Development of a preoperative predictive model for major complications following adult spinal deformity surgery

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.

Impact of Self-Reported Loss of Balance and Gait Disturbance on Outcomes following Adult Spinal Deformity Surgery

Background: The objective of this study was to evaluate if imbalance influences complication rates, radiological outcomes, and patient-reported outcomes (PROMs) following adult spinal deformity (ASD) surgery. Methods: ASD patients with baseline and 2-year radiographic and PROMs were included. Patients were grouped according to whether they answered yes or no to a recent history of pre-operative loss of balance. The groups were propensity-matched by age, pelvic incidence-lumbar lordosis (PI-LL), and surgical invasiveness score. Results: In total, 212 patients were examined (106 in each group). Patients with gait imbalance had worse baseline PROM measures, including Oswestry disability index (45.2 vs. 36.6), SF-36 mental component score (44 vs. 51.8), and SF-36 physical component score (p < 0.001 for all). After 2 years, patients with gait imbalance had less pelvic tilt correction (-1.2 vs. -3.6°, p = 0.039) for a comparable PI-LL correction (-11.9 vs. -15.1°, p = 0.144). Gait imbalance patients had higher rates of radiographic proximal junctional kyphosis (PJK) (26.4% vs. 14.2%) and implant-related complications (47.2% vs. 34.0%). After controlling for age, baseline sagittal parameters, PI-LL correction, and comorbidities, patients with imbalance had 2.2-times-increased odds of PJK after 2 years. Conclusions: Patients with a self-reported loss of balance/unsteady gait have significantly worse PROMs and higher risk of PJK.

Advances in Anterolateral Approaches to the Lumbar Spine: A Focus on Technological Developments

A historical overview of the evolution of anterolateral approaches to the lumber spine and associated patient outcomes is presented. In addition, the modern incorporation of new technologies is discussed, including interbody cages, intraoperative image guidance, robotics, augmented reality, and machine learning, which have significantly improved the spine surgery safety and efficacy profile. Current challenges and future directions are also covered, emphasizing the need for further research and development, particularly in robotic assistance and machine learning algorithms.

Artificial Intelligence in Spine Surgery

The amount and quality of data being used in our everyday lives continue to advance in an unprecedented pace. This digital revolution has permeated healthcare, specifically spine surgery, allowing for very advanced and complex computational analytics, such as artificial intelligence (AI) and machine learning (ML). The integration of these methods into clinical practice has just begun, and the following review article will describe AI/ML, demonstrate how it has been applied in adult spinal deformity surgery, and show its potential to improve patient care touching on future directions.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery

Background: Adult spinal deformities (ASD) are varied spinal abnormalities, often necessitating surgical intervention when associated with pain, worsening deformity, or worsening function. Predicting post-operative complications and revision surgery is critical for surgical planning and patient counseling. Due to the relatively small number of cases of ASD surgery, machine learning applications have been limited to traditional models (e.g., logistic regression or standard neural networks) and coarse clinical variables. We present the novel application of advanced models (CNN, LLM, GWAS) using complex data types (radiographs, clinical notes, genomics) for ASD outcome prediction. Methods: We developed a CNN trained on 209 ASD patients (1549 radiographs) from the Stanford Research Repository, a CNN pre-trained on VinDr-SpineXR (10,468 spine radiographs), and an LLM using free-text clinical notes from the same 209 patients, trained via Gatortron. Additionally, we conducted a GWAS using the UK Biobank, contrasting 540 surgical ASD patients with 7355 non-surgical ASD patients. Results: The LLM notably outperformed the CNN in predicting pulmonary complications (F1: 0.545 vs. 0.2881), neurological complications (F1: 0.250 vs. 0.224), and sepsis (F1: 0.382 vs. 0.132). The pre-trained CNN showed improved sepsis prediction (AUC: 0.638 vs. 0.534) but reduced performance for neurological complication prediction (AUC: 0.545 vs. 0.619). The LLM demonstrated high specificity (0.946) and positive predictive value (0.467) for neurological complications. The GWAS identified 21 significant (p < 10-5) SNPs associated with ASD surgery risk (OR: mean: 3.17, SD: 1.92, median: 2.78), with the highest odds ratio (8.06) for the LDB2 gene, which is implicated in ectoderm differentiation. Conclusions: This study exemplifies the innovative application of cutting-edge models to forecast outcomes in ASD, underscoring the utility of complex data in outcome prediction for neurosurgical conditions. It demonstrates the promise of genetic models when identifying surgical risks and supports the integration of complex machine learning tools for informed surgical decision-making in ASD.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Artificial Intelligence to Preoperatively Predict Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery: Soft Tissue Imaging May Be Necessary for Accurate Models

STUDY DESIGN

Retrospective cohort.

OBJECTIVE

In a cohort of patients undergoing adult spinal deformity (ASD) surgery, we used artificial intelligence to compare three models of preoperatively predicting radiographic proximal junction kyphosis (PJK) using: (1) traditional demographics and radiographic measurements, (2) raw preoperative scoliosis radiographs, and (3) raw preoperative thoracic magnetic resonance imaging (MRI).

SUMMARY OF BACKGROUND DATA

Despite many proposed risk factors, PJK following ASD surgery remains difficult to predict.

MATERIALS AND METHODS

A single-institution, retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009 to 2021. PJK was defined as a sagittal Cobb angle of upper-instrumented vertebra (UIV) and UIV+2>10° and a postoperative change in UIV/UIV+2>10°. For model 1, a support vector machine was used to predict PJK within 2 years postoperatively using clinical and traditional sagittal/coronal radiographic variables and intended levels of instrumentation. Next, for model 2, a convolutional neural network (CNN) was trained on raw preoperative lateral and posterior-anterior scoliosis radiographs. Finally, for model 3, a CNN was trained on raw preoperative thoracic T1 MRIs.

RESULTS

A total of 191 patients underwent ASD surgery with at least 2-year follow-up and 89 (46.6%) developed radiographic PJK within 2 years. Model 1: Using clinical variables and traditional radiographic measurements, the model achieved a sensitivity: 57.2% and a specificity: 56.3%. Model 2: a CNN with raw scoliosis x-rays predicted PJK with a sensitivity: 68.2% and specificity: 58.3%. Model 3: a CNN with raw thoracic MRIs predicted PJK with average sensitivity: 73.1% and specificity: 79.5%. Finally, an attention map outlined the imaging features used by model 3 elucidated that soft tissue features predominated all true positive PJK predictions.

CONCLUSIONS

The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared with raw scoliosis radiographs and traditional clinical/radiographic measurements. The improved predictive accuracy using MRI may indicate that PJK is best predicted by soft tissue degeneration and muscle atrophy.

PreOperative Planning for Adult Spinal Deformity Goals: Level Selection and Alignment Goals

Adult Spinal Deformity (ASD) is a complex pathologic condition with significant impact on quality of life, including pain, loss of function, and fatigue. Achieving realignment goals is crucial for long-term results. Reliable preoperative planning strategies, including nomograms, measurement tools, and level selection, are key to maximizing the likelihood of achieving a good outcome following ASD corrective surgery. This review covers recent literature on such strategies, including review of the different targets for realignment and their association with outcomes (both patients-reported outcomes and complications), selection of upper and lower instrumented vertebrae, and the latest innovation in preoperative planning for deformity surgery.

A Comparative Analysis of Frailty, Disability, and Sarcopenia With Patient Characteristics and Outcomes in Adult Spinal Deformity Surgery

STUDY DESIGN

Retrospective case series study.

OBJECTIVES

This study aims to compare preoperative indices, including the modified frailty index-11 (mFI-11), modified frailty index-5 (mFI-5), Oswestry Disability Index (ODI), and psoas muscle index (PMI), as they relate to outcomes in adult spinal deformity (ASD) surgery.

METHODS

We identified 235 patients who underwent thoracolumbar ASD surgery (≥4 levels). The mFI-11, mFI-5, ODI, and PMI were determined from preoperative visits and correlated to outcome measures, including perioperative transfusion, duration of anesthesia, hospital and ICU length of stay (LOS), discharge disposition, readmission, change in ODI at last follow-up, revision surgery, and mortality.

RESULTS

Our cohort had a mean age of 69.6 years and a male:female ratio of 1:2 with 177 undergoing an index surgery and 58 patients presenting after a failed multilevel fusion. The average number of levels fused was 9.3. The mFI-11 and mFI-5 were similar in predicting the need for intraoperative and postoperative transfusion. However, the mFI-11 was able to predict longer ICU and hospital LOS and mortality. The average preoperative ODI was 44.9% with an average decrease of 10.1% at the last follow-up. Preoperative ODI was the most significant predictor of postoperative change of ODI. Sarcopenia, defined as the lowest quartile of PMI values measured at L3 and L4, was not associated with any meaningful outcomes.

