Explainable Deep Learning Model for Predicting Serious Adverse Events in Hospitalized Geriatric Patients Within 72 Hours.

Background: The global aging population presents a significant challenge, with older adults experiencing declining physical and cognitive abilities and increased vulnerability to chronic diseases and adverse health outcomes. This study aims to develop an interpretable deep learning (DL) model to predict adverse events in geriatric patients within 72 hours of hospitalization. Methods: The study used retrospective data (2017-2020) from a major medical center in Taiwan. It included non-trauma geriatric patients who visited the emergency department and were admitted to the general ward. Data preprocessing involved collecting prognostic factors like vital signs, lab results, medical history, and clinical management. A deep feedforward neural network was developed, and performance was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC). Model interpretation utilized the Shapley Additive Explanation (SHAP) technique. Results: The analysis included 127,268 patients, with 2.6% experiencing imminent intensive care unit transfer, respiratory failure, or death during hospitalization. The DL model achieved AUCs of 0.86 and 0.84 in the validation and test sets, respectively, outperforming the Sequential Organ Failure Assessment (SOFA) score. Sensitivity and specificity values ranged from 0.79 to 0.81. The SHAP technique provided insights into feature importance and interactions. Conclusion: The developed DL model demonstrated high accuracy in predicting serious adverse events in geriatric patients within 72 hours of hospitalization. It outperformed the SOFA score and provided valuable insights into the model's decision-making process.

Association between organophosphate flame retardant exposure and lipid metabolism: data from the 2013-2014 National Health and Nutrition Examination Survey.

Organophosphate flame retardants (OPFRs) are emerging environmental pollutants that can be detected in water, dust, and biological organisms. Certain OPFRs can disrupt lipid metabolism in animal models and cell lines. However, the effects of OPFRs on human lipid metabolism remain unclear. We included 1,580 participants (≥20 years) from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) to explore the relationship between OPFR exposure and lipid metabolism biomarkers. After adjusting for confounding factors, results showed that one-unit increases in the log levels of diphenyl phosphate (DPhP) (regression coefficient = -5.755; S.E. = 2.289; p = 0.023) and log bis-(1-chloro-2-propyl) phosphate (BCPP) (regression coefficient = -4.637; S.E. = 2.019; p = 0.036) were negatively associated with the levels of total cholesterol (TC) in all participants. One-unit increases in the levels of DPhP (regression coefficient = -2.292; S.E. = 0.802; p = 0.012), log bis (1,3-dichloro-2-propyl) phosphate (BDCPP) (regression coefficient = -2.046; S.E. = 0.825; p = 0.026), and log bis-2-chloroethyl phosphate (BCEP) (regression coefficient = -2.604; S.E. = 0.704; p = 0.002) were negatively associated with the levels of high-density lipoprotein cholesterol (HDL-C). With increasing quartiles of urine BDCPP levels, the mean TC levels significantly decreased in all participants (p value for trend = 0.028), and quartile increases in the levels of DPhP (p value for trend = 0.01), BDCPP (p value for trend = 0.001), and BCEP (p value for trend<0.001) were negatively corelated with HDL-C, with approximately 5.9, 9.9, and 12.5% differences between the upper and lower quartiles. In conclusion, DPhP, BDCPP, and BCEP were negatively related to HDL-C concentration, whereas DPhP and BCPP levels were negatively associated with TC level. Thus, exposure to OPFRs may interfere with lipid metabolism.

RAPID-ED: A predictive model for risk assessment of patient's early in-hospital deterioration from emergency department.

Introduction: The objective of this multi-center retrospective cohort study was to devise a predictive tool known as RAPID-ED. This model identifies non-traumatic adult patients at significant risk for cardiac arrest within 48 hours post-admission from the emergency department. Methods: Data from 224,413 patients admitted through the emergency department (2016-2020) was analyzed, incorporating vital signs, lab tests, and administered therapies. A multivariable regression model was devised to anticipate early cardiac arrest. The efficacy of the RAPID-ED model was evaluated against traditional scoring systems like National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) and its predictive ability was gauged via the area under the receiver operating characteristic curve (AUC) in both hold-out validation set and external validation set. Results: RAPID-ED outperformed traditional models in predicting cardiac arrest with an AUC of 0.819 in the hold-out validation set and 0.807 in the external validation set. In this critical care update, RAPID-ED offers an innovative approach to assessing patient risk, aiding emergency physicians in post-discharge care decisions from the emergency department. High-risk score patients (≥13) may benefit from early ICU admission for intensive monitoring. Conclusion: As we progress with advancements in critical care, tools like RAPID-ED will prove instrumental in refining care strategies for critically ill patients, fostering an improved prognosis and potentially mitigating mortality rates.

