Sex-dependent effects of long-term clozapine or haloperidol medication on red blood cells and liver iron metabolism in Sprague Dawley rats as a model of metabolic syndrome

Background Patients with liver diseases often have some form of anemia. Hematological dyscrasias are known side effects of antipsychotic drug medication and the occurrence of agranulocytosis under clozapine is well described. However, the sex-dependent impact of clozapine and haloperidol on erythrocytes and symptoms like anemia, and its association with hepatic iron metabolism has not yet been completely clarified. Therefore, in the present study, we investigated the effect of both antipsychotic drugs on blood parameters and iron metabolism in the liver of male and female Sprague Dawley rats. Methods After puberty, rats were treated orally with haloperidol or clozapine for 12 weeks. Blood count parameters, serum ferritin, and liver transferrin bound iron were determined by automated counter. Hemosiderin (Fe3+) was detected in liver sections by Perl’s Prussian blue staining. Liver hemoxygenase (HO-1), 5’aminolevulinate synthase (ALAS1), hepcidin, heme-regulated inhibitor (HRI), cytochrome P4501A1 (CYP1A1) and 1A2 (CYP1A2) were determined by Western blotting. Results We found anemia with decreased erythrocyte counts, associated with lower hemoglobin and hematocrit, in females with haloperidol treatment. Males with clozapine medication showed reduced hemoglobin and increased red cell distribution width (RDW) without changes in erythrocyte numbers. High levels of hepatic hemosiderin were found in the female clozapine and haloperidol medicated groups. Liver HRI was significantly elevated in male clozapine medicated rats. CYP1A2 was significantly reduced in clozapine medicated females. Conclusions The characteristics of anemia under haloperidol and clozapine medication depend on the administered antipsychotic drug and on sex. We suggest that anemia in rats under antipsychotic drug medication is a sign of an underlying liver injury induced by the drugs. Changing hepatic iron metabolism under clozapine and haloperidol may help to reduce these effects of liver diseases.

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission.Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP).Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset.Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.

Monocyte distribution width (MDW): a useful biomarker to improve sepsis management in Emergency Department

Objectives Sepsis is a time-dependent and life-threating condition. Despite several biomarkers are available, none of them is completely reliable for the diagnosis. This study aimed to evaluate the diagnostic utility of monocyte distribution width (MDW) to early detect sepsis in adult patients admitted in the Emergency Department (ED) with a five part differential analysis as part of the standard clinical practice. Methods A prospective cohort study was conducted on 985 patients aged from 18 to 96 and included in the study between November 2019 and December 2019. Enrolled subjects were classified into four groups based on sepsis-2 diagnostic criteria: control, Systemic Inflammatory Response Syndrome (SIRS), infection and sepsis. The hematology analyzer DxH 900 (Beckman Coulter Inc.) provides the new reportable parameter MDW, included in the leukocyte 5 part differential analysis, cleared by Food and Drug administration (FDA) and European Community In-Vitro-Diagnostic Medical Device (CE IVD) marked as early sepsis indicator (ESId). Results MDW was able to differentiate the sepsis group from all other groups with Area Under the Curve (AUC) of 0.849, sensitivity of 87.3% and specificity of 71.7% at cut-off of 20.1. MDW in combination with white blood cell (WBC) improves the performance for sepsis detection with a sensitivity increased up to 96.8% when at least one of the two biomarkers are abnormal, and a specificity increased up to 94.6% when both biomarkers are abnormal. Conclusions MDW can predict sepsis increasing the clinical value of Leukocyte 5 Part Differential analysis and supporting the clinical decision making in sepsis management at the admission to the ED.

Red Blood Cell Distribution Width in Hospitalized COVID-19 Patients

Introduction: Coronavirus disease-2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is causing dramatic morbidity and mortality worldwide. The Red Blood Cell Distribution Width (RDW) has been strongly associated with increased morbidity and mortality in multiple diseases.Objective: To assess if elevated RDW is associated with unfavorable outcomes in hospitalized COVID-19.Methods: We retrospectively studied clinical outcomes of hospitalized COVID-19 patients for their RDW values. In-hospital mortality was defined as primary outcome, while septic shock, need for mechanical ventilation, and length of stay (LOS) were secondary outcomes.Results: A total of 294 COVID-19 patients were finally studied. Overall prevalence of increased RDW was 49.7% (146/294). RDW was associated with increased risk of in-hospital mortality (aOR, 4.6; 95%CI, 1.5-14.6) and septic shock (aOR, 4.6; 95%CI, 1.4-15.1) after adjusting for anemia, ferritin, lactate, and absolute lymphocyte count. The association remained unchanged even after adjusting for other clinical confounders such as age, sex, body mass index, coronary artery disease, hypertension, diabetes mellitus, and chronic obstructive pulmonary disease. No association was found instead with mechanical ventilation and median LOS.Conclusion: Elevated RDW in hospitalized COVID-19 patients is associated with a significantly increased risk of mortality and septic shock.