Impact of the 2016 American College of Surgeons Guideline Revision on Overlapping Lumbar Fusion Cases at a Large Academic Medical Center

OBJECTIVE: The American College of Surgeons (ACS) u pdated its guidelines on overlapping surgery in 2016. The objective was to examine differences in postoperative outcomes after overlapping surgery either pre-ACS guide-line revision or post-guideline revision, in a coarsened exact matching sample. -METHODS: A total of 3327 consecutive adult patients u ndergoing single-level posterior lumbar fusion from 2013 to 2019 were retrospectively analyzed. Patients were separated into a pre-ACS guideline revision cohort (surgery before April 2016) or a post-guideline revision cohort (surgery after October 2016) for comparison. The primary outcomes were proportion of cases performed with any degree of overlap, and adverse events including 30-day and 90-day rates of readmission, reoperation, emergency department visit, morbidity, and mortality. Subsequently, coarsened exact matching was used among overlapping surgery patients only to assess the impact of the ACS guideline revision on overlapping outcomes, and control-ling for attending surgeon and key patient characteristics known to affect surgical outcomes. -RESULTS: After the implementation of the ACS guide-lines, fewer cases were performed with overlap (22.0% vs. 53.7%; P \u3c 0.001). Patients in the post-ACS guideline revi-sion cohort experienced improved rates of readmission and reoperation within 30 and 90 days. However, when limited to overlapping cases only, no differences were observed in overlap outcomes pre-ACS versus post-ACS guideline revision. Similarly, when exact matched on risk-associated patient characteristics and attending surgeon, overlapping surgery patients pre-ACS and post-ACS guideline revision experienced similar rates of 30-day and 90-day outcomes. -CONCLUSIONS: After the ACS guideline revision, no discernable impact was observed on postoperative out-comes after lumbar fusion performed with overlap

Neurosurgeons Deliver Similar Quality Care Regardless of First Assistant Type: Resident Physician versus Nonphysician Surgical Assistant

OBJECTIVE: There are limited data evaluating the out-comes of attending neurosurgeons with different types of first assistants. This study considers a common neurosurgical procedure (single-level, posterior-only lumbar fusion surgery) and examines whether attending surgeons deliver equal patient outcomes, regardless of the type of first assistant (resident physician vs. nonphysician surgical assistant [NPSA]), among otherwise exact-matched patients. -METHODS: The authors retrospectively analyzed 3395 adult patients undergoing single-level, posterior-only lumbar fusion at a single academic medical center. Primary outcomes included readmissions, emergency department visits, reoperation, and mortality within 30 and 90 days after surgery. Secondary outcome measures included discharge disposition, length of stay, and length of surgery. Coarsened exact matching was used to match patients on key demographics and baseline characteristics known to independently affect neurosurgical outcomes. -RESULTS: Among exact-matched patients (n [ 1402), there was no significant difference in adverse postsurgical events (readmission, emergency department visits, reoperation, or mortality) within 30 days or 90 days of the index operation between patients who had resident physicians and those who had NPSAs as first assistants. Patients who had resident physicians as first assistants demonstrated a longer length of stay (mean: 100.0 vs. 87.4 hours, P \u3c 0.001) and a shorter duration of surgery (mean: 187.4 vs. 213.8 minutes, P \u3c 0.001). There was no significant difference between the two groups in the percentage of patients discharged home. -CONCLUSIONS: For single-level posterior spinal fusion, in the setting described, there are no differences in short-term patient outcomes delivered by attending surgeons assisted by resident physicians versus NPSAs

Spatiotemporal changes in along-tract profilometry of cerebellar peduncles in cerebellar mutism syndrome

