categorical variables were expressed as frequencies and percentages (%). Univariate and multivariate logistic regression analyses were performed to identify the independent predictors of a forgotten joint, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. To prevent the loss of important predictors, variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate analysis were included in the subsequent multivariate logistic regression. A backward stepwise method was used in to develop a predictive model. The discriminatory ability of the model was determined using the concordance index (C-index) and receiver operating characteristic curve (ROC) analysis. To better evaluate the predictive performance, internal validation was performed using 500 bootstrap resampling. Calibration curves that assessed the agreement between actual probability and predicted probability of achieving a forgotten joint, were also conducted using 500 bootstrap resampling. A Hosmer-Lemeshow test was employed, with p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating a high goodness of fit. Finally, a decision curve analysis (DCA) was performed to evaluate the clinical usefulness of the nomogram. Statistical analysis was conducted using R 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 199 patients were included in this study, with a mean age of 67.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.36 years. According to the FJS-12 at 1-year follow-up, 44.22% (88/199) of knees achieved a forgotten joint (FJS-12\u0026thinsp;\u0026gt;\u0026thinsp;77.1). Baseline demographic data, radiographic data and operative time of the patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the 25 variables collected, eight variables were selected based on a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 from univariate logistic regression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), including age, sex, ASA, preoperative aHKA, prosthesis type, operative time, HKA changes (ΔHKA) and aHKA changes (ΔaHKA). These variables were subsequently incorporated into the multivariate logistic regression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of all patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;199)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-forgotten joint group (n\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForgotten joint group (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.86\u0026thinsp;\u0026plusmn;\u0026thinsp;6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.62\u0026thinsp;\u0026plusmn;\u0026thinsp;6.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153(76.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95(84.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58(66.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103(51.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(52.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(50.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96(48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(47.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(49.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(37.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135(67.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81(72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(62.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91(45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42(48.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108(54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(51.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162(81.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89(79.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73(83.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(16.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of ipsilateral joint surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187(94.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106(94.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81(93.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(6.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u0026ndash;L grades\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118(59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(55.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(63.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81(40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(44.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(36.4\u0026amp;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative HKA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172.73\u0026thinsp;\u0026plusmn;\u0026thinsp;6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172.15\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173.47\u0026thinsp;\u0026plusmn;\u0026thinsp;5.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative LDFA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative MPTA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative aHKA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.42\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative JLO(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprosthesis type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(13.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158(79.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(74.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75(86.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoperative time(min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.61\u0026thinsp;\u0026plusmn;\u0026thinsp;21.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.87\u0026thinsp;\u0026plusmn;\u0026thinsp;22.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.13\u0026thinsp;\u0026plusmn;\u0026thinsp;18.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostoperative HKA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178.81\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostoperative LDFA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostoperative MPTA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostoperative aHKA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostoperative JLO(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔHKA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔLDFA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔMPTA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔaHKA(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔJLO(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFJS-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.12\u0026thinsp;\u0026plusmn;\u0026thinsp;22.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.30\u0026thinsp;\u0026plusmn;\u0026thinsp;19.