The Evolution of Risk Assessment in Spine Surgery: A Narrative Review

BACKGROUND

Risk assessment is critically important in elective and high-risk interventions, particularly spine surgery. This narrative review describes the evolution of risk assessment from the earliest instruments focused on general surgical risk stratification, to more accurate and spine-specific risk calculators that quantified risk, to the current era of big data.

METHODS

The PubMed and SCOPUS databases were queried on October 11, 2023 using search terms to identify risk assessment tools (RATs) in spine surgery. A total of 108 manuscripts were included after screening with full-text review using the following inclusion criteria: 1) study population of adult spine surgical patients, 2) studies describing validation and subsequent performance of preoperative RATs, and 3) studies published in English.

RESULTS

Early RATs provided stratified patients into broad categories and allowed for improved communication between physicians. Subsequent risk calculators attempted to quantify risk by estimating general outcomes such as mortality, but then evolved to estimate spine-specific surgical complications. The integration of novel concepts such as invasiveness, frailty, genetic biomarkers, and sarcopenia led to the development of more sophisticated predictive models that estimate the risk of spine-specific complications and long-term outcomes.

CONCLUSIONS

RATs have undergone a transformative shift from generalized risk stratification to quantitative predictive models. The next generation of tools will likely involve integration of radiographic and genetic biomarkers, machine learning, and artificial intelligence to improve the accuracy of these models and better inform patients, surgeons, and payers.

Post-operative neurosurgery outcomes by race/ethnicity among enhanced recovery after surgery (ERAS) participants

OBJECTIVE

Prior work reveals that Enhanced Recovery After Surgery (ERAS) programs decrease opioid use, improve mobilization, and shorten length of stay (LOS) among patients undergoing spine surgery. The impact of ERAS on outcomes by race/ethnicity is unknown. This study examined outcomes by race/ethnicity among neurosurgical patients enrolled in an ERAS program.

METHODS

Patients undergoing elective spine or peripheral nerve surgeries at a multi-hospital university health system from April 2017 to November 2020 were enrolled in an ERAS program that involves preoperative, perioperative, and postoperative phases focused on improving outcomes through measures such as specialty consultations for co-morbidities, multimodal analgesia, early mobilization, and wound care education. The following outcomes for ERAS patients were compared by race/ethnicity: length of stay, discharge disposition, complications, readmission, pain level at discharge, and post-operative health rating. We estimated the association between race/ethnicity and the outcomes using linear and logistic regression models adjusting for age, sex, insurance, BMI, comorbid conditions, and surgery type.

RESULTS

Among participants (n = 3449), 2874 (83.3%) were White and 575 (16.7%) were Black, Indigenous, and people of color (BIPOC). BIPOC patients had significantly longer mean length of stay compared to White patients (3.8 vs. 3.4 days, p = 0.005) and were significantly more likely to be discharged to a rehab or subacute nursing facility compared to White patients (adjusted odds ratio (95% CI): 3.01 (2.26-4.01), p < 0.001). The complication rate did not significantly differ between BIPOC and White patients (13.7% vs. 15.5%, p = 0.29). BIPOC patients were not significantly more likely to be readmitted within 30 days compared to White patients in the adjusted model (adjusted odds ratio (95% CI): 1.30 (0.91-1.86), p = 0.15) CONCLUSION: BIPOC as compared to White ERAS participants in ERAS undergoing neurosurgical procedures had significantly longer hospital stays and were significantly less likely to be discharged home. ERAS protocols present an opportunity to provide consistent high quality post-operative care, however while there is evidence that it improves care in aggregate, our results suggest significant disparities in outcomes by patient race/ethnicity despite enrollment in ERAS. Future inquiry must identify contributors to these disparities in the recovery pathway.

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.

RAPT score and preoperative factors to predict discharge location following adult spinal deformity surgery

PURPOSE

To assess factors, including RAPT score, predictive of non-home discharges following adult spinal deformity (ASD) operations.

METHODS

Adults who underwent thoracolumbar instrumented fusions to the pelvis for ASD (1/2019-1/2020) were reviewed. Patient demographics, RAPT metrics, hospital length of stay (LOS), operative details, and complications were compared between patients discharged home and non-home. Univariate and multivariate analyses were performed using logistic regression to determine the relative risk of non-home discharge. Area Under the Receiver Operating Characteristic curve (AUROC) for RAPT score and non-home discharge was also determined.

