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➤ As the Patient-Reported Outcomes Measurement Information System (PROMIS) is increasingly utilized in orthopaedic research and clinical practice, there is not a consensus regarding the minimum clinically important difference (MCID) calculation. ➤ The varied MCID calculation methods can lead to a range of possible values, which limits the translatability of research efforts. ➤ The completion rate and follow-up period also influence MCID values and should be reported alongside study results.

Background and purpose — Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in orthopedic surgery. Given their growing popularity and their likely implementation in clinical practice we evaluated which outcomes these new models have focused on and what methodologies are being employed. Material and methods — We performed a systematic search in PubMed, Embase, and Cochrane Library for studies published up to June 18, 2020. Studies reporting on non-ML prediction models or non-orthopedic outcomes were excluded. After screening 7,138 studies, 59 studies reporting on 77 prediction models were included. We extracted data regarding outcome, study design, and reported performance metrics. Results — Of the 77 identified ML prediction models the most commonly reported outcome domain was medical management (17/77). Spinal surgery was the most commonly involved orthopedic subspecialty (28/77). The most frequently employed algorithm was neural networks (42/77). Median size of datasets was 5,507 (IQR 635–26,364). The median area under the curve (AUC) was 0.80 (IQR 0.73–0.86). Calibration was reported for 26 of the models and 14 provided decision-curve analysis. Interpretation — ML prediction models have been developed for a wide variety of topics in orthopedics. Topics regarding medical management were the most commonly studied. Heterogeneity between studies is based on study size, algorithm, and time-point of outcome. Calibration and decision-curve analysis were generally poorly reported.

BACKGROUND CONTEXT Surgical decompression and stabilization in the setting of spinal metastasis is performed to relieve pain and preserve functional status. These potential benefits must be weighed against the risks of perioperative morbidity and mortality. Accurate prediction of a patient's postoperative survival is a crucial component of patient counseling. PURPOSE To externally validate the SORG machine learning algorithms for prediction of 90-day and 1-year mortality after surgery for spinal metastasis. STUDY DESIGN/SETTING Retrospective, cohort study PATIENT SAMPLE Patients 18 years or older at a tertiary care medical center treated surgically for spinal metastasis OUTCOME MEASURES Mortality within 90 days of surgery, mortality within 1 year of surgery METHODS This is a retrospective cohort study of 298 adult patients at a tertiary care medical center treated surgically for spinal metastasis between 2004 and 2020. Baseline characteristics of the validation cohort were compared to the derivation cohort for the SORG algorithms. The following metrics were used to assess the performance of the algorithms: discrimination, calibration, overall model performance, and decision curve analysis. RESULTS Sixty-one patients died within 90 days of surgery and 133 died within 1 year of surgery. The validation cohort differed significantly from the derivation cohort. The SORG algorithms for 90-day mortality and 1-year mortality performed excellently with respect to discrimination; the algorithm for 1-year mortality was well-calibrated. At both postoperative time points, the SORG algorithms showed greater net benefit than the default strategies of changing management for no patients or for all patients. CONCLUSIONS With an independent, contemporary, and geographically distinct population, we report successful external validation of SORG algorithms for preoperative risk prediction of 90-day and 1-year mortality after surgery for spinal metastasis. By providing accurate prediction of intermediate and long-term mortality risk, these externally validated algorithms may inform shared decision-making with patients in determining management of spinal metastatic disease.

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

BACKGROUND CONTEXT Patient-Reported Outcome Measurement Information System (PROMIS) scores are increasingly utilized in clinical care. However, it is unclear if PROMIS can discriminate surgeon performance on an individual level. PURPOSE The purpose of this study was to examine surgeon-level variance in rates of achieving minimal clinically important difference (MCID) after lumbar decompression. PATIENT SAMPLE This is a prospective, observational cohort study performed across a healthcare enterprise (two academic medical centers and three community centers). Patients 18 years or older undergoing one- to two-level primary decompression for lumbar disc herniation (LDH) or lumbar spinal stenosis (LSS) were included. OUTCOME MEASURES The primary outcome was achievement of MCID, using a distribution-based method, on paired PROMIS physical function scores. METHODS Descriptive statistics were generated to examine the baseline characteristics of the study cohort. Bivariate analyses were used to examine the impact of surgeon-level variance on rates of MCID. Multivariable analyses were used to examine the risk-adjusted impact of surgeon-level variance on rates of MCID. RESULTS Overall, 636 patients treated by nine surgeons were included. The median patient age was 58 [interquartile range (IQR): 46–70] and 62.3% (n=396) were female. Among all patients, 56.9% (n=362) underwent surgery for LDH. The overall rate of achieving MCID was 75.8% (n=482). Of the surgeons, the median years in practice were 12 (range 4–31) and 55.6% (n=5) were in academic practice settings. On bivariate analysis, patients treated by one of the surgeons had lower rates of achieving MICD (odds ratio=0.37, 95% confidence interval: 0.15–0.91, p=.03). However, on multivariable analysis adjusting for operative indication (LDH vs. LSS), body mass index, number of comorbidities, percent unemployment in patient zip code, and preoperative PROMIS physical function scores, all surgeons were equally likely to obtain MCID. CONCLUSIONS In this cohort, variance in PROMIS scores after primary lumbar decompression is influenced by patient-related factors and not by individual surgeon. Adequate risk adjustment is needed if ascertaining clinical improvement on an individual surgeon basis. Level of Evidence 2

BACKGROUND Surgical site infections are a major driver of morbidity and increased costs in the postoperative period after spine surgery. Current tools for surveillance of these adverse events rely on prospective clinical tracking, manual retrospective chart review, or administrative procedural and diagnosis codes. PURPOSE The purpose of this study was to develop natural language processing (NLP) algorithms for automated reporting of postoperative wound infection requiring reoperation after lumbar discectomy. PATIENT SAMPLE Adult patients undergoing discectomy at two academic and three community medical centers between January 1, 2000 and July 31, 2019 for lumbar disc herniation. OUTCOME MEASURES Reoperation for wound infection within 90 days after surgery METHODS Free-text notes of patients who underwent surgery from January 1, 2000 to December 31, 2015 were used for algorithm training. Free-text notes of patients who underwent surgery after January 1, 2016 were used for algorithm testing. Manual chart review was used to label which patients had reoperation for wound infection. An extreme gradient-boosting NLP algorithm was developed to detect reoperation for postoperative wound infection. RESULTS Overall, 5,860 patients were included in this study and 62 (1.1%) had a reoperation for wound infection. In patients who underwent surgery after January 1, 2016 (n=1,377), the NLP algorithm detected 15 of the 16 patients (sensitivity=0.94) who had reoperation for infection. In comparison, current procedural terminology and international classification of disease codes detected 12 of these 16 patients (sensitivity=0.75). At a threshold of 0.05, the NLP algorithm had positive predictive value of 0.83 and F1-score of 0.88. CONCLUSION Temporal validation of the algorithm developed in this study demonstrates a proof-of-concept application of NLP for automated reporting of adverse events after spine surgery. Adapting this methodology for other procedures and outcomes in spine and orthopedics has the potential to dramatically improve and automatize quality and safety reporting.

IMPORTANCE Preoperative determination of the potential for postoperative opioid dependence in previously naive patients undergoing elective spine surgery may facilitate targeted interventions. OBJECTIVE The purpose of this study was to develop supervised machine learning algorithms for preoperative prediction of prolonged opioid prescription use in opioid-naive patients following lumbar spine surgery. DESIGN Retrospective review of clinical registry data. Variables considered for prediction included demographics, insurance status, preoperative medications, surgical factors, laboratory values, comorbidities, and neighborhood characteristics. Five supervised machine learning algorithms were developed and assessed by discrimination, calibration, Brier score, and decision curve analysis. SETTING One healthcare entity (two academic medical centers, three community hospitals), 2000 to 2018. PARTICIPANTS Opioid-naive patients undergoing decompression and/or fusion for lumbar disk herniation, stenosis, and spondylolisthesis. MAIN OUTCOME Sustained prescription opioid use exceeding 90 days after surgery. RESULTS Overall, of 8,435 patients included, 359 (4.3%) were found to have prolonged postoperative opioid prescriptions. The elastic-net penalized logistic regression achieved the best performance in the independent testing set not used for algorithm development with c-statistic=0.70, calibration intercept=0.06, calibration slope=1.02, and Brier score=0.039. The five most important factors for prolonged opioid prescriptions were use of instrumented spinal fusion, preoperative benzodiazepine use, preoperative antidepressant use, preoperative gabapentin use, and uninsured status. Individual patient-level explanations were provided for the algorithm predictions and the algorithms were incorporated into an open access digital application available here: https://sorg-apps.shinyapps.io/lumbaropioidnaive/ . CONCLUSION AND RELEVANCE The clinician decision aid developed in this study may be helpful to preoperatively risk-stratify opioid-naive patients undergoing lumbar spine surgery. The tool demonstrates moderate discriminative capacity for identifying those at greatest risk of prolonged prescription opioid use. External validation is required to further support the potential utility of this tool in practice.

