European Journal of Cardiovascular Medicine

Coronary artery disease or CAD is a common cardiac condition linked with atherosclerotic plaque in coronary artery lumen which is asymptomatic for long and is identified as most common cause of disability and mortality globally. Cardiovascular diseases account for nearly 20 million deaths yearly. In countries like India, a high mortality burden is seen with CADs with 7 million yearly deaths and is associated with high mortality rates. India has highest cardiovascular disease incidence including CADs and has highest growth rate. In India, 265 adult deaths and 17% overall deaths are attributed to cardiovascular diseases. Also, the prevalence of cardiovascular diseases has increased in India in past few decades.¹

Owing to significant change in activity levels, dietary performances, and lifestyle, cardiovascular diseases are increasing in India at an alarming rate making India as highest susceptible population for cardiovascular diseases and contributing to 60% burden of heart disease in the World. Also, Indian subjects with cardiovascular diseases are 5-6 years younger compared to developed nations affecting middle aged subjects. Indians have cardiovascular diseases that are more complex as ischemic heart diseases which reduce the blood supply to heart.²

In subjects with blocked coronary artery, CABG (coronary artery bypass graft) or stenting is a common procedure along with PCI (percutaneous coronary intervention) which manage narrowed or blocked coronary artery to regulate blood supply to ischemic areas. PCI uses stents and presently, DESs (drug-eluting stents) have largely replaced the bare metal stents (BMS) owing to their efficacy to reduce restenosis rates. In outcomes of PCI, it has been linked to increased risk of mortality or morbidity compared to healthy subjects.³

Atherosclerosis can increase the CVDs (cardiovascular diseases) risk which can be assessed with markers as CK-MB (MB isoenzyme of creatine kinase) and troponin which are commonly used in subjects with MI (myocardial infarction), however, it is unable to diagnose the risk for chronic CAD. RDW (Red cell distribution width) which is used for complete blood counts measures variation in red blood cell volume. With the availability of automated cell counters, platelet count (PC) and the platelet volume indices (PVI)-mean platelet volume (MPV), platelet distribution width (PDW), and platelet large cell ratio (P-LCR) has become routinely available in most clinical laboratories. Therefore, using a multimarker approach may be of benefit to diagnose the spectrum of clinical manifestation, like CAD. All the blood elements as white blood cells (WBCs e.g, neutrophils, monocytes, lymphocytes, and eosinophils), red blood cells (RBCs), and platelets are involved in atherosclerosis pathogenesis and RDW has recently been included as risk marker for cardiovascular disease with high RDW levels in subjects with MI, CAD, and heart failure.⁴

Other hematological parameters like monocyte to high density lipoprotein ratio (MHR) and haemoglobin to red –cell distribution width ratio (HRR), have also come up as marker of atherosclerosis progression and subsequent adverse cardiovascular risk. Few studies in literature have reported that MHR and HRR high predictive value in post-PCI and heart failure patients. Concerning PCI, HRR can be predictive low HRR signifying increased cardiovascular events and mortality in subjects with CAD. HRR monitoring can help in identification of subjects at higher complication risk for better management and follow-up.⁵ Hnece, the present study was aimed to compare the novel predictive markers monocyte to high density lipoprotein ratio (MHR) and haemoglobin to red –cell distribution width ratio (HRR) in coronary heart disease post – PCI.

The present prospective clinical study was aimed to compare the novel predictive markers monocyte to high density lipoprotein ratio (MHR) and haemoglobin to red –cell distribution width ratio (HRR) in coronary heart disease post – PCI. The study was carried out at Department of Cardiology, BMRC Hospital, Barrackpore Trunk Rd, Talpukur, Titagarh, Barrackpore, West Bengal in the period between May 2024 to May 2025. An informed consent was obtained from all the patients

The study included subjects that were diagnosed for coronary heart disease attending the Department of Cardiology after confirmation with Echocardiography and Trop-I investigation. The inclusion criteria for the study were Patients with coronary heart diseases confirmed with ECG, Echocardiography and Trop-I investigation and subjects scheduled to undergo percutaneous intervention. The exclusion criteria for the study were subjects having malignant tumors, infectious diseases, hematological diseases, liver diseases, as well as severe heart failure, congenital, rheumatic, or valvular heart disease, and severe kidney or liver dysfunction.

