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Accurate prediction of spontaneous lumbar curve correction following posterior selective thoracic fusion in adolescent idiopathic scoliosis using logistic regression models and clinical rationale
Introduction Accurate prediction of spontaneous lumbar curve correction (SLCC) after selective thoracic fusion (STF) remains difficult. This study sought to improve prediction accuracy of SLCC. The hypothesis was preoperative and intraoperative variables could predict SLCC 20° at ≥ 2-year follow-up...
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| Published in: | European spine journal 2019-09, Vol.28 (9), p.1987-1997 |
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| Language: | English |
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| container_end_page | 1997 |
| container_issue | 9 |
| container_start_page | 1987 |
| container_title | European spine journal |
| container_volume | 28 |
| creator | Koller, H. Hitzl, W. Marks, M. C. Newton, P. O. |
| description | Introduction
Accurate prediction of spontaneous lumbar curve correction (SLCC) after selective thoracic fusion (STF) remains difficult. This study sought to improve prediction accuracy of SLCC. The hypothesis was preoperative and intraoperative variables could predict SLCC 20° at ≥ 2-year follow-up. Single and dual thresholds models in perspective of clinical rationales were applied to find models with the highest positive/negative predictive values (PPV/NPV). The secondary outcome measure was SRS scores at ≥ 2-year follow-up.
Results
410 patients were included. At ≥ 2-year follow-up 282 patients had LC ≤ 20°. These patients had better SRS-22 scores than those with LC > 20° (
P
= 0.02). The postoperative LC and LC ≤ 20° were predicted by preoperative LC and LC-bending Cobb angle (
P
|
| doi_str_mv | 10.1007/s00586-019-06000-6 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2246903827</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2246903827</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-babbd383ecfed5b1eec089ccf5eb55ebefa3f8c1d312815e8c413338df5b92263</originalsourceid><addsrcrecordid>eNp9kc2KFTEQhYMozvXqC7iQgBs3rUnnpn-Ww-DPwIAbXTfppHInQ7qrTaWVeT5fzNzbo4ILFyGQ89WpSh3GXkrxVgrRviMhdNdUQvaVaIQQVfOI7eRB1ZXoVf2Y7UR_KI-t7C_YM6I7IaTuRfOUXShZq6bR3Y79vLR2TSYDXxK4YHPAmaPntOCczQy4Eo_rNJrEC_cduMWUYMM8xog_wnzkC1KGFDBxgnhSC5hvMRkbLPcrnegwc-MwAlmYMw8u4GLybdHJYgxIgXgBi1nEY6BchATHBHQuntBBJG5mx20Mc7Am8jJ1kUyE5-yJN5HgxcO9Z18_vP9y9am6-fzx-uryprKq1bkazTg61SmwHpweJYAVXW-t1zDqcsAb5TsrXdlOJzV09iCVUp3zeuzrulF79mbzXRJ-W4HyMIXymxi3PQ11fWh6obq6Lejrf9A7XFMZ9kzpvlWyeO9ZvVE2IVECPywpTCbdD1IMp4iHLeKhRDycIx5OU7x6sF7HCdyfkt-ZFkBtABVpPkL62_s_tr8AfOa5ZA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2245973113</pqid></control><display><type>article</type><title>Accurate prediction of spontaneous lumbar curve correction following posterior selective thoracic fusion in adolescent idiopathic scoliosis using logistic regression models and clinical rationale</title><source>Springer Nature</source><creator>Koller, H. ; Hitzl, W. ; Marks, M. C. ; Newton, P. O.</creator><creatorcontrib>Koller, H. ; Hitzl, W. ; Marks, M. C. ; Newton, P. O.</creatorcontrib><description>Introduction
Accurate prediction of spontaneous lumbar curve correction (SLCC) after selective thoracic fusion (STF) remains difficult. This study sought to improve prediction accuracy of SLCC. The hypothesis was preoperative and intraoperative variables could predict SLCC < 20°.
Methods
A multicenter observational prospective analysis was conducted to determine predictors of SLCC in AIS patients that had posterior STF. Curve types included major thoracic curves (Lenke 1, 3–4).The primary outcome variable was to establish prediction models, and a postoperative lumbar curve (LC) ≤ 20° was defined as the target variable. Multivariate logistic regression models were established to study the relationship between selected variables and a LC ≤ 20° versus a LC > 20° at ≥ 2-year follow-up. Single and dual thresholds models in perspective of clinical rationales were applied to find models with the highest positive/negative predictive values (PPV/NPV). The secondary outcome measure was SRS scores at ≥ 2-year follow-up.
