Prediction calculator for nonroutine discharge and length of stay after spine surgery

A systematic literature review of predicting patient discharges using statistical methods and machine learning

Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.

Development and Validation of Machine Learning-Based Predictive Model for Prolonged Hospital Stay after Decompression Surgery for Lumbar Spinal Canal Stenosis

Introduction

Precise prediction of hospital stay duration is essential for maximizing resource utilization during surgery. Existing lumbar spinal stenosis (LSS) surgery prediction models lack accuracy and generalizability. Machine learning can improve accuracy by considering preoperative factors. This study aimed to develop and validate a machine learning-based model for estimating hospital stay duration following decompression surgery for LSS.

Methods

Data from 848 patients who underwent decompression surgery for LSS at three hospitals were examined. Twelve prediction models, using 79 preoperative variables, were developed for postoperative hospital stay estimation. The top five models were chosen. Fourteen models predicted prolonged hospital stay (≥14 days), and the most accurate model was chosen. Models were validated using a randomly divided training sample (70%) and testing cohort (30%).

Results

The top five models showed moderate linear correlations (0.576-0.624) between predicted and measured values in the testing sample. The ensemble of these models had moderate prediction accuracy for final length of stay (linear correlation 0.626, absolute mean error 2.26 days, standard deviation 3.45 days). The c5.0 decision tree model was the top predictor for prolonged hospital stay, with accuracies of 89.63% (training) and 87.2% (testing). Key predictors for longer stay included JOABPEQ social life domain, facility, history of vertebral fracture, diagnosis, and Visual Analogue Scale (VAS) of low back pain.

Conclusions

A machine learning-based model was developed to predict postoperative hospital stay after LSS decompression surgery, using data from multiple hospital settings. Numerical prediction of length of stay was not very accurate, although favorable prediction of prolonged stay was accomplished using preoperative factors. The JOABPEQ social life domain score was the most important predictor.

Development and external validation of a predictive model for prolonged length of hospital stay in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models

PURPOSE

This study aimed to develop a predictive model for prolonged length of hospital stay (pLOS) in elderly patients undergoing lumbar fusion surgery, utilizing multivariate logistic regression, single classification and regression tree (hereafter, "classification tree") and random forest machine-learning algorithms.

METHODS

This study was a retrospective review of a prospective Geriatric Lumbar Disease Database. The primary outcome measure was pLOS, which was defined as the LOS greater than the 75th percentile. All patients were grouped as pLOS group and non-pLOS. Three models (including logistic regression, single-classification tree and random forest algorithms) for predicting pLOS were developed using training dataset and internal validation using testing dataset. Finally, online tool based on our model was developed to assess its validity in the clinical setting (external validation).

RESULTS

The development set included 1025 patients (mean [SD] age, 72.8 [5.6] years; 632 [61.7%] female), and the external validation set included 175 patients (73.2 [5.9] years; 97[55.4%] female). Multivariate logistic analyses revealed that older age (odds ratio [OR] 1.06, p < 0.001), higher BMI (OR 1.08, p = 0.002), number of fused segments (OR 1.41, p < 0.001), longer operative time (OR 1.02, p < 0.001), and diabetes (OR 1.05, p = 0.046) were independent risk factors for pLOS in elderly patients undergoing lumbar fusion surgery. The single-classification tree revealed that operative time ≥ 232 min, delayed ambulation, and BMI ≥ 30 kg/m2 as particularly influential predictors for pLOS. A random forest model was developed using the remaining 14 variables. Intraoperative EBL, operative time, delayed ambulation, age, number of fused segments, BMI, and RBC count were the most significant variables in the final model. The predictive ability of our three models was comparable, with no significant differences in AUC (0.73 vs. 0.71 vs. 0.70, respectively). The logistic regression model had a higher net benefit for clinical intervention than the other models. The nomogram was developed, and the C-index of external validation for PLOS was 0.69 (95% CI, 0.65-0.76).

CONCLUSION

This investigation produced three predictive models for pLOS in elderly patients undergoing lumbar fusion surgery. The predictive ability of our three models was comparable. Logistic regression model had a higher net benefit for clinical intervention than the other models. Our predictive model could inform physicians about elderly patients with a high risk of pLOS after surgery.

Hospital-level variation in hospitalization costs for spinal fusion in the United States

BACKGROUND

With a growing emphasis on value of care, understanding factors associated with rising healthcare costs is increasingly important. In this national study, we evaluated the degree of center-level variation in the cost of spinal fusion.

METHODS

All adults undergoing elective spinal fusion were identified in the 2016 to 2020 National Inpatient Sample. Multilevel mixed-effect models were used to rank hospitals based on risk-adjusted costs. The interclass coefficient (ICC) was utilized to tabulate the amount of variation attributable to hospital-level characteristics. The association of high cost-hospital (HCH) status with in-hospital mortality, perioperative complications, and overall resource utilization was analyzed. Predictors of increased costs were secondarily explored.

RESULTS

An estimated 1,541,740 patients underwent spinal fusion, and HCH performed an average of 9.5% of annual cases. HCH were more likely to be small (36.8 vs 30.5%, p<0.001), rural (10.1 vs 8.8%, p<0.001), and located in the Western geographic region (49.9 vs 16.7%, p<0.001). The ICC demonstrated 32% of variation in cost was attributable to the hospital, independent of patient-level characteristics. Patients who received a spinal fusion at a HCH faced similar odds of mortality (0.74 [0.48-1.15], p = 0.18) and perioperative complications (1.04 [0.93-1.16], p = 0.52), but increased odds of non-home discharge (1.30 [1.17-1.45], p<0.001) and prolonged length of stay (β 0.34 [0.26-0.42] days, p = 0.18). Patient factors such as gender, race, and income quartile significantly impacted costs.

CONCLUSION

The present analysis identified 32% of the observed variation to be attributable to hospital-level characteristics. HCH status was not associated with increased mortality or perioperative complications.

Development and external validation of a nomogram for predicting postoperative adverse events in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models

BACKGROUND

The burden of lumbar degenerative diseases (LDD) has increased substantially with the unprecedented aging population. Identifying elderly patients with high risk of postoperative adverse events (AEs) and establishing individualized perioperative management is critical to mitigate added costs and optimize cost-effectiveness to the healthcare system. We aimed to develop a predictive tool for AEs in elderly patients with transforaminal lumbar interbody fusion (TLIF), utilizing multivariate logistic regression, single classification and regression tree (hereafter, "classification tree"), and random forest machine learning algorithms.