CONCLUSION

The mFI-11 better correlated with outcomes, indicating its increased prognostic value compared to other preoperative indices in ASD surgery. Preoperative ODI remains a significant predictor of postoperative change in ODI when evaluating ASD patients.

Machine Learning Applications in Spine Surgery

This literature review sought to identify and evaluate the current applications of artificial intelligence (AI)/machine learning (ML) in spine surgery that can effectively guide clinical decision-making and surgical planning. By using specific keywords to maximize search sensitivity, a thorough literature research was conducted in several online databases: Scopus, PubMed, and Google Scholar, and the findings were filtered according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 46 studies met the requirements and were included in this review. According to this study, AI/ML models were sufficiently accurate with a mean overall value of 74.9%, and performed best at preoperative patient selection, cost prediction, and length of stay. Performance was also good at predicting functional outcomes and postoperative mortality. Regression analysis was the most frequently utilized application whereas deep learning/artificial neural networks had the highest sensitivity score (81.5%). Despite the relatively brief history of engagement with AI/ML, as evidenced by the fact that 77.5% of studies were published after 2018, the outcomes have been promising. In light of the Big Data era, the increasing prevalence of National Registries, and the wide-ranging applications of AI, such as exemplified by ChatGPT (OpenAI, San Francisco, California), it is highly likely that the field of spine surgery will gradually adopt and integrate AI/ML into its clinical practices. Consequently, it is of great significance for spine surgeons to acquaint themselves with the fundamental principles of AI/ML, as these technologies hold the potential for substantial improvements in overall patient care.

The ISSG-AO Complication Intervention Score, but Not Major/Minor Designation, is Correlated With Length of Stay Following Adult Spinal Deformity Surgery

STUDY DESIGN

Retrospective review.

OBJECTIVES

The International Spine Study Group-AO (ISSG-AO) Adult Spinal Deformity (ASD) Complication Classification System was developed to improve classification, reporting, and study of complications among patients undergoing ASD surgery. The ISSG-AO system classifies interventions to address complications by level of invasiveness: grade zero (none); grade 1, mild (e.g., medication change); grade 2, moderate (e.g., ICU admission); grade 3, severe (e.g., reoperation related to surgery of interest). To evaluate the efficacy of the ISSG-AO ASD Complication Classification System, we aimed to compare correlations between postoperative length of stay (LOS) and complication severity as classified by the ISSG-AO ASD and traditional major/minor complication classification systems.

METHODS

Patients age ≥18 in a multicenter ASD database who sustained in-hospital complications were identified. Complications were classified with the major/minor and ISSG-AO systems and correlated with LOS using an ensemble-based machine learning algorithm (conditional random forest) and a generalized linear mixed model.

RESULTS

490 patients at 19 sites were included. 64.9% of complications were major, and 35.1% were minor. By ISSG-AO classification, 20.4%, 66.1%, 6.7%, and 6.7% were grades 0-3, respectively. ISSG-AO complication grading demonstrated significant correlation with LOS, whereas major/minor complication classification demonstrated inverse correlation with LOS. In conditional random forest analysis, ISSG-AO classification had the greatest relative importance when assessing correlations across multiple variables with LOS.

CONCLUSIONS

The ISSG-AO system may help identify specific complications associated with prolonged LOS. Targeted interventions to avoid or reduce these complications may improve ASD surgical quality and resource utilization.

Impact of Navigation on 30-Day Outcomes for Adult Spinal Deformity Surgery

STUDY DESIGN

Retrospective database study.

OBJECTIVE

Navigation has been increasingly used to treat degenerative disease, with positive radiographic and clinical outcomes and fewer adverse events and reoperations, despite increased operative time. However, short-term analysis on treating adult spinal deformity (ASD) surgery with navigation is limited, particularly using large nationally represented cohorts. This is the first large-scale database study to compare 30-day readmission, reoperation, morbidity, and value-per-operative time for navigated and conventional ASD surgery.

METHODS

Adults were identified in the National Surgical Quality Improvement Program (NSQIP) database. Multivariate regression was used to compare outcomes between navigated and conventional surgery and to control for predictors and baseline differences.

RESULTS

3190 ASD patients were included. Navigated and conventional patients were similar. Navigated cases had greater operative time (405 vs 320 min) and mean RVUs per case (81.3 vs 69.7), and had more supplementary pelvic fixations (26.1 vs 13.4%) and osteotomies (50.3 vs 27.7%) (P <.001).In univariate analysis, navigation had greater reoperation (9.9 vs 5.2%, P = .011), morbidity (57.8 vs 46.8%, P = .007), and transfusion (52.2 vs 41.8%, P = .010) rates. Readmission was similar (11.9 vs 8.4%). In multivariate analysis, navigation predicted reoperation (OR = 1.792, P = .048), but no longer predicted morbidity or transfusion. Most reoperations were infectious and hardware-related.

CONCLUSIONS

Despite controlling for patient-related and procedural factors, navigation independently predicted a 79% increased odds of reoperation but did not predict morbidity or transfusion. Readmission was similar between groups. This is explained, in part, by greater operative time and transfusion, which are risk factors for infection. Reoperation most frequently occurred for wound- and hardware-related reasons, suggesting navigation carries an increased risk of infectious-related events beyond increased operative time.

Are We Focused on the Wrong Early Postoperative Quality Metrics? Optimal Realignment Outweighs Perioperative Risk in Adult Spinal Deformity Surgery

BACKGROUND

While reimbursement is centered on 90-day outcomes, many patients may still achieve optimal, long-term outcomes following adult spinal deformity (ASD) surgery despite transient short-term complications.

OBJECTIVE

Compare long-term clinical success and cost-utility between patients achieving optimal realignment and suboptimally aligned peers.

STUDY DESIGN/SETTING

Retrospective cohort study of a prospectively collected multicenter database.

METHODS

ASD patients with two-year (2Y) data included. Groups were propensity score matched (PSM) for age, frailty, body mass index (BMI), Charlson Comorbidity Index (CCI), and baseline deformity. Optimal radiographic criteria are defined as meeting low deformity in all three (Scoliosis Research Society) SRS-Schwab parameters or being proportioned in Global Alignment and Proportionality (GAP). Cost-per-QALY was calculated for each time point. Multivariable logistic regression analysis and ANCOVA (analysis of covariance) adjusting for baseline disability and deformity (pelvic incidence (PI), pelvic incidence minus lumbar lordosis (PI-LL)) were used to determine the significance of surgical details, complications, clinical outcomes, and cost-utility.

RESULTS

A total of 930 patients were considered. Following PSM, 253 "optimal" (O) and 253 "not optimal" (NO) patients were assessed. The O group underwent more invasive procedures and had more levels fused. Analysis of complications by two years showed that the O group suffered less overall major (38% vs. 52%, p = 0.021) and major mechanical complications (12% vs. 22%, p = 0.002), and less reoperations (23% vs. 33%, p = 0.008). Adjusted analysis revealed O patients more often met MCID (minimal clinically important difference) in SF-36 PCS, SRS-22 Pain, and Appearance. Cost-utility-adjusted analysis determined that the O group generated better cost-utility by one year and maintained lower overall cost and costs per QALY (both p < 0.001) at two years.

CONCLUSIONS

Fewer late complications (mechanical and reoperations) are seen in optimally aligned patients, leading to better long-term cost-utility overall. Therefore, the current focus on avoiding short-term complications may be counterproductive, as achieving optimal surgical correction is critical for long-term success.

Preoperative medical assessment for adult spinal deformity surgery: a state-of-the-art review

INTRODUCTION

The purpose of this study is to provide a state-of-the-art review regarding risk factors for perioperative complications in adult spinal deformity (ASD) surgery. The review includes levels of evidence for risk factors associated with complications in ASD surgery.

METHODS

Using the PubMed database, we searched for complications, risk factors, and adult spinal deformity. The included publications were assessed for level of evidence as described in clinical practice guidelines published by the North American Spine Society, with summary statements generated for each risk factor (Bono et al. in Spine J 9:1046-1051, 2009).

RESULTS

Frailty had good evidence (Grade A) as a risk for complications in ASD patients. Fair evidence (Grade B) was assigned for bone quality, smoking, hyperglycemia and diabetes, nutritional status, immunosuppression/steroid use, cardiovascular disease, pulmonary disease, and renal disease. Indeterminate evidence (Grade I) was assigned for pre-operative cognitive function, mental health, social support, and opioid utilization.

