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Editorial - Archives of General Internal Medicine (2018) Volume 2, Issue 4

Role of Blood Biochemical Components on Cardiac Parameters for Shock Patients.

Rabindra Nath Das1,2*, Youngjo Lee2*

1Department of Statistics, The University of Burdwan, Burdwan, West Bengal, India

2Department of Statistics, College of Natural Science, Seoul National University, Seoul, 151-747, Korea

*Corresponding Author:
Rabindra Nath Das
Department of Statistics
The University of Burdwan
Burdwan
West Bengal, India
E-mail: [email protected]

Accepted on October 11, 2018

Citation: Das RN, Lee Y. Role of blood biochemical components on cardiac parameters for shock patients.. Arch Gen Intern Med. 2018;2(4):27-29. DOI: 10.4066/ 2591-7951.1000e101

Editorial

Generally, many blood components such as red blood cells (RBC), white blood cells (WBC), blood plasma volume (BPV), hemoglobin (HG), hematocrit (HCT), cholesterol, platelets, etc. may have an active role in atherosclerosis [1-3]. In clinical practice, complete blood count (CBC) is easily available but its practical utility as a potential risk factor for cardiac disease is uncertain [4,5]. A simple measure of RBC size heterogeneity is red blood cell distribution width (RDW), and the increased RDW value is correlated with atrial fibrillation(AF), peripheral artery disease (PAD), acute myocardial infarction (AMI), hypertension, stroke and heart failure (HF) [1,3,5,6]. Decreased hemoglobin level (HGL) is frequently associated with chronic heart failure (CHF), as HGL may affect the cardiovascular system (CVS) through blood viscosity and oxygen supply [3,6]. A low BPV is associated with shock, dehydration, and Addison's disease, while a high BPV is associated with liver and spleen disease, vitamin C deficiency [7,8] .

Most of the earlier studies have examined the association of only one blood component with some cardiac disease. It is little known the association of many blood components together on any cardiac parameter such as blood pressure (BP) (systolic BP [SBP], basal BP [BBP], diastolic BP [DBP], maximum BP [MBP], mean arterial pressure [MAP], mean central venous pressure [MCVP]), heart rate (HR) (basal HR [BHR], peak HR [PHR], maximum HR [MHR]]), ejection fraction (EF), cardiac index (CI) etc. It is stated above that low HGL, low RDW, low and high BPV have many effects on CVS and many diseases. Necessarily, the following queries arise.

· Is any blood component (HGL, BPV, HCT, RBC Index [RBCI]) associated with any or more cardiac parameters (SBP, DBP, HR, CI, MAP, MCVP) for shock patients?

· What is the association of any blood component with one or more cardiac parameters?

· What are the effects of the blood component on the cardiac parameters?

· How do we establish the association of the blood component with one or more cardiac parameters?

Answer of these queries is little examined in the previous articles (best of our knowledge). These answers are reported very shortly (detailed report will be appeared soon) in the current note with the help of real a data set of 113 shock patients with 20 study variables [9], and the data site is http://www.umass. edu/statdata/statdata/data/shock.txt The study characters of the data set are:

· Height (HEIGHT),

· Age (AGE),

· Sex (SEX) (male=0, female=1),

· Shock type (SHOCKT) (non-shock=1, hypovolemic=2, cardiogenic, or bacterial, or neurogenic or other=3),

· Survival stage (SURVIV) (survived=0, death=1),

· HR,

· SBP,

· DBP,

· MAP,

· Mean circulation time (MCT),

· Body surface index (BSI),

· MCVP,

· Appearance time (AT),

· CI,

· Urinary output (UO),

· HG,

· Blood plasma volume index (BPVI),

· HCT,

· RBCI,

· Card sequence record (initial=1, final =2) (CSO).