METHODS

This study was a retrospective review of a prospective Geriatric Lumbar Disease Database (age ≥ 65). Our outcome measure was postoperative AEs, including prolonged hospital stays, postoperative complications, readmission, and reoperation within 90 days. Patients were grouped as either having at least one adverse event (AEs group) or not (No-AEs group). Three models for predicting postoperative AEs were developed using training dataset and internal validation using testing dataset. Finally, online tool was developed to assess its validity in the clinical setting (external validation).

RESULTS

The development set included 1025 patients (mean [SD] age, 72.8 [5.6] years; 632 [61.7%] female), and the external validation set included 175 patients (73.2 [5.9] years; 97 [55.4%] female). The predictive ability of our three models was comparable, with no significant differences in AUC (0.73 vs. 0.72 vs. 0.70, respectively). The logistic regression model had a higher net benefit for clinical intervention than the other models. A nomogram based on logistic regression was developed, and the C-index of external validation for AEs was 0.69 (95% CI 0.65-0.76).

CONCLUSION

The predictive ability of our three models was comparable. Logistic regression model had a higher net benefit for clinical intervention than the other models. Our nomogram and online tool ( https://xuanwumodel.shinyapps.io/Model_for_AEs/ ) could inform physicians about elderly patients with a high risk of AEs within the 90 days after TLIF surgery.

Public health insurance, frailty, and lack of home support predict rehab discharge following elective anterior cervical discectomy and fusion

BACKGROUND CONTEXT

Anterior cervical discectomy and fusion (ACDF) is a commonly-performed and generally well-tolerated procedure used to treat cervical disc herniation. Rarely, patients require discharge to inpatient rehab, leading to inconvenience for the patient and increased healthcare expenditure for the medical system.

PURPOSE

The objective of this study was to create an accurate and practical predictive model for, as well as delineate associated factors with, rehab discharge following elective ACDF.

STUDY DESIGN

This was a retrospective, single-center, cohort study.

PATIENT SAMPLE

Patients who underwent ACDF between 2012 and 2022 were included. Those with confounding diagnoses or who underwent concurrent, staged, or nonelective procedures were excluded.

OUTCOME MEASURES

Primary outcomes for this study included measurements of accuracy for predicting rehab discharge. Secondary outcomes included associations of variables with rehab discharge.

METHODS

Current Procedural Terminology codes identified patients. Charts were reviewed to obtain additional demographic and clinical characteristics on which an initial univariate analysis was performed. Two logistic regression and two machine learning models were trained and evaluated on the data using cross-validation. A multimodel logistic regression was implemented to analyze independent variable associations with rehab discharge.

RESULTS

A total of 466 patients were included in the study. The logistic regression model with minimum corrected Akaike information criterion score performed best overall, with the highest values for area under the receiver operating characteristic curve (0.83), Youden's J statistic (0.71), balanced accuracy (85.7%), sensitivity (90.3%), and positive predictive value (38.5%). Rehab discharge was associated with a modified frailty index of 2 (p=.007), lack of home support (p=.002), and having Medicare or Medicaid insurance (p=.007) after correction for multiple hypotheses.

CONCLUSIONS

Nonmedical social determinants of health, such as having public insurance or a lack of support at home, may play a role in rehab discharge following elective ACDF. In combination with the modified frailty index and other variables, these factors can be used to predict rehab discharge with high accuracy, improving the patient experience and reducing healthcare costs.

Relationship Between Comorbidity Burden and Short-Term Outcomes Across 4680 Consecutive Spinal Fusions

OBJECTIVE

Preoperative management requires the identification and optimization of modifiable medical comorbidities, though few studies isolate comorbid status from related patient-level variables. This study evaluates Charlson Comorbidity Index (CCI)-an easily derived measure of aggregate medical comorbidity-to predict outcomes from spinal fusion surgery. Coarsened exact matching is employed to control for key patient characteristics and isolate CCI.

METHODS

We retrospectively assessed 4680 consecutive patients undergoing single-level, posterior-only lumbar fusion at a single academic center. Logistic regression evaluated the univariate relationship between CCI and patient outcomes. Coarsened exact matching generated exact demographic matches between patients with high comorbid status (CCI >6) or no medical comorbidities (matched n = 524). Patients were matched 1:1 on factors associated with surgical outcomes, and outcomes were compared between matched cohorts. Primary outcomes included surgical complications, discharge status, 30- and 90-day risk of readmission, emergency department (ED) visits, reoperation, and mortality.

RESULTS

Univariate regression of increasing CCI was significantly associated with non-home discharge, as well as 30- and 90-day readmission, ED visits, and mortality (all P < 0.05). Subsequent isolation of comorbidity between otherwise exact-matched cohorts found comorbid status did not affect readmissions, reoperations, or mortality; high CCI score was significantly associated with non-home discharge (OR = 2.50, P < 0.001) and 30-day (OR = 2.44, P = 0.02) and 90-day (OR = 2.29, P = 0.008) ED evaluation.

CONCLUSIONS

Comorbidity, measured by CCI, did not increase the risk of readmission, reoperation, or mortality. Single-level, posterior lumbar fusions may be safe in appropriately selected patients regardless of comorbid status. Future studies should determine whether CCI can guide discharge planning and postoperative optimization.

Modified Frailty Index as a Predictor of Postoperative Complications and Patient-Reported Outcomes after Posterior Cervical Decompression and Fusion

STUDY DESIGN

A retrospective cohort study.

PURPOSE

To determine whether the 11-item modified frailty index (mFI) is associated with readmission rates, complication rates, revision rates, or differences in patient-reported outcome measures (PROMs) for patients undergoing posterior cervical decompression and fusion (PCDF).

OVERVIEW OF LITERATURE

mFI incorporates preexisting medical comorbidities and dependency status to determine physiological reserve. Based on previous literature, it may be used as a predictive tool for identifying postoperative clinical and surgical outcomes.

METHODS

Patients undergoing elective PCDF at our urban academic medical center from 2014 to 2020 were included. Patients were categorized by mFI scores (0-0.08, 0.09-0.17, 0.18-0.26, and ≥0.27). Univariate statistics compared demographics, comorbidities, and clinical/surgical outcomes. Multiple linear regression analysis evaluated the magnitude of improvement in PROMs at 1 year.

