## 3 Results
### Correlation between MCV, RDW, MPV, PDW, and KILLIIP Class III-IV in NSTEMI Patients
#### 3.2.1 Correlation between MCV and KILLIIP Class III-IV in NSTEMI Patients  
We demonstrated the relationship between MCV and Killip class III-IV using a linear regression analysis, as displayed in Table 5.  
Based on the information provided in Table 5, we can draw the following conclusions. 1. The \(p\) value (0.029) linked to the MCV coefficient confirms that there is a significant association between higher MCV values and a higher risk of developing Killip class III-IV; 2. The R-square value of 0.0577 indicates that approximately 5.77% of the variability in developing Killip class III-IV can be accounted for by changes in the MCV values; 3. The coefficient for MCV is 0.1134, suggesting that for every one-unit increase in MCV, the score (which could be converted into probability) of developing Killip class III-IV increases by 0.1134. The resulting model is as follows: score (representing the likelihood of class III-IV Killip in NSTEMI) = \(-11.6527+0.1134\times\) MCV value. Using this model, we can calculate the scores for different patients, for example, in the case of a patient with an MCV value of 100 fl, the score is = \(-11.6527+0.1134\times\) 100 = \(-0.3127\). Since scores such as \(-0.3127\) are not easily interpreted, they can be translated into probabilities using the following equation: \(\frac{1}{1+e^{-i\omega t}}\). The calculated likelihood for the above individual is 0.47, indicating a 47% chance of developing Killip class III-IV. This information is further illustrated in Figure 4.  
In Figure 4, the graphical representation demonstrates that patients with a high MCV value (i.e., over 90 fl) possess a higher risk of being stratified as Killip class III-IV compared to those with low MCV values. The optimal cut-off value of MCV in predicting Killip class III-IV in NSTEMI patients was determined by the Youden index from the ROC results. An MCV of 104 fl was found to be the best threshold, with optimal sensitivity and specificity (Figure 5). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dependent Variable** & **r Squared** & **Independent Variable** & **Coefficient** &
\begin{tabular}{c} \(p\) \\ **Value** \\ \end{tabular} \\ \hline Killip class III/IV & 0.05756 & MCV & 0.1134 & 0.029 \\ \hline Killip class III/IV & 0.128 & MPV & 0.8224 & 0.002 \\ \hline Killip class III/IV & 0.04021 & PDW & 0.7878 & 0.105 \\ \hline Killip class III/IV & 0.006249 & RDW & 0.1581 & 0.439 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Correlation between MCV, RDW, MPV, PDW, and KILLIIP class III–IV in NSTEMI patients. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Measured** & \multicolumn{3}{c}{**Killip Class No. Observations: 97**} \\
**Variable:** & **III/IV** & & & & & \\ \hline \multirow{3}{*}{Intercept} & **Coefficient** & **Standard Error** & **Z** & \(p\) **>** | z | & **[0.025** & **0.975**] \\  & \(-11.6527\) & 4.727 & \(-2.465\) & 0 & \(-29.917\) & \(-2.38\) \\ \cline{1-1}  & 0.1134 & 0.052 & 2.178 & 0.029 & 0.011 & 0.215 \\ \hline \hline \end{tabular}  
The table is an original contribution by the authors.  
\end{table} 
Table 5: Logistic regression applied to NSTEMI patients based on MCV values. Figure 6 demonstrates the relationship between MCV values and the likelihood of a patient developing Killip class III-IV. As indicated by the logistic regression analysis, as MCV increases, the likelihood of patients being in Killip class III-IV also increases. For example, patients with an MCV of 100 fl had a 45% chance of developing Killip class III-IV, compared with a probability of 28% in individuals with an MCV of 90 fl. Figure 4: MCV levels in individuals with NSTEMI Killip class III–IV vs. without Killip class III–IV. The diamond symbol means an aberrant value. The colors represent different values. The figure is an original contribution by the authors. Figure 5: ROC curve for predicting Killip class III–IV based on the MCV value. The figure is an original contribution by the authors.