Effect of acute alcohol consumption on blunt bowel mesenteric injury: a retrospective analysis.

BACKGROUND: The effect of alcohol consumption on trauma remains controversial. The effects of alcohol on hemorrhage and peritonitis after blunt abdominal trauma have rarely been discussed. This study aimed to explore the effects of acute alcohol intoxication on the clinical characteristics, injury patterns, and outcomes in a surgical blunt bowel mesenteric injury (BBMI) cohort. METHODS: A retrospective data analysis was performed using trauma cases of patients who had been tested for alcohol and had surgically proven BBMI from a Trauma Registry System from 2009 to 2021. Patients were grouped according to their positive blood alcohol concentration (BAC; >0.5% vs. no BAC; less than 0.5% no BAC) upon arrival at the emergency department (ED). The injury characteristics, physiological parameters, and outcomes with respect to post-injury complications and mortality were assessed. RESULTS: In total, 142 patients with surgical BBMI were included. Of these, 116 and 26 patients were assigned to the BAC-negative and BAC-positive groups, respectively. The overall injury severity, injury pattern, and age were comparable between the groups. The patients in the BAC-positive group had a significantly lower systolic blood pressure (99 mmHg vs. 119 mmHg; p = 0.046), worse shock index (0.96 vs. 0.82; p = 0.048), and lower percentage and number of packed red blood cells transfused (34.6% vs. 57.8%; p = 0.032 and 0 U vs. 2 U; p = 0.031) than those in the BAC-negative group. Additionally, although not statistically significant, patients in the BAC-positive group had lower leukocyte counts (9,700 cells/mm3 vs. 11,600 cells/mm3; p = 0.165 ) at the ED. However, significantly reduced percentages of leukocytes ≥ 12,000 cells/mm3 (26.9% vs. 48.3%; p = 0.048) and ≥ 12,000 or ≤ 4,000 cells/mm3 (26.9% vs. 50.9%; p = 0.027) were observed in the BAC-positive group at the ED. Furthermore, the 30-day mortality rate did not show statistically significant differences, and there was a higher incidence of bowel-related mortality in the BAC-positive group (11.5% vs. 1.7%, p = 0.043). CONCLUSIONS: For patients with BBMI arriving alive to the hospital, acute alcohol consumption was associated with significantly worse hemodynamic parameters, interfered inflammation status, and higher bowel related mortality rate.

Levels of organophosphate flame retardants and their metabolites among 391 volunteers in Taiwan: difference between adults and children.

Background: Organophosphate flame retardants (OPFRs) are ubiquitous in the environment. The compositions and concentrations of different OPFRs metabolites vary in different environments depending on different human activities. The objective of the present study was to evaluate the exposure of different age groups to OPFRs in Taiwan. Methods: Volunteers provided urine samples and responded to questionnaires including demographic factors, underlying disease, lifestyle information, and occupation from October 2021 to January 2022. OPFR measurements were performed using a Waters Acquity Ultra-Performance Liquid Chromatography system coupled with a Waters Xevo TQ-XS mass spectrometer. Results: A total of 391 volunteers (74 children and 317 adults) were enrolled in this study. The concentrations (presented as µg/g creatinine) of bis(1,3-dichloro-2-propyl) phosphate (BDCPP, p = 0.029) and tri-n-butyl phosphate (TNBP, p = 0.008) were higher in the adult group, while the concentrations of bis-2-chloroethyl phosphate (BCEP, p = 0.024), diphenyl phosphate (DPHP, p < 0.001), tris(1,3-dichloro-2-propyl) phosphate (TDCPP, p = 0.009), and Tris(2-butoxyethyl) phosphate (TBEP, p = 0.007) were higher in the child group. Compared with school age children (>6 years), the concentration of di(2-n-butoxyethyl) phthalate (DBEP, 1.14 vs. 0.20 µg/g creatinine, p = 0.001), DPHP (1.23 vs. 0.54 µg/g creatinine, p = 0.036), TBEP (1.63 vs. 0.29 µg/g creatinine, p < 0.001), and the sum of OPFR metabolites (ΣOPFRs, 6.58 vs. 2.04 µg/g creatinine, p < 0.001) were statistically higher in preschool-aged children. After adjusting for confounding factors, pre-school age [odds ratio (OR): 4.579, 95% confidence interval (CI): 1.389-13.115] and current smoker (OR: 5.328, 95%CI: 1.858-14.955) were independently associated with the risk of ΣOPFRs higher than 90 percentile. Conclusion: This study revealed the distribution of different OPFRs metabolites in children and adults. DBEP, DPHP, TBEP, and ΣOPFR were higher in preschool-aged children. Pre-school age and current smoking status were independent risk factors for ΣOPFRs higher than 90 percentile.