RESULTS

A total of 165 patients were included and grouped by mFI scores: 0 (n=36), 0.09 (n=62), 0.18 (n=42), and ≥0.27 (n=30). The severe frailty group (mFI ≥0.27) was significantly more likely to be diabetic (p <0.001) and have a greater Elixhauser comorbidity index (p =0.001). They also had worse baseline Physical Component Score-12 (PCS-12) (p =0.011) and modified Japanese Orthopaedic Association (mJOA) (p =0.012) scores and worse 1-year postoperative PCS-12 (p =0.008) and mJOA (p =0.001) scores. On regression analysis, an mFI score of 0.18 was an independent predictor of greater improvement in ΔVisual Analog Scale neck (β =-2.26, p =0.022) and ΔVAS arm (β =-1.76, p =0.042). Regardless of frailty status, patients had similar 90-day readmission rates (p =0.752), complication rates (p =0.223), and revision rates (p =0.814), but patients with severe frailty were more likely to have longer hospital length of stay (p =0.006) and require non-home discharge (p <0.001).

CONCLUSIONS

Similar improvements across most PROMs can be expected irrespective of the frailty status of patients undergoing PCDF. Complication rates, 90-day readmission rates, and revision rates are not significantly different when stratified by frailty status. However, patients with severe frailty are more likely to have longer hospital stays and require non-home discharge.

Predictive models to assess risk of extended length of stay in adults with spinal deformity and lumbar degenerative pathology: development and internal validation

BACKGROUND CONTEXT

Postoperative recovery after adult spinal deformity (ASD) operations is arduous, fraught with complications, and often requires extended hospital stays. A need exists for a method to rapidly predict patients at risk for extended length of stay (eLOS) in the preoperative setting.

PURPOSE

To develop a machine learning model to preoperatively estimate the likelihood of eLOS following elective multi-level lumbar/thoracolumbar spinal instrumented fusions (≥3 segments) for ASD.

STUDY DESIGN/SETTING

Retrospectively from a state-level inpatient database hosted by the Health care cost and Utilization Project.

PATIENT SAMPLE

Of 8,866 patients of age ≥50 with ASD undergoing elective lumbar or thoracolumbar multilevel instrumented fusions.

OUTCOME MEASURES

The primary outcome was eLOS (>7 days).

METHODS

Predictive variables consisted of demographics, comorbidities, and operative information. Significant variables from univariate and multivariate analyses were used to develop a logistic regression-based predictive model that use six predictors. Model accuracy was assessed through area under the curve (AUC), sensitivity, and specificity.

RESULTS

Of 8,866 patients met inclusion criteria. A saturated logistic model with all significant variables from multivariate analysis was developed (AUC=0.77), followed by generation of a simplified logistic model through stepwise logistic regression (AUC=0.76). Peak AUC was reached with inclusion of six selected predictors (combined anterior and posterior approach, surgery to both lumbar and thoracic regions, ≥8 level fusion, malnutrition, congestive heart failure, and academic institution). A cutoff of 0.18 for eLOS yielded a sensitivity of 77% and specificity of 68%.

CONCLUSIONS

This predictive model can facilitate identification of adults at risk for eLOS following elective multilevel lumbar/thoracolumbar spinal instrumented fusions for ASD. With a fair diagnostic accuracy, the predictive calculator will ideally enable clinicians to improve preoperative planning, guide patient expectations, enable optimization of modifiable risk factors, facilitate appropriate discharge planning, stratify financial risk, and accurately identify patients who may represent high-cost outliers. Future prospective studies that validate this risk assessment tool on external datasets would be valuable.

Factors contributing to a longer length of stay in adults admitted to a quaternary spinal care center

BACKGROUND

Longer hospital length of stay (LOS) has been associated with worse outcomes and increased resource utilization. However, diagnostic and patient-level factors associated with LOS have not been well studied on a large scale. The goal was to identify patient, surgical and organizational factors associated with longer patient LOS for adult patients at a high-volume quaternary spinal care center.

METHODS

We performed a retrospective analysis of 13,493 admissions from January 2006 to December 2019. Factors analyzed included age, sex, admission status (emergent vs scheduled), ASIA grade, operative vs non-operative management, mean blood loss, operative time, and adverse events. Specific adverse events included surgical site infection (SSI), other infection (systemic or UTI), neuropathic pain, delirium, dural tear, pneumonia, and dysphagia. Diagnostic categories included trauma, oncology, deformity, degenerative, and "other". A multivariable linear regression model was fit to log-transformed LOS to determine independent factors associated with patient LOS, with effects expressed as multipliers on mean LOS.

RESULTS

Mean LOS for the population (SD) was 15.8 (34.0) days. Factors significantly (p < 0.05) associated with longer LOS were advanced patient age [multiplier on mean LOS 1.011/year (95% CI: 1.007-1.015)], emergency admission [multiplier on mean LOS 1.615 (95% CI: 1.337-1.951)], ASIA grade [multiplier on mean LOS 1.125/grade (95% CI: 1.051-1.205)], operative management [multiplier on mean LOS 1.211 (95% CI: 1.006-1.459)], and the occurrence of one or more AEs [multiplier on mean LOS 2.613 (95% CI: 2.188-3.121)]. Significant AEs included postoperative SSI [multiplier on mean LOS 1.749 (95% CI: 1.250-2.449)], other infections (systemic infections and UTI combined) [multiplier on mean LOS 1.650 (95% CI: 1.359-2.004)], delirium [multiplier on mean LOS 1.404 (95% CI: 1.103-1.787)], and pneumonia [multiplier on mean LOS 1.883 (95% CI: 1.447-2.451)]. Among the diagnostic categories explored, degenerative patients experienced significantly shorter LOS [multiplier on mean LOS 0.672 (95%CI: 0.535-0.844), p < 0.001] compared to non-degenerative categories.

CONCLUSION

This large-scale study taking into account diagnostic categories identified several factors associated with patient LOS. Future interventions should target modifiable factors to minimize LOS and guide hospital resource allocation thereby improving patient outcomes and quality of care and decreasing healthcare-associated costs.

Development and validation of a prediction model for outcomes after transaxillary first rib resection for neurogenic thoracic outlet syndrome following strict Society for Vascular Surgery diagnostic criteria

OBJECTIVE

Neurogenic thoracic outlet syndrome (NTOS) is the most common form of thoracic outlet syndrome. However, NTOS has remained difficult to diagnose and treat successfully. The purpose of the present study was to generate a predictive clinical calculator for postoperative outcomes after first rib resection (FRR) for NTOS.

