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Abstract. The neutrophil-to-lymphocyte ratio (NLR) is an important predictive prognostic tool. However, its accuracy for predicting sepsis remains debatable. This study aimed to evaluate the factors influencing the ability of NLR to predict sepsis. Adult patients with sepsis or septic shock from the eICU database were enrolled in this study. Results showed that there was a significant difference in NLR between the survival and nonsurvival groups (median [lower quartile–upper quartile], 15.27 [7.92–26.28] vs. 17.97 [8.94–31.85], P = 0.008). Area under the curve (AUC) of NLR on the sixth day was the highest. AUC of NLR was significantly higher in the Simplified Acute Physiological Score (SAPS) 3 ≤52 group than that in the SAPS3 > 52 group (0.78 ± 0.05 vs. 0.69 ± 0.03, P = 0.007). AUC of NLR in the nonshock group was significantly higher than that in the shock group (0.72 ± 0.04 vs. 0.65 ± 0.05, P < 0.01). The ability of NLR as a prognosis predictor of sepsis was influenced by admission time, SAPS3, and shock.

BackgroundSepsis-associated encephalopathy (SAE) is a critical and common problem in elderly patients with sepsis, which is still short of efficient predictive tools. Therefore, this study aims to screen the risk factors and establish a useful predictive nomogram for SAE in elderly patients with sepsis in the intensive care unit (ICU).Patients and methodsElderly patients (age ≥ 65 years) with sepsis were selected from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Data from demographics and laboratory examinations were collected on the first day of admission to the ICU. SAE was defined by two criteria in the presence of sepsis: ① a Glasgow Coma Scale (GCS) score of < 15 or ② delirium. Differences in demographics and laboratory tests were calculated between SAE and non-SAE groups. Participants were randomly divided into a training set and a validation set without replacement at a ratio of 6:4. A predictive nomogram was constructed in the training set by logistic regression analysis and then validated. The predictive capability of the nomogram was demonstrated by receiver operating characteristic (ROC) analysis and calibration curve analysis.ResultsA total of 22,361 patients were selected, of which 2,809 patients (12.7%) died in the hospital and 8,290 patients (37.1%) had SAE. In-hospital mortality in the SAE group was higher than that in the non-SAE group (18.8 vs. 8.9%, p < 0.001). Based on the results of logistic regression analysis, a nomogram integrating age, Na+, Sequential Organ Failure Assessment (SOFA) score, heart rate, and body temperature were constructed. The area under the curve (AUC) of the nomogram was 80.2% in the training set and 80.9% in the validation set. Calibration curve analysis showed a good predictive capacity of the nomogram.ConclusionSAE is an independent risk of in-hospital mortality in elderly patients in the intensive care unit. The nomogram has an excellent predictive capability of SAE and helps in clinical practice.

Climate change with increasing temperature is making a significant impact on human health, including more heat-related diseases, and increasing the burden on the healthcare system. Although many studies have explored the association between increasing temperatures and negative health outcomes, research on the associated costs of heat-related diseases remains relatively sparse. Furthermore, estimations of future costs associated with heat-attributable hospital healthcare have not been well explored. This study used a distributed lag nonlinear model to estimate heat-attributable hospital healthcare costs in Perth, Western Australia. Using 2006–2012 as the baseline, future costings for 2026–2032 and 2046–2052 were estimated under RCP2.6, RCP4.5, and RCP8.5. Higher temperatures were found to be associated with increased hospital healthcare costs. The total hospital costs attributable to heat over the baseline period 2006–2012 was estimated to be 79.5 million AUD, with costs for mental health hospitalizations being the largest contributor of the heat-related conditions examined. Costs are estimated to increase substantially to 125.8–129.1 million AUD in 2026–2032, and 174.1–190.3 million AUD by midcentury under climate change scenarios. Our findings of a notable burden of heat-attributable healthcare costs now and in the future emphasize the importance of climate change adaptation measures to reduce the adverse health effects of increasing temperatures and heat exposure on the people of Perth.

