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.

Reliable Prediction of Discharge Disposition Following Cervical Spine Surgery With Ensemble Machine Learning and Validation on a National Cohort

STUDY DESIGN

A retrospective cohort study.

OBJECTIVE

The purpose of this study is to develop a machine learning algorithm to predict nonhome discharge after cervical spine surgery that is validated and usable on a national scale to ensure generalizability and elucidate candidate drivers for prediction.

SUMMARY OF BACKGROUND DATA

Excessive length of hospital stay can be attributed to delays in postoperative referrals to intermediate care rehabilitation centers or skilled nursing facilities. Accurate preoperative prediction of patients who may require access to these resources can facilitate a more efficient referral and discharge process, thereby reducing hospital and patient costs in addition to minimizing the risk of hospital-acquired complications.

METHODS

Electronic medical records were retrospectively reviewed from a single-center data warehouse (SCDW) to identify patients undergoing cervical spine surgeries between 2008 and 2019 for machine learning algorithm development and internal validation. The National Inpatient Sample (NIS) database was queried to identify cervical spine fusion surgeries between 2009 and 2017 for external validation of algorithm performance. Gradient-boosted trees were constructed to predict nonhome discharge across patient cohorts. The area under the receiver operating characteristic curve (AUROC) was used to measure model performance. SHAP values were used to identify nonlinear risk factors for nonhome discharge and to interpret algorithm predictions.

RESULTS

A total of 3523 cases of cervical spine fusion surgeries were included from the SCDW data set, and 311,582 cases were isolated from NIS. The model demonstrated robust prediction of nonhome discharge across all cohorts, achieving an area under the receiver operating characteristic curve of 0.87 (SD=0.01) on both the SCDW and nationwide NIS test sets. Anterior approach only, age, elective admission status, Medicare insurance status, and total Elixhauser Comorbidity Index score were the most important predictors of discharge destination.

CONCLUSIONS

Machine learning algorithms reliably predict nonhome discharge across single-center and national cohorts and identify preoperative features of importance following cervical spine fusion surgery.

A Turkish Study to Identify the Discharge Learning Needs of Spinal Surgery Patients

ABSTRACT

PURPOSE: The aim of this study was to identify the learning needs of spinal surgery patients before hospital discharge. METHODS: This cross-sectional study consisted of 117 spinal surgery patients admitted to the neurosurgery department between October 2019 and March 2020. Data were collected using a descriptive information form, visual analog scale, and the Patient Learning Needs Scale. Data were analyzed using descriptive statistics, Mann-Whitney U and Kruskal-Wallis tests, and Spearman correlation analysis. RESULTS: The mean age of the participants was 54 years, 54.7% were male, and 59% underwent surgery because of spinal disc herniation. The mean Patient Learning Needs Scale score was 188.74. The primary learning needs of the patients were related to the dimensions of activities of living, medication, treatment, and complications, whereas the feelings related to condition were the least-demanded dimension of learning needs. Sex and occupation were the primary factors influencing learning needs. CONCLUSION: The level of learning needs in spinal surgery patients was relatively high. Therefore, discharge education may be planned in line with the learning needs and priorities of these patients, and sex and occupation may be considered while planning discharge education.

Time of Discharge and 30-Day Re-Presentation to an Acute Care Setting After Elective Lumbar Decompression Surgery

BACKGROUND

Evidence regarding the consequence of efforts to increase patient throughput and decrease length of stay in the context of elective spine surgery is limited.

OBJECTIVE

To evaluate whether early time of discharge results in increased rates of hospital readmission or return to emergency department for patients admitted after elective, posterior, lumbar decompression surgery.

METHODS

We conducted a retrospective cohort study of 779 patients admitted to hospital after undergoing elective, posterior, lumbar decompression surgery. Multiple logistic regression evaluated the relationship between time of discharge and the primary outcome of return to acute care within 30 days, while controlling for sociodemographic, procedural, and discharge characteristics.

