Outcomes of Laser-Assisted Closure (SiLaC) Surgery for Chronic Pilonidal Sinus Disease.

Background: Management of pilonidal sinus (PNS) disease has changed notably in the past decade, with the introduction of novel surgical techniques and technological innovation. In this study, we summarized our initial experience with sinus laser-assisted closure (SiLaC) of pilonidal disease. Methods: A retrospective analysis of a prospective database of all patients who underwent minimally invasive surgery combined with laser therapy for PNS between September 2018 and December 2020 was performed. Patients' demographics, clinical and perioperative data, as well postoperative outcomes were recorded and analyzed. Results: A total of 92 patients (86 males, 93.4%) underwent SiLaC surgery for pilonidal sinus disease during the study period. Patients' median age was 22 (range 16-62 years), and 60.8% of them previously underwent abscess drainage due to PNS. SiLaC was performed under local anesthesia in 85.7% of cases (78 patients) with a median energy of 1081 J (range 13-5035 J). One patient was lost to follow-up, leaving 91 patients for final analysis. The primary outcome was complete healing rate, standing at 81.3% (74/91 patients). In 8 patients (8.8%), there was minor incomplete healing that did not require reintervention. Recurrent/nonhealing disease was seen in 9 patients (9.9%), requiring reoperation in 7 patients (8.4%). Of those, 4 patients underwent repeat SiLaC and 3 patients underwent wide excision. Analysis of risk factors for PNS recurrence demonstrated that general anesthesia (P = .02) was associated with increased risk for recurrence along with a trend for increased risk in patients with significant hairiness (P = .078). No differences were seen in age (P = .621), gender (P = .475), median sinus length (P = .397), and energy used (P = .904). Conclusion: Primary healing rate after SiLaC surgery for chronic PNS was >80% in our series. Ten percent of patients did not achieve complete healing but did not require surgery due to lack of symptoms.

A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding.

(1) Background: Predicting which patients with upper gastro-intestinal bleeding (UGIB) will receive intervention during urgent endoscopy can allow for better triaging and resource utilization but remains sub-optimal. Using machine learning modelling we aimed to devise an improved endoscopic intervention predicting tool. (2) Methods: A retrospective cohort study of adult patients diagnosed with UGIB between 2012−2018 who underwent esophagogastroduodenoscopy (EGD) during hospitalization. We assessed the correlation between various parameters with endoscopic intervention and examined the prediction performance of the Glasgow-Blatchford score (GBS) and the pre-endoscopic Rockall score for endoscopic intervention. We also trained and tested a new machine learning-based model for the prediction of endoscopic intervention. (3) Results: A total of 883 patients were included. Risk factors for endoscopic intervention included cirrhosis (9.0% vs. 3.8%, p = 0.01), syncope at presentation (19.3% vs. 5.4%, p < 0.01), early EGD (6.8 h vs. 17.0 h, p < 0.01), pre-endoscopic administration of tranexamic acid (TXA) (43.4% vs. 31.0%, p < 0.01) and erythromycin (17.2% vs. 5.6%, p < 0.01). Higher GBS (11 vs. 9, p < 0.01) and pre-endoscopy Rockall score (4.7 vs. 4.1, p < 0.01) were significantly associated with endoscopic intervention; however, the predictive performance of the scores was low (AUC of 0.54, and 0.56, respectively). A combined machine learning-developed model demonstrated improved predictive ability (AUC 0.68) using parameters not included in standard GBS. (4) Conclusions: The GBS and pre-endoscopic Rockall score performed poorly in endoscopic intervention prediction. An improved predictive tool has been proposed here. Further studies are needed to examine if predicting this important triaging decision can be further optimized.