TY - JOUR
T1 - 166 Predictive Modeling of Length of Hospital Stay Following Adult Spinal Deformity Correction
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
Y1 - 2016/8/1
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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U2 - 10.1227/01.neu.0000489735.46846.2b
DO - 10.1227/01.neu.0000489735.46846.2b
M3 - Article
C2 - 27399445
AN - SCOPUS:85031912107
SN - 0148-396X
VL - 63
SP - 166
EP - 167
JO - Neurosurgery
JF - Neurosurgery
ER -