Gastrointestinal manifestations of critical ill heatstroke patients and their associations with outcomes: A multicentre, retrospective, observational study.

BACKGROUND: Extreme heat exposure is a growing health problem, and the effects of heat on the gastrointestinal (GI) tract is unknown. This study aimed to assess the incidence of GI symptoms associated with heatstroke and its impact on outcomes. AIM: To assess the incidence of GI symptoms associated with heatstroke and its impact on outcomes. METHODS: Patients admitted to the intensive care unit (ICU) due to heatstroke were included from 83 centres. Patient history, laboratory results, and clinically relevant outcomes were recorded at ICU admission and daily until up to day 15, ICU discharge, or death. GI symptoms, including nausea/vomiting, diarrhoea, flatulence, and bloody stools, were recorded. The characteristics of patients with heatstroke concomitant with GI symptoms were described. Multivariable regression analyses were performed to determine significant predictors of GI symptoms. RESULTS: A total of 713 patients were included in the final analysis, of whom 132 (18.5%) patients had at least one GI symptom during their ICU stay, while 26 (3.6%) suffered from more than one symptom. Patients with GI symptoms had a significantly higher ICU stay compared with those without. The mortality of patients who had two or more GI symptoms simultaneously was significantly higher than that in those with one GI symptom. Multivariable logistic regression analysis revealed that older patients with a lower GCS score on admission were more likely to experience GI symptoms. CONCLUSION: The GI manifestations of heatstroke are common and appear to impact clinically relevant hospitalization outcomes.

[Introduction on a forecasting model for infectious disease incidence rate based on radial basis function network].

It is important to forecast incidence rates of infectious disease for the development of a better program on its prevention and control. Since the incidence rate of infectious disease is influenced by multiple factors, and the action mechanisms of these factors are usually unable to be described with accurate mathematical linguistic forms, the radial basis function (RBF) neural network is introduced to solve the nonlinear approximation issues and to predict incidence rates of infectious disease. The forecasting model is constructed under data from hepatitis B monthly incidence rate reports from 1991-2002. After learning and training on the basic concepts of the network, simulation experiments are completed, and then the incidence rates from Jan. 2003-Jun. 2003 forecasted by the established model. Through comparing with the actual incidence rate, the reliability of the model is evaluated. When comparing with ARIMA model, RBF network model seems to be more effective and feasible for predicting the incidence rates of infectious disease, observed in the short term.

[Study on the factors influencing the quality of life among patients with malignant lymphoma].

OBJECTIVE: To measure and assess the quality of life (QOL) and to explore the influencing factors on patients with malignant lymphoma. METHODS: QOL of 110 patients with malignant lymphoma were marked using EORTC QLQ-C30 short form, and multiple linear regression models were used to study the main factors influencing the QOL of patients with malignant lymphoma on five functional scales (physical, role, cognitive, emotional, and social) and the total scores. RESULTS: The influencing factors of quality of life on patients with malignant lymphoma appeared to be: history of relapse, refraining from smoking, older age, educational level, space for living, exercises, medical care system, and available health care programs. Relapse (beta = 5.997, P= 0.020) and refraining from smoking (beta = -6.526, P= 0.006) were associated with total QOL scores, educational level (beta = -2.144, P= 0.057), History of relapse (beta = 5.857, P = 0.003) was associated with total functional scales while exercises (beta= -0.771, P = 0.097) and refraining from smoking (beta= -4.106, P = 0.005) were with physical scales, refraining from smoking (beta = -4.644,P = 0.008) and older age (beta = 0.989, P= 0.029) were with role scales, relapse (beta = 14.035, P= 0.001) and older age (beta = 2.230, P= 0.023) were with cognitive scales, relapse (beta = 8.500, P= 0.031) and living space (beta = - 3.054, P= 0.0901) were with emotional scales and medical care system and available health care programs (beta = -6.577, P= 0.018) were with social scales respectively. CONCLUSION: Factors as prevention of relapse, correct cognition on malignant lymphoma, reasonable exercise, refrain from bad habits, improving medical care system could all increase the functions of malignant lymphoma patient, and to improve their quality of life.

[Study on the relations between serum insulin-like growth factor-1, insulin-like growth factor binding protein-3 and colorectal cancer: a meta-analysis].

OBJECTIVE: To evaluate the relationship between circulating levels of insulin-like growth factor-1 (IGF-1), IGF-binding protein-3 (IGFBP-3) and colorectal cancer. METHODS: A meta-analysis of 6 epidemiological studies on insulin-like growth factors and risk of colorectal cancer were performed. RESULTS: The pooled odds ratio (OR) of IGF-1 and IGFBP-3 were 1.56 (95% CI: 1.14-2.13) and 0.78 (95% CI: 0.43-1.44) respectively. According to the results from different measurements (enzyme-linked immunoabsorbent assay and immunoradiometric assay), the pooled OR were 1.92 and 1.23 for IGF-1, 0.46 and 1.44 for IGFBP-3 respectively. CONCLUSION: High serum levels of IGF-1 were independent risk factors of colorectal cancer but the OR of IGFBP-3 was not statistically significant. The heterogeneity between studies on IGFBP-3 and colorectal cancer was caused by different measurements used, but there was still a need to conduct simultaneous large size study under 2 different measurements for further conclusion.

[Study on the application of artificial neural network on diabetes mellitus/insulin-glucose tolerance classification].

OBJECTIVE: To discuss the potential application of artificial neural network (ANN) on the epidemiological classification of disease. METHODS: Learning vector quantification neural network (LVQNN) and discriminate analysis were applied to data from epidemiological survey in a mine in 1996. RESULTS: The structure of LVQNN was 25-->13-->3. The total veracity rates was 96.98%, and 92.45% among the abnormal blood glucose individuals. Through stepwise discriminate analysis, the discriminate equations were established including 11 variables with a total veracity rate of 87.34%, but was 85.53% in the abnormal blood glucose individuals. Further analysis on 30 cases with missing values showed that the disagreement ratio of LVQ was 1/30, lower than that of discriminate analysis of 7/30. CONCLUSIONS: Compared to the conventional statistics method, LVQ not only showed better prediction precision, but could treat data with missing values satisfactorily plus it had no limit to the type or distribution of relevant data, thus provided a new powerful method to epidemiologic prediction.