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe data were showed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or as frequencies and percentages (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI, body mass index; ASA, American Society of Anesthesiologists; K-L, Kellgren\u0026ndash;Lawrence; HKA, hip-knee-ankle angle; HKA, hip-knee-ankle angle; LDFA, lateral distal femoral angle; MPTA, medial proximal tibial angle; aHKA, arithmetic hip-knee-ankle angle; JLO, joint line obliquity; FJS-12, Forgotten Joint Score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable logistic regression analyses of predictive factors for achieving a forgotten joint.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable logistic regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariable logistic regression (Backward Stepwise)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64(0.42\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6(0.38\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02(0.94\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.79(1.41\u0026ndash;5.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.73(1.30\u0026ndash;5.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09(0.62\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59(0.32\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.47\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74(0.36\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehistory of ipsilateral joint surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31(0.41\u0026ndash;4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u0026ndash;L grades\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72(0.41\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative HKA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04(0.99\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative LDFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93(0.85\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative MPTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07(0.98\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative aHKA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05(0.99\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreoperative JLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01(0.94\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprosthesis type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.18(1.01\u0026ndash;4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.52(1.10\u0026ndash;5.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoperative time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98(0.96\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.96\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostoperative HKA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02(0.93\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostoperative LDFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95(0.81\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostoperative MPTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06(0.90\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostoperative aHKA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06(0.94\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostoperative JLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00(0.90\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔHKA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89(0.82\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔLDFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04(0.89\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔMPTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92(0.83\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔaHKA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86(0.77\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88(0.79\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔJLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00(0.92\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*The age was divided into three age groups (\u0026le;\u0026thinsp;60, 60\u0026ndash;70, \u0026ge;\u0026thinsp;70). OR: Odds ratio; CI: Confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI, body mass index; ASA, American Society of Anesthesiologists; K-L, Kellgren\u0026ndash;Lawrence; HKA, hip-knee-ankle angle; HKA, hip-knee-ankle angle; LDFA, lateral distal femoral angle; MPTA, medial proximal tibial angle; aHKA, arithmetic hip-knee-ankle angle; JLO, joint line obliquity; FJS-12, Forgotten Joint Score;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDevelopment of an individualized prediction model\u003c/h3\u003e\n\u003cp\u003eMultivariable logistic regression was performed based on previously determined variables. The results showed that age, sex, prosthesis type, operative time and ΔaHKA were independent predictors of achieving a forgotten joint after RA-TKA (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Thus, these independent predictors were then used to develop a predictive model and displayed as a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The corresponding score can be determined based on the variable values. This total score can be calculated and then be used to estimate the probability of achieving a forgotten joint.\u003c/p\u003e \n\u003ch3\u003ePerformance and validation of the model\u003c/h3\u003e\n\u003cp\u003eThe C-index for this nomogram was 0.726 and was internally validated to be 0.725 (95% CI: 0.660\u0026ndash;0.788) by 500 bootstrap resampling, indicating good discriminatory ability of the prediction model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The model also showed good calibration, as evidenced by calibration curves generated from 500 bootstrap resampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, the Hosmer\u0026ndash;Lemeshow test (p\u0026thinsp;=\u0026thinsp;0.886) confirmed a goodness-of-fit between the predicted probability and observed probability.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical usefulness of the model\u003c/h2\u003e \u003cp\u003eThe DCA was conducted to evaluate the clinical usefulness of this prediction model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The decision curve showed that if the threshold probability ranged from 20\u0026ndash;75%, applying this nomogram to predict a forgotten joint could acquire greater net benefits compared to both strategies of predicting that all patients would achieve a forgotten joint or that none would.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, an individualized nomogram for predicting a forgotten joint 1 year after RA-TKA was developed and internally validated. We identified five variables including age, sex, prosthesis type, operative time and ΔaHKA, that had a significant importance in predicting the joint awareness. This model demonstrated good discriminative ability and calibration, which have been validated through C-index and calibration curves. Additionally, the wide threshold probability range displayed by the DCA curve confirmed its clinical usefulness.