Urinary levels of organophosphate flame retardants metabolites in a young population from Southern Taiwan and potential health effects.

Background: Organophosphate flame retardants (OPFRs) are widely distributed in the environment and their metabolites are observed in urine, but little is known regarding OPFRs in a broad-spectrum young population from newborns to those aged 18 years. Objectives: Investigate urinary levels of OPFRs and OPFR metabolites in Taiwanese infants, young children, schoolchildren, and adolescents within the general population. Methods: Different age groups of subjects (n=136) were recruited from southern Taiwan to detect 10 OPFR metabolites in urine samples. Associations between urinary OPFRs and their corresponding metabolites and potential health status were also examined. Results: The mean level of urinary Σ10 OPFR in this broad-spectrum young population is 2.25 µg/L (standard deviation (SD) of 1.91 µg/L). Σ10 OPFR metabolites in urine are 3.25 ± 2.84, 3.06 ± 2.21, 1.75 ± 1.10, and 2.32 ± 2.29 µg/L in the age groups comprising of newborns, 1-5 year-olds, 6-10 year-olds, and 11-18 year-olds, respectively, and borderline significant differences were found in the different age groups (p=0.125). The OPFR metabolites of TCEP, BCEP, DPHP, TBEP, DBEP, and BDCPP predominate in urine and comprise more than 90% of the total. TBEP was highly correlated with DBEP in this population (r=0.845, p<0.001). The estimated daily intake (EDI) of Σ5OPFRs (TDCPP, TCEP, TBEP, TNBP, and TPHP) was 2,230, 461, 130, and 184 ng/kg bw/day for newborns, 1-5 yr children, 6-10 yr children, and 11-17 yr adolescents, respectively. The EDI of Σ5OPFRs for newborns was 4.83-17.2 times higher than the other age groups. Urinary OPFR metabolites are significantly correlated with birth length and chest circumference in newborns. Conclusion: To our knowledge, this is the first investigation of urinary OPFR metabolite levels in a broad-spectrum young population. There tended to be higher exposure rates in both newborns and pre-schoolers, though little is known about their exposure levels or factors leading to exposure in the young population. Further studies should clarify the exposure levels and factor relationships.

Association between Air Pollution and Short-Term Outcome of ST-Segment Elevation Myocardial Infarction in a Tropical City, Kaohsiung, Taiwan.

ST-segment elevation myocardial infarction (STEMI), one of the primary factors leading to global mortality, has been shown through epidemiological studies to have a relationship with short-term exposure to air pollutants; however, the association between air pollutants and the outcome of STEMI has not been well studied. The aim of this study was to estimate the impact of air pollutants on the outcomes of STEMI. Data on particulate matter <2.5 µm (PM2.5), <10 µm (PM10), nitrogen dioxide (NO2), and ozone (O3) at each of the 11 air monitoring stations in Kaohsiung City were collected between 1 January 2012 and 31 December 2017. Medical records of non-trauma patients aged > 20 years who had presented to the Emergency Department (ED) with a principal diagnosis of STEMI were extracted. The primary outcome measure was in-hospital mortality. After adjusting for potential confounders and meteorological variables, we found that an increase in the interquartile range (IQR) in NO2 was associated with an elevated risk of in-hospital mortality in patients with STEMI. Moreover, there was an observed higher risk of in-hospital mortality associated with an increase in the IQR of NO2 during the warm season, specifically in lag 3 (3 days prior to the onset, OR = 3.266; 95%CI: 1.203-8.864, p = 0.02). Conversely, an IQR increase in PM10 was associated with an increased risk of in-hospital mortality in patients with STEMI in lag 3 (OR = 2.792; 95%CI: 1.115-6.993, p = 0.028) during the cold season. Our study suggests that exposure to NO2 (during the warm season) and PM10 (during the cold season) may contribute to a higher risk of poor prognosis in patients with STEMI.