METHODS

We performed a retrospective review of patients who had undergone FRR for NTOS at a single tertiary care institution between 2016 and 2020. A multivariate stepwise logistic regression analysis was performed to assess the association of the percentage of improvement after FRR with the patient baseline characteristics, pertinent clinical characteristics, and diagnostic criteria set by the Society for Vascular Surgery. The primary outcome was subjective patient improvement after FRR. A prediction risk calculator was developed using backward stepwise multivariate logistic regression coefficients. Bootstrapping was used for internal validation.

RESULTS

A total of 208 patients (22.2% male; mean age, 35.8 ± 12.8 years; median follow-up, 44.9 months) had undergone 243 FRRs. Of the 208 patients, 94.7% had had symptoms localized to the supraclavicular area, and 97.6% had had symptoms in the hand. All the patients had had positive symptoms reproduced by the elevated arm stress test and upper limb tension test. Another reasonably likely diagnosis was absent for all the patients. Of the 196 patients who had received a lidocaine injection, 180 (93.3%) had experienced improvement of NTOS symptoms. Of the 95 patients who had received a Botox injection, 82 (74.6%) had experienced improvement of NTOS symptoms. Receiver operating characteristic curve analysis was used to assess the model. The area under the curve for the backward stepwise multivariate logistic regression model was 0.8. The multivariate logistic regression analyses revealed that the significant predictors of worsened clinical outcomes included hand weakness (adjusted odds ratio [aOR], 4.28; 95% confidence interval [CI], 1.04-17.74), increasing age (aOR, 0.93; 95% CI, 0.88-0.99), workers' compensation or litigation case (aOR, 0.09; 95% CI, 0.01-0.82), and symptoms in the dominant hand (aOR, 0.20; 95% CI, 0.05-0.88).

CONCLUSIONS

Using retrospective data from a single-institution database, we have developed a prediction calculator with moderate to high predictive ability, as demonstrated by an area under the curve of 0.8. The tool (available at: https://jhhntosriskcalculator.shinyapps.io/NTOS_calc/) is an important adjunct to clinical decision-making that can offer patients and providers realistic and personalized expectations of the postoperative outcome after FRR for NTOS. The findings from the present study have reinforced the diagnostic criteria set by the Society for Vascular Surgery. The calculator could aid physicians in surgical planning, referrals, and counseling patients on whether to proceed with surgery.

Development and internal validation of predictive models to assess risk of post-acute care facility discharge in adults undergoing multi-level instrumented fusions for lumbar degenerative pathology and spinal deformity

PURPOSE

To develop a model for factors predictive of Post-Acute Care Facility (PACF) discharge in adult patients undergoing elective multi-level (≥ 3 segments) lumbar/thoracolumbar spinal instrumented fusions.

METHODS

The State Inpatient Databases acquired from the Healthcare Cost and Utilization Project from 2005 to 2013 were queried for adult patients who underwent elective multi-level thoracolumbar fusions for spinal deformity. Outcome variables were classified as discharge to home or PACF. Predictive variables included demographic, pre-operative, and operative factors. Univariate and multivariate logistic regression analyses informed development of a logistic regression-based predictive model using seven selected variables. Performance metrics included area under the curve (AUC), sensitivity, and specificity.

RESULTS

Included for analysis were 8866 patients. The logistic model including significant variables from multivariate analysis yielded an AUC of 0.75. Stepwise logistic regression was used to simplify the model and assess number of variables needed to reach peak AUC, which included seven selected predictors (insurance, interspaces fused, gender, age, surgical region, CCI, and revision surgery) and had an AUC of 0.74. Model cut-off for predictive PACF discharge was 0.41, yielding a sensitivity of 75% and specificity of 59%.

CONCLUSIONS

The seven variables associated significantly with PACF discharge (age > 60, female gender, non-private insurance, primary operations, instrumented fusion involving 8+ interspaces, thoracolumbar region, and higher CCI scores) may aid in identification of adults at risk for discharge to a PACF following elective multi-level lumbar/thoracolumbar spinal fusions for spinal deformity. This may in turn inform discharge planning and expectation management.

Predictive Models for Length of Stay and Discharge Disposition in Elective Spine Surgery: Development, Validation, and Comparison to the ACS NSQIP Risk Calculator

STUDY DESIGN

A retrospective study at a single academic institution.

OBJECTIVE

The purpose of this study is to utilize machine learning to predict hospital length of stay (LOS) and discharge disposition following adult elective spine surgery, and to compare performance metrics of machine learning models to the American College of Surgeon's National Surgical Quality Improvement Program's (ACS NSQIP) prediction calculator.

SUMMARY OF BACKGROUND DATA

A total of 3678 adult patients undergoing elective spine surgery between 2014 and 2019, acquired from the electronic health record.

METHODS

Patients were divided into three stratified cohorts: cervical degenerative, lumbar degenerative, and adult spinal deformity groups. Predictive variables included demographics, body mass index, surgical region, surgical invasiveness, surgical approach, and comorbidities. Regression, classification trees, and least absolute shrinkage and selection operator (LASSO) were used to build predictive models. Validation of the models was conducted on 16% of patients (N=587), using area under the receiver operator curve (AUROC), sensitivity, specificity, and correlation. Patient data were manually entered into the ACS NSQIP online risk calculator to compare performance. Outcome variables were discharge disposition (home vs. rehabilitation) and LOS (days).

RESULTS

Of 3678 patients analyzed, 51.4% were male (n=1890) and 48.6% were female (n=1788). The average LOS was 3.66 days. In all, 78% were discharged home and 22% discharged to rehabilitation. Compared with NSQIP (Pearson R2 =0.16), the predictions of poisson regression ( R2 =0.29) and LASSO ( R2 =0.29) models were significantly more correlated with observed LOS ( P =0.025 and 0.004, respectively). Of the models generated to predict discharge location, logistic regression yielded an AUROC of 0.79, which was statistically equivalent to the AUROC of 0.75 for NSQIP ( P =0.135).

CONCLUSION

The predictive models developed in this study can enable accurate preoperative estimation of LOS and risk of rehabilitation discharge for adult patients undergoing elective spine surgery. The demonstrated models exhibited better performance than NSQIP for prediction of LOS and equivalent performance to NSQIP for prediction of discharge location.

AM-PAC mobility scores predict non-home discharge following adult spinal deformity surgery

BACKGROUND CONTEXT

Adult spinal deformity (ASD) surgery requires an extended recovery period and often non-routine discharge. The Activity Measure for Post-Acute Care (AM-PAC) Basic Mobility Inpatient Short Form (6-Clicks) is a prediction tool, validated for other orthopedic procedures, to assess a patient's ability to mobilize after surgery.

