Implementation of a novel computer assisted telephone follow-up model for older patients after emergency department discharge in an Asian population.

BACKGROUND: While the impact of telephone follow-up (TFU) for older emergency department (ED) patients is controversial, its effects on the Asian population remain uncertain. In this study, we evaluated the effectiveness of a novel computer assisted TFU model specifically for this demographic. METHODS: At a Taiwanese tertiary medical center, we developed a TFU protocol that included a referral and case management system within the ED hospital information system. We provided TFU to older discharged patients between April 1, 2021, and May 31, 2021. We compared this cohort with a non-TFU cohort of older ED patients and analyzed demographic characteristics and post-ED discharge outcomes. RESULTS: The TFU model was successfully implemented, with 395 patients receiving TFU and 191 without TFU. TFU patients (median age: 76 years, male proportion: 48.9%) differed from non-TFU patients (median age: 74 years, male proportion: 43.5%). Compared with the non-TFU cohort, the multivariate logistic regression analysis revealed that the TFU cohort had a lower total medical expenditure < 1 month (adjusted odds ratio [AOR]: 0.32; 95% CI: 0.21 - 0.47 for amounts exceeding 5,000 New Taiwan Dollars), and higher satisfaction (AOR: 2.80; 95% CI: 1.46 - 5.36 for scores > 3 on a five-point Likert Scale). However, the TFU cohort also had a higher risk of hospitalization < 1 month (AOR: 2.50; 95% CI: 1.31 - 4.77) compared to the non-TFU cohort. CONCLUSION: Computer-assisted TFU appears promising. Further research involving a larger number of patients and validation in other hospitals is necessary to bolster the evidence and extend the findings to a broader context.

Hypothyroidism following carbon monoxide poisoning: An epidemiological study supported by animal experiments.

Previous studies have suggested a possible association between carbon monoxide poisoning (COP) and hypothyroidism, but the evidence is limited. Therefore, the aim of this study was to further investigate this relationship. Using data from the Taiwan National Health Research Database, we identified 32,162 COP patients and matched with 96,486 non-COP patients by age and index date for an epidemiological study. The risk of hypothyroidism was compared between the two cohorts until 2018. Independent predictors of hypothyroidism were analyzed using competing risk analysis. An animal study was also conducted to support the findings. COP patients had an increased risk of hypothyroidism compared to non-COP patients in the overall analysis (adjusted hazard ratio [AHR]= 3.88; 95 % confidence interval [CI]: 3.27-4.60) and in stratified analyses by age, sex, and comorbidities. The increase in the overall risk persisted even after more than six years of follow-up (AHR= 4.19; 95 % CI: 3.18-5.53). Independent predictors of hypothyroidism, in addition to COP, included age ≥65 years, female sex, hyperlipidemia, and mental disorder. The animal study showed damages in the hypothalamus, pituitary gland, and thyroid, as well as altered hormone levels 28 days after COP exposure. The epidemiological results showed an increased risk of hypothyroidism in COP patients, which was further supported by the animal study. These findings suggest the need for close monitoring of thyroid function in COP patients, especially in those who are age ≥65 years, female, and have hyperlipidemia or mental disorder.

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.

BACKGROUND: Hyperglycemic crises are associated with high morbidity and mortality. Previous studies have proposed methods to predict adverse outcomes of patients in hyperglycemic crises; however, artificial intelligence (AI) has never been used to predict adverse outcomes. We implemented an AI model integrated with the hospital information system (HIS) to clarify whether AI could predict adverse outcomes. METHODS: We included 2,666 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. The patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from the electronic medical records were collected. The performance of the multilayer perceptron (MLP), logistic regression, random forest, Light Gradient Boosting Machine (LightGBM), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms was compared. We selected the best algorithm to construct an AI model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. The outcomes between the non-AI and AI groups were compared after implementing the HIS and predicting the hyperglycemic crisis death (PHD) score. RESULTS: The MLP had the best performance in predicting the three adverse outcomes, compared with the random forest, logistic regression, SVM, KNN, and LightGBM models. The areas under the curves (AUCs) using the MLP model were 0.852 for sepsis or septic shock, 0.743 for ICU admission, and 0.796 for all-cause mortality. Furthermore, we integrated the AI predictive model with the HIS to assist decision making in real time. No significant differences in ICU admission or all-cause mortality were detected between the non-AI and AI groups. The AI model performed better than the PHD score for predicting all-cause mortality (AUC 0.796 vs. 0.693). CONCLUSIONS: A real-time AI predictive model is a promising method for predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies recruiting more patients are warranted.