Use of Machine Learning to Differentiate Children With Kawasaki Disease From Other Febrile Children in a Pediatric Emergency Department.

Importance: Early awareness of Kawasaki disease (KD) helps physicians administer appropriate therapy to prevent acquired heart disease in children. However, diagnosing KD is challenging and relies largely on subjective diagnosis criteria. Objective: To develop a prediction model using machine learning with objective parameters to differentiate children with KD from other febrile children. Design, Setting, and Participants: This diagnostic study included 74 641 febrile children younger than 5 years who were recruited from 4 hospitals, including 2 medical centers and 2 regional hospitals, between January 1, 2010, and December 31, 2019. Statistical analysis was performed from October 2021 to February 2023. Main Outcomes and Measures: Demographic data and laboratory values from electronic medical records, including complete blood cell count with differential, urinalysis, and biochemistry, were collected as possible parameters. The primary outcome was whether the febrile children fulfilled the diagnostic criteria of KD. The supervised eXtreme Gradient Boosting (XGBoost) machine learning method was applied to establish a prediction model. The confusion matrix and likelihood ratio were used to evaluate the performance of the prediction model. Results: This study included a total of 1142 patients with KD (mean [SD] age, 1.1 [0.8] years; 687 male patients [60.2%]) and 73 499 febrile children (mean [SD] age, 1.6 [1.4] years; 41 465 male patients [56.4%]) comprising the control group. The KD group was predominantly male (odds ratio, 1.79; 95% CI, 1.55-2.06) with younger age (mean difference, -0.6 years [95% CI, -0.6 to -0.5 years]) compared with the control group. The prediction model's best performance in the testing set was able to achieve 92.5% sensitivity, 97.3% specificity, 34.5% positive predictive value, 99.9% negative predictive value, and a positive likelihood ratio of 34.0, which indicates outstanding performance. The area under the receiver operating characteristic curve of the prediction model was 0.980 (95% CI, 0.974-0.987). Conclusions and Relevance: This diagnostic study suggests that results of objective laboratory tests had the potential to be predictors of KD. Furthermore, these findings suggested that machine learning with XGBoost can help physicians differentiate children with KD from other febrile children in pediatric emergency departments with excellent sensitivity, specificity, and accuracy.

Use of a Deep-Learning Algorithm to Guide Novices in Performing Focused Assessment With Sonography in Trauma.

This quality improvement study compares the diagnostic quality and completion time between ultrasonography operators guided by artificial intelligence vs those without such assistance.

Impact of Pelvic Fracture on Patients with Blunt Bowel Mesenteric Injury: Is Immediate Laparotomy Warranted?

The management of blunt abdominopelvic trauma with combined hemoperitoneum and pelvic fractures is challenging for trauma surgeons. Although angioembolization can achieve hemostasis in most visceral organ injuries and pelvic fractures after blunt abdominal trauma, it cannot effectively control hemorrhage in patients with blunt bowel mesenteric injury (BBMI). This study aimed to determine the risk factors associated with hemodynamically unstable patients with BBMI and to test the hypothesis that pelvic fracture is an independent risk factor for patients with unstable BBMI and concomitant pelvic fracture to guide the therapeutic sequence for difficult-to-manage patients. This retrospective study reviewed the data of hospitalized patients with trauma between 2009 and 2021 and included 158 adult patients with surgically proven BBMI. The patients were divided on the basis of the presence of a shock episode before emergency laparotomy. The shock group included 44.3% of all patients in the study (n = 70). Clinical injury severity and prognosis for patients in the shock group were poorer than those for patients in the non-shock group, and more invasive treatments and transfusions were performed for patients in the shock group than for those in the non-shock group. Pelvic fractures were more frequently associated with the shock group than with the non-shock group (21.4% vs. 5.7%; p = 0.003). In multivariate analysis, the presence of intracerebral hemorrhage (odds ratio [OR] = 10.87, 95% confidence intervals [CIs]: 1.70-69.75) and rib fracture (OR = 5.94, 95% CIs = 1.06-33.45) was identified as an independent predictor of shock, whereas the effect of pelvic fracture did not achieve statistical significance (OR = 2.94, 95% CIs = 0.66-13.13) after adjusting for confounding factors. For patients with BBMI, outcomes need to be improved during early diagnosis, and treatments should be expeditiously performed on the basis of the rapid identification of unstable hemodynamic status. Our results support the recommendation of emergency laparotomy in unstable patients with concomitant pelvic fractures, followed by damage control TAE if needed.

Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach.

BACKGROUND: Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors. OBJECTIVE: This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data. METHODS: This retrospective cohort study used open source data from the Waveform Database Matched Subset of the Medical Information Mart From Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results and matched ECG data from the MIMIC-III database. A 1D convolutional neural network-based deep learning model was first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-the-art performance, it was used in an active transfer learning process to perform patient-adaptive heartbeat classification tasks. RESULTS: The results show that by acquiring data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly, from an average of 0.604 (SD 0.211) to 0.980 (SD 0.078), when compared with the generic model. Moreover, the area under the receiver operating characteristic curve level improved from 0.729 (SD 0.240) to 0.945 (SD 0.094). CONCLUSIONS: By using the deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection using ambulatory ECG monitors. These findings could potentially be extended to applications that continuously monitor one's ECGs for early alerts of hyperkalemia and help avoid unnecessary blood tests.

The associations between renal disease severity and exposure to organophosphate flame retardants in patients with chronic kidney disease.

Organophosphate flame retardants (OPFRs) are emerging and widespread environmental pollutants with potential health hazards, including nephrotoxicity. However, the exposure patterns and nephrotoxic potential of OPFRs are yet to be investigated in patients with chronic kidney disease (CKD). We conducted a cross-sectional study involving 166 patients with CKD stratified by estimated glomerular filtration rate (eGFR) and severity of proteinuria. The urinary concentrations of 10 OPFR compounds were measured to evaluate the exposure patterns. Clinical and urinary OPFR profiles were compared among subgroups to identify whether the OPFR compounds were independently correlated with eGFR and proteinuria. Additionally, lifestyle factors were compared among subgroups stratified by median concentrations of urinary OPFR compounds associated with renal disease severity. This study revealed universal exposure to OPFRs in the CKD population, with an overall urinary detection rate of 98.80 %. Furthermore, after adjusting for covariates, the urinary concentration of bis(2-chloroethyl) phosphate (BCEP) was identified as an independent predictor of lower eGFR (low vs high eGFR, odds ratio (OR) (95 % confidence interval (CI)), 1.761 (1.032-3.005) per log µg/g creatinine, p = 0.038), and the urinary concentration of bis(2-butoxyethyl) phosphate (BBOEP) was independently correlated with overt proteinuria in CKD patients (with vs without overt proteinuria, OR (95 % CI), 1.813 (1.065-3.086) per log µg/g creatinine, p = 0.028). Moreover, frequent seafood consumption was negatively correlated with urinary BCEP concentration (high vs low BCEP, OR (95 % CI), 0.455 (0.228-0.908), p = 0.025), and age was inversely associated with urinary BBOEP concentration (high vs low BBOEP, OR (95 % CI), 0.968 (0.937-0.999) per year, p = 0.048). In conclusion, our investigation highlights the extensive exposure to OPFRs and the independent association between renal disease severity and urinary BCEP/BBOEP concentrations in the CKD population, indicating the nephrotoxic potential of these pollutants.

Clinical Validation of the Shock Index, Modified Shock Index, Delta Shock Index, and Shock Index-C for Emergency Department ST-Segment Elevation Myocardial Infarction.

Background: ST-segment elevation myocardial infarction (STEMI) is a leading cause of death worldwide. A shock index (SI), modified SI (MSI), delta-SI, and shock index-C (SIC) are known predictors of STEMI. This retrospective cohort study was designed to compare the predictive value of the SI, MSI, delta-SI, and SIC with thrombolysis in myocardial infarction (TIMI) risk scales. Method: Patients > 20 years old with STEMI who underwent percutaneous coronary intervention (PCI) were included. Receiver operating characteristic (ROC) curve analysis with the Youden index was performed to calculate the optimal cutoff values for these predictors. Results: Overall, 1552 adult STEMI cases were analyzed. The thresholds for the emergency department (ED) SI, MSI, SIC, and TIMI risk scales for in-hospital mortality were 0.75, 0.97, 21.00, and 5.5, respectively. Accordingly, ED SIC had better predictive power than the ED SI and ED MSI. The predictive power was relatively higher than TIMI risk scales, but the difference did not achieve statistical significance. After adjusting for confounding factors, the ED SI > 0.75, MSI > 0.97, SIC > 21.0, and TIMI risk scales > 5.5 were statistically and significantly associated with in-hospital mortality of STEMI. Compared with the ED SI and MSI, SIC (>21.0) had better sensitivity (67.2%, 95% CI, 58.6−75.9%), specificity (83.5%, 95% CI, 81.6−85.4%), PPV (24.8%, 95% CI, 20.2−29.6%), and NPV (96.9%, 95% CI, 96.0−97.9%) for in-hospital mortality of STEMI. Conclusions: SIC had better discrimination ability than the SI, MSI, and delta-SI. Compared with the TIMI risk scales, the ACU value of SIC was still higher. Therefore, SIC might be a convenient and rapid tool for predicting the outcome of STEMI.