CONCLUSIONS

Identification of risk factors for perioperative complications in ASD surgery is a priority for empowering informed choices for patients and surgeons and managing patient expectations. Risk factors with grade A and B evidence should be identified prior to elective surgery and modified to reduce the risk of perioperative complications.

Association of telomere length with risk of complications in adult spinal deformity surgery: a pilot study of 43 patients

OBJECTIVE

Risk stratification is a critical element of surgical planning. Early tools were fairly crude, while newer instruments incorporate disease-specific elements and markers of frailty. It is unknown if discrepancies between chronological and cellular age can guide surgical planning or treatment. Telomeres are DNA-protein complexes that serve an important role in protecting genomic DNA. Their shortening is a consequence of aging and environmental exposures, with well-established associations with diseases of aging and mortality. There are compelling data to suggest that telomere length can provide insight toward overall health. The authors sought to determine potential associations between telomere length and postoperative complications.

METHODS

Adults undergoing elective surgery for spinal deformity were prospectively enrolled. Telomere length was measured from preoperative whole blood using quantitative polymerase chain reaction and expressed as the ratio of telomere (T) to single-copy gene (S) abundance (T/S ratio), with higher T/S ratios indicating longer telomere length. Demographic and patient data included age, BMI, and results for the following rating scales: the Adult Spinal Deformity Frailty Index (ASD-FI), Oswestry Disability Index (ODI), Scoliosis Research Society-22r (SRS-22r), American Society of Anesthesiology (ASA) classification, and Charlson Comorbidity Index (CCI). Operative and postoperative complication data (medical or surgical within 90 days) were also collected.

RESULTS

Forty-three patients were enrolled, including 31 women (53%), with a mean age of 66 years and a mean BMI of 28.5. The mean number of levels fused was 11, with 21 (48.8%) combined anterior-posterior approaches. Twenty-two patients (51.2%) had a medical or surgical complication. Patients with a postoperative complication had a significantly lower T/S ratio (0.712 vs 0.813, p = 0.008), indicating shorter telomere length, despite a mild difference in age compared with patients without a postoperative complication (68 vs 63 years, p = 0.069). Patients with complications also had higher CCI scores than patients without complications (2.3 vs 3.8, p = 0.004). There were no significant differences in sex, BMI, ASD-FI score, ASA class, preoperative ODI and SRS-22r scores, number of levels fused, or use of three-column osteotomies. In a multivariate model including age, frailty, ASA class, use of an anterior-posterior approach, CCI score, and telomere length, the authors found that short telomere length was significantly associated with postoperative complications. Patients whose telomere length fell in the shortest quartile had the highest risk (OR 18.184, p = 0.030).

CONCLUSIONS

Short telomere length was associated with an increased risk of postoperative complications despite only a mild difference in chronological age. Increasing comorbidity scores also trended toward significance. Larger prospective studies are needed; however, these data provide a compelling impetus to investigate the role of biological aging as a component of surgical risk stratification.

Risk factors of early complications after thoracic and lumbar spinal deformity surgery: a systematic review and meta-analysis

PURPOSE

To determine risk factors increasing susceptibility to early complications (intraoperative and postoperative within 6 weeks) associated with surgery to correct thoracic and lumbar spinal deformity.

METHODS

We systematically searched the PubMed and EMBASE databases for studies published between January 1990 and September 2021. Observational studies evaluating predictors of early complications of thoracic and lumbar spinal deformity surgery were included. Pooled odds ratio (OR) or standardized mean difference (SMD) with 95% confidence intervals (CI) was calculated via the random effects model.

RESULTS

Fifty-two studies representing 102,432 patients met the inclusion criteria. Statistically significant patient-related risk factors for early complications included neurological comorbidity (OR = 3.45, 95% CI 1.83-6.50), non-ambulatory status (OR = 3.37, 95% CI 1.96-5.77), kidney disease (OR = 2.80, 95% CI 1.80-4.36), American Society of Anesthesiologists score > 2 (OR = 2.23, 95% CI 1.76-2.84), previous spine surgery (OR = 1.98, 95% CI 1.41-2.77), pulmonary comorbidity (OR = 1.94, 95% CI 1.21-3.09), osteoporosis (OR = 1.60, 95% CI 1.17-2.20), cardiovascular diseases (OR = 1.46, 95% CI 1.20-1.78), hypertension (OR = 1.37, 95% CI 1.23-1.52), diabetes mellitus (OR = 1.84, 95% CI 1.30-2.60), preoperative Cobb angle (SMD = 0.43, 95% CI 0.29, 0.57), number of comorbidities (SMD = 0.41, 95% CI 0.12, 0.70), and preoperative lumbar lordotic angle (SMD = - 0.20, 95% CI - 0.35, - 0.06). Statistically significant procedure-related factors were fusion extending to the sacrum or pelvis (OR = 2.53, 95% CI 1.53-4.16), use of osteotomy (OR = 1.60, 95% CI 1.12-2.29), longer operation duration (SMD = 0.72, 95% CI 0.05, 1.40), estimated blood loss (SMD = 0.46, 95% CI 0.07, 0.85), and number of levels fused (SMD = 0.37, 95% CI 0.03, 0.70).

CONCLUSION

These data may contribute to development of a systematic approach aimed at improving quality-of-life and reducing complications in high-risk patients.

Artificial intelligence in spine surgery

The continuous progress of research and clinical trials has offered a wide variety of information concerning the spine and the treatment of the different spinal pathologies that may occur. Planning the best therapy for each patient could be a very difficult and challenging task as it often requires thorough processing of the patient's history and individual characteristics by the clinician. Clinicians and researchers also face problems when it comes to data availability due to patients' personal information protection policies. Artificial intelligence refers to the reproduction of human intelligence via special programs and computers that are trained in a way that simulates human cognitive functions. Artificial intelligence implementations to daily clinical practice such as surgical robots that facilitate spine surgery and reduce radiation dosage to medical staff, special algorithms that can predict the possible outcomes of conservative versus surgical treatment in patients with low back pain and disk herniations, and systems that create artificial populations with great resemblance and similar characteristics to real patients are considered to be a novel breakthrough in modern medicine. To enhance the body of the related literature and inform the readers on the clinical applications of artificial intelligence, we performed this review to discuss the contribution of artificial intelligence in spine surgery and pathology.

Use of artificial neural networks in the prognosis of musculoskeletal diseases-a scoping review

To determine the current evidence on artificial neural network (ANN) in prognostic studies of musculoskeletal diseases (MSD) and to assess the accuracy of ANN in predicting the prognosis of patients with MSD. The scoping review was reported under the Preferred Items for Systematic Reviews and the Meta-Analyses extension for Scope Reviews (PRISMA-ScR). Cochrane Library, Embase, Pubmed, and Web of science core collection were searched from inception to January 2023. Studies were eligible if they used ANN to make predictions about MSD prognosis. Variables, model prediction accuracy, and disease type used in the ANN model were extracted and charted, then presented as a table along with narrative synthesis. Eighteen Studies were included in this scoping review, with 16 different types of musculoskeletal diseases. The accuracy of the ANN model predictions ranged from 0.542 to 0.947. ANN models were more accurate compared to traditional logistic regression models. This scoping review suggests that ANN can predict the prognosis of musculoskeletal diseases, which has the potential to be applied to different types of MSD.

Timing of inpatient medical complications after adult spinal deformity surgery: early ambulation matters

BACKGROUND

The substantial risk for medical complication after adult spinal deformity (ASD) surgery is well known. However, the timing of medical complications during the inpatient stay have not been previously described. Accurate anticipation of complications and adverse events may improve patient counseling and postoperative management.

PURPOSE

(1) Describe the rate of medical complication and adverse events by postoperative day after ASD surgery and (2) determine whether early ambulation is protective for complications.

STUDY DESIGN/SETTING

Single institution retrospective cohort study.

PATIENT SAMPLE

Two hundred thirty-five patients with ASD who underwent posterior-only fusion of the lumbar spine (≥5 levels to the pelvis) between 2013 and 2020.

OUTCOME MEASURES

Medical complications, categorized per the International Spine Study Group-AO system (cardiopulmonary [CP], gastrointestinal [GI], central nervous system [CNS], infectious [nonsurgical site], and renal) and adverse events (postoperative blood transfusion, urinary retention, and electrolyte abnormalities).

METHODS

Patients were identified from an institutional ASD database. Outcome measures were classified by the first postoperative day the event was recognized. Demographics, year of surgery, surgical factors, radiographic parameters, surgical invasiveness (ASD-S Index), frailty (Modified Frailty Index-5 [mFI-5]), Charlson Comorbidity Index (CCI), obstructive sleep apnea (OSA), smoking, preoperative opioid use, depression, and post-operative day of ambulation were assessed as risk factors for inpatient medical complications.

