The Role of Red Cell Index on Cardiac Factors for Shock Patients

Special Article - Hemoglobin

Austin Hematol. 2019; 4(1): 1024.

The Role of Red Cell Index on Cardiac Factors for Shock Patientss

Das RN1,2* and Lee Y2

¹Department of Statistics, The University of Burdwan, India

²Department of Statistics, Seoul National University, Korea

*Corresponding author: Rabindra Nath Das, Department of Statistics, The University of Burdwan, Department of Statistics, College of Natural Science, Seoul National University, Seoul, 151-747, Korea

Received: October 21, 2019; Accepted: October 24, 2019; Published: October 31, 2019


The Red Blood Cells (RBCs) are acquainted as erythrocytes. The normal size of RBCs usually lies between 80 and 100fL. Practically, Red Cell Index (RCI) is a blood trail, which provides information about the hemoglobin content & Red Blood Size (RBS) Mean Corpuscular Volume (MCV), while MCV explains the average Red Blood Cell Size (RBCS), which is calculated by dividing the Hematocrit (HCT) by the red cell count [1-4]. Many research articles have reported as effective predictors of cardiac disease treating, RCI associated with HCT & MCV, Blood Plasma Volume (BPV), and White Blood Cells (WBC), along with its subtypes such as lymphocytes, monocytes, and neutrophils [1,4-6]. The following queries are examined in the current report.

• Is there any association of RCI with some cardiac factors for shock patients?

• If it is affirmative, what are the associations?

• What are the effects of RCI on cardiac factors?

These queries are studied in the report with the help of a real data set of 113 shock patients containing 20 characters, which is given in: and the patient population and data collection method are well described in [7]. For ready reference, the 20 study characters are reported as follows.

• Age,

• Sex (male=0, female=1),

• Height,

• Systolic Blood Pressure (SBP),

• Shock Type (Shock) (non-shock=1, hypovolemic=2, cardiogenic, or bacterial, or neurogenic or other=3),

• Diastolic Blood Pressure (DBP),

• Survival Status (SURVIV) (survived=1, death=2),

• Heart Rate (HR),

• Hematocrit (HCT),

• Hemoglobin (HG),

• Plasma Volume Index (PVI),

• Cardiac Index (CI),

• Appearance Time (AT),

• Mean Arterial Pressure (MAP),

• Urinary Output (UO),

• Mean Central Venous Pressure (MCVP),

• Mean Circulation Time (MCT),

• RCI,

• Body Surface Index (BSI),

• Card Record Order (initial=1, final=2) (CRO).

The above data set contains seven cardiac factors such as DBP, SBP, MAP, HR, MCVP, CI and shock type. The above queries should be examined in two ways such as modeling of a cardiac factor on RCI, along with the remaining others, and also modeling of RCI on all the cardiac factors, along with the remaining others. Note that DBP, SBP, MAP, HR, MCVP, CI are all continuous variables, while shock type is an attribute character.

Let us examine the RCI modeling on all the cardiac factors, along with the remaining other variables. Here all the continuous variables are positive, heterogeneous, continuous and non-normally distributed. These are modeled using Joint Generalized Linear Models (JGLMs) under both the Log-normal & Gamma distributions [8- 10]. Log-normal JGLMs fit of RCI is better than the Gamma, which is shown in Table 1, and its fit verification is displayed in Figure 1. Figure 1(a) shows the absolute residuals plot against the predicted RCI values, which is approximately a flat straight line, concluding that variance is constant with the running means. On the hand, Figure 1(b) shows the normal probability plot of mean RCI Lognormal fitted model in Table 1. Both the plots do not reveal any fit discrepancy. Therefore, Log-normal fitted RCI model (Table 1) is approximately a very close to its true model. RCI mean & dispersion models are as follows.