Ambient Air Pollution and Risk for Stroke Hospitalization: Impact on Susceptible Groups.

Stroke is a leading cause of death, and air pollution is associated with stroke hospitalization. However, the susceptibility factors are unclear. Retrospective studies from 2014 to 2018 in Kaohsiung, Taiwan, were analyzed. Adult patients (>17 years) admitted to a medical center with stroke diagnosis were enrolled and patient characteristics and comorbidities were recorded. Air pollutant measurements, including those of particulate matter (PM) with aerodynamic diameters < 10 µm (PM10) and < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3), were collected from air quality monitoring stations. During the study period, interquartile range (IQR) increments in PM2.5 on lag3 and lag4 were 12.3% (95% CI, 1.1−24.7%) and 11.5% (95% CI, 0.3−23.9%) concerning the risk of stroke hospitalization, respectively. Subgroup analysis revealed that the risk of stroke hospitalization after exposure to PM2.5 was greater for those with advanced age (≥80 years, interaction p = 0.045) and hypertension (interaction p = 0.034), after adjusting for temperature and humidity. A dose-dependent effect of PM2.5 on stroke hospitalization was evident. This is one of few studies focusing on the health effects of PM2.5 for patients with risk factors of stroke. We found that patients with risk factors, such as advanced age and hypertension, are more susceptible to PM2.5 impacts on stroke hospitalization.

Kidney damage induced by repeated fine particulate matter exposure: Effects of different components.

BACKGROUND: Exposure to fine particulate matter with an aerodynamic diameter of ≤2.5 µm (PM2.5) is associated with adverse health effects. This study aimed to evaluate the toxic effects of the constituents of PM2.5 on mouse kidneys. METHODS: We collected PM2.5 near an industrial complex located in southern Kaohsiung, Taiwan, that was divided into water extract and insoluble particles. Male C57BL/6 mice were divided into five groups: control, low- and high-dose insoluble particle exposure, and low- and high-dose water extract exposure. Biochemical analysis, Western blot analysis, histological examination, and immunohistochemistry were performed to evaluate the impact of PM2.5 constituents on mice kidneys. RESULTS: PM2.5 was collected from January 1, 2021, to February 8, 2021, from an industrial complex in Kaohsiung, Taiwan. Metallic element analysis showed that Pb, Ni, V, and Ti were non-essential metals with enrichment factors >10. Polycyclic aromatic hydrocarbon and nitrate polycyclic aromatic hydrocarbon analyses revealed that the toxic equivalents are, in the order, benzo(a)pyrene (BaP), indeno(1,2,3-cd) pyrene (IP), dibenzo(a,h)anthracene (DBA), and benzo(b)fluoranthene (BbF), which are potential carcinogens. Both water extract and insoluble particle exposure induced inflammatory cytokine upregulation, inflammatory cell infiltration, antioxidant activity downregulation, and elevation of kidney injury molecule 1 (KIM-1) level in mouse kidneys. A dose-dependent effect of PM2.5 water extract and insoluble particle exposure on angiotensin converter enzyme 2 downregulation in mouse kidneys was observed. CONCLUSION: We found that water-soluble extract and insoluble particles of PM2.5 could induce oxidative stress and inflammatory reactions, influence the regulation of renin-angiotensin system (RAS), and lead to kidney injury marker level elevation in mouse kidneys. The lowest-observed-adverse-effect level for renal toxicity in mice was 40 µg water-soluble extract/insoluble particle inhalation per week, which was approximately equal to the ambient PM2.5 concentration of 44 µg/m3 for mice.

