Frailty and Other Factors Associated With Early Outcomes in Middle-to Older Age Trauma Patients: A Prospective Cohort Study.

OBJECTIVES: To prospectively investigate associations of frailty and other predictor variables with functional recovery and health outcomes in middle-aged and older patients with trauma. DESIGN: Single-center prospective cohort study. SETTING: Emergency department of Wan Fang Hospital in Taiwan. PARTICIPANTS: Trauma patients aged 45 and older. MEASUREMENTS: Frailty was assessed with the Clinical Frailty Scale (CFS). Injury mechanisms, pre-existing diseases, and fracture locations were recorded at baseline. The primary outcome was functional recovery assessed using the Barthel Index (BI). Secondary outcomes were new care needs, unscheduled return visits, and falls 3 months postinjury. RESULTS: A total of 588 participants were included in the final analysis. For every one-point increase in the CFS, the multivariable-adjusted odds ratio (OR, 95% confidence interval [CI]) of failure to retain the preinjury BI was 1.34 (1.16-1.55); associations were consistent across levels of age and injury severities. Significant joint associations of frailty and age with poor functional recovery were observed. CFS was also associated with new care needs (OR for every one-point increase, 1.36, 95% CI, 1.17-1.58), unscheduled return visits (OR 1.26, 95% CI, 1.04-1.51), and falls (OR 1.23, 95% CI, 1.01-1.51). Other variables associated with failure to retain preinjury BI included road traffic accident and presence of hip fracture. CONCLUSION: Frailty was significantly associated with poor functional and health outcomes regardless of injury severity in middle-aged and older patients with trauma. Injury mechanisms and fracture locations were also significant predictors of functional recovery postinjury.

Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling.

Introduction: Age-specific risk factors may delay posttraumatic functional recovery; complex interactions exist between these factors. In this study, we investigated the prediction ability of machine learning models for posttraumatic (6 months) functional recovery in middle-aged and older patients on the basis of their preexisting health conditions. Methods: Data obtained from injured patients aged ≥45 years were divided into training-validation (n = 368) and test (n = 159) data sets. The input features were the sociodemographic characteristics and baseline health conditions of the patients. The output feature was functional status 6 months after injury; this was assessed using the Barthel Index (BI). On the basis of their BI scores, the patients were categorized into functionally independent (BI >60) and functionally dependent (BI ≤60) groups. The permutation feature importance method was used for feature selection. Six algorithms were validated through cross-validation with hyperparameter optimization. The algorithms exhibiting satisfactory performance were subjected to bagging to construct stacking, voting, and dynamic ensemble selection models. The best model was evaluated on the test data set. Partial dependence (PD) and individual conditional expectation (ICE) plots were created. Results: In total, nineteen of twenty-seven features were selected. Logistic regression, linear discrimination analysis, and Gaussian Naive Bayes algorithms exhibited satisfactory performances and were, therefore, used to construct ensemble models. The k-Nearest Oracle Elimination model outperformed the other models when evaluated on the training-validation data set (sensitivity: 0.732, 95% CI: 0.702-0.761; specificity: 0.813, 95% CI: 0.805-0.822); it exhibited compatible performance on the test data set (sensitivity: 0.779, 95% CI: 0.559-0.950; specificity: 0.859, 95% CI: 0.799-0.912). The PD and ICE plots showed consistent patterns with practical tendencies. Conclusion: Preexisting health conditions can predict long-term functional outcomes in injured middle-aged and older patients, thus predicting prognosis and facilitating clinical decision-making.

Association between short-term air pollution exposure and traumatic intracranial hemorrhage: pilot evidence from Taiwan.

