Can the Charlson Comorbidity Index be used to predict the ASA grade in patients undergoing spine surgery?
Background The American Society of Anaesthesiologists' Physical Status Score (ASA) is a key variable in predictor models of surgical outcome and "appropriate use criteria". However, at the time when such tools are being used in decision-making, the ASA rating is typically unknown. We...
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| Veröffentlicht in: | European spine journal 2020-12, Vol.29 (12), p.2941-2952 |
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| creator | Mannion, A. F. Bianchi, G. Mariaux, F. Fekete, T. F. Reitmeir, R. Moser, B. Whitmore, R. G. Ratliff, J. Haschtmann, D. |
| description | Background
The American Society of Anaesthesiologists' Physical Status Score (ASA) is a key variable in predictor models of surgical outcome and "appropriate use criteria". However, at the time when such tools are being used in decision-making, the ASA rating is typically unknown. We evaluated whether the ASA class could be predicted statistically from Charlson Comorbidy Index (CCI) scores and simple demographic variables.
Methods
Using established algorithms, the CCI was calculated from the ICD-10 comorbidity codes of 11′523 spine surgery patients (62.3 ± 14.6y) who also had anaesthetist-assigned ASA scores. These were randomly split into training (
N
= 8078) and test (
N
= 3445) samples. A logistic regression model was built based on the training sample and used to predict ASA scores for the test sample and for temporal (
N
= 341) and external validation (
N
= 171) samples.
Results
In a simple model with just CCI predicting ASA, receiver operating characteristics (ROC) analysis revealed a cut-off of CCI ≥ 1 discriminated best between being ASA ≥ 3 versus |
| doi_str_mv | 10.1007/s00586-020-06595-1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2444377357</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2473362788</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-befd641f1cf4f61828020fb29dbe018baaff9eaf1ebcb577023cdfa472e986e93</originalsourceid><addsrcrecordid>eNp9kTtvFDEUhS0EIkvgD1AgSzQ0Q_wae1yh1YhHpEgpgNqyx9cbR7v2YM9I2X8fkw0gUaS6xf3OuY-D0FtKPlJC1EUlpB9kRxjpiOx139FnaEMFZx3RnD1HG6IF6aSi-gy9qvWWENprIl-iM8606LXkGxRHm_ByA3i8sWVfc8JjPuTioo_LEV8mD3fYAV4reLxkPBfwcVoeFNvvW7wr1gOOCc92iZCWitcmKbsc0w7XOSbAdS07KMdPr9GLYPcV3jzWc_Tzy-cf47fu6vrr5bi96iau-qVzELwUNNApiCDpwIZ2X3BMeweEDs7aEDTYQMFNrleKMD75YIVioAcJmp-jDyffueRfK9TFHGKdYL-3CfJaDRNCcKV4rxr6_j_0Nq8lte0apTiXTA1Do9iJmkqutUAwc4kHW46GEvM7CHMKwrRFzUMQhjbRu0fr1R3A_5X8-XwD-AmorZXah_7NfsL2HpHylAU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2473362788</pqid></control><display><type>article</type><title>Can the Charlson Comorbidity Index be used to predict the ASA grade in patients undergoing spine surgery?</title><source>SpringerNature Journals</source><creator>Mannion, A. F. ; Bianchi, G. ; Mariaux, F. ; Fekete, T. F. ; Reitmeir, R. ; Moser, B. ; Whitmore, R. G. ; Ratliff, J. ; Haschtmann, D.</creator><creatorcontrib>Mannion, A. F. ; Bianchi, G. ; Mariaux, F. ; Fekete, T. F. ; Reitmeir, R. ; Moser, B. ; Whitmore, R. G. ; Ratliff, J. ; Haschtmann, D.</creatorcontrib><description>Background
The American Society of Anaesthesiologists' Physical Status Score (ASA) is a key variable in predictor models of surgical outcome and "appropriate use criteria". However, at the time when such tools are being used in decision-making, the ASA rating is typically unknown. We evaluated whether the ASA class could be predicted statistically from Charlson Comorbidy Index (CCI) scores and simple demographic variables.
