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Marike L. D. Broekman, Janneke van Beijnum, Wilco C. Peul and Luca Regli

Many neurosurgeons remove their patients' hair before surgery. They claim that this practice reduces the chance of postoperative surgical site infections, and facilitates planning, attachment of the drapes, and closure. However, most patients dread this procedure. The authors performed the first systematic review on shaving before neurosurgical procedures to investigate whether this commonly performed procedure is based on evidence. They systematically reviewed the literature on wound infections following different shaving strategies. Data on the type of surgery, surgeryrelated infections, preoperative shaving policy, decontamination protocols, and perioperative antibiotics protocols were collected. The search detected 165 articles, of which 21 studies—involving 11,071 patients—were suitable for inclusion. Two of these studies were randomized controlled trials. The authors reviewed 13 studies that reported on the role of preoperative hair removal in craniotomies, 14 on implantation surgery, 5 on bur hole procedures, and 3 on spine surgery. Nine studies described shaving policies in pediatric patients. None of these papers provided evidence that preoperative shaving decreases the occurrence of postoperative wound infections. The authors conclude that there is no evidence to support the routine performance of preoperative hair removal in neurosurgery. Therefore, properly designed studies are needed to provide evidence for preoperative shaving recommendations.

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Enrico Martin, Joeky T. Senders, Aislyn C. DiRisio, Timothy R. Smith and Marike L. D. Broekman

OBJECTIVE

Ideal timeframes for operating on traumatic stretch and blunt brachial plexus injuries remain a topic of debate. Whereas on the one hand spontaneous recovery might occur, on the other hand, long delays are believed to result in poorer functional outcomes. The goal of this review is to assess the optimal timeframe for surgical intervention for traumatic brachial plexus injuries.

METHODS

A systematic search was performed in January 2017 in PubMed and Embase databases according to the PRISMA guidelines. Search terms related to “brachial plexus injury” and “timing” were used. Obstetric plexus palsies were excluded. Qualitative synthesis was performed on all studies. Timing of operation and motor outcome were collected from individual patient data. Patients were categorized into 5 delay groups (0–3, 3–6, 6–9, 9–12, and > 12 months). Median delays were calculated for Medical Research Council (MRC) muscle grade ≥ 3 and ≥ 4 recoveries.

RESULTS

Forty-three studies were included after full-text screening. Most articles showed significantly better motor outcome with delays to surgery less than 6 months, with some studies specifying even shorter delays. Pain and quality of life scores were also significantly better with shorter delays. Nerve reconstructions performed after long time intervals, even more than 12 months, can still be useful. All papers reporting individual-level patient data described a combined total of 569 patients; 65.5% of all patients underwent operations within 6 months and 27.4% within 3 months. The highest percentage of ≥ MRC grade 3 (89.7%) was observed in the group operated on within 3 months. These percentages decreased with longer delays, with only 35.7% ≥ MRC grade 3 with delays > 12 months. A median delay of 4 months (IQR 3–6 months) was observed for a recovery of ≥ MRC grade 3, compared with a median delay of 7 months (IQR 5–11 months) for ≤ MRC grade 3 recovery.

CONCLUSIONS

The results of this systematic review show that in stretch and blunt injury of the brachial plexus, the optimal time to surgery is shorter than 6 months. In general, a 3-month delay appears to be appropriate because while recovery is better in those operated on earlier, this must be considered given the potential for spontaneous recovery.

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David McKalip

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Ken R. Winston

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Aditya V. Karhade, Paul Ogink, Quirina Thio, Marike Broekman, Thomas Cha, William B. Gormley, Stuart Hershman, Wilco C. Peul, Christopher M. Bono and Joseph H. Schwab

OBJECTIVE

If not anticipated and prearranged, hospital stay can be prolonged while the patient awaits placement in a rehabilitation unit or skilled nursing facility following elective spine surgery. Preoperative prediction of the likelihood of postoperative discharge to any setting other than home (i.e., nonroutine discharge) after elective inpatient spine surgery would be helpful in terms of decreasing hospital length of stay. The purpose of this study was to use machine learning algorithms to develop an open-access web application for preoperative prediction of nonroutine discharges in surgery for elective inpatient lumbar degenerative disc disorders.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program was queried to identify patients who underwent elective inpatient spine surgery for lumbar disc herniation or lumbar disc degeneration between 2011 and 2016. Four machine learning algorithms were developed to predict nonroutine discharge and the best algorithm was incorporated into an open-access web application.

RESULTS

The rate of nonroutine discharge for 26,364 patients who underwent elective inpatient surgery for lumbar degenerative disc disorders was 9.28%. Predictive factors selected by random forest algorithms were age, sex, body mass index, fusion, level, functional status, extent and severity of comorbid disease (American Society of Anesthesiologists classification), diabetes, and preoperative hematocrit level. On evaluation in the testing set (n = 5273), the neural network had a c-statistic of 0.823, calibration slope of 0.935, calibration intercept of 0.026, and Brier score of 0.0713. On decision curve analysis, the algorithm showed greater net benefit for changing management over all threshold probabilities than changing management on the basis of the American Society of Anesthesiologists classification alone or for all patients or for no patients. The model can be found here: https://sorg-apps.shinyapps.io/discdisposition/.

CONCLUSIONS

Machine learning algorithms show promising results on internal validation for preoperative prediction of nonroutine discharges. If found to be externally valid, widespread use of these algorithms via the open-access web application by healthcare professionals may help preoperative risk stratification of patients undergoing elective surgery for lumbar degenerative disc disorders.

