Heatstroke: a multicenter study in Southwestern China.

Background: An increase in Heatstroke cases occurred in southwest China in 2022 due to factors like global warming, abnormal temperature rise, insufficient power supply, and other contributing factors. This resulted in a notable rise in Heatstroke patients experiencing varying degrees of organ dysfunction. This descriptive study aims to analyze the epidemiology and clinical outcomes of Heatstroke patients in the ICU, providing support for standardized diagnosis and treatment, ultimately enhancing the prognosis of Heatstroke. Methods: A retrospective, multicenter, descriptive analysis was conducted on Heatstroke patients admitted to ICUs across 83 hospitals in southwest China. Electronic medical records were utilized for data collection, encompassing various aspects such as epidemiological factors, onset symptoms, complications, laboratory data, concurrent infections, treatments, and patient outcomes. Results: The dataset primarily comprised classic heatstroke, with 477 males (55% of total). The patient population had a median age of 72 years (range: 63-80 years). The most common initial symptoms were fever, mental or behavioral abnormalities, and fainting. ICU treatment involved respiratory support, antibiotics, sedatives, and other interventions. Among the 700 ICU admissions, 213 patients had no infection, while 487 were diagnosed with infection, predominantly lower respiratory tract infection. Patients presenting with neurological symptoms initially (n = 715) exhibited higher ICU mortality risk compared to those without neurological symptoms (n = 104), with an odds ratio of 2.382 (95% CI 1.665, 4.870) (p = 0.017). Conclusion: In 2022, the majority of Heatstroke patients in southwest China experienced classical Heatstroke, with many acquiring infections upon admission to the ICU. Moreover, Heatstroke can result in diverse complications.

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.

Risk Factor Analysis and Nomogram for Predicting In-Hospital Mortality in ICU Patients with Heat Stroke: A National Multicenter Study.

Objective: The aim of this nationwide multicenter study was to ascertain the risk factors associated with in-hospital mortality in patients with heat stroke admitted to intensive care units (ICUs) and to develop a nomogram for prognostic prediction. Methods: A retrospective analysis was conducted on clinical data collected from ICU patients diagnosed with heat stroke across multiple centers nationwide. Univariate and multivariate logistic regression analyses were performed to identify significant risk factors for in-hospital mortality. Based on the results of the multivariate analysis, a nomogram was constructed to estimate the individualized probability of mortality. Internal validation of the nomogram was performed, and its performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Results: A total of 292 ICU patients with heat stroke were included in this study. Three risk factors, namely Cr (creatinine), AST (aspartate aminotransferase), and SBP (systolic blood pressure), were found to be significantly associated with in-hospital mortality. These risk factors were incorporated into the nomogram, which exhibited good discriminative ability (area under the ROC curve of the training and validation cohorts were 0.763 and 0.739, respectively) and calibration. Internal validation and decision curve analysis confirmed the stability and reliability of the nomogram. Conclusion: This nationwide multicenter study identified key risk factors for in-hospital mortality in ICU patients with heat stroke. The developed nomogram provides an individualized prediction of mortality risk and can serve as a valuable tool for clinicians in the assessment and management of ICU patients with heat stroke.

Albumin Corrected Anion Gap and the Risk of in-Hospital Mortality in Patients with Acute Pancreatitis: A Retrospective Cohort Study.

Purpose: To explore the prognostic value of albumin corrected anion gap (ACAG) within 24 hours of admission to the intensive care unit (ICU) for acute pancreatitis (AP). Patients and Methods: This was a retrospective cohort study. Adult AP patients admitted to ICU from June 2016 to December 2019 were included in the study, who were divided into three groups according to initial serum ACAG within 24 hours upon ICU admission: ACAG ≤ 14.87 mmol/L, 14.87 < ACAG ≤ 19.03 mmol/L, and ACAG > 19.03 mmol/L. The primary study outcome indicator was in-hospital mortality. Age, sex, Glasgow Coma Scale score, and Acute Physiology and Chronic Health Evaluation II (APACHE II) score were matched through propensity score matching (PSM) method to balance the baseline between the survivors and non-survivors. Multivariate Cox regression was used to determine the relationship between ACAG and in-hospital mortality. Results: A total of 344 patients (of them 81 non-survivors) were analyzed in this study. Patients with higher ACAG intended to present significantly higher in-hospital mortality, APACHE II score, creatine, lower albumin, and bicarbonate. Multivariate Cox regression analysis after matching demonstrated that white blood cell count, platelet count, and higher ACAG were independently associated with higher in-hospital mortality (ACAG ≤ 14.87 as a reference, 14.87 < ACAG ≤ 19.03 mmol/L with HR of 2.34 and 95% CI of 1.15-4.76, ACAG >19.03 with HR of 3.46 and 95% CI of 1.75-6.84). Conclusion: Higher ACAG was independently associated with higher in-hospital mortality in patients with AP after matching the baseline between the survivors and non-survivors.

CONUT score is associated with short-term prognosis in patients with severe acute pancreatitis: a propensity score matching cohort study.

Background: The Controlling Nutritional Status (CONUT) score was designed to assess the immune-nutritional status in patients. This study aimed to investigate the role of the CONUT score in the short-term prognosis of severe acute pancreatitis. Methods: This was a retrospective cohort study. 488 patients with severe acute pancreatitis at the Department of Critical Care Medicine of the West China Hospital of Sichuan University (Chengdu, China) were enrolled in the study. Baseline data were collected from the West China Hospital of Sichuan University database. The primary outcome during follow-up was all-cause mortality. The secondary outcomes were 28 day mortality, renal insufficiency, length of stay (LOS) in the ICU, and length of stay (LOS) in the hospital. Patients were divided into two groups based on a median CONUT score of 7, and baseline differences between the two groups were eliminated by propensity matching. Univariate Cox regression analyses were performed to estimate the association between CONUT score and outcomes. The Kaplan-Meier method was used to estimate the survival rate of patients. Results: CONUT score was an independent predictor of all-cause mortality (hazard ratio [HR]:2.093; 95%CI: 1.342-3.263; p < 0.001) and 28 day mortality (hazard ratio [HR]:1.813; 95%CI: 1.135-2.896; p < 0.013). CONUT score was not statistically significant in predicting the incidence of renal insufficiency. The high CONUT group had significantly higher all-cause mortality (p < 0.001), and 28 day mortality (p < 0.011) than the low CONUT group. Conclusion: The CONUT score is an independent predictor of short-term prognosis in patients with severe acute pancreatitis, and timely nutritional support is required to reduce mortality in patients with severe acute pancreatitis.

[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.