Methods
Using established algorithms, the CCI was calculated from the ICD-10 comorbidity codes of 11′523 spine surgery patients (62.3 ± 14.6y) who also had anaesthetist-assigned ASA scores. These were randomly split into training (
N
= 8078) and test (
N
= 3445) samples. A logistic regression model was built based on the training sample and used to predict ASA scores for the test sample and for temporal (
N
= 341) and external validation (
N
= 171) samples.
Results
In a simple model with just CCI predicting ASA, receiver operating characteristics (ROC) analysis revealed a cut-off of CCI ≥ 1 discriminated best between being ASA ≥ 3 versus < 3 (area under the curve (AUC), 0.70 ± 0.01, 95%CI,0.82–0.84). Multiple logistic regression analyses including age, sex, smoking, and BMI in addition to CCI gave better predictions of ASA (Nagelkerke’s pseudo-R
2
for predicting ASA class 1 to 4, 46.6%; for predicting ASA ≥ 3 vs. < 3, 37.5%). AUCs for discriminating ASA ≥ 3 versus < 3 from multiple logistic regression were 0.83 ± 0.01 (95%CI, 0.82–0.84) for the training sample and 0.82 ± 0.01 (95%CI, 0.81–0.84), 0.85 ± 0.02 (95%CI, 0.80–0.89), and 0.77 ± 0.04 (95%CI,0.69–0.84) for the test, temporal and external validation samples, respectively. Calibration was adequate in all validation samples.
Conclusions
It was possible to predict ASA from CCI. In a simple model, CCI ≥ 1 best distinguished between ASA ≥ 3 and < 3. For a more precise prediction, regression algorithms were created based on CCI and simple demographic variables obtainable from patient interview. The availability of such algorithms may widen the utility of decision aids that rely on the ASA, where the latter is not readily available.</description><identifier>ISSN: 0940-6719</identifier><identifier>EISSN: 1432-0932</identifier><identifier>DOI: 10.1007/s00586-020-06595-1</identifier><identifier>PMID: 32945963</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Back surgery ; Bone surgery ; Comorbidity ; Decision making ; Medicine ; Medicine & Public Health ; Neurosurgery ; Original Article ; Surgical Orthopedics</subject><ispartof>European spine journal, 2020-12, Vol.29 (12), p.2941-2952</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-befd641f1cf4f61828020fb29dbe018baaff9eaf1ebcb577023cdfa472e986e93</citedby><cites>FETCH-LOGICAL-c375t-befd641f1cf4f61828020fb29dbe018baaff9eaf1ebcb577023cdfa472e986e93</cites><orcidid>0000-0002-1203-1096</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00586-020-06595-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00586-020-06595-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32945963$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mannion, A. F.</creatorcontrib><creatorcontrib>Bianchi, G.</creatorcontrib><creatorcontrib>Mariaux, F.</creatorcontrib><creatorcontrib>Fekete, T. F.</creatorcontrib><creatorcontrib>Reitmeir, R.</creatorcontrib><creatorcontrib>Moser, B.</creatorcontrib><creatorcontrib>Whitmore, R. G.</creatorcontrib><creatorcontrib>Ratliff, J.</creatorcontrib><creatorcontrib>Haschtmann, D.</creatorcontrib><title>Can the Charlson Comorbidity Index be used to predict the ASA grade in patients undergoing spine surgery?</title><title>European spine journal</title><addtitle>Eur Spine J</addtitle><addtitle>Eur Spine J</addtitle><description>Background
The American Society of Anaesthesiologists' Physical Status Score (ASA) is a key variable in predictor models of surgical outcome and "appropriate use criteria". However, at the time when such tools are being used in decision-making, the ASA rating is typically unknown. We evaluated whether the ASA class could be predicted statistically from Charlson Comorbidy Index (CCI) scores and simple demographic variables.