RESULTS

In multiple logistic regression, time of discharge earlier in the day was not associated with increased odds of return to acute care within 30 days (odds ratio [OR] 1.18, 95% CI 0.92-1.52, P = .19). Weekend discharge (OR 1.99, 95% CI 1.04-3.79, P = .04) increased the likelihood of return to acute care. Surgeon experience (<1 year of attending practice, OR 0.43, 95% CI 0.19-1.00, P = .05 and 2-5 years of attending practice, OR 0.50, 95% CI 0.25-1.01, P = .054), weekend discharge (OR 0.49, 95% CI 0.27-0.89, P = .02), and physical therapy evaluation (OR 0.20, 95% CI 0.12-0.33, P < .001) decreased the likelihood of discharge before noon.

CONCLUSION

Time of discharge is not associated with risk of readmission or presentation to the emergency department after elective lumbar decompression. Weekend discharge is independently associated with increased risk of readmission and decreased likelihood of prenoon discharge.

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.

Patient frailty association with cerebral arteriovenous malformation microsurgical outcomes and development of custom risk stratification score: an analysis of 16,721 nationwide admissions

OBJECTIVE

Patient frailty is associated with poorer perioperative outcomes for several neurosurgical procedures. However, comparative accuracy between different frailty metrics for cerebral arteriovenous malformation (AVM) outcomes is poorly understood and existing frailty metrics studied in the literature are constrained by poor specificity to neurosurgery. This aim of this paper was to compare the predictive ability of 3 frailty scores for AVM microsurgical admissions and generate a custom risk stratification score.

METHODS

All adult AVM microsurgical admissions in the National (Nationwide) Inpatient Sample (2002–2017) were identified. Three frailty measures were analyzed: 5-factor modified frailty index (mFI-5; range 0–5), 11-factor modified frailty index (mFI-11; range 0–11), and Charlson Comorbidity Index (CCI) (range 0–29). Receiver operating characteristic curves were used to compare accuracy between metrics. The analyzed endpoints included in-hospital mortality, routine discharge, complications, length of stay (LOS), and hospitalization costs. Survey-weighted multivariate regression assessed frailty-outcome associations, adjusting for 13 confounders, including patient demographics, hospital characteristics, rupture status, hydrocephalus, epilepsy, and treatment modality. Subsequently, k-fold cross-validation and Akaike information criterion–based model selection were used to generate a custom 5-variable risk stratification score called the AVM-5. This score was validated in the main study population and a pseudoprospective cohort (2018–2019).

RESULTS

The authors analyzed 16,271 total AVM microsurgical admissions nationwide, with 21.0% being ruptured. The mFI-5, mFI-11, and CCI were all predictive of lower rates of routine discharge disposition, increased perioperative complications, and longer LOS (all p < 0.001). Their AVM-5 risk stratification score was calculated from 5 variables: age, hydrocephalus, paralysis, diabetes, and hypertension. The AVM-5 was predictive of decreased rates of routine hospital discharge (OR 0.26, p < 0.001) and increased perioperative complications (OR 2.42, p < 0.001), postoperative LOS (+49%, p < 0.001), total LOS (+47%, p < 0.001), and hospitalization costs (+22%, p < 0.001). This score outperformed age, mFI-5, mFI-11, and CCI for both ruptured and unruptured AVMs (area under the curve [AUC] 0.78, all p < 0.001). In a pseudoprospective cohort of 2005 admissions from 2018 to 2019, the AVM-5 remained significantly associated with all outcomes except for mortality and exhibited higher accuracy than all 3 earlier scores (AUC 0.79, all p < 0.001).

CONCLUSIONS

Patient frailty is predictive of poorer disposition and elevated complications, LOS, and costs for AVM microsurgical admissions. The authors’ custom AVM-5 risk score outperformed age, mFI-5, mFI-11, and CCI while using threefold less variables than the CCI. This score may complement existing AVM grading scales for optimization of surgical candidates and identification of patients at risk of postoperative medical and surgical morbidity.

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.

