Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule.

OBJECTIVE: Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect. METHODS: Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP). RESULTS: The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817). CONCLUSIONS: The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.

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.

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.

FDG PET/CT and Endoscopic Ultrasound for Preoperative T-Staging of Esophageal Squamous Cell Carcinoma.

This study aimed to compare the diagnostic performances of endoscopic ultrasound (EUS) and FDG PET/CT in the preoperative T-staging of esophageal squamous cell carcinoma (ESCC) and determine whether their innovative coordination achieves better prediction. In total, 100 patients diagnosed with ESCC, 57 without (CRT[-]sub) and 43 with (CRT[+]sub) neoadjuvant chemoradiotherapy, undergoing EUS and FDG PET/CT, followed by surgical resection of the tumor, were included in this analysis. EUS classified T-stages based on the depth of primary tumor invasion, and FDG PET/CT used thresholded maximal standardized uptake value (SUVmax) classifications. By employing pathology results as the reference standard, we assessed the accuracy of EUS and FDG PET/CT, evaluated their concordance using the κ statistic, and conducted a comparative analysis between the two modalities through McNemar's chi-square test. FDG PET/CT had higher overall accuracy than EUS (for CRT[-]sub: 71.9%, κ = 0.56 vs. 56.1%, κ = 0.31, p = 0.06; for CRT[+]sub: 65.1%, κ = 0.50 vs. 18.6%, κ = 0.05, p < 0.01) in predicting pT- and ypT-stage. Our proposed method of incorporating both FDG PET/CT and EUS information could achieve higher accuracies in differentiating between early and locally advanced disease in the CRT[-]sub group (82.5%) and determining residual viable tumor in the CRT[+]sub group (83.7%) than FDG PET/CT or EUS alone. FDG PET/CT had a better diagnostic ability than EUS to predict the (y)pT-stage of ESCC. Our complementary method, which combines the advantages of both imaging modalities, can deliver higher accuracy for clinical applications of ESCC.

Real-time artificial intelligence system for bacteremia prediction in adult febrile emergency department patients.

BACKGROUND: Artificial intelligence (AI) holds significant potential to be a valuable tool in healthcare. However, its application for predicting bacteremia among adult febrile patients in the emergency department (ED) remains unclear. Therefore, we conducted a study to provide clarity on this issue. METHODS: Adult febrile ED patients with blood cultures at Chi Mei Medical Center were divided into derivation (January 2017 to June 2019) and validation groups (July 2019 to December 2020). The derivation group was utilized to develop AI models using twenty-one feature variables and five algorithms to predict bacteremia. The performance of these models was compared with qSOFA score. The AI model with the highest area under the receiver operating characteristics curve (AUC) was chosen to implement the AI prediction system and tested on the validation group. RESULTS: The study included 5,647 febrile patients. In the derivation group, there were 3,369 patients with a mean age of 61.4 years, and 50.7% were female, including 508 (13.8%) with bacteremia. The model with the best AUC was built using the random forest algorithm (0.761), followed by logistic regression (0.755). All five models demonstrated better AUC than the qSOFA score (0.560). The random forest model was adopted to build a real-time AI prediction system integrated into the hospital information system, and the AUC achieved 0.709 in the validation group. CONCLUSION: The AI model shows promise to predict bacteremia in adult febrile ED patients; however, further external validation in different hospitals and populations is necessary to verify its effectiveness.

Efficacy and safety of intravenous tranexamic acid in urologic surgery: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: This meta-analysis compared the effects of intravenous Tranexamic acid (TXA) and a placebo on hemostasis, hospital course, and complications in adult patients undergoing various urologic surgeries. METHODS: The literature was extensively searched using various databases. The primary outcomes were standardized mean differences (SMDs) of intraoperative blood loss and odds ratios (ORs) of necessary blood product transfusion. The secondary outcomes included SMDs of operative time, SMDs of decreased hemoglobulin levels at 24 hours after surgery, and ORs of thromboembolic events. RESULTS: The meta-analysis included 13 randomized controlled trials (RCT) comprising 1814 participants in total. The SMD of intraoperative blood loss for TXA versus placebo was -0.705 (95% confidence interval [CI]: -1.113 to -0.297). The pooled ORs of transfusion in the TXA group compared with the placebo group was 0.426 (95% CI: 0.290-0.625). These findings indicated a significantly lower intraoperative blood loss and a reduced need for transfusion following intravenous TXA. The pooled ORs of thromboembolic events in the TXA group compared with the placebo group was 0.664 (95% CI: 0.146-3.024). CONCLUSIONS: Intravenous TXA can reduce intraoperative blood loss, decrease the need for transfusion, and shorten operative time, and it does not increase the risk of thromboembolic events.

