Analysis of Risk Factors for Major Complications Following Elective Posterior Lumbar Fusion

Adverse Events After Posterior Lumbar Fusion Are Not Sufficiently Characterized With 30-day Follow-up: A Database Study

INTRODUCTION

Many studies track outcomes after procedures, such as posterior lumbar fusion (PLF), for only 30 days because of database limitations. However, adverse events may not have plateaued by this time. Thus, this study used an alternate database to evaluate the timing of adverse events for 90 days after PLF.

METHODS

Adult PLF patients were identified from the 2010 to 2020 Q2 M53Ortho PearlDiver administrative data set. Ninety-day rates of multiple adverse events were determined. The time of diagnosis for each event in the 90-day postoperative period was determined. Data were dichotomized by occurrence in days 0 to 30 and 31 to 90. Median, interquartile range, and middle 80% for the time of diagnosis were determined for each adverse event.

RESULTS

Of 51,915 patients undergoing PLF, 7,141 (13.8%) had an adverse event within 90 days of PLF. Of these, 5,174 (72.5%) experienced an event within 30 days and 2,544 (35.6%) after 30 days. For individual adverse outcomes studied, the percent that occurred 31 to 90 days after surgery ranged from 9% to 42%. The time of diagnosis (median; interquartile range; middle 80%) for each adverse event was as follows: transfusion (2 days; 2 to 5 days; 1 to 26 days), acute kidney injury (9; 2 to 29; 1 to 60), hematoma (9; 4 to 20; 3 to 39), cardiac event (11; 3 to 43; 1 to 71), pneumonia (12; 4 to 38; 2 to 68), venous thromboembolism (15; 6 to 33; 3 to 62), sepsis (19; 9 to 39; 4 to 63), surgical site infection (21; 14 to 34; 8 to 48), urinary tract infection (22; 8 to 49; 4 to 72), and wound dehiscence (27; 17 to 39; 9 to 54).

DISCUSSION

This study highlights the importance of looking past the 30-day mark for adverse events after PLF because approximately one-third of adverse events in this study were diagnosed 31 to 90 days after surgery. This can affect research studies, patient counseling about the incidence of specific adverse events, and the development of mechanisms for surveillance at key time points.

Machine learning in orthopaedic surgery

Artificial intelligence and machine learning in orthopaedic surgery has gained mass interest over the last decade or so. In prior studies, researchers have demonstrated that machine learning in orthopaedics can be used for different applications such as fracture detection, bone tumor diagnosis, detecting hip implant mechanical loosening, and grading osteoarthritis. As time goes on, the utility of artificial intelligence and machine learning algorithms, such as deep learning, continues to grow and expand in orthopaedic surgery. The purpose of this review is to provide an understanding of the concepts of machine learning and a background of current and future orthopaedic applications of machine learning in risk assessment, outcomes assessment, imaging, and basic science fields. In most cases, machine learning has proven to be just as effective, if not more effective, than prior methods such as logistic regression in assessment and prediction. With the help of deep learning algorithms, such as artificial neural networks and convolutional neural networks, artificial intelligence in orthopaedics has been able to improve diagnostic accuracy and speed, flag the most critical and urgent patients for immediate attention, reduce the amount of human error, reduce the strain on medical professionals, and improve care. Because machine learning has shown diagnostic and prognostic uses in orthopaedic surgery, physicians should continue to research these techniques and be trained to use these methods effectively in order to improve orthopaedic treatment.

Effects of Total Psoas Area Index on Surgical Outcomes of Single-Level Lateral Lumbar Interbody Fusion

OBJECTIVE

We evaluated the effect of the total psoas area index (TPAI = total psoas muscle area [cm²]/height squared [m²]) on neurological complications and clinical outcomes after lateral lumbar interbody fusion and identified the appropriate TPAI to achieve a substantial clinical benefit (SCB).

METHODS

A consecutive series of 123 patients who had undergone single-level lateral lumbar interbody fusion at a single center with ≥2 years of follow-up were retrospectively reviewed. The patient characteristics and operative data were evaluated. The neurological complications were classified as transient and persistent symptoms. The visual analog scale score for back pain was assessed preoperatively and at 1 and 2 years postoperatively.

