İstanbul Medical Journal (Feb 2023)

Investigation of the Prognostic Values of the Shock Index and Modified Shock Index in Predicting the Clinical Outcomes in Elderly Hospitalized Patients with Coronavirus Disease-2019

  • Serdar Yeşiltaş,
  • Saadet Öztop,
  • Mustafa Günay,
  • İsmail Sümer,
  • Sedat Akbaş,
  • Sinan Yılmaz,
  • Özge Pasin,
  • Kazım Karaaslan

DOI
https://doi.org/10.4274/imj.galenos.2023.44380
Journal volume & issue
Vol. 24, no. 1
pp. 65 – 70

Abstract

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Introduction:Advanced age is an independent risk factor for increased mortality in coronavirus disease-2019 (COVID-19). However, the best method for estimating mortality in elderly patients with COVID-19 is still under debate. We performed this study to assess the shock index (SI) and the modified shock index (MSI) for the abovementioned problem.Methods:A retrospective study was conducted including elderly cases (≥65 years) confirmed with COVID-19 who admitted to a tertiary university hospital between March-December 2020. The SI and MSI at the time of the emergency department visits were used to evaluate the intensive care unit admission, ventilator support, septic shock, and 30-day mortality in all patients. The receiver operating characteristic and area under the curve (AUC) were used to measure the overall ability of SI and MSI to predict clinical outcomes.Results:We recruited 334 consecutive COVID-19 patients with a mean age of 75.2±7.3 and an almost equal gender distribution [170 males (50.9%)]. In deceased and surviving patients, the SI was 0.66±0.16 and 0.6±0.1 (p=0.014), while the MSI was 0.95±0.22 and 1.09±0.34 (p=0.003), respectively. In predicting mortality, the AUC of the SI and MSI were 0.590 [95% confidence interval (CI): 0.535 to 0.643] and 0.608 (95% CI: 0.553 to 0.660), respectively.Conclusion:Increased SIs and MSIs are associated with 30-day mortality. SI and MSI can benefit the triage of elderly patients hospitalized for COVID-19. However, it was found that there is no single cut-off value of SI or MSI with optimum accuracy for predicting COVID-19-related clinical outcomes.

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