Association of Patient Frailty With Vestibular Schwannoma Resection Outcomes and Machine Learning Development of a Vestibular Schwannoma Risk Stratification Score

BACKGROUND

Patient frailty is predictive of higher neurosurgical morbidity and mortality. However, existing frailty measures are hindered by lack of specificity to neurosurgery.

OBJECTIVE

To analyze the association between 3 risk stratification scores and outcomes for nationwide vestibular schwannoma (VS) resection admissions and develop a custom VS risk stratification score.

METHODS

We identified all VS resection admissions in the National Inpatient Sample (2002-2017). Three risk stratification scores were analyzed: modified Frailty Index-5, modified Frailty Index-11(mFI-11), and Charlson Comorbidity Index (CCI). Survey-weighted multivariate regression evaluated associations between frailty and inpatient outcomes, adjusting for patient demographics, hospital characteristics, and disease severity. Subsequently, we used k-fold cross validation and Akaike Information Criterion-based model selection to create a custom risk stratification score.

RESULTS

We analyzed 32 465 VS resection admissions. High frailty, as identified by the mFI-11 (odds ratio [OR] = 1.27, P = .021) and CCI (OR = 1.72, P < .001), predicted higher odds of perioperative complications. All 3 scores were also associated with lower routine discharge rates and elevated length of stay (LOS) and costs (all P < .05). Our custom VS-5 score (https://skullbaseresearch.shinyapps.io/vs-5_calculator/) featured 5 variables (age ≥60 years, hydrocephalus, preoperative cranial nerve palsies, diabetes mellitus, and hypertension) and was predictive of higher mortality (OR = 6.40, P = .001), decreased routine hospital discharge (OR = 0.28, P < .001), and elevated complications (OR = 1.59, P < .001), LOS (+48%, P < .001), and costs (+23%, P = .001). The VS-5 outperformed the modified Frailty Index-5, mFI-11, and CCI in predicting routine discharge (all P < .001), including in a pseudoprospective cohort (2018-2019) of 3885 admissions.

CONCLUSION

Patient frailty predicted poorer inpatient outcomes after VS surgery. Our custom VS-5 score outperformed earlier risk stratification scores.

Influence of Time of Discharge and Length of Stay on 30-Day Outcomes After Elective Anterior Cervical Spine Surgery

BACKGROUND

Encouraging early time of discharge (TOD) for medical inpatients is commonplace and may potentially improve patient throughput. It is unclear, however, whether early TOD after elective spine surgery achieves this goal without a consequent increase in re-presentations to the hospital.

OBJECTIVE

To evaluate whether early TOD results in increased rates of hospital readmission or return to the emergency department after elective anterior cervical spine surgery.

METHODS

We analyzed 686 patients who underwent elective uncomplicated anterior cervical spine surgery at a single institution. Logistic regression was used to evaluate the relationship between sociodemographic, procedural, and discharge characteristics, and the outcomes of readmission or return to the emergency department and TOD.

RESULTS

In multiple logistic regression, TOD was not associated with increased risk of readmission or return to the emergency department within 30 days of surgery. Weekend discharge (odds ratio [OR] 0.33, 95% CI 0.21-0.53), physical therapy evaluation (OR 0.44, 95% CI 0.28-0.71), and occupational therapy evaluation (OR 0.32, 95% CI 0.17-0.63) were all significantly associated with decreased odds of discharge before noon. Disadvantaged status, as measured by area of deprivation index, was associated with increased odds of readmission or re-presentation (OR 1.86, 95% CI 0.95-3.66), although this result did not achieve statistical significance.

CONCLUSION

There does not appear to be an association between readmission or return to the emergency department and early TOD after elective spine surgery. Overuse of inpatient physical and occupational therapy consultations may contribute to decreased patient throughput in surgical admissions.