Objectives: Recovery after shoulder arthroscopy can be impaired by sustained postoperative opioid use, yet there are few validated risk calculators for this outcome. The purpose of this study is to develop and validate a machine learning algorithm that can reliably and effectively predict sustained opioid use in patients following elective shoulder arthroscopy. Methods: A retrospective review of an institutional outcomes database was performed at a tertiary academic medical center to identify adult patients who underwent shoulder arthroscopy between January 1, 2014 and October 1, 2019. Extended postoperative opioid consumption was defined as opioid consumption at least 150 days following surgery. Five machine learning algorithms were developed to predict this outcome. Performance of the algorithms were assessed through discrimination, calibration, and decision curve analysis. Results: Overall, of the 1504 patients included, 132 (8.8%) demonstrated sustained postoperative opioid consumption. The factors determined for prediction of prolonged postoperative opioid prescriptions were preoperative opioid use, age, median percentage of population in patient zip code living at the federal poverty line, preoperative pain score, duration of symptoms, BMI. The random forest model achieved the best performance based on discrimination (AUC = 0.78), calibration, and decision curve analysis. This model was integrated into a web-based open-access application able to provide both predictions and explanations. Conclusions: If externally validated in independent populations, the algorithm developed presently could effectively guide preoperative screening in patients at high risk for extended postoperative opioid prescriptions. Early identification and interdisciplinary counseling in high-risk cases can optimize both resource allocation and surgical outcomes.

Neutrophil to lymphocyte ratio and platelet to lymphocyte ratio have been previously identified as markers for overall survival in oncology but remain heretofore unexplored in spinal epidural abscess (SEA).The purpose of this study was to determine the impact of these routinely collected assessments on 90-day mortality in SEA.Retrospective, case-control study.Patients 18 years or older diagnosed with SEA at 2 academic medical centers and 3 community hospitals.Ninety-day postdischarge and in-hospital mortality.Complete blood count with differential obtained on the day immediately preceding or on the day of admission was used to calculate platelet to lymphocyte and neutrophil to lymphocyte ratios. Multivariate analyses were used to determine if these ratios were independent risk factors for 90-day mortality.For 1,053 SEA patients included in the study, the rate of 90-day mortality was 134 (12.7%). The rate of 90-day mortality with neutrophil to lymphocyte ratio (≥8) was (20.5%) compared to (8.1%) with neutrophil to lymphocyte ratio8. Neutrophil to lymphocyte ratio was positively associated with bacteremia, elevated erythrocyte sedimentation rate, and concurrent systemic infections (endocarditis, meningitis) and negatively associated with duration of symptoms prior to presentation. On multivariate analysis, elevated neutrophil to lymphocyte remained an independent risk factor for 90-day mortality (odds ratio=2.62, 95% confidence interval=1.66-4.17, p.001). Platelet to lymphocyte ratio was not associated with 90-day mortality.Absolute neutrophil to lymphocyte ratio is a routinely collected but overlooked biomarker in patients with spinal epidural abscess that is a novel independent risk factor for 90-day mortality.

The foot and ankle surgery literature lacks an analysis of studies published in general medical journals. Studies published in general medical journals have high visibility and significant impact on decision making by medical practitioners and health care policy makers. Knowledge of the subject matter and methodologic characteristics of foot and ankle publications being read by general medical practitioners and the lay public is important to the practicing foot and ankle surgeon. A systematic review of foot and ankle studies published from 2000 to 2017 in 5 high-impact general medical journals was undertaken. Data extracted included study topic, study design, study demographics, and methodologic characteristics of randomized controlled trials (RCTs). There were 47 foot and ankle studies published in the selected medical journals examined during the study time period. Most common topics were diabetic foot ulcers (n = 25 [53.2%]), plantar fasciitis (n = 6 [12.8%]), and Achilles tendinopathy (n = 5 [10.6%]). The most common study types were reviews (n = 24 [51.1%]), RCTs (n = 12 [25.4%], and prospective studies (n = 6 [12.8%]). Of the published RCTs, the most common journal of publication was the Journal of the American Medical Association (n = 7 [58.3%]). The median number of citations for RCTs published before 2016 was 242.5. Of the small number of RCTs that compared operative with nonoperative treatment, the majority supported nonoperative management (n = 3 [75%]). Diabetic foot ulcers, plantar fasciitis, and Achilles tendinopathy are the most commonly published foot and ankle topics in general medical journals.

Introduction Numerous prognostication models have been developed to estimate survival in patients with extremity metastatic bone disease, but few include albumin despite albumin's role in malnutrition and inflammation. The purpose of this study was to examine two independent datasets to determine the value for albumin in prognosticating survival in this population. Materials and methods Extremity metastatic bone disease patients undergoing surgical management were identified from two independent populations. Population 1: Retrospective chart review at two tertiary care centers. Population 2: A large, national, North American multicenter surgical registry with 30-day follow-up. Bivariate and multivariate analyses were used to examine albumin's value for prognostication at 1-, 3-, and 12-month after surgery. Results In Population 1, 1,090 patients were identified with 1-, 3-, and 12-month mortality rates of 95 (8.8%), 305 (28.9%), and 639 (62.0%), respectively. In Population 2, 1,675 patients were identified with one-month postoperative mortality rates of 148 (8.8%). In both populations, hypoalbuminemia was an independent prognostic factor for mortality at 30 days. In the institutional set, hypoalbuminemia was additionally associated with 3- and 12-month mortality. Conclusions Hypoalbuminemia is a marker for mortality in extremity metastatic bone disease. Further consideration of this marker could improve existing prognostication models in this population. Level of evidence III.

Machine learning (ML) studies are becoming increasingly popular in orthopedics but lack a critically appraisal of their adherence to peer-reviewed guidelines. The objective of this review was to (1) evaluate quality and transparent reporting of ML prediction models in orthopedic surgery based on the transparent reporting of multivariable prediction models for individual prognosis or diagnosis (TRIPOD), and (2) assess risk of bias with the Prediction model Risk Of Bias ASsessment Tool. A systematic review was performed to identify all ML prediction studies published in orthopedic surgery through June 18th, 2020. After screening 7138 studies, 59 studies met the study criteria and were included. Two reviewers independently extracted data and discrepancies were resolved by discussion with at least two additional reviewers present. Across all studies, the overall median completeness for the TRIPOD checklist was 53% (interquartile range 47%-60%). The overall risk of bias was low in 44% (n = 26), high in 41% (n = 24), and unclear in 15% (n = 9). High overall risk of bias was driven by incomplete reporting of performance measures, inadequate handling of missing data, and use of small datasets with inadequate outcome numbers. Although the number of ML studies in orthopedic surgery is increasing rapidly, over 40% of the existing models are at high risk of bias. Furthermore, over half incompletely reported their methods and/or performance measures. Until these issues are adequately addressed to give patients and providers trust in ML models, a considerable gap remains between the development of ML prediction models and their implementation in orthopedic practice.