Association between chronic pain and acute coronary syndrome in the older population: a nationwide population-based cohort study.

BACKGROUND: Chronic pain (CP) may increase the risk of acute coronary syndrome (ACS); however, this issue in the older population remains unclear. Therefore, this study was conducted to clarify it. METHODS: We used the Taiwan National Health Insurance Research Database to identify older patients with CP between 2001 and 2005 as the study cohort. Comparison cohort was the older patients without CP by matching age, sex, and index date at 1:1 ratio with the study cohort in the same period. We also included common underlying comorbidities in the analyses. The risk of ACS was compared between the two cohorts by following up until 2015. RESULTS: A total of 17241 older patients with CP and 17241 older patients without CP were included in this study. In both cohorts, the mean age (± standard deviation) and female percentage were 73.5 (± 5.7) years and 55.4%, respectively. Spinal disorders (31.9%) and osteoarthritis (27.0%) were the most common causes of CP. Older patients with CP had an increased risk for ACS compared to those without CP after adjusting for all underlying comorbidities (adjusted sub-distribution hazard ratio [sHR] 1.18; 95% confidence interval: 1.07-1.30). The increasement of risk of ACS was more when the follow-up period was longer (adjusted sHR of < 3 years: 1.8 vs. <2 years: 1.75 vs. <1 year: 1.55). CONCLUSIONS: CP was associated with an increased risk of ACS in the older population, and the association was more prominent when the follow-up period was longer. Early detection and intervention for CP are suggested in this population.

Associations between dietary patterns and stages of chronic kidney disease.

BACKGROUND: Studies have revealed that patients with chronic kidney disease (CKD) have dietary patterns different from those of the general population. However, no studies have compared the dietary patterns of between patients with early-stages (stages 1-3a) and late-stages (stages 3b-5) of CKD. Our objective was to investigate the associations between dietary patterns in early and late-stage CKD. METHODS: We analyzed 4480 participants with CKD at various stages based on the data recorded between 2007 and 2016 from the database of the American National Health and Nutrition Examination Survey. RESULTS: In total, 3683 and 797 participants had early and late-stage CKD, respectively. Through principal components analysis, the dietary intake dimension was reduced from 63 variables to 3 dietary patterns. We adopted logistic regression for analysis. The three dietary patterns are as follows: (1) saturated fatty acids and mono-unsaturated fatty acids (MUFA); (2) vitamins and minerals; and (3) cholesterols and polyunsaturated fatty acids (PUFA). These 3 patterns explained > 50% of dietary nutrient intake. Results indicated that among participants with dietary patterns 2 (vitamins and minerals) and 3 (cholesterols and PUFA), those with low intakes were more likely to have late-stage CKD. The odds ratios for patterns 2 and 3 were 1.74 (95% CI: 1.21-2.50) and 1.66 (95% CI: 1.13-2.43), respectively. CONCLUSIONS: This study revealed that intakes of vitamins and minerals and cholesterols and PUFA were associated with the stages of CKD.

Interdisciplinary collaboration and computer-assisted home healthcare referral in the emergency department: a retrospective cohort study.

AIM: Home healthcare (HHC) provides continuous care for disabled patients. However, HHC referral after the emergency department (ED) discharge remains unclear. Thus, this study aimed its clarification. METHODS: A computer-assisted HHC referral by interdisciplinary collaboration among emergency physicians, case managers, nurse practitioners, geriatricians, and HHC nurses was built in a tertiary medical center in Taiwan. Patients who had HHC referrals after ED discharge between February 1, 2020 and September 31, 2020, were recruited into the study. A non-ED HHC cohort who had HHC referrals after hospitalization from the ED was also identified. Comparison for clinical characteristics and uses of medical resources was performed between ED HHC and non-ED HHC cohorts. RESULTS: The model was successfully implemented. In total, 34 patients with ED HHC and 40 patients with non-ED HHC were recruited into the study. The female proportion was 61.8% and 67.5%, and the mean age was 81.5 and 83.7 years in ED HHC and non-ED HHC cohorts, respectively. No significant difference was found in sex, age, underlying comorbidities, and ED diagnoses between the two cohorts. The ED HHC cohort had a lower median total medical expenditure within 3 months (34,030.0 vs. 56,624.0 New Taiwan Dollars, p = 0.021) compared with the non-ED HHC cohort. Compared to the non-ED HHC cohort, the ED HHC cohort had a lower ≤ 1 month ED visit, ≤ 6 months ED visit, and ≤ 3 months hospitalization; however, differences were not significant. CONCLUSION: An innovative ED HHC model was successfully implemented. Further studies with more patients are warranted to investigate the impact.

