Utility of Preoperative Laboratory Testing in Assessing Risk of Adverse Outcomes After Anterior Cervical Discectomy and Fusion: Insights from National Surgical Registry

Pre-Operative Anemia is an Unsuspecting Driver of Machine Learning Prediction of Adverse Outcomes after Lumbar Spinal Fusion

BACKGROUND CONTEXT

Pre-operative risk assessment remains a challenge in spinal fusion operations. Predictive modeling provides data-driven estimates of post-surgical outcomes, guiding clinical decisions and improving patient care. Moreover, automated machine learning models are both effective and user-friendly, allowing healthcare professionals with minimal technical expertise to identify high-risk patients who may need additional pre-operative support.

PURPOSE

This study investigated the use of automated machine learning models to predict discharge disposition, length of hospital stay, and readmission post-surgery by analyzing pre-operative patient electronic medical record data and identifying key factors influencing adverse outcomes.

STUDY DESIGN/SETTING

Retrospective cohort study.

PATIENT SAMPLE

The sample includes electronic medical records of 3,006 unique surgical events from 2,855 patients who underwent lumbar spinal fusion surgeries at a single institution.

OUTCOME MEASURES

The adverse outcomes assessed were discharge disposition (non-home facility), length of hospital stay (extended stay), and readmission within 90 days post-surgery.

METHODS

We employed several inferential and predictive approaches, including the automated machine learning tool TPOT2 (Tree-based Pipeline Optimization Tool-2). TPOT2, which uses genetic programming to select optimal machine learning pipelines in a process inspired by molecular evolution, constructed, optimized and identified robust predictive models for all outcomes. Feature importance values were derived to identify major pre-operative predictive features driving optimal models.

RESULTS

Adverse outcome rates were 25.9% for discharge to non-home facilities, 23.9% for extended hospital stay, and 24.7% for readmission within 90 days post-surgery. TPOT2 delivered the best-performing predictive models, achieving balanced accuracies ((Sensitivity [true positive rate] + Specificity [true negative rate)]) / 2) of 0.72 for discharge disposition, 0.72 for length of stay, and 0.67 for readmission. Notably, preoperative hemoglobin emerged as a consistently strong predictor in best-performing models across outcomes. Patients with severe anemia (hemoglobin <80g/dL) demonstrated higher associations with all adverse outcomes and common comorbidities associated with frailty (e.g., hypertension, type II diabetes, and chronic pain). Additional patient variables and comorbidities, including body mass index, age, and mental health status, influencing post-surgical outcomes were also highly predictive.

CONCLUSIONS

This study demonstrates the effectiveness of automated machine learning in predicting post-surgical adverse outcomes and identifying key pre-operative predictors associated with such outcomes. While factors like age, BMI, insurance type, and specific comorbidities showed notable effects on outcomes, preoperative hemoglobin consistently emerged as a significant predictor across outcomes, suggesting its critical role in pre-surgical assessment. These findings underscore the potential of enhancing patient care and preoperative assessment through advanced predictive modeling.

Association Between Age-stratified Cohorts and Perioperative Complications and 30-day and 90-day Readmission in Patients Undergoing Single-level Anterior Cervical Discectomy and Fusion

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

To assess perioperative complication rates and readmission rates after ACDF in a patient population of advanced age.

SUMMARY OF BACKGROUND DATA

Readmission rates after ACDF are important markers of surgical quality and, with recent shifts in reimbursement schedules, they are rapidly gaining weight in the determination of surgeon and hospital reimbursement.

METHODS

Patients 18 years of age and older who underwent elective single-level ACDF were identified in the National Readmissions Database (NRD) and stratified into 4 cohorts: 18-39 ("young"), 40-64 ("middle"), 65-74 ("senior"), and 75+ ("elderly") years of age. For each cohort, the perioperative complications, frequency of those complications, and number of patients with at least 1 readmission within 30 and 90 days of discharge were analyzed. χ 2 tests were used to calculate likelihood of complications and readmissions.

