Mapping the scientific research on integrated care: a bibliometric and social network analysis.

Background: Integrated care (IC) is the cornerstone of the sustainable development of the medical and health system. A thorough examination of the existing scientific literature on IC is essential for assessing the present state of knowledge on this subject. This review seeks to offer an overview of evidence-based knowledge, pinpoint existing knowledge gaps related to IC, and identify areas requiring further research. Methods: Data were retrieved from the Web of Science Core Collection, from 2010 to 2020. Bibliometrics and social network analysis were used to explore and map the knowledge structure, research hotspots, development status, academic groups and future development trends of IC. Results: A total of 7,501 articles were obtained. The number of publications on IC was rising in general. Healthcare science services were the most common topics. The United States contributed the highest number of articles. The level of collaboration between countries and between authors was found to be relatively low. The keywords were stratified into four clusters: IC, depression, integrative medicine, and primary health care. In recent years, complementary medicine has become a hotspot and will continue to be a focus. Conclusion: The study provides a comprehensive analysis of global research hotspots and trends in IC, and highlights the characteristics, challenges, and potential solutions of IC. To address resource fragmentation, collaboration difficulties, insufficient financial incentives, and poor information sharing, international collaboration needs to be strengthened to promote value co-creation and model innovation in IC. The contribution of this study lies in enhancing people's understanding of the current state of IC research, guiding scholars to discover new research perspectives, and providing valuable references for researchers and policymakers in designing and implementing effective IC strategies.

The T stage of esophageal cancer can be effectively predicted by muscularis propria thickness and muscularis propria + mucosa thickness under ultrasonic gastroscopy.

OBJECTIVES: The latest version of the National Comprehensive Cancer Network recommends neoadjuvant therapy followed by surgical treatment or radical chemoradiotherapy for patients with cT3N0M0. Neoadjuvant therapy can improve the prognosis of patients with locally advanced esophageal cancer. Therefore, the evaluation or prediction of T stage is particularly important because the treatment could differently affect the prognosis. Here, we establish a model to predict the T stage of patients with T2-3N0M0 to help choose the best treatment strategy. METHODS: From 1637 patents with esophageal cancer, we enrolled 48 patients and performed least absolute shrinkage and selection operator regression to screen for independent factors influencing pathological T stage. We, then, trained the decision tree to obtain the decision tree diagram and divided the T stages obtained by different methods into two categories, T2 and T3, for survival analysis. RESULTS: A total of 21 and 27 cases were predicted to be T2 and T3, respectively, under ultrasonic gastroscopy, 19 and 29 under magnetic resonance imaging, and 22 and 26 under pathological examination. Multivariate logistic regression analysis revealed that the muscularis propria thickness (MPT) (p = 0.0097) and the muscularis propria + mucosa thickness (MPMT) in the largest tumor cross-section (p = 0.0239) were independent influencing factors. We plotted a decision tree diagram with these two factors. MPT in the largest tumor cross-section >1.3 mm could be judged as pT3; if ≤1.3 mm, MPMT should be considered a thickness ≥1.7 mm could be judged as pT2 (otherwise pT3). Corresponding survival analysis was performed according to the T stage under different examination modalities. CONCLUSION: MPT in the largest tumor cross-section and MPMT in the largest tumor cross-section are independent predicting factors of pathological T stage.

A clinical nomogram for predicting tumor regression grade in esophageal squamous-cell carcinoma treated with immune neoadjuvant immunotherapy.

Background: There are various treatment options for esophageal squamous cell cancer. including surgery, peri-operative chemotherapy, and radiation. More recently, neoadjuvant immunotherapy has also been shown improve outcomes. In this study, we addressed the question, "Can we predict which patients with esophageal squamous cell cancer will benefit from neoadjuvant immunotherapy?". Methods: All patients with thoracic esophageal squamous-cell carcinoma (T2N+M0-T3-4N0/+M0) (according to the eighth edition of the National Comprehensive Cancer Network guidelines) who underwent immune neoadjuvant immunochemotherapy with programmed cell death protein 1 (PD-1) combined with paclitaxel plus cisplatin or nedaplatin in the Affiliated Cancer Hospital of Zhengzhou University, China, between November 2019 and August 2021 were included in this study. All patients underwent surgical resection. We developed a response [tumor regression grade (TRG)] prediction model using the least absolute shrinkage and selection operator (LASSO) regression incorporating factors associated with response. The accuracy of the prediction model was then validated. Results: We included 79 patients who underwent neoadjuvant immunotherapy combined with chemotherapy, aged 48-78 years (62.05±6.67), including 21 males and 58 females. There were five cases of immune-related pneumonia, of which three cases were diagnosed as immune-related pneumonia during the perioperative period, and one case of immune-related thyroid dysfunction changes. After LASSO regression, the factors that were independently associated with TRG were clinical T stage before neoadjuvant therapy, clinical N stage before neoadjuvant therapy, albumin level difference from before to after neoadjuvant therapy, white blood cell (WBC) count before neoadjuvant therapy, and T stage before surgery. We constructed a prediction model, plotted the nomogram, and verified its accuracy. Its Brier score was 0.13, its calibration slope was 0.98, and its C-index was 0.90 (95% CI: 0.82-0.97). Conclusions: Our prediction model can predict the likelihood of TRG in patients with esophageal squamous cell cancer after immunotherapy combined with neoadjuvant chemotherapy. Using this prediction model, we plan to conduct a subsequent neoadjuvant radiotherapy in patients with of TRG 2-3 patients with neoadjuvant radiotherapy.

Effect of an Integrated Payment System on the Direct Economic Burden and Readmission of Rural Cerebral Infarction Inpatients: Evidence from Anhui, China.

Rural China is piloting an integrated payment system, which prepays a budget to a medical alliance rather than a single hospital. This study aims to evaluate the effect of this reform on the direct economic burden and readmission rates of cerebral infarction inpatients. The settlement records of 78,494 cerebral infarction inpatients were obtained from the New Rural Cooperative Medical Scheme (NRCMS) database in Dingyuan and Funan Counties in the Anhui Province. The direct economic burden was estimated by total costs, out-of-pocket expenditures, the out-of-pocket ratio, and the compensation ratio of the NRCMS. Generalized additive models and multivariable linear/logistic regression were applied to measure the changes of the dependent variables along with the year. Within the county, the total costs positively correlated to the year (ß = 313.10 in 2015; 163.06 in 2016). The out-of-pocket expenditures, out-of-pocket ratios, and the length-of-stay positively correlated to the year in 2015 (ß = 105.10, 0.01, and 0.18 respectively), and negatively correlated to the year in 2016 (ß = -58.40, -0.03, and -0.30, respectively). The odds ratios of the readmission rates were less than one within the county (0.70 in 2015; 0.53 in 2016). The integrated payment system in the Anhui Province has considerably reduced the direct economic burden for the rural cerebral infarction inpatients, and the readmission rate has decreased within the county. Inpatients' health outcomes should be given further attention, and the long-term effect of this reform model awaits further evaluation.