Carpal tunnel syndrome in dentists compared to other populations: A nationwide population-based study in Taiwan.

BACKGROUND: Dentists may be at a higher risk of developing carpal tunnel syndrome (CTS) because of their use of frequent wrist and vibratory instruments at work; however, this issue remains unclear. Therefore, we conducted this study to clarify it. METHODS: Taiwan National Health Insurance Research Database was used for this nationwide population-based study. We identified 11,084 dentists, 74,901 non-dentist healthcare professionals (HCPs), and identical number of age- and sex-matched participants from the general population. Participants who had the diagnosis of CTS before 2007 were excluded. Between 2007 and 2011, the risk of developing CTS among dentists, non-dentist HCPs, and the general population was compared by following their medical histories. RESULTS: The cumulative incidence rate of CTS among dentists was 0.5% during the 5-year follow-up period. In dentists, the risk was higher in women (women: 0.7%; men: 0.4%) and older individuals (≥60 years: 1.0%; <60 years: 0.4%). After adjusting for age, sex, and underlying comorbidities, dentists had a lower risk of CTS than the general population (adjusted odds ratio [AOR]: 0.65, 95% confidence interval [CI]: 0.45-0.92). Dentists had a higher risk for CTS compared with non-dentist HCPs, although the difference was not statistically significant (AOR: 1.21; 95% CI: 0.90-1.64). CONCLUSIONS: In CTS, dentists had a lower risk than the general population and a trend of higher risk than non-dentist HCPs. The difference between dentists and non-dentist HCPs suggests that we should pay attention to dentists for potential occupational risk of this disease. However, further studies are warranted to better clarify it.

Pulmonary Fat Embolism Following Liposuction and Fat Grafting: A Review of Published Cases.

BACKGROUND: One of the most severe complications of liposuction and fat grafting is pulmonary fat embolism (PFE). However, most healthcare workers are not familiar with PFE. We performed a systematic review to describe the details of PFE. METHODS: PubMed, EMBASE, and Google Scholar were searched up to October 2022. Further analysis focused on clinical, diagnostic, and outcome parameters. RESULTS: A total of 40 patients from 19 countries were included. Chest computed tomography (CT) yielded 100% accuracy in the diagnosis of PFE. More than 90% of the deceased died within 5 days after surgery, and in 69% of patients, onset of symptoms occurred within 24 h after surgery. The proportions of patients who required mechanical ventilation, had a cardiac arrest event, or died among all patients and among those whose onset of symptoms occurred within 24 h after surgery were 76%, 38%, and 34% versus 86%, 56%, and 54%, respectively. CONCLUSIONS: The earlier the onset of symptoms was, the more severe the clinical course was. Once a patient presents with PFE-related symptoms, surgery should be halted, supportive care initiated, and chest CT used to diagnose PFE. According to our review results, if a patient with PFE survives the initial episode without permanent sequelae, a complete recovery can be anticipated.

AFP Response to Locoregional Therapy Can Stratify the Risk of Tumor Recurrence in HCC Patients after Living Donor Liver Transplantation.

(1) Background: Alpha-fetoprotein (AFP) has been incorporated into the selection criteria of liver transplantation and been used to predict the outcome of hepatocellular carcinoma (HCC) recurrence. Locoregional therapy (LRT) is recommended for bridging or downstaging in HCC patients listed for liver transplantation. The aim of this study was to evaluate the effect of the AFP response to LRT on the outcomes of hepatocellular carcinoma patients after living donor liver transplantation (LDLT). (2) Methods: This retrospective study included 370 HCC LDLT recipients with pretransplant LRT from 2000 to 2016. The patients were divided into four groups according to AFP response to LRT. (3) Results: The nonresponse group had the worst 5-year cumulative recurrence rates whereas the complete-response group (patients with abnormal AFP before LRT and with normal AFP after LRT) had the best 5-year cumulative recurrence rate among the four groups. The 5-year cumulative recurrence rate of the partial-response group (AFP response was over 15% lower) was comparable to the control group. (4) Conclusions: AFP response to LRT can be used to stratify the risk of HCC recurrence after LDLT. If a partial AFP response of over 15% declineis achieved, a comparable result to the control can be expected.

External validation of geriatric influenza death score: A multicenter study.

