What Is the Risk of Irrigation and Debridement Following Foot and Ankle Surgery? Development of a Risk Severity Scoring System.

BACKGROUND: Surgical site infection (SSI) after foot and ankle surgery has serious negative effects on patient health and quality of life. While previous studies have looked at predisposing factors for SSI, to our knowledge, no study has proposed a risk severity score. QUESTIONS/PURPOSES: Can a risk severity score, based on patient demographic characteristics and surgical variables, be developed for preoperative use in patients undergoing foot and ankle surgery that will calculate the risk of an irrigation and debridement (I&D) procedure within 90 days of surgery utilizing data from previous surgeries? METHODS: A retrospective chart study was performed on patients undergoing foot and ankle surgery. Data on demographic characteristics including age, sex, and BMI were recorded. Data on patient factors including diabetes and smoking history were also recorded. Surgical details including length of surgery, procedure type, surgeon, antibiotic delivery time, antibiotic type, and antibiotic dose were analyzed. Of 2979 procedures, 1% (36) of I&Ds were performed within 90 days. The mean age at surgery was 49 ± 17 years, and 57% (1702) of patients were female. The mean BMI was 28 ± 6 kg/m2. The primary outcome was I&D within 90 days postoperatively. Descriptive statistics of differences in patient characteristics between those who underwent I&D and those who did not were examined using chi-square tests and t-tests (p < 0.05 was taken as significant). Significant variables from a simple regression analysis were included in a multiple logistic regression model with a forward stepwise procedure for variable selection. We required all data in the model to be categorical; thus, continuous variables such as time were dichotomized. We factored odds ratios determined by multiple regression for significant variables into the final risk severity score, and an easy-to-use tool based on this risk severity score was created in Excel (Microsoft). RESULTS: Current tobacco use, diabetes, and longer operative times were the only factors associated with I&D within 90 days postoperatively. A risk severity score was developed using current tobacco use, diabetes, and length of surgery greater than 60 minutes as factors. A patient with a severity score of 0 (no risk factors) had a 0.6% chance of I&D within 90 days, while a severity score of 1 indicated a 1.1% chance, a score of 2 indicated a 2.1% chance, a score of 3 indicated a 4.0% chance, and a score of 4 (all risk factors) indicated a 7.5% chance of I&D within 90 days. A spreadsheet that can be used at the point of care was created based on these findings. CONCLUSION: Our risk severity score may help inform preoperative patient guidance and operative planning. Calculating the score in the office setting during preoperative visits can also improve communication between physician and patient. Future research should focus on validation of this risk severity score at multiple institutions. LEVEL OF EVIDENCE: Level III, prognostic study.

What's New in Peripheral Nerve Stimulation.

Peripheral nerve stimulation (PNS) is a powerful interventional option for the management of otherwise intractable pain. This technique involves the implantation of electrodes to apply electrical stimulation to named peripheral nerves, thereby alleviating pain in the territory of the target nerves. Recent advancements, largely driven by physician-industry relationships, have transformed the therapy into one that is minimally invasive, safe, evidence-based, and effective. Ongoing research has expanded the indications beyond chronic neuropathic pain in a peripheral nerve distribution. This article provides an overview of recent advances in this field.

Vector Angle Analysis of Multimodal Neuromonitoring Data for Continuous Prediction of Delayed Cerebral Ischemia.

BACKGROUND: Dysfunctional cerebral autoregulation often precedes delayed cerebral ischemia (DCI). Currently, there are no data-driven techniques that leverage this information to predict DCI in real time. Our hypothesis is that information using continuous updated analyses of multimodal neuromonitoring and cerebral autoregulation can be deployed to predict DCI. METHODS: Time series values of intracranial pressure, brain tissue oxygenation, cerebral perfusion pressure (CPP), optimal CPP (CPPOpt), ΔCPP (CPP - CPPOpt), mean arterial pressure, and pressure reactivity index were combined and summarized as vectors. A validated temporal signal angle measurement was modified into a classification algorithm that incorporates hourly data. The time-varying temporal signal angle measurement (TTSAM) algorithm classifies DCI at varying time points by vectorizing and computing the angle between the test and reference time signals. The patient is classified as DCI+ if the error between the time-varying test vector and DCI+ reference vector is smaller than that between the time-varying test vector and DCI- reference vector. Finally, prediction at time point t is calculated as the majority voting over all the available signals. The leave-one-patient-out cross-validation technique was used to train and report the performance of the algorithms. The TTSAM and classifier performance was determined by balanced accuracy, F1 score, true positive, true negative, false positive, and false negative over time. RESULTS: One hundred thirty-one patients with aneurysmal subarachnoid hemorrhage who underwent multimodal neuromonitoring were identified from two centers (Columbia University: 52 [39.7%], Aachen University: 79 [60.3%]) and included in the analysis. Sixty-four (48.5%) patients had DCI, and DCI was diagnosed 7.2 ± 3.3 days after hemorrhage. The TTSAM algorithm achieved a balanced accuracy of 67.3% and an F1 score of 0.68 at 165 h (6.9 days) from bleed day with a true positive of 0.83, false positive of 0.16, true negative of 0.51, and false negative of 0.49. CONCLUSIONS: A TTSAM algorithm using multimodal neuromonitoring and cerebral autoregulation calculations shows promise to classify DCI in real time.