## 3 Results
### Correlation between MCV, RDW, MPV, PDW, and 30-Day Mortality  
Regarding the potential use of MCV, MPV, RDW, and PDW as predictors of one-month mortality, our findings indicate that only PDW demonstrated this predictive capability (Table 11). \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Measured** **Variable:**} & \multicolumn{3}{c}{**Triple No. Observations: 97**} & \multirow{2}{*}{} \\  & **Vessel** & & & & & \\  & **Disease** & & & & & \\ \hline \multirow{3}{*}{Intercept} & **Coefficient** & **Standard Error** & **Z** & \(p>\) **lz** & **[0.025** & **0.975**] \\  & \(-\)5.8627 & 2.585 & \(-\)2.568 & 0.023 & \(-\)10.93 & \(-\)0.795 \\ \cline{1-1}  & RDW & 0.3729 & 0.181 & 2.058 & 0.004 & 0.018 & 0.728 \\ \hline \end{tabular} The table is an original contribution by the authors.  
\end{table} 
Table 10: Logistic regression for NSTEMI individuals and triple vessel disease considering an RDW value. Figure 14: Receiver operating characteristics analysis and curve for predicting triple vessel disease based on an RDW value. The figure is an original contribution by the authors. Based on the information provided in the table below (Table 12), the following conclusions can be drawn. 1. The \(p\) value = 0.03 linked with the PDW coefficient validates a reliable relationship between elevated PDW values and an increased risk of one-month mortality; 2. The R-square value of 0.1689 indicates that approximately 16.89% of the variability in 30-day mortality can be accounted for by changes in the PDW values; 3. The coefficient for PDW is 1.6545, suggesting that for every one-unit increase in PDW, the one-month mortality rises by 11.65%. The estimated model is the following: score (representing the likelihood of one-month mortality) = \(-\)31.2314 + 1.6545 \(\times\) PDW value. Using this model, we can calculate the scores for different patients, for instance, for a patient with a PDW value of 18%, the score is = \(-\)31.2314 + 1.6545 \(\times\) 18= \(-\)1.4504. Since these results (i.e., scores such as \(-\)1.4504) do not provide much ease of interpretation, we can convert them into probabilities using the equation = \(\frac{1}{1+e^{-\cos\theta}}\). The probability for the patient above is 0.189, representing an 18.9% chance of one-month mortality. The optimal cut-off value of PDW in predicting one-month mortality in NSTEMI patients was determined by the Youden index from the ROC results. A PDW at 19% was found to be the best threshold, featuring optimal sensitivity and specificity (Figure 15). The three graphics below show that patients with NSTEMI and high values of PDW tend to have a higher risk of one-month mortality--more exactly, patients with a PDW value above 17.5% (Figure 16). Figure 17 displays the link between PDW value and one-month mortality. By observing the logistic regression, we can see that as the PDW value increases, the likelihood of one-month mortality increases as well. More concretely, we found that patients with a PDW of 17% had a 4% probability of one-month mortality. Therefore, based on the arguments outlined above, it can be affirmed that the PDW value provides insight into the likelihood of one-month mortality in individuals with NSTEMI. A higher PDW value correlates with an increased likelihood of this event occurring. Due to the small cohort size, the association between PDW and one-month mortality is statistically reliable(\(p<0.05\)) but holds little practical significance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dependent Variable** & **r Squared** & **Independent Variable** & **Coefficient** &
\begin{tabular}{c} \(p\) \\ Value \\ \end{tabular} \\ \hline One-month mortality & 0.0002991 & MPV & \(-\)0.0448 & 0.914 \\ \hline One-month mortality & 0.006695 & MCV & \(-\)0.0379 & 0.598 \\ \hline One-month mortality & 0.03529 & RDW & 0.3883 & 0.215 \\ \hline One-month mortality & 0.1689 & PDW & 1.6545 & 0.03 \\ \hline \hline \end{tabular} The table is an original contribution by the authors.  
\end{table}