PURPOSE

To assess the thresholds of AM-PAC scores that determine non-home discharge disposition in patients who have undergone ASD surgery.

STUDY DESIGN

Retrospective review PATIENT SAMPLE: Ninety consecutive ASD patients with ≥5 levels fused who underwent surgery from 2015 to 2018, with postoperative AM-PAC scores measured before discharge, were included.

OUTCOME MEASURES

Non-home discharge disposition METHODS: Patients with routine home discharge were compared to those with non-home discharge. Bivariate analysis was first conducted to compare these groups by preoperative demographics, comorbidities, radiographic alignment, surgical characteristics, HRQOLs, and AM-PAC measurements. Threshold linear regression with Bayesian information criteria was utilized to identify the optimal cutoffs for AM-PAC scores associated with increased likelihood of non-home discharge. Finally, multivariable analysis controlling for age, sex, comorbidities, levels fused, perioperative complication, and home support was conducted to assess each threshold.

RESULTS

Thirty-six (40%) of 90 patients analyzed had non-home discharge. On bivariate analysis, first AM-PAC score (13.5 vs. 17), last AM-PAC score (17 vs. 20), and AM-PAC change per day (+.387 vs. +1) were all significantly associated with non-home discharge. Threshold regression identified that cutoffs of ≤15 for first AM-PAC score, <17 for last AM-PAC score, and <+0.625 for daily AM-PAC change were associated with non-home discharge. On multivariable analysis, first AM-PAC score ≤15 (odds ratio [OR] 11.28; confidence interval [CI] 2.96-42.99; p<.001), last AM-PAC score <17 (OR 33.57; CI 5.85-192.82; p<.001), and AM-PAC change per day <+0.625 (OR 6.24; CI 2.01-19.43; p<.001) were all associated with increased odds of non-home discharge.

CONCLUSIONS

First AM-PAC score of 15 or less can help predict non-home discharge. A goal of daily AM-PAC increases of 0.625 points toward a final AM-PAC score of 17 can aid in achieving home discharge. The early AM-PAC mobility threshold of ≤15 may help prepare for non-home discharge, while AM-PAC daily changes per day <0.625 and final AM-PAC <17 may provide goals for mobility improvement during the early postoperative period in order to prevent non-home discharge.

Does Age Younger Than 65 Affect Clinical Outcomes in Medicare Patients Undergoing Lumbar Fusion?

STUDY DESIGN

This was a retrospective cohort study.

OBJECTIVE

To determine if age (younger than 65) and Medicare status affect patient outcomes following lumbar fusion.

SUMMARY OF BACKGROUND DATA

Medicare is a common spine surgery insurance provider, but most qualifying patients are older than age 65. There is a paucity of literature investigating clinical outcomes for Medicare patients under the age of 65.

MATERIALS AND METHODS

Patients 40 years and older who underwent lumbar fusion surgery between 2014 and 2019 were queried from electronic medical records. Patients with >2 levels fused, >3 levels decompressed, incomplete patient-reported outcome measures (PROMs), revision procedures, and tumor/infection diagnosis were excluded. Patients were placed into 4 groups based on Medicare status and age: no Medicare under 65 years (NM<65), no Medicare 65 years or older (NM≥65), yes Medicare under 65 (YM<65), and yes Medicare 65 years or older (YM≥65). T tests and χ 2 tests analyzed univariate comparisons depending on continuous or categorical type. Multivariate regression for ∆PROMs controlled for confounders. Alpha was set at 0.05.

RESULTS

Of the 1097 patients, 567 were NM<65 (51.7%), 133 were NM≥65 (12.1%), 42 were YM<65 (3.8%), and 355 were YM≥65 (32.4%). The YM<65 group had significantly worse preoperative Visual Analog Scale back ( P =0.01) and preoperative and postoperative Oswestry Disability Index (ODI), Short-Form 12 Mental Component Score (MCS-12), and Physical Component Score (PCS-12). However, on regression analysis, there were no significant differences in ∆PROMs for YM <65 compared with YM≥65, and NM<65. NM<65 (compared with YM<65) was an independent predictor of decreased improvement in ∆ODI following surgery (β=12.61, P =0.007); however, overall the ODI was still lower in the NM<65 compared with the YM<65.

CONCLUSION

Medicare patients younger than 65 years undergoing lumbar fusion had significantly worse preoperative and postoperative PROMs. The perioperative improvement in outcomes was similar between groups with the exception of ∆ODI, which demonstrated greater improvement in Medicare patients younger than 65 compared with non-Medicare patients younger than 65.

LEVEL OF EVIDENCE

Level III (treatment).

Simple and actionable preoperative prediction of postoperative healthcare needs of single-level lumbar fusion patients

OBJECTIVE

Preoperative prediction of a patient's postoperative healthcare utilization is challenging, and limited guidance currently exists. The objective of the present study was to assess the capability of individual risk-related patient characteristics, which are available preoperatively, that may predict discharge disposition prior to lumbar fusion.

METHODS

In total, 1066 consecutive patients who underwent single-level, posterior-only lumbar fusion at a university health system were enrolled. Patients were prospectively asked 4 nondemographic questions from the Risk Assessment and Prediction Tool during preoperative office visits to evaluate key risk-related characteristics: baseline walking ability, use of a gait assistive device, reliance on community supports (e.g., Meals on Wheels), and availability of a postoperative home caretaker. The primary outcome was discharge disposition (home vs skilled nursing facility/acute rehabilitation). Logistic regression was performed to analyze the ability of each risk-related characteristic to predict likelihood of home discharge.

RESULTS

Regression analysis demonstrated that improved baseline walking ability (OR 3.17), ambulation without a gait assistive device (OR 3.13), and availability of a postoperative home caretaker (OR 1.99) each significantly predicted an increased likelihood of home discharge (all p < 0.0001). However, reliance on community supports did not significantly predict discharge disposition (p = 0.94).

CONCLUSIONS

Patient mobility and the availability of a postoperative caretaker, when determined preoperatively, strongly predict a patient's healthcare utilization in the setting of single-level, posterior lumbar fusion. These findings may help surgeons to streamline preoperative clinic workflow and support the patients at highest risk in a targeted fashion.

Does Age and Medicare Status Affect Clinical Outcomes in Patients Undergoing Anterior Cervical Discectomy and Fusion?

OBJECTIVE

The objective of this study was to determine if Medicare status and age affect clinical outcomes following anterior cervical discectomy and fusion.