\u003c/p\u003e \u003cp\u003eOnly few prediction models have been established for patients undergoing RA-TKA. Motesharei et al designed a prediction model to estimate the operative time for RA-TKA based on demographic and anatomic data [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Duan et al developed a nomogram to predict the early functional outcome in patients after RA-TKA, which demonstrated excellent prediction performance and clinical application potential [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Another nomogram model based on computed tomography radiomics was also developed to predict the satisfaction of patients 3 months after RA-TKA [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To our knowledge, this is the first nomogram that has been developed for predicting a forgotten joint after RA-TKA. The forgotten joint, as the ultimate goal of TKA, has a low ceiling effect, allowing it to discriminate patients with good to excellent joint function [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In our study, the average 1-year FJS-12 was 69.21\u0026thinsp;\u0026plusmn;\u0026thinsp;22.11, with 44.22% (88/199) of knees achieving a forgotten joint. Our result was comparable to that of another multicenter study on RA-TKA, which reported an average FJS-12 of 70.2\u0026thinsp;\u0026plusmn;\u0026thinsp;27.8 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Although there is still controversy over the differences in postoperative outcomes between RA-TKA and conventional TKA, RA-TKA offers the advantage of reducing errors associated with manual procedures [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, surgeons can better customize an individualized alignment based on the patient's specific anatomy and native soft-tissue envelope [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur research found that the changes in aHKA had a negative impact on achieving a forgotten joint after RA-TKA. Before the concept of constitutional alignment emerged, some studies have explored the relationship between mechanical HKA and FJS-12. It has been found that patients with more than \u0026plusmn;\u0026thinsp;1 changes in functional HKA and femoral phenotype had significantly lower FJS-12 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A systematic review and meta-analysis reported that residual mild varus alignment after TKA can lead to a higher FJS-12 than neutral alignment in patients with preoperative varus knees [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These views potentially suggested that pursuing a one-size-fits-all neutral alignment may not be the optimal approach. Instead, respecting and restoring a patient\u0026rsquo;s specific native alignment could lead to better outcomes, reinforcing the importance of personalized surgical strategies.\u003c/p\u003e \u003cp\u003eTo enhance patient satisfaction, the philosophy of restoring a patient\u0026rsquo;s prearthritic alignment has gained popularity, as it was believed to promote a more natural knee kinematic model and improve soft-tissue balance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In 2021, MacDessi et al proposed the coronal plane alignment of the knee (CPAK) classification, which suggested that aHKA can be used to predict the constitutional alignment in cases of unicompartmental joint space narrowing without bone loss [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Franceschetti et al observed that patients with preoperative varus aHKA had the worst FJS-12 at 1-year follow-up compared to other aHKA classifications [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A recent study revealed that alternation in varus/valgus alignment before and after TKA was a negative predictive factor for FJS-12 at a mean 55-month follow-up. But they did not find any correlation between postoperative apex proximal and the FJS-12 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our findings were similar to theirs, showing that greater changes in aHKA significantly reduced the probability of achieving a forgotten joint, while JLO changes were not relevant. Altering a patient\u0026rsquo;s constitutional alignment to a certain extent could disrupt their original kinematics and lose respect for their native soft-tissue envelope. Moreover, these changes in constitutional alignment often required increased manipulation and trauma to the soft-tissue, raising the risk of soft-tissue imbalance. This may be the potential cause of postoperative pain and discomfort, thereby reducing the probability of achieving a forgotten joint.\u003c/p\u003e \u003cp\u003eIn our model, the prosthesis type was another variable for predicting a forgotten joint. Although both PS and CR implants have been widely used for years, their impact on outcomes remains inconclusive. Some studies reported findings contrary to ours. For example, Daffara et al found that there was no significant difference in FJS-12 between CR and PS implants in RA-TKA patients at a minimum 2-year follow-up [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, no differences in FJS-12 were observed between CR and PS groups in a RA-TKA cohort [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Consistent with our results, Thuysbaert et al reported significantly higher FJS-12 for patients with CR implants compared to PS implants at 2-year follow-up [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This could be attributed to the preservation of posterior-cruciate ligament in CR designs, which enhanced proprioceptive function and maintained normal femoral rollback, resulting in a more physiological knee kinematic model [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVariables such as age, sex, and BMI have been widely debated as predictive factors for outcomes in TKA. Nielsen et al found that FJS-12 increased with age for women, whereas for men, scores peaked at age 67 and declined in both younger and older groups [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, Rissolio et al also reported poorer FJS-12 in women, regardless of age [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A cluster analysis demonstrated that men around the age of 63 with lower BMI were most likely to achieve better FJS-12 at 1-year follow-up [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similar to their results, our study identified older age and woman as negative predictors for achieving a forgotten joint. To enhance clinical relevance, we categorized age into three groups (\u0026le;\u0026thinsp;60, 60\u0026ndash;70, \u0026ge;\u0026thinsp;70). However, no correlation between BMI and joint awareness was observed in our cohort. This could be attributed to differences in patient characteristics, women often underwent TKA at more advanced stages of arthritis compared to men and tended to experience more residual pain and functional limitations postoperatively [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, advanced age can negatively affect overall physical health, which may indirectly reduce surgical satisfaction [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Previous studies linking BMI to poorer outcomes often focused on patients with high BMI (more than 30\u0026ndash;40 kg/m\u0026sup2;), which was rare in our cohort, where the mean BMI was 27.