166 Predictive Modeling of Length of Hospital Stay Following Adult Spinal Deformity Correction: Analysis of 653 Patients With an Accuracy of 75% Within 2 Days

Justin K. Scheer, Tamir T. Ailon, Justin S. Smith, Robert Hart, Douglas C. Burton, Shay Bess, Brian J. Neuman, Peter G. Passias, Emily Miller, Christopher I. Shaffrey, Frank Schwab, Virginie Lafage, Eric Klineberg, Christopher P. Ames

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

INTRODUCTION: The length of stay (LOS) following adult spinal deformity (ASD) surgery is a critical time period allowing for recovery to levels safe enough to return home or to rehabilitation. Thus, the goal is to minimize it for conserving hospital resources and third-party payer pressure. Factors related to LOS have not been studied nor has a predictive model been created. The goal of this study was to construct a preadmission predictive model based on patients' baseline variables and modifiable surgical parameters.

METHODS: Retrospective review of a multicenter, prospective ASD database.

INCLUSION CRITERIA: operative patients, age >18 years, ASD. Patients with staged surgery at a separate hospitalization or LOS >30 days were excluded. Sixty-six variables were initially evaluated with 40 being used for model building following univariable predictor importance = 0.90, redundancy, and collinearity testing. Variables included: demographics, comorbidities, preoperative health-related quality of life, preoperative coronal and sagittal radiographic parameters, and modifiable surgical factors (Figure). A generalized linear model was constructed by using a training data set developed from a bootstrapped sample with replacement using a random number generator. Patients randomly omitted from the bootstrapped sample composed the testing data set. Accuracy was calculated by comparison of predicted LOS with the actual LOS.

RESULTS: A total of 689 patients were eligible; 653 met inclusion criteria. The mean LOS was 7.9 ± 4.1 days (range: 1-28). Following bootstrapping, 893 patients were modeled in total, Training: 653, TESTING: 240 (36.6%). The linear correlations for the training and testing data sets were 0.632 and 0.507, respectively. TESTING dataset accuracy within 2 days of actual LOS was 75.4% (181/240 patients).

CONCLUSION: A successful model was created to predict LOS to an accuracy of 75% within 2 days. There are some factors related to LOS that are not likely captured in large databases, which may partially explain the 75% accuracy, such as rehabilitation bed availability and social support resources.

Original languageEnglish (US)
Pages (from-to)166-167
Number of pages2
JournalNeurosurgery
Volume63
DOIs
StatePublished - Aug 1 2016

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Length of Stay
Rehabilitation
Databases
Health Insurance Reimbursement
Social Support
Comorbidity
Linear Models
Hospitalization
Quality of Life
Demography
Pressure
Datasets

ASJC Scopus subject areas

  • Surgery
  • Clinical Neurology

Cite this

166 Predictive Modeling of Length of Hospital Stay Following Adult Spinal Deformity Correction : Analysis of 653 Patients With an Accuracy of 75% Within 2 Days. / Scheer, Justin K.; Ailon, Tamir T.; Smith, Justin S.; Hart, Robert; Burton, Douglas C.; Bess, Shay; Neuman, Brian J.; Passias, Peter G.; Miller, Emily; Shaffrey, Christopher I.; Schwab, Frank; Lafage, Virginie; Klineberg, Eric; Ames, Christopher P.

In: Neurosurgery, Vol. 63, 01.08.2016, p. 166-167.

Research output: Contribution to journalArticle

Scheer, JK, Ailon, TT, Smith, JS, Hart, R, Burton, DC, Bess, S, Neuman, BJ, Passias, PG, Miller, E, Shaffrey, CI, Schwab, F, Lafage, V, Klineberg, E & Ames, CP 2016, '166 Predictive Modeling of Length of Hospital Stay Following Adult Spinal Deformity Correction: Analysis of 653 Patients With an Accuracy of 75% Within 2 Days', Neurosurgery, vol. 63, pp. 166-167. https://doi.org/10.1227/01.neu.0000489735.46846.2b
Scheer, Justin K. ; Ailon, Tamir T. ; Smith, Justin S. ; Hart, Robert ; Burton, Douglas C. ; Bess, Shay ; Neuman, Brian J. ; Passias, Peter G. ; Miller, Emily ; Shaffrey, Christopher I. ; Schwab, Frank ; Lafage, Virginie ; Klineberg, Eric ; Ames, Christopher P. / 166 Predictive Modeling of Length of Hospital Stay Following Adult Spinal Deformity Correction : Analysis of 653 Patients With an Accuracy of 75% Within 2 Days. In: Neurosurgery. 2016 ; Vol. 63. pp. 166-167.
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abstract = "INTRODUCTION: The length of stay (LOS) following adult spinal deformity (ASD) surgery is a critical time period allowing for recovery to levels safe enough to return home or to rehabilitation. Thus, the goal is to minimize it for conserving hospital resources and third-party payer pressure. Factors related to LOS have not been studied nor has a predictive model been created. The goal of this study was to construct a preadmission predictive model based on patients' baseline variables and modifiable surgical parameters.METHODS: Retrospective review of a multicenter, prospective ASD database.INCLUSION CRITERIA: operative patients, age >18 years, ASD. Patients with staged surgery at a separate hospitalization or LOS >30 days were excluded. Sixty-six variables were initially evaluated with 40 being used for model building following univariable predictor importance = 0.90, redundancy, and collinearity testing. Variables included: demographics, comorbidities, preoperative health-related quality of life, preoperative coronal and sagittal radiographic parameters, and modifiable surgical factors (Figure). A generalized linear model was constructed by using a training data set developed from a bootstrapped sample with replacement using a random number generator. Patients randomly omitted from the bootstrapped sample composed the testing data set. Accuracy was calculated by comparison of predicted LOS with the actual LOS.RESULTS: A total of 689 patients were eligible; 653 met inclusion criteria. The mean LOS was 7.9 ± 4.1 days (range: 1-28). Following bootstrapping, 893 patients were modeled in total, Training: 653, TESTING: 240 (36.6{\%}). The linear correlations for the training and testing data sets were 0.632 and 0.507, respectively. TESTING dataset accuracy within 2 days of actual LOS was 75.4{\%} (181/240 patients).CONCLUSION: A successful model was created to predict LOS to an accuracy of 75{\%} within 2 days. There are some factors related to LOS that are not likely captured in large databases, which may partially explain the 75{\%} accuracy, such as rehabilitation bed availability and social support resources.",
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T2 - Analysis of 653 Patients With an Accuracy of 75% Within 2 Days

