Global Leadership Initiative on Malnutrition criteria using calf and upper arm circumference as phenotypic criteria for assessing muscle mass demonstrate satisfactory validity for diagnosing malnutrition in hospitalized patients: A prospective cohort study.

BACKGROUND: Hospitalized individuals present high rates of malnutrition and loss of muscle mass (MM). Imaging techniques for assessing MM are expensive and scarcely available in hospital practice. The Global Leadership Initiative on Malnutrition (GLIM) proposed a framework for malnutrition diagnosis that includes simple measurements to assess MM, such as calf circumference (CC) and mid-upper arm circumference (MUAC). This study aimed to analyze the validity of the GLIM criteria with CC and MUAC for malnutrition diagnosis, using Subjective Global Assessment (SGA) as the reference standard, in inpatients. METHODS: A prospective cohort study was conducted on 453 inpatient adults in a university hospital. The presence of malnutrition was assessed within 48 h of hospital admission using SGA and GLIM criteria using CC and MUAC as phenotypic criteria for malnutrition diagnosis. Accuracy, agreement tests, and logistic regression analysis adjusted for confounders were performed to test the validity of the GLIM criteria for malnutrition diagnosis. RESULTS: The patients were aged 59 (46-68) years, 51.4% were male, and 67.8% had elective surgery. Compared with SGA, the GLIM criteria using the two MM assessment measures showed good accuracy (area under the curve > 0.80) and substantial agreement (κ > 0.60) for diagnosing malnutrition. The highest sensitivity was obtained with GLIMCC (89%), whereas GLIMMUAC showed high specificity (>90%). Also, malnutrition identified by GLIMCC and GLIMMUAC was significantly associated with prolonged hospitalization and in-hospital death. CONCLUSION: In the absence of imaging techniques to assess MM, the use of CC and MUAC measurements from the GLIM criteria demonstrated satisfactory validity for diagnosing malnutrition in hospitalized patients.

Albumin corrected anion gap for predicting in-hospital death among patients with acute myocardial infarction: A retrospective cohort study.

OBJECTIVE: To explore the relationship between Anion Gap (AG), Albumin Corrected AG (ACAG), and in-hospital mortality of Acute Myocardial Infarction (AMI) patients and develop a prediction model for predicting the mortality in AMI patients. METHODS: This was a retrospective cohort study based on the Medical Information Mart for Intensive Care (MIMIC)-Ⅲ, MIMIC-IV, and eICU Collaborative Study Database (eICU). A total of 9767 AMI patients who were admitted to the intensive care unit were included. The authors employed univariate and multivariable cox proportional hazards analyses to investigate the association between AG, ACAG, and in-hospital mortality; p < 0.05 was considered statistically significant. A nomogram incorporating ACAG and clinical indicators was developed and validated for predicting mortality among AMI patients. RESULTS: Both ACAG and AG exhibited a significant association with an elevated risk of in-hospital mortality in AMI patients. The C-index of ACAG (C-index = 0.606) was significantly higher than AG (C-index = 0.589). A nomogram (ACAG combined model) was developed to predict the in-hospital mortality for AMI patients. The nomogram demonstrated a good predictive performance by Area Under the Curve (AUC) of 0.763 in the training set, 0.744 and 0.681 in the external validation cohort. The C-index of the nomogram was 0.759 in the training set, 0.756 and 0.762 in the validation cohorts. Additionally, the C-index of the nomogram was obviously higher than the ACAG and age shock index in three databases. CONCLUSION: ACAG was related to in-hospital mortality among AMI patients. The authors developed a nomogram incorporating ACAG and clinical indicators, demonstrating good performance for predicting in-hospital mortality of AMI patients.

Albumin corrected anion gap for predicting in-hospital death among patients with acute myocardial infarction: A retrospective cohort study

Abstract Objective: To explore the relationship between Anion Gap (AG), Albumin Corrected AG (ACAG), and in-hospital mortality of Acute Myocardial Infarction (AMI) patients and develop a prediction model for predicting the mortality in AMI patients. Methods: This was a retrospective cohort study based on the Medical Information Mart for Intensive Care (MIMIC)-III, MIMIC-IV, and eICU Collaborative Study Database (eICU). A total of 9767 AMI patients who were admitted to the intensive care unit were included. The authors employed univariate and multivariable cox proportional hazards analyses to investigate the association between AG, ACAG, and in-hospital mortality; p < 0.05 was considered statistically significant. A nomogram incorporating ACAG and clinical indicators was developed and validated for predicting mortality among AMI patients. Results: Both ACAG and AG exhibited a significant association with an elevated risk of in-hospital mortality in AMI patients. The C-index of ACAG (C-index = 0.606) was significantly higher than AG (C-index = 0.589). A nomo-gram (ACAG combined model) was developed to predict the in-hospital mortality for AMI patients. The nomo-gram demonstrated a good predictive performance by Area Under the Curve (AUC) of 0.763 in the training set, 0.744 and 0.681 in the external validation cohort. The C-index of the nomogram was 0.759 in the training set, 0.756 and 0.762 in the validation cohorts. Additionally, the C-index of the nomogram was obviously higher than the ACAG and age shock index in three databases. Conclusion: ACAG was related to in-hospital mortality among AMI patients. The authors developed a nomogram incorporating ACAG and clinical indicators, demonstrating good performance for predicting in-hospital mortality of AMI patients.

The Neutrophil-to-Monocyte Ratio and Lymphocyte-to-Neutrophil Ratio at Admission Predict In-Hospital Mortality in Mexican Patients with Severe SARS-CoV-2 Infection (Covid-19).

There is a deep need for mortality predictors that allow clinicians to quickly triage patients with severe coronavirus disease 2019 (Covid-19) into intensive care units at the time of hospital admission. Thus, we examined the efficacy of the lymphocyte-to-neutrophil ratio (LNR) and neutrophil-to-monocyte ratio (NMR) as predictors of in-hospital death at admission in patients with severe Covid-19. A total of 54 Mexican adult patients with Covid-19 that met hospitalization criteria were retrospectively enrolled, followed-up daily until hospital discharge or death, and then assigned to survival or non-survival groups. Clinical, demographic, and laboratory parameters were recorded at admission. A total of 20 patients with severe Covid-19 died, and 75% of them were men older than 62.90 ± 14.18 years on average. Type 2 diabetes, hypertension, and coronary heart disease were more prevalent in non-survivors. As compared to survivors, LNR was significantly fourfold decreased while NMR was twofold increased. LNR ≤ 0.088 predicted in-hospital mortality with a sensitivity of 85.00% and a specificity of 74.19%. NMR ≥ 17.75 was a better independent risk factor for mortality with a sensitivity of 89.47% and a specificity of 80.00%. This study demonstrates for the first time that NMR and LNR are accurate predictors of in-hospital mortality at admission in patients with severe Covid-19.