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45 kg/m\u0026sup2; [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This may explain why BMI did not emerge as a significant factor in predicting joint awareness in our analysis.\u003c/p\u003e \u003cp\u003eOperative time was a novel variable to predict joint awareness in our study which has not been reported before. Most previous researches have focused on the correlation between operative time and short-term periprosthetic joint infection [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In our cohort, patients who were readmitted were due to flexion restriction or soft-tissue imbalance rather than infection. Our team possessed extensive expertise in performing RA-TKA, having transcended the learning curve. We believed that prolonged operative time was often due to severe joint deformities that required multiple adjustments to achieve target alignment and soft-tissue balance. This interpretation has also been confirmed by Motesharei et al, revealing that the presence of osteophytes required additional operative time. Furthermore, the removal of posterior osteophytes also impacted the gap balance, potentially resulting in extra bone cuttings or soft-tissue releases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These could also increase the risk of soft-tissue imbalance and postoperative discomfort, which may ultimately reduce the likelihood of achieving a forgotten joint.\u003c/p\u003e \u003cp\u003eThis predictive model has clinical guidance significance, as some of the predictive factors are modifiable. This allows for the identification of patients with a lower probability of achieving a forgotten joint, enabling targeted clinical interventions to improve outcomes. For example, implementing a robotic surgical plan aimed at restoring constitutional alignment and prioritizing the use of CR implants, may help optimize alignment while also effectively reducing operative time. Additionally, this model can enhance patient selection by providing evidence-based preoperative consultations and setting realistic postoperative expectations, ultimately contributing to improved surgical satisfaction.\u003c/p\u003e \u003cp\u003eHowever, this study still has several limitations. First, our research was based on a retrospective study with a limited sample size, which was inevitable to selection bias. Thus, a multicenter and prospective study is needed to expand the sample size in the future. Second, the predictive model which was validated through 500 bootstrap resampling may lead to overfitting. Third, other variables with potential relevance to the outcome need to be further included in future research. Last, multicenter external validation is still necessary to further determine the efficacy and reliability of this predictive model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we developed a nomogram for predicting a forgotten joint of patients 1 year after RA-TKA. This model has been verified to have good discrimination and calibration, with potential clinical application value. By using this model, clinicians can improve patient selection, guide the development of preoperative robotic plans, and optimize intraoperative decisions, ultimately leading to better clinical outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFJS-12\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForgotten Joint Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRA-TKA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erobotic-assisted total knee arthroplasty\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaHKA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earithmetic hip-knee-ankle angle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTKA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal knee arthroplasty\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eK-L\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKellgren\u0026ndash;Lawrence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPAK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoronal plane alignment of knee\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecruciate-retaining\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eposterior-stabilized\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Society of Anesthesiologists\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHKA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehip-knee-ankle angle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elateral distal femoral angle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMPTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emedial proximal tibial angle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eJLO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ejoint line obliquity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC-index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econcordance index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHKA changes\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eΔHKA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaHKA changes\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eΔaHKA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the staff in the Department of Orthopedics, Beijing Jishuitan Hospital, Capital\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMedical University, Beijing, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part\u0026nbsp;by National Key R\u0026amp;D Program of China (Grant No. 2023YFC2411000 to Y. Z.), Beijing Hospitals Authority Clinical Medicine Development of special funding support (ZLRK202505 to Y. Z.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the first author (Chengshuai Zhang, [email protected]) on reasonable request and with permission of the hospital\u0026rsquo;s ethics institutional review board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Beijing Jishuitan Hospital (approval No. K2022-092-01). All methods were performed in line with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eScott CEH, Turnbull GS, MacDonald D, Breusch SJ. Activity levels and return to work following total knee arthroplasty in patients under 65 years of age. 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Orthop Clin North Am. 2016;47:301\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ocl.2015.09.005\u003c/span\u003e\u003cspan address=\"10.1016/j.ocl.2015.09.