The sample size for the study was assessed using Sample size Formula.^95,96 Sample size was calculated using the formula: N = [Z2 α/2xVF] L2 where, Zα/2 = 1.96 for a 95% CI. L= desired half-width of the CI. VF= (0.0099 x e-AxA/2) x [5 x A2 + 8) + (A2 + 8)/k].

In all the subjects, 4 ml of whole blood was collected in ethylene diamine tetra acetic acid (EDTA) and plain vials which was used for testing hematological parameters. Tests were done by H 560 Automated blood cell count analyser Transasia XS-800i and manual cross check by microscopy. For the present study, the parameters considered were Hemoglobin, RDW-CV, DLC: Monocyte count. Then the ratio of Haemoglobin to RDW-CV and Monocyte to HDL ratio was calculated. All the subjects were followed for 6 months.

Hemoglobin was measured using colorimeter. Prior to the addition of the blood sample, the baseline voltage of the diluent is first measured. Then the blood sample and lyse are mixed well for a complete reaction so that the parameter voltage of the sample can be measured. Hemoglobin can then be calculated based on local voltage and parameter voltage according to Lambert Beer’s Law. Measurement Process: The measurement process for the WBC/haemoglobin module is as follow: Dosing: The diluent syringe is first applied to add the diluent into the WBC bath, and then the sample probe is used to add the blood sample into the WBC bath, where they are mixed evenly. After aspirating the diluted sample for the first time, Lyse1-H580 is added into the WBC bath for incubation. Mixing: Open valve LV15 generates air bubbles through the intermittent valve opening to mix the sample well. Measurement: Open Valve LV15 aspirates the sample out of the WBC bath through the aperture by means of the negative pressure chamber (with a negative pressure of -30 Kpa). The WBC particles generate electric pulses when travelling through the aperture, allowing the WBC cells to be counted according to the number of pulses emitted. Cleaning: To clean LV4 and LV8 are opened and diluent is added to the WBC bath using the diluent syring. Waste discharge: Waste is discharged by opening LV25 (or Valve 21) and Pump P1.

To assess HDL, blood sample was collected in plain vials, Test was done by Vitros 5600. The VITROS 5600 Integrated System uses dry slide technology for HDL (High-Density Lipoprotein) cholesterol testing, which is an essential aspect of lipid profiling to assess cardiovascular health. Here’s a detailed explanation of how the VITROS 5600 measures HDL cholesterol: Principle of HDL Measurement on the VITROS 5600 Separation of HDL Cholesterol: HDL cholesterol is separated from other lipoproteins in the blood sample. This is typically done using a selective detergent or other chemical reagents that precipitate non-HDL lipoproteins, leaving HDL cholesterol in the solution. Dry Slide Technology: The VITROS 5600 employs dry slide technology, which is a multi-layered analytical device that contains all the necessary reagents for the HDL cholesterol test. The dry slide consists of several layers, each with a specific function, including spreading, reagent, and indicator layers.

Sample Application: A small amount of the blood sample (serum or plasma) is applied to the dry slide. Reaction Process Spreading Layer: The sample spreads evenly across this layer to ensure uniform distribution. Reagent Layer: Contains specific enzymes and reagents that react with HDL cholesterol. Typically, the cholesterol is converted to cholest-4-en3-one and hydrogen peroxide by the action of cholesterol oxidase. Indicator Layer: The hydrogen peroxide produced in the reagent layer reacts with a chromogen (a color-producing reagent) in the indicator layer to produce a colored product. Measurement: The intensity of the color produced is proportional to the concentration of HDL cholesterol in the sample. The VITROS 5600 measures this color intensity using reflectance photometry. The system uses a spectrophotometer to measure the optical density of the colored product at a specific wavelength, which correlates with the HDL cholesterol concentration. Calculation: The instrument's software calculates the HDL cholesterol concentration based on the measured optical density and the calibration curve obtained from known standards.

