Ling Lan

Interdepartmental consultations are essential for managing complex inpatient care but are often inefficient. Hospital-wide, data-driven analyses are needed to guide process improvements; yet, most existing studies have focused on single departments or specific diseases, leaving a gap in understanding hospital-level collaboration networks. Understanding these patterns is crucial for optimizing clinical workflows, reducing delays, and improving patient outcomes in large tertiary hospitals. This study aimed to analyze the distribution and network characteristics of interdepartmental consultations across a large tertiary hospital, focusing on high-frequency collaboration pairs and their disease associations. This retrospective cohort study included all interdepartmental consultations for inpatients and emergency patients at Peking Union Medical College Hospital (Beijing, China) from January 1 to December 31, 2024. Secondary data were extracted from the Hospital Information System. In total, 102,858 valid consultations involving 42 clinical departments were analyzed. Outcome measures included consultation requests and receptions per department, per capita request intensity, and pairwise collaboration volume. High-frequency collaboration pairs were defined as those with an annual consultation volume of 300 or more. Descriptive statistics (medians and IQRs) and proportions with 95% CIs were used. Consultation activity exhibited marked concentration. The Emergency Department issued the most consultation requests (19,698/102,858, 19.15%), far exceeding the median departmental request volume of 1899.5 (IQR 1359.5-3759.25). Meanwhile, the Internal Medicine Consultation Service received the highest number of consultations (10,428/102,858, 10.14%), substantially above the median reception volume of 1886.0 (IQR 521.25-4056.75) across departments. The per capita request intensity varied widely, with Critical Care Medicine highest value (21.64) vs a hospital-wide average of 0.32. Collaboration demonstrated a strong Pareto distribution: the top 5.37% (65/1210) of department pairs (65 pairs) accounted for 42.02% (43,221/102,858) of all consultations. These high-volume pairs were predominantly disease-specific. Examples include: Endocrinology-Ophthalmology primarily for diabetic and thyroid eye disease (1287/102,858, 1.25%); General Surgery-Otolaryngology mainly for preoperative thyroid airway assessment (1225/102,858, 1.19%); General Surgery-Clinical Nutrition for perioperative support (1032/102,858, 1.00%); Endocrinology-Clinical Nutrition for metabolic disease management (703/102,858, 0.68%); Orthopedics-Rehabilitation Medicine for postoperative rehabilitation (686/102,858, 0.67%); Oncology Medical Center-Clinical Nutrition for nutrition support in patients with cancer (681/102,858, 0.66%). Recurring clinical scenarios generated these stable, predictable consultation pathways. This study provides a novel, hospital-wide, network-based mapping of interdepartmental consultations using real-world data. Unlike prior work limited to single departments or diseases, it reveals that collaboration is concentrated, Pareto-like, and disease-driven. The identification of stable, disease-specific consultation pairs offers a data-driven framework for understanding multidisciplinary collaboration. These findings offer a data-driven framework for understanding multidisciplinary collaboration as a networked system. In practice, administrators and clinicians can use this evidence to prioritize resources, design standardized multidisciplinary team pathways, and implement spatial or digital interventions to reduce delays and improve patient flow and outcomes.

