Is Abdominal Sonography a Useful Adjunct to Abdominal Radiography in Evaluating Neonates with Suspected Necrotizing Enterocolitis?

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Development and Validation of a Laboratory Risk Score (LabScore) to Predict Outcomes after Resection for Intrahepatic Cholangiocarcinoma

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Implementation and Evaluation of a Novel High-Value Care Curriculum in a Single Academic Surgery Department

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Live Quality Assurance: Using a Multimedia Messaging Service Group Chat to Instantly Grade Intraoperative Images

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Epidemiological features of primary breast lymphoma patients and development of a nomogram to predict survival

BackgroundStudies on the epidemiology and prognosis of primary breast lymphoma (PBL) are lack for low incidence. Therefore, we aimed to investigate the epidemiological characteristics of PBL and develop nomograms to predict patient survival.MethodsData of patients who were diagnosed with PBL from 1975 to 2011 and incidence rate of PBL from 1975 to 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Time-varying multivariable Cox regression analysis was performed to identify independent prognostic factors for overall survival (OS) and disease-specific survival (DSS). Nomograms were constructed based on the independent prognostic factors identified in multivariate Cox regression analysis.ResultsA total of 1427 patients diagnosed with PBL were identified with the average age of 67.1 years. The overall incidence of PBL is 1.35/1,000,000 (adjusted to the United States standard population in 2000) from 1975 to 2017, with a significant upward trend by an annual percentage change (APC) of 2.91 (95%CI 2.29–3.94, P < 0.05). Age, sex, race, year of diagnosis, marital status, histological subtype, Ann Arbor Stage, and treatment modality were assessed as independent prognostic factors for OS and DSS by multivariable Cox regression (P < 0.05). Nomograms were constructed to predict the 1-, 3-, 5-, and 10- year OS and DSS. The concordance index (C-index) and calibration plots showed robustness and accuracy of the nomogram.ConclusionThe overall incidence of PBL was steadily increasing over the past four decades. Nomograms constructed can predicting 1-, 3-, 5-, and 10-year OS and identify patients with high-risk PBL.     

Clinical impact of the heart team on the outcomes of surgical aortic valve replacement among octogenarians

ObjectivesThe effectiveness of a multidisciplinary heart team in the management of patients with severe symptomatic aortic stenosis is unknown. This study evaluated the impact of a heart team on the outcomes of surgical aortic valve replacement in octogenarians.MethodsBetween May 2007 and January 2016, 528 patients aged 80 years or more were referred to our institutional heart team for a transcatheter aortic valve replacement. Among these, 101 were redirected to surgical aortic valve replacement (heart team group). These patients were compared with a surgical aortic valve replacement cohort (n = 506) without prior heart team screening (non-heart team group), taken from the same time period. Propensity score matching with bootstrap analysis was performed; 76 heart team patients were matched to 76 non-heart team patients. Early and late outcomes including survival and readmission for cardiovascular causes were compared.ResultsMatched subgroups were largely comparable; congestive heart failure and echocardiographic pulmonary hypertension were more prevalent in the heart team group. In-hospital mortality was significantly lower in the matched heart team group (0% vs 6.0%, bootstrap mean difference 6.0%, 95% confidence interval, 2.2-9.8). The risk of stroke, low cardiac output state, reexploration for bleeding, pneumonia, and prolonged ventilation was also significantly lower in the heart team group. There was no significant between-group difference regarding late survival (hazard ratio, 0.86, 95% confidence interval, 0.55-1.33, P = .49) or readmission for cardiovascular reasons (hazard ratio, 0.70, 95% confidence interval, 0.41-1.20, P = .19).ConclusionsPreoperative multidisciplinary assessment of octogenarians by a heart team was associated with lower in-hospital mortality and adverse events after surgical aortic valve replacement.     

Albumin-Bilirubin Score vs Model for End-Stage Liver Disease in Predicting Post-Hepatectomy Outcomes

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Validation of a prognostic scoring system for postmastectomy locoregional recurrence in breast cancer

