How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment

Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Chen, Ziming [verfasserIn] Deng, Zhantao [verfasserIn] Li, Qingtian [verfasserIn] Chen, Junfeng [verfasserIn] Ma, Yuanchen [verfasserIn] Zheng, Qiujian [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Total-knee arthroplasty Predictors Scoring system Clinical outcome Alignment Grade approach

Übergeordnetes Werk:	Enthalten in: BMC musculoskeletal disorders - London : BioMed Central, 2000, 21(2020), 1 vom: 03. Aug.
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:1 ; day:03 ; month:08

Links:	Volltext

DOI / URN:	10.1186/s12891-020-03528-3

Katalog-ID:	SPR040547329

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520			\|a Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA.
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allfields	10.1186/s12891-020-03528-3 doi (DE-627)SPR040547329 (SPR)s12891-020-03528-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Chen, Ziming verfasserin aut How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA. Total-knee arthroplasty (dpeaa)DE-He213 Predictors (dpeaa)DE-He213 Scoring system (dpeaa)DE-He213 Clinical outcome (dpeaa)DE-He213 Alignment (dpeaa)DE-He213 Grade approach (dpeaa)DE-He213 Deng, Zhantao verfasserin aut Li, Qingtian verfasserin aut Chen, Junfeng verfasserin aut Ma, Yuanchen verfasserin aut Zheng, Qiujian verfasserin aut Enthalten in BMC musculoskeletal disorders London : BioMed Central, 2000 21(2020), 1 vom: 03. Aug. (DE-627)326643745 (DE-600)2041355-5 1471-2474 nnns volume:21 year:2020 number:1 day:03 month:08 https://dx.doi.org/10.1186/s12891-020-03528-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 21 2020 1 03 08
spelling	10.1186/s12891-020-03528-3 doi (DE-627)SPR040547329 (SPR)s12891-020-03528-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Chen, Ziming verfasserin aut How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA. Total-knee arthroplasty (dpeaa)DE-He213 Predictors (dpeaa)DE-He213 Scoring system (dpeaa)DE-He213 Clinical outcome (dpeaa)DE-He213 Alignment (dpeaa)DE-He213 Grade approach (dpeaa)DE-He213 Deng, Zhantao verfasserin aut Li, Qingtian verfasserin aut Chen, Junfeng verfasserin aut Ma, Yuanchen verfasserin aut Zheng, Qiujian verfasserin aut Enthalten in BMC musculoskeletal disorders London : BioMed Central, 2000 21(2020), 1 vom: 03. Aug. (DE-627)326643745 (DE-600)2041355-5 1471-2474 nnns volume:21 year:2020 number:1 day:03 month:08 https://dx.doi.org/10.1186/s12891-020-03528-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 21 2020 1 03 08
allfields_unstemmed	10.1186/s12891-020-03528-3 doi (DE-627)SPR040547329 (SPR)s12891-020-03528-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Chen, Ziming verfasserin aut How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA. Total-knee arthroplasty (dpeaa)DE-He213 Predictors (dpeaa)DE-He213 Scoring system (dpeaa)DE-He213 Clinical outcome (dpeaa)DE-He213 Alignment (dpeaa)DE-He213 Grade approach (dpeaa)DE-He213 Deng, Zhantao verfasserin aut Li, Qingtian verfasserin aut Chen, Junfeng verfasserin aut Ma, Yuanchen verfasserin aut Zheng, Qiujian verfasserin aut Enthalten in BMC musculoskeletal disorders London : BioMed Central, 2000 21(2020), 1 vom: 03. Aug. (DE-627)326643745 (DE-600)2041355-5 1471-2474 nnns volume:21 year:2020 number:1 day:03 month:08 https://dx.doi.org/10.1186/s12891-020-03528-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 21 2020 1 03 08
allfieldsGer	10.1186/s12891-020-03528-3 doi (DE-627)SPR040547329 (SPR)s12891-020-03528-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Chen, Ziming verfasserin aut How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA. Total-knee arthroplasty (dpeaa)DE-He213 Predictors (dpeaa)DE-He213 Scoring system (dpeaa)DE-He213 Clinical outcome (dpeaa)DE-He213 Alignment (dpeaa)DE-He213 Grade approach (dpeaa)DE-He213 Deng, Zhantao verfasserin aut Li, Qingtian verfasserin aut Chen, Junfeng verfasserin aut Ma, Yuanchen verfasserin aut Zheng, Qiujian verfasserin aut Enthalten in BMC musculoskeletal disorders London : BioMed Central, 2000 21(2020), 1 vom: 03. Aug. (DE-627)326643745 (DE-600)2041355-5 1471-2474 nnns volume:21 year:2020 number:1 day:03 month:08 https://dx.doi.org/10.1186/s12891-020-03528-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 21 2020 1 03 08
allfieldsSound	10.1186/s12891-020-03528-3 doi (DE-627)SPR040547329 (SPR)s12891-020-03528-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Chen, Ziming verfasserin aut How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA. Total-knee arthroplasty (dpeaa)DE-He213 Predictors (dpeaa)DE-He213 Scoring system (dpeaa)DE-He213 Clinical outcome (dpeaa)DE-He213 Alignment (dpeaa)DE-He213 Grade approach (dpeaa)DE-He213 Deng, Zhantao verfasserin aut Li, Qingtian verfasserin aut Chen, Junfeng verfasserin aut Ma, Yuanchen verfasserin aut Zheng, Qiujian verfasserin aut Enthalten in BMC musculoskeletal disorders London : BioMed Central, 2000 21(2020), 1 vom: 03. Aug. (DE-627)326643745 (DE-600)2041355-5 1471-2474 nnns volume:21 year:2020 number:1 day:03 month:08 https://dx.doi.org/10.1186/s12891-020-03528-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 21 2020 1 03 08