Introduction Surgical resection is the cornerstone therapy for pancreatic neoplasms, particularly in cases of resectable pancreatic carcinoma identified in the early stages. Clinically relevant postoperative pancreatic fistula (CR-POPF) persists as a prevalent and formidable postsurgical complication, substantially elevating patient morbidity and mortality. The current literature reveals a gap in the understanding of the relationship between perioperative glucose levels and the incidence of CR-POPF in patients undergoing pancreatic surgery. This study aims to investigate the association between perioperative glycaemic levels, specifically monitoring both blood and interstitial fluid glucose levels via non-invasive continuous glucose monitoring (CGM) and the occurrence of CR-POPF. Methods and analysis This observational prospective cohort study will be conducted at high-volume general hospitals in China. Adult patients scheduled for elective pancreatic surgeries from 1 March 2024 to 31 March 2025 will be prospectively enrolled, and the end date of the whole study is 31 July 2025. Eligible patients will wear a CGM system before surgery, and real-time interstitial fluid blood glucose levels will be monitored up to 14 days after surgery, or until the patient is discharged from the hospital. Patient characteristics and perioperative data will be collected using a standard case report form. The primary outcome is the incidence of CR-POPF during the hospital stay. The secondary outcomes include other postoperative complications, such as bile leakage, chyle leak, postoperative haemorrhage, intra-abdominal infection, and delayed gastric emptying, and in-hospital mortality. Ethics and dissemination The study protocol was approved by the Research Ethics Committee of Peking Union Medical College Hospital (IRB K-2501). Written informed consent will be obtained from all participants before study enrolment. The results of this study will be disseminated at scientific conferences and published in international peer-reviewed journals. Trial registration number NCT06289530.

Introduction: Pheochromocytoma/paraganglioma (PPGL) resection remains a high-risk surgical procedure owing to severe haemodynamic instability, which can lead to myocardial injury after non-cardiac surgery (MINS). MINS is the most common and easily overlooked cardiovascular complication and results in increased postoperative mortality and prolonged inpatient hospitalisation. We aimed to develop and validate a predictive model for MINS in patients undergoing laparoscopic PPGL resection. Methods and analysis: The PPGL-MINS study is a single-centre, observational, ambispective cohort study that includes patients undergoing elective laparoscopic PPGL resection under general anaesthesia at the Peking Union Medical College Hospital (PUMCH) between 1 January 2013 and 31 May 2025. We expect to enrol 700 patients, including at least 550 patients retrospectively and 150 patients prospectively. A prediction model will be developed for the retrospective cohort (training cohort) of patients from 1 January 2013 to 31 December 2022. Possible clinically relevant variables, particularly intraoperative blood pressure and heart rate, will be selected as candidate predictors. Stepwise and least absolute shrinkage and selection operator regression will be used for predictor selection. Multivariate logistic regression will be used to develop the prediction model, which will be presented as a nomogram. The developed model will be used to assess discrimination with the receiver operating characteristic curve and area under the curve value, calibration with the Hosmer-Lemeshow test and calibration curve, and clinical usefulness with decision curve analysis. Internal validation will be assessed with bootstrap. For external validation, we will use an independent prospective cohort (validation cohort) of patients from 1 March 2023 to 31 May 2025. Ethics and dissemination: The study protocol has been approved by the Research Ethics Committee of PUMCH (IRB-K2893). Written informed consent will be obtained from all participants in the prospective cohort before enrolment in the study. We aim to publish and disseminate the findings in peer-reviewed journals and at scientific conferences. Trial registration number: NCT05752773 .

Background/Objectives: The automatic left ventricle segmentation in transesophageal echocardiography (TEE) is of significant importance. In this paper, we constructed a large-scale TEE apical four-chamber view (A4CV) image dataset and proposed an automatic left ventricular segmentation method for the TEE A4CV based on the UNeXt deep neural network. Methods: UNeXt, a variant of U-Net integrating a multilayer perceptron, was employed for left ventricle segmentation in the TEE A4CV because it could yield promising segmentation performance while reducing both the number of network parameters and computational complexity. We also compared the proposed method with U-Net, TransUNet, and Attention U-Net models. Standard TEE A4CV videos were collected from 60 patients undergoing cardiac surgery, from the onset of anesthesia to the conclusion of the procedure. After preprocessing, a dataset comprising 3000 TEE images and their corresponding labels was generated. The dataset was randomly divided into training, validation, and test sets in an 8:1:1 ratio on the patient level. The training and validation sets were used to train the UNeXt, U-Net, TransUNet, and Attention U-Net models for left ventricular segmentation. The dice similarity coefficient (DSC) and Intersection over Union (IoU) were used to evaluate the segmentation performance of each model, and the Kruskal–Wallis test was employed to analyze the significance of DSC differences. Results: On the test set, the UNeXt model achieved an average DSC of 88.60%, outperforming U-Net (87.76%), TransUNet (85.75%, p < 0.05), and Attention U-Net (79.98%; p < 0.05). Additionally, the UNeXt model had a smaller number of parameters (1.47 million) and floating point operations (2.28 giga) as well as a shorter average inference time per image (141.73 ms), compared to U-Net (185.12 ms), TransUNet (209.08 ms), and Attention U-Net (201.13 ms). The average IoU of UNeXt (77.60%) was also higher than that of U-Net (76.61%), TransUNet (77.35%), and Attention U-Net (68.86%). Conclusions: This study pioneered the construction of a large-scale TEE A4CV dataset and the application of UNeXt to left ventricle segmentation in the TEE A4CV. The proposed method may be used for automatic segmentation of the left ventricle in the TEE A4CV.