Introduction: The detrimental effects of air pollution on the brain are well established. However, few studies have examined the effect of air pollution on traumatic brain injury (TBI). This pilot study evaluated the association between short-term air pollution exposure and traumatic intracranial hemorrhage (TIH). Methods: Hospital data of patients with TBI following road traffic accidents were retrospectively collected from the electronic medical records at five trauma centers in Taiwan between 1 January and 31 December 2017. TIH was employed as an outcome measure. All road accident locations were geocoded, and air quality data were collected from the nearest monitoring stations. Air pollutants were entered into five multivariable models. A sensitivity analysis was performed on patients who are vulnerable to suffering TBI after road accidents, including motorcyclists, bicyclists, and pedestrians. Results: Among 730 patients with TBI, 327 had TIH. The ages of ≥65 [odds ratio (OR), 3.24; 95% confidence interval (CI), 1.85-5.70], 45-64 (OR, 2.61; 95% CI, 1.64-4.15), and 25-44 (OR, 1.79; 95% CI, 1.13-2.84) years were identified as significant risk factors in the multivariable analysis. In the best-fit multivariable model, exposure to higher concentrations of particulate matter ≤ 2.5 µm in aerodynamic diameter (PM2.5) was associated with an elevated TIH risk (OR, 1.50; 95% CI, 1.17-1.94). The concentration of nitrogen oxides (NOX) did not increase the risk of TIH (OR, 0.45; 95% CI, 0.32-0.61). After categorizing the air pollution concentration according to quartile, the trend tests in the multivariate model showed that the concentrations of PM2.5 and NOX were significant (p = 0.017 and p < 0.001, respectively). There was a negative borderline significant association between temperature and TIH risk (OR, 0.75; 95% CI, 0.56-1.00, p = 0.05). Notably, the single-vehicle crash was a significant risk factor (OR, 2.11; 95% CI, 1.30-3.42) for TIH. Discussion: High PM2.5 concentrations and low temperatures are risk factors for TIH in patients with TBI. High NOX concentrations are associated with a lower TIH risk.

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.

Background: Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods. Objective: This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19. Methods: Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. Results: The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902. Conclusion: This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries.

OBJECTIVES: The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. METHODS: This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. RESULTS: This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. CONCLUSIONS: This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.

Environmental Factors Associated with Severe Motorcycle Crash Injury in University Neighborhoods: A Multicenter Study in Taiwan.

University neighborhoods in Taiwan have high-volume traffic, which may increase motorcyclists' risk of injury. However, few studies have analyzed the environmental factors affecting motorcycle crash injury severity in university neighborhoods. In this multicenter cross-sectional study, we explored the factors that increase the severity of such injuries, especially among young adults. We retrospectively connected hospital data to the Police Traffic Accident Dataset. Areas within 500 m of a university were considered university neighborhoods. We analyzed 4751 patients, including 513 with severe injury (injury severity score ≥ 8). Multivariate analysis revealed that female sex, age ≥ 45 years, drunk driving, early morning driving, flashing signals, and single-motorcycle crashes were risk factors for severe injury. Among patients aged 18-24 years, female sex, late-night and afternoon driving, and flashing signals were risk factors. Adverse weather did not increase the risk. Time to hospital was a protective factor, reflecting the effectiveness of urban emergency medical services. Lifestyle habits among young adults, such as drunk driving incidents and afternoon and late-night driving, were also explored. We discovered that understanding chaotic traffic in the early morning, flashing signals at the intersections, and roadside obstacles is key for mitigating injury severity from motorcycle crashes in university neighborhoods.

Association of Air Pollution and Weather Factors with Traffic Injury Severity: A Study in Taiwan.

Exposure to air pollutants may elevate the injury severity scores (ISSs) for road traffic injuries (RTIs). This multicenter cross-sectional study aimed to investigate the associations between air pollution, weather conditions, and RTI severity. This retrospective study was performed in Taiwan in 2018. The location of each road traffic accident (RTA) was used to determine the nearest air quality monitoring and weather station, and the time of each RTA was matched to the corresponding hourly air pollutant concentration and weather factors. Five multiple logistic regression models were used to compute the risk of sustaining severe injury (ISS ≥ 9). Of the 14,973 patients with RTIs, 2853 sustained severe injury. Moderate or unhealthy air quality index, higher exposure to particulate matter ≤2.5 µm in diameter, bicyclists or pedestrians, greater road width, nighttime, and higher temperature and relative humidity were significant risk factors for severe injury. Exposure to nitrogen oxide and ozone did not increase the risk. Auto occupants and scene-to-hospital time were the protective factors. Sensitivity analyses showed consistent results between air pollutants and the risk of severe injury. Poor air quality and hot and humid weather conditions were associated with severe RTIs. Active commuters were at higher risk of sustaining severe RTI.

Effects of the COVID-19 Pandemic on Treatment Efficiency for Traumatic Brain Injury in the Emergency Department: A Multicenter Study in Taiwan.