RESULTS

There were 1174 "elderly" patients in 2016, 1072 in 2017, and 1010 in 2018 who underwent ACDF. Their rate of any complication was 8.95%, 11.00%, and 13.47%, respectively ( P <0.0001), with dysphagia and acute posthemorrhagic anemia being the most common across all 3 years. They experienced complications at a greater frequency than their younger counterparts (15.80%, P <0.0001; 16.98%, P <0.0001; 21.68%, P <0.0001). They also required 30-day and 90-day readmission more frequently ( P <0.0001).

CONCLUSION

It has been well-established that advanced patient age brings greater risk of perioperative complications in ACDF surgery. What remains unsettled is the characterization of this age-complication relationship within specific age cohorts and how these complications inform patient hospital course. Our study provides an updated analysis of age-specific complications and readmission rates in ACDF patients. Orthopedic surgeons may account for the rise in complication and readmission rates in this population with the corresponding reduction in length and stay and consider this relationship before discharging elderly ACDF patients.

Development and External Validation of a Risk Calculator for Prediction of Major Complications and Readmission After Anterior Cervical Discectomy and Fusion

STUDY DESIGN

A retrospective, case-control study.

OBJECTIVE

We aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF).

SUMMARY OF BACKGROUND DATA

The average age and proportion of patients with at least one comorbidity undergoing ACDF have increased in recent years. Given the increased morbidity and cost associated with perioperative complications and unplanned readmission, accurate risk stratification of patients undergoing ACDF is of great clinical utility.

METHODS

This is a retrospective cohort study of adults who underwent anterior cervical fusion at any nonfederal California hospital between 2015 and 2017. The primary outcome was major perioperative complication or 30-day readmission. We built standard and ensemble machine learning models for risk prediction, assessing discrimination, and calibration. The best-performing model was validated on an external cohort comprised of consecutive adult patients who underwent ACDF at our institution between 2013 and 2020.

RESULTS

A total of 23,184 patients were included in this study; there were 1886 cases of major complication or readmissions. The ensemble model was well calibrated and demonstrated an area under the receiver operating characteristic curve of 0.728. The variables most important for the ensemble model include male sex, medical comorbidities, history of complications, and teaching hospital status. The ensemble model was evaluated on the validation cohort (n=260) with an area under the receiver operating characteristic curve of 0.802. The ensemble algorithm was used to build a web-based risk calculator.

CONCLUSION

We report derivation and external validation of an ensemble algorithm for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion. This model has excellent discrimination and is well calibrated when tested on a contemporaneous external cohort of ACDF cases.

Spine Surgery and Preoperative Hemoglobin, Hematocrit, and Hemoglobin A1c: A Systematic Review

STUDY DESIGN

Systematic review.

OBJECTIVES

Synthesize previous studies evaluating clinical utility of preoperative Hb/Hct and HbA1c in patients undergoing common spinal procedures: anterior cervical discectomy and fusion (ACDF), posterior cervical fusion (PCF), posterior lumbar fusion (PLF), and lumbar decompression (LD).

METHODS

We queried PubMed, Embase, Cochrane Library, and Web of Science for literature on preoperative Hb/Hct and HbA1c and post-operative outcomes in adult patients undergoing ACDF, PCF, PLF, or LD surgeries.

RESULTS

Total of 4,307 publications were assessed. Twenty-one articles met inclusion criteria.

PCF AND ACDF

Decreased preoperative Hb/Hct were significant predictors of increased postoperative morbidity, including return to operating room, pulmonary complications, transfusions, and increased length of stay (LOS). For increased HbA1c, there was significant increase in risk of postoperative infection and cost of hospital stay.

PLF

Decreased Hb/Hct was reported to be associated with increased risk of postoperative cardiac events, blood transfusion, and increased LOS. Elevated HbA1c was associated with increased risk of infection as well as higher visual analogue scores (VAS) and Oswestry disability index (ODI) scores.

LD

LOS and total episode of care cost were increased in patients with preoperative HbA1c elevation.