METHODS

Patients who underwent cervical discectomy and fusion between 2014 and 2020 with complete preoperative and 1-year postoperative patient-reported outcome measures (PROMs) were grouped based on Medicare status and age: no Medicare under 65 years (NM < 65), Medicare under 65 years (M < 65), no Medicare 65 years or older (NM ≥ 65), and Medicare 65 years or older (M ≥ 65). Multivariate regression for ΔPROMs (Δ: postoperative minus preoperative) controlled for confounding differences between groups. Significant was set at P < 0.05.

RESULTS

A total of 1288 patients were included, with each group improving in the visual analog score (VAS) Neck (all, P < 0.001), VAS Arm (M < 65: P = 0.003; remaining groups: P < 0.001), and Neck Disability Index (M < 65: P = 0.009; remaining groups: P < 0.001) following surgery. Only M < 65 did not significantly improve in the Physical Component Score (PCS-12) and modified Japanese Orthopaedic Association (mJOA) score (P = 0.256 and P = 0.092, respectively). When comparing patients under 65 years, non-Medicare patients had better preoperative PCS-12 (P < 0.001), Neck Disability Index (P < 0.001), and modified Japanese Orthopaedic Association (P < 0.001), as well as better postoperative values for all PROMs (P < 0.001), but there were no differences in ΔPROMs. Multivariate analysis identified M < 65 to be an independent predictor of decreased improvement in ΔPCS-12 (β = -4.07, P = 0.015), ΔVAS Neck (β = 1.17, P = 0.010), and ΔVAS Arm (β = 1.15, P = 0.025) compared to NM < 65.

CONCLUSIONS

Regardless of age and Medicare status, all patients undergoing cervical discectomy and fusion had significant clinical improvement postoperatively. However, Medicare patients under age 65 have a smaller magnitude of improvement in PROMs.

Frailty in Patients Undergoing Surgery for Brain Tumors: A Systematic Review of the Literature

BACKGROUND

Emerging literature suggests that frailty may be an important driver of postoperative outcomes in patients undergoing surgery for brain tumors. We systematically reviewed the literature on frailty in patients with brain tumor with respect to 3 questions: What methods of frailty assessment have been applied to patients with brain tumor? What thresholds have been defined to distinguish between different levels of frailty? What clinical outcomes does frailty predict in patients with brain tumor?

METHODS

A literature search was conducted using PubMed, Embase, The Cochrane Library, Web of Science, Scopus, and ClinicalTrials.gov. Included studies were specific to patients with brain tumor, used a validated instrument to assess frailty, and measured the impact of frailty on postoperative outcomes.

RESULTS

Of 753 citations, 21 studies met our inclusion criteria. Frailty instruments were studied, in order of frequency reported, including the 5-factor modified frailty index, 11-factor modified frailty index, Johns Hopkins Adjusted Clinical Groups frailty-defining diagnosis indicator, and Hopkins Frailty Score. Multiple different conventions and thresholds were reported for distinguishing the levels of frailty. Clinical outcomes associated with frailty included mortality, survival, complications, length of stay, charges, costs, discharge disposition, readmissions, and operative time.

CONCLUSIONS

Frailty is an increasingly popular concept in patients with brain tumor that is associated with important clinical outcomes. However, the extant literature is largely comprised of retrospective studies with heterogeneous definitions of frailty, thresholds for defining levels of frailty, and patient populations. Further work is needed to understand best practices in assessing frailty in patients with brain tumor and applying these concepts to clinical practice.

Neighborhood-level Socioeconomic Status Predicts Extended Length of Stay Following Elective Anterior Cervical Spine Surgery

BACKGROUND

A significant portion of healthcare spending is driven by a small percentage of the overall population. Understanding risk factors predisposing patients to disproportionate utilization of healthcare resources is critical. Our objective was to identify risk factors leading to a prolonged length of stay (LOS) following cervical spine surgery.

METHODS

A single center cohort analysis was performed on patients who underwent elective anterior spine surgery from 2015-2021. Multivariate logistic regression evaluated the effects of sociodemographic factors including Area of Deprivation Index (quantifies income, education, employment, and housing quality), procedural, and discharge characteristics on postoperative LOS. Extended LOS was defined as greater than the 90^th percentile in midnights for the study population (greater than or equal to three midnights).

RESULTS

There were 686 patients included in the study, with a mean age of 57 years (range 26-92), median of 1 level (1-4) fused, and median LOS of 1 midnight (IQR 1,2). After adjusting for confounders, patients had increased odds of extended LOS if they were highly disadvantaged on the Area of Deprivation Index (ADI, OR=2.24, 95% CI=1.04 - 4.82; p=.039); had surgery on Thursday or Friday (OR=1.94; 1.01 - 3.72; p=.046); had a corpectomy performed (OR=2.81; 1.26 - 6.28; p=.012); or discharged not to home (OR=8.24; 2.88 - 23.56; p<.001). Patients with extended LOS were more likely to present to the emergency department or be re-admitted within 30 days after discharge (p=.024).

CONCLUSION

After adjusting for potential cofounders, patients most disadvantaged on ADI were more likely to have an extended LOS.

Predicting High-Value Care Outcomes After Surgery for Non-Skull Base Meningiomas

OBJECTIVE

A need exists to better understand the prognostic factors that influence high-value care outcomes after meningioma surgery. The goal of the present study was to develop predictive models to determine the patients at risk of experiencing an extended hospital length of stay (LOS), nonroutine discharge disposition, and/or a 90-day hospital readmission after non-skull base meningioma resection.

METHODS

In the present study, we analyzed the data from 396 patients who had undergone surgical resection of non-skull base meningiomas at a single institution between January 1, 2005 and December 31, 2020. The Mann-Whitney U test was used for bivariate analysis of the continuous variables and the Fisher exact test for bivariate analysis of the categorical variables. A multivariate analysis was conducted using logistic regression models.

RESULTS

Most patients had had a falcine or parasagittal meningioma (66.2%), with the remainder having convexity (31.8%) or intraventricular (2.0%) tumors. Nonelective surgery (P < 0.0001) and an increased tumor volume (P = 0.0022) were significantly associated with a LOS >4 days on multivariate analysis. The independent predictors of a nonroutine discharge disposition included male sex (P = 0.0090), nonmarried status (P = 0.024), nonelective surgery (P = 0.0067), tumor location within the parasagittal or intraventricular region (P = 0.0084), and an increased modified frailty index score (P = 0.039). Hospital readmission within 90 days was independently associated with nonprivate insurance (P = 0.010) and nonmarried status (P = 0.0081). Three models predicting for a prolonged LOS, nonroutine discharge disposition, and 90-day readmission were implemented in the form of an open-access, online calculator (available at: https://neurooncsurgery3.shinyapps.io/non_skull_base_meningiomas/).

