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Jeff Ehresman, Zach Pennington, James Feghali, Andrew Schilling, Andrew Hersh, Bethany Hung, Daniel Lubelski, and Daniel M. Sciubba

OBJECTIVE

More than 8000 patients are treated annually for vertebral column tumors, of whom roughly two-thirds will be discharged to an inpatient facility (nonroutine discharge). Nonroutine discharge is associated with increased care costs as well as delays in discharge and poorer patient outcomes. In this study, the authors sought to develop a prediction model of nonroutine discharge in the population of vertebral column tumor patients.

METHODS

Patients treated for primary or metastatic vertebral column tumors at a single comprehensive cancer center were identified for inclusion. Data were gathered regarding surgical procedure, patient demographics, insurance status, and medical comorbidities. Frailty was assessed using the modified 5-item Frailty Index (mFI-5) and medical complexity was assessed using the modified Charlson Comorbidity Index (mCCI). Multivariable logistic regression was used to identify independent predictors of nonroutine discharge, and multivariable linear regression was used to identify predictors of prolonged length of stay (LOS). The discharge model was internally validated using 1000 bootstrapped samples.

RESULTS

The authors identified 350 patients (mean age 57.0 ± 13.6 years, 53.1% male, and 67.1% treated for metastatic vs primary disease). Significant predictors of prolonged LOS included higher mCCI score (β = 0.74; p = 0.026), higher serum absolute neutrophil count (β = 0.35; p = 0.001), lower hematocrit (β = −0.34; p = 0.001), use of a staged operation (β = 4.99; p < 0.001), occurrence of postoperative pulmonary embolism (β = 3.93; p = 0.004), and surgical site infection (β = 9.93; p < 0.001). Significant predictors of nonroutine discharge included emergency admission (OR 3.09; p = 0.001), higher mFI-5 score (OR 1.90; p = 0.001), lower serum albumin level (OR 0.43 per g/dL; p < 0.001), and operations with multiple stages (OR 4.10; p < 0.001). The resulting statistical model was deployed as a web-based calculator (https://jhuspine4.shinyapps.io/Nonroutine_Discharge_Tumor/).

CONCLUSIONS

The authors found that nonroutine discharge of patients with surgically treated vertebral column tumors was predicted by emergency admission, increased frailty, lower serum albumin level, and staged surgical procedures. The resulting web-based calculator tool may be useful clinically to aid in discharge planning for spinal oncology patients by preoperatively identifying patients likely to require placement in an inpatient facility postoperatively.

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Jeff Ehresman, Daniel Lubelski, Zach Pennington, Bethany Hung, A. Karim Ahmed, Tej D. Azad, Kurt Lehner, James Feghali, Zorica Buser, James Harrop, Jefferson Wilson, Shekar Kurpad, Zoher Ghogawala, and Daniel M. Sciubba

OBJECTIVE

The objective of this study was to evaluate the characteristics and performance of current prediction models in the fields of spine metastasis and degenerative spine disease to create a scoring system that allows direct comparison of the prediction models.

METHODS

A systematic search of PubMed and Embase was performed to identify relevant studies that included either the proposal of a prediction model or an external validation of a previously proposed prediction model with 1-year outcomes. Characteristics of the original study and discriminative performance of external validations were then assigned points based on thresholds from the overall cohort.

RESULTS

Nine prediction models were included in the spine metastasis category, while 6 prediction models were included in the degenerative spine category. After assigning the proposed utility of prediction model score to the spine metastasis prediction models, only 1 reached the grade of excellent, while 2 were graded as good, 3 as fair, and 3 as poor. Of the 6 included degenerative spine models, 1 reached the excellent grade, while 3 studies were graded as good, 1 as fair, and 1 as poor.

CONCLUSIONS

As interest in utilizing predictive analytics in spine surgery increases, there is a concomitant increase in the number of published prediction models that differ in methodology and performance. Prior to applying these models to patient care, these models must be evaluated. To begin addressing this issue, the authors proposed a grading system that compares these models based on various metrics related to their original design as well as internal and external validation. Ultimately, this may hopefully aid clinicians in determining the relative validity and usability of a given model.