OBJECTIVE The role of spine surgeons in precipitating and mediating sustained prescription opioid use remains controversial at this time. The purpose of this study was to identify prescription opioid use following lumbar discectomy and characterize the source of opioid prescriptions by clinician specialty (surgeon vs nonsurgeon). METHODS Using a retrospective review, the authors identified adult patients undergoing lumbar discectomy for a primary diagnosis of disc herniation between 2010 and 2017. The primary outcome was sustained prescription opioid use, defined as issue of an opioid prescription at a time point 90 days or longer after the surgical procedure. The primary predictor variable was prescriber specialty (surgeon vs nonsurgeon). The independent effect of provider specialty on the number of opioid prescriptions issued to patients was assessed using multivariable Poisson regression that accounted for confounding from all other clinical and sociodemographic variables. RESULTS This study included 622 patients who underwent a lumbar discectomy. A total of 610 opioid prescriptions were dispensed for this population after surgery. In total, 126 patients (20.3%) had at least one opioid prescription in the period beyond 90 days following their surgery. The majority of opioid prescriptions, 494 of 610 (81%), were non–inpatient prescriptions. Among these, only a minority (26%) of outpatient opioid prescriptions were written by surgical providers. Following multivariable Poisson regression analysis, surgical providers were found to have a lower likelihood of issuing an opioid prescription compared to nonsurgical clinicians (incidence rate ratio [IRR] 0.78; 95% CI 0.68–0.89; p = 0.001). CONCLUSIONS A minority of lumbar discectomy patients continue to receive opioid prescriptions up to 15 months after surgery. Many of these prescriptions are written by nonsurgical providers unaffiliated with the operative team.

The widespread use of electronic patient-generated health data has led to unprecedented opportunities for automated extraction of clinical features from free-text medical notes. However, processing this rich resource of data for clinical and research purposes, depends on labor-intensive and potentially error-prone manual review. The aim of this study was to develop a natural language processing (NLP) algorithm for binary classification (single metastasis versus two or more metastases) in bone scintigraphy reports of patients undergoing surgery for bone metastases.Bone scintigraphy reports of patients undergoing surgery for bone metastases were labeled each by three independent reviewers using a binary classification (single metastasis versus two or more metastases) to establish a ground truth. A stratified 80:20 split was used to develop and test an extreme-gradient boosting supervised machine learning NLP algorithm.A total of 704 free-text bone scintigraphy reports from 704 patients were included in this study and 617 (88%) had multiple bone metastases. In the independent test set (NLP has the potential to automate clinical data extraction from free text radiology notes in orthopedics, thereby optimizing the speed, accuracy, and consistency of clinical chart review. Pending external validation, the NLP algorithm developed in this study may be implemented as a means to aid researchers in tackling large amounts of data.

BACKGROUND Anterior lumbar spine surgery (ALSS) requires mobilization of the great vessels, resulting in a high risk of iatrogenic vascular injury (VI). It remains unclear whether VI is associated with increased risk of postoperative complications and other related adverse outcomes. PURPOSE The purpose of this study was to (1) assess the incidence of postoperative complications attributable to VI during ALSS, and (2) outcomes secondary to VI such as procedural blood loss, transfusion of blood products, length of stay (LOS), and in hospital mortality. STUDY DESIGN Retrospective propensity-score matched, case-control study at 2 academic and 3 community medical centers, PATIENT SAMPLE Patients 18 years of age or older, undergoing ALSS between January 1st, 2000 and July 31st, 2019 were included in this analysis. OUTCOME MEASURES The primary outcome was the incidence of postoperative complications attributable to VI, such as venous thromboembolism, compartment syndrome, transfusion reaction, limb ischemia, and reoperations. The secondary outcomes included estimated operative blood loss (milliliter), transfused blood products, LOS (days), and in-hospital mortality. METHODS In total, 1,035 patients were identified, of which 75 (7.2%) had a VI. For comparative analyses, the 75 VI patients were paired with 75 comparable non-VI patients by propensity-score matching. The adequacy of the matching was assessed by testing the standardized mean differences (SMD) between VI and non-VI group (>0.25 SMD). RESULTS Two patients (2.7%) had VI-related postoperative complications in the studied period, which consisted of two deep venous thromboembolisms (DVTs) occurring on day 3 and 7 postoperatively. Both DVTs were located in the distal left common iliac vein (CIV). The VI these patients suffered were to the distal inferior vena cava and the left CIV, respectively. Both patients did not develop additional complications in consequence of their DVTs, however, did require systemic anticoagulation and placement of an inferior vena cava filter. There was no statistical difference with the non-VI group where no instances (0%) of postoperative complications were reported (p=.157). No differences were found in LOS or in hospital mortality between the two groups (p=.157 and p=.999, respectively). Intraoperative blood loss and blood transfusion were both found to be higher in the VI group in comparison to the non-VI group (650 mL, interquartile range [IQR] 300–1400 vs. 150 mL, IQR 50–425, p≤.001; 0 units, IQR 0–3 vs. 0 units, IQR 0–1, p=.012, respectively). CONCLUSION This study found a low number of serious postoperative complications related to VI in ALSS. In addition, these complications were not significantly different between the VI and matched non-VI ALSS cohort. Although not significant, the found DVT incidence of 2.7% after VI in ALSS warrants vigilance and preventive measures during the postoperative course of these patients.

BACKGROUND The ability to preoperatively predict which patients will achieve a minimal clinically important difference (MCID) after lumbar spine decompression surgery can help determine the appropriateness and timing of surgery. Patient-Reported Outcome Measurement Information System (PROMIS) scores are an increasingly popular outcome instrument. PURPOSE The purpose of this study was to develop algorithms predictive of achieving MCID after primary lumbar decompression surgery. PATIENT SAMPLE This was a retrospective study at two academic medical centers and three community medical centers including adult patients 18 years or older undergoing one or two level posterior decompression for lumbar disc herniation or lumbar spinal stenosis between January 1, 2016 and April 1, 2019. OUTCOME MEASURES The primary outcome, MCID, was defined using distribution-based methods as one half the standard deviation of postoperative patient-reported outcomes (PROMIS physical function, pain interference, pain intensity). METHODS Five machine learning algorithms were developed to predict MCID on these surveys and assessed by discrimination, calibration, Brier score, and decision curve analysis. The final model was incorporated into an open access digital application. RESULTS Overall, 906 patients completed at least one PROMs survey in the 90 days before surgery and at least one PROMs survey in the year after surgery. Attainment of MCID during the study period by PROMIS instrument was 74.3% for physical function, 75.8% for pain interference, and 79.2% for pain intensity. Factors identified for preoperative prediction of MCID attainment on these outcomes included preoperative PROs, percent unemployment in neighborhood of residence, comorbidities, body mass index, private insurance, preoperative opioid use, surgery for disc herniation, and federal poverty level in neighborhood of residence. The discrimination (c-statistic) of the final algorithms for these outcomes was 0.79 for physical function, 0.74 for pain interference, and 0.69 for pain intensity with good calibration. The open access digital application for these algorithms can be found here: https://sorg-apps.shinyapps.io/promis_pld_mcid/ CONCLUSION Lower preoperative PROMIS scores, fewer comorbidities, and certain sociodemographic factors increase the likelihood of achieving MCID for PROMIS after lumbar spine decompression.

None of the existing prognostic scoring systems for spinal epidural abscess (SEA) include albumin despite albumin's established role in inflammation, nutrition, lipid peroxidation, and regulation of apoptosis. The purpose of the present study was to determine the prognostic value of albumin in SEA.We performed a retrospective, case-control study of 2 independent data sets: patients with SEA in an institutional population and patients in the National Surgical Quality Improvement Program (NSQIP). Bivariate analyses and multivariate analyses were used to determine whether albumin is an independent prognostic factor for survival in both data sets.For the 1053 patients with SEA in the institutional cohort, the 90-day postdischarge mortality was 134 (12.7%). Overall, 633 (60.1%) underwent surgery in the initial admission, with a 30-day postoperative mortality rate of 5.5% (n = 35). For the 1154 patients with SEA in the NSQIP database, the 30-day postoperative mortality rate was 3.6% (n = 42). The rate of 90-day postdischarge mortality in the institutional cohort for patients with albumin2.3 g/dL was 25.1%. In contrast, the rate for patients with albumin3.3 g/dL was 4.5%. On multivariate analysis of the NSQIP database, hypoalbuminemia was an independent prognostic factor for 30-day postoperative mortality. On multivariate analysis of the institutional cohort, hypoalbuminemia remained a prognostic factor for 90-day postdischarge mortality.Albumin was validated as an independent prognostic factor in patients with SEA. The lack of this marker in existing scoring systems underscores the need for updated models to optimize risk stratification and shared decision-making before surgery.

