Can subjective symptoms predict objective findings in gastroesophageal reflux disease patients?

Comparative analysis of laparoscopic nissen fundoplication and rossetti modification in gastroesophageal reflux disease: A focus on life quality enhancement

OBJECTIVE

This study aims to investigate the focus of surgical treatment of gastroesophageal reflux disease (GERD) on enhancing life quality beyond symptom relief. The comparison involves laparoscopic Nissen fundoplication and Rossetti modification techniques.

METHODS

Patients intolerant to or experiencing relapse after medical therapy underwent either standard Nissen procedure (Group 1, n = 61) or Rossetti modification (Group 2, n = 42). A disease-specific quality of life questionnaire for GERD was utilized for evaluating life quality preoperatively and 2 years postoperatively. Symptom scores and patient satisfaction were also assessed.

RESULTS

Preoperatively, groups were similar in symptom duration, hiatal hernia presence, and DeMeester scores (p = 0.127, p = 0.427, and 0.584, respectively). Both groups exhibited a statistically significant increase in life quality postoperatively (p < 0.001), with no significant intergroup difference. Symptoms decreased after both surgeries, except for dysphagia and bloating. Bloating significantly increased in both groups after surgery (p = 0.018 and p = 0.017, respectively), and dysphagia increased significantly only in Group 2 (p = 0.007). The surgery refusal rate was significantly higher in Group 2 for similar preoperative symptoms (p = 0.040).

CONCLUSION

Despite increased life quality scores, the combination of increased dysphagia and bloating in patients undergoing Rossetti modification resulted in a decreased satisfaction rate.

Predictability of Magnetic Sphincter Augmentation Device Explantation: A Nomogram-based Scoring Tool from an Experienced Quaternary Center

BACKGROUND

Magnetic sphincter augmentation (MSA) explantation is an uncommon occurrence, and there are limited studies characterizing factors predictive for explanation. The aim of this study was to create a nomogram to aid in determining the probability of explantation in patients before undergoing MSA implantation.

METHODS

An institutional review board-approved, prospectively maintained database was retrospectively reviewed for all patients undergoing anti-reflux surgery between February 2015 and May 2023. All patients who underwent MSA-related procedures were included. Patients were divided into two groups, explant group and non-explant group, and differences were analyzed. A multivariable logistic regression model was fitted to identify independent risk factors for predicting MSA explantation, and a nomogram-based scoring tool was developed.

RESULTS

There were 227 patients (134 females; 93 males) with a mean age of 51.4 years. The explant group included 28 patients (12.3%), whereas the non-explant group included 199 patients (87.7%). Patient sociodemographic characteristics, medical comorbidities, preoperative testing results, and surgical history were included in our analysis. The multivariable regression model resulted in 4 significant variables that were included in the nomogram. These included preoperative DeMeester score, preoperative gastroesophageal reflux disease health-related quality of life (GERD-HRQL) score, preoperative distal contractile integral (DCI) value on manometry, and body mass index (BMI). Based on these variables, a scoring nomogram was developed with values from 0 to 18.

CONCLUSION

Our data was used to develop a scoring calculator capable of predicting the probability of MSA explantation. This scoring tool can guide preoperative patient selection and treatment decisions.

Validation of an artificial intelligence platform for the guidance of safe laparoscopic cholecystectomy

BACKGROUND

Many surgical adverse events, such as bile duct injuries during laparoscopic cholecystectomy (LC), occur due to errors in visual perception and judgment. Artificial intelligence (AI) can potentially improve the quality and safety of surgery, such as through real-time intraoperative decision support. GoNoGoNet is a novel AI model capable of identifying safe ("Go") and dangerous ("No-Go") zones of dissection on surgical videos of LC. Yet, it is unknown how GoNoGoNet performs in comparison to expert surgeons. This study aims to evaluate the GoNoGoNet's ability to identify Go and No-Go zones compared to an external panel of expert surgeons.

METHODS

A panel of high-volume surgeons from the SAGES Safe Cholecystectomy Task Force was recruited to draw free-hand annotations on frames of prospectively collected videos of LC to identify the Go and No-Go zones. Expert consensus on the location of Go and No-Go zones was established using Visual Concordance Test pixel agreement. Identification of Go and No-Go zones by GoNoGoNet was compared to expert-derived consensus using mean F1 Dice Score, and pixel accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

RESULTS

A total of 47 frames from 25 LC videos, procured from 3 countries and 9 surgeons, were annotated simultaneously by an expert panel of 6 surgeons and GoNoGoNet. Mean (± standard deviation) F1 Dice score were 0.58 (0.22) and 0.80 (0.12) for Go and No-Go zones, respectively. Mean (± standard deviation) accuracy, sensitivity, specificity, PPV and NPV for the Go zones were 0.92 (0.05), 0.52 (0.24), 0.97 (0.03), 0.70 (0.21), and 0.94 (0.04) respectively. For No-Go zones, these metrics were 0.92 (0.05), 0.80 (0.17), 0.95 (0.04), 0.84 (0.13) and 0.95 (0.05), respectively.

CONCLUSIONS

AI can be used to identify safe and dangerous zones of dissection within the surgical field, with high specificity/PPV for Go zones and high sensitivity/NPV for No-Go zones. Overall, model prediction was better for No-Go zones compared to Go zones. This technology may eventually be used to provide real-time guidance and minimize the risk of adverse events.