CONCLUSION

In adult patients undergoing spine surgery, preoperative Hb/Hct are clinically useful predictors for postoperative complications, transfusion rates, and LOS, and HbA1c is predictive for postoperative infection and functional outcomes. Using Hct values <35-38% and HbA1c >6.5%-6.9% for identifying patients at higher risk of postoperative complications is most supported by the literature. We recommend obtaining these labs as part of routine pre-operative risk stratification.

LEVEL OF EVIDENCE

III.

Evaluation of Postoperative Complication with Medically Necessary, Time-Sensitive Scoring System During Acute COVID-19 Pandemic: A Prospective Observational Study

Background

High scores in the Medically Necessary, Time-Sensitive (MeNTS) scoring system, used for elective surgical prioritization during the coronavirus disease 2019 pandemic, are assumed to be associated with worse outcomes. We aimed to evaluate the MeNTS scoring system in patients undergoing elective surgery during restricted capacity of our institution, with or without moderate or severe postoperative complications.

Study Design

In this prospective observational study, MeNTS scores of patients undergoing elective operations during May and June 2020 were calculated. Postoperative complication severity (classified as Group Clavien-Dindo < II or Group Clavien-Dindo ≥ II), as well as Duke Activity Index, American Society of Anesthesiologists (ASA) physical status, presence of smoking, leukocytosis, lymphopenia, elevated C-reactive protein (CRP), operation and anesthesia characteristics, intensive care requirement and duration, length of hospital stay, rehospitalization, and mortality were noted.

Results

There were 223 patients analyzed. MeNTS score was higher in the Clavien-Dindo ≥ II Group compared with the Clavien-Dindo < II Group (50.98 ± 8.98 vs 44.27 ± 8.90 respectively, p < 0.001). Duke activity status index (DASI) scores were lower, and American Society of Anesthesiologists physical status class, presence of smoking, leukocytosis, lymphopenia, elevated CRP, and intensive care requirement were higher in the Clavien-Dindo ≥ II Group (p < 0.01). Length of hospital stay was longer in the Clavien-Dindo ≥ II Group (15 [range 2–90] vs 4 [1–30] days; p < 0.001). Mortality was observed in 8 patients. Area under the receiver operating characteristic curve of MeNTS and DASI were 0.69 and 0.71, respectively, for predicting moderate/severe complications.

Conclusions

Although significant, MeNTS score had low discriminating power in distinguishing patients with moderate/severe complications. Incorporation of a cardiovascular functional capacity measure could improve the scoring system.

Utility of the 5-Item Modified Frailty Index for Predicting Adverse Outcomes Following Elective Anterior Cervical Discectomy and Fusion

BACKGROUND

Frailty is an increasingly studied tool for preoperative risk stratification, but its prognostic value for anterior cervical discectomy and fusion (ACDF) patients is unclear. We sought to evaluate the association of the 5-item modified Frailty Index (5i-mFI) with 30-day adverse outcomes following ACDF and its predictive performance compared with other common metrics.

METHODS

The National Surgical Quality Improvement Program was queried from 2016-2018 for patients undergoing elective ACDF for degenerative diseases. Outcomes of interest included 30-day complications, extended length of stay (LOS), non-home discharge, and unplanned readmissions. Unadjusted/adjusted odds ratios were calculated. The discriminatory performance of the 5i-mFI compared with age, American Society of Anesthesiologists (ASA) classification, and body mass index was computed using the area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 23,754 patients were identified. On adjusted analysis, an index of 1 was significantly associated with extended LOS, non-home discharge, and unplanned readmissions (P < 0.001, P = 0.023, P = 0.003, respectively), but not complications (all P > 0.05). An index ≥2 was significantly associated with each outcome (all P < 0.001). The 5i-mFI was found to have a significantly higher AUC than body mass index for each outcome, a similar AUC compared with ASA classification and age for complications and unplanned readmissions, and a significantly lower AUC than ASA classification and age for extended LOS and non-home discharge.

CONCLUSIONS

The 5i-mFI was found to be significantly associated with 30-day adverse outcomes following ACDF but had similar or lesser predictive performance compared with more universally available and easily implemented metrics, such as ASA classification and age.