Background Determination of the appropriateness of invasive management in patients with spinal metastatic disease requires accurate pre-operative estimation of survival. The purpose of this study was to examine serum alkaline phosphatase as a prognostic marker in spinal metastatic disease. Methods Chart reviews from two tertiary care centres were used to identify spinal metastatic disease patients. Bivariate and multivariate analyses were used to determine if serum alkaline phosphatase was an independent prognostic marker for survival. Results Overall, 732 patients were included with 90-day and 1-year survival of n = 539 (74.9%) and n = 324 (45.7%), respectively. The 1-year survival of patients in the first quartile of alkaline phosphatase (≤73 IU/L) was 78 (57.8%) compared to 31 (24.0%) for patients in the fourth quartile (>140 IU/L). Preoperative serum alkaline phosphatase levels were significantly elevated in patients with multiple spine metastases, non-spine bone metastasis, and visceral metastasis but not in patients with brain metastasis. On multivariate analysis, elevated serum alkaline phosphatase was identified as an independent prognostic factor for survival in spinal metastatic disease. Conclusion Serum alkaline phosphatase is associated with preoperative metastatic tumour burden and is a biomarker for overall survival in spinal metastatic disease.

BACKGROUND: The Skeletal Oncology Research Group (SORG) machine learning algorithm for predicting survival in patients with chondrosarcoma was developed using data from the Surveillance, Epidemiology, and End Results (SEER) registry. This algorithm was externally validated on a dataset of patients from the United States in an earlier study, where it demonstrated generally good performance but overestimated 5-year survival. In addition, this algorithm has not yet been validated in patients outside the United States; doing so would be important because external validation is necessary as algorithm performance may be misleading when applied in different populations. QUESTIONS/PURPOSES: Does the SORG algorithm retain validity in patients who underwent surgery for primary chondrosarcoma outside the United States, specifically in Italy? METHODS: A total of 737 patients were treated for chondrosarcoma between January 2000 and October 2014 at the Italian tertiary care center which was used for international validation. We excluded patients whose first surgical procedure was performed elsewhere (n = 25), patients who underwent nonsurgical treatment (n = 27), patients with a chondrosarcoma of the soft tissue or skull (n = 60), and patients with peripheral, periosteal, or mesenchymal chondrosarcoma (n = 161). Thus, 464 patients were ultimately included in this external validation study, as the earlier performed SEER study was used as the training set. Therefore, this study-unlike most of this type-does not have a training and validation set. Although the earlier study overestimated 5-year survival, we did not modify the algorithm in this report, as this is the first international validation and the prior performance in the single-institution validation study from the United States may have been driven by a small sample or non-generalizable patterns related to its single-center setting. Variables needed for the SORG algorithm were manually collected from electronic medical records. These included sex, age, histologic subtype, tumor grade, tumor size, tumor extension, and tumor location. By inputting these variables into the algorithm, we calculated the predicted probabilities of survival for each patient. The performance of the SORG algorithm was assessed in this study through discrimination (the ability of a model to distinguish between a binary outcome), calibration (the agreement of observed and predicted outcomes), overall performance (the accuracy of predictions), and decision curve analysis (establishment on the ability of a model to make a decision better than without using the model). For discrimination, the c-statistic (commonly known as the area under the receiver operating characteristic curve for binary classification) was calculated; this ranged from 0.5 (no better than chance) to 1.0 (excellent discrimination). The agreement between predicted and observed outcomes was visualized with a calibration plot, and the calibration slope and intercept were calculated. Perfect calibration results in a slope of 1 and an intercept of 0. For overall performance, the Brier score and the null-model Brier score were calculated. The Brier score ranges from 0 (perfect prediction) to 1 (poorest prediction). Appropriate interpretation of the Brier score requires comparison with the null-model Brier score. The null-model Brier score is the score for an algorithm that predicts a probability equal to the population prevalence of the outcome for every patient. A decision curve analysis was performed to compare the potential net benefit of the algorithm versus other means of decision support, such as treating all or none of the patients. There were several differences between this study and the earlier SEER study, and such differences are important because they help us to determine the performance of the algorithm in a group different from the initial study population. In this study from Italy, 5-year survival was different from the earlier SEER study (71% [319 of 450 patients] versus 76% [1131 of 1487 patients]; p = 0.03). There were more patients with dedifferentiated chondrosarcoma than in the earlier SEER study (25% [118 of 464 patients] versus 8.5% [131 of 1544 patients]; p < 0.001). In addition, in this study patients were older, tumor size was larger, and there were higher proportions of high-grade tumors than the earlier SEER study (age: 56 years [interquartile range {IQR} 42 to 67] versus 52 years [IQR 40 to 64]; p = 0.007; tumor size: 80 mm [IQR 50 to 120] versus 70 mm [IQR 42 to 105]; p < 0.001; tumor grade: 22% [104 of 464 had Grade 1], 42% [196 of 464 had Grade 2], and 35% [164 of 464 had Grade 3] versus 41% [592 of 1456 had Grade 1], 40% [588 of 1456 had Grade 2], and 19% [276 of 1456 had Grade 3]; p ≤ 0.001). RESULTS: Validation of the SORG algorithm in a primarily Italian population achieved a c-statistic of 0.86 (95% confidence interval 0.82 to 0.89), suggesting good-to-excellent discrimination. The calibration plot showed good agreement between the predicted probability and observed survival in the probability thresholds of 0.8 to 1.0. With predicted survival probabilities lower than 0.8, however, the SORG algorithm underestimated the observed proportion of patients with 5-year survival, reflected in the overall calibration intercept of 0.82 (95% CI 0.67 to 0.98) and calibration slope of 0.68 (95% CI 0.42 to 0.95). The Brier score for 5-year survival was 0.15, compared with a null-model Brier of 0.21. The algorithm showed a favorable decision curve analysis in the validation cohort. CONCLUSIONS: The SORG algorithm to predict 5-year survival for patients with chondrosarcoma held good discriminative ability and overall performance on international external validation; however, it underestimated 5-year survival for patients with predicted probabilities from 0 to 0.8 because the calibration plot was not perfectly aligned for the observed outcomes, which resulted in a maximum underestimation of 20%. The differences may reflect the baseline differences noted between the two study populations. The overall performance of the algorithm supports the utility of the algorithm and validation presented here. The freely available digital application for the algorithm is available here: https://sorg-apps.shinyapps.io/extremitymetssurvival/. LEVEL OF EVIDENCE: Level III, prognostic study.

Background Preoperative prognostication of short-term postoperative mortality in patients with spinal metastatic disease can improve shared decision making around end-of-life care. Objective To (1) develop machine learning algorithms for prediction of short-term mortality and (2) deploy these models in an open access web application. Methods The American College of Surgeons, National Surgical Quality Improvement Program was used to identify patients that underwent operative intervention for metastatic disease. Four machine learning algorithms were developed, and the algorithm with the best performance across discrimination, calibration, and overall performance was integrated into an open access web application. Results The 30-d mortality for the 1790 patients undergoing surgery for spinal metastatic disease was 8.49%. Preoperative factors used for prognostication were albumin, functional status, white blood cell count, hematocrit, alkaline phosphatase, spinal location (cervical, thoracic, lumbosacral), and severity of comorbid systemic disease (American Society of Anesthesiologist Class). In this population, machine learning algorithms developed to predict 30-d mortality performed well on discrimination (c-statistic), calibration (assessed by calibration slope and intercept), Brier score, and decision analysis. An open access web application was developed for the best performing model and this web application can be found here: https://sorg-apps.shinyapps.io/spinemets/. Conclusion Machine learning algorithms are promising for prediction of postoperative outcomes in spinal oncology and these algorithms can be integrated into clinically useful decision tools. As the volume of data in oncology continues to grow, creation of learning systems and deployment of these systems as accessible tools may significantly enhance prognostication and management.