BackgroundTo date, it remains unclear which patients with breast cancer (BC) benefit from post-mastectomy radiotherapy (PMRT). Cheng et al. developed and validated a scoring system based on 4 prognostic factors for locoregional recurrence (LRR) to identify patients in need for PMRT. These factors include age, estrogen receptor status, lymphovascular status and number of affected axillary lymph nodes.PurposeTo validate the scoring system for LRR in BC developed by Cheng et al. by using an independent BC database.Methods and materialsWe retrospectively identified 1989 BC cases, treated with mastectomy (ME) with or without PMRT at the University Hospitals Leuven between 2000 and 2007. The primary endpoint was 5-year locoregional control rate with and without PMRT, according to the LRR score.ResultsMedian follow-up time was 11.4 years. After excluding patients with missing variables 1103 patients were classified using the LRR scoring system: 688 (62.38%) patients were at low risk of recurrence (LRR score 0–1), 335 (30.37%) patients were at intermediate risk of recurrence (LRR score 2–3) and 80 (7.25%) patients were at high risk of recurrence (LRR score ≥4). 5-year locoregional control rates with and without PMRT were 99.20% versus 99.21% (p = 0.43) in the low-risk group; 98.24% versus 85.74% (p < 0.0001) in the intermediate-risk group and 96.87% versus 85.71% (p = 0.10) in the high-risk group respectively.ConclusionOur validation of the LRR scoring system suggests it can be used to point out patients that would benefit from PMRT. We recommend further validation of this scoring system by other independent institutions before application in clinical practice.     

Derivation and Validation of a Machine Learning Algorithm for Predicting Venous Thromboembolism in Injured Children

BackgroundVenous thromboembolism (VTE) causes significant morbidity in pediatric trauma patients. We applied machine learning algorithms to the Trauma Quality Improvement Program (TQIP) database to develop and validate a risk prediction model for VTE in injured children.MethodsPatients ≤18 years were identified from TQIP (2017–2019, n = 383,814). Those administered VTE prophylaxis ≤24 h and missing the outcome (VTE) were removed (n = 347,576). Feature selection identified 15 predictors: intubation, need for supplemental oxygen, spinal injury, pelvic fractures, multiple long bone fractures, major surgery (neurosurgery, thoracic, orthopedic, vascular), age, transfusion requirement, intracranial pressure monitor or external ventricular drain placement, and low Glasgow Coma Scale score. Data was split into training (n = 251,409) and testing (n = 118,175) subsets. Machine learning algorithms were trained, tested, and compared.ResultsLow-risk prediction: For the testing subset, all models outperformed the baseline rate of VTE (0.15%) with a predicted rate of 0.01–0.02% (p < 2.2e⁻¹⁶). 88.4–89.4% of patients were classified as low risk by the models.High-risk predictionAll models outperformed baseline with a predicted rate of VTE ranging from 1.13 to 1.32% (p < 2.2e⁻¹⁶). The performance of the 3 models was not significantly different.ConclusionWe developed a predictive model that differentiates injured children for development of VTE with high discrimination and can guide prophylaxis use.Level of EvidencePrognostic, Level II.Type of StudyRetrospective, Cross-sectional.     

Protective Effects of a Hydrogen-Rich Preservation Solution in a Canine Lung Transplantation Model

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Outcomes of lung transplantation from donors with a history of substance abuse

ObjectiveThe study objective was to determine whether donor substance abuse (opioid overdose death, opioid use, cigarette or marijuana smoking) impacts lung acceptance and recipient outcomes.MethodsDonor offers to a single center from 2013 to 2019 were reviewed to determine if lung acceptance rates and recipient outcomes were affected by donor substance abuse.ResultsThere were 3515 donor offers over the study period. A total of 154 offers (4.4%) were opioid use and 117 (3.3%) were opioid overdose deaths. A total of 1744 donors (65.0%) smoked cigarettes and 69 donors (2.6%) smoked marijuana. Of smokers, 601 (35.0%) had less than 20 pack-year history and 1117 (65.0%) had more than 20 pack-year history. Substance abuse donors were younger (51.5 vs 55.2 P < .001), more often male (65.6 vs 54.8%, P < .001), more often White (86.2 vs 68.7%, P < .001), and had hepatitis C (8.3 vs 0.8%, P < .001). Donor acceptance was significantly associated with brain dead donors (odds ratio, 1.56, P < .001), donor smoking history (odds ratio, 0.56, P < .001), hepatitis C (odds ratio, 0.35, P < .001), younger age (odds ratio, 0.98, P < .001), male gender (odds ratio, 0.74, P = .004), and any substance abuse history (odds ratio, 0.50, P < .001), but not opioid use, opioid overdose death, or marijuana use. Recipient survival was equivalent when using lungs from donors who had opioid overdose death, who smoked marijuana, or who smoked cigarettes for less than 20 patient-years or more than 20 patient-years, and significantly longer in recipients of opioid use lungs. There was no significant difference in time to chronic lung allograft dysfunction for recipients who received lungs from opioid overdose death or with a history of opioid use, marijuana smoking, or cigarette smoking.ConclusionsDonor acceptance was impacted by cigarette smoking but not opioid use, opioid overdose death, or marijuana use. Graft outcomes and recipient survival were similar for recipients of lungs from donors who abused substances.     

Circumferential Resection Margin as a Hospital Quality Assessment Tool for Rectal Cancer Surgery

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