The outcomes assessed in the study subjects were Major adverse cardiovascular event (MACE) Manifestation of mild signs and symptoms like anxiety, fatigue, chest pain – not amounting to hospital visit, mortality, and restenosis. Demographic data and clinical data of patient was obtained from case files and was recorded on a separate sheet. Findings of laboratory investigations done were also recorded on the same sheet. Patients were followed till discharge or final outcome.

 The data was analysed using SPSS 25.0 software. Qualitative/ categorical data has been shown as numbers and percentages. Continuous data has been shown as Mean ± SD. Chi-square, Independent samples ‘t’ test and ANOVA followed by Tukey’s HSD were used to compare the data. Receiver-operator characteristic curve (ROC) analysis was performed to derive study specific cut-off values of predictors. A ‘p’ value less than 0.05 was considered as 0.05.

The present study was conducted to assess the role of monocyte to high density lipoprotein ratio (MHR) and haemoglobin to red cell distribution width ratio on clinical outcome of coronary heart disease in post-percutaneous coronary intervention (PCI). All the patients of coronary heart disease undergoing PCI were informed about the objectives of the study of these 138 patients fulfilling the inclusion criteria and giving their consent for participation in the study were enrolled in the present study after obtaining an informed consent.

Age of patients ranged from 27 to 84 years, mean age was 56.64±10.05 years, most common age groups were 51-60 years (37.0%) and 61-70 years (28.3%) while least common age groups were ≤40 years (5.1%) and >70 years (6.5%). Out of 138 patients approximately one-fourth (n=35; 25.4%) were females, rest were males (74.6%). Male: Female ratio was 2.94. For range and mean values of Serum hemoglobin (6.10- 16.20; 11.91±1.82 g/dl), Red cell distribution width (11.0-16.1; 12.52±1.10%), High density lipoprotein levels (14-78; 36.52±8.79 mg/dl), monocytes (1-9; 3.17±1.51), MHR (0.020-0.2727; 0.0918±0.0496) and HRR (0.5169-1.3304; 0.9579±1.600). Approximately half of the patients (50.7%) patients did not suffer from anaemia, 39.9% had mild anemia, 12 (8.7%) had moderate anemia, only 1 (0.7%) had severe anemia. Out of 138 patients enrolled in the study 82 (59.4%) had HRR level. Only 21 (15.2%) patients had MHR levels ≥0.14, majority of patients had MHR levels <0.14. Out of 138 patients who underwent PCI, 117 (84.8%) had no adverse symptoms after PCI, 17 (12.3%) had mild symptoms and 4 (2.9%) expired during/after PCI (Table 1).

Table 1: Demographic and disease data in study subjects

HRR of younger patients i.e. ≤40 yrs (1.0405±0.1703) & 41-50 yrs (0.9893±0.1183) were higher as compared to older patients i.e. 51-60 yrs (0.9435±0.1792), 61-70 yrs (0.9643±0.1469) and >70 yrs (0.8351±0.1749) but this difference was not found to be significant statistically (p=0.062). MHR of younger patients i.e. ≤40 yrs (0.1291±0.0498) & 41-50 yrs (0.0957±0.0474) were higher as compared to older patients i.e. 51-60 yrs (0.0857±0.0454), 61-70 years (0.0923±0.0569) and >70 yrs (0.0817±0.0417), when compared statistically this difference was not found to be significant (p=0.261). Mean HRR of males (0.9905±0.1523) was significantly higher as compared to that of females (0.8617±0.1442). Mean MHR of males (0.0964±0.0519) was higher as compared to that of females (0.0784±0.0402) but this difference was not found to be significant statistically (p=0.064). Mean HRR of patients with no problem after PCI (0.9666±0.1582) was maximum followed by those with mild problem (0.9479±0.1399), minimum HRR was observed for patients who Expired after PCI (0.7437±0.1793). This difference was found to be significant. On exploring the between group differences, significant difference was found only between Expired patients and patients with no problem. Mean MHR of patients with no symptom after PCI (0.0861±0.0427) was minimum followed by those with mild symptoms (0.1134±0.0699), maximum MHR was observed for patients who Expired after PCI (0.1686±0.0662). This difference was found to be significant. On exploring the between group differences, significant difference was found only between Expired patients and patients with no symptoms (Table 2 and 3).