Background: Elevated serum alkaline phosphatase has been previously studied as a biomarker for progression of metastatic disease and implicated in adverse skeletal events and worsened survival. The purpose of this study was to determine if serum alkaline phosphatase was a predictor of short-term mortality of patients undergoing surgery for spinal metastatic disease. Methods: The American College of Surgeons National Surgical Quality Improvement Program was queried for patients undergoing spinal surgery for metastatic disease. Bivariate and multivariable analyses was undertaken to determine the relationship between serum alkaline phosphatase and 30-day mortality. Results: For the 1788 patients undergoing operative intervention for spinal metastatic disease between 2009 and 2016 the 30-day mortality was 8.49% (n = 151). In patients who survived beyond 30-days after surgery, n = 1627 (91.5%) the median [interquartile range] serum alkaline phosphatase levels were 126.4 [75–138], whereas in patients who had 30-day mortality, the serum alkaline phosphatase levels were 179.8 [114–187]. The optimal cut-off for alkaline phosphatase was determined to be 113 IU/L. On multivariable analysis, elevated serum alkaline phosphatase levels were associated with 30-day mortality (OR 1.61, 95% CI 1.12–2.32, p = 0.011). Conclusion: Elevated preoperative serum alkaline phosphatase is a marker for 30-day mortality in patients undergoing surgery for spinal metastatic disease. Future retrospective and prospective study designs should incorporate assessment of this serum biomarker to better understand the role for serum alkaline phosphatase in improving prognostication in spinal metastatic disease.

Background Modern medical management of metastatic renal cell carcinoma (RCC) includes therapies targeting tyrosine kinases, growth pathways (mammalian target of rapamycin (mTOR)), and immune checkpoints. Objective To test our hypothesis that patients with spinal metastases would benefit from postoperative systemic therapy despite presenting with disease that, in many cases, was resistant to prior systemic therapy. Methods This is an Institutional Review Board-approved clinical retrospective cohort analysis. A sample of adult patients with RCC metastatic to the spine who underwent operative intervention between January 2010 and December 2017 at 2 large academic medical centers was used in this study. Results We identified 78 patients with metastatic RCC in whom instrumented stabilization was performed in 79% and postoperative stereotactic radiosurgery was performed in 41% of patients. Of patients presenting with weakness or myelopathy, 93% noted postoperative improvement and 78% reported improvement in radicular and axial paraspinal pain severity. Increased overall survival (OS) (913 d (95% CI: 633-1975 d, n = 49) vs 222 d (95% CI: 143-1005 d, n = 29), P = .003) following surgery was noted in patients who received postoperative systemic therapy a median of 80 d (interquartile range 48-227 d) following the surgical intervention. Conclusion Postoperative outcomes and palliation of symptoms for metastatic RCC without targeted therapies in this cohort are similar to those reported in earlier series prior to the adoption of these systemic therapies. We observed a significantly longer OS among patients who received modern systemic therapies postoperatively. These findings have implications for the preoperative evaluation of patients with systemic disease who may have been deemed poor surgical candidates prior to the availability of these systemic therapies.

Home Hospital (HH) is a clinical service involving the delivery of acute inpatient care in the home setting. Acute care services provided via HH include continuous telemonitoring, intravenous fluids and medications, nursing care, point-of-care imaging and laboratory tests, and in-person and virtual clinician visits. Despite offering an inpatient level of care, HH has lower fixed costs and less overhead than conventional hospital settings and offers rapid scalability. Originally implemented for acute medical conditions, HH has proven to be a safe and value-based care-delivery model for a variety of medical conditions, ranging from heart failure to chronic obstructive pulmonary disease exacerbations. For surgical conditions, HH represents an opportunity to reduce adverse hospital-acquired conditions, improve patient and caregiver satisfaction, and decrease cost. The patient profile of orthopaedic surgery inpatients matches that of patients who are most likely to benefit from HH-namely, those who are prone to functional decline, delirium, and nosocomial infections. A focus on surgeon leadership, quality and safety, and digital health with collection of patient-reported outcome measures (PROMs) will ensure that the potential of HH is realized as implementation and widespread rollout proceed.

Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML’s potential to assist and improve neurosurgical care. A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment. Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson’s disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction. ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.

Background Quality improvement, value-based care delivery, and personalized patient care depend on robust clinical, financial, and demographic data streams of neurosurgical outcomes. The neurosurgical literature lacks a comprehensive review of large national databases. Objective To assess the strengths and limitations of various resources for outcomes research in neurosurgery. Methods A review of the literature was conducted to identify surgical outcomes studies using national data sets. The databases were assessed for the availability of patient demographics and clinical variables, longitudinal follow-up of patients, strengths, and limitations. Results The number of unique patients contained within each data set ranged from thousands (Quality Outcomes Database [QOD]) to hundreds of millions (MarketScan). Databases with both clinical and financial data included PearlDiver, Premier Healthcare Database, Vizient Clinical Data Base and Resource Manager, and the National Inpatient Sample. Outcomes collected by databases included patient-reported outcomes (QOD); 30-day morbidity, readmissions, and reoperations (National Surgical Quality Improvement Program); and disease incidence and disease-specific survival (Surveillance, Epidemiology, and End Results-Medicare). The strengths of large databases included large numbers of rare pathologies and multi-institutional nationally representative sampling; the limitations of these databases included variable data veracity, variable data completeness, and missing disease-specific variables. Conclusion The improvement of existing large national databases and the establishment of new registries will be crucial to the future of neurosurgical outcomes research.

Study Design: Review and technical report. Objective: Intraoperative ultrasound has been used by spine surgeons since the early 1980s. Since that time, more advanced modes of intraoperative imaging and navigation have become widely available. Although the use of ultrasound during spine surgery has fallen out of favor, it remains the only true real-time imaging modality that allows surgeons to visualize soft tissue anatomy instantly and continuously while operating. It is our objective to demonstrate that for this reason, ultrasound is a useful adjunctive technique for spine surgeons, especially when approaching intradural lesions or when addressing pathology in the ventral spinal canal via a posterior approach. Methods: Using PubMed, the existing literature regarding the use of intraoperative ultrasound during spinal surgery was evaluated. Also, surgical case logs were reviewed to identify spinal operations during which intraoperative ultrasound was used. Illustrative cases were selected and reviewed in detail. Results: This article provides a brief review of the history of intraoperative ultrasound in spine surgery and describes certain surgical scenarios during which this technique might be useful. Several illustrative cases are provided from our own experience. Conclusions: Surgeons should consider the use of intraoperative ultrasound when approaching intradural lesions or when addressing pathology ventral to the thecal sac via a posterior approach.

Background Context : Reliable estimation of the likelihood for prolonged postoperative opioid use may aid targeted interventions for high-risk patients. Previous studies have recommended differing methodologies for prediction of sustained postoperative opioid use. Purpose : To compare the performance of the Stopping Opioids after Surgery (SOS) score and preoperative morphine milligram equivalents (MME) for postoperative opioid prescription exposure in a contemporary cohort of lumbar surgery patients. Patient Sample : Adult patients undergoing posterior decompression with or without fusion for degenerative lumbar conditions between January 31st, 2016 and May 31st, 2019. Study Design/Setting : Retrospective review at two academic medical centers and three community hospitals. Outcome Measures : The primary outcome was sustained postoperative prescription opioid exposure at 3-months and 6-months. Reoperations and readmissions were considered secondarily. Methods : SOS score and MME were assigned to patients based on data from their pre-operative surgical evaluation. Performance for both measures was assessed for all outcomes by discrimination, including c-statistic and receiver-operating curve analysis. Calibration of the low, medium and high-risk strata with the observed rates of postoperative adverse events were examined. Results : Overall, 4165 patients were included in this study. Pre-operative prevalence of prescription opioid use was 31%. Rates of postoperative opioid prescriptions at 3-months and 6-months, were 3.3% (n = 136) and 1.5% (n = 61). The c-statistics of preoperative oral MME and SOS score for 3-month sustained opioid prescriptions were 0.64 and 0.78, respectively. The c-statistics of preoperative oral MME and SOS score for 6-month sustained opioid prescriptions were 0.64 and 0.82, respectively. C-statistics of preoperative oral MME and SOS score were much lower for reoperation and readmission, although SOS score outperformed MME for both outcomes. Conclusion : The SOS score clinically outperformed oral MME as a predictive measure for outcomes following lumbar spine surgery. The SOS score may be valuable for identifying individuals at high-risk for sustained prescription opioid use and associated adverse events following spine surgery.

