0
selected
-
1.
Contribution of non-socioeconomic factors to healthy quality of life in socioeconomically deprived patients with advanced gastrointestinal cancer: Measuring attributable fraction.
An, Z, Nie, J, Huang, Y, Fang, P, Meng, X, Cai, C, Yu, L
Journal of advanced nursing. 2024;(3):1154-1165
Abstract
BACKGROUND The quality of life of patients with advanced gastrointestinal cancer is seriously impaired, and socioeconomic deprivation often has a serious impact on their quality of life. However, little is known about the relative contribution of non-socioeconomic factors to the quality of life of patients with advanced gastrointestinal cancer with socioeconomic deprivation. AIM: This study aims to investigate the situation and predictors of quality of life of patients with socioeconomic deprivation and evaluate the independent effects of some non-socioeconomic factors. DESIGN A retrospective study based on cross-sectional design. METHODS Data were obtained from 1075 patients with advanced gastrointestinal cancer who received family palliative treatment in the hospice ward of Zhongnan Hospital of Wuhan University from March 2010 to October 2020, including demographic and clinical questionnaires, Karnofsky Performance Status scale and Cancer Pain and Quality of Life Questionnaire of Chinese Cancer Patients. RESULTS The quality of life of patients with advanced gastrointestinal cancer with socioeconomic deprivation is impaired and is affected by gait, self-care ability, abdominal distension, nutritional status, weight loss, constipation and posture. Improvement in six of these factors-gait, self-care ability, abdominal distension, nutritional status, weight loss and posture-has an independent positive impact on the development of a healthy quality of life for patients. CONCLUSIONS Gait, self-care ability, abdominal distension, nutritional status, weight loss and posture are important determinants of healthy quality of life in patients with advanced gastrointestinal cancer with socioeconomic deprivation, and early identification and strength management of these non-socioeconomic factors may neutralize the negative impact of socioeconomic factors on the quality of life. IMPLICATIONS FOR PRACTICE This study provides new ideas and intervention entry points for global nurses in practice innovations to improve the quality of life of socioeconomically deprived patients with advanced gastrointestinal cancer. It enables them to focus on the effectiveness of non-socioeconomic factors in the development and implementation of targeted care plans for patients with advanced gastrointestinal cancer experiencing socioeconomic deprivation globally. REPORTING METHOD This study was reported in strict compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline. PATIENT OR PUBLIC CONTRIBUTION No patient or public contribution.
-
2.
Prognostic Value of Four Preimplantation Malnutrition Estimation Tools in Predicting Heart Failure Hospitalization of the Older Diabetic Patients with Right Ventricular Pacing.
Fu, B, Yu, Y, Cheng, S, Huang, H, Long, T, Yang, J, Gu, M, Cai, C, Chen, X, Niu, H, et al
The journal of nutrition, health & aging. 2023;(12):1262-1270
-
-
Free full text
-
Abstract
OBJECTIVES The prognostic value of preimplantation nutritional status is not yet known for older diabetic patients that received right ventricular pacing (RVP). The study aimed to investigate the clinical value of the four malnutrition screening tools for the prediction of heart failure hospitalization (HFH) in older diabetic patients that received RVP. DESIGN Retrospective observational cohort study. SETTING AND PARTICIPANTS This study was conducted between January 2017 and January 2018 at the Fuwai Hospital, Beijing, China, and included older (age ≥ 65 years) diabetic patients that received RVP for the first time Measurements: The Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Naples Prognostic Score (NPS), and the Controlling Nutritional Status (CONUT) score were used to estimate the preimplantation nutritional status of the patients. Univariate and multivariate Cox proportional hazard regression analyses were performed to investigate the association between preimplantation malnutrition and HFH. RESULTS Overall, 231 older