The Geriatric Influenza Death (GID) score was developed to help decision making in older patients with influenza in the emergency department (ED), but external validation is unavailable. Thus, we conducted a study was to fill the data gap. We recruited all older patients (≥65 years) who visited the ED of three hospitals between 2009 and 2018. Demographic data and clinical characteristics were retrospectively collected. Discrimination, goodness of fit, and performance of the GID score were evaluated. Of the 5,508 patients (121 died) with influenza, the mean age was 76.6±7.4 (standard deviation) years, and 49.3% were males. The GID score was higher in the mortality group (1.7±1.1 vs. 0.8±0.8, p <0.01). With 0 as the reference, the odds ratio for morality with score of 1, 2 and ≥3 was 3.08 (95% confidence interval [CI]: 1.66-5.71), 6.69 (95% CI: 3.52-12.71), and 23.68 (95% CI: 11.95-46.93), respectively. The area under the curve was 0.722 (95% CI: 0.677-0.766), and the Hosmer-Lemeshow goodness of fit test was 1.000. The GID score had excellent negative predictive values with different cut-offs. The GID score had good external validity, and further studies are warranted for wider application.

Yttrium-90 Radioembolization as the Major Treatment of Hepatocellular Carcinoma.

Background: The purpose of this study was to assess the safety and efficacy of Yttrium-90 radioembolization using in unresectable hepatocellular carcinoma. Methods: From 2017 to 2021, 32 patients with unresectable hepatocellular carcinoma, with mean tumor diameter about 7cm (21 males, 11 females; median age, 57.5 years of age), treated with Yttrium-90 radioembolization using resin microspheres were reviewed at pre-Yttrium-90 and post-Yttrium-90 follow-up. Tumor response was assessed according to the modified Response Evaluation Criteria in Solid Tumors. Outcomes including overall survival and progression-free survival were reported. Results: Median follow-up was 18 months. At follow-up examinations at 3-, 6-, and 12-months follow-up, the overall survival rates were 94%, 87% and 59%, and the progression-free survival rates were 78%, 64% and 60%, respectively. Complete response, partial response, stable disease, and progressive disease were noted in 7 (21.9%), 14 (43.7%), 4 (12.5%), and 7 (21.9%) patients, respectively. The disease control rate was 78.1%, the objective response rate was 65.6%, and the successful downstage rate was 34.4% (11 of 32). Nine of thirty-two patients underwent resection or transplantation after Yttrium-90 radioembolization with 2-year overall survival being 100%. No serious adverse events occurred after Yttrium-90 treatment. Worse overall survival was related to the larger tumor, higher stage, Eastern Cooperative Oncology Group performance status, and Child-Pugh score. And worse progression-free survival was related to the higher tumor burden, and pre-Yttrium-90 serum α-fetoprotein level >100. Conclusion: Yttrium-90 Radioembolization can control hepatocellular carcinoma well even in advanced diseases. Patients successfully downstaging/bridging to resection or transplantation have excellent overall survival.

Risks of Developing Diabetes and Hyperglycemic Crisis Following Carbon Monoxide Poisoning: A Study Incorporating Epidemiologic Analysis and Animal Experiment.

Purpose: Carbon monoxide (CO) poisoning may damage the pancreas, but the effects of CO poisoning on the development of diabetes and on existing diabetes remain unclear. We conducted a study incorporating data from epidemiologic analyses and animal experiments to clarify these issues. Methods: Using the National Health Insurance Database of Taiwan, we identified CO poisoning patients diagnosed between 2002 and 2016 (CO poisoning cohort) together with references without CO poisoning who were matched by age, sex, and index date at a 1:3 ratio. We followed participants until 2017 and compared the risks of diabetes and hyperglycemic crisis between two cohorts using Cox proportional hazards regressions. In addition, a rat model was used to assess glucose and insulin levels in blood as well as pathological changes in the pancreas and hypothalamus following CO poisoning. Results: Among participants without diabetes history, 29,141 in the CO poisoning cohort had a higher risk for developing diabetes than the 87,423 in the comparison cohort after adjusting for potential confounders (adjusted hazard ratio [AHR]=1.23; 95% confidence interval [CI]: 1.18-1.28). Among participants with diabetes history, 2302 in the CO poisoning cohort had a higher risk for developing hyperglycemic crisis than the 6906 in participants without CO poisoning (AHR = 2.12; 95% CI: 1.52-2.96). In the rat model, CO poisoning led to increased glucose and decreased insulin in blood and damages to pancreas and hypothalamus. Conclusion: Our epidemiological study revealed that CO poisoning increased the risks of diabetes and hyperglycemic crisis, which might be attributable to damages in the pancreas and hypothalamus as shown in the animal experiments.