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David J. Cote, Naci Balak, Jannick Brennum, Daniel T. Holsgrove, Neil Kitchen, Herbert Kolenda, Wouter A. Moojen, Karl Schaller, Pierre A. Robe, Tiit Mathiesen and Marike L. Broekman

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Marike L. D. Broekman, Eelco W. Hoving, Kuan H. Kho, Lucienne Speleman, K. Sen Han and Patrick W. Hanlo

✓ Beckwith–Wiedemann syndrome (BWS) is a rare congenital syndrome characterized by gigantism, macroglossia, exophthalmos, postpartum hypoglycemia, and multiple midline defects such as omphalocele. The authors describe, to the best of their knowledge, the first case of a child in whom BWS was diagnosed and who was subsequently treated for a nasal encephalocele.

Because the authors believe that this feature might not be an incidental finding in patients with BWS, intranasal masses in these patients should be carefully differentiated, as complications might be severe.

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Predicting nonroutine discharge after elective spine surgery: external validation of machine learning algorithms

Presented at the 2019 AANS/CNS Joint Section on Disorders of the Spine and Peripheral Nerves

Brittany M. Stopa, Faith C. Robertson, Aditya V. Karhade, Melissa Chua, Marike L. D. Broekman, Joseph H. Schwab, Timothy R. Smith and William B. Gormley

OBJECTIVE

Nonroutine discharge after elective spine surgery increases healthcare costs, negatively impacts patient satisfaction, and exposes patients to additional hospital-acquired complications. Therefore, prediction of nonroutine discharge in this population may improve clinical management. The authors previously developed a machine learning algorithm from national data that predicts risk of nonhome discharge for patients undergoing surgery for lumbar disc disorders. In this paper the authors externally validate their algorithm in an independent institutional population of neurosurgical spine patients.

METHODS

Medical records from elective inpatient surgery for lumbar disc herniation or degeneration in the Transitional Care Program at Brigham and Women’s Hospital (2013–2015) were retrospectively reviewed. Variables included age, sex, BMI, American Society of Anesthesiologists (ASA) class, preoperative functional status, number of fusion levels, comorbidities, preoperative laboratory values, and discharge disposition. Nonroutine discharge was defined as postoperative discharge to any setting other than home. The discrimination (c-statistic), calibration, and positive and negative predictive values (PPVs and NPVs) of the algorithm were assessed in the institutional sample.

RESULTS

Overall, 144 patients underwent elective inpatient surgery for lumbar disc disorders with a nonroutine discharge rate of 6.9% (n = 10). The median patient age was 50 years and 45.1% of patients were female. Most patients were ASA class II (66.0%), had 1 or 2 levels fused (80.6%), and had no diabetes (91.7%). The median hematocrit level was 41.2%. The neural network algorithm generalized well to the institutional data, with a c-statistic (area under the receiver operating characteristic curve) of 0.89, calibration slope of 1.09, and calibration intercept of −0.08. At a threshold of 0.25, the PPV was 0.50 and the NPV was 0.97.

CONCLUSIONS

This institutional external validation of a previously developed machine learning algorithm suggests a reliable method for identifying patients with lumbar disc disorder at risk for nonroutine discharge. Performance in the institutional cohort was comparable to performance in the derivation cohort and represents an improved predictive value over clinician intuition. This finding substantiates initial use of this algorithm in clinical practice. This tool may be used by multidisciplinary teams of case managers and spine surgeons to strategically invest additional time and resources into postoperative plans for this population.

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Alexander F. C. Hulsbergen, Sandra C. Yan, Brittany M. Stopa, Aislyn DiRisio, Joeky T. Senders, Max J. van Essen, Stéphanie M. E. van der Burgt, Timothy R. Smith, William B. Gormley and Marike L. D. Broekman

OBJECTIVE

The value of CT scanning after burr hole surgery in chronic subdural hematoma (CSDH) patients is unclear, and practice differs between countries. At the Brigham and Women’s Hospital (BWH) in Boston, Massachusetts, neurosurgeons frequently order routine postoperative CT scans, while the University Medical Center Utrecht (UMCU) in the Netherlands does not have this policy. The aim of this study was to compare the use of postoperative CT scans in CSDH patients between these hospitals and to evaluate whether there are differences in clinical outcomes.

METHODS

The authors collected data from both centers for 391 age- and sex-matched CSDH patients treated with burr hole surgery between January 1, 2002, and July 1, 2016, and compared the number of postoperative scans up to 6 weeks after surgery, the need for re-intervention, and postoperative neurological condition.

RESULTS

BWH patients were postoperatively scanned a median of 4 times (interquartile range [IQR] 2–5), whereas UMCU patients underwent a median of 0 scans (IQR 0–1, p < 0.001). There was no significant difference in the number of re-operations (20 in the BWH vs 27 in the UMCU, p = 0.34). All re-interventions were preceded by clinical decline and no recurrences were detected on scans performed on asymptomatic patients. Patients’ neurological condition was not worse in the UMCU than in the BWH (p = 0.43).

CONCLUSIONS

While BWH patients underwent more scans than UMCU patients, there were no differences in clinical outcomes. The results of this study suggest that there is little benefit to routine scanning in asymptomatic patients who have undergone surgical treatment of uncomplicated CSDH and highlight opportunities to make practice more efficient.