RESULTS

After exclusions of patients with incomplete medical chart data, 191 patients were available for analysis, mean age 66±10 y, BMI 28±5 kg/m2, PI-LL 24±20°, T1PA 28±13°. Inpatient medical complications occurred in 55 (28.8%) patients; adverse events occurred in 137 (71.7%). Length of stay was higher in patients with medical complications (mean 8.5±3.8 vs. 5.8±2.3 days, p<.001). However, for patients who had an adverse event, but no medical complication, LOS was comparable (p>.05). Most medical complications occurred by POD3 (58% of all complications). Risk (defined as the proportion of patients with that complication out of all inpatients that day) of CNS, CP, and GI complication peaked early in the postoperative course (CNS on POD1 [2.1% risk]; GI on POD2 [3.7%]; CP on POD3 [2.7%]). Risk for infectious and renal complications (infectious POD8 [2.7%]; renal POD7 [0.9%]) peaked later (Figure 1). On univariate analysis, patients with medical complications had higher rates of OSA (9.1% vs. 2.2%, p=.045), ASD-S (45.9 vs. 40.8, p=.04), max coronal cobb (46.9 vs. 36.7°, p=.003), ASA class (2.5 vs. 2.3, p=.01), and POD of ambulation (1.9 vs. 1.3, p=.01). On multivariate logistic regression (c-statistic 0.78), larger coronal cobb and later POD of ambulation were independent risk factors for complications (OR 1.04, 95% CI 1.01-1.07 and OR 2.3, 95% CI 1.2-4.7, respectively).

CONCLUSIONS

Our data may inform peri-operative management and patient expectations for hospitalization after ASD surgery. Early ambulation may reduce the risk of complications.

Predicting Bone Health Using Machine Learning in Patients undergoing Spinal Reconstruction Surgery

STUDY DESIGN

Retrospective study of data collected prospectively.

OBJECTIVE

The goal of this study is to create a predictive model of preoperative bone health status in adult patients undergoing adult spinal reconstructive (ASR) surgery using machine learning (ML).

SUMMARY OF BACKGROUND DATA

Despite understanding that bone health impacts spine surgery outcomes, spine surgeons lack the tools to risk stratify patients preoperatively to determine who should undergo bone health screening. An ML approach mines patterns in data to determine the risk for poor bone health in ASR patients.

MATERIALS AND METHODS

Two hundred and eleven subjects over the age of 30 with dual energy X-ray absorptiometry scans, who underwent spinal reconstructive surgery were reviewed. Data was collected by manual and automated collection from the electronic health records. The Weka software was used to develop predictive models for multiclass classification of healthy, osteopenia, and osteoporosis (OPO) bone status. Bone status was labeled according to the World Health Organization (WHO) criteria using dual energy X-ray absorptiometry T scores. The accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated. The model was evaluated on a test set of unseen data for generalizability.

RESULTS

The prevalence of OPO was 23.22% and osteopenia was 52.61%. The random forest model achieved optimal performance with an average sensitivity of 0.81, specificity of 0.95, and AUC of 0.96 on the training set. The model yielded an averaged sensitivity of 0.64, specificity of 0.78, and AUC of 0.69 on the test set. The model was best at predicting OPO in patients. Numerous patient features exhibited predictive value, such as body mass index, insurance type, serum sodium level, serum creatinine level, history of bariatric surgery, and the use of medications such as selective serotonin reuptake inhibitors.

CONCLUSION

Predicting bone health status in ASR patients is possible with an ML approach. Additionally, data mining using ML can find unrecognized risk factors for bone health in ASR surgery patients.

Development and validation of a point-of-care clinical risk score to predict surgical site infection following open spinal fusion

Background

Surgical site infection (SSI) after open spine surgery increases healthcare costs and patient morbidity. Predictive analytics using large databases can be used to develop prediction tools to aid surgeons in identifying high-risk patients and strategies for optimization. The purpose of this study was to develop and validate an SSI risk-assessment score for patients undergoing open spine surgery.

Methods

The Premier Healthcare Database of adult open spine surgery patients (n = 157,664; 2,650 SSIs) was used to create an SSI risk scoring system using mixed effects logistic regression modeling. Full and reduced multilevel logistic regression models were developed using patient, surgery or facility predictors. The full model used 38 predictors and the reduced used 16 predictors. The resulting risk score was the sum of points assigned to 16 predictors.

Results

The reduced model showed good discriminatory capability (C-statistic = 0.75) and good fit of the model ([Pearson Chi-square/DF] = 0.90, CAIC=25,517) compared to the full model (C-statistic = 0.75, [Pearson Chi-square/DF] =0.90, CAIC=25,578). The risk scoring system, based on the reduced model, included the following: female (5 points), hypertension (4), blood disorder (8), peripheral vascular disease (9), chronic pulmonary disease (6), rheumatic disease (16), obesity (12), nicotine dependence (5), Charlson Comorbidity Index (2 per point), revision surgery (14), number of ICD-10 procedures (1 per procedure), operative time (1 per hour), and emergency/urgent surgery (12). A final risk score as the sum of the points for each surgery was validated using a 1,000-surgery random hold-out (independent from the study cohort) sample (C-statistic = 0.77).

Conclusions

The resulting SSI risk score composed of readily obtainable clinical information could serve as a strong prediction tool for SSI in preoperative settings when open spine surgery is considered.

Risk factors for extended length of stay and non-home discharge in adults treated with multi-level fusion for lumbar degenerative pathology and deformity

PURPOSE

To identify independent risk factors, including the Risk Assessment and Prediction Tool (RAPT) score, associated with extended length of stay (eLOS) and non-home discharge following elective multi-level instrumented spine fusion operations for diagnosis of adult spinal deformity (ASD) and lumbar degenerative pathology.

METHODS

Adults who underwent multi-level ([Formula: see text] segments) instrumented spine fusions for ASD and lumbar degenerative pathology at a single institution (2016-2021) were reviewed. Presence of a pre-operative RAPT score was used as an inclusion criterion. Excluded were patients who underwent non-elective operations, revisions, operations for trauma, malignancy, and/or infections. Outcomes were eLOS (> 7 days) and discharge location (home vs. non-home). Predictor variables included demographics, comorbidities, operative information, Surgical Invasiveness Index (SII), and RAPT score. Fisher's exact test was used for univariate analysis, and significant variables were implemented in multivariate binary logistic regression, with generation of 95% percent confidence intervals (CI), odds ratios (OR), and p-values.

RESULTS

Included for analysis were 355 patients. Post-operatively, 36.6% (n = 130) had eLOS and 53.2% (n = 189) had a non-home discharge. Risk factors significant for a non-home discharge were older age (> 70 years), SII > 36, pre-op RAPT < 10, DMII, diagnosis of depression or anxiety, and eLOS. Risk factors significant for an eLOS were SII > 20, RAPT < 6, and an ASA score of 3.

CONCLUSION

The RAPT score and SII were most important significant predictors of eLOS and non-home discharges following multi-level instrumented fusions for lumbar spinal pathology and deformity. Preoperative optimization of the RAPT's individual components may provide a useful strategy for decreasing LOS and modifying discharge disposition.

Applications of artificial intelligence in orthopaedic surgery

The practice of medicine is rapidly transforming as a result of technological breakthroughs. Artificial intelligence (AI) systems are becoming more and more relevant in medicine and orthopaedic surgery as a result of the nearly exponential growth in computer processing power, cloud based computing, and development, and refining of medical-task specific software algorithms. Because of the extensive role of technologies such as medical imaging that bring high sensitivity, specificity, and positive/negative prognostic value to management of orthopaedic disorders, the field is particularly ripe for the application of machine-based integration of imaging studies, among other applications. Through this review, we seek to promote awareness in the orthopaedics community of the current accomplishments and projected uses of AI and ML as described in the literature. We summarize the current state of the art in the use of ML and AI in five key orthopaedic disciplines: joint reconstruction, spine, orthopaedic oncology, trauma, and sports medicine.

Predicting complications of spine surgery: external validation of three models

BACKGROUND CONTEXT

Numerous prediction tools are available for estimating postoperative risk following spine surgery. External validation and comparison of these tools is critical prior to clinical use. No model for adverse events after spine surgery has undergone decision curve analysis.

PURPOSE

External validation, comparison, and decision curve analysis of 3 previously described models [SpineSage, Risk Assessment Tool (RAT), National Surgical Quality Improvement Program Risk Calculator (NSQIP)] for predicting 30-day postoperative complications after spine surgery STUDY DESIGN: Retrospective cohort study.

