Internal and Emergency Medicine最新文献

Background Predicting potential overcrowding is a significant tool in efficient emergency department (ED) management. Our aim was to develop and validate overcrowding predictive models using accessible and high quality information. Methods Retrospective cohort study of consecutive days in the Hospital Italiano de Buenos Aires ED from june 2016 to may 2018. We estimated hourly NEDOCS score for the entire period, and defined the outcome as Sustained Critical ED Overcrowding (EDOC) equal to occurrence of 8 or more hours with a NEDOCS score ≥ 180. We generated 3 logistic regression predictive models with different related outcomes: beginning, ending or occurrence of Sustained Critical EDOC. We estimated calibration and discrimination as internal (random validation group and bootstrapping) and external validation (different period and different ED). Results The main model included both the beginning and occurrence of NEDOCS, including weather variables, variables related to NEDOCS itself and patient flow variables. The second model considered only the beginning of Sustained Critical EDOC and included variables related to NEDOCS. The last model considered the end of Sustained Critical EDOC and it included variables related to NEDOCS, weather, bed occupancy and management. Discrimination for the main model had an area under the receiveroperator curve of 0.997 (95% CI 0.994 - 1) in the validation group. Calibration for the model was very high on internal validation and acceptable on external validation. Conclusion The Sustained Critical EDOC predictive model includes variables that are easily obtained and can be used for effective resource management in situations of overcrowding.

The aim was to identify predictors for early identification of HFNC failure risk in patients with severe community-acquired (CAP) pneumonia or COVID-19. Data from adult critically ill patients admitted with CAP or COVID-19 and the need for ventilatory support were retrospectively analysed. HFNC failure was defined as the need for invasive ventilation or death before intubation. 60 patients with CAP and 185 with COVID-19 were included. 27 (45%) patients with CAP and 69 (37.3%) patients with COVID-19 showed HFNC failure. Lower oxygenation index, lower respiratory oxygenation (ROX) index, and higher respiratory rate at the start of HFNC were significantly associated with HFNC failure. ROC-analysis identified a respiratory rate of 27/min as the optimal cut-off for predicting HFNC failure, with a specificity of 59% and a sensitivity of 75%, and an oxygenation index after HFNC initiation of 99.6 (specificity 81%, sensitivity 74%). In COVID-19, an elevated CRB65-score at hospital admission and at HFNC-initiation was significantly associated with HFNC failure. In CAP and COVID patients an oxygenation index < 99.6, a respiratory rate > 27/min and a ROX index < 4.88 were predictors for HFNC failure whereas a CRB65 score > 3 at hospital admission and > 2 at HFNC start was predictive for HFNC failure in COVID-19.

The accuracy of using HEART (history, electrocardiogram, age, risk factors, and troponin) scores with high-sensitivity cardiac troponin (hs-cTn) to risk stratify emergency department (ED) chest pain patients remains uncertain. We aim to compare the performance accuracy of determining major adverse cardiac event (MACE) among three modified HEART (mHEART) scores with the use of hs-cTn to risk stratify ED chest pain patients. This retrospective single-center observational study included ED patients with suspected acute coronary syndrome who had HEAR scores calculated and at least one hs-cTnI result. Various hs-cTnI parameters, including 99th percentile upper reference limit (URL, i.e., positive, ≥ 53 ng/l for females and ≥ 78 ng/l for males), limit of quantitation (LoQ, i.e., negative: < 20 ng/l), and limit of detection (LoD, < 3 ng/l), were used to calculate a troponin score (T-score). Patients with a T-score of 0 or mHEART score of 0-3 were considered low risk. The study compared the accuracy of different mHEART scores in predicting 30-day and 180-day MACE outcomes. A total of 10,495 patients were included, with 337 (3.21%) and 647 (6.16%) experiencing 30-day and 180-day MACE. The 30-day MACE rates were 0.53%, 1.37%, and 2.00% for patients whose hs-cTnI was beyond the cutoffs of LoD, LoQ, and URL, respectively. However, when low risk was defined as an mHEART score of 0-3, the 30-day MACE rates ranged from 0.33 to 0.62% across different mHEART scores. The mHEART score for risk stratification of low-risk chest pain patients shows acceptable accuracy in predicting MACE outcomes.

Eosinophilic oesophagitis (EoE) is a chronic and progressive immune-mediated condition, typically affecting young atopic male adults and potentially leads to organ dysfunction and fibrosis. The clinical spectrum widely varies -from non-troublesome dysphagia to food impaction- and hence the rate of misdiagnosis and diagnostic delay are high, especially when presenting with minor symptoms, such as heartburn and acid regurgitation. There have been several major therapeutic breakthroughs for the management of EoE in recent years. Highly effective conventional agents with oesophagus-specific formulations (i.e. orodispersible budesonide) and a biological agent (i.e. dupilumab) now have a formal indication. Oesophageal dilation may be indicated in case of strictures, which are more common in longstanding and untreated disease. Therefore, the early diagnosis of this disorder and specialist referral is if of great importance. The evaluation of alarm signs and typical presentation patterns should allow a more straightforward recognition. The emergency and internal medicine doctors should actively be involved in this process and take part to the multidisciplinary care of patients with EoE, to allow better patient care and clinical outcomes.

