{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T20:11:00Z","timestamp":1793823060544,"version":"4.6.1"},"reference-count":46,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>The American Spinal Injury Association Impairment Scale (AIS) assigned at patient admission is an important predictor of outcomes following spinal cord injury (SCI). However, nearly 80% of records in the Spinal Cord Injury Model Systems (SCIMS) database\u2014a multicenter prospective database of patients with SCI\u2014lack admission AIS grades. Accurate imputation of this missing data could enable more robust analyses and insights into SCI recovery. This study aims to develop and validate methods for imputing missing admission AIS data in the SCIMS database.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>The study included 16,062 patients with SCI from the publicly available SCIMS database (1988\u20132020). Five machine learning algorithms\u2014random forest (RF), linear discriminant analysis, K-nearest neighbors, naive Bayes, and support vector machine\u2014were compared using performance metrics (accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and multiclass area under the receiver operating characteristic curve) using five-fold cross-validation on a training subset of 6054 patients with complete AIS admission grades. The model with the highest performance was trained on all 16,062 patients. The imputed AIS grades were validated by predicting discharge functional independence measure (FIM) scores (range 13\u201391) with simple and multiple linear regression models on a 1:1 propensity score\u2013matched cohort (n = 5828). Model performance was compared using differences in root mean square error (\u2206RMSE) with bootstrapped 95% confidence intervals (CIs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>The full cohort contained a representative distribution of AIS grades (45% grade A, 13% grade B, 18% grade C, and 24% grade D), and the propensity score\u2013matched cohort characteristics were well balanced. The RF algorithm demonstrated the highest validation accuracy (81.7%). Predictive models showed no significant differences between models using true versus imputed AIS grades, with 95% CIs for \u2206RMSE of \u22120.60 to 0.47 for simple regression and \u22120.63 to 0.46 for multiple regression models. The coefficients of AIS grades also did not significantly differ between models with true versus imputed values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>A data-driven approach to imputation resulted in a robust method for imputing admission AIS grades that demonstrated clinical validity in the SCIMS database. This approach extends the utility of this longitudinal database and may provide a framework for other SCI databases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2025.1.spine241135","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T22:27:29Z","timestamp":1746829649000},"page":"90-97","source":"Crossref","is-referenced-by-count":7,"title":["Assessing and validating machine learning\u2013enhanced imputation of admission American Spinal Injury Association Impairment Scale grades for spinal cord injury"],"prefix":"10.3171","volume":"43","author":[{"given":"Ritvik R.","family":"Jillala","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos A.","family":"Aude","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vikas N.","family":"Vattipally","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathleen R.","family":"Ran","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelly","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carly","family":"Weber-Levine","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. Daniel","family":"Davidar","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew M.","family":"Hersh","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Lubelski","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Bydon","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Witham","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Theodore","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tej D.","family":"Azad","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"issue":"22","key":"b1","series-title":"JAMA","first-page":"2236","article-title":"Traumatic spinal cord injury in the United States, 1993-2012","volume":"313","author":"Jain NB","year":"2015","unstructured":"Jain NB, Ayers GD, Peterson EN,  Traumatic spinal cord injury in the United States, 1993-2012. 