Table 2: Correlation of various study parameters to MHR and HHR values

Table 3: Association of HHR and MHR to post PCI-outcomes in study subjects

HRR ≤1.0 was observed in lower proportion of patients with No problem as compared to those with Mild problem or Expiry (56.4% vs. 70.6% & 100%). This difference was not found to be significant statistically. Majority of cases with No problem and Mild problem had MHR ≤1.4 (below cut-off) while majority of Expired cases MHR was ≥0.14 (above cutoff). Receiver-operator curve of HRR and MHR for prediction of mortality in the present study was obtained (Table 4).

Table 4: Association of HRR Abnormality (Cut-off ≤1.0) and MHR Abnormality (Cut-off ≥0.14) with Post-PCI Outcome (N=138)

Based on the direction of assessment, HRR level was evaluated for prediction of post-PCI mortality at a cut-off with a lower value indicating positive result. Area under curve for HRR was 0.838 (indicating a projected accuracy of 83.8%). A cut off of HRR ≤0.8116 was found to be 75% Sensitive and 85.1% Specific. MHR level was evaluated for prediction of post-PCI mortality at a cut-off with a higher value indicating positive result. Area under curve for MHR was 0.863 (indicating a projected accuracy of approximately 86.3%). A cut off of MHR ≥0.1017 was found to be 100% Sensitive and 88.9% Specific. Receiver operator Analysis was done to predict poor outcome (Mild problem or mortality) for Novel marker HRR. HRR level was evaluated for prediction of post-PCI problems (mild symptom + mortality) at a cut-off with a lower value predicted positive result. Area under curve for HRR was 0.605 but was not found to be statistically significant. Hence, no cut off of HRR was drawn. MHR level was evaluated for prediction of post-PCI problems (mild symptom + mortality) at a cut-off with a higher value indicating positive result. Area under curve for MHR was 0.655. A cut off of MHR ≥0.1342 was found to be 47.6% Sensitive and 86.3% Specific (Table 5).

Table 5: ROC analysis of MHR and HRR for Prediction of Mortality and Poor outcome (Mild symptom + mortality)

CAD (coronary artery disease) is a condition having characteristic of blockage or narrowing of coronary arteries majorly due to atherosclerosis which can result in decrease of blood flow to the muscles of the heart which can result in heart attack, chest pain, or other serious cardiac conditions. Post PCI (percutaneous coronary intervention) outcomes are governed by many factors including procedural details, postprocedural data, clinical characteristics, and demographics. Age is a vital predictor with old subjects showing worse outcomes from high comorbidities as chronic kidney disease, diabetes, and hypertension all of which can complicate recovery and increase the risk of adverse events such as restenosis and stent thrombosis.⁶ The clinical presentation at the time of PCI significantly affects outcomes.

In the present study, we take up two such novel markers, i.e. monocyte to high density lipoprotein ratio (MHR) and haemoglobin to red–cell distribution width ratio (HRR) for their value in prediction of clinical outcome of coronary heart disease in post-PCT patients. For this purpose, a total of 138 patients fulfilling the eligibility criteria of the study (age range 27-84 years; mean age 56.64±10.05 years; 74.6% males) were enrolled in the study. Compared to the present study, Wu et al⁷ carried out their study on 673 CAD patients with mean age 59.1 years and a dominance of males (80.7%). Thus, sample size of their study was much larger than ours but age and sex profile was almost similar to that in the present study. Xiu et al⁸ also had a much larger sample size (6,046) but had a dominance of males (74.3%) like our study and mean age of patients close to 60 years. Çiçek et al⁹ in another study had 682 patients with mean age 56.6 years which is close to that in the present study and more than 80% males. Thus, though most of the studies have age and sex profile of patients like ours but had sample size much larger than ours. As such, sample size is one of the limitations of the study.