RESULTS

The present study included 31 men and 92 women. The mean TPAI was 8.97 cm²/m² for the men and 5.04 cm²/m² for the women. The mean TPAI was not significantly different between the patients with and without perioperative neurological complications. Multiple logistic regression analysis showed that solid interbody fusion was the most significant factor for achieving an SCB regarding back pain in men (odds ratio [OR], 2.453; P = 0.019) and women (OR, 2.906; P = 0.042). The TPAI was one of the predictors for achieving an SCB in men (OR, 1.251; P = 0.038) and women (OR, 1.795; P = 0.023). The optimal cutoff point of the TPAI for an SCB was 8.18 cm²/m² for the men and 4.43 cm²/m² for the women.

CONCLUSIONS

The TPAI had little effect on the incidence of perioperative neurological complications. However, the TPAI was identified as one of the predictors for achieving an SCB regarding back pain.

Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach

BACKGROUND

Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readmission after lumbar fusion. We also aim to identify the factors most important to performance of each tested model.

METHODS

We identified 38,788 adult patients who underwent lumbar fusion at any California hospital between 2015 and 2017. The primary outcome was major perioperative complication or readmission within 30 days. We build logistic regression and advanced machine learning models: XGBoost, AdaBoost, Gradient Boosting, and Random Forest. Discrimination and calibration were assessed using area under the receiver operating characteristic curve and Brier score, respectively.

RESULTS

There were 4470 major complications (11.5%). The XGBoost algorithm demonstrates the highest discrimination of the machine learning models, outperforming regression. The variables most important to XGBoost performance include angina pectoris, metastatic cancer, teaching hospital status, history of concussion, comorbidity burden, and workers' compensation insurance. Teaching hospital status and concussion history were not found to be important for regression.

CONCLUSIONS

We report a machine learning algorithm for prediction of major complications and readmission after lumbar fusion that outperforms logistic regression. Notably, the predictors most important for XGBoost differed from those for regression. The superior performance of XGBoost may be due to the ability of advanced machine learning methods to capture relationships between variables that regression is unable to detect. This tool may identify and address potentially modifiable risk factors, helping risk-stratify patients and decrease complication rates.

Frailty and Post-Operative Outcomes in the Older Patients Undergoing Elective Posterior Thoracolumbar Fusion Surgery

Background and Aim

Frailty is an independent predictor of mortality and adverse events (AEs) in patients undergoing surgery. This study aimed to quantify the ability of Modified Frailty Index (mFI) to predict AEs in older patients undergoing elective posterior thoracolumbar fusion surgery.

Methods

We retrospectively reviewed the results of 426 patients with the following diagnoses and follow-up evaluations of at least 12 months duration: lumbar disc herniation, 125; degenerative spondylolisthesis, 81; lumbar spinal canal stenosis, 187; and adult spinal deformities, 33. The cases were divided into two groups. The long spinal fusion (LSF) group was defined as ≥3 spinal levels with segmental pedicle-screw fixation. Short spinal fusion (SSF) were defined with at most two levels. The mFI used in the present study is an 11-variable assessment. The association of frailty with AEs was determined after adjusting for known and suspected confounders.

Results

Frailty was presented in 66 patients (15.5%) within the total population (LSF, 21.9% and SSF, 11.8%). Rates of AEs assessed in the study increased stepwise with an increase in the mFI for the two groups. The severity of frailty was an independent predictor of any, major, and minor complications in the LSF group and any, minor complication in the SSF group (P<0.05). A comparison of post-operative clinical outcomes showed that the ODI and SF-36 scores deteriorated as the mFI increased.

Conclusion

Frailty was shown to be an independent predictor of AEs in older patients undergoing elective posterior thoracolumbar fusion surgery, especially for patients undergoing LSF.