PATIENT SAMPLE

Three hundred fifteen patients who underwent spine surgery at a tertiary academic surgical center in New Zealand between January 2019 and April 2020.

OUTCOME MEASURES

As defined by each risk prediction tool and objectively using the Comprehensive Complication Index.

METHODS

We retrospectively reviewed risk of postoperative complication was calculated for each patient according to the 3 models. Overall model fit, calibration, discrimination, and decision curve analysis for each model were assessed in line with the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines.

RESULTS

100 (35%) patients experienced complications. SpineSage and RAT were well calibrated, NSQIP systematically underestimated risk. Area under the curve was greatest for SpineSage (0.75) compared with the NSQIP (0.72) and the RAT (0.69). Decision curve analysis showed SpineSage resulted in greatest net benefit across all risk thresholds.

CONCLUSIONS

Of the models studied, SpineSage most accurately predicted risk and can be expected to perform better than a strategy of treating all patients if patient or surgeon deem complication risk >10% significant. NSQIP may not be suitable for the clinical use in our local population.

Artificial Intelligence in Spinal Imaging: Current Status and Future Directions

Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care. Imaging investigations can provide information and insights that are not visible through ordinary visual inspection. Multiscale in vivo interrogation has the potential to improve the assessment and monitoring of pathologies thanks to the convergence of imaging, artificial intelligence (AI), and radiomic techniques. AI is revolutionizing computer vision, autonomous driving, natural language processing, and speech recognition. These revolutionary technologies are already impacting radiology, diagnostics, and other fields, where automated solutions can increase precision and reproducibility. In the first section of this narrative review, we provide a brief explanation of the many approaches currently being developed, with a particular emphasis on those employed in spinal imaging studies. The previously documented uses of AI for challenges involving spinal imaging, including imaging appropriateness and protocoling, image acquisition and reconstruction, image presentation, image interpretation, and quantitative image analysis, are then detailed. Finally, the future applications of AI to imaging of the spine are discussed. AI has the potential to significantly affect every step in spinal imaging. AI can make images of the spine more useful to patients and doctors by improving image quality, imaging efficiency, and diagnostic accuracy.

A scoping review of complication prediction models in spinal surgery: An analysis of model development, validation and impact

Background

Predictive analytics are being used increasingly in the field of spinal surgery with the development of models to predict post-surgical complications. Predictive models should be valid, generalizable, and clinically useful. The purpose of this review was to identify existing post-surgical complication prediction models for spinal surgery and to determine if these models are being adequately investigated with internal/external validation, model updating and model impact studies.

Methods

This was a scoping review of studies pertaining to models for the prediction of post-surgical complication after spinal surgery published over 10 years (2010-2020). Qualitative data was extracted from the studies to include study classification, adherence to Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and risk of bias (ROB) assessment using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). Model evaluation was determined using area under the curve (AUC) when available. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement was used as a basis for the search methodology in four different databases.

Results

Thirty studies were included in the scoping review and 80% (24/30) included model development with or without internal validation. Twenty percent (6/30) were exclusively external validation studies and only one study included an impact analysis in addition to model development and internal validation. Two studies referenced the TRIPOD guidelines and there was a high ROB in 100% of the studies using the PROBAST tool.

Conclusions

The majority of post-surgical complication prediction models in spinal surgery have not undergone standardized model development and internal validation or adequate external validation and impact evaluation. As such there is uncertainty as to their validity, generalizability, and clinical utility. Future efforts should be made to use existing tools to ensure standardization in development and rigorous evaluation of prediction models in spinal surgery.

Artificial Learning and Machine Learning Applications in Spine Surgery: A Systematic Review

OBJECTIVES

This current systematic review sought to identify and evaluate all current research-based spine surgery applications of AI/ML in optimizing preoperative patient selection, as well as predicting and managing postoperative outcomes and complications.

METHODS

A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA guidelines.

RESULTS

After application of inclusion and exclusion criteria, 41 studies were included in this review. Bayesian networks had the highest average AUC (.80), and neural networks had the best accuracy (83.0%), sensitivity (81.5%), and specificity (71.8%). Preoperative planning/cost prediction models (.89,82.2%) and discharge/length of stay models (.80,78.0%) each reported significantly higher average AUC and accuracy compared to readmissions/reoperation prediction models (.67,70.2%) (P < .001, P = .005, respectively). Model performance also significantly varied across postoperative management applications for average AUC and accuracy values (P < .001, P < .027, respectively).

CONCLUSIONS

Generally, authors of the reviewed studies concluded that AI/ML offers a potentially beneficial tool for providers to optimize patient care and improve cost-efficiency. More specifically, AI/ML models performed best, on average, when optimizing preoperative patient selection and planning and predicting costs, hospital discharge, and length of stay. However, models were not as accurate in predicting postoperative complications, adverse events, and readmissions and reoperations. An understanding of AI/ML-based applications is becoming increasingly important, particularly in spine surgery, as the volume of reported literature, technology accessibility, and clinical applications continue to rapidly expand.

The 5-factor modified frailty index (mFI-5) is predictive of 30-day postoperative complications and readmission in patients with adult spinal deformity (ASD)

BACKGROUND

There is limited research regarding the association between the mFI-5 and postoperative complications among adult spinal deformity (ASD) patients.

METHODS

Using the National Surgical Quality Improvement Project (NSQIP) database, patients with Current Procedural Terminology (CPT) codes for > 7-level fusion or < 7-level fusion with International Classification of Diseases, Ninth Revision (ICD-9) codes for ASD were identified between 2008 and 2016. Univariate analyses with post-hoc Bonferroni correction for demographics and preoperative factors were performed. Logistic regression assessed associations between mFI-5 scores and 30-day post-operative outcomes.

RESULTS

2,120 patients met criteria. Patients with an mFI-5 score of 4 or 5 were excluded, given there were<20 patients with those scores. Patients with mFI-5 scores of 1 and 2 had increased 30-day rates of pneumonia (3.5 % and 4.3 % vs 1.6 %), unplanned postoperative ventilation for > 48 h (3.1 % and 4.3 % vs 0.9 %), and UTIs (4.4 % and 7.4 % vs 2.0 %) than patients with a score of 0 (all, p < 0.05). Logistic regression revealed that compared to an mFI-5 of 0, a score of 1 was an independent predictor of 30-day reoperations (OR = 1.4; 95 % CI 1.1-18). A score of 2 was an independent predictor of overall (OR = 2.4; 95 % CI 1.4-4.1) and related (OR = 2.2; 95 % CI 1.2-4.1) 30-day readmissions. A score of 3 was not predictive of any adverse outcome.

CONCLUSION

The mFI-5 score predicted complications and postoperative events in the ASD population. The mFI-5 may effectively predict 30-day readmissions. Further research is needed to identify the benefits and predictive value of mFI-5 as a risk assessment tool.

Artificial intelligence in spine care: current applications and future utility

PURPOSE

The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research.

METHODS

A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review.

RESULTS

This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems.

CONCLUSION

Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.

Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery

STUDY DESIGN

Retrospective study at a unique center.

OBJECTIVE

The aim of this study is twofold, to develop a virtual patients model for lumbar decompression surgery and to evaluate the precision of an artificial neural network (ANN) model designed to accurately predict the clinical outcomes of lumbar decompression surgery.

METHODS

We performed a retrospective study of complete Electronic Health Records (EHR) to identify potential unfavorable criteria for spine surgery (predictors). A cohort of synthetics EHR was created to classify patients by surgical success (green zone) or partial failure (orange zone) using an Artificial Neural Network which screens all the available predictors.

RESULTS

In the actual cohort, we included 60 patients, with complete EHR allowing efficient analysis, 26 patients were in the orange zone (43.4%) and 34 were in the green zone (56.6%). The average positive criteria amount for actual patients was 8.62 for the green zone (SD+/- 3.09) and 10.92 for the orange zone (SD 3.38). The classifier (a neural network) was trained using 10,000 virtual patients and 2000 virtual patients were used for test purposes. The 12,000 virtual patients were generated from the 60 EHR, of which half were in the green zone and half in the orange zone. The model showed an accuracy of 72% and a ROC score of 0.78. The sensitivity was 0.885 and the specificity 0.59.

CONCLUSION

Our method can be used to predict a favorable patient to have lumbar decompression surgery. However, there is still a need to further develop its ability to analyze patients in the "failure of treatment" zone to offer precise management of patient health before spinal surgery.