Results
410 patients were included. At ≥ 2-year follow-up 282 patients had LC ≤ 20°. These patients had better SRS-22 scores than those with LC > 20° (
P
= 0.02). The postoperative LC and LC ≤ 20° were predicted by preoperative LC and LC-bending Cobb angle (
P
< 0.01,
r
= 0.4–0.6). Logistic regression models could be established to identify patients at risk for failing the target LC ≤ 20°.For preoperative LC and LC-bending, the prediction model achieved a NPV/PPV of 80%/72%. If the postoperative main thoracic curve is combined with the preoperative LC and a gray area for difficult decisions was allowed, model accuracy could even be improved (NPV/PPV = 96%/81%).
Conclusion
An accurate prediction model for postoperative SLCC was established based on a large analysis of prospective STF cases. These models can support prediction and understanding of postoperative SLCC aiding in surgical decision making when contemplating a selective thoracic fusion.
Graphical abstract
These slides can be retrieved under Electronic Supplementary Material.</description><identifier>ISSN: 0940-6719</identifier><identifier>EISSN: 1432-0932</identifier><identifier>DOI: 10.1007/s00586-019-06000-6</identifier><identifier>PMID: 31236658</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Decision making ; Medicine ; Medicine & Public Health ; Neurosurgery ; Original Article ; Prediction models ; Regression analysis ; Scoliosis ; Surgical Orthopedics ; Thorax</subject><ispartof>European spine journal, 2019-09, Vol.28 (9), p.1987-1997</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>European Spine Journal is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-babbd383ecfed5b1eec089ccf5eb55ebefa3f8c1d312815e8c413338df5b92263</citedby><cites>FETCH-LOGICAL-c375t-babbd383ecfed5b1eec089ccf5eb55ebefa3f8c1d312815e8c413338df5b92263</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31236658$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Koller, H.</creatorcontrib><creatorcontrib>Hitzl, W.</creatorcontrib><creatorcontrib>Marks, M. C.</creatorcontrib><creatorcontrib>Newton, P. O.</creatorcontrib><title>Accurate prediction of spontaneous lumbar curve correction following posterior selective thoracic fusion in adolescent idiopathic scoliosis using logistic regression models and clinical rationale</title><title>European spine journal</title><addtitle>Eur Spine J</addtitle><addtitle>Eur Spine J</addtitle><description>Introduction
Accurate prediction of spontaneous lumbar curve correction (SLCC) after selective thoracic fusion (STF) remains difficult. This study sought to improve prediction accuracy of SLCC. The hypothesis was preoperative and intraoperative variables could predict SLCC < 20°.
Methods
A multicenter observational prospective analysis was conducted to determine predictors of SLCC in AIS patients that had posterior STF. Curve types included major thoracic curves (Lenke 1, 3–4).The primary outcome variable was to establish prediction models, and a postoperative lumbar curve (LC) ≤ 20° was defined as the target variable. Multivariate logistic regression models were established to study the relationship between selected variables and a LC ≤ 20° versus a LC > 20° at ≥ 2-year follow-up. Single and dual thresholds models in perspective of clinical rationales were applied to find models with the highest positive/negative predictive values (PPV/NPV). The secondary outcome measure was SRS scores at ≥ 2-year follow-up.
Results
410 patients were included. At ≥ 2-year follow-up 282 patients had LC ≤ 20°. These patients had better SRS-22 scores than those with LC > 20° (
P
= 0.02). The postoperative LC and LC ≤ 20° were predicted by preoperative LC and LC-bending Cobb angle (
P
< 0.01,
r
= 0.4–0.6). Logistic regression models could be established to identify patients at risk for failing the target LC ≤ 20°.For preoperative LC and LC-bending, the prediction model achieved a NPV/PPV of 80%/72%. If the postoperative main thoracic curve is combined with the preoperative LC and a gray area for difficult decisions was allowed, model accuracy could even be improved (NPV/PPV = 96%/81%).
Conclusion
An accurate prediction model for postoperative SLCC was established based on a large analysis of prospective STF cases. These models can support prediction and understanding of postoperative SLCC aiding in surgical decision making when contemplating a selective thoracic fusion.