Cerebellar mutism syndrome, characterised by mutism, emotional lability and cerebellar motor signs, occurs in up to 39% of children following resection of medulloblastoma, the most common malignant posterior fossa tumour of childhood. Its pathophysiology remains unclear, but prior studies have implicated damage to the superior cerebellar peduncles. In this study, the objective was to conduct high-resolution spatial profilometry of the cerebellar peduncles and identify anatomic biomarkers of cerebellar mutism syndrome. In this retrospective study, twenty-eight children with medulloblastoma (mean age 8.8 ± 3.8 years) underwent diffusion MRI at four timepoints over one year. Forty-nine healthy children (9.0 ± 4.2 years), scanned at a single timepoint, served as age- and sex-matched controls. Automated Fibre Quantification was used to segment cerebellar peduncles and compute fractional anisotropy (FA) at 30 nodes along each tract. Thirteen patients developed cerebellar mutism syndrome. FA was significantly lower in the distal third of the left superior cerebellar peduncle pre-operatively in all patients compared to controls (FA in proximal third 0.228, middle and distal thirds 0.270, p = 0.01, Cohen's d = 0.927). Pre-operative differences in FA did not predict cerebellar mutism syndrome. However, post-operative reductions in FA were highly specific to the distal left superior cerebellar peduncle, and were most pronounced in children with cerebellar mutism syndrome compared to those without at the 1–4 month follow up (0.325 vs 0.512, p = 0.042, d = 1.36) and at the 1-year follow up (0.342, vs 0.484, p = 0.038, d = 1.12). High spatial resolution cerebellar profilometry indicated a site-specific alteration of the distal segment of the superior cerebellar peduncle seen in cerebellar mutism syndrome which may have important surgical implications in the treatment of these devastating tumours of childhood

Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model.

IMPORTANCE: Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic. OBJECTIVE: To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxel-by-voxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians\u27 intracranial aneurysm diagnostic performance. DESIGN, SETTING, AND PARTICIPANTS: In this diagnostic study, a 3-dimensional convolutional neural network architecture was developed using a training set of 611 head CTA examinations to generate aneurysm segmentations. Segmentation outputs from this support model on a test set of 115 examinations were provided to clinicians. Between August 13, 2018, and October 4, 2018, 8 clinicians diagnosed the presence of aneurysm on the test set, both with and without model augmentation, in a crossover design using randomized order and a 14-day washout period. Head and neck examinations performed between January 3, 2003, and May 31, 2017, at a single academic medical center were used to train, validate, and test the model. Examinations positive for aneurysm had at least 1 clinically significant, nonruptured intracranial aneurysm. Examinations with hemorrhage, ruptured aneurysm, posttraumatic or infectious pseudoaneurysm, arteriovenous malformation, surgical clips, coils, catheters, or other surgical hardware were excluded. All other CTA examinations were considered controls. MAIN OUTCOMES AND MEASURES: Sensitivity, specificity, accuracy, time, and interrater agreement were measured. Metrics for clinician performance with and without model augmentation were compared. RESULTS: The data set contained 818 examinations from 662 unique patients with 328 CTA examinations (40.1%) containing at least 1 intracranial aneurysm and 490 examinations (59.9%) without intracranial aneurysms. The 8 clinicians reading the test set ranged in experience from 2 to 12 years. Augmenting clinicians with artificial intelligence-produced segmentation predictions resulted in clinicians achieving statistically significant improvements in sensitivity, accuracy, and interrater agreement when compared with no augmentation. The clinicians\u27 mean sensitivity increased by 0.059 (95% CI, 0.028-0.091; adjusted P = .01), mean accuracy increased by 0.038 (95% CI, 0.014-0.062; adjusted P = .02), and mean interrater agreement (Fleiss κ) increased by 0.060, from 0.799 to 0.859 (adjusted P = .05). There was no statistically significant change in mean specificity (0.016; 95% CI, -0.010 to 0.041; adjusted P = .16) and time to diagnosis (5.71 seconds; 95% CI, 7.22-18.63 seconds; adjusted P = .19). CONCLUSIONS AND RELEVANCE: The deep learning model developed successfully detected clinically significant intracranial aneurysms on CTA. This suggests that integration of an artificial intelligence-assisted diagnostic model may augment clinician performance with dependable and accurate predictions and thereby optimize patient care