Background The neurosurgery literature lacks a comprehensive report of neurosurgical randomized controlled trials (RCTs) published in general medical journals. RCTs published in these journals have high visibility and impact on decision-making by general medical practitioners and health care policymakers. Methods A systematic review of neurosurgical RCTs in the New England Journal of Medicine, The Lancet, Journal of the American Medical Association, The BMJ, and Annals of Internal Medicine was completed. Results There were 78 neurosurgical RCTs published in the selected high-impact journals from 2000 to 2017. The most common study topics were neurovascular (n = 39, 50%) and spine (n = 24, 30.8%). Of these RCTs, 44 (56.4%) compared operative with nonoperative management. For studies published before 2017, the mean number of citations was 899. Approximately half of the studies showed superiority of operative management over nonoperative management in the intent to treat primary outcome of interest (n = 24, 54.5%). However, stratified by subsubspecialty, 7 (87.5%) of the functional RCTs, 9 (50%) of the neurovascular RCTs, 1 (50%) of the trauma RCTs, and 7 (43.8%) of the spinal RCTs demonstrated superiority of operative management over nonoperative management. Additionally, there were large subspecialty differences in study characteristics, such as rate of double blinding, proportion of patient enrollment from patients screened, and proportion of crossover from nonsurgical to surgical arm. Conclusions Neurosurgical RCTs in general medical journals have large subspecialty differences in characteristics such as crossovers from nonsurgical to surgical treatment arms and the proportion of studies demonstrating benefit of operative intervention over nonoperative management.

Objective Age and comorbidity burden of patients going anterior cervical discectomy and fusion (ACDF) have increased significantly over the past 2 decades, resulting in increased expenditures. Non-home discharge after ACDF contributes to increased direct and indirect costs of postoperative care. The purpose of this study was to identify independent prognostic factors for discharge disposition in patients undergoing ACDF. Methods A retrospective review was conducted at 5 medical centers to identify patients undergoing ACDF for degenerative conditions. The primary outcome was non-home discharge. Additional outcomes considered included discharge to rehabilitation and home discharge with services. Bivariate and multivariable analyses were used to identify independent prognostic factors for non-home discharge. Results Of 2070 patients undergoing ACDF, 114 (5.5%) had non-home discharge and 63 (3.0%) had discharge to inpatient rehabilitation. Factors independently associated with non-home discharge included older age, marital status, Medicare insurance, Medicaid insurance, previous spine surgery, myelopathy, preoperative comorbidities (hemiplegia/paraplegia, congestive heart failure, cerebrovascular accident), anemia, and leukocytosis. C-statistic for the overall model was 0.85. Results were relatively similar for patients younger than the age of 65 years as well as for discharge to inpatient rehabilitation and discharge home with services. Conclusions Numerous sociodemographic and clinical characteristics influence the risk of non-home discharge and discharge to inpatient rehabilitation in patients undergoing ACDF. Policy makers and payers should consider these factors when determining appropriate preoperative adjustment for risk-based reimbursements.

Advances in cancer biology and therapy have increased survival of metastatic disease patients and, in turn, the rates of metastatic epidural spinal cord compression (MESCC). Surgery can improve patient quality of life, but accurate estimation of postoperative survival is critical for appropriate patient selection, multidisciplinary management, and shared decision making. Survival estimation on the basis of clinician judgement alone has been shown to be inaccurate and unreliable. Numerous prognostic scoring systems have been developed to address this need but the inputs to these models, the modeling methodologies, and the model outputs have evolved significantly over time. Here we discuss the available scoring systems, existing limitations, and future opportunities.

Spine surgery is expensive, invasive and associated with important risks. The potential benefits of intervention must be balanced against these harms and risks. Decision curve analysis is a statistical method for evaluating models, rules and tests to indicate patients for intervention. The net benefit of a proposed tool can be compared with the clinical default strategies of “treat all” vs. “treat none” across a range of clinically reasonable threshold probabilities, thereby demonstrating whether the use of a prediction model or diagnostic test is clinically useful. Here we discuss the current applications of decision curve analysis within the spine population.

Strategies for improving value-based healthcare in anterior cervical discectomy and fusion (ACDF) include a shift towards outpatient procedures. The primary outcomes was prolonged length of stay defined as length of stay greater than one day. Overall, 2917 patients underwent ACDF, of which 35.2% (n = 1027) had length of stay greater than one day. The variables used for prediction of prolonged LOS were age, sex, marital status, private insurance, myelopathy, patient-reported drug allergies, presence of at least one medical comorbidity, preoperative medications, and preoperative laboratory values. These models may be helpful for triaging the patients most likely to benefit from outpatient ACDF.

Background The existing body of literature on postoperative neurosurgical infections lacks large multicenter reports on postoperative neurosurgical infections. This is the largest study to date of postoperative neurosurgical infections rates, time to event, and risk factors. Methods Demographics, medical history, and postoperative infections were assessed for all adult patients in the large, randomized, multicenter American College of Surgeons–National Surgical Quality Improvement Program (ACS-NSQIP) database undergoing an operation with a surgeon whose primary specialty was neurological surgery from 2006 to 2014. Results Of 3,723,797 cases from 517 institutions in the NSQIP from 2006 to 2014, 132,063 neurosurgery cases were identified. Within these patients, the 30-day rate of postoperative infections was 5.3%. Postoperatively, 1.8% of patients developed surgical site infections (SSI), and 3.9% developed other infections including pneumonia and urinary tract infections. Superficial SSI had a cumulative incidence of 0.8% at a median of 16 (interquartile range [IQR], 11–22) days. Pneumonia had a cumulative incidence of 1.4% at a median of 5 days (IQR, 2–10 days). Systemic inflammatory response syndrome had a cumulative incidence of 1.6% at a median of 3 days (IQR, 8–16 days). Predictors of postoperative infections on multivariable analysis included female sex, older age, obesity, functionally dependent status before surgery, ventilator dependence, preoperative steroid use, bleeding disorders, hyponatremia, lymphocytosis, anemia, thrombocytosis, emergent case status, wound class II–IV, American Society of Anesthesiologists class 3–5, and longer operative times. Conclusion The overall ACS-NSQIP reported rate of postoperative infections was 5.3% from 2006 to 2014. Multivariable analysis demonstrated several predictive factors for postoperative infections.

Many studies have implicated operative length as a predictor of post-operative complications, including venous thromboembolism [deep vein thrombosis (DVT) and pulmonary embolism (PE)]. We analyzed the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database from 2006 to 2014, to evaluate whether length of operation had a statistically significant effect on post-operative complications in patients undergoing surgical resection of meningioma. Patients were included for this study if they had a post-operative diagnosis of meningioma. Patient demographics, pre-operative comorbidities, and post-operative 30-day complications were analyzed. Of 3743 patients undergoing craniotomy for meningioma, 13.6 % experienced any complication. The most common complications and their median time to occurrence were urinary tract infection (2.6 %) at 10 days postoperatively (IQR 7-15), unplanned intubation (2.5 %) at 3 days (IQR 1-7), failure to wean from ventilator (2.4 %) at 2.0 days (IQR 2-4), and DVT (2.4 %) at 6 days (IQR 11-19). Postoperatively, 3.6 % developed VTE; 2.4 % developed DVT and 1.7 % developed PE. Multivariable analysis identified older age (third and upper quartile), obesity, preoperative ventilator dependence, preoperative steroid use, anemia, and longer operative time as significant risk factors for VTE. Separate multivariable logistic regression models demonstrated longer operative time as a significant risk factor for VTE, all complications, major complications, and minor complications. Meningioma resection is associated with various post-operative complications that increase patient morbidity and mortality risk. this large, multi-institutional patient sample, longer operative length was associated with increased risk for postoperative venous thromboembolisms, as well as major and minor complications.