RESULTS

Ninety-nine patients (average age 68 ± 9 years; female-64; average RAPT 8.6 ± 2.2) were analyzed. Operations had the following characteristics: average # levels fused 11 ± 3, revisions 54%, anterior-posterior 70%, 3-column osteotomies 23%. Average LOS was 8.5 ± 3.6 days. The majority of patients (75.8%) had non-home discharges. Non-home discharges had significantly lower RAPT scores (8.3 vs. 9.6; p = 0.02), more advanced age (70 vs. 63 years; p = 0.01), and higher Charlson Comorbidity Index (CCI) scores (3.6 vs. 2.5; p < 0.01) compared to home discharges. On univariate analysis, factors significantly associated with non-home discharge were older age [relative risk (RR) 1.09, p < 0.01], higher CCI (RR 1.73, p = 0.01), total # levels fused (RR 1.24, p = 0.04), and lower RAPT scores (RR 0.71, p = 0.01). RAPT score < 8 was most predictive of non-home discharge (RR 4.87, p = 0.04). An AUROC relating RAPT scores and non-home discharge was 0.7.

CONCLUSIONS

Non-home discharges after ASD operations are common. Of the four factors associated with non-home discharges (elderly age, higher CCI, total number of levels fused, RAPT score), a RAPT score < 8 was most predictive. The RAPT score holds promising utility for pre-operative patient counseling and discharge planning for adults undergoing operations for spinal deformity.

Composite Score for Outcome Prediction in Gynecologic Surgery Patients

BACKGROUND

The LACE+ index is a well-studied metric that compacts patient data in an effort to assess readmission risk.

PURPOSE

Assess the capacity of LACE+ scores for predicting short-term undesirable outcomes in an entire single-center population of patients undergoing gynecologic surgery.

IMPORTANCE AND RELEVANCE TO HEALTHCARE QUALITY

Proactive identification of high-risk patients, with tools such as the LACE+ index, may serve as the first step toward appropriately engaging resources for reducing readmissions.

METHODS

This study was a retrospective analysis that used coarsened exact matching. All gynecologic surgery cases over 2 years within a single health system (n = 12,225) were included for analysis. Outcomes of interest were unplanned readmission, emergency room (ER) evaluation, and return to surgery. Composite LACE+ scores were separated into quartiles and matched. For outcome comparison, matched patients were assessed by LACE+ quartile, using Q4 as the reference group.

RESULTS

Increasing LACE+ score reflected a higher rate of readmission (p = .003, p = .001) and visits to the ER at 30 postoperative days (p < .001).

CONCLUSION

The data presented here suggest that LACE+ index is a viable metric for patient outcome prediction following gynecologic surgery.

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.

The Risk Assessment and Prediction Tool Accurately Predicts Discharge Destination After Revision Hip and Knee Arthroplasty

BACKGROUND

The Risk Assessment and Prediction Tool (RAPT) was developed and validated to predict discharge disposition after primary total hip and knee arthroplasty (THA/TKA). To date, there are no studies evaluating the applicability and accuracy of RAPT for revision THA/TKA. This study aims to determine the predictive accuracy of the RAPT for revision THA/TKA.

METHODS

Prospectively collected data from a single tertiary academic medical center were retrospectively analyzed for patients undergoing revision THA/TKA between January 2016 and July 2019. RAPT score was used to predict their postoperative discharge destination and its predictive accuracy was calculated. Patient risk (low, intermediate, and high) for postoperative inpatient rehabilitation facilities or skilled nursing facilities were determined based on the predictive accuracy of each RAPT score. Other factors evaluated included patient-reported discharge expectation, body mass index, and American Society of Anesthesiologists scores.

RESULTS

A total of 716 consecutive revision THA/TKA episodes were analyzed. Overall, predictive accuracy of RAPT for discharge disposition was 83%. RAPT scores <3 and >8 were deemed high and low risk of discharge to a post-acute care facility, respectively. RAPT scores of 4 to 7 were still accurate 65%-71% of the time and were deemed to be intermediate-risk. RAPT score and patient-reported discharge expectation had the strongest correlation with actual discharge disposition.

CONCLUSION

The RAPT has high predictive accuracy for discharge planning in revision THA/TKA patients. Patient-expected discharge destination is a powerful modulator of the RAPT score and we suggest that it be taken into consideration for preoperative discharge planning.

Assessment of Short-Term Patient Outcomes Following Overlapping Orthopaedic Surgery at a Large Academic Medical Center

BACKGROUND

Overlapping surgery is a long-standing practice that has not been well studied. The aim of this study was to assess whether overlapping surgery is associated with untoward outcomes for orthopaedic patients.

METHODS

Coarsened exact matching was used to assess the impact of overlap on outcomes among elective orthopaedic surgical interventions (n = 18,316) over 2 years (2014 and 2015) at 1 health-care system. Overlap was categorized as any overlap, and subcategories of exclusively beginning overlap and exclusively end overlap. Study subjects were matched on the Charlson comorbidity index score, duration of surgery, surgical costs, body mass index, length of stay, payer, and race, among others. Serious unanticipated events were studied.