The coronavirus disease 2019 (COVID-19) pandemic has impacted emergency department (ED) practice, including the treatment of traumatic brain injury (TBI), which is commonly encountered in the ED. Our study aimed to evaluate TBI treatment efficiency in the ED during the COVID-19 pandemic. A retrospective observational study was conducted using the electronic medical records from three hospitals in metropolitan Taipei, Taiwan. The time from ED arrival to brain computed tomography (CT) and the time from ED arrival to surgical management were used as measures of treatment efficiency. TBI treatment efficiencies in the ED coinciding with a small-scale local COVID-19 outbreak in 2020 (P1) and large-scale community spread in 2021 (P2) were compared against the pre-pandemic efficiency recorded in 2019. The interval between ED arrival and brain CT was significantly shortened during P1 and P2 compared with the pre-pandemic interval, and no significant delay between ED arrival and surgical management was found, indicating increased treatment efficiency for TBI in the ED during the COVID-19 pandemic. Minimizing viral spread in the community and the hospital is vital to maintaining ED treatment efficiency and capacity. The ED should retain sufficient capacity to treat older patients with serious TBI during the COVID-19 pandemic.

Effect of motorcycle helmet types on head injuries: evidence from eight level-I trauma centres in Taiwan.

BACKGROUND: Motorcycle full-coverage helmet use may reduce fatalities and head injuries. METHODS: This retrospective cohort study extracted injury data from eight level-I trauma centres in Taiwan and performed a questionnaire survey to investigate injuries sustained by motorcyclists for the period between January 2015 and June 2017. RESULTS: As many as 725 patients participated in the questionnaire survey and reported their helmet types or phone use during crashes. The results of multivariate logistic models demonstrated that nonstandard helmet (half or open-face helmet) use was associated with an increased risk of head injuries and more severe injuries (injury severity score ≥ 8). Drunk riding and phone use appeared to be two important risk factors for head injuries and increased injury severity. Anaemia was also found to be a determinant of head injuries." CONCLUSIONS: Compared to full-coverage helmets, nonstandard provide less protection against head injuries and increased injury severity among motorcyclists.

Determination of contact force by compression testing of cylindrical specimens.

This paper presents a method for determining values of dynamic parameters of the Hunt and Crossley model in order to estimate the amount of force generated at the point of contact (contact force) in an impact. A two-degree-of-freedom lumped-mass-system based on a non-linear visco-elastic model as proposed by Hunt and Crossley has been widely used to accurately model contact force. The primary difficulty associated with the Hunt and Crossley contact force model is the need to determine the unknown dynamic parameters of the model, which can be obtained by calibrating the model against results from high-speed impact experiments. Spherical impactors have to be placed in the gas-gun barrel for accelerating onto the target specimen. An innovative and inexpensive method proposed in this paper describes the use of compression testing on a test rig employing cylindrical specimens of colliding bodies to obtain the dynamic parameters thereby waiving away the need of costly and time-consuming impact experiments.

Rider factors associated with severe injury after a light motorcycle crash: A multicentre study in an emerging economy setting.

OBJECTIVE: In Taiwan, light motorcycles (LMCs) with cylinder capacities between 50 and 250 cc are widely used for daily commute. These vehicles are operated in a mixed traffic environment and prohibited on highways. In light of increasing motorcycle casualties, we conducted a multicentre study to analyse rider factors affecting injury severity. METHODS: Riders hospitalised upon LMC crashes were contacted. Information on demographics, comorbidities, and riding behaviours was collected through questionnaires and linked to hospital data. The injury severity score (ISS) and length of hospitalisation (LOH) were used as injury severity measures. RESULTS: In total, 725 patients (mean age: 37.7 years; 64% men) completed their questionnaires. Multivariate analysis results showed that age ≥ 65 years, half-face helmets, protective clothing, collisions with a bus/truck or car, and fatigue riding were risk factors for having an ISS of ≥9. Age ≥ 65 years; motorcycle crashes ≥2 times in the previous year; anaemia; rural crashes; half-face helmets; protective boots; collisions with a bus/truck, car, or a stationary object; alcohol/stimulating refreshment consumption; and fatigue riding were risk factors for increased LOH. A protective factor was individuals working in commerce. Collisions with opening car doors caused low risks of having an ISS of ≥9 and a short LOH. CONCLUSION: Certain factors were significantly associated with riders' injury severity and related medical resource consumption. Because of differences in the power output, use, and riding environment, risk factors for severe injuries in LMC crashes are dissimilar from those for heavy motorcycles (cylinder capacities > 250 cc) in developed countries and deserve more attention for injury prevention. Further in-depth evaluation of significant factors based on this study's results can yield valuable information to reduce severe injuries after LMC crashes in countries and areas with a high dependency on motorcycles, even considering the popularity of electric motorcycles.