Graphical abstract
These slides can be retrieved under Electronic Supplementary Material.</description><subject>Decision making</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neurosurgery</subject><subject>Original Article</subject><subject>Prediction models</subject><subject>Regression analysis</subject><subject>Scoliosis</subject><subject>Surgical Orthopedics</subject><subject>Thorax</subject><issn>0940-6719</issn><issn>1432-0932</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kc2KFTEQhYMozvXqC7iQgBs3rUnnpn-Ww-DPwIAbXTfppHInQ7qrTaWVeT5fzNzbo4ILFyGQ89WpSh3GXkrxVgrRviMhdNdUQvaVaIQQVfOI7eRB1ZXoVf2Y7UR_KI-t7C_YM6I7IaTuRfOUXShZq6bR3Y79vLR2TSYDXxK4YHPAmaPntOCczQy4Eo_rNJrEC_cduMWUYMM8xog_wnzkC1KGFDBxgnhSC5hvMRkbLPcrnegwc-MwAlmYMw8u4GLybdHJYgxIgXgBi1nEY6BchATHBHQuntBBJG5mx20Mc7Am8jJ1kUyE5-yJN5HgxcO9Z18_vP9y9am6-fzx-uryprKq1bkazTg61SmwHpweJYAVXW-t1zDqcsAb5TsrXdlOJzV09iCVUp3zeuzrulF79mbzXRJ-W4HyMIXymxi3PQ11fWh6obq6Lejrf9A7XFMZ9kzpvlWyeO9ZvVE2IVECPywpTCbdD1IMp4iHLeKhRDycIx5OU7x6sF7HCdyfkt-ZFkBtABVpPkL62_s_tr8AfOa5ZA</recordid><startdate>20190901</startdate><enddate>20190901</enddate><creator>Koller, H.</creator><creator>Hitzl, W.</creator><creator>Marks, M. C.</creator><creator>Newton, P. O.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope></search><sort><creationdate>20190901</creationdate><title>Accurate prediction of spontaneous lumbar curve correction following posterior selective thoracic fusion in adolescent idiopathic scoliosis using logistic regression models and clinical rationale</title><author>Koller, H. ; Hitzl, W. ; Marks, M. C. ; Newton, P. O.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-babbd383ecfed5b1eec089ccf5eb55ebefa3f8c1d312815e8c413338df5b92263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Decision making</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neurosurgery</topic><topic>Original Article</topic><topic>Prediction models</topic><topic>Regression analysis</topic><topic>Scoliosis</topic><topic>Surgical Orthopedics</topic><topic>Thorax</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Koller, H.</creatorcontrib><creatorcontrib>Hitzl, W.</creatorcontrib><creatorcontrib>Marks, M. C.</creatorcontrib><creatorcontrib>Newton, P. O.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><jtitle>European spine journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Koller, H.</au><au>Hitzl, W.</au><au>Marks, M. C.</au><au>Newton, P. O.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accurate prediction of spontaneous lumbar curve correction following posterior selective thoracic fusion in adolescent idiopathic scoliosis using logistic regression models and clinical rationale</atitle><jtitle>European spine journal</jtitle><stitle>Eur Spine J</stitle><addtitle>Eur Spine J</addtitle><date>2019-09-01</date><risdate>2019</risdate><volume>28</volume><issue>9</issue><spage>1987</spage><epage>1997</epage><pages>1987-1997</pages><issn>0940-6719</issn><eissn>1432-0932</eissn><abstract>Introduction
Accurate prediction of spontaneous lumbar curve correction (SLCC) after selective thoracic fusion (STF) remains difficult. This study sought to improve prediction accuracy of SLCC. The hypothesis was preoperative and intraoperative variables could predict SLCC < 20°.
Methods
A multicenter observational prospective analysis was conducted to determine predictors of SLCC in AIS patients that had posterior STF. Curve types included major thoracic curves (Lenke 1, 3–4).The primary outcome variable was to establish prediction models, and a postoperative lumbar curve (LC) ≤ 20° was defined as the target variable. Multivariate logistic regression models were established to study the relationship between selected variables and a LC ≤ 20° versus a LC > 20° at ≥ 2-year follow-up. Single and dual thresholds models in perspective of clinical rationales were applied to find models with the highest positive/negative predictive values (PPV/NPV). The secondary outcome measure was SRS scores at ≥ 2-year follow-up.
Results
410 patients were included. At ≥ 2-year follow-up 282 patients had LC ≤ 20°. These patients had better SRS-22 scores than those with LC > 20° (
P
= 0.02). The postoperative LC and LC ≤ 20° were predicted by preoperative LC and LC-bending Cobb angle (
P
< 0.01,
r
= 0.4–0.6). Logistic regression models could be established to identify patients at risk for failing the target LC ≤ 20°.For preoperative LC and LC-bending, the prediction model achieved a NPV/PPV of 80%/72%. If the postoperative main thoracic curve is combined with the preoperative LC and a gray area for difficult decisions was allowed, model accuracy could even be improved (NPV/PPV = 96%/81%).
Conclusion
An accurate prediction model for postoperative SLCC was established based on a large analysis of prospective STF cases. These models can support prediction and understanding of postoperative SLCC aiding in surgical decision making when contemplating a selective thoracic fusion.
Graphical abstract
These slides can be retrieved under Electronic Supplementary Material.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>31236658</pmid><doi>10.1007/s00586-019-06000-6</doi><tpages>11</tpages></addata></record> |
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| source | Springer Nature |
| subjects | Decision making Medicine Medicine & Public Health Neurosurgery Original Article Prediction models Regression analysis Scoliosis Surgical Orthopedics Thorax |
| title | Accurate prediction of spontaneous lumbar curve correction following posterior selective thoracic fusion in adolescent idiopathic scoliosis using logistic regression models and clinical rationale |
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