Computerized tool and interdisciplinary care for older patients with delirium in the emergency department: a novel model in Taiwan.

AIMS: A computerized tool and interdisciplinary care were implemented to develop a novel model for older patients with delirium in the emergency department (ED). METHODS: We developed a computerized tool using a delirium triage screen and brief confusion assessment in the hospital information system, performed education for the healthcare providers, and developed a continuous care protocol. Comparisons for outcomes between pre- and post-intervention periods were performed. RESULTS: Compared with the pre-intervention period, patients in the post-intervention period had shorter hospitalization stay, lower expenditure of hospitalization, more likely to return home, lower ED revisits of ≤ 3 days, re-hospitalization of ≤ 14 days, and mortality of ≤ 1 month. All mentioned differences were not statistically significant. CONCLUSIONS: A novel model was successfully developed for delirium management in older patients in the ED. Outcome differences were not significant; however, the result is promising, which gives us an important reference in the future.

Prediction of Prognosis in Patients with Trauma by Using Machine Learning.

Background and Objectives: We developed a machine learning algorithm to analyze trauma-related data and predict the mortality and chronic care needs of patients with trauma. Materials and Methods: We recruited admitted patients with trauma during 2015 and 2016 and collected their clinical data. Then, we subjected this database to different machine learning techniques and chose the one with the highest accuracy by using cross-validation. The primary endpoint was mortality, and the secondary endpoint was requirement for chronic care. Results: Data of 5871 patients were collected. We then used the eXtreme Gradient Boosting (xGBT) machine learning model to create two algorithms: a complete model and a short-term model. The complete model exhibited an 86% recall for recovery, 30% for chronic care, 67% for mortality, and 80% for complications; the short-term model fitted for ED displayed an 89% recall for recovery, 25% for chronic care, and 41% for mortality. Conclusions: We developed a machine learning algorithm that displayed good recall for the healthy recovery group but unsatisfactory results for those requiring chronic care or having a risk of mortality. The prediction power of this algorithm may be improved by implementing features such as age group classification, severity selection, and score calibration of trauma-related variables.

Selective brain cooling achieves peripheral organs protection in hemorrhagic shock resuscitation via preserving the integrity of the brain-gut axis.

Background: Although whole-body cooling has been reported to improve the ischemic/reperfusion injury in hemorrhagic shock (HS) resuscitation, it is limited by its adverse reactions following therapeutic hypothermia. HS affects the experimental and clinical bowel disorders via activation of the brain-gut axis. It is unknown whether selective brain cooling achieves beneficial effects in HS resuscitation via preserving the integrity of the brain-gut axis. Methods: Male Sprague-Dawley rats were bled to hypovolemic HS and resuscitated with blood transfusion followed by retrograde jugular vein flush (RJVF) with 4 °C or 36 °C normal saline. The mean arterial blood pressure, cerebral blood flow, and brain and core temperature were measured. The integrity of intestinal tight junction proteins and permeability, blood pro-inflammatory cytokines, and multiple organs damage score were determined. Results: Following blood transfusion resuscitation, HS rats displayed gut barrier disruption, increased blood levels of pro-inflammatory cytokines, and peripheral vital organ injuries. Intrajugular-based infusion cooled the brain robustly with a minimal effect on body temperature. This brain cooling significantly reduced the HS resuscitation-induced gut disruption, systemic inflammation, and peripheral vital organ injuries in rats. Conclusion: Resuscitation with selective brain cooling achieves peripheral vital organs protection in hemorrhagic shock resuscitation via preserving the integrity of the brain-gut axis.

Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system.

BACKGROUND: Predicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML. METHODS: We recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for model training and testing. Using 10 clinical variables from their electronic health records, a prediction model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes. RESULTS: The best areas under the curves of predicting outcomes were: random forest model for hospitalization (0.840), pneumonia (0.765), and sepsis or septic shock (0.857), XGBoost for intensive care unit admission (0.902), and logistic regression for in-hospital mortality (0.889) in the testing data. The predictive model was further applied in the hospital information system to assist physicians' decisions in real time. CONCLUSIONS: ML is a promising way to assist physicians in predicting outcomes in older ED patients with influenza in real time. Evaluations of the effectiveness and impact are needed in the future.

Validation of shock index for predicting mortality in older patients with dengue fever.

BACKGROUND: Older adults have a higher mortality for dengue fever (DF). However, the best method for predicting mortality is still unclear. AIMS: We conducted this study to evaluate the shock index (SI) for this issue. METHODS: A retrospective case-control study was conducted by recruiting older patients (≥ 65 years old) with DF who visited the study hospital in southern Taiwan during the 2015 DF outbreak. Demographic data, vital signs, past histories, decision groups, complications, and mortality were included in the analyses. We evaluated the accuracy of SI ≥ 1 for predicting 30-day mortality in this population. RESULTS: A total of 626 patients with a mean age of 74.1 years and nearly equal sex distribution were recruited. The mean of SI (± standard deviation [SD]) was 0.6 (± 0.2) and patients with a SI ≥ 1 accounted for 3.5% of the total patients. Logistic regression showed that patients with SI ≥ 1 had a higher mortality than those with SI < 1 (odds ratio: 8.49; 95% confidence interval: 1.76-17.92). The area under the receiver-operating characteristic was 0.76, and the Hosmer-Lemeshow goodness of fit test was 0.48. The SI ≥ 1 had a sensitivity, specificity, positive predictive value, and negative predictive value of 14.8%, 97.0%, 18.2%, and 96.2% for predicting mortality. CONCLUSIONS: The SI ≥ 1 is an easy tool that can be potentially used to predict 30-day mortality in older DF patients, especially in DF outbreak. It has a high specificity and negative predictive value for excluding patients with high-risk mortality.

Risk of Dementia in Diabetic Patients with Hyperglycemic Crisis: A Nationwide Taiwanese Population-Based Cohort Study.

BACKGROUND: A hyperglycemic crisis episode (HCE) signifies poor control of diabetes and may increase the risk of dementia via microvascular and macrovascular injuries. OBJECTIVES: We conducted this study to clarify this issue, which remains unclear. METHODS: Using the Taiwan National Health Insurance Database, we identified 9,466 diabetic patients with HCE and the identical number of diabetic patients without HCE who were matched by age and sex for this nationwide population-based cohort study. The risk of dementia was compared between the 2 cohorts by following up until 2014. Investigation of independent predictors of dementia was also done. RESULTS: In the overall analysis, the risk of dementia between the 2 cohorts was not different. However, stratified analyses showed that patients with HCE had a higher risk of subsequent dementia in the age subgroup of 45-54 and 55-64 years (adjusted odds ratio [AOR]: 2.4, 95% confidence interval [CI]: 1.6-3.6, and AOR: 1.2, 95% CI: 1.0-1.5, respectively). In the overall analysis, older age, female sex, ≥3 HCEs, hypertension, hyperlipidemia, depression, cerebrovascular disease, Parkinson's disease, and head injury were independent predictors. CONCLUSIONS: HCE increased the risk of dementia in diabetic patients aged 45-64 years. Dementia was predicted by ≥3 HCEs. Prevention of recurrent HCE, control of comorbidities, and close follow-up of cognitive decline and dementia are suggested in patients with HCE.

Chronic Pain Increases the Risk for Major Adverse Cardiac and Cerebrovascular Events: A Nationwide Population-Based Study in Asia.