Coronal Alignment in Adult Spinal Deformity Surgery: Definitions, Measurements, Treatment Algorithms, and Impact on Clinical Outcomes

The major focus of realignment in adult spinal deformity (ASD) surgery has been on the sagittal plane, and less emphasis has been given to the coronal plane. In light of this overlooked area within ASD surgery, we aimed to refocus the narrative on coronal alignment in ASD surgery. The objectives of the current narrative review were to (1) define coronal alignment and discuss existing measurements; (2) report the incidence and prevalence of coronal malalignment (CM); (3) discuss the impact of CM on clinical outcomes; and (4) describe our preferred treatment algorithm of surgical correction of CM in ASD.

Adult Spinal Deformity Surgery and Frailty: A Systematic Review

STUDY DESIGN

Systematic review.

OBJECTIVES

Adult spinal deformity (ASD) can be a debilitating condition with a profound impact on patients' health-related quality of life (HRQoL). Many reports have suggested that the frailty status of a patient can have a significant impact on the outcome of the surgery. The present review aims to identify all pre-operative patient-specific frailty markers that are associated with postoperative outcomes following corrective surgery for ASD of the lumbar and thoracic spine.

METHODS

A systematic review of the literature was performed to identify findings regarding pre-operative markers of frailty and their association with postoperative outcomes in patients undergoing ASD surgery of the lumbar and thoracic spine. The search was performed in the following databases: PubMed, Embase, Cochrane and CINAHL.

RESULTS

An association between poorer performance on frailty scales and worse postoperative outcomes. Comorbidity indices were even more frequently employed with similar patterns of association between increased comorbidity burden and postoperative outcomes. Regarding the assessment of HRQoL, worse pre-operative ODI, SF-36, SRS-22 and NRS were shown to be predictors of post-operative complications, while ODI, SF-36 and SRS-22 were found to improve post-operatively.

CONCLUSIONS

The findings of this review highlight the true breadth of the concept of "frailty" in ASD surgical correction. These parameters, which include frailty scales and various comorbidity and HRQoL indices, highlight the importance of identifying these factors preoperatively to ensure appropriate patient selection while helping to limit poor postoperative outcomes.

External validation of a predictive model of adverse events following spine surgery

BACKGROUND CONTEXT

We lack models that reliably predict 30-day postoperative adverse events (AEs) following spine surgery.

PURPOSE

We externally validated a previously developed predictive model for common 30-day adverse events (AEs) after spine surgery.

STUDY DESIGN/SETTING

This prospective cohort study utilizes inpatient and outpatient data from a tertiary academic medical center.

PATIENT SAMPLE

We assessed a prospective cohort of all 276 adult patients undergoing spine surgery in the Department of Neurosurgery at a tertiary academic institution between April 1, 2018 and October 31, 2018. No exclusion criteria were applied.

OUTCOME MEASURES

Incidence of observed AEs was compared with predicted incidence of AEs. Fifteen assessed AEs included: pulmonary complications, congestive heart failure, neurological complications, pneumonia, cardiac dysrhythmia, renal failure, myocardial infarction, wound infection, pulmonary embolus, deep venous thrombosis, wound hematoma, other wound complication, urinary tract infection, delirium, and other infection.

METHODS

Our group previously developed the Risk Assessment Tool for Adverse Events after Spine Surgery (RAT-Spine), a predictive model of AEs within 30 days following spine surgery using a cohort of approximately one million patients from combined Medicare and MarketScan databases. We applied RAT-Spine to the single academic institution prospective cohort by entering each patient's preoperative medical and demographic characteristics and surgical type. The model generated a patient-specific overall risk score ranging from 0 to 1 representing the probability of occurrence of any AE. The predicted risks are presented as absolute percent risk and divided into low (<17%), medium (17%-28%), and high (>28%).

RESULTS

Among the 276 patients followed prospectively, 76 experienced at least one 30-day postoperative AE. Slightly more than half of the cohort were women (53.3%). The median age was slightly lower in the non-AE cohort (63 vs. 66.5 years old). Patients with Medicaid comprised 2.5% of the non-AE cohort and 6.6% of the AE cohort. Spinal fusion was performed in 59.1% of cases, which was comparable across cohorts. There was good agreement between the predicted AE and observed AE rates, Area Under the Curve (AUC) 0.64 (95% CI 0.56-0.710). The incidence of observed AEs in the prospective cohort was 17.8% among the low-risk group, 23.0% in the medium-risk group, and 38.4% in the high risk group (p =.003).

CONCLUSIONS

We externally validated a model for postoperative AEs following spine surgery (RAT-Spine). The results are presented as low-, moderate-, and high-risk designations.

Perioperative Predictive Factors for Positive Outcomes in Spine Fusion for Adult Deformity Correction

Purpose: Identifying perioperative factors that may influence the outcomes of long spine fusion for the treatment of adult deformity is key for tailored surgical planning and targeted informed consent. The aim of this study was to analyze the association between demographic or perioperative factors and clinical outcomes 2 years after long spine fusion for the treatment of adult deformity. Methods: This study is a multivariate analysis of retrospectively collected data. All patients who underwent long fusion of the lumbar spine for adult spinal deformity (January 2016–June 2019) were included. The outcomes of interest were the Oswestry disability index (ODI), visual analogic scale (VAS) preoperatively and at 1 and 2 years’ follow up, age, body mass index, American Society of Anaesthesiologists (ASA) score, upper and lowest instrumented vertebrae (UIV and LIV, respectively), length of surgery, estimated blood loss, and length of hospital stay. Results: Data from 192 patients were available. The ODI at 2 years correlated weakly to moderately with age (r = 0.4), BMI (r = 0.2), ASA (r = 0.3), and LIV (r = 0.2), and strongly with preoperative ODI (r = 0.6). The leg VAS at 2 years moderately correlated with age (r = 0.3) and BMI (r = 0.3). Conclusion: ODI and VAS at 2 years’ follow-up had no to little association to preoperative age, health status, LIV, or other peroperative data, but showed a strong correlation with preoperative ODI and pain level.

Artificial Intelligence in Adult Spinal Deformity

Artificial Intelligence is gaining traction in medicine for its ease of use and advancements in technology. This study evaluates the current literature on the use of artificial intelligence in adult spinal deformity.

Artificial intelligence for adult spinal deformity: current state and future directions

As we experience a technological revolution unlike any other time in history, spinal surgery as a discipline is poised to undergo a dramatic transformation. As enormous amounts of data become digitized and more readily available, medical professionals approach a critical juncture with respect to how advanced computational techniques may be incorporated into clinical practices. Within neurosurgery, spinal disorders in particular, represent a complex and heterogeneous disease entity that can vary dramatically in its clinical presentation and how it may impact patients' lives. The spectrum of pathologies is extremely diverse, including many different etiologies such as trauma, oncology, spinal deformity, infection, inflammatory conditions, and degenerative disease among others. The decision to perform spine surgery, especially complex spine surgery, involves several nuances due to the interplay of biomechanical forces, bony composition, neurologic deficits, and the patient's desired goals. Adult spinal deformity as an example is one of the most complex, given its involvement of not only the spine, but rather the entirety of the skeleton in order to appreciate radiographic completeness. With the vast array of variables contributing to spinal disorders, treatment algorithms can vary significantly, and it is very difficult for surgeons to predict how patients will respond to surgery. As such, it will become imperative for spine surgeons to utilize the burgeoning availability of advanced computational tools to process unprecedented amounts of data and provide novel insights into spinal disease. These tools range from predictive models built using machine learning algorithms, to deep learning methods for imaging analysis, to natural language processing that can mine text from electronic medical records or transcribed patient visits - all to better treat the intricacies of spinal disorders. The adoption of such techniques will empower patients and propel spine surgeons into the era of personalized medicine, by allowing clinical plans to be tailored to address individual patients' needs. This paper, which exists in the context of a larger body of literatutre, provides a comprehensive review of the current state and future of artificial intelligence and machine learning with a particular emphasis on Adult spinal deformity surgery.

Predictors of serious, preventable, and costly medical complications in a population of adult spinal deformity patients

BACKGROUND CONTEXT

In 2008, the Centers for Medicare and Medicaid Services (CMS) established a list of hospital-acquired conditions (HACs) with significant deleterious effects on both patients and providers. Adult spinal deformity (ASD) surgery is complex and highly invasive, and as such may result in significant morbidity including these HACs.

PURPOSE

Identify predictors for developing the most common HACs among adult spinal deformity (ASD) patients undergoing corrective surgery.

STUDY DESIGN/SETTING

Retrospective analysis.

PATIENT SAMPLE

One thousand one hundred and seventy-one ASD patients.

