Internal and Emergency Medicine最新文献

Acute myocardial infarction (AMI) is a critical medical emergency worldwide and a leading cause of mortality. This study aims to investigate the predictive utility of the neutrophil-to-platelet ratio (NPR) in identifying AMI patients at an increased risk of in-hospital mortality. We enrolled 664 patients, including 421 with ST-elevation myocardial infarction (STEMI) and 243 with non-ST-elevation myocardial infarction (NSTEMI), at Zhongshan Hospital, Fudan University, from January 2020 to September 2023. NPR was calculated as the neutrophil count divided by the platelet count. The primary outcome was defined as in-hospital mortality. The overall in-hospital mortality among AMI patients was 6.78%. Mortality was notably higher in the high NPR group compared to the low NPR group. Univariate analysis identified several variables significantly associated with in-hospital mortality, including age, left ventricular ejection fraction (LVEF), neutrophil-to-lymphocyte ratio (NLR), and NPR. NPR demonstrated a strong independent association with in-hospital mortality following adjustment for potential confounders. Receiver operating characteristic (ROC) curve analyses were performed to assess the discriminative power of NPR and NLR in predicting in-hospital mortality. NPR exhibited an area under the curve (AUC) of 0.755 (95% CI, 0.682-0.829, p < 0.001), indicating good discriminative ability. Similarly, NLR showed a discriminative AUC of 0.674 (95% CI, 0.586-0.762, p < 0.001). The optimal cutoff values for predicting mortality were determined as 0.042 for NPR (sensitivity 80%, specificity 62.2%) and 8.02 for NLR (sensitivity 62.2%, specificity 67.5%). Bootstrap validation with 1000 iterations confirmed the robustness of these findings, with validated AUCs of 0.755 (95% CI, 0.681-0.826) for NPR and 0.674 (95% CI, 0.587-0.766) for NLR. This study identifies NPR as an independent and valuable predictor of in-hospital mortality among AMI patients, The findings underscore NPR's potential utility in clinical practice for risk stratification and early intervention strategies aimed at reducing mortality rates in this high-risk patient population.

Carbon monoxide (CO) poisoning continues to result in hospitalization and mortality. We sought to analyze risk factors associated with inpatient admission for CO poisoning. Retrospective study of the US National Inpatient Sample (NIS) database. All adults with ICD-10 code for CO poisoning from 01/01/2016 to 12/31/2020 were identified. Demographics, co-morbid conditions, insurance type, household income, length of stay (LOS), total charges, and mortality were abstracted. Univariable logistic regression was used to calculate unadjusted odds ratios (ORs) for CO poisoning admissions. Variables with p-value ≤ 0.2 were included in a multivariable logistic regression model. Of 148,767,786 total hospitalizations, 14,625 had a principal diagnosis of CO poisoning. Compared to non-CO hospitalizations, the CO group was younger (median age 54 vs 61 years; p < 0.001), included more males (61.1 vs 42.7%; p < 0.001), and higher in-hospital mortality (4.0 vs 2.4%; p < 0.001). Although LOS was similar (3 vs 3; p = NS), the CO group had lower median hospital charges ($24,368 vs $ 32,667; p < 0.001). In multivariable analysis, male sex (OR 1.98; 95% CI 1.83-2.15), alcohol use disorder (OR 1.89; 95% CI 1.71-2.10), cannabis use disorder (OR 1.26; 95% CI 1.09-1.47), mood disorders (OR 2.65; 95% CI 2.44-2.88), and suicide ideation (OR 1.74; 95% CI 1.50-2.02) were independently associated with hospitalization. In this 5-year analysis, cannabis use, mood disorders, and suicidality were significant risk factors for CO-related hospitalization in addition to previously known risks of alcohol use and male gender. Since mood and substance use disorders are increasing globally, these modifiable risk factors deserve priority attention from clinicians and policymakers.

The aim was to estimate the prevalence of low muscle mass (LMM) and low muscle mass associated with obesity (LMM-O) in healthy adult, and to verify the performance of raw bioelectrical impedance parameters (BIA) and vector analysis (BIVA) in the screening of this tow conditions. This is a cross-sectional study including 1025 healthy adults. Body composition was assessed by the BIA technique. The appendicular skeletal muscle mass index (ASMMI) and body fat percentage (BF%) were used for the screening of LMM and LMM-O. The raw BIA parameters were: resistance (R), reactance (X_c), phase angle (PhA), and impedance (Z). The vectors, R and X_c, were adjusted for height and projected on the RX_c graph. Associations were checked by the correlation test, binary logistic regression, adjusted for age and body water, and ROC curve. LMM was found in 30.8% of the subjects, and 20.9 and 21.4% of the men and women were with LMM-O. PhA and R/H were the most powerful discriminators of LMM with a sensitivity of 62-100% and a specificity of 71-90%. Cutoff values of PhA ranged between 4.95° and 5.75° for women and men. The RX_c graph was able to identify LMM subjects, with clustering on the right side: area of low cellularity, high R/H and low-phase angle. Traditional anthropometric indices were the least effective in identifying LMM-O. The BIVA approach, PhA, R and R/H are effective in the screening of LMM and LMM-O, irrespective of age, gender, intra- and extracellular hydration status.

Background: Patients with an elevated admission National Early Warning Score (NEWS) are more likely to die while in hospital. However, it is not known if this increased mortality risk is the same for all diagnoses. The aim of this study was to determine and compare the increased risk of in-hospital mortality associated with an elevated NEWS and different primary discharge diagnoses in unselected emergency admissions to a UK university teaching hospital.

Methods: A non-interventional observational study of 122,321 consecutive, unselected, adult patients with complete data admitted as an emergency between 2014 and 2022.

Results: The overall in-hospital mortality was 4.3%. Eighty diagnostic groupings accounted for 85.8% of all admissions and 89.4% of all in-hospital deaths. Depending on diagnostic grouping, the risk of mortality associated with an admission NEWS ≥ 3 ranged from 2.3- to 100-fold. For example, the in-hospital mortality of COPD patients increased from 1.9% for those with admission NEWS < 3 to 35.6% for those with NEWS ≥ 3, for chest pain mortality increased from 0.1 to 3.9%, and for patients with an opiate overdose from 0.2 to 7.7%. Conversely, for admission NEWS < 3, aspiration pneumonia and intracranial hemorrhage had in-hospital mortalities of 13.7% and 12.1%, respectively.

Discussion: There is enormous variation in the mortality risk associated with an increased admission NEWS in different commonly encountered diagnoses. Therefore, the mortality risk of some 'low risk' conditions can be dramatically increased if their admission NEWS is elevated, whereas some 'high risk' conditions are still likely to die even if their admission NEWS is low.