Optimizing discharge after major surgery using an artificial intelligence–based decision support tool (DESIRE): An external validation study

Background: In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we have previously developed and validated a machine learning concept in 1,677 gastrointestinal and oncology surgery patients that can predict safe hospital discharge after the second postoperative day. Despite strong model performance (area under the receiver operating characteristics curve of 0.88) in an academic surgical population, it remains unknown whether these findings can be translated to other hospitals and surgical populations. We therefore aimed to determine the generalizability of the previously developed machine learning concept. Methods: We externally validated the machine learning concept in gastrointestinal and oncology surgery patients admitted to 3 nonacademic hospitals in The Netherlands between January 2017 and June 2021, who remained admitted 2 days after surgery. Primary outcome was the ability to predict hospital interventions after the second postoperative day, which were defined as unplanned reoperations, radiological interventions, and/or intravenous antibiotics administration. Four forest models were locally trained and evaluated with respect to area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value. Results: All models were trained on 1,693 epsiodes, of which 731 (29.9%) required a hospital intervention and demonstrated strong performance (area under the receiver operating characteristics curve only varied 4%). The best model achieved an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [0.81–0.85]), sensitivity of 77.9% (0.67–0.87), specificity of 79.2% (0.72–0.85), positive predictive value of 61.6% (0.54–0.69), and negative predictive value of 89.3% (0.85–0.93). Conclusion: This study showed that a previously developed machine learning concept can predict safe discharge in different surgical populations and hospital settings (academic versus nonacademic) by training a model on local patient data. Given its high accuracy, integration of the machine learning concept into the clinical workflow could expedite surgical discharge and aid hospitals in addressing capacity challenges by reducing avoidable bed-days

Evaluation of National Surgical Practice for Lateral Lymph Nodes in Rectal Cancer in an Untrained Setting

BACKGROUND: Involved lateral lymph nodes (LLNs) have been associated with increased local recurrence (LR) and ipsi-lateral LR (LLR) rates. However, consensus regarding the indication and type of surgical treatment for suspicious LLNs is lacking. This study evaluated the surgical treatment of LLNs in an untrained setting at a national level.METHODS: Patients who underwent additional LLN surgery were selected from a national cross-sectional cohort study regarding patients undergoing rectal cancer surgery in 69 Dutch hospitals in 2016. LLN surgery consisted of either 'node-picking' (the removal of an individual LLN) or 'partial regional node dissection' (PRND; an incomplete resection of the LLN area). For all patients with primarily enlarged (≥7 mm) LLNs, those undergoing rectal surgery with an additional LLN procedure were compared to those undergoing only rectal resection.RESULTS: Out of 3057 patients, 64 underwent additional LLN surgery, with 4-year LR and LLR rates of 26% and 15%, respectively. Forty-eight patients (75%) had enlarged LLNs, with corresponding recurrence rates of 26% and 19%, respectively. Node-picking (n = 40) resulted in a 20% 4-year LLR, and a 14% LLR after PRND (n = 8; p = 0.677). Multivariable analysis of 158 patients with enlarged LLNs undergoing additional LLN surgery (n = 48) or rectal resection alone (n = 110) showed no significant association of LLN surgery with 4-year LR or LLR, but suggested higher recurrence risks after LLN surgery (LR: hazard ratio [HR] 1.5, 95% confidence interval [CI] 0.7-3.2, p = 0.264; LLR: HR 1.9, 95% CI 0.2-2.5, p = 0.874).CONCLUSION: Evaluation of Dutch practice in 2016 revealed that approximately one-third of patients with primarily enlarged LLNs underwent surgical treatment, mostly consisting of node-picking. Recurrence rates were not significantly affected by LLN surgery, but did suggest worse outcomes. Outcomes of LLN surgery after adequate training requires further research.</p

Surgeons’ practice and preferences for the anal fissure treatment: results from an international survey

The best nonoperative or operative anal fissure (AF) treatment is not yet established, and several options have been proposed. Aim is to report the surgeons' practice for the AF treatment. Thirty-four multiple-choice questions were developed. Seven questions were about to participants' demographics and, 27 questions about their clinical practice. Based on the specialty (general surgeon and colorectal surgeon), obtained data were divided and compared between two groups. Five-hundred surgeons were included (321 general and 179 colorectal surgeons). For both groups, duration of symptoms for at least 6 weeks is the most important factor for AF diagnosis (30.6%). Type of AF (acute vs chronic) is the most important factor which guide the therapeutic plan (44.4%). The first treatment of choice for acute AF is ointment application for both groups (59.6%). For the treatment of chronic AF, this data is confirmed by colorectal surgeons (57%), but not by the general surgeons who prefer the lateral internal sphincterotomy (LIS) (31.8%) (p = 0.0001). Botulin toxin injection is most performed by colorectal surgeons (58.7%) in comparison to general surgeons (20.9%) (p = 0.0001). Anal flap is mostly performed by colorectal surgeons (37.4%) in comparison to general surgeons (28.3%) (p = 0.0001). Fissurectomy alone is statistically significantly most performed by general surgeons in comparison to colorectal surgeons (57.9% and 43.6%, respectively) (p = 0.0020). This analysis provides useful information about the clinical practice for the management of a debated topic such as AF treatment. Shared guidelines and consensus especially focused on operative management are required to standardize the treatment and to improve postoperative results