There are six cardiac parameters such as DBP, MAP, SBP, MCVP, HR, CI and four blood components such as RBCI, BPVI, HCT, HG in the above data set. An appropriate model of each cardiac parameter with the rest variables may give the association of the respective cardiac parameter with the remaining others. Similarly, a good blood component model can present its association with the remaining others. All these above 10 interested positive continuous variables are heteroscedastic, and non-normally distributed, and they are modeled by joint generalized Log-normal and gamma models [10,11]. The models of the above six cardiac parameters (completely or partially) are given in [12-16]. Models of the above four blood components are appeared soon. Cardiac parameter (DBP, MAP, SBP, MCVP, HR, CI) analyses [12-16] (only from the mean models) interpret the following associations of a cardiac parameter with all the blood components.

· DBP is inversely correlated (associated) with BPVI (P=0.0696), indicating that DBP increases as BPVI decreases.

· DBP is directly correlated with HG (P=0.0773), concluding that DBP increases as HG rises.

· MAP is inversely correlated with HCT (P=0.0988), interpreting that MAP increases as HCT decreases.

· SBP is inversely correlated with HG (P=0.0051), interpreting that SBP increases as HG decreases.

· MCVP is directly correlated with BPVI (P=0.0333), indicating that MCVP increases as BPVI rises.

· MCVP is directly insignificantly correlated with RBCI (P=0.3136), indicating that MCVP increases as RBCI rises.

· HR is directly insignificantly correlated with HCT (P=0.1002), indicating that HR increases as HCT rises.

· CI is directly correlated with BPVI (P<0.0001), concluding that CI increases as BPVI rises.

· CI is inversely correlated with HG (P=0.0053), interpreting that CI increases as HG decreases.

Blood component (HG, HCT, BPVI, RBCI) analyses (only from the mean models) interpret the following association of a blood component with all the cardiac parameters.

· HG is directly correlated with MCVP (P<0.0001), concluding that MCVP increases as HG rises.

· HCT is inversely correlated with SHOCKT 2 (P=0.0014), interpreting that shock status is higher at level 1, than at level 2, if HCT increases.

· HCT is inversely correlated with SHOCKT 3 (P=0.0281), interpreting that shock status is higher at levels 1 & 2, than at level 3, if HCT increases.

· BPVI is inversely correlated with DBP (P<0.0001), interpreting that DBP increases as BPVI decreases.

· BPVI is directly correlated with MCVP (P<0.0001), interpreting that MCVP increases as BPVI increases.

· BPVI is directly correlated with CI (P<0.0001), concluding that CI increases as BPVI rises.

· BPVI is inversely correlated with SHOCKT 2 (P=0.0103), indicating that shock status is higher at level 1, than at level 2, if BPVI increases.

· RBCI is inversely correlated with SHOCKT 2 (P=0.0522), concluding that shock status is higher at level 1, than at level 2, if RBCI increases.

All the above interpretations are displayed in Table 1 as a summarized form. The report presents some blood component associations (arising only from mean models) with the cardiac parameters, but there are many more (arising from variance modes), which will be appeared very soon, along with detailed derivations. It is observed that all the blood components have some active role on cardiac parameters. Based on the above knowledge of association, medical practitioners may be able to predict the cardiac parameters, depending on the clinical reports of HG, BPVI, HCT and RBCI.

Variable Associated with Association type P-value
DBP BPVI negative 0.0694
DBP HG positive 0.0773
MAP HCT negative 0.0988
SBP HG negative 0.0051
MCVP BPVI positive 0.0333
MCVP RBCI positive 0.3136
HR HCT positive 0.1002
CI BPVI positive <0.0001
CI HG negative 0.0053
HG MCVP positive <0.0001
HCT SHOCKT-2 negative 0.0014
HCT SHOCKT-3 negative 0.0281
BPVI DVP negative <0.0001
BPVI MCVP positive <0.0001
BPVI CI positive <0.0001
BPVI SHOCKT-2 negative 0.0103
RBCI SHOCKT-2 negative 0.0522

Table 1: Associations of blood components with cardiac parameters.

Conflict of Interest

The authors confirm that this article content has no conflict of interest.

Acknowledgement

This research was supported by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2014M3C7A1062896).

References