OUTCOME MEASURES

HACs, Health-Related Quality of Life scores(HRQLs), Reoperation, Integrated Health State (IHS) METHODS: ASD pts undergoing surgery (>18 years, scoliosis ≥20°, SVA ≥5 cm, PT ≥25° and/or TK >60°) with complete data at BL and up to 2 years post-op were included. Patients were stratified by presence of >1 HAC, defined as at least one superficial/deep SSI, UTI, DVT, or PE within a 30-day post-op window. Random forest analysis generated 5,000 Conditional Inference Trees to compute a variable importance table for top predictors of HACs. An area-under-the-curve (AUC) methodology compared normalized HRQL scores between groups to determine an IHS with 2-year follow-up.

RESULTS

Total of 1,171 pts (59.8 years, 76.2%F, 28.1kg/m²) underwent corrective ASD surgery, with 1,053 pts in the non-HAC group and 118 in the HAC group. Of these pts, 25.4% had UTI, 15.4% DVT, 19.2% superficial SSI, 20.8% deep SSI, and 19.2% PE. HAC pts were on average older (63.5 vs 59.3, p=.004) and more often frail (51.3 vs 39.7%, p=.021) than non-HAC pts. Postop LOS and reoperation were most associated with HAC groups: [1] LOS >7 days [2] reoperation. Patient-related predictors of HACs were [3] age >50 yerr, [4] frailty, and [13] BMI >31. Procedure-related predictors of HACs were [5] operative-time >405 minutes, [6] levels fused >9, EBL >1450 mL, and [11] decompression. BL radiographic predictors were [7] PT >20°, [9] PI-LL>6°, [10] TL Cobb angle >15°, [12] SVA C7-S1 >29 mm. No differences were observed between groups with regards to IHS ODI (0.73 vs 0.74, p=.863), SRS (1.3 vs1.3, p=.374), NRS Back (0.6 vs 0.6, p=.158). HAC had higher rates of reoperation than non-HAC (0.08 vs 0.01, p=.066), and any HAC within 30-days of index was a significant predictor of reoperation (OR: 2.448 [1.94-3.09], p<.001).

CONCLUSIONS

In a population of ASD patients, HACs were associated with length of stay, reoperation, age, and frailty. Radiographic parameters such as pelvic tilt >20°, PI-LL >6°, & SVA >29 mm also increased odds of HACs, and should raise postoperative awareness for HAC development.

Defining a Surgical Invasiveness Threshold for Increased Risk of a Major Complication Following Adult Spinal Deformity Surgery

STUDY DESIGN

Retrospective review.

OBJECTIVES

The aim of this study was to define a surgical invasiveness threshold that predicts major complications after adult spinal deformity (ASD) surgery; use this threshold to categorize patients into quartiles by invasiveness; and determine the odds of major complications by quartile.

SUMMARY OF BACKGROUND DATA

Understanding the relationship between surgical invasiveness and major complications is important for estimating the likelihood of major complications after ASD surgery.

METHODS

Using a multicenter database, we identified 574 ASD patients (more than 5 levels fused; mean age, 60 ± 15 years) with minimum 2-year follow-up. Invasiveness was calculated as the ASD Surgical and Radiographic (ASD-SR) score. Youden index was used to identify the invasiveness score cut-off associated with optimal sensitivity and specificity for predicting major complications. Resulting high- and low-invasiveness groups were divided in half to create quartiles. Odds of developing a major complication were analyzed for each quartile using logistic regression (alpha = 0.05).

RESULTS

The ASD-SR cutoff score that maximally predicted major complications was 90 points. ASD-SR quartiles were 0 to 65 (Q1), 66 to 89 (Q2), 90 to 119 (Q3), and ≥120 (Q4). Risk of a major complication was 17% in Q1, 21% in Q2, 35% in Q3, and 33% in Q4 (P < 0.001). Comparisons of adjacent quartiles showed an increase in the odds of a major complication from Q2 to Q3 (odds ratio [OR] 1.8; 95% confidence interval [CI]: 1.0-3.0), but not from Q1 to Q2 or from Q3 to Q4. Patients with ASD-SR scores ≥90 were 1.9 times as likely to have a major complication than patients with scores <90 (OR 1.9, 95% CI 1.3-2.9). Mean ASD-SR scores above and below 90 points were 121 ± 25 and 63 ± 17, respectively.

CONCLUSION

The odds of major complications after ASD surgery are significantly greater when the procedure has an ASD-SR score ≥90. ASD-SR score can be used to counsel patients regarding these increased odds.Level of Evidence: 3.

American Society of Anesthesiologists Physical Status Classification as a reliable predictor of postoperative medical complications and mortality following ambulatory surgery: an analysis of 2,089,830 ACS-NSQIP outpatient cases

BACKGROUND

Seventy percent of surgical procedures are currently performed in the outpatient setting. Although the American Society of Anesthesiologists (ASA) Physical Classification ability to predict risk has been evaluated for in-patient surgeries, an evaluation in outpatient surgeries has yet to be performed. The major goal of the current study is to determine if the ASA classification is an independent predictor for morbidity and mortality for outpatient surgeries.

METHODS

The 2005 through 2016 NSQIP Participant Use Data Files were queried to extract all patients scheduled for outpatient surgery. ASA PS class was the primary independent variable of interest. The primary outcome was 30-day medical complications, defined as having one or more of the following postoperative outcomes: (1) deep vein thrombosis, (2) pulmonary embolism, (3) reintubation, (4) failure to wean from ventilator, (5) renal insufficiency, (6) renal failure, (7) stroke, (8) cardiac arrest, (9) myocardial infarction, (10) pneumonia, (11) urinary tract infection, (12) systemic sepsis or septic shock. Mortality was also evaluated as a separate outcome.

RESULTS

A total of 2,089,830 cases were included in the study. 24,777 (1.19%) patients had medical complications and 1,701 (0.08%) died within 30 days. ASA PS IV patients had a much greater chance of dying when compared to healthy patients, OR (95%CI) of 89 (55 to 143), P < 0.001. Nonetheless, over 30,000 ASA PS IV patients had surgery in the outpatient setting. Multivariable analysis demonstrated a stepwise independent association between ASA PS class and medical complications (C statistic = 0.70), mortality (C statistic = 0.74) and readmissions (C statistic = 0.67). Risk stratifying ability was maintained across surgical procedures and anesthesia techniques.

CONCLUSIONS

ASA PS class is a simple risk stratification tool for surgeries in the outpatient setting. Patients with higher ASA PS classes subsequently developed medical complications or mortality at a greater frequency than patients with lower ASA PS class after outpatient surgery. Our results suggest that the ambulatory setting may not be able to match the needs of high-risk patients.

Artificial Intelligence in Spine Care

Artificial intelligence is an exciting and growing field in medicine to assist in the proper diagnosis of patients. Although the use of artificial intelligence in orthopedics is currently limited, its utility in other fields has been extremely valuable and could be useful in orthopedics, especially spine care. Automated systems have the ability to analyze complex patterns and images, which will allow for enhanced analysis of imaging. Although the potential impact of artificial intelligence integration into spine care is promising, there are several limitations that must be overcome. Our goal is to review current advances that machine learning has been used for in orthopedics, and discuss potential application to spine care in the clinical setting in which there is a need for the development of automated systems.

Narrative Review of Predictive Analytics of Patient-Reported Outcomes in Adult Spinal Deformity Surgery

STUDY DESIGN

Narrative review.

OBJECTIVE

Decision making in surgery for adult spinal deformity (ASD) is complex due to the multifactorial etiology, numerous surgical options, and influence of multiple medical and psychosocial factors on patient outcomes. Predictive analytics provide computational tools to analyze large data sets and generate hypotheses regarding new data. In this review, we examine the use of predictive analytics to predict patient-reported outcomes (PROs) in ASD surgery.

METHODS

A search of PubMed, Web of Science, and Embase databases was performed to identify all potentially relevant studies up to February 1, 2020. Studies were included based on the use of predictive analytics to predict PROs in ASD.

RESULTS

Of 57 studies identified and reviewed, 7 studies were included. Multiple algorithms including supervised and unsupervised methods were used. Significant heterogeneity was observed with choice of PROs modeled including ODI, SRS22, and SF36, assessment of model accuracy, and with the model accuracy and area under the receiver operating curve values (ranging from 30% to 86% and 0.57 to 0.96, respectively). Models were built with data sets of patients ranging from 89 to 570 patients with a range of 22 to 267 variables.