CONCLUSIONS

After external validation, our open-access, online calculator could be useful for assessing the likelihood of adverse postoperative outcomes for patients undergoing surgery of non-skull base meningioma.

Deployment of Clinical Prediction Models: A Practical Guide to Nomograms and Online Calculators

The use of predictive models within neurosurgery is increasing and many models described in published journal articles are made available to readers in formats such as nomograms and online calculators. The present chapter details a step-by-step methodology with accompanying R code that may be used to implement models both in the form of traditional nomograms and as open-access, online calculators through RStudio's Shinyapps. The chapter assumes a basic understanding of predictive modeling in R and utilizes open-access files created by the Machine Intelligence in Clinical Neuroscience (MICN) Lab (Department of Neurosurgery and the Clinical Neuroscience Center of the University Hospital Zurich). When implemented correctly, tools such as nomograms and predictive calculators have the potential to improve user understanding of the underlying statistical models, facilitate broader adoption, and to streamline the eventual use of such models in clinical settings.

Establishment and validation of a nomogram and web calculator for the risk of new vertebral compression fractures and cement leakage after percutaneous vertebroplasty in patients with osteoporotic vertebral compression fractures

PURPOSE

The aim of this work was to investigate the risk factors for cement leakage and new-onset OVCF after Percutaneous vertebroplasty (PVP) and to develop and validate a clinical prediction model (Nomogram).

METHODS

Patients with Osteoporotic VCF (OVCF) treated with PVP at Liuzhou People's Hospital from June 2016 to June 2018 were reviewed and met the inclusion criteria. Relevant data affecting bone cement leakage and new onset of OVCF were collected. Predictors were screened using univariate and multi-factor logistic analysis to construct Nomogram and web calculators. The consistency of the prediction models was assessed using calibration plots, and their predictive power was assessed by tenfold cross-validation. Clinical value was assessed using Decision curve analysis (DCA) and clinical impact plots.

RESULTS

Higher BMI was associated with lower bone mineral density (BMD). Higher BMI, lower BMD, multiple vertebral fractures, no previous anti-osteoporosis treatment, and steroid use were independent risk factors for new vertebral fractures. Cement injection volume, time to surgery, and multiple vertebral fractures were risk factors for cement leakage after PVP. The development and validation of the Nomogram also demonstrated the predictive ability and clinical value of the model.

CONCLUSIONS

The established Nomogram and web calculator (https://dr-lee.shinyapps.io/RefractureApp/) (https://dr-lee.shinyapps.io/LeakageApp/) can effectively predict the occurrence of cement leakage and new OVCF after PVP.

Does state malpractice environment affect outcomes following spinal fusions? A robust statistical and machine learning analysis of 549,775 discharges following spinal fusion surgery in the United States

OBJECTIVE

Spine surgery is especially susceptible to malpractice claims. Critics of the US medical liability system argue that it drives up costs, whereas proponents argue it deters negligence. Here, the authors study the relationship between malpractice claim density and outcomes.

METHODS

The following methods were used: 1) the National Practitioner Data Bank was used to determine the number of malpractice claims per 100 physicians, by state, between 2005 and 2010; 2) the Nationwide Inpatient Sample was queried for spinal fusion patients; and 3) the Area Resource File was queried to determine the density of physicians, by state. States were categorized into 4 quartiles regarding the frequency of malpractice claims per 100 physicians. To evaluate the association between malpractice claims and death, discharge disposition, length of stay (LOS), and total costs, an inverse-probability-weighted regression-adjustment estimator was used. The authors controlled for patient and hospital characteristics. Covariates were used to train machine learning models to predict death, discharge disposition not to home, LOS, and total costs.

RESULTS

Overall, 549,775 discharges following spinal fusions were identified, with 495,640 yielding state-level information about medical malpractice claim frequency per 100 physicians. Of these, 124,425 (25.1%), 132,613 (26.8%), 130,929 (26.4%), and 107,673 (21.7%) were from the lowest, second-lowest, second-highest, and highest quartile states, respectively, for malpractice claims per 100 physicians. Compared to the states with the fewest claims (lowest quartile), surgeries in states with the most claims (highest quartile) showed a statistically significantly higher odds of a nonhome discharge (OR 1.169, 95% CI 1.139-1.200), longer LOS (mean difference 0.304, 95% CI 0.256-0.352), and higher total charges (mean difference [log scale] 0.288, 95% CI 0.281-0.295) with no significant associations for mortality. For the machine learning models-which included medical malpractice claim density as a covariate-the areas under the curve for death and discharge disposition were 0.94 and 0.87, and the R2 values for LOS and total charge were 0.55 and 0.60, respectively.

CONCLUSIONS

Spinal fusion procedures from states with a higher frequency of malpractice claims were associated with an increased odds of nonhome discharge, longer LOS, and higher total charges. This suggests that medicolegal climate may potentially alter practice patterns for a given spine surgeon and may have important implications for medical liability reform. Machine learning models that included medical malpractice claim density as a feature were satisfactory in prediction and may be helpful for patients, surgeons, hospitals, and payers.

Does the ACS NSQIP Surgical Risk Calculator Accurately Predict Complications Rates After Anterior Lumbar Interbody Fusion Procedures?

STUDY DESIGN

Clinical case series.

OBJECTIVE

The aim of this study was to determine the effectiveness of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator in the prediction of complications after anterior lumbar interbody fusion (ALIF).

SUMMARY OF BACKGROUND DATA

Identifying at-risk patients may aid in the prevention of complications after spine procedures. The ACS NSQIP surgical risk calculator was developed to predict 30-day postoperative complications for a variety of operative procedures.

METHODS

Medical records of patients undergoing ALIF at our institution from 2009 to 2019 were retrospectively reviewed. Demographic and comorbidity variables were entered into the ACS NSQIP surgical risk calculator to generate percentage predictions for complication incidence within 30 days postoperatively. The observed incidences of these complications were also abstracted from the medical record. The predictive ability of the ACS NSQIP surgical risk calculator was assessed in comparison to the observed incidence of complications using area under the curve (AUC) analyses.

RESULTS

Two hundred fifty-three (253) patients were analyzed. The ACS NSQIP surgical risk calculator was a fair predictor of discharge to non-home facility (AUC 0.71) and surgical site infection (AUC 0.70). The ACS NSQIP surgical risk calculator was a good predictor of acute kidney injury/progressive renal insufficiency (AUC 0.81). The ACS NSQIP surgical risk calculator was not an adequate predictive tool for any other category, including: pneumonia, urinary tract infections, venous thromboembolism, readmission, reoperations, and aggregate complications (AUC < 0.70).