GPR109A, a G-protein–coupled receptor, is activated by niacin and butyrate. Upon activation in colonocytes, GPR109A potentiates anti-inflammatory pathways, induces apoptosis, and protects against inflammation-induced colon cancer. In contrast, GPR109A activation in keratinocytes induces flushing by activation of Cox-2–dependent inflammatory signaling, and the receptor expression is upregulated in human epidermoid carcinoma. Thus, depending on the cellular context and tissue, GPR109A functions either as a tumor suppressor or a tumor promoter. However, the expression status and the functional implications of this receptor in the mammary epithelium are not known. Here, we show that GPR109A is expressed in normal mammary tissue and, irrespective of the hormone receptor status, its expression is silenced in human primary breast tumor tissues, breast cancer cell lines, and in tumor tissues of three different murine mammary tumor models. Functional expression of this receptor in human breast cancer cell lines decreases cyclic AMP production, induces apoptosis, and blocks colony formation and mammary tumor growth. Transcriptome analysis revealed that GPR109A activation inhibits genes, which are involved in cell survival and antiapoptotic signaling, in human breast cancer cells. In addition, deletion of Gpr109a in mice increased tumor incidence and triggered early onset of mammary tumorigenesis with increased lung metastasis in MMTV-Neu mouse model of spontaneous breast cancer. These findings suggest that GPR109A is a tumor suppressor in mammary gland and that pharmacologic induction of this gene in tumor tissues followed by its activation with agonists could be an effective therapeutic strategy to treat breast cancer. Cancer Res; 74(4); 1166–78. ©2013 AACR.

GPR109A, a G-protein–coupled receptor, is activated by niacin and butyrate. Upon activation in colonocytes, GPR109A potentiates anti-inflammatory pathways, induces apoptosis, and protects against inflammation-induced colon cancer. In contrast, GPR109A activation in keratinocytes induces flushing by activation of Cox-2–dependent inflammatory signaling, and the receptor expression is upregulated in human epidermoid carcinoma. Thus, depending on the cellular context and tissue, GPR109A functions either as a tumor suppressor or a tumor promoter. However, the expression status and the functional implications of this receptor in the mammary epithelium are not known. Here, we show that GPR109A is expressed in normal mammary tissue and, irrespective of the hormone receptor status, its expression is silenced in human primary breast tumor tissues, breast cancer cell lines, and in tumor tissues of three different murine mammary tumor models. Functional expression of this receptor in human breast cancer cell lines decreases cyclic AMP production, induces apoptosis, and blocks colony formation and mammary tumor growth. Transcriptome analysis revealed that GPR109A activation inhibits genes, which are involved in cell survival and antiapoptotic signaling, in human breast cancer cells. In addition, deletion of Gpr109a in mice increased tumor incidence and triggered early onset of mammary tumorigenesis with increased lung metastasis in MMTV-Neu mouse model of spontaneous breast cancer. These findings suggest that GPR109A is a tumor suppressor in mammary gland and that pharmacologic induction of this gene in tumor tissues followed by its activation with agonists could be an effective therapeutic strategy to treat breast cancer. Cancer Res; 74(4); 1166–78. ©2013 AACR.