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.

Metabolomic analysis of cerebral spinal fluid from patients with severe brain injury.

Proton nuclear magnetic resonance (H-NMR) spectroscopic analysis of cerebral spinal fluid provides a quick, non-invasive modality for evaluating the metabolic activity of brain-injured patients. In a prospective study, we compared the CSF of 44 TBI patients and 13 non-injured control subjects. CSF was screened for ten parameters: ß-glucose (Glu), lactate (Lac), propylene glycol (PG), glutamine (Gln), alanine (Ala), α-glucose (A-Glu), pyruvate (PYR), creatine (Cr), creatinine (Crt), and acetate (Ace). Using mixed effects measures, we discovered statistically significant differences between control and trauma concentrations (mM). TBI patients had significantly higher concentrations of PG, while statistical trends existed for lactate, glutamine, and creatine. TBI patients had a significantly decreased concentration of total creatinine. There were no significant differences between TBI patients and non-injured controls regarding ß- or α-glucose, alanine, pyruvate or acetate. Correlational analysis between metabolites revealed that the strongest significant correlations in non-injured subjects were between ß- and α-glucose (r = 0.74), creatinine and pyruvate (r = 0.74), alanine and creatine (r = 0.62), and glutamine and α-glucose (r = 0.60). For TBI patients, the strongest significant correlations were between lactate and α-glucose (r = 0.54), lactate and alanine (r = 0.53), and α-glucose and alanine (r = 0.48). The GLM and multimodel inference indicated that the combined metabolites of PG, glutamine, α-glucose, and creatinine were the strongest predictors for CMRO2, ICP, and GOSe. By analyzing the CSF of patients with TBI, our goal was to create a metabolomic fingerprint for brain injury.

Using machine learning and big data for the prediction of venous thromboembolic events after spine surgery: A single-center retrospective analysis of multiple models on a cohort of 6869 patients.

Objective: Venous thromboembolic event (VTE) after spine surgery is a rare but potentially devastating complication. With the advent of machine learning, an opportunity exists for more accurate prediction of such events to aid in prevention and treatment. Methods: Seven models were screened using 108 database variables and 62 preoperative variables. These models included deep neural network (DNN), DNN with synthetic minority oversampling technique (SMOTE), logistic regression, ridge regression, lasso regression, simple linear regression, and gradient boosting classifier. Relevant metrics were compared between each model. The top four models were selected based on area under the receiver operator curve; these models included DNN with SMOTE, linear regression, lasso regression, and ridge regression. Separate random sampling of each model was performed 1000 additional independent times using a randomly generated training/testing distribution. Variable weights and magnitudes were analyzed after sampling. Results: Using all patient-related variables, DNN using SMOTE was the top-performing model in predicting postoperative VTE after spinal surgery (area under the curve [AUC] =0.904), followed by lasso regression (AUC = 0.894), ridge regression (AUC = 0.873), and linear regression (AUC = 0.864). When analyzing a subset of only preoperative variables, the top-performing models were lasso regression (AUC = 0.865) and DNN with SMOTE (AUC = 0.864), both of which outperform any currently published models. Main model contributions relied heavily on variables associated with history of thromboembolic events, length of surgical/anesthetic time, and use of postoperative chemoprophylaxis. Conclusions: The current study provides promise toward machine learning methods geared toward predicting postoperative complications after spine surgery. Further study is needed in order to best quantify and model real-world risk for such events.

Frailty predicts readmission, reoperation, and infection after posterior spinal fusion: An institutional series of 3965 patients.

OBJECTIVE: Frailty is a measure of physiologic vulnerability conceptualized as the accumulation of deficits with aging, and may be useful for predicting risk of adverse events following posterior spinal fusion. Our objective was to investigate the utility of the Canadian Study on Health and Aging (CHSA) Modified Frailty Index (mFI) in patients undergoing posterior spinal fusion (PSF) as a predictor of several surgical quality metrics including readmission, reoperation, and surgical site infection. METHODS: We examined 3965 consecutive PSF patients treated at our institution between 2000 and 2015, and collected demographic, clinical, and frailty and comorbid disease burden measures using the mFI and Charlson Comorbidity Index (CCI). We examined trends and changes in these clinical and demographic characteristics over the course of the study period. We performed multivariable regression to identify independent predictors of readmission, reoperation, and surgical site infection. RESULTS: Over the course of the study period, the mean patient age increased linearly year-over-year (ß=0.60 [0.48, 0.72], p < 0.0001, R=0.94), while the SSI rate decreased linearly (ß=-0.14 [-0.27, -0.02], p = 0.0249, R=0.56), and frailty scores did not change significantly (p = 0.8124, R=0.065). Among all patients undergoing PSF, postoperative wound infection was independently associated with number of levels fused (OR=1.104 p < 0.001), frailty as measured by mFI (OR=1.150 p = 0.006), and BMI (OR=1.041 p = 0.008). Frailty was also independently associated with postoperative ICU admission (OR=1.1080 p = 0.005), 30-day readmission (OR=1.181 p < 0.001), and 30-day reoperation (OR=1.128 p < 0.001). Among all patients, rate of postoperative wound infection increased with increasing frailty (p = 0.0002) and increasing comorbid disease burden (chi-square p = 0.0012). CONCLUSION: The mFI predicts adverse events among patients undergoing PSF, including readmission, reoperation, and surgical site infection. When controlling for frailty, age was not an independent predictor of adverse events.

Surgical Site Infection in the Intensive Care Setting After Posterior Spinal Fusion: A Case Series Highlighting the Microbial Profile, Risk Factors, and the Importance of Comorbid Disease Burden.

BACKGROUND: Most posterior spinal fusion (PSF) patients do not require admission to an intensive care unit (ICU), and those who do may represent an underinvestigated, high-risk subpopulation. OBJECTIVE: To identify the microbial profile of and risk factors for surgical site infection (SSI) in PSF patients admitted to the ICU postoperatively. METHODS: We examined 3965 consecutive PSF patients treated at our institution between 2000 and 2015 and collected demographic, clinical, and procedural data. Comorbid disease burden was quantified using the Charlson Comorbidity Index (CCI). We performed multivariable logistic regression to identify risk factors for SSI, readmission, and reoperation. RESULTS: Anemia, more levels fused, cervical surgery, and cerebrospinal fluid leak were positively associated with ICU admission, and minimally invasive surgery was negatively associated. The median time to infection was equivalent for ICU patients and non-ICU patients, and microbial culture results were similar between groups. Higher CCI and undergoing a staged procedure were associated with readmission, reoperation, and SSI. When stratified by CCI into quintiles, SSI rates show a strong linear correlation with CCI ( P = .0171, R = 0.941), with a 3-fold higher odds of SSI in the highest risk group than the lowest (odds ratio = 3.15 [1.19, 8.07], P = .032). CONCLUSION: Procedural characteristics drive the decision to admit to the ICU postoperatively. Patients admitted to the ICU have higher rates of SSI but no difference in the timing of or microorganisms that lead to those infections. Comorbid disease burden drives SSI in this population, with a 3-fold greater odds of SSI for high-risk patients than low-risk patients.