The Role of Minimally Invasive Spinal Surgical Procedures in the Elderly Patient: An Analysis of 49 Patients Between 75 and 95 Years of Age

As the population gets older, yet remains active, the number of patients presenting with symptomatic spinal disease over the age of 75 increases. These include pain from osteoporotic spinal fractures, lumbar degenerative disease, as well as radiculopathy or neurogenic claudication from stenosis over the age of 75 and older increases. While some of these patients are very healthy, taking minimal medication, many are not good candidates for more invasive surgical procedures under general anesthesia because of medical co-morbidities such as insulin-dependent diabetes and medication use such as anti-coagulants. Past reviews of lumbar surgery in elderly patients have examined the risk factors with spinal fusion and multilevel surgery and many were written before the recent advent of more minimally invasive spinal procedures that reduce both operative time and the need for general anesthesia. This review examines effectiveness in return to activity and reduction in pain in these elderly patients stratified by underlying disease category, i.e. fractures, stenosis with neurogenic claudication and chronic pain, rather than just by the procedure, since there are often several minimally invasive procedures that are available. This review demonstrates very similar pain relief outcomes as measured by the visual analog scale (VAS) scores which dropped in the range of 70% or more with the different procedures. Since the majority of these procedures involve short surgical times and minimal blood loss with small incisions that lower the risk of wound infection as well as cardio-respiratory stress and can be performed under local anesthesia as an outpatient, they are particularly advantageous for the properly selected elderly patient.

Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry

OBJECTIVE

Nonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion.

METHODS

The authors queried the 2012-2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets.

RESULTS

A total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85-0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data.

CONCLUSIONS

In an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.

Clinical Outcomes Associated With Allogeneic Red Blood Cell Transfusions in Spinal Surgery: A Systematic Review

STUDY DESIGN

Systematic review.

OBJECTIVES

The objectives of this systematic review were to report the available clinical evidence on patient outcomes associated with perioperative allogeneic red blood cell (RBC) transfusions in adult patients undergoing spinal surgery and to determine whether there is any evidence to support an association between transfusion timing and clinical outcomes.

METHODS

A systematic review of the PubMed, EMBASE, and Cochrane Library databases was performed to identify all articles examining outcomes of adult spinal surgery patients who received perioperative allogeneic RBC transfusions. The level of evidence for each study was assessed using the "Oxford Levels of Evidence 2" classification system. Meta-analysis was not performed due to the heterogeneity of reports.

RESULTS

A total of 2759 unique citations were identified and 76 studies underwent full-text review. Thirty-four studies were selected for analysis. All the studies, except one, were retrospective. Eleven studies investigated intraoperative or postoperative transfusions. Only one article compared outcomes related to intraoperative versus postoperative transfusions.

CONCLUSIONS

Perioperative transfusion is associated with increased rates of postoperative complications, especially infectious complications, and prolonged length of stay. Some evidence suggests that a dose-response relationship may exist between morbid events and the number of RBC units administered, but these findings are inconsistent. Because of the heterogeneity of reports and inconsistent findings, the incidence of specific complications remains unclear. Limited research activity has focused on intraoperative versus postoperative transfusions, or the effect of transfusion on functional outcomes of spine surgery patients. Further research is warranted to address these clinical issues.

Age Is a Risk Factor for Postoperative Complications Following Excisional Laminectomy for Intradural Extramedullary Spinal Tumors

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

The incidence of intradural extramedullary (IDEM) spinal tumors is increasing. Excisional laminectomy for removal and decompression is the standard of care, but complications associated with patient age are unreported in the literature. Our objective is to identify if age is a risk factor for postoperative complications after excisional laminectomy of IDEM spinal tumors.

METHODS

A retrospective analysis was performed on the 2011 to 2014 ACS-NSQIP (American College of Surgeons National Surgical Quality Improvement Program) database for patients undergoing excisional laminectomy of IDEM spinal tumors. Age groups were determined by interquartile analysis. Chi-squared tests, t tests, and multivariate logistic regression models were employed to identify independent risk factors. Institutional review board approval was not needed.