OBJECTIVE: Chronic pain (CP) may increase the risk for major adverse cardiac and cerebrovascular events (MACCEs); however, this issue is still unclear in the Asian population. We conducted this study to delineate it. DESIGN: From the Taiwan National Health Insurance Research Database, we identified 17,614 participants (<65 years) with CP and matched them by age and sex at a 1:2 ratio to participants without CP, who made up the comparison cohort. Several causes of CP and its underlying comorbidities were also analyzed. OUTCOME MEASURE: A comparison of MACCE occurring in the two cohorts was performed via follow-up until 2015. RESULTS: The mean age (SD) was 50.2 (11.5) years and 50.4 (11.7) years in participants with and without CP, respectively. In both cohorts, the percentage of female participants was 55.5%. Common causes of CP were spinal disorders (23.9%), osteoarthritis (12.4%), headaches (11.0%), gout (10.2%), malignancy (6.2%), and osteoporosis (4.5%). After adjusting for hypertension, diabetes, chronic obstructive pulmonary disease, renal diseases, hyperlipidemia, liver diseases, dementia, and depression, participants with CP had a higher risk for MACCE than those without CP (adjusted hazard ratio [AHR] = 1.3, 95% confidence interval [CI] = 1.3 - 1.4). After conducting subgroup analyses, an increased risk was also found for all-cause mortality (AHR = 1.4, 95% CI = 1.1 - 1.8), acute myocardial infarction (AHR = 1.2, 95% CI = 1.0 - 1.4), and stroke (AHR = 1.3, 95% CI = 1.3 - 1.4). CONCLUSIONS: CP is associated with increased occurrence of MACCE. Early detection and interventions for CP are suggested.

Quick-SOFA score ≥ 2 predicts prolonged hospital stay in geriatric patients with influenza infection.

PURPOSE: The quick Sepsis-Related Organ Failure Assessment (qSOFA) score was designed to predict mortality among sepsis patients. However, it has never been used to identify prolonged length of hospital stay (pLOS) in geriatric patients with influenza infection. We conducted this study to clarify this issue. METHODS: We conducted a retrospective case-control study, including geriatric patients (aged ≥ 65 years) with influenza infection visiting the emergency department (ED) of a medical center between January 01, 2010 and December 31, 2015. The included patients were divided into two groups on the basis of their qSOFA score: qSOFA < 2, and qSOFA ≥ 2. Data regarding demographics, vital signs, qSOFA score, underlying diseases, subtypes of influenza, and outcomes were included in the analysis. We investigated the association between qSOFA score ≥ 2 and pLOS (>9 days) via logistic regression. RESULTS: Four hundred and nine geriatric patients were included in this study with a mean age of 79.5 (standard deviation [SD], 8.3) years. The median length of stay (LOS) was 7.0 (interquartile range [IQR], 4-12) days, while the rate of pLOS (> 9 days) was 32%. The median LOS in the qSOFA ≥ 2 group, 11.0 (7-15) days, was longer than the qSOFA < 2 group, 6.0 (4-10) days (p-value <0.01). Logistic regression showed that qSOFA ≥ 2 predicts pLOS with an odds ratio of 3.78 (95% confidence interval, 2.04-6.97). CONCLUSION: qSOFA score ≥ 2 is a prompt and simple tool to predict pLOS in geriatric patients with influenza infection.

Association between exercise and health-related quality of life and medical resource use in elderly people with diabetes: a cross-sectional population-based study.

BACKGROUND: Exercise improves glycemic control and functional capacity in elderly people with diabetes; however, its effect on health-related quality of life (HRQoL) and medical resource use remains unclear. This study aims to clarify the effect of exercise. METHODS: Using the data from National Health and Nutrition Examination Survey between 2007 and 2016, we identified 1572 elderly people with diabetes for this cross-sectional population-based study. Demographic characteristics, health conditions, comorbidities, HRQoL, and medical resource were compared among four groups (no exercise, low-intensity exercise, moderate-intensity exercise, and high-intensity exercise). RESULTS: The mean age of all participants was between 71.5 and 73.3 years. Male participants with higher education performed more exercise than their counterparts. The moderate- and high-intensity groups reported better general health condition than the no exercise group. Depression and worse health were more common in the no exercise group. Participants in the moderate-intensity exercise group had lower risk for depression than those in the no exercise group (adjusted odds ratio: 0.13, 95% confidence interval: 0.02-0.92) after adjusting for demographic characteristics, health conditions, and comorbidities, whereas participants in the low- and high-intensity exercise did not have a lower risk. The no exercise group had the highest proportions of emergency, hospitalization, and total healthcare visits. CONCLUSIONS: Exercise is associated with better HRQoL, and lack of exercise is associated with higher medical resource use in elderly people with diabetes. Encouraging exercise is recommended in this population.