Global warming has contributed to more frequent and severe extreme weather events, which has led to increased research on the health impacts of extreme heat. However, research on heatwaves, air quality, and their spatial impact on health service demand is limited. This study used machine learning (ML) approaches to obtain the optimised model to predict health service demand associated with those risk factors for an all-age model and compared it with young children (0–4 years) model in Perth. Ten years’ data (2006–2015) on emergency department attendances (EDA), socioeconomic status (SES), heatwaves, landscape fires, and gaseous and particulate air pollutants were collected. ML approaches, including decision tree, random forest (RF), and geographical random forest (GRF) models, were used to compare and select the best model for predicting EDA and identify important risk factors. Five-hundred cross validations were performed using the testing data, and a construct validation was performed by comparing actual and predicted EDA data. The results showed that the RF model outperformed other models, and SES, air quality, and heatwaves were among the important risk factors to predict EDA. The GRF model was fitted well to the data (R ^2 = 0.975) and further showed that heatwaves had significant geographic variations and a joint effect with PM _2.5 in the southern suburbs of the study area for young children. The RF and GRF models have satisfactory performance in predicting the impact of heatwaves, air quality, and SES on EDA. Heatwaves and air quality have great spatial heterogeneity. Spatial interactions between heatwaves, SES, and air quality measures were the most important predictive risk factors of EDA for young children in the Perth southern suburbs. Future studies are warranted to confirm the findings from this study on a wider scale.

Purpose: To compare the effectiveness and safety of three methods of open necrosectomy, minimally invasive surgery and endoscopic step-up approach for necrotizing pancreatitis.Methods: We searched Pubmed, Embase, ScienceDirect, and CNKI full text database (CNKI) (to December 25, 2019). RCT, prospective cohort study (PCS), and retrospective cohort study (RCS) comparing the effectiveness and safety of any two of above-mentioned three methods were included.Results: There was no significant difference in major complications or death, and mortality between the minimally invasive surgery treatment group and the endoscopic step-up approach treatment group (RR = 1.66, 95%CI: 0.83–3.33, P = 0.15; RR = 1.05, 95%CI: 0.59–1.86, P = 0.87); the incidence rate of new-onset multiple organ failure, enterocutaneous fistula, pancreatic-cutaneous fistula, intra-abdominal bleeding, and endocrine pancreatic insufficiency in the endoscopic step-up approach treatment group was significantly lower than minimally invasive surgery group (RR = 2.65, 95%CI: 1.10–6.36, P = 0.03; RR = 6.63, 95%CI: 1.59–27.60, P = 0.009; RR = 7.73, 95%CI: 3.00–19.89, P < 0.0001; RR = 1.91, 95%CI: 1.13–3.24, P = 0.02; RR = 1.83, 95%CI: 1.9–3.16, P = 0.02); hospital stay in the endoscopic step-up approach group was significantly shorter than minimally invasive surgical treatment group (MD = 11.26, 95%CI: 5.46–17.05, P = 0.0001). The incidence of pancreatic-cutaneous fistula in the endoscopic escalation step therapy group was significantly lower than that in the open necrosectomy group (RR = 0.11, 95%CI: 0.02–0.58, P = 0.009).Conclusion: Compared with minimally invasive surgery and open necrosectomy, although endoscopic step-up approach cannot reduce the main complications or death and mortality of patients, it can significantly reduce the incidence of some serious complications, such as pancreatic-cutaneous fistula, enterocutaneous fistula, intra-abdominal bleeding, endocrine pancreatic insufficiency, and can significantly shorten the patient's hospital stay.

Background: In a remote region of Western Australia, Kimberley, residents have nearly twice the State average per capita consumption of alcohol, four and a half times the level of alcohol-related hospitalizations and nearly three times the level of alcohol-related deaths. This study aimed to evaluate the long term effects of alcohol sale restrictions on health service utilization in two remote towns in Kimberley.Methods: Sale of high strength packaged alcohol was restricted in Fitzroy Crossing and Halls Creek since October 2007 and May 2009, respectively. Alcohol-related Emergency Department (ED) attendances and hospitalizations utilized by local residents before and after the intervention between 2003 and 2013 was compared by using yearly rates (/1,000 person-years) and interrupted time series analysis with Autoregressive Integrated Moving Average (ARIMA) modeling. The Western Australia specific aetiological fractions (AAFs) were applied to hospital inpatient data for estimation of the proportion of hospital separations attributable to alcohol.Results: In Fitzroy Crossing, there was a significant reduction of over 40% on rates (/1,000 person-years) of alcohol-related acute hospitalizations (54.2 [95% CI: 53.8–54.7] vs. 31.7 [31.4–32.1]) and ED attendances (534.1[532.8–535.5] vs. 294.5 [293.5–295.4]). In Halls Creek, there was a significant reduction of over 50% on rates (/1,000 person-years) of alcohol- related acute hospitalizations (17.7 [17.6–17.8] vs. 8.0 [7.9–8.1]) and ED attendance (248.4 [247.9–248.9] vs. 111.1[110.8–111.5]). Domestic violence and injury related hospitalization rates were also reduced by over 20% in both towns.Conclusions: The total restriction of selling high strength alcohol through a community driven process has shown to be effective in reducing alcohol-related health service utilization in post-intervention period. Continue monitoring is required to address new emerging issues. Future research on health service utilization related to alcohol by using interrupted time series analysis incorporating ARIMA modeling and applying AAFs are recommended for evaluating alcohol-related interventions.