RESULTS

A total of 1368 patients met the inclusion criteria for the study. Group 1 (age ≤ 44) contained 372 patients, group 2 (age 45-54) contained 314 patients, group 3 (age 55-66) contained 364 patients, and group 4 (age > 66) contained 318 patients. The univariate analysis showed that mortality and unplanned readmission were highest among patients in group 4 (1.26%, P = .011, and 10.00%, P = .039, respectively). Postoperative wound complications were highest among patients in group 1 (2.15%, P = .009), and postoperative venous thromboembolism and cardiac complications were highest among patients in group 3 (4.4%, P = .007, and 1.10%, P = .032, respectively). Multivariate logistic regression revealed that elderly age was an independent risk factor for postoperative venous thromboembolism (group 3 vs group 1; odds ratio = 6.739, confidence interval = 1.522-29.831, P = .012).

CONCLUSIONS

This analysis revealed that increased age is an independent risk factor for postoperative venous thromboembolism in patients undergoing excisional laminectomy for IDEM spinal tumors.

Frailty status as a predictor of 3-month cognitive and functional recovery following spinal surgery: a prospective pilot study

BACKGROUND CONTEXT

As increasing numbers of elderly Americans undergo spinal surgery, it is important to identify which patients are at highest risk for poor cognitive and functional recovery. Frailty is a geriatric syndrome that has been closely linked to poor outcomes, and short-form screening may be a helpful tool for preoperative identification of at-risk patients.

PURPOSE

This study aimed to conduct a pilot study on the usefulness of a short-form screening tool to identify elderly patients at increased risk for prolonged cognitive and functional recovery following elective spine surgery.

STUDY DESIGN/SETTING

This is a prospective, comparative cohort study.

PATIENT SAMPLE

The sample comprised 100 patients over age 65 who underwent elective spinal surgery (cervical or lumbar) at a single, large academic medical center from 2013 to 2014.

OUTCOME MEASURES

Fatigue, Resistance, Ambulation, Illnesses, Loss of Weight (FRAIL) scale, Postoperative Quality of Recovery Scale (PQRS), and instrumental activities of daily living (IADL) scores were the outcome measures.

METHODS

Included patients were assessed with the FRAIL scale and stratified as robust, pre-frail, or frail. The PQRS and IADL scores were also obtained. Patients were re-examined at 1 day, 3 days, 1 month, and 3 months after surgery for cognitive recovery at 3 months, and secondarily, functional recovery at 3 months.

RESULTS

At 3 months, only 50% of frail patients had recovered to their cognitive baseline compared with 60.7% of pre-frail and 69.2% of robust patients (trend). At 3 months, 66.7% of frail patients had recovered to their functional baseline compared with 57% of pre-frail and 76.9% of robust patients (trend). Using multivariate regression modeling, at 3 months, frail patients were less likely to have recovered to their cognitive baseline compared with pre-frail and robust patients (odds ratio 0.39, confidence interval 0.131-1.161).

CONCLUSIONS

This pilot study demonstrates a trend toward poorer cognitive recovery 3 months following elective spinal surgery for frail patients. Frailty screening can help preoperatively identify patients who may experience protracted cognitive and functional recovery.

Evaluating the effect of growing patient numbers and changing data elements in the National Surgical Quality Improvement Program (NSQIP) database over the years: a study of posterior lumbar fusion outcomes

BACKGROUND CONTEXT

The use of national databases in spinal surgery outcomes research is increasing. A number of variables collected by the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) changed between 2010 and 2011, coinciding with a rapid increase in the number of patients included per year. However, there has been limited study evaluating the effect that these changes may have on the results of outcomes studies.

PURPOSE

The present study aimed to investigate the influence of changing data elements and growth of the NSQIP database on results of lumbar fusion outcomes studies.

STUDY DESIGN/SETTING

This is a retrospective cohort study of prospectively collected data.

PATIENT SAMPLE

The NSQIP database was retrospectively queried to identify 19,755 patients who underwent elective posterior lumbar fusion surgery with or without interbody fusion between 2005 and 2014. Patients were split into two groups based on year of surgery: 2,802 from 2005 to 2010 and 16,953 from 2011 to 2014.

OUTCOME MEASURES

The occurrence of adverse events after discharge from the hospital, within postoperative day 30, was determined.

METHODS

Preoperative characteristics and 30-day perioperative outcomes were compared between the era groups using bivariate analysis. To illustrate the effect of such changing data elements, the association between age and postoperative outcomes in the era groups was analyzed using multivariate Poisson regression. The present study had no funding sources, and there were no study-related conflicts of interest for any authors.