diabetic patients receiving RVP were included. The median follow-up period after RVP was 53 months. HFH was reported for 19.9% of the included patients. Our results showed preimplantation malnutrition for 18.2%, 15.2%, 86.6% and 66.2% of the included patients based on the PNI, GNRI, NPS, and CONUT score, respectively. The cumulative rate of HFH during follow-up period was significantly higher for patients in the preimplantation malnutrition group based on the PNI (log-rank = 13.0, P = 0.001), GNRI (log-rank = 8.5, P = 0.01), and NPS (log-rank = 15.7, P < 0.001) compared to the normal nutrition group, but was not statistically significant for those in the preimplantation malnutrition group based on the CONUT score (log-rank = 2.7, P = 0.3). As continuous variables, all the nutritional indices showed significant correlation with HFH (all P < 0.05). However, multivariate analysis showed that only GNRI was independently associated with HFH (HR = 0.97, 95% CI: 0.937-0.997, P = 0.032). As categorical variables, PNI, GNRI, and NPS showed significant correlation with HFH. After adjustment of confounding factors, moderate-to-severe degree of malnutrition was an independent predictor of HFH based on the PNI (HR = 4.66, 95% CI: 1.03-21.00, P = 0.045) and GNRI (HR = 3.02, 95% CI: 1.02-9.00, P = 0.047). CONCLUSION Preimplantation malnutrition was highly prevalent in older diabetic patients that received RVP. The malnutrition prediction tools, PNI and GNRI, showed significant prognostic value in accurately predicting HFH in older diabetic patients with RVP.
-
3.
Soy foods and nuts consumption during early pregnancy are associated with decreased risk of gestational diabetes mellitus: a prospective cohort study.
Pang, X, Cai, C, Dong, H, Lan, X, Zhang, Y, Bai, D, Hao, L, Sun, H, Li, F, Zeng, G
The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians. 2022;(25):9122-9130
Abstract
AIMS: To study the relationship of soy foods and nuts consumption during early pregnancy with the risk of gestational diabetes mellitus (GDM). METHODS This was a prospective observational study conducted in Southwest China. Dietary information was assessed through 3-day 24-h dietary recalls at 6-14 gestational weeks. For soy foods and nuts, non-consumers were used as the reference category and the consumers were categorized into tertiles. GDM was assessed with the 75-g, 2-h oral glucose tolerance test at 24-28 gestational weeks. Log-binomial models were used to assess the effects of soy foods and nuts on GDM. RESULTS Of the 1495 pregnant women, 529 were diagnosed with GDM. Median (IQRs) intakes of soy foods and nuts were 2.9 (0.0, 10.3) and 5.0 (0.0, 15.0) g/d, respectively. Our study found that, compared with the non-consumers, the highest tertile of soy foods intake was associated with a decrease in risk of GDM (RR = 0.73, 95%CI: 0.54-0.99, p = .049). Similarly, compared with the non-consumers, a negative relationship between the highest tertile of nuts intake and GDM risk was identified (RR = 0.65, 95%CI: 0.48-0.89, p = .007). CONCLUSIONS Consumption of soy foods and nuts are independently inversely associated with the risk of GDM during early pregnancy.
-
4.
A deep learning system for detecting diabetic retinopathy across the disease spectrum.
Dai, L, Wu, L, Li, H, Cai, C, Wu, Q, Kong, H, Liu, R, Wang, X, Hou, X, Liu, Y, et al
Nature communications. 2021;(1):3242
Abstract
Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR is trained for real-time image quality assessment, lesion detection and grading using 466,247 fundus images from 121,342 patients with diabetes. Evaluation is performed on a local dataset with 200,136 fundus images from 52,004 patients and three external datasets with a total of 209,322 images. The area under the receiver operating characteristic curves for detecting microaneurysms, cotton-wool spots, hard exudates and hemorrhages are 0.901, 0.941, 0.954 and 0.967, respectively. The grading of diabetic retinopathy as mild, moderate, severe and proliferative achieves area under the curves of 0.943, 0.955, 0.960 and 0.972, respectively. In external validations, the area under the curves for grading range from 0.916 to 0.970, which further supports the system is efficient for diabetic retinopathy grading.