CONCLUSION

The ACS NSQIP surgical risk calculator is an adequate predictive tool for a subset of complications after ALIF including acute kidney injury/progressive renal insufficiency, surgical site infections, and discharge to non-home facilities. However, it is a poor predictor for all other complication groups. The reliability of the ACS NSQIP surgical risk calculator is limited, and further identification of models for risk stratification is necessary for patients undergoing ALIF.Level of Evidence: 3.

Prediction Models in Degenerative Spine Surgery: A Systematic Review

STUDY DESIGN

Systematic review.

OBJECTIVES

To review the existing literature of prediction models in degenerative spinal surgery.

METHODS

Review of PubMed/Medline and Embase databases was conducted to identify articles between January 1, 2000 and March 1, 2020 that reported prediction model performance for outcomes following elective degenerative spine surgery.

RESULTS

Thirty-one articles were included. Twenty studies were of thoracolumbar, 5 were of cervical, and 6 included all spine patients. Five studies were externally validated. Prediction models were developed using machine learning (42%) and logistic regression (42%) as well as other techniques. Web-based calculators were included in 45% of published articles. Various outcomes were investigated, including complications, infection, length of stay, discharge disposition, reoperation, readmission, disability score, back pain, leg pain, return to work, and opioid dependence.

CONCLUSIONS

Significant heterogeneity exists in methods used to develop prediction models of postoperative outcomes after degenerative spine surgery. Most internally validate their scores, but a few have been externally validated. Areas under the curve for most models range from 0.6 to 0.9. Techniques for development are becoming increasingly sophisticated with different machine learning tools. With further external validation, these models can be deployed online for patient, physician, and administrative use, and have the potential to optimize outcomes and maximize value in spine surgery.

Compilation and Analysis of Web-Based Orthopedic Personalized Predictive Tools: A Scoping Review

Web-based personalized predictive tools in orthopedic surgery are becoming more widely available. Despite rising numbers of these tools, many orthopedic surgeons may not know what tools are available, how these tools were developed, and how they can be utilized. The aim of this scoping review is to compile and synthesize the profile of existing web-based orthopedic tools. We conducted two separate PubMed searches-one a broad search and the second a more targeted one involving high impact journals-with the aim of comprehensively identifying all existing tools. These articles were then screened for functional tool URLs, methods regarding the tool's creation, and general inputs and outputs required for the tool to function. We identified 57 articles, which yielded 31 unique web-based tools. These tools involved various orthopedic conditions (e.g., fractures, osteoarthritis, musculoskeletal neoplasias); interventions (e.g., fracture fixation, total joint arthroplasty); outcomes (e.g., mortality, clinical outcomes). This scoping review highlights the availability and utility of a vast array of web-based personalized predictive tools for orthopedic surgeons. Increased awareness and access to these tools may allow for better decision support, surgical planning, post-operative expectation management, and improved shared decision-making.

Predictive Model and Online Calculator for Discharge Disposition in Brain Tumor Patients

BACKGROUND

In the era of value-based payment models, it is imperative for neurosurgeons to eliminate inefficiencies and provide high-quality care. Discharge disposition is a relevant consideration with clinical and economic ramifications in brain tumor patients. We developed a predictive model and online calculator for postoperative non-home discharge disposition in brain tumor patients that can be incorporated into preoperative workflows.

METHODS

We reviewed all brain tumor patients at our institution from 2017 to 2019. A predictive model of discharge disposition containing preoperatively available variables was developed using stepwise multivariable logistic regression. Model performance was assessed using receiver operating characteristic curves and calibration curves. Internal validation was performed using bootstrapping with 2000 samples.

RESULTS

Our cohort included 2335 patients who underwent 2586 surgeries with a 16% non-home discharge rate. Significant predictors of non-home discharge were age >60 years (odds ratio [OR], 2.02), African American (OR, 1.73) or Asian (OR, 2.05) race, unmarried status (OR, 1.48), Medicaid insurance (OR, 1.90), admission from another health care facility (OR, 2.30), higher 5-factor modified frailty index (OR, 1.61 for 5-factor modified frailty index ≥2), and lower Karnofsky Performance Status (increasing OR with each 10-point decrease in Karnofsky Performance Status). The model was well calibrated and had excellent discrimination (optimism-corrected C-statistic, 0.82). An open-access calculator was deployed (https://neurooncsurgery.shinyapps.io/discharge_calc/).

CONCLUSIONS

A strongly performing predictive model and online calculator for non-home discharge disposition in brain tumor patients was developed. With further validation, this tool may facilitate more efficient discharge planning, with consequent improvements in quality and value of care for brain tumor patients.

Utility of the 5-Item Modified Frailty Index for Predicting Adverse Outcomes Following Elective Anterior Cervical Discectomy and Fusion

BACKGROUND

Frailty is an increasingly studied tool for preoperative risk stratification, but its prognostic value for anterior cervical discectomy and fusion (ACDF) patients is unclear. We sought to evaluate the association of the 5-item modified Frailty Index (5i-mFI) with 30-day adverse outcomes following ACDF and its predictive performance compared with other common metrics.

METHODS

The National Surgical Quality Improvement Program was queried from 2016-2018 for patients undergoing elective ACDF for degenerative diseases. Outcomes of interest included 30-day complications, extended length of stay (LOS), non-home discharge, and unplanned readmissions. Unadjusted/adjusted odds ratios were calculated. The discriminatory performance of the 5i-mFI compared with age, American Society of Anesthesiologists (ASA) classification, and body mass index was computed using the area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 23,754 patients were identified. On adjusted analysis, an index of 1 was significantly associated with extended LOS, non-home discharge, and unplanned readmissions (P < 0.001, P = 0.023, P = 0.003, respectively), but not complications (all P > 0.05). An index ≥2 was significantly associated with each outcome (all P < 0.001). The 5i-mFI was found to have a significantly higher AUC than body mass index for each outcome, a similar AUC compared with ASA classification and age for complications and unplanned readmissions, and a significantly lower AUC than ASA classification and age for extended LOS and non-home discharge.

CONCLUSIONS

The 5i-mFI was found to be significantly associated with 30-day adverse outcomes following ACDF but had similar or lesser predictive performance compared with more universally available and easily implemented metrics, such as ASA classification and age.