Methods
Using established algorithms, the CCI was calculated from the ICD-10 comorbidity codes of 11′523 spine surgery patients (62.3 ± 14.6y) who also had anaesthetist-assigned ASA scores. These were randomly split into training (
N
= 8078) and test (
N
= 3445) samples. A logistic regression model was built based on the training sample and used to predict ASA scores for the test sample and for temporal (
N
= 341) and external validation (
N
= 171) samples.
Results
In a simple model with just CCI predicting ASA, receiver operating characteristics (ROC) analysis revealed a cut-off of CCI ≥ 1 discriminated best between being ASA ≥ 3 versus < 3 (area under the curve (AUC), 0.70 ± 0.01, 95%CI,0.82–0.84). Multiple logistic regression analyses including age, sex, smoking, and BMI in addition to CCI gave better predictions of ASA (Nagelkerke’s pseudo-R
2
for predicting ASA class 1 to 4, 46.6%; for predicting ASA ≥ 3 vs. < 3, 37.5%). AUCs for discriminating ASA ≥ 3 versus < 3 from multiple logistic regression were 0.83 ± 0.01 (95%CI, 0.82–0.84) for the training sample and 0.82 ± 0.01 (95%CI, 0.81–0.84), 0.85 ± 0.02 (95%CI, 0.80–0.89), and 0.77 ± 0.04 (95%CI,0.69–0.84) for the test, temporal and external validation samples, respectively. Calibration was adequate in all validation samples.
Conclusions
It was possible to predict ASA from CCI. In a simple model, CCI ≥ 1 best distinguished between ASA ≥ 3 and < 3. For a more precise prediction, regression algorithms were created based on CCI and simple demographic variables obtainable from patient interview. The availability of such algorithms may widen the utility of decision aids that rely on the ASA, where the latter is not readily available.</description><subject>Algorithms</subject><subject>Back surgery</subject><subject>Bone surgery</subject><subject>Comorbidity</subject><subject>Decision making</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neurosurgery</subject><subject>Original Article</subject><subject>Surgical Orthopedics</subject><issn>0940-6719</issn><issn>1432-0932</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp9kTtvFDEUhS0EIkvgD1AgSzQ0Q_wae1yh1YhHpEgpgNqyx9cbR7v2YM9I2X8fkw0gUaS6xf3OuY-D0FtKPlJC1EUlpB9kRxjpiOx139FnaEMFZx3RnD1HG6IF6aSi-gy9qvWWENprIl-iM8606LXkGxRHm_ByA3i8sWVfc8JjPuTioo_LEV8mD3fYAV4reLxkPBfwcVoeFNvvW7wr1gOOCc92iZCWitcmKbsc0w7XOSbAdS07KMdPr9GLYPcV3jzWc_Tzy-cf47fu6vrr5bi96iau-qVzELwUNNApiCDpwIZ2X3BMeweEDs7aEDTYQMFNrleKMD75YIVioAcJmp-jDyffueRfK9TFHGKdYL-3CfJaDRNCcKV4rxr6_j_0Nq8lte0apTiXTA1Do9iJmkqutUAwc4kHW46GEvM7CHMKwrRFzUMQhjbRu0fr1R3A_5X8-XwD-AmorZXah_7NfsL2HpHylAU</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Mannion, A. F.</creator><creator>Bianchi, G.</creator><creator>Mariaux, F.</creator><creator>Fekete, T. F.</creator><creator>Reitmeir, R.</creator><creator>Moser, B.</creator><creator>Whitmore, R. G.</creator><creator>Ratliff, J.</creator><creator>Haschtmann, D.</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>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-1203-1096</orcidid></search><sort><creationdate>20201201</creationdate><title>Can the Charlson Comorbidity Index be used to predict the ASA grade in patients undergoing spine surgery?</title><author>Mannion, A. F. ; Bianchi, G. ; Mariaux, F. ; Fekete, T. F. ; Reitmeir, R. ; Moser, B. ; Whitmore, R. G. ; Ratliff, J. ; Haschtmann, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-befd641f1cf4f61828020fb29dbe018baaff9eaf1ebcb577023cdfa472e986e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Back surgery</topic><topic>Bone surgery</topic><topic>Comorbidity</topic><topic>Decision making</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neurosurgery</topic><topic>Original Article</topic><topic>Surgical Orthopedics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mannion, A. F.</creatorcontrib><creatorcontrib>Bianchi, G.</creatorcontrib><creatorcontrib>Mariaux, F.</creatorcontrib><creatorcontrib>Fekete, T. F.