Hypocapnia is associated with increased in-hospital mortality and 1 year mortality in acute heart failure patients.

AIMS: Both hypercapnia and hypocapnia are common in patients with acute heart failure (AHF), but the association between partial pressure of arterial carbon dioxide (PaCO2) and AHF prognosis remains unclear. The objective of this study was to investigate the connection between PaCO2 within 24 h after admission to the intensive care unit (ICU) and mortality during hospitalization and at 1 year in AHF patients. METHODS AND RESULTS: AHF patients were enrolled from the Medical Information Mart for Intensive Care IV database. The patients were divided into three groups by PaCO2 values of <35, 35-45, and >45 mmHg. The primary outcome was to investigate the connection between PaCO2 and in-hospital mortality and 1 year mortality in AHF patients. The secondary outcome was to assess the prediction value of PaCO2 in predicting in-hospital mortality and 1 year mortality in AHF patients. A total of 2374 patients were included in this study, including 457 patients in the PaCO2 < 35 mmHg group, 1072 patients in the PaCO2 = 35-45 mmHg group, and 845 patients in the PaCO2 > 45 mmHg group. The in-hospital mortality was 19.5%, and the 1 year mortality was 23.9% in the PaCO2 < 35 mmHg group. Multivariate logistic regression analysis showed that the PaCO2 < 35 mmHg group was associated with an increased risk of in-hospital mortality [hazard ratio (HR) 1.398, 95% confidence interval (CI) 1.039-1.882, P = 0.027] and 1 year mortality (HR 1.327, 95% CI 1.020-1.728, P = 0.035) than the PaCO2 = 35-45 mmHg group. The PaCO2 > 45 mmHg group was associated with an increased risk of in-hospital mortality (HR 1.387, 95% CI 1.050-1.832, P = 0.021); the 1 year mortality showed no significant difference (HR 1.286, 95% CI 0.995-1.662, P = 0.055) compared with the PaCO2 = 35-45 mmHg group. The Kaplan-Meier survival curves showed that the PaCO2 < 35 mmHg group had a significantly lower 1 year survival rate. The area under the receiver operating characteristic curve for predicting in-hospital mortality was 0.591 (95% CI 0.526-0.656), and the 1 year mortality was 0.566 (95% CI 0.505-0.627) in the PaCO2 < 35 mmHg group. CONCLUSIONS: In AHF patients, hypocapnia within 24 h after admission to the ICU was associated with increased in-hospital mortality and 1 year mortality. However, the increase in 1 year mortality may be influenced by hospitalization mortality. Hypercapnia was associated with increased in-hospital mortality.

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning.

BACKGROUND: Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear. METHODS: The Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram. RESULTS: Among the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram. CONCLUSIONS: The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.

Cytogenetics and associated mutation profile in patients with acute monocytic leukemia.

INTRODUCTION: Cytogenetics and molecular testings for disease classifying and prognosis estimation are becoming routine in clinical practice. However, the molecular characteristics of acute monocytic leukemia (AML-M5) remain unclear. The aim of this study was to investigate the association between karyotypes and gene mutations, especially in AML-M5 patients with 11q23/KMT2A (MLL) rearrangement and normal karyotype. METHODS: A total of 126 de novo AML-M5 patients were screened for mutations in the 51 genes known or suspected to have a role in myeloid malignancies or in monocytic differentiation using next-generation sequencing (NGS). Chromosome karyotype analysis was performed by R-banding method and further confirmed either by fluorescence in situ hybridization (FISH) and/or by multiple reverse transcription polymerase chain reaction (multiple RT-PCR). RESULTS: Of the 126 patients, one or more mutations were detected in 83.3% patients. FLT3-ITD and NRAS had the highest mutation frequency, followed by NPM1, DNMT3A, TET2, KRAS, and RUNX1. We also identified a significant difference in mutational spectrums between KMT2A-rearranged (KMT2Ar) patients and normal karyotype (NK) patients, as reflected in the average number of gene mutations per patient (1.66 vs 2.46), and in the frequencies of commonly mutated genes (FLT3-ITD: 6% vs 43.5%; NPM1: 0% vs 43.5%; RUNX1: 2.0% vs 15.2%; DNMT3A: 4% vs 26.1%; KRAS: 24.0% vs 4.35%). Patients harboring ≥3 mutations showed much lower complete remission rate than that with double mutations (P = 0.043) in high-risk group. CONCLUSION: There was a significantly different mutation profile between KMT2Ar-patients and NK patients. Our research provided new insight into the molecular characteristics of AML-M5.

Study of the Influence of Age in 18F-FDG PET Images Using a Data-Driven Approach and Its Evaluation in Alzheimer's Disease.

Objectives: 18F-FDG PET scan is one of the most frequently used neural imaging scans. However, the influence of age has proven to be the greatest interfering factor for many clinical dementia diagnoses when analyzing 18F-FDG PET images, since radiologists encounter difficulties when deciding whether the abnormalities in specific regions correlate with normal aging, disease, or both. In the present paper, the authors aimed to define specific brain regions and determine an age-correction mathematical model. Methods: A data-driven approach was used based on 255 healthy subjects. Results: The inferior frontal gyrus, the left medial part and the left medial orbital part of superior frontal gyrus, the right insula, the left anterior cingulate, the left median cingulate, and paracingulate gyri, and bilateral superior temporal gyri were found to have a strong negative correlation with age. For evaluation, an age-correction model was applied to 262 healthy subjects and 50 AD subjects selected from the ADNI database, and partial correlations between SUVR mean and three clinical results were carried out before and after age correction. Conclusion: All correlation coefficients were significantly improved after the age correction. The proposed model was effective in the age correction of both healthy and AD subjects.

Routine Hemostasis and Hemogram Parameters: Valuable Assessments for Coagulation Disorder and Chemotherapy in Cancer Patients.

BACKGROUND: The clotting system abnormalities are the common complication in cancer patients. The aim of this retrospective study was to evaluate the coagulation state, clinical features, and treatment in cancer patients by routine tests. METHODS: A total of 2328 patients with different types of cancer were classified as the positive group (n = 1419, including 53 patients with thrombosis) and the negative group (n = 909) based on D-dimer (DD) value. Of the 2328 cases, 354 were admitted for chemotherapy. Hemostasis test and complete blood count (CBC) were performed during treatment or following-up. RESULTS: This study showed that the hypercoagulable state was affected not only by clinical staging (P < 0.0001) but also by metastasis site (P < 0.0001 for bone vs. lung). Compared to negative DD group, the higher fibrinogen level, the extended activated partial thromboplastin time, and prothrombin time interacted markedly with disease clinical stage (P < 0.05) in the positive group. Between positive DD groups with and without thrombus, the significantly statistic difference in white blood cell (WBC) and DD (P < 0.05) rather than in red blood cell (RBC) and platelet count was observed. However, the higher DD level was not correlated with WBC, RBC, and platelet count in the positive DD group. Furthermore, the hypercoagulable plasma profile in cancer patients was moderated 2-3 weeks after chemotherapy (P < 0.05 for first six cycles). CONCLUSIONS: The routine hemostatic parameters and CBC are valuable to assessment for thrombosis and chemotherapy even for disease prognosis.