In the present study, we followed up the patients up to 6 months post-PCI interval. This could be considered only a short-term follow-up. Compared to the present study, other workers had a much longer follow-up duration. Wu et al.¹⁰ in their study reported follow-up duration ranging from 31 to 45 months. In the study of Xiu et al.43 mean follow-up length was 35.9 months. In other studies, too this duration ranged from 25.8 to 62.27 months.^9,11,12

In the present study proportion of patients having HRR>1.0 was 40.6%. In the study of Xiu et al,⁸ higher HRR was recorded in 61% of the patients, thus showing a relatively higher risk. In the present study, we found higher HRR to be significantly associated with male sex which is also in agreement with the study by Xiu et al.⁸ In their study, they also found it to be significantly associated with other factors like smoking, diabetes, hypertension, non-alcohol use, haematocrit, uric acid, glucose, blood urea, creatinine and absence of statin use. However, in the present study, we did not record most of these factors. In their study, younger age was also found to be significantly associated with increased HRR. However, in our study, no such association was seen. We agree that HRR can be influenced by most of these factors as they are recognized cardiovascular risk factors that can interfere with the PCI outcomes, however, as the purpose of the study was not to evaluate the relationship of all these factors with HRR but with the PCI outcome, hence we did not delve into this issue. However, these factors tend to explain the difference in proportion of patients with increased HRR in two studies as it may be variable depending upon the overall difference in cardiovascular risk profile of patients in different studies. Nevertheless, the predictive value of HRR for post-PCI outcomes is of major consideration.

In the present study, we did not find out a significant association between lower HRR levels and symptomatic manifestation. However, lower HRR levels were found to be significantly associated with mortality. Notably, there were no adverse cardiovascular events except for 4 (2.9%) mortalities which may solely be attributed to shorter duration of follow-up in the present study as compared to that in the earlier studies. It may be noted that mortality rates close to 5% have been reported by Xiu et al⁸ in their study with nearly three years of follow up duration while major adverse cardiovascular events (MACE) were reported to be close to 13%. In their study, both MACE and all other cause of mortality showed a significant association with HRR. Within the limitations of the present study, we could also get similar trends in our study, though both sample size as well as length of follow-up acted as the barriers to achieve more concrete and robust evidence. Hence, the findings of the present study depict the usefulness of HRR as a predictor of post-PCI outcomes.

In the present study, there were 21 (15.2%) patients having MHR levels above selected cut-off and showed a significant association with mortality as well as mild symptomatic manifestation. Compared to the present study, Çiçek et al⁹ found 74.8% patients with MHR above the cut-off value while Wu et al.87 found 75.2% patients with MHR above >0.19. Moreover, both these showed a significant association of MHR with age and other cardiovascular risk factors. In the present study, however, we did not find them to be significantly affected by age. Considering higher MHR levels to be associated with increased risk of adverse outcomes, compared to the present study they had a relatively higher proportion of at-risk population as compared to that in the present study. In the present study, we observed MHR to be significantly associated with short-term mortality which is in agreement with the findings of Çiçek et al⁹ who also found MHR to be significantly associated with in-hospital mortality. Both Wu et al¹⁰ as well as Çiçek et al⁹ also observed it to be associated with long-term major adverse cardiovascular events as well as all cause/ cardiovascular mortality.

In the present study, both MHR as well as HRR to hold value in prognosis even during the short-term follow-up. These findings are in agreement with the observations of Xiu et al⁸ Cicek et al⁹ and Wu et al¹⁰ who observed it for the long-term. The two major limitations of the study were sample size and short duration of follow-up. Apart from these major limitations absence of detailed risk profile of patients was also a limitation in assessing the value of these two predictive markers in a multivariate scenario. Further studies on larger sample size, with longer duration of follow-up and exhaustive details of risk profile of patients are recommended to reach at a more robust, concrete and reliable evidence as observed in the present study at a pilot level.

The findings of the study showed the scope for utilization of MHR and HHR for prognostic purposes in post-PCI coronary heart disease patients. In the present study, despite the limitation of a short-term follow-up and limited sample size they depicted some predictive value. Further studies on a larger sample size and longer duration of follow-up are recommended to validate and substantiate the findings of the present study.

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