Improving the timeliness and accuracy of injury severity data in road traffic accidents in an emerging economy setting.

Road traffic injuries (RTIs) are among the leading causes of injury and fatality worldwide. RTI casualties are continually increasing in Taiwan; however, because of a lack of an advanced method for classifying RTI severity data, as well as the fragmentation of data sources, road traffic safety and health agencies encounter difficulties in analyzing RTIs and their burden on the healthcare system and national resources. These difficulties lead to blind spots during policy-making for RTI prevention and control. After compiling classifications applied in various countries, we summarized data sources for RTI severity in Taiwan, through which we identified data fragmentation. Accordingly, we proposed a practical classification for RTI severity, as well as a feasible model for collecting and integrating these data nationwide. This model can provide timely relevant data recorded by medical professionals and is valuable to healthcare providers. The proposed model's pros and cons are also compared to those of other current models.

Treatment outcomes of patients with non-bacteremic pneumonia caused by extensively drug-resistant Acinetobacter calcoaceticus-Acinetobacter baumannii complex isolates: Is there any benefit of adding tigecycline to aerosolized colistimethate sodium?

Few therapeutic options exist for various infections caused by extensively drug-resistant Acinetobacter calcoaceticus-Acinetobacter baumannii (XDR-Acb) complex isolates, including pneumonia. This study investigated the clinical efficacy between aerosolized colistimethate sodium (AS-CMS, 2 million units thrice a day) treatment alone or in combination with standard-dose tigecycline (TGC) in patients with non-bacteremic pneumonia due to XDR-Acb, and explored the factors influencing patients' 30-day mortality.A 1:1 case (n = 106; receiving TGC plus AS-CMS) control (receiving AS-CMS alone with matching scores) observational study was conducted among adult patients with non-bacteremic XDR-Acb complex pneumonia in a Taiwanese medical center from January 2014 through December 2016. The clinically relevant data were retrospectively recorded. The primary endpoint was 30-day case fatality. Secondary endpoints investigated that if the co-morbidities, XDR-A. baumannii as a pneumonic pathogen, therapy-related factors, or airway colonization with colistin-resistant Acb negatively influenced the 14-day clinical condition of enrolled patients.A higher 30-day mortality rate was noted among the group receiving combination therapy (34.0% vs 22.6%; P = .17). The ≥7-day AS-CMS therapy successfully eradicated > 90% of airway XDR-Acb isolates. Nevertheless, follow-up sputum specimens from 10 (6.4% [10/156]) patients were colonized with colistin-resistant Acb isolates. After the conditional factors were adjusted by multivariate logistic analysis, the only factor independently predicting the 30-day case-fatality was the failure of treating XDR-Acb pneumonia at 14 days (adjusted odds ratio [aOR] = 38.2; 95% confidence interval [CI] = 9.96-142.29; P < .001). Cox proportional regression analysis found that chronic obstructive pulmonary disease (COPD) (adjusted hazard ratio [aHR] = 2.08; 95% CI = 1.05-4.10; P = .035), chronic renal failure (aHR = 3.00; 95% CI = 1.52-5.90; P = .002), non-invasive ventilation use (aHR = 2.68; 95% CI = 1.37-5.25; P = .004), and lack of TGC therapy (aHR = 0.52; 95% CI = 0.27-1.00; P = .049) adversely influenced the 14-day clinical outcomes. Conversely, the emergence of colistin-resistant Acb isolates in the follow-up sputum samples was not statistically significantly associated with curing or improving XDR-Acb pneumonia.In conclusion, aggressive pulmonary hygiene care, the addition of TGC, and corticosteroid dose tapering were beneficial in improving the 14-day patients' outcomes.

Risk factors for 14-day rehospitalization following trauma with new traumatic spinal cord injury diagnosis: A 10-year nationwide study in Taiwan.