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.

Comparison of the risk of gastrointestinal perforation between patients with and without rheumatoid arthritis: A nationwide cohort study in Asia.

Objectives: Patients with rheumatoid arthritis (RA) may have an increased risk for gastrointestinal perforation (GIP) caused by medications or chronic inflammation. However, the risk of GIP between patients with and without RA remains unclear. Therefore, we conducted this study to clarify it. Methods: Using the Taiwan National Health Insurance Research Database, we identified patients with and without RA matched at 1:1 ratio by age, sex, and index date between 2000 and 2013 for this study. Comparison of the risk of GIP between the two cohorts was performed by following up until 2014 using Cox proportional hazard regression analyses. Results: In total, 11,666 patients with RA and an identical number of patients without RA were identified for this study. The mean age (±standard deviation) and female ratio were 55.3 (±15.2) years and 67.6% in both cohorts. Patients with RA had a trend of increased risk for GIP than patients without RA after adjusting for underlying comorbidities, medications, and monthly income [adjusted hazard ratio (AHR) 1.42; 95% confidence interval (CI) 0.99-2.04, p = 0.055]. Stratified analyses showed that the increased risk was significant in the female population (AHR 2.06; 95% CI 1.24-3.42, p = 0.005). Older age, malignancy, chronic obstructive pulmonary disease, and alcohol abuse were independent predictors of GIP; however, NSAIDs, systemic steroids, and DMARDs were not. Conclusion: RA may increase the risk of GIP, particularly in female patients. More attention should be paid in female population and those with independent predictors above for prevention of GIP.

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.

Association between carbon monoxide poisoning and adrenal insufficiency: a nationwide cohort study.

Carbon monoxide poisoning may damage the brain and adrenal glands, but it is unclear whether it is associated with adrenal insufficiency. We identified all COP patients diagnosed between 1999 and 2012 in Taiwan using the Nationwide Poisoning Database and selected a reference cohort (participants without COP) from the same database by exact matching of age and index date at a 1:2 ratio. Participants with a history of adrenal insufficiency or steroid use of more than 14 days were excluded. We followed up participants until 2013 and compared the risk of developing adrenal insufficiency between the two cohorts. The 21,842 COP patients had a higher risk for adrenal insufficiency than the 43,684 reference participants (adjusted hazard ratio [AHR] = 2.5; 95% confidence interval [CI]: 1.8-3.5) after adjustment for sex and underlying comorbidities (liver disease, thyroid disease, mental disorder). The risk continued to elevate even after 1 year (AHR = 2.1; 95% CI: 1.4-3.4). The COP patients who had acute respiratory failure had an even higher risk for adrenal insufficiency than those without acute respiratory failure, which may indicate a dose-response relationship. Stratified analyses showed that female patients had an elevated risk (AHR = 3.5; 95% CI: 2.1-6.0), but not male patients. Younger patients (< 50 years) had higher risks, and the AHR reached statistical significance in the age groups 20-34 (AHR = 5.5; 95% CI: 1.5-20.6) and 35-49 (AHR = 4.9; 95% CI: 2.3-10.6) years old. The risk for developing adrenal insufficiency elevated after COP, especially in female and younger patients. Carbon monoxide is the most common gaseous agent causing acute intoxication worldwide. Results of the current study call for monitoring adrenal function of patients with COP.

Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients.

The emergency department (ED) is at the forefront of medical care, and the medical team needs to make outright judgments and treatment decisions under time constraints. Thus, knowing how to make personalized and precise predictions is a very challenging task. With the advancement of artificial intelligence (AI) technology, Chi Mei Medical Center (CMMC) adopted AI, the Internet of Things (IoT), and interaction technologies to establish diverse prognosis prediction models for eight diseases based on the ED electronic medical records of three branch hospitals. CMMC integrated these predictive models to form a digital AI dashboard, showing the risk status of all ED patients diagnosed with any of these eight diseases. This study first explored the methodology of CMMC's AI development and proposed a four-tier AI dashboard architecture for ED implementation. The AI dashboard's ease of use, usefulness, and acceptance was also strongly affirmed by the ED medical staff. The ED AI dashboard is an effective tool in the implementation of real-time risk monitoring of patients in the ED and could improve the quality of care as a part of best practice. Based on the results of this study, it is suggested that healthcare institutions thoughtfully consider tailoring their ED dashboard designs to adapt to their unique workflows and environments.

Association between osteoarthritis and urinary tract infection in older adults: A nationwide population-based cohort study.

