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. 2023 Apr 10;15(4):e37413.
doi: 10.7759/cureus.37413. eCollection 2023 Apr.

Role of Routine Blood Parameters in Predicting Mortality Among Surgical Patients With Sepsis

Srishti Dixit  1 , Jainendra K Arora  1 , Rakesh Kumar  2 , Rashmi Arora  3
Affiliations

Role of Routine Blood Parameters in Predicting Mortality Among Surgical Patients With Sepsis

Srishti Dixit et al. Cureus. .

Abstract

Background: Outcome prediction for surgical patients with sepsis may be conducive to early aggressive interventions. In several studies, changes in the level of numerous biomarkers like red cell distribution width (RDW), platelet count (PC), mean platelet volume (MPV), and platelet distribution width (PDW) have been demonstrated to be associated with mortality in critically ill patients. We aimed at investigating the prognostic significance of dynamic changes in RDW, PC, MPV, and PDW in surgical patients with sepsis.

Methods: We prospectively enrolled 110 surgical patients of sepsis in our study admitted to the surgical ward and ICU. We measured RDW, PC, MPV, and PDW on days 1, day 4, and day 8. Receiver operating characteristics (ROC) were generated for prognostic validation of these parameters and mortality in surgical patients with sepsis. Results: We found that higher RDW and PDW on day 1 among non-survivors as compared to survivors on day 1 were significantly associated with mortality. ROC curves showed that RDW and PDW on day 1 could be used to predict mortality in surgical patients with sepsis and it was dynamic changes in PC on day 4 and day 8 along with a change in MPV on day 8, which was significantly associated with mortality.

Conclusion: The major findings of our study were baseline value of RDW and PDW on day 1 and continuous decrease in PC and increase in MPV over one week were significantly associated with mortality. So, it is better to monitor dynamic changes in PC and MPV in combination with baseline RDW and PDW. So, these parameters can be promising markers to assess prognosis in surgical patients with sepsis.

Keywords: mean platelet volume (mpv); mortality; platelet count (plt); platelet distribution width; red cell distribution width (rdw); sepsis.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Trend of RDW (%) on day 1, day 4, and day 8
RDW: red cell distribution width
Figure 2
Figure 2. Receiver operating characteristic curve of mean RDW (%) on day 1
RDW: red cell distribution width
Figure 3
Figure 3. Association of mean RDW (%) on day 1 with outcome using box and whisker plot
RDW: red cell distribution width
Figure 4
Figure 4. Trend of PC in lakhs (cells/mm3) on day 1, day 4, and day 8
PC: platelet count
Figure 5
Figure 5. Receiver operating characteristic curve of mean change in PC in lakhs (cells/mm3) on day 4
PC: platelet count
Figure 6
Figure 6. Receiver operating characteristic curve of mean change in PC in lakhs (cells/mm3) on day 8
PC: platelet count
Figure 7
Figure 7. Association between change in mean PC in lakhs (cells/mm3) on day 4 and day 8 with outcome
PC: platelet count
Figure 8
Figure 8. Trend of MPV (cm3) on day 1, day 4, and day 8
MPV: mean platelet volume
Figure 9
Figure 9. Receiver operating characteristic curve of mean of change in MPV (cm3) on day 8 for mortality assessment
MPV: mean platelet volume
Figure 10
Figure 10. Association between change in MPV (cm3) on day 4 and day 8 with outcome
MPV: mean platelet volume
Figure 11
Figure 11. Trend of PDW (%) on day 1, day 4, and day 8
PDW: platelet distribution width
Figure 12
Figure 12. Receiver operating characteristic curve of mean PDW (%) on day 1 for mortality assessment
PDW: platelet distribution width
Figure 13
Figure 13. Association of mean PDW (%) on day 1 with outcome using box and whisker plot
PDW: platelet distribution width
See this image and copyright information in PMC

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