Exposure to tris(2-butoxyethyl) phosphate induces abnormal sperm morphology and testicular histopathology in male rats.

Tris(2-butoxyethyl) phosphate (TBEP) is one of the most abundant organophosphate flame retardants in the environment. This study aimed to evaluate the effect of TBEP exposure during adolescence on male reproductive function in adult rats. Male Sprague-Dawley rats were treated with 20 and 200 mg/kg body weight of TBEP or corn oil from postnatal day (PND) 42 to PND 105. A significant increase in the proportion of sperm with abnormal morphology (flattened head and bent tail) and superoxide anion (O2-.) production in the sperm of the 200 mg/kg treated group was observed (p < 0.05). Excessive production of sperm hydrogen peroxide (H2O2) was found in both the 20 and 200 mg/kg treatment groups (p < 0.05). Disruption of testicular structure was observed in the 20 and 200 mg/kg treated groups and seminiferous tubule degeneration was observed in the 200 mg/kg treated group. Our study demonstrated the adverse effects of TBEP on male reproductive function in rats.

Experience of carbon monoxide poisoning and the outcome predicting score: A multicenter retrospective study.

BACKGROUND: Carbon monoxide poisoning (COP), resulting from accidental and intentional exposure, is a leading cause of fatal poisoning worldwide. Except for early death, neurological sequelae are common and impose a large burden on patients, caregivers, and the society. MATERIALS AND METHODS: This retrospective study included patients who visited the emergency departments (EDs) of the medical institutes of Chang Gung Memorial Hospital after COP with a carboxyhemoglobin level > 10% between January 2009 and October 2018. Patients who experienced out-of-hospital cardiac arrest (OHCA) were excluded. Poor outcome was defined as mortality or a Glasgow coma scale (GCS) <13 at discharge. Stepwise regression analysis was performed, and a receiver operating characteristic (ROC) curve was applied to analyze our newly created scoring system for prognosis prediction. RESULTS: This study enrolled 1171 patients. Fire scene (F) (aOR, 20.635; 95% CI, 8.345-51.023), intentional CO exposure (I) (aOR, 2.634; 95% CI, 1.335-5.196), respiratory failure (R) (aOR, 9.944; 95% CI, 5.533-17.873), every point of reduced GCS (E) (aOR, 1.253; 95% CI, 1.186-1.323), and diabetes mellitus (D) (aOR, 2.749; 95% CI, 1.201-6.292) were identified as predictors of poor outcomes. The FIRED score was created. CONCLUSION: The FIRED score could predict the outcomes of non-OHCA patients with a carboxyhemoglobin level > 10% after COP using five factors that can be obtained by history taking and basic examination. An FIRED score ≥ 10 was associated with a poor outcome (sensitivity, 89.6%; specificity, 82.4%; AUC0.930).

Association between Ambient Air Pollution and Emergency Room Visits for Pediatric Respiratory Diseases: The Impact of COVID-19 Pandemic.

The level and composition of air pollution have changed during the coronavirus disease 2019 (COVID-19) pandemic. However, the association between air pollution and pediatric respiratory disease emergency department (ED) visits during the COVID-19 pandemic remains unclear. The study was retrospectively conducted between 2017 and 2020 in Kaohsiung, Taiwan, from 1 January 2020 to 1 May 2020, defined as the period of the COVID-19 pandemic, and 1 January 2017 to 31 May 2019, defined as the pre-COVID-19 pandemic period. We enrolled patients under 17 years old who visited the ED in a medical center and were diagnosed with respiratory diseases such as pneumonia, asthma, bronchitis, and acute pharyngitis. Measurements of particulate matter (PM) with aerodynamic diameters of <10 µm (PM10) and < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and Ozone (O3) were collected. During the COVID-19 pandemic, an increase in the interquartile range of PM2.5, PM10, and NO2 levels was associated with increases of 72.5% (95% confidence interval [CI], 50.5−97.7%), 98.0% (95% CI, 70.7−129.6%), and 54.7% (95% CI, 38.7−72.6%), respectively, in the risk of pediatric respiratory disease ED visits on lag 1, which were greater than those in the pre-COVID-19 pandemic period. After adjusting for temperature and humidity, the risk of pediatric respiratory diseases after exposure to PM2.5 (inter p = 0.001) and PM10 (inter p < 0.001) was higher during the COVID-19 pandemic. PM2.5, PM10, and NO2 may play important roles in pediatric respiratory events in Kaohsiung, Taiwan. Compared with the pre-COVID-19 pandemic period, the levels of PM2.5 and PM10 were lower; however, the levels were related to a greater increase in ED during the COVID-19 pandemic.