Heart failure (HF) with improved ejection fraction (HFimpEF) has gradually attracted widespread attention in recent years for its better clinical prognosis. In this study, we attempted to investigate the relationship between subclinical hypothyroidism (SCH) and HFimpEF. This study retrospectively collected clinical data on patients with HF with reduced ejection fraction (HFrEF) hospitalized at the First Affiliated Hospital of USTC from March 2015 to September 2023, and divided into two groups as euthyroidism or SCH according to the baseline thyroid function. Then patients were further categorized into HFimpEF (follow-up LVEF > 40% and absolute increase ≥ 10%) and persistent HFrEF based on their LVEF on the echocardiograms during the follow-up period. Afterward, logistic regression was used to estimate the effect of SCH on HFimpEF. A total of 916 patients with HFrEF met the inclusion and exclusion criteria, and 396 patients (43.2%) progressed to HFimpEF status during the follow-up period. Compared with HFrEF patients, the prevalence of SCH is lower in HFimpEF patients (9.3% vs. 14.4%, P = 0.020). Univariate logistic regression analysis indicates that SCH was a potential predictor for HFimpEF (OR: 0.612 [95% CI 0.403-0.928], P = 0.021). After adjusting for multiple factors in logistic regression, the odds ratios of HFrEF patients with SCH progressing to HFimpEF decreased by 37.8% (OR: 0.622 [95% CI 0.397-0.974], P = 0.038) compared with patients with euthyroidism. This study suggests that thyroid function affects the improvement of cardiac function in patients with HFrEF and SCH is an independent predictor for HFimpEF.

Diaphragmatic dysfunction is an important contributor to hypercapnic respiratory failure, but its presence is often challenging to determine at the bedside. Diaphragm ultrasound provides an opportunity to evaluate the function of the diaphragm noninvasively by evaluating the following parameters that can help define diaphragmatic dysfunction: diaphragm excursion, diaphragm muscle thickness, and thickening fraction. Its evaluation has the potential to assist with diagnosis of respiratory failure, provide prognosis, and assist with patient monitoring and should be considered as part of an internal medicine physician's and emergency physician's skill set. This article provides an overview on how to perform diaphragm ultrasound, review its pitfalls, and discuss the evidence of its use in patients with neuromuscular disorders and chronic obstructive pulmonary disease. Finally, its potential emerging uses in the perioperative setting and for evaluation of acute heart failure are discussed.

Adhering to bundle-based care recommendations within stringent time constraints presents a profound challenge. Elements within these bundles hold varying degrees of significance. We aimed to evaluate the Surviving Sepsis Campaign (SSC) hour-one bundle compliance patterns and their association with patient outcomes. Utilizing the Medical Information Mart for Intensive Care-IV 1.0 dataset, this retrospective cohort study evaluated patients with sepsis who developed shock and were admitted to the intensive care unit between 2008 and 2019. The execution of five hour-one bundle interventions were assessed. Patients with similar treatment profiles were categorized into clusters using unsupervised machine learning. Primary outcomes included in-hospital and 1-year mortality. Four clusters were identified: C#0 (n = 4716) had the poorest bundle compliance. C#1 (n = 1117) had perfect antibiotic adherence with modest fluid and serum lactate measurement adherence. C#2 (n = 850) exhibited full adherence to lactate measurement and low adherence to fluid administration, blood culture, and vasopressors, while C#3 (n = 381) achieved complete adherence to fluid administration and the highest adherence to vasopressor requirements in the entire cohort. Adjusting for covariates, C#1 and C#3 were associated with reduced odds of in-hospital mortality compared to C#0 (adjusted odds ratio [aOR] = 0·83; 95% confidence interval [CI] 0·7-0·97 and aOR = 0·7; 95% CI 0·53-0·91, respectively). C#1 exhibited significantly better 1-year survival (adjusted hazard ratio [aHR] = 0·9; 95%CI 0·81-0·99). We were able to identify distinct clusters of SSC hour-one bundle adherence patterns using unsupervised machine learning techniques, which were associated with patient outcomes.

Background and aims: Some studies have explored the relationship between metabolic abnormalities, elevated liver enzymes and gallstone risk, but the results have been inconsistent. The aim of this study was to comprehensively assess the relationship between metabolic abnormalities, liver enzymes, and gallstone risk through a cross-sectional study and Mendelian randomization analysis.

Methods: Firstly, a cross-sectional study was conducted involving 555,31 subjects who underwent physical examinations at the Second Affiliated Hospital of Xi'an Jiaotong University between January 2021 and December 2021. The diagnosis of gallstones was based on ultrasound findings. Multivariable logistic regression analysis was employed to investigate the associations between metabolic abnormalities, liver enzymes, and the risk of gallstones. Additionally, two-sample and multivariate Mendelian randomization analyses were performed to further explore the genetic causal associations.

Results: In the cross-sectional analysis, the detection rate of gallstones was 4.8%, showing a gradual increase with age and BMI (body mass index) in both men and women. Gallstones were found to be comorbid with various metabolic disorders and were associated with elevated liver enzymes. Multivariate analysis showed that female sex, age, body mass index, diastolic blood pressure, fatty liver, alanine aminotransferase, and gamma-glutamyltransferase may be independent risk factors for gallstones, whereas high-density lipoprotein cholesterol may be a protective factor. Two-sample and multivariate Mendelian randomization analyses further revealed a causal association between genetically predicted BMI, type 2 diabetes mellitus, alanine aminotransferase, gamma-glutamyl transferase, and an increased risk of gallstones.

Conclusion: The findings suggest that metabolic abnormalities and elevated liver enzymes may increase the risk of developing gallstones.