OBJECTIVES: Fourteen-day rehospitalization with new traumatic spinal cord injury (tSCI) diagnosis is used as an indicator for the diagnostic quality of the first hospitalization. In this nationwide population-based cohort study, we identified risk factors for this indicator. METHODS: We conducted a nested case-control study by using the data of patients who received a first hospitalization for trauma between 2001 and 2011. The data were retrieved from Taiwan's National Health Insurance Research Database. Variables including demographic and trauma characteristics were compared between patients diagnosed with tSCI at the first hospitalization and those receiving a 14-day rehospitalization with new tSCI diagnosis. RESULTS: Of the 23 617 tSCI patients, 997 had 14-day rehospitalization with new tSCI diagnosis (incidence rate, 4.22%). The risk of 14-day rehospitalization with new tSCI diagnosis was significantly lower in patients with severe (injury severity score [ISS] = 16-24; odds ratio [OR], 0.17; 95% confidence interval [CI], 0.13-0.21) and profound (ISS > 24; OR, 0.11; 95% CI, 0.07-0.18) injuries. Interhospital transfer (OR, 8.20; 95% CI, 6.48-10.38) was a significant risk factor, along with injuries at the thoracic (OR, 1.62; 95% CI, 1.21-2.18), lumbar (OR, 1.30; 95% CI, 1.02-1.65), and multiple (OR, 3.23; 95% CI, 1.86-5.61) levels. Brain (OR, 2.82), chest (OR, 2.99), and abdominal (OR, 2.74) injuries were also identified as risk factors. In addition, the risk was higher in patients treated at the orthopedic department (OR, 2.26; 95% CI, 1.78-2.87) and those of other surgical disciplines (OR, 1.89; 95% CI, 1.57-2.28) than in those treated at the neurosurgery department. CONCLUSIONS: Delayed tSCI diagnoses are not uncommon, particularly among trauma patients with ISSs < 16 or those who are transferred from lower-level hospitals. Further validation and implementation of evidence-based decision rules is essential for improving the diagnostic quality of traumatic thoracolumbar SCI.

First Fall-Related Injuries Requiring Hospitalization Increase the Risk of Recurrent Injurious Falls: A Nationwide Cohort Study in Taiwan.

OBJECTIVES: Recurrent falls not only have risk factors different from those of single falls but also have less favorable outcomes. The aim of our study was to determine the association between the injury characteristics of a first fall and the likelihood of recurrent injurious falls in a cohort of hospitalized patients. METHODS: We designed a nationwide retrospective cohort study and selected hospitalized patients who had injurious falls between 2001 and 2010. Cox proportional hazards models were used to estimate the hazard ratios (HRs) of recurrent injurious falls requiring hospitalization in the following year on the basis of the patients' demographic characteristics, comorbidities, and the characteristics of injuries from the first injurious fall requiring hospitalization. RESULTS: Among the 504 512 patients hospitalized for injurious falls, 19 442 experienced recurrent injurious falls requiring hospitalization. The 1-year incidence of recurrent injurious falls requiring hospitalization was 3.85%. The incidence density was the highest within the 3-month period after the first injurious fall. The risk of recurrent injurious falls among patients aged 40 to 64, 65 to 74, and ≥ 75 years increased progressively (HR: 2.11, 95% confidence interval [CI]: 1.90-2.34; HR: 2.80, 95% CI: 2.51-3.11; and HR: 3.80, 95% CI: 3.42-4.23, respectively). The length of hospitalization (LOH) ≥ 15 or ≥ 31 days (HR: 1.39, 95% CI: 1.30-1.48; and HR: 1.59, 95% CI: 1.43-1.77, respectively) and injury to the head (HR: 1.59, 95% CI: 1.53-1.65) or spine (HR: 1.66, 95% CI: 1.59-1.74) were also found to be major risk factors. CONCLUSIONS: Our findings show that the LOH and head and spine injuries are associated with an increased risk of recurrent injurious falls leading to hospitalization. The risk of recurrent injurious falls requiring hospitalization increased significantly among adults older than 40 years. We suggest further research on the effects of injury characteristics associated with the first injurious fall requiring hospitalization and resultant anatomical damages on the risk of recurrent injurious falls requiring hospitalization. High-risk patients should receive tailored rehabilitation addressing their respective injuries within 3 months after hospital discharge.

Comparison of the clinical efficacy between tigecycline plus extended-infusion imipenem and sulbactam plus imipenem against ventilator-associated pneumonia with pneumonic extensively drug-resistant Acinetobacter baumannii bacteremia, and correlation of clinical efficacy with in vitro synergy tests.