CONCLUSIONS

Predictive analytics makes accurate predictions regarding PROs regarding pain, disability, and work and social function; PROs regarding satisfaction, self-image, and psychologic aspects of ASD were predicted with the lowest accuracy. Our review demonstrates a relative paucity of studies on ASD with limited databases. Future studies should include larger and more diverse databases and provide external validation of preexisting models.

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

BACKGROUND

Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making.

METHODS

This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist.

RESULTS

A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies.

CONCLUSIONS

A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

Attitudes of the Surgical Team Toward Artificial Intelligence in Neurosurgery: International 2-Stage Cross-Sectional Survey

BACKGROUND

Artificial intelligence (AI) has the potential to disrupt how we diagnose and treat patients. Previous work by our group has demonstrated that the majority of patients and their relatives feel comfortable with the application of AI to augment surgical care. The aim of this study was to similarly evaluate the attitudes of surgeons and the wider surgical team toward the role of AI in neurosurgery.

METHODS

In a 2-stage cross sectional survey, an initial open-question qualitative survey was created to determine the perspective of the surgical team on AI in neurosurgery including surgeons, anesthetists, nurses, and operating room practitioners. Thematic analysis was performed to develop a second-stage quantitative survey that was distributed via social media. We assessed the extent to which they agreed and were comfortable with real-world AI implementation using a 5-point Likert scale.

RESULTS

In the first-stage survey, 33 participants responded. Six main themes were identified: imaging interpretation and preoperative diagnosis, coordination of the surgical team, operative planning, real-time alert of hazards and complications, autonomous surgery, and postoperative management and follow-up. In the second stage, 100 participants responded. Responders somewhat agreed or strongly agreed about AI being used for imaging interpretation (62%), operative planning (82%), coordination of the surgical team (70%), real-time alert of hazards and complications (85%), and autonomous surgery (66%). The role of AI within postoperative management and follow-up was less agreeable (49%).

CONCLUSIONS

This survey highlights that the majority of surgeons and the wider surgical team both agree and are comfortable with the application of AI within neurosurgery.

A Simpler, Modified Frailty Index Weighted by Complication Occurrence Correlates to Pain and Disability for Adult Spinal Deformity Patients

BACKGROUND

The Miller et al adult spinal deformity frailty index (ASD-FI) correlates with complication risk; however, its development was not rooted in clinical outcomes, and the 40 factors needed for its calculation limit the index's clinical utility. The present study aimed to develop a simplified, weighted frailty index for ASD patients METHODS: This study is a retrospective review of a single-center database. Component ASD-FI parameters contributing to overall ASD-FI score were assessed via Pearson correlation. Top significant, clinically relevant factors were regressed against ASD-FI score to generate the modified ASD-FI (mASD-FI). Component mASD-FI factors were regressed against incidence of medical complications, and factor weights were calculated from regression of these coefficients. Total mASD-FI score ranged from 0 to 21, and was calculated by summing weights of expressed parameters. Linear regression and published ASD-FI cutoffs generated corresponding mASD-FI frailty cutoffs: not frail (NF, <7), frail (7-12), severely frail (SF, >12). Analysis of variance assessed the relationship between frailty category and validated baseline measures of pain and disability at baseline.

RESULTS

The study included 50 ASD patients. Eight factors were included in the mASD-FI. Overall mean mASD-FI score was 5.7 ± 5.2. Combined, factors comprising the mASD-FI showed a trend of predicting the incidence of medical complications (Nagelkerke R ² = 0.558; Cox & Snell R ² = 0.399; P = .065). Breakdown by frailty category is NF (70%), frail (12%), and SF (18%). Increasing frailty category was associated with significant impairments in measures of pain and disability: Oswestry Disability Index (NF: 23.4; frail: 45.0; SF: 49.3; P < .001), SRS-22r (NF: 3.5; frail: 2.6; SF: 2.4; P = .001), Pain Catastrophizing Scale (NF: 41.9; frail: 32.4; SF: 27.6; P < .001), and NRS Leg Pain (NF: 2.3; frail: 7.2; SF: 5.6; P = .001).

CONCLUSIONS

This study modifies an existing ASD frailty index and proposes a weighted, shorter mASD-FI. The mASD-FI relies less on patient-reported variables, and it weights component factors by their contribution to adverse outcomes. Because increasing mASD-FI score is associated with inferior clinical measures of pain and disability, the mASD-FI may serve as a valuable tool for preoperative risk assessment.

A narrative review of machine learning as promising revolution in clinical practice of scoliosis

Machine learning (ML), as an advanced domain of artificial intelligence (AI), is progressively changing our view of the world. By implementing its algorithms, our ability to detect previously undiscoverable patterns in data has the potential to revolutionize predictive analytics. Scoliosis, as a relatively specialized branch in the spine field, mainly covers the pediatric, adult and the elderly populations, and its diagnosis and treatment remain difficult. With recent efforts and interdisciplinary cooperation, ML has been widely applied to investigate issues related to scoliosis, and surprisingly augment a surgeon's ability in clinical practice related to scoliosis. Meanwhile, ML models penetrate in every stage of the clinical practice procedure of scoliosis. In this review, we first present a brief description of the application of ML in the clinical practice procedures regarding scoliosis, including screening, diagnosis and classification, surgical decision making, intraoperative manipulation, complication prediction, prognosis prediction and rehabilitation. Meanwhile, the ML models and specific applications adopted are presented. Additionally, current limitations and future directions are briefly discussed regarding its use in the field of scoliosis. We believe that the implementation of ML is a promising revolution to assist surgeons in all aspects of clinical practice related to scoliosis in the near future.

Does the Global Alignment and Proportion score overestimate mechanical complications after adult spinal deformity correction?

OBJECTIVE

The goal of this study was to validate the Global Alignment and Proportion (GAP) score in a cohort of patients undergoing adult spinal deformity (ASD) surgery. The GAP score is a novel measure that uses sagittal parameters relative to each patient's lumbosacral anatomy to predict mechanical complications after ASD surgery. External validation is required.

METHODS

Adult ASD patients undergoing > 4 levels of posterior fusion with a minimum 2-year follow-up were included. Six-week postoperative standing radiographs were used to calculate the GAP score, classified into a spinopelvic state as proportioned (P), moderately disproportioned (MD), or severely disproportioned (SD). A chi-square analysis, receiver operating characteristic curve, and Cochran-Armitage analysis were performed to assess the relationship between the GAP score and mechanical complications.

RESULTS

Sixty-seven patients with a mean age of 52.5 years (range 18-75 years) and a mean follow-up of 2.04 years were included. Patients with < 2 years of follow-up were included only if they had an early mechanical complication. Twenty of 67 patients (29.8%) had a mechanical complication. The spinopelvic state breakdown was as follows: P group, 21/67 (31.3%); MD group, 23/67 (34.3%); and SD group, 23/67 (34.3%). Mechanical complication rates were not significantly different among all groups: P group, 19.0%; MD group, 30.3%; and SD group, 39.1% (χ2 = 1.70, p = 0.19). The rates of mechanical complications between the MD and SD groups (30.4% and 39.1%) were less than those observed in the original GAP study (MD group 36.4%-57.1% and SD group 72.7%-100%). Within the P group, the rates in this study were higher than in the original study (19.0% vs 4.0%, respectively).

CONCLUSIONS

The authors found no statistically significant difference in the rate of mechanical complications between the P, MD, and SD groups. The current validation study revealed poor generalizability toward the authors' patient population.

State-of-the-art reviews predictive modeling in adult spinal deformity: applications of advanced analytics

Adult spinal deformity (ASD) is a complex and heterogeneous disease that can severely impact patients' lives. While it is clear that surgical correction can achieve significant improvement of spinopelvic parameters and quality of life measures in adults with spinal deformity, there remains a high risk of complication associated with surgical approaches to adult deformity. Over the past decade, utilization of surgical correction for ASD has increased dramatically as deformity correction techniques have become more refined and widely adopted. Along with this increase in surgical utilization, there has been a massive undertaking by spine surgeons to develop more robust models to predict postoperative outcomes in an effort to mitigate the relatively high complication rates. A large part of this revolution within spine surgery has been the gradual adoption of predictive analytics harnessing artificial intelligence through the use of machine learning algorithms. The development of predictive models to accurately prognosticate patient outcomes following ASD surgery represents a dramatic improvement over prior statistical models which are better suited for finding associations between variables than for their predictive utility. Machine learning models, which offer the ability to make more accurate and reproducible predictions, provide surgeons with a wide array of practical applications from augmenting clinical decision making to more wide-spread public health implications. The inclusion of these advanced computational techniques in spine practices will be paramount for improving the care of patients, by empowering both patients and surgeons to more specifically tailor clinical decisions to address individual health profiles and needs.