Comparison of the risk for dementia between physicians and the general population: a nationwide population-based cohort study.

BACKGROUND: Physicians have better medical knowledge, which may decrease the risk of dementia; however, this issue remains unclear. This study was performed to clarify it. METHODS: We conducted a nationwide population-based study that recruited 29,388 physicians, 50,000 participants from the general population, and 30,446 other healthcare professionals (HCPs; excluding physicians) for this study. The prevalence of dementia was compared among the three groups and physician subgroups by tracing their medical histories from 2006 to 2012. RESULTS: Physicians had a lower prevalence of dementia than the general population after adjusting for age, sex, head trauma, hypothyroidism, hypertension, diabetes mellitus, stroke, vascular disease, atrial fibrillation, hypercholesterolemia, depression, and alcoholism [adjusted odds ratio (AOR) 0.56; 95% confidence interval (CI) 0.47-0.67]. Other HCPs also had a lower prevalence for dementia than the general population (AOR 0.46; 95% CI 0.36-0.60). Compared with other HCPs, physicians had no difference in the prevalence for dementia (AOR 0.98 95% CI 0.71-1.36). Physicians who were older, specialized in pediatrics and worked at local hospitals and clinics had a higher prevalence for dementia than their counterparts did. CONCLUSIONS: Physicians had a lower prevalence for dementia than the general population. The prevalence for dementia in specific subgroups of physicians was higher, which needs to be clarified by further studies.

A Shape Approximation for Medical Imaging Data.

This study proposes a shape approximation approach to portray the regions of interest (ROI) from medical imaging data. An effective algorithm to achieve an optimal approximation is proposed based on the framework of Particle Swarm Optimization. The convergence of the proposed algorithm is derived under mild assumptions on the selected family of shape equations. The issue of detecting Parkinson's disease (PD) based on the Tc-99m TRODAT-1 brain SPECT/CT images of 634 subjects, with 305 female and an average age of 68.3 years old from Kaohsiung Chang Gung Memorial Hospital, Taiwan, is employed to demonstrate the proposed procedure by fitting optimal ellipse and cashew-shaped equations in the 2D and 3D spaces, respectively. According to the visual interpretation of 3 experienced board-certified nuclear medicine physicians, 256 subjects are determined to be abnormal, 77 subjects are potentially abnormal, 174 are normal, and 127 are nearly normal. The coefficients of the ellipse and cashew-shaped equations, together with some well-known features of PD existing in the literature, are employed to learn PD classifiers under various machine learning approaches. A repeated hold-out with 100 rounds of 5-fold cross-validation and stratified sampling scheme is adopted to investigate the classification performances of different machine learning methods and different sets of features. The empirical results reveal that our method obtains 0.88 ±0.04 classification accuracy, 0.87 ±0.06 sensitivity, and 0.88 ±0.08 specificity for test data when including the coefficients of the ellipse and cashew-shaped equations. Our findings indicate that more constructive and useful features can be extracted from proper mathematical representations of the 2D and 3D shapes for a specific ROI in medical imaging data, which shows their potential for improving the accuracy of automated PD identification.

Exposure Duration and History of Hypertension Predicted Neurological Sequelae in Patients with Carbon Monoxide Poisoning.