Post-operative respiratory failure can occur after neurosurgical operations. Identification of risk factors for respiratory failure after neurosurgery may help guide clinical decision-making, decrease length of stay, improve patient outcomes, and lower costs. We performed a search of the ACS-NSQIP database for all patients undergoing operations with a neurosurgeon from 2006 to 2013. We analyzed demographics, past medical history, and post-operative respiratory failure, defined as unplanned intubation and/or ventilator dependence for more than 48 h post-operatively. Of 94,621 NSQIP-reported neurosurgical patients from 2006 to 2013, 2325 (2.5 %) developed post-operative respiratory failure. Of these patients, 1270 (54.6 %) were male, with an overall mean age of 60.59 years; 571 (24.56 %) were current smokers and 756 (32.52 %) were ventilator-dependent. Past medical history included dyspnea in 204 patients (8.8 %), COPD in 198 (8.5 %), and congestive heart failure in 66 (2.8 %). The rate of post-operative respiratory failure decreased from 4.1 % in 2006 to 2.1 % in 2013 (p

Background: Poor sleep quality is prevalent among patients with rotator cuff tears (RCTs) and negatively influences the potential for healing and quality of life. However, there is a paucity of literature describing the magnitude and timing of changes in sleep quality after arthroscopic rotator cuff repair (RCR). Purpose: (1) To evaluate the prevalence of poor sleep quality in patients undergoing arthroscopic RCR and (2) to determine the timing and magnitude of changes in sleep quality after RCR. Study Design: Systematic review; Level of evidence, 4. Methods: PubMed, OVID/Medline, and Cochrane databases were queried in January 2020 for literature investigating the prevalence of poor sleep quality in patients with RCTs or changes in sleep quality after arthroscopic RCR. Data pertaining to study characteristics, risk of bias, sleep quality assessments, and clinical outcomes were extracted. A qualitative analysis of the prevalence of poor sleep quality and changes in sleep quality was performed. Results: A total of 8 studies (1034 patients) were included. The mean Pittsburgh Sleep Quality Index (PSQI) ranged from 5.2 to 15.0 preoperatively among all studies, while the frequency of patients experiencing poor sleep quality ranged from 40.8% to 89.0% in 4 studies. Four studies reported the mean PSQI at a minimum of 6 months postoperatively, which ranged from 4.2 to 7.1. Four studies did not report the PSQI score or the proportion of patients who experienced poor postoperative sleep quality. One study evaluated the PSQI at 12 months postoperatively, which decreased to 4.2 from 5.8 at 6 months. One study evaluated the PSQI at 24 months postoperatively, which decreased to 5.5 from 6.2 at 6 months. Conclusion: Patients with RCTs have a high prevalence of poor sleep quality. Consistent improvements in sleep quality are observed in the 6 months after arthroscopic RCR, but there is limited evidence based on the available data to characterize changes in sleep quality beyond this time. More evidence is needed to characterize changes in sleep quality beyond 6 months and how these changes are perceived by this patient population.

A randomized controlled trial (RCT) remains the pinnacle of clinical research design. However, RCTs in neurosurgery, especially those comparing surgery to non-operative treatment, are rare and their relevance and applicability have been questioned. This study set out to assess trial design and quality and identify their influence on outcomes in recent neurosurgical trials that compare surgery to non-operative treatment. From 2000 to 2017, PubMed and Embase databases and four trial registries were searched. RCTs were evaluated for study design, funding, adjustments to reported outcome measures, accrual of patients, and academic impact. Eighty-two neurosurgical RCTs were identified, 40 in spine disorders, 19 neurovascular and neurotrauma, 11 functional neurosurgery, ten peripheral nerve, and two pituitary surgery. Eighty-four RCTs were registered, of which some are ongoing. Trial registration rate differed per subspecialty. Funding was mostly from non-industry institutions (58.5%), but 25.6% of RCTs did not report funding sources. 36.4% of RCTs did not report a difference between surgical and non-operative treatment, 3.7% favored non-operative management. Primary and secondary outcome measures were changed in 13.2% and 34.2% of RCTs respectively and varied by subspecialty. 41.9% of RCTs subtracted ≥ 10% of the anticipated accrual and 12.9% of RCTs added ≥ 10%. 7.3% of registered RCTs were terminated, mostly due to too slow recruitment. Subspecialty, registration, funding, masking, population size, and changing outcome measures were not significantly associated with a reported benefit of surgery. High Jadad scores (≥ 4) were negatively associated with a demonstration of surgical benefit (P

BACKGROUND CONTEXT Spine surgery has been identified as a risk factor for prolonged postoperative opioid use. Preoperative prediction of opioid use could improve risk stratification, shared decision-making, and patient counseling before surgery. PURPOSE The primary purpose of this study was to develop algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation. STUDY DESIGN/SETTING Retrospective, case-control study at five medical centers. PATIENT SAMPLE Chart review was conducted for patients undergoing surgery for lumbar disc herniation between January 1, 2000 and March 1, 2018. OUTCOME MEASURES The primary outcome of interest was sustained opioid prescription after surgery to at least 90 to 180 days postoperatively. METHODS Five models (elastic-net penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict prolonged opioid prescription. Explanations of predictions were provided globally (averaged across all patients) and locally (for individual patients). RESULTS Overall, 5,413 patients were identified, with sustained postoperative opioid prescription of 416 (7.7%) at 90 to 180 days after surgery. The elastic-net penalized logistic regression model had the best discrimination (c-statistic 0.81) and good calibration and overall performance; the three most important predictors were: instrumentation, duration of preoperative opioid prescription, and comorbidity of depression. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found here: https://sorg-apps.shinyapps.io/lumbardiscopioid/ CONCLUSION Preoperative prediction of prolonged postoperative opioid prescription can help identify candidates for increased surveillance after surgery. Patient-centered explanations of predictions can enhance both shared decision-making and quality of care.

Background Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-d) and long-term (1-yr) mortality. Objective To develop predictive algorithms for spinal metastatic disease at these time points and to provide patient-specific explanations of the predictions generated by these algorithms. Methods Retrospective review was conducted at 2 large academic medical centers to identify patients undergoing initial operative management for spinal metastatic disease between January 2000 and December 2016. Five models (penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict 90-d and 1-yr mortality. Results Overall, 732 patients were identified with 90-d and 1-yr mortality rates of 181 (25.1%) and 385 (54.3%), respectively. The stochastic gradient boosting algorithm had the best performance for 90-d mortality and 1-yr mortality. On global variable importance assessment, albumin, primary tumor histology, and performance status were the 3 most important predictors of 90-d mortality. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found at https://sorg-apps.shinyapps.io/spinemetssurvival/. Conclusion Preoperative estimation of 90-d and 1-yr mortality was achieved with assessment of more flexible modeling techniques such as machine learning. Integration of these models into applications and patient-centered explanations of predictions represent opportunities for incorporation into healthcare systems as decision tools in the future.

Objective The aims of this study were to assess baseline musculoskeletal radiology knowledge among Haitian orthopedists and to determine the impact of an adult and pediatric musculoskeletal radiology lecture series. Design Participants were given lectures reviewing normal and abnormal elbow radiographs and received assessments before and after the intervention. Bivariate and multivariate analyses were used to identify factors associated with baseline and postintervention scores. Setting This study was carried out as part of the 2018 Haitian Annual Assembly of Orthopaedic Trauma. This is an annual continuing medical educational conference in the capital city of Port-au-Prince open to all Haitian orthopedic surgeons and associated care providers, with a strong focus on resident training. Participants Haitian orthopedic surgery residents and surgeons attending the 2018 Haitian Annual Assembly of Orthopaedic Trauma. Results Thirty-seven residents and faculty consented to participate in this study and 32 (86.5%) were male with a median age of 33 (interquartile rage: 30-35). On multivariate analysis controlling for the title (resident versus attending), total years of orthopedics (beginning of residency and beyond), and formal radiology teaching in medical school or residency, conference attendance in the past was significantly associated with higher preintervention assessment scores (odds ratio = 1.24, 95% confidence interval = 1.06-1.44, p = 0.010]. The mean total preintervention accuracy for correctly identification of pathology, if present, was 70% compared to 83% at the postassessment (p Conclusions Overall, this study demonstrates that a brief lecture series at a continuing medical conference in Port-au-Prince, Haiti improved upper extremity radiographic interpretation based on pre and postassessments, and that prior conference attendance may be associated with higher baseline scores.