RESULTS

There were significant differences between the era groups for a variety of preoperative characteristics. Postoperative events such blood transfusion and deep vein thrombosis were also significantly different between the era groups. For the 2005-2010 cohort, age was significantly associated with septic shock by multivariate analysis. For the 2011-2014 cohort, age was significantly associated with septic shock, urinary tract infection, blood transfusion, myocardial infarction, and extended length of stay.

CONCLUSIONS

The NSQIP database has undergone substantial changes between 2005 and 2014. These changes may contribute to different results in analyses, such as the association between age and postoperative outcomes, when using older versus newer data. Conclusions from early studies using this database may warrant reconsideration.

Risk Factors for Adverse Cardiac Events After Lumbar Spine Fusion

Background

To determine the incidence and risk factors for adverse cardiac events after lumbar spine fusion.

Methods

A total of 50 495 patients were identified through the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database who underwent lumbar spine fusion between 2005 and 2015. The 30-day postoperative data were analyzed to assess for the incidence of adverse cardiac events including cardiac arrest or myocardial infarction. Of those who experienced an event, patient- and surgery-specific parameters were evaluated to assess for risk factors.

Results

A total of 240 cardiac events occurred in the studied cohort (4.76 events/1000 patients). Factors that were associated with an increased cardiac risk were age (odds ratio [OR] = 1.039, 95% confidence interval [CI] = 1.03, 1.05, P < .001), male sex (OR = 1.51, 95% CI = 1.17, 1.94, P = .001), insulin-dependent diabetes (OR = 1.83, 95% CI = 1.29, 2.6, P = .001), American Society of Anesthesiologists (ASA) score >3 (OR = 1.92, 95% CI = 1.00, 3.65, P = .048), absolute hematocrit different from 45 (OR = 1.07, 95% CI = 1.04, 1.10, P < .001), and smoking (OR = 1.39, 95% CI = 1.02, 1.90, P = .04). The impact of sustaining a cardiac event in the setting of single-level lumbar fusion is catastrophic as the 30-day postoperative mortality rate for those sustaining an event was 24.6% (59/240 patients), compared to 0.2% (87/50 255) for those not sustaining an event (P < .001).

Conclusions

Cardiac events after lumbar fusion are a rare but devastating series of complications. Several risk factors were identified, including insulin-dependent diabetes mellitus, smoking, advanced age, male sex, ASA score of >3, and anemia/polycythemia. Considering the severity of these consequences, appropriate risk stratification is imperative, and optimization of modifiable risk factors may mitigate this risk.

Does Obesity Explain the Effect of the Metabolic Syndrome on Complications Following Elective Lumbar Fusion? A Propensity Score Matched Analysis

STUDY DESIGN

Propensity score matched retrospective cohort study.

OBJECTIVES

Obesity is a major confounder in determining the independent effect of metabolic syndrome (MetS) on complications after spinal surgery. The purpose of this study is to differentiate MetS from obesity as an independent influence on perioperative outcomes after elective lumbar spine fusion.

METHODS

One- to 3-level posterior spinal fusion cases were identified from the 2011-2014 American College of Surgeons' National Surgical Quality Improvement Program. To determine the effects of MetS outside of obesity itself, patients with MetS were first compared to a no-MetS cohort and then to an obese-only no-MetS cohort. Two propensity score matches based on demographics, comorbidities, surgical complexity, and diagnosis were used to match patients in 1:1 ratios and compare outcomes. Logistic regression with propensity score adjustment was further utilized as a secondary method of reducing selection bias.

RESULTS

Out of 18 605 patients that met criteria for inclusion, 1903 (10.2%) met our definition of MetS. Patients with MetS had a higher rate of wound complications (3.8% vs 2.7% obese no MetS, P = .045; vs 2.6% no MetS, P = .035), readmissions (7.4% vs 2.2% obese no MetS, P < .001; vs 4.6% no MetS, P < .001), and extended length of stay (29.1% vs 23.9% obese no MetS, P < .001; vs 23.5% no MetS, P < .001). Patients with MetS were more likely to experience a wound complication (odds ratio = 1.47, 95% confidence interval = 1.02-2.12) or readmission (odds ratio = 1.48, 95% confidence interval = 1.22-1.80).