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRissolio L, Sabatini L, Risitano S, Bistolfi A, Galluzzo U, Masse A, Indelli PF. Is It the Surgeon, the Patient, or the Device? A Comprehensive Clinical and Radiological Evaluation of Factors Influencing Patient Satisfaction in 648 Total Knee Arthroplasties. J Clin Med. 2021;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jcm10122599\u003c/span\u003e\u003cspan address=\"10.3390/jcm10122599\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParsley BS, Bertolusso R, Harrington M, Brekke A, Noble PC. Influence of gender on age of treatment with TKA and functional outcome. Clin Orthop Relat Res. 2010;468:1759\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11999-010-1348-y\u003c/span\u003e\u003cspan address=\"10.1007/s11999-010-1348-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClement ND, Burnett R. Patient satisfaction after total knee arthroplasty is affected by their general physical well-being. Knee Surg Sports Traumatol Arthrosc. 2013;21:2638\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00167-013-2523-y\u003c/span\u003e\u003cspan address=\"10.1007/s00167-013-2523-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Apuzzo MR, Novicoff WM, Browne JA. The John Insall Award: Morbid obesity independently impacts complications, mortality, and resource use after TKA. Clin Orthop Relat Res. 2015;473:57\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11999-014-3668-9\u003c/span\u003e\u003cspan address=\"10.1007/s11999-014-3668-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahomed N, Gandhi R, Daltroy L, Katz JN. 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EClinicalMedicine. 2019;16:74\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eclinm.2019.09.015\u003c/span\u003e\u003cspan address=\"10.1016/j.eclinm.2019.09.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuchman KR, Pugely AJ, Martin CT, Gao Y, Bedard NA, Callaghan JJ. Operative Time Affects Short-Term Complications in Total Joint Arthroplasty. J Arthroplasty. 2017;32:1285\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arth.2016.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.arth.2016.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Robotic-assisted total knee arthroplasty, Forgotten Joint Score, Clinical prediction model, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-6163272/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6163272/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe Forgotten Joint Score (FJS-12) was designed to assess the extent to which patients were unaware of their artificial joints during daily activities, representing an ideal outcome of TKA. This study aimed to identify the individual predictors and develop a nomogram to predict a forgotten joint in patients 1 year after robotic-assisted total knee arthroplasty (RA-TKA).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study involved 199 patients with knee osteoarthritis who underwent RA-TKA. All participants completed the FJS-12 questionnaire at 1-year follow-up, with scores above 77.1 considered indicative of a forgotten joint. The demographic data, surgical data, preoperative and postoperative imaging data were collected for analysis. Univariate and multivariate logistic regression analyses were conducted to determine predictors and establish a predictive model. The receiver operating characteristic curve, calibration curve analysis and decision curve analysis (DCA) were used to evaluate the discriminatory ability, calibration and clinical usefulness of the model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 44.22% (88/199) of knees achieved a forgotten joint 1 year after RA-TKA. Five variables were identified as independent predictors, including age, sex, prothesis type, operative time and changes in the arithmetic hip-knee-ankle angle (aHKA). The area under the curve (AUC) of the nomogram was 0.726 and 0.725 (95% CI 0.660\u0026ndash;0.788) using 500 bootstrap resampling. The Hosmer\u0026ndash;Lemeshow test showed that the model was of goodness-of-fit (p\u0026thinsp;=\u0026thinsp;0.886). And the DCA showed net benefits when the threshold probability was between 20%-75%.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA nomogram was developed for predicting a forgotten joint 1 year after RA-TKA. This model showed good discrimination and calibration, which could assist surgeons in optimizing patient selection, preoperative planning and intraoperative decisions, ultimately improving outcomes of RA-TKA.\u003c/p\u003e","manuscriptTitle":"Development and validation of a nomogram for predicting a forgotten joint in patients one year after robotic-assisted total knee arthroplasty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 09:51:50","doi":"10.21203/rs.3.rs-6163272/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-08T16:17:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-02T14:35:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T14:50:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-20T10:57:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304025639508949532169069655827998892450","date":"2025-04-20T06:48:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49387038764537934611908529100436250364","date":"2025-04-16T12:30:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T16:55:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-31T11:22:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239297635295791500666067537429225617604","date":"2025-03-29T17:32:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201524962081396349485896322216954423967","date":"2025-03-26T21:24:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46170296071992615957785893044937357054","date":"2025-03-23T07:57:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54969791751566728942293962420205067916","date":"2025-03-21T16:21:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63267055864976832401648342323829525545","date":"2025-03-21T13:00:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-21T10:25:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-21T10:19:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-21T10:06:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-21T09:30:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2025-03-21T09:29:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b1109767-8751-4bc8-a80b-c29ea4e7c113","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T16:04:57+00:00","versionOfRecord":{"articleIdentity":"rs-6163272","link":"https://doi.org/10.1186/s12891-025-08789-4","journal":{"identity":"bmc-musculoskeletal-disorders","isVorOnly":false,"title":"BMC Musculoskeletal Disorders"},"publishedOn":"2025-05-31 15:57:24","publishedOnDateReadable":"May 31st, 2025"},"versionCreatedAt":"2025-03-25 09:51:50","video":"","vorDoi":"10.1186/s12891-025-08789-4","vorDoiUrl":"https://doi.org/10.1186/s12891-025-08789-4","workflowStages":[]},"version":"v1","identity":"rs-6163272","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6163272","identity":"rs-6163272","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}