Increasing temperature and its impact on population health is an emerging significant public health issue in the context of climate change in Australia. While previous studies have primarily focused on risk assessment, very few studies have evaluated heat-attributable emergency department (ED) visits and associated healthcare costs, or projected future health and economic burdens. This study used a distributed lag non-linear model to estimate heat attributable ED visits and associated healthcare costs from 13 hospitals in Perth, Western Australia, and to project the future healthcare costs in 2030s and 2050s under three climate change scenarios—Representative Concentration Pathways (RCPs)2.6, RCP4.5 and RCP8.5. There were 3697 ED visits attributable to heat (temperatures above 20.5 °C) over the study period 2012–2019, accounting for 4.6% of the total ED visits. This resulted in AU$ 2.9 million in heat-attributable healthcare costs. The number of ED visits projected to occur in the 2030s and 2050s ranges from 5707 to 9421 under different climate change scenarios, which would equate to AU$ 4.6–7.6 million in heat associated healthcare costs. The heat attributable fraction for ED visits and associated healthcare costs would increase from 4.6% and 4.1% in 2010s to 5.0%–6.3% and 4.4%–5.6% in 2030s and 2050s, respectively. Future heat attributable ED visits and associated costs will increase in Perth due to climate change. Excess heat will generate a substantial population health challenge and economic burdens on the healthcare system if there is insufficient heat adaptation. It is vital to reduce greenhouse gas emissions, develop heat-related health interventions and optimize healthcare resources to mitigate the negative impact on the healthcare system and population health in the face of climate change.

Abstract Objective: To determine which measures of heatwave have the greatest predictive power for increases in health service utilisation in Perth, Western Australia. Methods: Three heatwave formulas were compared, using Poisson or zero‐inflated Poisson regression, against the number of presentations to emergency departments from all causes, and the number of inpatient admissions from heat‐related causes. The period from July 2006 to June 2013 was included. A series of standardised thresholds were calculated to allow comparison between formulas, in the absence of a gold standard definition of heatwaves. Results: Of the three heatwave formulas, Excess Heat Factor (EHF) produced the most clear dose‐response relationship with Emergency Department presentations. The EHF generally predicted periods that resulted in a similar or higher rate of health service utilisation, as compared to the two other formulas, for the thresholds examined. Conclusions: The EHF formula, which considers a period of acclimatisation as well as the maximum and minimum temperature, best predicted periods of greatest health service demand. The strength of the dose‐response relationship reinforces the validity of the measure as a predictor of hazardous heatwave intensity. Implications: The findings suggest that the EHF formula is well suited for use as a means of activating heatwave plans and identifies the required level of response to extreme heatwave events as well as moderate heatwave events that produce excess health service demand.

BACKGROUND: The molecular mechanism of sepsis-associated acute kidney injury (SA-AKI) is unclear. We analyzed co-differentially expressed genes (co-DEGs) to elucidate the underlying mechanism and intervention targets of SA-AKI. METHODS: The microarray datasets GSE65682, GSE30718, and GSE174220 were downloaded from the Gene Expression Omnibus (GEO) database. We identified the co-DEGs and constructed a gene co-expression network to screen the hub genes. We analyzed immune correlations and disease correlations and performed functional annotation of the hub genes. We also performed single-cell and microenvironment analyses and investigated the enrichment pathways and the main transcription factors. Finally, we conducted a correlation analysis to evaluate the role of the hub genes. RESULTS: Interleukin 32 (IL32) was identified as the hub gene in SA-AKI, and the main enriched signaling pathways were associated with hemopoiesis, cellular response to cytokine stimulus, inflammatory response, and regulation of kidney development. Additionally, IL32 was significantly associated with mortality in SA-AKI patients. Monocytes, macrophages, T cells, and NK cells were closely related to IL32 and were involved in the immune microenvironment in SA-AKI patients. IL32 expression increased significantly in the kidney of septic mouse. Toll-like receptor 2 (TLR2) was significantly and negatively correlated with IL32. CONCLUSION: IL32 is the key gene involved in SA-AKI and is significantly associated with prognosis. TLR2 and relevant immune cells are closely related to key genes. [ABSTRACT FROM AUTHOR]