AU - Scheer, Justin K.

AU - Ailon, Tamir T.

AU - Smith, Justin S.

AU - Hart, Robert

AU - Burton, Douglas C.

AU - Bess, Shay

AU - Neuman, Brian J.

AU - Passias, Peter G.

AU - Miller, Emily

AU - Shaffrey, Christopher I.

AU - Schwab, Frank

AU - Lafage, Virginie

AU - Klineberg, Eric

AU - Ames, Christopher P.

PY - 2016/8/1

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N2 - INTRODUCTION: The length of stay (LOS) following adult spinal deformity (ASD) surgery is a critical time period allowing for recovery to levels safe enough to return home or to rehabilitation. Thus, the goal is to minimize it for conserving hospital resources and third-party payer pressure. Factors related to LOS have not been studied nor has a predictive model been created. The goal of this study was to construct a preadmission predictive model based on patients' baseline variables and modifiable surgical parameters.METHODS: Retrospective review of a multicenter, prospective ASD database.INCLUSION CRITERIA: operative patients, age >18 years, ASD. Patients with staged surgery at a separate hospitalization or LOS >30 days were excluded. Sixty-six variables were initially evaluated with 40 being used for model building following univariable predictor importance = 0.90, redundancy, and collinearity testing. Variables included: demographics, comorbidities, preoperative health-related quality of life, preoperative coronal and sagittal radiographic parameters, and modifiable surgical factors (Figure). A generalized linear model was constructed by using a training data set developed from a bootstrapped sample with replacement using a random number generator. Patients randomly omitted from the bootstrapped sample composed the testing data set. Accuracy was calculated by comparison of predicted LOS with the actual LOS.RESULTS: A total of 689 patients were eligible; 653 met inclusion criteria. The mean LOS was 7.9 ± 4.1 days (range: 1-28). Following bootstrapping, 893 patients were modeled in total, Training: 653, TESTING: 240 (36.6%). The linear correlations for the training and testing data sets were 0.632 and 0.507, respectively. TESTING dataset accuracy within 2 days of actual LOS was 75.4% (181/240 patients).CONCLUSION: A successful model was created to predict LOS to an accuracy of 75% within 2 days. There are some factors related to LOS that are not likely captured in large databases, which may partially explain the 75% accuracy, such as rehabilitation bed availability and social support resources.

AB - INTRODUCTION: The length of stay (LOS) following adult spinal deformity (ASD) surgery is a critical time period allowing for recovery to levels safe enough to return home or to rehabilitation. Thus, the goal is to minimize it for conserving hospital resources and third-party payer pressure. Factors related to LOS have not been studied nor has a predictive model been created. The goal of this study was to construct a preadmission predictive model based on patients' baseline variables and modifiable surgical parameters.METHODS: Retrospective review of a multicenter, prospective ASD database.INCLUSION CRITERIA: operative patients, age >18 years, ASD. Patients with staged surgery at a separate hospitalization or LOS >30 days were excluded. Sixty-six variables were initially evaluated with 40 being used for model building following univariable predictor importance = 0.90, redundancy, and collinearity testing. Variables included: demographics, comorbidities, preoperative health-related quality of life, preoperative coronal and sagittal radiographic parameters, and modifiable surgical factors (Figure). A generalized linear model was constructed by using a training data set developed from a bootstrapped sample with replacement using a random number generator. Patients randomly omitted from the bootstrapped sample composed the testing data set. Accuracy was calculated by comparison of predicted LOS with the actual LOS.RESULTS: A total of 689 patients were eligible; 653 met inclusion criteria. The mean LOS was 7.9 ± 4.1 days (range: 1-28). Following bootstrapping, 893 patients were modeled in total, Training: 653, TESTING: 240 (36.6%). The linear correlations for the training and testing data sets were 0.632 and 0.507, respectively. TESTING dataset accuracy within 2 days of actual LOS was 75.4% (181/240 patients).CONCLUSION: A successful model was created to predict LOS to an accuracy of 75% within 2 days. There are some factors related to LOS that are not likely captured in large databases, which may partially explain the 75% accuracy, such as rehabilitation bed availability and social support resources.

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