language	English
source	Enthalten in BMC musculoskeletal disorders 21(2020), 1 vom: 03. Aug. volume:21 year:2020 number:1 day:03 month:08
sourceStr	Enthalten in BMC musculoskeletal disorders 21(2020), 1 vom: 03. Aug. volume:21 year:2020 number:1 day:03 month:08
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Total-knee arthroplasty Predictors Scoring system Clinical outcome Alignment Grade approach
dewey-raw	610
isfreeaccess_bool	true
container_title	BMC musculoskeletal disorders
authorswithroles_txt_mv	Chen, Ziming @@aut@@ Deng, Zhantao @@aut@@ Li, Qingtian @@aut@@ Chen, Junfeng @@aut@@ Ma, Yuanchen @@aut@@ Zheng, Qiujian @@aut@@
publishDateDaySort_date	2020-08-03T00:00:00Z
hierarchy_top_id	326643745
dewey-sort	3610
id	SPR040547329
language_de	englisch
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In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. 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author	Chen, Ziming
spellingShingle	Chen, Ziming ddc 610 bkl 44.00 misc Total-knee arthroplasty misc Predictors misc Scoring system misc Clinical outcome misc Alignment misc Grade approach How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment
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topic_title	610 ASE 44.00 bkl How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment Total-knee arthroplasty (dpeaa)DE-He213 Predictors (dpeaa)DE-He213 Scoring system (dpeaa)DE-He213 Clinical outcome (dpeaa)DE-He213 Alignment (dpeaa)DE-He213 Grade approach (dpeaa)DE-He213
topic	ddc 610 bkl 44.00 misc Total-knee arthroplasty misc Predictors misc Scoring system misc Clinical outcome misc Alignment misc Grade approach
topic_unstemmed	ddc 610 bkl 44.00 misc Total-knee arthroplasty misc Predictors misc Scoring system misc Clinical outcome misc Alignment misc Grade approach
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title	How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment
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title_full	How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment
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author_browse	Chen, Ziming Deng, Zhantao Li, Qingtian Chen, Junfeng Ma, Yuanchen Zheng, Qiujian
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title_sort	how to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment
title_auth	How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment
abstract	Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA.
abstractGer	Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA.
abstract_unstemmed	Background A method that can accurately predict the outcome of surgery can give patients timely feedback. In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. Conclusions This study first developed a validated, evidence-based scoring system based on lower-extremity angles of alignment to predict early clinical outcomes and to objectively evaluate the quality of TKA.
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title_short	How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment
url	https://dx.doi.org/10.1186/s12891-020-03528-3
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author2	Deng, Zhantao Li, Qingtian Chen, Junfeng Ma, Yuanchen Zheng, Qiujian
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In addition, to some extent, an objective evaluation method can help the surgeon quickly summarize the patient’s surgical experience and lessen dependence on the long wait for follow-up results. However, there was still no precise tool to predict clinical outcomes of total knee arthroplasty (TKA). This study aimed to develop a scoring system to predict clinical results of TKA and then grade the quality of TKA. Methods We retrospectively reviewed 98 primary TKAs performed between April 2013 and March 2017 to determine predictors of clinical outcomes among lower-extremity angles of alignment. Applying multivariable linear-regression analysis, we built Models (i) and (ii) to predict detailed clinical outcomes which were evaluated using the Knee Society Score (KSS). Multivariable logistic-regression analysis was used to establish Model (iii) to predict probability of getting a good clinical outcome (PGGCO) which was evaluated by Knee Injury and Osteoarthritis Outcome Score (KOOS) score. Finally, we designed a new scoring system consisting of 3 prediction models and presented a method of grading TKA quality. Thirty primary TKAs between April and December 2017 were enrolled for external validation. Results We set up a scoring system consisting of 3 models. The interpretations of Model (i) and (ii) were good ($ R^{2} $ = 0.756 and 0.764, respectively). Model (iii) displayed good discrimination, with an area under the curve (AUC) of 0.936, and good calibration according to the calibration curve. Quality of surgery was stratified as follows: “A” = PGGCO ≥0.8, “B” = PGGCO ≤0.6 but < 0.8, and “C” = PGGCO < 0.6. The scoring system performed well in external validation. 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How to predict early clinical outcomes and evaluate the quality of primary total knee arthroplasty: a new scoring system based on lower-extremity angles of alignment

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