Enhanced Recovery After Surgery (ERAS) for Cranial Tumor Resection: A Review

Enhanced Recovery After Surgery (ERAS) protocols describe a standardized method of preoperative, perioperative, and postoperative care to enhance outcomes and minimize complication risks surrounding elective surgical intervention. A growing body of evidence is being generated as we learn to apply principles of ERAS standardization to neurosurgical patients. First applied in spinal surgery, ERAS protocols have been extended to cranial neuro-oncological procedures. This review synthesizes recent findings to generate evidence-based guidelines to manage neurosurgical oncology patients with standardized systems and assess ability of these systems to coordinate multidisciplinary, patient-centric care efforts. Furthermore, we highlight the potential utility of multimedia, app-based communication platforms to facilitate patient education, autonomy, and team communication within each of the three settings.

Challenges in Predicting Discharge Disposition for Trauma and Emergency General Surgery Patients

BACKGROUND

Changes in discharge disposition and delays in discharge negatively impact the patient and hospital system. Our objectives were¹ to determine the accuracy with which trauma and emergency general surgery (TEGS) providers could predict the discharge disposition for patients and² determine the factors associated with incorrect predictions.

METHODS

Discharge dispositions and barriers to discharge for 200 TEGS patients were predicted individually by members of the multidisciplinary TEGS team within 24 h of patient admission. Univariate analyses and multivariable logistic least absolute shrinkage and selection operator regressions determined the associations between patient characteristics and correct predictions.

RESULTS

A total of 1,498 predictions of discharge disposition were made by the multidisciplinary TEGS team for 200 TEGS patients. Providers correctly predicted 74% of discharge dispositions. Prediction accuracy was not associated with clinical experience or job title. Incorrect predictions were independently associated with older age (OR 0.98; P < 0.001), trauma admission as compared to emergency general surgery (OR 0.33; P < 0.001), higher Injury Severity Scores (OR 0.96; P < 0.001), longer lengths of stay (OR 0.90; P < 0.001), frailty (OR 0.43; P = 0.001), ICU admission (OR 0.54; P < 0.001), and higher Acute Physiology and Chronic Health Evaluation II scores (OR 0.94; P = 0.006).

CONCLUSION

The TEGS team can accurately predict the majority of discharge dispositions. Patients with risk factors for unpredictable dispositions should be flagged to better allocate appropriate resources and more intensively plan their discharges.

Utility of prediction model score: a proposed tool to standardize the performance and generalizability of clinical predictive models based on systematic review

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.

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.

Web-Based Calculator Predicts Surgical-Site Infection After Thoracolumbar Spine Surgery

BACKGROUND

Surgical-site infection (SSI) after spine surgery leads to increased length of stay, reoperation, and worse patient quality of life. We sought to develop a web-based calculator that computes an individual's risk of a wound infection following thoracolumbar spine surgery.

METHODS

We performed a retrospective review of consecutive patients undergoing elective degenerative thoracolumbar spine surgery at a tertiary-care institution between January 2016 and December 2018. Patients who developed SSI requiring reoperation were identified. Regression analysis was performed and model performance was assessed using receiver operating curve analysis to derive an area under the curve. Bootstrapping was performed to check for overfitting, and a Hosmer-Lemeshow test was employed to evaluate goodness-of-fit and model calibration.

RESULTS

In total, 1259 patients were identified; 73% were index operations. The overall infection rate was 2.7%, and significant predictors of SSI included female sex (odds ratio [OR] 3.0), greater body mass index (OR 1.1), active smoking (OR 2.8), worse American Society of Anesthesiologists physical status (OR 2.1), and greater surgical invasiveness (OR 1.1). The prediction model had an optimism-corrected area under the curve of 0.81. A web-based calculator was created: https://jhuspine2.shinyapps.io/Wound_Infection_Calculator/.

CONCLUSIONS

In this pilot study, we developed a model and simple web-based calculator to predict a patient's individualized risk of SSI after thoracolumbar spine surgery. This tool has a predictive accuracy of 83%. Through further multi-institutional validation studies, this tool has the potential to alert both patients and providers of an individual's SSI risk to improve informed consent, mitigate risk factors, and ultimately drive down rates of SSIs.

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.

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).