OBJECTIVEIf not anticipated and prearranged, hospital stay can be prolonged while the patient awaits placement in a rehabilitation unit or skilled nursing facility following elective spine surgery. Preoperative prediction of the likelihood of postoperative discharge to any setting other than home (i.e., nonroutine discharge) after elective inpatient spine surgery would be helpful in terms of decreasing hospital length of stay. The purpose of this study was to use machine learning algorithms to develop an open-access web application for preoperative prediction of nonroutine discharges in surgery for elective inpatient lumbar degenerative disc disorders.METHODSThe American College of Surgeons National Surgical Quality Improvement Program was queried to identify patients who underwent elective inpatient spine surgery for lumbar disc herniation or lumbar disc degeneration between 2011 and 2016. Four machine learning algorithms were developed to predict nonroutine discharge and the best algorithm was incorporated into an open-access web application.RESULTSThe rate of nonroutine discharge for 26,364 patients who underwent elective inpatient surgery for lumbar degenerative disc disorders was 9.28%. Predictive factors selected by random forest algorithms were age, sex, body mass index, fusion, level, functional status, extent and severity of comorbid disease (American Society of Anesthesiologists classification), diabetes, and preoperative hematocrit level. On evaluation in the testing set (n = 5273), the neural network had a c-statistic of 0.823, calibration slope of 0.935, calibration intercept of 0.026, and Brier score of 0.0713. On decision curve analysis, the algorithm showed greater net benefit for changing management over all threshold probabilities than changing management on the basis of the American Society of Anesthesiologists classification alone or for all patients or for no patients. The model can be found here: https://sorg-apps.shinyapps.io/discdisposition/.CONCLUSIONSMachine learning algorithms show promising results on internal validation for preoperative prediction of nonroutine discharges. If found to be externally valid, widespread use of these algorithms via the open-access web application by healthcare professionals may help preoperative risk stratification of patients undergoing elective surgery for lumbar degenerative disc disorders.

Background. While investigations have been performed examining the quality of US-based cost-utility analyses for other orthopaedic subspecialties and have provided important insights, a similar analysis has not been performed examining the foot and ankle literature. Methods. A systematic review of foot and ankle studies was conducted to identify cost-utility analyses published between 2000 and 2017. Of 687 studies screened by abstract, 4 cost-utility studies were identified and scored by the Quality of Health Economic Studies instrument. Results. Of these 4 studies, 3 examined end-stage arthritis and 1 examined unstable ankle fractures. Cost-effective interventions identified by these studies included the performance of total ankle arthroplasty over ankle arthrodesis or nonoperative treatment for end-stage arthritis and suture button fixation over syndesmotic screws for unstable supination–external rotation ankle fractures. The mean Quality of Health Economic Studies scores for these studies was 87.5. Conclusion. Despite the increasing focus on value-based care delivery in the United States, there are few foot and ankle cost-utility analyses. Nonetheless, the quality of existing analyses is high. Certain interventions have been identified as cost-effective as highlighted above and the findings of this review can be used to help design future analyses in order to best demonstrate the cost-effectiveness of foot and ankle interventions.Levels of Evidence: Level III: Systematic Review of level III studies

The Computational Neurosurgery Outcomes Center at Brigham and Women's Hospital focuses on new knowledge creation and value-based innovation with big data, predictive analytics, patient-reported outcomes, and national databases. Work with artificial intelligence, including machine learning for structured data such as online medical records and natural language processing for unstructured data such as clinic notes, allows for analysis of high-velocity, high-volume, and high-complexity neurosurgical data. Use of mobile-based actively collected patient reported outcomes has been paralleled by the development of digital phenotyping and passive data collection. The study of surgical outcomes has evolved tremendously since the work of Ernest Amory Codman and big data, predictive analytics, and passively collected patient reported outcomes are the next frontier for outcomes research.

Background Postoperative recovery after total hip arthroplasty (THA) can lead to the development of prolonged opioid use but there are few tools for predicting this adverse outcome. The purpose of this study is to develop machine learning algorithms for preoperative prediction of prolonged opioid prescriptions after THA. Methods A retrospective review of electronic health records was conducted at 2 academic medical centers and 3 community hospitals to identify adult patients who underwent THA for osteoarthritis between January 1, 2000 and August 1, 2018. Prolonged postoperative opioid prescriptions were defined as continuous opioid prescriptions after surgery to at least 90 days after surgery. Five machine learning algorithms were developed to predict this outcome and were assessed by discrimination, calibration, and decision curve analysis. Results Overall, 5507 patients underwent THA, of which 345 (6.3%) had prolonged postoperative opioid prescriptions. The factors determined for prediction of prolonged postoperative opioid prescriptions were age, duration of opioid exposure, preoperative hemoglobin, and preoperative medications (antidepressants, benzodiazepines, nonsteroidal anti-inflammatory drugs, and beta-2-agonists). The elastic-net penalized logistic regression model achieved the best performance across discrimination (c-statistic = 0.77), calibration, and decision curve analysis. This model was incorporated into a digital application able to provide both predictions and explanations (available at https://sorg-apps.shinyapps.io/thaopioid/ ). Conclusion If externally validated in independent populations, the algorithms developed in this study could improve preoperative screening and support for THA patients at high risk for prolonged postoperative opioid prescriptions. Early identification and intervention in high-risk cases may mitigate the long-term adverse consequence of opioid dependence. Level of Evidence III.

BACKGROUND CONTEXT Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-day) and long-term (one-year) mortality. PURPOSE The purpose of this study was to develop predictive algorithms for spinal metastatic disease at these time points and to provide patient-specific explanations of the predictions generated by these algorithms. STUDY DESIGN/SETTING Retrospective review was conducted at two large academic medical centers. PATIENT SAMPLE Patients undergoing initial operative management for spinal metastatic disease between January 2000 and December 2016. OUTCOME MEASURES Ninety-day and one-year overall survival. METHODS Five models (penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict ninety-day and one-year mortality. RESULTS Overall, 732 patients were identified with ninety-day and one-year mortality rates of 181 (25.1%) and 385 (54.3%), respectively. The stochastic gradient boosting algorithm had the best performance for 90-day mortality and one-year mortality. On global variable importance assessment, albumin, primary tumor histology, and performance status were the three most important predictors of 90-day mortality. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found at: https://sorg-apps.shinyapps.io/spinemetssurvival/ CONCLUSIONS Preoperative estimation of 90-day and one-year mortality was achieved with assessment of more flexible modeling techniques such as machine learning. Integration of these models into applications and patient-centered explanations of predictions represent opportunities for incorporation into health care systems as decision tools in the future. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed.To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence."A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature.Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P-value was provided or calculated, ML models outperformed clinical experts (P.05). In 18 of 50 (36%), no difference was seen between ML and expert performance (P.05), while in 3 of 50 (6%) clinical experts outperformed ML models (P.05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group.We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human-vs-machine to a human-and-machine paradigm could be essential to overcome these hurdles.

Background Patients who undergo craniotomy for brain tumor have an increased risk of developing venous thromboembolism (VTE). Using the National Surgical Quality Improvement Program (NSQIP) registry, patients undergoing craniotomy for brain tumor from 2006 and 2014 were analyzed to identify risk factors for postoperative VTE. Methods The study population, identified by Current Procedural Terminology codes, included all NSQIP-reported patients who underwent a craniotomy for brain tumor resection. Results There were 629 instances of VTE among 19,409 craniotomies for brain tumor (3.2%) recorded in NSQIP. Occurrence of VTE was associated with other postoperative complications on univariate analysis, including pneumonia, respiratory failure, stroke, and sepsis (all p < 0.001). On multivariate analysis, independent predictors of VTE included age 46 to 57 years (odds ratio [OR], 1.432; p = 0.006), 57 to 66 years (OR, 1.550; p = 0.001), or over 66 years (OR, 2.493; p < 0.001), body mass index (BMI) over 32.1 kg/m2 (OR, 1.835; p < 0.001), functional dependence (OR, 1.657; p < 0.001), ventilator dependence (OR, 2.516; p < 0.001), steroid use (OR, 1.661; p < 0.001), prior sepsis (OR, 1.845; p < 0.001), and total operative time 183 to 271 minutes (OR, 1.462; p = 0.032) and longer than 271 minutes (OR, 1.945; p < 0.001). Conclusions VTE occurs in approximately 3% of patients undergoing craniotomy for brain tumor resection. Independent predictors for developing VTE include older age, higher BMI, recent steroid use, and total operative time.