BACKGROUND: Carbon monoxide poisoning (COP) accounts for a large number of emergency department visits worldwide and is fatal in many cases. In surviving patients, neurological sequelae (NS) attributable to cerebral hypoxia are the most devastating outcome, but reliable predictors are limited. Therefore, we conducted a study to identify predictors of NS in patients with COP and evaluate their effects. METHODS: In this retrospective case-control study, we identified patients with COP in a medical center in Southern Taiwan between January 2005 and December 2014. Cases were patients with NS, and controls were patients without NS. We obtained information on potential predictors of NS from medical records and evaluated their association with NS, including demographic characteristics, exposure source, suicide attempts, duration of exposure (by tertile), histories, symptoms, signs, laboratory data, treatment, and the length of hospital stay. RESULTS: We included 371 patients with COP. Of them, 93 developed NS, and their mean ages (41.4 ± 14.7 years vs. 39.7 ± 14.2 years) and proportions of males (59.1% vs. 58.6%) were similar to those in the 298 controls. Multivariate logistic regression showed that a history of hypertension (adjusted odds ratio = 2.1; 95% confidence interval = 1.0, 4.5) and a longer duration of carbon monoxide exposure (adjusted odds ratio = 1.7; 95% confidence interval = 1.1, 2.8; the longest tertile [>5 hours] vs. the other two tertiles [≤5 hours]) were independent predictors for NS, but not the level of carboxyhemoglobin. CONCLUSIONS: This study identified two independent predictors for NS that may be useful for public healthcare workers and physicians in predicting outcomes and deciding on treatment strategies for COP patients.

Utilization of systemic inflammatory response syndrome criteria in predicting mortality among geriatric patients with influenza in the emergency department.

BACKGROUND: Systemic Inflammatory Response Syndrome (SIRS) criteria are often used to evaluate the risk of sepsis and to identify in-hospital mortality among patients with suspected infection. However, utilization of the SIRS criteria in mortality prediction among geriatric patients with influenza in the emergency department (ED) remains unclear. Therefore, we conducted a research to delineate this issue. METHODS: This is a retrospective case-control study including geriatric patients (age ≥ 65 years) with influenza, who presented to the ED of a medical center between January 1, 2010 and December 31, 2015. Vital signs, past history, subtype of influenza, demographic data, and outcomes were collected from all patients and analyzed. We calculated the accuracy for predicting 30-days mortality using the SIRS criteria. We also performed covariate adjustment of the area under the receiver operating characteristic curve (AUROC) via regression modeling. RESULTS: We recruited a total of 409 geriatric patients in the ED, with mean age 79.5 years and an equal sex ratio. The mean SIRS criteria score was 1.9 ± 1.1. The result of a Hosmer-Lemeshow goodness-of-fit test was 0.34 for SIRS criteria. SIRS criteria score ≥ 3 showed better mortality prediction, with odds ratio (OR) 3.37 (95% confidence interval (CI), 1.05-10.73); SIRS score ≥ 2 showed no statistical significance, with p = 0.85 (OR, 1.15; 95% CI, 0.28-4.69). SIRS score ≥ 3 had acceptable 30-days mortality discrimination, with AUROC 0.77 (95% CI, 0.68-0.87) after adjustment. SIRS score ≥ 3 also had a notable negative predictive value of 0.97 (95% CI, 0.94-0.99). CONCLUSION: The presence of a higher number of SIRS criteria (≥ 3) showed greater accuracy for predicting mortality among geriatric patients with influenza.

Shock index predicted mortality in geriatric patients with influenza in the emergency department.

BACKGROUND: The shock index is a rapid and simple tool used to predict mortality in patients with acute illnesses including sepsis, multiple trauma, and postpartum hemorrhage. However, its ability to predict mortality in geriatric patients with influenza in the emergency department (ED) remains unclear. This study was conducted to clarify this issue. METHODS: We conducted a retrospective case-control study, recruiting geriatric patients (≥ 65 years) with influenza visiting the ED of a medical center between January 01, 2010 and December 31, 2015. Demographic data, vital signs, shock index, past histories, subtypes of influenza, and outcomes were included for the analysis. We investigated the association between shock index ≥1 and 30-day mortality. RESULTS: In total, 409 geriatric ED patients with mean age of 79.5 years and nearly equal sex ratio were recruited. The mean shock index ±â€¯standard deviation was 0.7 ±â€¯0.22 and shock index ≥1 was accounted for in 7.1% of the total patients. Logistic regression showed that shock index ≥1 predicted mortality (odds ratio: 6.80; 95% confidence interval: 2.39-19.39). The area under the receiver operating characteristic was 0.62 and the result of the Hosmer-Lemeshow goodness-of-fit test was 0.23. The sensitivity, specificity, positive predictive value, and negative predictive value of a shock index ≥1 were 30.0%, 94.1%, 20.0%, and 96.4%. CONCLUSIONS: A shock index ≥1 has a high specificity, negative predictive value, and good reliability to predict 30-day mortality in geriatric ED patients with influenza.