Preoperative factors predicting 6-month Lysholm score after 3D-printed osteotomy guide-assisted total knee arthroplasty: A retrospective cohort study

This study analyzes preoperative factors influencing the clinical outcome of 3D-printed osteotomy guide-assisted knee arthroplasty in patients with knee osteoarthritis (OA) through a retrospective cohort study. A retrospective analysis was carried out on 200 eligible knee OA patients who met inclusion/exclusion criteria (111 males, 89 females; median age 68.5 years, range 46-92) undergoing 3D-printed osteotomy guide-assisted knee arthroplasty at our institution between January 2021 and December 2023. Clinical outcomes were evaluated using the Lysholm knee score at 6-month postoperative follow-up, based on these assessments, patients were categorized into the good outcome group and the poor outcome group. All kinds of data of the research subjects were collected on preoperative day 1, and logistic regression analysis was conducted on the items with significant differences to explore the related factors influencing the therapeutic effect of 3D-printed osteotomy guide-assisted total knee arthroplasty in patients with knee OA. The predictive value of these factors on the clinical outcome was assessed by constructing receiver operating characteristic curves. Additionally, Pearson correlation analysis was performed to examine the relationships between these factors and the therapeutic efficacy of 3D-printed osteotomy guide-assisted knee arthroplasty in knee OA patients. Based on clinical evaluations, 158 of the 200 patients (79.00%) were categorized into the good outcome group, while 42 patients (21.00%) were categorized into the poor outcome group. Preoperative body mass index (BMI), Visual Analogue Scale (VAS) score, preoperative mean trajectory error (MTE), and preoperative lipopolysaccharides (LPS) were identified as risk factors (OR > 1, P < .05) for the efficacy of 3D-printed osteotomy guide-assisted knee arthroplasty in knee OA patients, while the preoperative Lysholm score was a protective factor (OR < 1, P < .05), and the receiver operating characteristic curve showed that the area under curve value of each factor for predicting the efficacy ranged from 0.608 to 0.971. Pearson correlation analysis revealed that the therapeutic efficacy of 3D-printed osteotomy guide-assisted knee arthroplasty in patients with knee OA showed positive correlations with BMI, preoperative VAS score, preoperative MTE, preoperative LPS levels, and negative correlation with preoperative Lysholm score. Preoperative BMI, Lysholm score, VAS score, MTE, and LPS are all preoperative factors for the efficacy of 3D-printed osteotomy guide-assisted knee arthroplasty in knee OA patients. In clinical practice, the prediction model based on the above factors can identify high-risk patients early, and timely intervention measures can be taken to improve the surgical effect of patients.

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Journal
Medicine
Published
2026-01-09
DOI
https://doi.org/10.1097/md.0000000000044751
Primary Topic
Total Knee Arthroplasty Outcomes
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article
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article

Preoperative factors predicting 6-month Lysholm score after 3D-printed osteotomy guide-assisted total knee arthroplasty: A retrospective cohort study

Qi Zhang, Xuan Sun, Jingbo Pan, Gang Kong
Medicine
Total Knee Arthroplasty Outcomes
article

Preoperative factors predicting 6-month Lysholm score after 3D-printed osteotomy guide-assisted total knee arthroplasty: A retrospective cohort study

Qi Zhang, Xuan Sun, Jingbo Pan, Gang Kong
article en

Abstract

This study analyzes preoperative factors influencing the clinical outcome of 3D-printed osteotomy guide-assisted knee arthroplasty in patients with knee osteoarthritis (OA) through a retrospective cohort study. A retrospective analysis was carried out on 200 eligible knee OA patients who met inclusion/exclusion criteria (111 males, 89 females; median age 68.5 years, range 46-92) undergoing 3D-printed osteotomy guide-assisted knee arthroplasty at our institution between January 2021 and December 2023. Clinical outcomes were evaluated using the Lysholm knee score at 6-month postoperative follow-up, based on these assessments, patients were categorized into the good outcome group and the poor outcome group. All kinds of data of the research subjects were collected on preoperative day 1, and logistic regression analysis was conducted on the items with significant differences to explore the related factors influencing the therapeutic effect of 3D-printed osteotomy guide-assisted total knee arthroplasty in patients with knee OA. The predictive value of these factors on the clinical outcome was assessed by constructing receiver operating characteristic curves. Additionally, Pearson correlation analysis was performed to examine the relationships between these factors and the therapeutic efficacy of 3D-printed osteotomy guide-assisted knee arthroplasty in knee OA patients. Based on clinical evaluations, 158 of the 200 patients (79.00%) were categorized into the good outcome group, while 42 patients (21.00%) were categorized into the poor outcome group. Preoperative body mass index (BMI), Visual Analogue Scale (VAS) score, preoperative mean trajectory error (MTE), and preoperative lipopolysaccharides (LPS) were identified as risk factors (OR > 1, P < .05) for the efficacy of 3D-printed osteotomy guide-assisted knee arthroplasty in knee OA patients, while the preoperative Lysholm score was a protective factor (OR < 1, P < .05), and the receiver operating characteristic curve showed that the area under curve value of each factor for predicting the efficacy ranged from 0.608 to 0.971. Pearson correlation analysis revealed that the therapeutic efficacy of 3D-printed osteotomy guide-assisted knee arthroplasty in patients with knee OA showed positive correlations with BMI, preoperative VAS score, preoperative MTE, preoperative LPS levels, and negative correlation with preoperative Lysholm score. Preoperative BMI, Lysholm score, VAS score, MTE, and LPS are all preoperative factors for the efficacy of 3D-printed osteotomy guide-assisted knee arthroplasty in knee OA patients. In clinical practice, the prediction model based on the above factors can identify high-risk patients early, and timely intervention measures can be taken to improve the surgical effect of patients.

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