Development and Validation of a Predictive Model for Failure of Medical Management in Spinal Epidural Abscesses

External Validation of Predictive Models for Failed Medical Management of Spinal Epidural Abscess

OBJECTIVE

There is limited consensus regarding management of spinal epidural abscesses (SEAs), particularly in patients without neurologic deficits. Several models have been created to predict failure of medical management in patients with SEA. We evaluate the external validity of 5 predictive models in an independent cohort of patients with SEA.

METHODS

One hundred seventy-six patients with SEA between 2010 and 2019 at our institution were identified, and variables relevant to each predictive model were collected. Published prediction models were used to assign probability of medical management failure to each patient. Predicted probabilities of medical failure and actual patient outcomes were used to create receiver operating characteristic (ROC) curves, with the area under the receiver operating characteristic curve used to quantify a model's discriminative ability. Calibration curves were plotted using predicted probabilities and actual outcomes. The Spiegelhalter z-test was used to determine adequate model calibration.

RESULTS

One model (Kim et al) demonstrated good discriminative ability and adequate model calibration in our cohort (ROC = 0.831, P value = 0.83). Parameters included in the model were age >65, diabetes, methicillin-resistant Staphylococcus aureus infection, and neurologic impairment. Four additional models did not perform well for discrimination or calibration metrics (Patel et al, ROC = 0.580, P ≤ 0.0001; Shah et al, ROC = 0.653, P ≤ 0.0001; Baum et al, ROC = 0.498, P ≤ 0.0001; Page et al, ROC = 0.534, P ≤ 0.0001).

CONCLUSIONS

Only 1 published predictive model demonstrated acceptable discrimination and calibration in our cohort, suggesting limited generalizability of the evaluated models. Multi-institutional data may facilitate the development of widely applicable models to predict medical management failure in patients with SEA.

Development of a Predictive Model for Persistent Instability Following Conservative Management of Type II Odontoid Fractures

BACKGROUND

Odontoid fractures are common cervical spine fractures; however, significant controversy exists regarding their treatment. Risk factors for failure of conservative therapy have been identified, although no predictive risk score has been developed to aid in decision-making.

METHODS

A retrospective review was conducted of all patients evaluated at a level 1 trauma center. Patients identified with type II odontoid fractures as classified by the D'Alonzo Classification system who were treated with external orthosis were included in analysis. Patients were considered to have failed conservative therapy if they were offered surgical intervention. A machine learning method (Risk-SLIM) was then utilized to create a risk stratification score based on risk factors to identify patients at high risk for requiring surgical intervention due to persistent instability.

RESULTS

A total of 138 patients were identified as presenting with type II odontoid fractures that were treated conservatively; 38 patients were offered surgery for persistent instability. The Odontoid Fracture Predictive Model (OFPM) was created using a machine learning algorithm with a 5-fold cross validation area under the curve of 0.7389 (95% CI: 0.671 to 0.808). Predictive factors were found to include fracture displacement, displacement greater than 5 mm, comminution at the fracture base, and history of smoking. The probability of persistent instability was <5% with a score of 0 and 88% with a score of 5.

CONCLUSIONS

The OFPM model is a unique, quick, and accurate tool to assist in clinical decision-making in patients with type II odontoid fractures. External validation is necessary to evaluate the validity of these findings.

Keeping It Simple: Developing a Prognostic Tool for Spinal Epidural Abscess

STUDY DESIGN

A retrospective study.

OBJECTIVE

To develop a prognostic score for mortality and treatment failure in Spinal epidural abscess (SEA), based on simplicity and multidimensional assessment principles.

METHODS

One-hundred-fifty patients were reviewed. Variables assessed included comorbidities, functional status, clinical presentation, Frankel classification, and biochemical and radiological parameters. The main outcomes were the 90-day mortality and treatment failure, corresponding to any intensification of the initial treatment plan. Variables were sorted out with a factorial analysis. Logistic regressions were performed, and the new score was derived from the coefficients. ROC curves with Area Under Curve, calibration plots, and cross-validation were performed.

RESULTS

Forty-three patients (29%) had treatment failure, and 15 died (10%) by 90 days. Factorization created 3 groups: Comorbidities (C), Severity (S), and Function (F). For 90-day mortality, Odds ratios were 1.20 (P = .0002), 1.15, (P = .03), 1.36, (P < 10-4) for C, S, F, respectively. The new score 'CSF' had 1 point per item, ranging from zero to 3. OR increased by 1.2/point for 90-day mortality (P < 10-4), AUC was .86. For failures OR increased by 1.15/point (P = .014), AUC was .58, and increased to .64 for patients who survived after 90 days, probably due to competing risks.

CONCLUSIONS

Comorbidities, Severity, and Function is a new simplistic tool, easy to use in daily practice; its performances were excellent for 90-day mortality, and acceptable for failures. Simple tools are more likely to be adopted into practice. External validation of this technique is desirable.

Patients Follow-up for Spinal Epidural Abscess as a Critical Treatment Plan Consideration

INTRODUCTION

 Spinal epidural abscess (SEA) is a rare process with significant risk for morbidity and mortality. Treatment includes an extended course of antibiotics with or without surgery depending on the clinical presentation. Both non-operative and surgically treated patients require close follow-up to ensure the resolution of the infection without recurrence and/or progression of neurologic deficits. No previous study has looked specifically at follow-up in the SEA population, but the review of the literature does show evidence of varying degrees of difficulty with follow-up for this patient population.

METHODS

 This retrospective review looked at follow-up for 147 patients with SEA at a single institution from 2012 to 2021. Statistical analyses were performed to assess differences between groups of surgical versus non-surgical patients and those with adequate versus inadequate follow-up.

RESULTS

Sixty-two of 147 (42.2%) patients had inadequate follow-up (less than 90 days) with their surgical team, and 112 of 147 (76.2%) patients had inadequate follow-up (less than 90 days) with infectious disease (ID). The primary statistically significant difference between patients with adequate versus inadequate follow-up was found to be surgical status with those treated surgically more likely to have adequate follow-up than those treated non-operatively.

CONCLUSION

Improved follow-up in surgical patients should be considered as a factor when deciding on surgical versus non-operative treatment in the SEA patient population. Extra efforts coordinating follow-up care should be made for SEA patients.