Osteoarthritis (OA) may increase urinary tract infection (UTI) in older adults. However, this issue remains unclear. We identified 8599 older patients (≥65 years) with OA, and an equal number of older patients without OA, matched by age, sex, and index date from the Taiwan National Health Insurance Research Database between 2001 and 2005. Past histories, including UTI and underlying comorbidities, were included in the analyses. Comparisons for any UTI, ≥1 hospitalization for UTI, and ≥3 hospitalizations for UTI between the 2 cohorts by following up until 2015 were performed. In both cohorts, the percentages of age subgroups were 65-74 years (65.7%), 75-84 years (30.1%), and ≥85 years (4.2%). The male sex was 42.4%. Patients with OA had an increased risk of any UTI compared with those without OA after adjusting for all past histories (adjusted hazard ratio [AHR]: 1.72; 95% confidence interval [CI]: 1.64-1.80). Compared with patients without OA, patients with OA also had an increased risk of ≥1 hospitalization for UTI and ≥3 hospitalizations for UTI (AHR: 1.13; 95% CI: 1.06-1.19 and AHR: 1.25; 95% CI: 1.13-1.38, respectively). In addition to OA, age 75-84 years, female sex, history of UTI, benign prostatic hyperplasia, indwelling urinary catheter, cerebrovascular disease, dementia, and urolithiasis were independent predictors for any UTI. This study showed that OA was associated with UTI in older adults. We suggest appropriately managing OA and controlling underlying comorbidities to prevent subsequent UTI.

The Presence of Bacteremia Indicates Higher Inflammatory Response and Augments Disease Severity in Adult Patients with Urinary Tract Infections.

BACKGROUND: This study investigated the association between the presence of bacteremia and increase in the requirement for intensive care in adult patients with urinary tract infection (UTI). The study also analyzed the differences in clinical features between patients with versus without bacteremia. METHODS: We conducted a retrospective screening of the medical records of adult patients admitted during a 4-month period at a single medical center. We excluded patients with concomitant infections and patients whose urine and blood samples were not collected in the emergency department (ED). The included patients were allocated to two groups-bacteremia and nonbacteremia groups-according to the blood culture results for samples collected in the ED. RESULTS: The study cohort comprised 637 patients, including 158 (24.8%) patients in the bacteremia group and 479 (75.2%) patients in the nonbacteremia group. Compared with the patients in the nonbacteremia group, those in the bacteremia group satisfied more systemic inflammatory response syndrome (SIRS) criteria; they had a higher white cell count, C-reactive protein level, and sequential organ failure assessment (SOFA) scores; and had a greater requirement for intensive care (bacteremia vs. nonbacteremia; SIRS: 79.1% vs. 49.9%, p = 0.000; leukocytosis: 68.2% vs. 57.6%, p = 0.000; elevation of CRP: 96.2% vs. 78.6%, p = 0.000; SOFA: 39.2% vs. 23.2%, p = 0.000; requirement for intensive care: 13.9% vs. 4.4%, p = 0.000, respectively). According to the results of multivariate logistic regression, bacteremia and sepsis were independent factors associated with the requirement for intensive care. CONCLUSIONS: Bacteremia increased the requirement for intensive care in patients with UTI. Physicians can identify bacteremia using inflammatory markers, the SIRS criteria, and SOFA scores.

Comprehensive comparison between geriatric and nongeriatric patients with trauma.

ABSTRACT: The incidence of geriatric trauma is increasing due to the growing elderly population. Healthcare providers require a global perspective to differentiate critical factors that might alter patients' prognosis.We retrospectively reviewed all adult patients admitted to a trauma center during a 4-year period. We identified 655 adult trauma patients aged from 18 to 64 (nongeriatric group) and 273 trauma patients ≥65 years (geriatric group). Clinical data were collected and compared between the 2 groups.The geriatric group had a higher incidence of trauma and higher Injury Severity Scores than did the nongeriatric group. Fewer geriatric patients underwent surgical treatment (all patients: geriatric vs nongeriatric: 65.9% vs 70.7%; patients with severe trauma: geriatric vs nongeriatric: 27.6% vs 44.5%). Regarding prognosis, the geriatric group exhibited higher mortality rate and less need for long-term care (geriatric vs nongeriatric: mortality: 5.5% vs 1.8%; long-term care: 2.2% vs 5.0%).We observed that geriatric patients had higher trauma incidence and higher trauma mortality rate. Aging is a definite predictor of poor outcomes for trauma patients. Limited physiological reserves and preference for less aggressive treatment might be the main reasons for poor outcomes in elderly individuals.