This is the first study to evaluate the utility of the LACE index in patients with SI. Herein, we describe the utility of the LACE index in patients with primary or secondary SI who underwent surgical management. Spinal infections are feared and life-threatening conditions with reported mortality rates of up to 20%.1 In our cohort of 164 patients with SI who were managed surgically, 30-day mortality rate was as high as 6%. When including the rate of unplanned hospital readmission to mortality within 30 days, the adverse event rate increased to 28%. Our results showed a good association between mortality and readmission rates, with higher LACE indices for these adverse events. Logistic regression analysis indicated that patients with a higher LACE index had significantly higher rates of readmission and mortality. The ASA Physical Status class remains one of the most widely used risk-stratification metrics for medical complications and mortality after surgery, and it also showed a good association with higher mortality within 30 days in our present study [8]. However, in our patient cohort, the LACE index clearly outperformed the ASA score as a predictor of mortality and readmission based on logistic regression analysis. Furthermore, our analysis showed that for patients with a LACE index of ≥ 12 points, the sensitivity and specificity for either death or readmission within 30 days following hospital discharge were 70% and 69%, respectively.

As with any surgical specialty, it is crucial to assess patient outcomes including mortality and readmission rates. This is especially true for the surgical management of patients with SI since complication and mortality rates are known to be relatively high, as mentioned above. In addition, patients with SI are at high risk for perioperative complications and have higher morbidity and unplanned readmission rates [9]. For this patient population, accurate prediction of a patient’s individual risk profile regarding readmission and mortality is of utmost importance and value to allow for enhanced preoperative patient evaluation, risk stratification, and postoperative monitoring. However, a LACE index is generated for a patient only after discharge from hospital. Evidently, this limits its utility during acute care in hospital. Still, as healthcare professionals and policymakers alike strive to contain healthcare costs, accurate prediction of mortality after discharge from hospital and hospital readmission has become increasingly important. Therefore, reducing hospital readmission rates has long become a clinical and policy priority. In addition, readmission following surgery has gained increasing attention as a performance measure [10]. In this regard, the LACE index may serve as an easy-to-use tool in clinical practice and for hospital administrators alike.

In recent years, the LACE index has gained attention as a promising tool for predicting the likelihood of adverse events following surgery [11, 12]. Regarding outcome prediction, age, insurance status, paralysis, and medical comorbidities are thought to be possible predictors of morbidity, mortality, and expense of care for patients following surgical treatment of spinal epidural abscess [13]. In contrast to widely used traditional statistical approaches, recent studies have reported on machine learning based models for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion surgery, or for readmission and estimated cost savings for patients undergoing posterior lumbar fusion surgery [14, 15]. Interestingly, despite acknowledging the discriminatory ability of the LACE index, Rezaii and colleagues maintained superior predictive ability of their machine learning model compared to the LACE index in predicting readmissions and projected cost reductions based on their data [1, 14]. However, these machine learning algorithms lack external validation and are therefore not yet fit for clinical implementation. In 2022, an assessment score for the preoperative estimation of mortality to support decision-making in the treatment of SI was published by Lener and colleagues [1].

The LACE index, although it accounts for only four general risk domains, seems to provide a sufficiently comprehensive assessment of patient risk after hospital discharge. It can therefore help provide guidance to clinicians and surgeons in making informed decisions. By quantifying patient risk, surgeons can more accurately determine the most appropriate postacute care management, and effectively allocate resources.

Our study has several limitations. First, our analysis is limited by the retrospective nature of data collection. Therefore, there is an inherent risk of underreporting the actual rate of adverse events or other data inaccuracies. As such, it is possible that not all deaths were captured and accounted for in our hospital record system. Second, the generalizability of our analysis is limited by the distinct pathology of surgically managed spinal infections in adult patients. In addition, as we present data from a single, tertiary care, university-affiliated teaching hospital, our results may not be generalizable to the broader spine surgery community, as SI patients are often critically ill and require highly specialized multidisciplinary treatments. Finally, the LACE index was originally developed primarily for medical patients. For this reason, factors specific to spine surgery, such as surgical techniques and approaches, intraoperative complications, and specific comorbidities associated with spinal disorders, were not accounted for.