RESULTS

A total of 3,395 patients had any overlap and were matched (a match rate of 90.8% of 3,738). For beginning and end overlap, matched groups were created, with a match rate of 95.2% of 1043 and 94.7% of 863, respectively. Among matched patients, any overlap did not predict an unanticipated return to surgery at 30 days (8.2% for any overlap and 8.3% for no overlap; p = 0.922) or 90 days (14.1% and 14.1%, respectively; p = 1.000). Patients who had surgery with any overlap demonstrated no difference compared with controls with respect to reoperation, readmission, or emergency room (ER) visits at 30 or 90 days (a reoperation rate of 3.1% and 3.2%, respectively [p = 0.884] at 30 days and 4.2% and 3.5% [p = 0.173] at 90 days; a readmission rate of 10.3% and 11.0% [p = 0.352] at 30 days and 5.5% and 5.2% [p = 0.570] at 90 days; and an ER visit rate of 5.2% and 4.6% [p = 0.276] at 30 days and 4.8% and 4.3% [p = 0.304] at 90 days). Patients with surgical overlap showed reduced mortality compared with controls during follow-up (1.8% and 2.6%, respectively; p = 0.029). Patients with beginning and/or end overlap had a similar lack of association with serious unanticipated events; however, patients with end overlap showed an increased unexpected rate of return to the operating room after reoperation at 90 days (13.3% versus 9.7%; p = 0.015).

CONCLUSIONS

Nonconcurrent overlapping surgery was not associated with adverse outcomes in a large, matched orthopaedic surgery population across 1 academic health system.

LEVEL OF EVIDENCE

Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Undiagnosed Obstructive Sleep Apnea as Predictor of 90-Day Readmission for Brain Tumor Patients

BACKGROUND

Previously undiagnosed obstructive sleep apnea (OSA) is a known contributor to negative postoperative outcomes. The STOP-Bang questionnaire is a screening tool for OSA that has been validated in both medical and surgical populations. The authors have previously studied this screening tool in a brain tumor population at 30 days. The present study seeks to investigate the effectiveness of this questionnaire, for predicting 90-day readmissions in a population of brain tumor patients with previously undiagnosed OSA.

METHODS

Included for analysis were all patients undergoing craniotomy for supratentorial neoplasm at a multihospital, single academic medical center. Data were collected from supratentorial craniotomy cases for which the patient was alive at 90 days after surgery (n = 238). Simple logistic regression analyses were used to assess the ability of the STOP-Bang questionnaire and subsequent single variables to accurately predict patient outcomes at 90 days.

RESULTS

The sample included 238 brain tumor admissions, of which 50% were female (n = 119). The average STOP-Bang score was 1.95 ± 1.24 (range 0-7). A 1-unit higher increase in STOP-Bang score accurately predicted 90-day readmissions (odds ratio [OR] = 1.65, P = 0.001), 30- to 90-day emergency department visits (OR = 1.85, P < 0.001), and 30- to 90-day reoperation (OR = 2.32, P < 0.001) with fair accuracy as confirmed by the receiver operating characteristic (C-statistic = 0.65-0.76). However, the STOP-Bang questionnaire did not correlate with home discharge (P = 0.315).

CONCLUSIONS

The results of this study suggest that undiagnosed OSA, as evaluated by the STOP-Bang questionnaire, is an effective predictor of readmission risk and health system utilization in a brain tumor craniotomy population with previously undiagnosed OSA.

The Risk Assessment and Prediction Tool (RAPT) for Discharge Planning in a Posterior Lumbar Fusion Population

BACKGROUND

As the use of bundled care payment models has become widespread in neurosurgery, there is a distinct need for improved preoperative predictive tools to identify patients who will not benefit from prolonged hospitalization, thus facilitating earlier discharge to rehabilitation or nursing facilities.

OBJECTIVE

To validate the use of Risk Assessment and Prediction Tool (RAPT) in patients undergoing posterior lumbar fusion for predicting discharge disposition.

METHODS

Patients undergoing elective posterior lumbar fusion from June 2016 to February 2017 were prospectively enrolled. RAPT scores and discharge outcomes were recorded for patients aged 50 yr or more (n = 432). Logistic regression analysis was used to assess the ability of RAPT score to predict discharge disposition. Multivariate regression was performed in a backwards stepwise logistic fashion to create a binomial model.