CONCLUSIONS

Even after controlling for obesity, MetS is an independent risk factor for adverse short-term outcomes. These findings have various implications for preoperative risk stratification and reduction strategies.

Revised Cardiac Risk Index versus ASA Status as a Predictor for Noncardiac Events After Posterior Lumbar Decompression

BACKGROUND

The Revised Cardiac Risk Index (RCRI) was designed to predict risk for cardiac events after noncardiac surgery. However, there is a paucity of literature that directly addresses the relationship between RCRI and noncardiac outcomes after posterior lumbar decompression (PLD). The objective of this study is to determine the ability of RCRI to predict noncardiac adverse events after PLD.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program was used to identify patients undergoing PLD from 2006 to 2014. Multivariate and receiver operating characteristic analysis was used to identify associations between RCRI and postoperative complications.

RESULTS

A total of 52,066 patients met the inclusion criteria. Membership in the RCRI=1 cohort independently predicted unplanned intubation, ventilation >48 hours, progressive renal insufficiency, acute renal failure, urinary tract infection (UTI), sepsis, septic shock, and readmission. Membership in the RCRI=2 cohort independently predicted for superficial surgical site infection, pneumonia, unplanned intubation, ventilation >48 hours, bleeding transfusion, progressive renal insufficiency, acute renal failure, UTI, sepsis, septic shock, and readmission. Membership in the RCRI=3 cohort independently predicted unplanned intubation (odds ratio [OR], 11.8), ventilation >48 hours (OR, 23.0), acute renal failure (OR, 84.5), and UTI (OR, 3.6). RCRI had a poor discriminative ability (DA) (area under the curve = 0.623), and American Society of Anesthesiologists status had a fair DA (area under the curve = 0.770) to predict a composite of noncardiac complications.

CONCLUSIONS

RCRI was predictive of a wide range of noncardiac complications after PLD but had a diminished DA to predict a composite of any noncardiac complication than did American Society of Anesthesiologists score. Consideration of the RCRI as a component of preoperative surgical risk stratification can minimize patient morbidity and mortality after lumbar decompression.

Anterior Lumbar Fusion: Differences in Patient Selection and Surgical Outcomes Between Neurosurgeons and Orthopaedic Surgeons

OBJECTIVE

Anterior lumbar fusion (ALF) is performed by both neurosurgeons and orthopaedic surgeons. The aim of this study was to determine differences between the 2 surgical subspecialties in terms of patient selection and postoperative outcomes after ALF.

METHODS

A retrospective cohort study of adult patients undergoing ALF in the American College of Surgeons National Surgical Quality Improvement Program database from 2010 to 2014 was performed. Univariate analyses were performed to identify differences in baseline patient demographics, comorbidities, operative characteristics, and 30-day postoperative outcomes between neurosurgery and orthopaedic surgery patients. Multivariate logistic regression analysis was used to determine whether surgical subspecialty was an independent risk factor for postoperative complications.

RESULTS

The study included 3182 patients, with 1629 (51.2%) neurosurgery patients and 1553 (48.8%) orthopaedic surgery patients. A greater proportion of neurosurgery patients were >65 years old, were being treated with preoperative steroids, had cardiac or pulmonary comorbidities, and had an American Society of Anesthesiologists classification III or higher. ALF procedures performed by neurosurgeons more frequently involved use of intervertebral devices and bone graft. On multivariate logistic regression analysis, ALF procedures performed by neurosurgeons were independently associated with a higher risk of reoperation (odds ratio = 1.61; 95% confidence interval, 1.02-2.56; P = 0.042) and urinary tract infection (odds ratio = 1.94; 95% confidence interval, 1.02-3.68; P = 0.043).

CONCLUSIONS

In addition to differences in baseline patient demographics and comorbidities and operative characteristics, ALF performed by neurosurgeons had a higher risk of 30-day reoperation and urinary tract infection compared with ALF performed by orthopaedic surgeons.