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J Neurosurg Spine. 2012;17(1 Suppl):11\u201326."},{"issue":"6","key":"b4","series-title":"Arch Phys Med Rehabil","first-page":"644","article-title":"Early predictors of functional independence 2 years after spinal cord injury","volume":"78","author":"Saboe LA","year":"1997","unstructured":"Saboe LA, Darrah JM, Pain KS, Guthrie J Early predictors of functional independence 2 years after spinal cord injury. Arch Phys Med Rehabil. 1997;78(6):644\u2013650."},{"key":"b5","series-title":"Handb Clin Neurol","first-page":"47","article-title":"Clinical diagnosis and prognosis following spinal cord injury","volume":"109","author":"Burns AS","year":"2012","unstructured":"Burns AS, Marino RJ, Flanders AE, Flett H Clinical diagnosis and prognosis following spinal cord injury. 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J Inf Sci Eng. 2021;37(5):1039."},{"issue":"4","key":"b16","series-title":"Eur J Hum Genet","first-page":"487","article-title":"Imputing missing genotypic data of single-nucleotide polymorphisms using neural networks","volume":"16","author":"Sun YV","year":"2008","unstructured":"Sun YV, Kardia SLR Imputing missing genotypic data of single-nucleotide polymorphisms using neural networks. Eur J Hum Genet. 2008;16(4):487\u2013495."},{"issue":"12","key":"b17","series-title":"Pattern Recognit","first-page":"3692","article-title":"Impact of imputation of missing values on classification error for discrete data","volume":"41","author":"Farhangfar A","year":"2008","unstructured":"Farhangfar A, Kurgan L, Dy J Impact of imputation of missing values on classification error for discrete data. 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Bioinformatics. 2012;28(1):112-118."},{"issue":"22","key":"b1","series-title":"JAMA","first-page":"2236","article-title":"Traumatic spinal cord injury in the United States, 1993-2012","volume":"313","author":"Jain NB","year":"2015","unstructured":"Jain NB, Ayers GD, Peterson EN,  Traumatic spinal cord injury in the United States, 1993-2012. JAMA. 2015;313(22):2236\u20132243."},{"issue":"10","key":"b2","series-title":"Arch Phys Med Rehabil","first-page":"1797","article-title":"Spinal Cord Injury Model Systems: review of program and national database from 1970 to 2015","volume":"97","author":"Chen Y","year":"2016","unstructured":"Chen Y, DeVivo MJ, Richards JS, SanAgustin TB Spinal Cord Injury Model Systems: review of program and national database from 1970 to 2015. Arch Phys Med Rehabil. 2016;97(10):1797\u20131804."},{"issue":"1 Suppl","key":"b3","series-title":"J Neurosurg Spine","first-page":"11","article-title":"Clinical predictors of neurological outcome, functional status, and survival after traumatic spinal cord injury: a systematic review","volume":"17","author":"Wilson JR","year":"2012","unstructured":"Wilson JR, Cadotte DW, Fehlings MG Clinical predictors of neurological outcome, functional status, and survival after traumatic spinal cord injury: a systematic review. J Neurosurg Spine. 2012;17(1 Suppl):11\u201326."},{"issue":"6","key":"b4","series-title":"Arch Phys Med Rehabil","first-page":"644","article-title":"Early predictors of functional independence 2 years after spinal cord injury","volume":"78","author":"Saboe LA","year":"1997","unstructured":"Saboe LA, Darrah JM, Pain KS, Guthrie J Early predictors of functional independence 2 years after spinal cord injury. Arch Phys Med Rehabil. 1997;78(6):644\u2013650."},{"key":"b5","series-title":"Handb Clin Neurol","first-page":"47","article-title":"Clinical diagnosis and prognosis following spinal cord injury","volume":"109","author":"Burns AS","year":"2012","unstructured":"Burns AS, Marino RJ, Flanders AE, Flett H Clinical diagnosis and prognosis following spinal cord injury. Handb Clin Neurol. 2012;109:47\u201362."},{"issue":"9","key":"b6","series-title":"Paraplegia","first-page":"573","article-title":"Definition of complete spinal cord injury","volume":"29","author":"Waters RL","year":"1991","unstructured":"Waters RL, Adkins RH, Yakura JS Definition of complete spinal cord injury. Paraplegia. 