An Update on Measures of Preoperative Glycemic Control

Early Experience With an Endocrinology Preoperative Fast-Track Program for Optimizing Spine Surgery Candidates With Poorly Controlled Diabetes Mellitus

INTRODUCTION

Previous studies demonstrated an increase in adverse outcomes for patients undergoing elective surgeries with poorly controlled diabetes mellitus. At a large, tertiary-care spine center with a notable population of patients with poorly controlled diabetes, an Endocrinology Fast-Track (EFT) program was developed to improve patient optimization before elective spine surgery. The purpose of this study was to report our early experience.

METHODS

Seventy-five patients who were scheduled for elective spine surgery were noted to have Hgb A1c > 7 on preoperative evaluation. Thirty-two patients accepted referral to the EFT program and 43 declined (non-EFT). Mean preoperative Hgb A1c was similar between groups (EFT: 8.79 versus non-EFT: 9.26, P = 0.221). The Fast-Track program included counseling, education, and medical management.

RESULTS

Patients in the two groups were similar in number of females (EFT: 17 versus non-EFT: 17, P = 0.348), age (EFT: 57.7 versus non-EFT: 60.6, P = 0.280), and body mass index (EFT: 34.6 versus non-EFT: 33.0, P = 0.341). A smaller number of smokers were there in the EFT group (1) compared with the non-EFT group (15, P = 0.003). The proportion of patients on insulin (EFT: 19 versus non-EFT: 23, P = 0.814) and oral hypoglycemics (EFT: 29 versus non-EFT: 39, P = 0.983) was similar between the two groups. Surgical clearance for patients with a documented preoperative Hgb A1c of < 7 or equivalent fructosamine level was significantly better in the Fast-Track group compared with the non-Fast-Track group (91% versus 42%, P = 0.000). Although most patients who declined the Fast-Track program did not obtain clearance for surgery, mean Hgb A1c was improved at the latest follow-up in both groups for those who obtained surgical clearance (7.10 versus 6.78, P = 0.470).

DISCUSSION

A novel EFT program was successful in obtaining acceptable blood glucose control in patients recommended for spine surgery who had poorly controlled diabetes mellitus on preoperative evaluation. The improved blood glucose control was maintained at the latest follow-up.

Association of Preoperative Hemoglobin A1c and Body Mass Index with Wound Infection Rate in Spinal Surgery

BACKGROUND

The deleterious effect of diabetes mellitus on surgical outcomes is well documented for joint replacement surgery. We analyzed the large national US Department of Veterans Affairs (VA) database for patients who had undergone elective spinal surgery.

METHODS

We retrospectively searched the VA database and identified 174 520 spine cases.

RESULTS

There were 7766 (4.5%) wound infections and 49 271 (28%) had hemoglobin A1c (HbA1c) testing (range: 3.0-17.8) prior to surgery. In the preoperative HbA1c-checked group, there were 2941 (6.0% of 49 271) infections and in the without-preoperative HbA1c group, there were 4825 (3.9% of 125 249) infections. The distribution of infections was significantly different (χ² = 372.577, P < .0001) and suggests a 2.12% increase in the absolute risk of infection based on the presence of preoperative HbA1c testing, regardless of the result. Logistic regression revealed a preoperative HbA1c test was associated with an odds ratio of 1.435 for infection (confidence interval 1.367-1.505, P < .0001). In a separate model based on HbA1c levels, we found that HbA1c is a significant predictor of infection with an odds ratio of 1.042 (confidence interval 1.017-1.068, P = .0009) for each 1% increase in the test result. This analysis differs from using a strict cutoff value of HbA1c of 6.5%. Similar testing for body mass index and age yielded an odds ratio of 1.027 for each increase of 1 kg/m² and an odds ratio of 1.009 for each 1-year increase in age respectively.

CONCLUSIONS

Hemoglobin A1c testing, HgA1c value, body mass index, and age all contribute to the risk of wound infection after elective spine surgery in a large national VA population. These data can be used to estimate surgical risks and to aid in patient counseling about proposed spine surgery.

LEVEL OF EVIDENCE

4.

Web-Based Calculator Predicts Surgical-Site Infection After Thoracolumbar Spine Surgery

BACKGROUND

Surgical-site infection (SSI) after spine surgery leads to increased length of stay, reoperation, and worse patient quality of life. We sought to develop a web-based calculator that computes an individual's risk of a wound infection following thoracolumbar spine surgery.

METHODS

We performed a retrospective review of consecutive patients undergoing elective degenerative thoracolumbar spine surgery at a tertiary-care institution between January 2016 and December 2018. Patients who developed SSI requiring reoperation were identified. Regression analysis was performed and model performance was assessed using receiver operating curve analysis to derive an area under the curve. Bootstrapping was performed to check for overfitting, and a Hosmer-Lemeshow test was employed to evaluate goodness-of-fit and model calibration.

RESULTS

In total, 1259 patients were identified; 73% were index operations. The overall infection rate was 2.7%, and significant predictors of SSI included female sex (odds ratio [OR] 3.0), greater body mass index (OR 1.1), active smoking (OR 2.8), worse American Society of Anesthesiologists physical status (OR 2.1), and greater surgical invasiveness (OR 1.1). The prediction model had an optimism-corrected area under the curve of 0.81. A web-based calculator was created: https://jhuspine2.shinyapps.io/Wound_Infection_Calculator/.

CONCLUSIONS

In this pilot study, we developed a model and simple web-based calculator to predict a patient's individualized risk of SSI after thoracolumbar spine surgery. This tool has a predictive accuracy of 83%. Through further multi-institutional validation studies, this tool has the potential to alert both patients and providers of an individual's SSI risk to improve informed consent, mitigate risk factors, and ultimately drive down rates of SSIs.