Clinical Validation of Cardiac Arrest Hospital Prognosis (CAHP) Score and MIRACLE2 Score to Predict Neurologic Outcomes after Out-of-Hospital Cardiac Arrest.

Background. Out-of-hospital cardiac arrest (OHCA) remains a challenge for emergency physicians, given the poor prognosis. In 2020, MIRACLE2, a new and easier to apply score, was established to predict the neurological outcome of OHCA. Objective. The aim of this study is to compare the discrimination of MIRACLE2 score with cardiac arrest hospital prognosis (CAHP) score for OHCA neurologic outcomes. Methods. This retrospective cohort study was conducted between January 2015 and December 2019. Adult patients (>17 years) with cardiac arrest who were brought to the hospital by an emergency medical service crew were included. Deaths due to trauma, burn, drowning, resuscitation not initiated due to pre-ordered "do not resuscitate" orders, and patients who did not achieve return of spontaneous circulation were excluded. Receiver operating characteristic curve analysis with Youden Index was performed to calculate optimal cut-off values for both scores. Results. Overall, 200 adult OHCA cases were analyzed. The threshold of the MIRACLE2 score for favorable neurologic outcomes was 5.5, with an area under the curve (AUC) value of 0.70 (0.61−0.80, p < 0.001); the threshold of the CAHP score was 223.4, with an AUC of 0.77 (0.68−0.86, p < 0.001). On setting the MIRACLE2 score cut-off value, we documented 64.7% sensitivity (95% confidence interval [CI], 56.9−71.9%), 66.7.0% specificity (95% CI, 48.2−82.0%), 90.8% positive predictive value (PPV; 95% CI, 85.6−94.2%), and 27.2% negative predictive value (NPV; 95% CI, 21.4−33.9%). On establishing a CAHP cut-off value, we observed 68.2% sensitivity (95% CI, 60.2−75.5%), 80.6% specificity (95% CI, 62.5−92.6%), 94.6% PPV (95% CI, 88.6%−98.0%), and 33.8% NPV (95% CI, 23.2−45.7%) for unfavorable neurologic outcomes. Conclusions. The CAHP score demonstrated better discrimination than the MIRACLE2 score, affording superior sensitivity, specificity, PPV, and NPV; however, the CAHP score remains relatively difficult to apply. Further studies are warranted to establish scores with better discrimination and ease of application.

An IoT-Based Smart System with an MQTT Broker for Individual Patient Vital Sign Monitoring in Potential Emergency or Prehospital Applications.

Emergency care is a critical area of medicine whose outcomes are influenced by the time, availability, and accuracy of contextual information. The success of critical or emergency care is determined by the quality and accuracy of the information received during the emergency call and the data collected during emergency transportation. The Internet of Things (IoT) consists of many smart devices and components that communicate via their connection to the Internet, which is used to collect data with sensors that obtain personal health parameters. In the past, most health measurement systems were based on a single dedicated orientation, and few systems had multiple devices on the same platform. In addition to traditional health measurement technologies, most such systems use centralized data transmission, which means that health measurement data have become the exclusive intellectual asset of the system developer. Therefore, this study develops an IoT-based message-broker system that is deployed and demonstrated for five health devices: blood oxygen, blood pressure, forehead temperature, body temperature, and body weight sensors. A central controller accessed by radio-frequency identification (RFID) collects clients' health profiles on the cloud platform. All collected data can be quickly shared, analyzed, and visualized, and the health devices can be changed, added to, and removed reliably when the requirements change. Additionally, following the message queuing telemetry transport (MQTT) protocol, all devices can communicate with each other and be integrated into a higher-level health measurement standard (such as blood pressure plus weight or body temperature plus blood oxygen). We implement a smart healthcare monitoring system (SHMS) and verify its reliability. We use MQTT to establish an open communication format that other organizations can follow to perform individual patient vital sign monitoring in potential applications. The robustness and flexibility of this research can be verified through the addition of other systems. Through this structure, more large-scale health detection devices can be integrated into the method proposed in this research in the future. Personal RFID or health insurance cards can be used for personal services or in medical institutions, and the data can easily be shared through the mechanism of this research. Such information sharing will enable the utilization of medical resources to be maximized.