{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:06:36Z","timestamp":1784073996005,"version":"3.55.0"},"reference-count":23,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,10]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>Preoperative risk-stratification tools, including frailty, nutritional, and surgical risk metrics, are used to predict complications after spine surgery. The relative performance of these tools across complication types and surgical subgroups is not well characterized. This study aimed to compare the predictive performance of 5 risk metrics, American College of Surgeons Surgical Risk Calculator (ACS SRC), serum albumin, Risk Analysis Index (RAI), modified 5-item frailty index (mFI-5), and Geriatric Nutritional Risk Index (GNRI), for perioperative complications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>The authors analyzed 362,145 adult spine surgery patients from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) from 2017 to 2022. Adjusted odds ratios were estimated via multivariable logistic regression, controlling for age, sex, urgency, and procedure type (defined by Current Procedural Terminology [CPT] codes). Subgroup analyses were stratified by ICD-10 diagnosis category, including degenerative disease, tumor, trauma, infection, and spinal deformity. Discrimination for predicting complications was assessed using C-statistics and DeLong\u2019s test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Across all endpoints, ACS SRC had the best predictive accuracy: mortality C-statistic (95% CI) 0.908 (0.900\u20130.916), Clavien-Dindo grade IV (CD-IV) 0.823 (0.816\u20130.830), and major complications 0.749 (0.745\u20130.752). Serum albumin, despite being a single laboratory value, ranked second with mortality C-statistic (95% CI) 0.820 (0.807\u20130.833), CD-IV 0.734, and major complications 0.682 and showed strong discrimination for infectious complications (e.g., sepsis, septic shock, surgical site infection, and urinary tract infection), as well as for hospital length of stay and nonhome discharge. Compared to frailty-based metrics, albumin showed significantly better predictive value (p &lt; 0.001 for pairwise comparisons) and maintained its advantages across all subgroups, including high-risk groups such as infection, trauma, and tumor cases. RAI provided moderate mortality prediction (C-statistic 0.807) and was most effective for predicting cardiovascular events, while both GNRI (0.753) and mFI-5 (0.647) were less consistent and demonstrated weaker associations with adverse outcomes. Multivariable regression confirmed that lower preoperative albumin and higher ACS SRC predictions were robust, independent predictors of increased risk for major complications, CD-IV events, and mortality. These performance patterns remained stable across surgical indications and in subgroup analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>ACS SRC remains among the comprehensive tools for risk stratification in spine surgery. Serum albumin offers strong, consistent predictive value, especially for infectious, respiratory, and life-threatening complications, and may be a valuable alternative when calculator inputs are incomplete.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2026.2.spine251657","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:01:26Z","timestamp":1783468886000},"page":"1-15","source":"Crossref","is-referenced-by-count":0,"title":["Benchmarking risk prediction tools in spine surgery: a national registry analysis of albumin, frailty, and surgical calculators"],"prefix":"10.3171","author":[{"given":"Michael","family":"Farid","sequence":"first","affiliation":[{"name":"Weill Cornell Medical College, Cornell University, New York;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolai","family":"Blasdel","sequence":"additional","affiliation":[{"name":"New York University Grossman School of Medicine, New York, New York;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Wang","sequence":"additional","affiliation":[{"name":"New York University Grossman School of Medicine, New York, New York;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Blake","family":"Perdikis","sequence":"additional","affiliation":[{"name":"New York University Grossman School of Medicine, New York, New York;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"O\u2019Leary","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University of Texas Medical Branch, Galveston, Texas;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwadwo","family":"Darko","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Korle Bu Teaching Hospital, Accra, Ghana; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umaru","family":"Barrie","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, NYU Langone Health, New York, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hammad A.","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, NYU Langone Health, New York, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darryl","family":"Lau","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, NYU Langone Health, New York, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"key":"b1","series-title":"World Neurosurg","first-page":"1","article-title":"The evolution of risk assessment in spine surgery: a narrative review","volume":"188","author":"Ton A","year":"2024","unstructured":"Ton A, Wishart D, Ball JR,  The evolution of risk assessment in spine surgery: a narrative review. World Neurosurg. 2024;188:1\u201314. doi:10.1016\/j.wneu.2024.04.117"},{"issue":"2","key":"b2","series-title":"J Am Coll Surg","first-page":"173","article-title":"New 5-factor modified frailty index using American College of Surgeons NSQIP data","volume":"226","author":"Subramaniam S","year":"2018","unstructured":"Subramaniam S, Aalberg JJ, Soriano RP, Divino CM. New 5-factor modified frailty index using American College of Surgeons NSQIP data. J Am Coll Surg. 2018;226(2):173\u2013181 e8. doi:10.1016\/j.jamcollsurg.2017.11.005"},{"issue":"1","key":"b3","series-title":"J Neurosurg Spine","first-page":"136","article-title":"Comparison of the Risk Analysis Index and the modified 5-factor frailty index in predicting 30-day morbidity and mortality after spine surgery","volume":"39","author":"Bowers CA","year":"2023","unstructured":"Bowers CA, Varela S, Conlon M,  Comparison of the Risk Analysis Index and the modified 5-factor frailty index in predicting 30-day morbidity and mortality after spine surgery. J Neurosurg Spine. 2023;39(1):136\u2013145. doi:10.3171\/2023.2.SPINE221019"},{"issue":"1","key":"b4","series-title":"J Orthop Surg Res","first-page":"289","article-title":"Do nutritional assessment tools (PNI, CONUT, GNRI) predict adverse events after spinal surgeries? A systematic review and meta-analysis","volume":"19","author":"Huang Z","year":"2024","unstructured":"Huang Z, Wang H, Da Y, Liu S, Zheng W, Li F. 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