Age Stratification of 30-Day Postoperative Outcomes Following Excisional Laminectomy for Extradural Cervical and Thoracic Tumors

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

To evaluate age as an independent predictive factor for perioperative morbidity and mortality in patients undergoing surgical decompression for metastatic cervical and thoracic spinal tumors using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database from 2011 to 2014.

METHODS

We identified 1673 adult patients undergoing excisional laminectomy of cervical and thoracic extradural tumors. Patients were stratified into quartiles based on age, with Q1 including patients aged 18 to 49 years, Q2 including patients aged 50 to 60 years, Q3 including patients aged 61 to 69 years, and Q4 including patients ≥70 years. Univariate and multivariate regression analyses were performed to examine the association between age and 30-day perioperative morbidity and mortality.

RESULTS

Age was an independent risk factor for 30-day venous thromboembolism (VTE) and reoperation. Patients in Q3 for age had nearly a 4 times increased risk of VTE than patients in Q1 (odds ratio [OR] 3.97; 95% CI 1.91-8.25; P < .001). However, there was no significant difference in VTE between patients in Q4 and Q1 (P = .069). Patients in Q2 (OR 1.99; 95% CI 1.06-3.74; P = .032) and Q4 (OR 2.18; 95% CI 1.06-4.52; P = .036) for age had a 2 times increased risk of reoperation compared with patients in Q1.

CONCLUSIONS

Age was an independent predictive factor for perioperative VTE and reoperation, but there was no clear age-dependent relationship between increasing age and the risk of these perioperative complications.

Short-term complication rate following orthopedic surgery in a tertiary care center in Argentina

INTRODUCTION

Registration of adverse events following orthopedic surgery has a critical role in patient safety and has received increasing attention. The purpose of this study was to determine the prevalence and severity of postoperative complications in the department of orthopedic unit in a tertiary hospital.

METHODS

A retrospective review from the postoperative complication registry of a cohort of consecutive patients operated in the department of orthopedic surgery from May 2015 to June 2016 was performed. Short-term complications (3 months after surgery), age gender, types of surgery (elective, scheduled urgency, non-scheduled urgency, and emergency), operative time, surgical start time (morning, afternoon or evening), American Society of Anesthesiologists score and surgeon's experience were assessed. Complications were classified based on their severity according to Dindo-Clavien system: Grade I complications do not require alterations in the postoperative course or additional treatment; Grade II complications require pharmacological treatment; Grade III require surgical, endoscopic, or radiological interventions without (IIIa) or with (IIIb) general anesthesia; Grade IV are life-threatening with single (IVa) or multi-organ (IVb) dysfunction(s), and require ICU management; and Grade V result in death of the patient. Complications were further classified in minor (Dindo I, II, IIIa) and major (Dindo IIIb, IVa, IVb and V), according to clinical severity.

RESULTS

1960 surgeries were performed. The overall 90-day complication rate was 12.7% (249/1960). Twenty-three complications (9.2 %) were type I, 159 (63.8%) type II, 9 (3.6%) type IIIa, 42 (16.8%) type IIIb, 7 (2.8%) type IVa and 9 (3.6%) were grade V according to Dindo-Clavien classification (DCC). The most frequent complication was anemia that required blood transfusion (27%) followed by wound infection (15.6%) and urinary tract infection (6%).

DISCUSSION

The overall complication rate after orthopedic surgery in our department was 12.7%. The implementation of the DCC following orthopedic surgery was an important tool to measure the standard of care.

Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion

STUDY DESIGN

A cross-sectional database study.

OBJECTIVE

The aim of this study was to train and validate machine learning models to identify risk factors for complications following posterior lumbar spine fusion.

SUMMARY OF BACKGROUND DATA

Machine learning models such as artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex datasets. ANNs have yet to be used for risk factor analysis in orthopedic surgery.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent posterior lumbar spine fusion. This query returned 22,629 patients, 70% of whom were used to train our models, and 30% were used to evaluate the models. The predictive variables used included sex, age, ethnicity, diabetes, smoking, steroid use, coagulopathy, functional status, American Society for Anesthesiology (ASA) class ≥3, body mass index (BMI), pulmonary comorbidities, and cardiac comorbidities. The models were used to predict cardiac complications, wound complications, venous thromboembolism (VTE), and mortality. Using ASA class as a benchmark for prediction, area under receiver operating curves (AUC) was used to determine the accuracy of our machine learning models.