1991;29(9):573\u2013581."},{"issue":"11","key":"b7","series-title":"Spinal Cord","first-page":"809","article-title":"Is determination between complete and incomplete traumatic spinal cord injury clinically relevant? 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Arch Phys Med Rehabil. 1997;78(1):39\u201343."},{"issue":"17-18","key":"b9","series-title":"J Neurotrauma","first-page":"2133","article-title":"Clinical subsets of central cord syndrome: is it a distinct entity from other forms of incomplete tetraplegia for research?","volume":"41","author":"Engel-Haber E","year":"2024","unstructured":"Engel-Haber E, Snider B, Botticello A, Eren F, Kirshblum S Clinical subsets of central cord syndrome: is it a distinct entity from other forms of incomplete tetraplegia for research?. J Neurotrauma. 2024;41(17-18):2133\u20132145."},{"key":"b10","article-title":"Multiple Imputation for Nonresponse in Surveys","author":"Rubin DB","year":"2004","unstructured":"Rubin DB Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons;2004."},{"issue":"2-3","key":"b11","series-title":"Sociol Methods Res","first-page":"292","article-title":"The analysis of social science data with missing values","volume":"18","author":"Little RJA","year":"1989","unstructured":"Little RJA, Rubin DB The analysis of social science data with missing values. Sociol Methods Res. 1989;18(2-3):292\u2013326."},{"key":"b12","article-title":"Statistical Analysis with Missing Data","author":"Little RJA","year":"2019","unstructured":"Little RJA, Rubin DB Statistical Analysis with Missing Data. John Wiley & Sons;2019."},{"issue":"1","key":"b13","series-title":"Stat Methods Med Res","first-page":"3","article-title":"Multiple imputation: a primer","volume":"8","author":"Schafer JL","year":"1999","unstructured":"Schafer JL Multiple imputation: a primer. Stat Methods Med Res. 1999;8(1):3\u201315."},{"issue":"3","key":"b14","series-title":"J Stat Softw","first-page":"1","article-title":"mice: multivariate imputation by chained equations in R","volume":"45","author":"van Buuren S","year":"2011","unstructured":"van Buuren S, Groothuis-Oudshoorn K mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3):1\u201367."},{"issue":"5","key":"b15","series-title":"J Inf Sci Eng","first-page":"1039","article-title":"Ensemble case based reasoning imputation in breast cancer classification","volume":"37","author":"Chlioui I","year":"2021","unstructured":"Chlioui I, Idri A, Abnane I, Ezzat M Ensemble case based reasoning imputation in breast cancer classification. J Inf Sci Eng. 2021;37(5):1039."},{"issue":"4","key":"b16","series-title":"Eur J Hum Genet","first-page":"487","article-title":"Imputing missing genotypic data of single-nucleotide polymorphisms using neural networks","volume":"16","author":"Sun YV","year":"2008","unstructured":"Sun YV, Kardia SLR Imputing missing genotypic data of single-nucleotide polymorphisms using neural networks. Eur J Hum Genet. 2008;16(4):487\u2013495."},{"issue":"12","key":"b17","series-title":"Pattern Recognit","first-page":"3692","article-title":"Impact of imputation of missing values on classification error for discrete data","volume":"41","author":"Farhangfar A","year":"2008","unstructured":"Farhangfar A, Kurgan L, Dy J Impact of imputation of missing values on classification error for discrete data. 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J Big Data. 2021;8(1):140."},{"key":"b20","series-title":"Presented at: Fifth International Conference on Hybrid Intelligent Systems (HIS\u201905); November 6\u20139, 2005;","article-title":"Naive Bayes as an imputation tool for classification problems","author":"Garcia AJT","unstructured":"Garcia AJT, Hruschka ER Naive Bayes as an imputation tool for classification problems. Presented at: Fifth International Conference on Hybrid Intelligent Systems (HIS\u201905); November 6\u20139, 2005;Rio de Janeiro, Brazil."},{"issue":"6","key":"b21","series-title":"Am J Epidemiol","first-page":"764","article-title":"Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study","volume":"179","author":"Shah AD","year":"2014","unstructured":"Shah AD, Bartlett JW, Carpenter J, Nicholas O, Hemingway H Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study. 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