</creatorcontrib><creatorcontrib>Reitmeir, R.</creatorcontrib><creatorcontrib>Moser, B.</creatorcontrib><creatorcontrib>Whitmore, R. G.</creatorcontrib><creatorcontrib>Ratliff, J.</creatorcontrib><creatorcontrib>Haschtmann, D.</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 Edition)</collection><collection>ProQuest Central UK/Ireland</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>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>European spine journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mannion, A. F.</au><au>Bianchi, G.</au><au>Mariaux, F.</au><au>Fekete, T. F.</au><au>Reitmeir, R.</au><au>Moser, B.</au><au>Whitmore, R. G.</au><au>Ratliff, J.</au><au>Haschtmann, D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Can the Charlson Comorbidity Index be used to predict the ASA grade in patients undergoing spine surgery?</atitle><jtitle>European spine journal</jtitle><stitle>Eur Spine J</stitle><addtitle>Eur Spine J</addtitle><date>2020-12-01</date><risdate>2020</risdate><volume>29</volume><issue>12</issue><spage>2941</spage><epage>2952</epage><pages>2941-2952</pages><issn>0940-6719</issn><eissn>1432-0932</eissn><abstract>Background
The American Society of Anaesthesiologists' Physical Status Score (ASA) is a key variable in predictor models of surgical outcome and "appropriate use criteria". However, at the time when such tools are being used in decision-making, the ASA rating is typically unknown. We evaluated whether the ASA class could be predicted statistically from Charlson Comorbidy Index (CCI) scores and simple demographic variables.
Methods
Using established algorithms, the CCI was calculated from the ICD-10 comorbidity codes of 11′523 spine surgery patients (62.3 ± 14.6y) who also had anaesthetist-assigned ASA scores. These were randomly split into training (
N
= 8078) and test (
N
= 3445) samples. A logistic regression model was built based on the training sample and used to predict ASA scores for the test sample and for temporal (
N
= 341) and external validation (
N
= 171) samples.
Results
In a simple model with just CCI predicting ASA, receiver operating characteristics (ROC) analysis revealed a cut-off of CCI ≥ 1 discriminated best between being ASA ≥ 3 versus < 3 (area under the curve (AUC), 0.70 ± 0.01, 95%CI,0.82–0.84). Multiple logistic regression analyses including age, sex, smoking, and BMI in addition to CCI gave better predictions of ASA (Nagelkerke’s pseudo-R
2
for predicting ASA class 1 to 4, 46.6%; for predicting ASA ≥ 3 vs. < 3, 37.5%). AUCs for discriminating ASA ≥ 3 versus < 3 from multiple logistic regression were 0.83 ± 0.01 (95%CI, 0.82–0.84) for the training sample and 0.82 ± 0.01 (95%CI, 0.81–0.84), 0.85 ± 0.02 (95%CI, 0.80–0.89), and 0.77 ± 0.04 (95%CI,0.69–0.84) for the test, temporal and external validation samples, respectively. Calibration was adequate in all validation samples.
Conclusions
It was possible to predict ASA from CCI. In a simple model, CCI ≥ 1 best distinguished between ASA ≥ 3 and < 3. For a more precise prediction, regression algorithms were created based on CCI and simple demographic variables obtainable from patient interview. The availability of such algorithms may widen the utility of decision aids that rely on the ASA, where the latter is not readily available.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>32945963</pmid><doi>10.1007/s00586-020-06595-1</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-1203-1096</orcidid></addata></record> |
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| source | SpringerNature Journals |
| subjects | Algorithms Back surgery Bone surgery Comorbidity Decision making Medicine Medicine & Public Health Neurosurgery Original Article Surgical Orthopedics |
| title | Can the Charlson Comorbidity Index be used to predict the ASA grade in patients undergoing spine surgery? |
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