BACKGROUND/PURPOSE: To compare the clinical efficacy between salvage antimicrobial regimen consisting of tigecycline plus extended-infusion imipenem/cilastatin (TIC) and regimen of sulbactam plus imipenem/cilastatin (SIC) for patients with ventilator-associated pneumonia and pneumonic bacteremia due to extensively drug-resistant (XDR) Acinetobacter baumannii (Ab) isolates, and determine the correlation of results of in vitro tigecycline-imipenem synergy test with clinical efficacy. METHODS: The comparative survey was conducted at a medical center in Taiwan in 2013. Patients comprising the TIC group (n = 28) received tigecycline plus extended-infusion imipenem/cilastatin following unresponsiveness to 3-day sulbactam-imipenem/cilastatin therapy, and those in the SIC group (n = 56) received sulbactam-imipenem/cilastatin throughout the course. Univariate and multivariate analyses were applied to explore 30-day case-fatality independent predictors. Additionally, the checkerboard test and time-kill analysis were performed for the bloodstream XDR-Ab isolates from patients in the TIC group, and molecular characterization was done for the bloodstream XDR-Ab strains of all patients. RESULTS: We found that the TIC scheme has a significant benefit on improving patients' survival status (the mortality rate of TIC and SIC group patients was 14.3% and 64.3%, respectively), corresponding well with in vitro synergy or additivity results by the checkerboard test. Twenty TIC group cases had monomicrobial XDR-Ab cultured from tracheal aspirates after 10 days of tigecycline-imipenem/cilastatin therapy, but none developed subsequent pneumonia. However, breakthrough primary Burkholderia cepacia (n = 3) and Pseudomonas aeruginosa (n = 1) bacteremias were attributed to four TIC case fatalities. Shock, SIC regimen usage, and development of breakthrough bacteremia were independent predictors of 30-day in-hospital mortality. CONCLUSION: Although the TIC regimen showed good efficacy, its value regarding managing XDR-Ab ventilator-associated pneumonia bacteremia needs further evaluation.

Rates of susceptibility of carbapenems, ceftobiprole, and colistin against clinically important bacteria collected from intensive care units in 2007: Results from the Surveillance of Multicenter Antimicrobial Resistance in Taiwan (SMART).

BACKGROUND: Data on susceptibility to ceftobiprole and colistin, and the complete evolutionary trends of minimum inhibitory concentrations (MICs) of important carbapenem agents among important pathogens collected in intensive care units (ICUs) in Taiwan are lacking. METHODS: We surveyed the MIC distribution patterns of ceftobiprole and colistin and susceptibility profiles of some important pathogens collected from patients hospitalized in intensive care units (ICUs) of major teaching hospitals throughout Taiwan in 2007. We also investigated the rates of nonsusceptibility to powerful carbapenems (imipenem, meropenem) among four important species of Enterobacteriaceae (Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, and Proteus mirabilis) collected during the same period. MIC breakpoints recommended by the Clinical and Laboratory Standards Institute in 2014 were applied. RESULTS: Colistin showed excellent in vitro activity (susceptibility rate, 96%) against Acinetobacter baumannii isolates but moderate (73-77% susceptibility rate) activity against isolates of Pseudomonas aeruginosa and E. cloacae. The ceftobiprole MIC90 value was 4 µg/mL for methicillin-resistant Staphylococcus aureus and 16 µg/mL for P. aeruginosa. The phenotype of methicillin resistance did not markedly increase the MIC value of ceftobiprole among S. aureus isolates. Interestingly, the proportion of isolates that displayed nonsusceptibility to imipenem was significantly higher among P. mirabilis isolates than among isolates of the other three Enterobacteriaceae species, regardless of the production of extended-spectrum ß-lactamase. CONCLUSION: Continuous monitoring of susceptibility profiles of ICU pathogens to important antibiotics is warranted to provide appropriate antimicrobial regimens against infections in the ICU.

Leclercia adecarboxylata bacteremia in a patient with long-term use of nonsteroidal anti-inflammatory drugs.

Leclercia adecarboxylata, a ubiquitous Gram-negative bacillus, is generally viewed as an opportunistic pathogen because it is rarely cultured from clinical samples. Although rare, bacteremia due to L. adecarboxylata tends to occur in immunocompromised hosts and patients with systemic comorbidities. Only one case of bacteremia due to L. adecarboxylata has been reported in a previously healthy patient. We describe a male patient with an active peptic ulcer who developed L. adecarboxylata bacteremia after a long-term use of nonsteroidal anti-inflammatory drugs. The abdomen is believed to have been the most probable portal of entry. After appropriate medical management, the patient recovered without sequela.