## 3 Results
### Correlation between MCV, RDW, MPV, PDW, and KILLIIP Class III-IV in NSTEMI Patients
#### 3.2.2 Correlation between MPV and Killip Class III/IV  
On the other hand, as mentioned previously, MPV serves as an important indicator for predicting Killip class III-IV in NSTEMI patients. Concerning the possible pattern of interdependence between MPV and the development of Killip class III-IV in NSTEMI individuals, it was found that as the MPV level increases, the risk of Killip III/IV also rises. These data are graphically represented in Figure 7.  
Figure 6: Likelihood of individuals developing Killip class III–IV derived from MCV value. The figure is an original contribution by the authors. Figure 7: MPV value in patients 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. The phenomenon described above can be better understood by utilizing a logistic regression model (Table 6). This model helps us forecast the likelihood of a patient with NSTEMI developing Killip class III-IV in accordance with the MPV values. The model utilized in this instance is as follows: the occurrence of Killip class III-IV = \(-9.5357\) + \(0.8224\)\(\times\) MPV. For a patient with an MPV of 9 fl, the computation using the formula yields a score of \(-2.1\). Consequently, the likelihood of a patient with NSTEMI and an MPV of 9 fl to develop Killip class III-IV is 0.108, which signifies a 10.8% chance. Based on the data above, it can be concluded that the MPV value can be used as a predictor of the probability of developing Killip class III-IV in NSTEMI patients. Regarding the optimal cut-off value of MPV in predicting Killip class III-IV in NSTEMI patients, this was also determined by the Youden index from the ROC results. An MPV of 11.7 fl shows optimal sensitivity and specificity and appears to be the best threshold value (Figure 8). According to the previously discussed points, it can be concluded that the MPV value can predict the development of severe heart failure following NSTEMI. Furthermore, by \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Measured** & \multicolumn{3}{c}{**Killip Class No. Observations: 96**} \\
**Variable:** & **III/IV** & & & & & \\ \hline  & **Coefficient** & **Standard Error** & **Z** & **\(p>1\)**z** & **[0.025** & **0.975**]** \\ Intercept & \(-9.5357\) & 2.602 & \(-3.665\) & 0 & \(-14.636\) & \(-4.436\) \\ MPV & 0.8224 & 0.26 & 3.168 & 0.002 & 0.314 & 1.331 \\ \hline \hline \end{tabular} The table is an original contribution by the authors.  
\end{table} 
Table 6: Application of the logistic regression analysis to NSTEMI individuals using MPV values. Figure 8: Receiver operating characteristics analysis and curve for predicting Killip class III–IV based on MPV value. The figure is an original contribution by the authors. using a threshold value of 11.7, we can identify individuals who may be at heightened risk of experiencing this complication. Correlation between MCV, RDW, MPV, PDW, and the Necessity of Surgical Revascularization in NSTEMI Patients  
In our research, we discovered a reliable relationship between the value of RDW and the clinician's ability to predict the necessity of surgical revascularization in NSTEMI patients. However, we did not demonstrate any notable association between MCV, MPV, PDW, and the need for surgical revascularization in NSTEMI patients (Table 7). Our findings revealed that NSTEMI patients with high RDW values have a higher probability of needing surgical revascularization. This relationship is depicted in Figure 9, which demonstrates that patients with NSTEMI and RDW exceeding 15 had higher probabilities of necessitating surgical revascularization. The logistic regression model, as illustrated in Table 8, provides us with the coefficients required to predict the necessity of surgical revascularization in patients with NSTEMI based on an RDW value.