RESULTS

Escalating RAPT score predicts disposition to home (P &lt; .0001). Every unit increase in RAPT score increases the chance of home disposition by 55.8% and 38.6% than rehab and skilled nursing facility, respectively. Further, RAPT score was significant in predicting length of stay (P = .0239), total surgical cost (P = .0007), and 30-d readmission (P &lt; .0001). Amongst RAPT score subcomponents, walk, gait, and postoperative care availability were all predictive of disposition location (P &lt; .0001) for both models. In a generalized multiple logistic regression model, the 3 top predictive factors for disposition were the RAPT score, length of stay, and age (P &lt; .0001, P &lt; .0001 and P = .0001, respectively).

CONCLUSION

Preoperative RAPT score is a highly predictive tool in lumbar fusion patients for discharge disposition.

Predicting nonroutine discharge after elective spine surgery: external validation of machine learning algorithms

OBJECTIVE

Nonroutine discharge after elective spine surgery increases healthcare costs, negatively impacts patient satisfaction, and exposes patients to additional hospital-acquired complications. Therefore, prediction of nonroutine discharge in this population may improve clinical management. The authors previously developed a machine learning algorithm from national data that predicts risk of nonhome discharge for patients undergoing surgery for lumbar disc disorders. In this paper the authors externally validate their algorithm in an independent institutional population of neurosurgical spine patients.

METHODS

Medical records from elective inpatient surgery for lumbar disc herniation or degeneration in the Transitional Care Program at Brigham and Women's Hospital (2013-2015) were retrospectively reviewed. Variables included age, sex, BMI, American Society of Anesthesiologists (ASA) class, preoperative functional status, number of fusion levels, comorbidities, preoperative laboratory values, and discharge disposition. Nonroutine discharge was defined as postoperative discharge to any setting other than home. The discrimination (c-statistic), calibration, and positive and negative predictive values (PPVs and NPVs) of the algorithm were assessed in the institutional sample.

RESULTS

Overall, 144 patients underwent elective inpatient surgery for lumbar disc disorders with a nonroutine discharge rate of 6.9% (n = 10). The median patient age was 50 years and 45.1% of patients were female. Most patients were ASA class II (66.0%), had 1 or 2 levels fused (80.6%), and had no diabetes (91.7%). The median hematocrit level was 41.2%. The neural network algorithm generalized well to the institutional data, with a c-statistic (area under the receiver operating characteristic curve) of 0.89, calibration slope of 1.09, and calibration intercept of -0.08. At a threshold of 0.25, the PPV was 0.50 and the NPV was 0.97.

CONCLUSIONS

This institutional external validation of a previously developed machine learning algorithm suggests a reliable method for identifying patients with lumbar disc disorder at risk for nonroutine discharge. Performance in the institutional cohort was comparable to performance in the derivation cohort and represents an improved predictive value over clinician intuition. This finding substantiates initial use of this algorithm in clinical practice. This tool may be used by multidisciplinary teams of case managers and spine surgeons to strategically invest additional time and resources into postoperative plans for this population.

Assessing the utility of an IoS application in the perioperative care of spine surgery patients: the NeuroPath Pilot study

BACKGROUND

In an attempt to improve care while decreasing costs and postoperative pain, we developed a novel IoS mobile health application, NeuroPath. The objective of this innovative app is to integrate enhanced recovery after surgery (ERAS) principles, patient education, and real-time pain and activity monitoring in a home setting with unencumbered two-way communication.

METHODS

The NeuroPath application was built over 18 months, with support from Apple, Medable, the Department of Information-Technology and the Department of Neurosurgery. Target areas addressed by NeuroPath include patient prep for surgery, perioperative risk mitigation, activity monitoring, wound care, and opioid use management. These target areas are monitored through a provider app, which is downloaded to the care providers IPad Mini. The provider app permits real time viewing of wound healing (patient incision photographs), activity levels, pain levels, and narcotic usage. Participants are given a daily To-Do list, via the Care Card section of the interface. The To-Do list presents the patient with specific tasks for exercise, instructions to wash incision area, pre-operative instructions, directions for discussing medication with care team, among other patient specific recommendations.

RESULTS

Of the 30 patients enrolled in the pilot study, there was a range of activity on the app. Patients with high involvement in the app logged in nearly every day from a week pre-op to >45 days post-op. Data for patients that utilized the app and uploaded regularly show trends of appropriately healing wounds, decreasing levels of pain, increasing step counts, and discontinuation of narcotics.

CONCLUSIONS

This pilot study of the NeuroPath app demonstrates its potential utility for improving quality of patient care without increased costs. Participants who regularly used the app showed consistent improvement throughout the post-operative recovery period (increasing ambulation, decreasing pain and guided reduction in narcotic usage).