RESULTS

On the basis of AUC values, ANN and LR both outperformed ASA class for predicting all four types of complications. ANN was the most accurate for predicting cardiac complications, and LR was most accurate for predicting wound complications, VTE, and mortality, though ANN and LR had comparable AUC values for predicting all types of complications. ANN had greater sensitivity than LR for detecting wound complications and mortality.

CONCLUSION

Machine learning in the form of logistic regression and ANNs were more accurate than benchmark ASA scores for identifying risk factors of developing complications following posterior lumbar spine fusion, suggesting they are potentially great tools for risk factor analysis in spine surgery.

LEVEL OF EVIDENCE

3.

Risk Factors for and Complications After Surgical Delay in Elective Single-Level Lumbar Fusion

STUDY DESIGN

Retrospective cohort.

OBJECTIVE

To assess the incidence of and risk factors for delay of elective lumbar fusion surgery, as well as medical and surgical complications associated with surgical delay.

SUMMARY OF BACKGROUND DATA

Lumbar fusion is a well-established treatment for patients with degenerative spondylolisthesis with stenosis who have failed conservative management. Rarely, patients admitted for elective lumbar fusion may experience a delay in surgery past the day of admission. The incidence of, and risk factors for, delay of elective lumbar fusion surgery and the complications associated therewith have never been previously evaluated.

METHODS

We retrospectively reviewed the ACS-NSQIP registry utilizing Current Procedural Terminology (CPT) codes 22612, 22558, 22630, and 22633 to identify all patients undergoing a single level spinal fusion. The data were then subdivided into cohorts consisting of patients with and without surgical delay. Demographic information, preoperative risk factors for delay, as well as intraoperative and postoperative complications were compared between the groups.

RESULTS

We identified 2758 (5.46%) patients as experiencing a delay before lumbar fusion. Multivariate analysis was then performed and identified male sex, American Society of Anesthesiologists classes 3 and 4, and chronic steroid use as risk factors increasing the rate of surgical delay. Multiple complication rates were also significantly higher in the delayed group, including an almost 10-fold increase in mortality rate (0.2% vs. 1.9%, respectively, P < 0.001).

CONCLUSION

Delays in elective surgery can affect medical system resource utilization, increasing costs and leading to worse patient outcomes. Patients with chronic steroid use and higher American Society of Anesthesiologists class may be at risk for surgical delay in lumbar fusion beyond the day of admission, and are at increased risk for significant complications postoperatively. Thorough medical evaluation and preoperative optimization may be indicated for these patients.

LEVEL OF EVIDENCE

4.

Relationship between depression and clinical outcome following anterior cervical discectomy and fusion

BACKGROUND

Anterior cervical discectomy and fusion (ACDF) is a commonly performed procedure for patients with symptomatic degenerative conditions of the cervical spine. The objective is to assess the impact of preoperative depression and other baseline characteristics on patient reported clinical outcomes following ACDF surgery based on the experience at our institution.

METHODS

This was a retrospective cohort study of some patients undergoing ACDF at a single institution from 2012 to 2014. Ninety-three patients that underwent an ACDF procedure were included. The primary outcome measure was post-operative Nurick score.

RESULTS

Sixteen (17.2%) patients had a formal diagnosis of depression compared to 77 (82.8%) patients without depression. On univariate analysis, patients with depression had statistically significantly higher Nurick scores compared to patients without depression after surgery (coefficient =0.55, 95% CI: 0.21-0.90, P=0.002). On multivariate analysis, there was a trend toward higher postoperative Nurick scores in patients that had depression (coefficient =0.31, 95% CI: -0.01-0.63, P=0.057).

CONCLUSIONS

This small retrospective study reveals an inverse relationship between preoperative depression and functional outcome. Further research should be performed to investigate this relationship and to investigate if treating depression can improve postoperative outcomes.