Prediction of median survival time in sepsis patients by the SOFA score combined with different predictors

Wen Li; Meiping Wang; Bo Zhu; Yibing Zhu; Xiuming Xi

doi:10.1093/burnst/tkz006

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Prediction of median survival time in sepsis patients by the SOFA score combined with different predictors

更新时间：2022-06-09

- Prediction of median survival time in sepsis patients by the SOFA score combined with different predictors
- Burns & Trauma Vol. 8, Issue 1, (2020)
- 作者机构：
  
  1. Department of Critical Care Medicine
  2. Department of Epidemiology and Health Statistics
  3. Department of Statistics
- 作者简介：
- 基金信息：
- DOI：10.1093/burnst/tkz006
  CLC：
- Published：2020，
  
  Published Online：16 January 2020，
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Wen Li, Meiping Wang, Bo Zhu, Yibing Zhu, Xiuming Xi, Prediction of median survival time in sepsis patients by the SOFA score combined with different predictors, Burns & Trauma, Volume 8, 2020, tkz006, https://doi.org/10.1093/burnst/tkz006
DOI：

Wen Li, Meiping Wang, Bo Zhu, Yibing Zhu, Xiuming Xi, Prediction of median survival time in sepsis patients by the SOFA score combined with different predictors, Burns & Trauma, Volume 8, 2020, tkz006, https://doi.org/10.1093/burnst/tkz006 DOI：

摘要

Abstract

BackgroundSepsis is the leading cause of intensive care unit (ICU) admission. The purpose of this study was to explore the prognostic value of the Sequential Organ Failure Assessment (SOFA) score

the Acute Physiological and Chronic Health Evaluation II (APACHE II) score

and procalcitonin (PCT)

albumin (ALB)

and lactate (LAC) levels in patients with sepsis.MethodsConsecutive adult patients with suspected or documented sepsis at ICU admission were recruited. Their basic vital signs and related auxiliary examinations to determine their PCT and ALB levels and APACHE II score were recorded at ICU admission

and their LAC levels and SOFA scores were recorded for one week after admission. The influence of these variables on hospital mortality was evaluated. Logistic regression was used to derive the Sepsis Hospital Mortality Score (SHMS)

a prediction equation describing the relationship between predictors and hospital mortality. The median survival time was calculated by the Kaplan–Meier method. In the validation group

the kappa value was calculated to evaluate the stability of the derived formula.ResultsThis study included 894 sepsis patients admitted to 18 ICUs in 16 tertiary hospitals. Patients were randomly assigned to an experimental group (626 cases) and validation group (258 cases). In addition

a nonsurvival group (248 patients) of the experimental group was established according to the outcome at the time of discharge. The hospital mortality rate in the experimental group was 39.6% (248/626). Univariate and multivariate regression analyses revealed that the APACHE II score (odds ratio [OR] = 1.178)

△SOFA (OR = 1.186)

△LAC (OR = 1.157)

and SOFA mean score (OR = 1.086) were independently associated with hospital mortality. The SHMS was calculated as logit(p) = 4.715 – (0.164 × APACHE II) – (0.171 × △SOFA) – (0.145 × △LAC) – (0.082 × SOFA mean). A receiver operating characteristic curve was constructed to further investigate the accuracy of the SHMS

with an area under the curve of 0.851 (95% confidence interval [CI] 0.821–0.882; p < 0.001) for hospital mortality. In the low-risk group and high-risk groups

the corresponding median survival times were 15 days and 11 days

respectively.ConclusionThe APACHE II score

△SOFA

△LAC and SOFA mean score were independently associated with hospital mortality in sepsis patients and accurately predicted the hospital mortality rate and median survival time. Data on the median survival time in sepsis patients could be provided to clinicians to assist in the rational use of limited medical resources by facilitating prudent resource allocation.Trial registrationChiCTR-ECH-13003934

retrospectively registered on August 03

2013.

关键词

Keywords

SepsisSOFAAPACHE IILactateMortalitySequential organ failure assessmentAcute Physiological and Chronic Health Evaluation II

references

1.De Backer D, Dorman T. Surviving sepsis guidelines: A continuous move toward better care of patients with sepsis. JAMA. 2017;317:807–8.

2.Yongming Y, Yingyi L. Precision evaluation of immune status and its significance in sepsis after burns or trauma. Chin J Burns 2018;34:786–9.

3.Esper AM, Martin GS. Extending international sepsis epidemiology: the impact of organ dysfunction. Crit Care. 2009;13:120.

4.Harrison DA, Welch CA, Eddleston JM. The epidemiology of severe sepsis in England, Wales and Northern Ireland, 1996 to 2004: secondary analysis of a high quality clinical database, the ICNARC case mix programme database. Crit Care. 2006;10:R42.

5.Yealy DM, Kellum JA, Huang DT, Barnato AE, Weissfeld LA, Pike F, et al. A randomized trial of protocol-based care for early septic shock. N Engl J Med. 2014; 370: 1683–93.

6.Rowan KM, Angus DC, Bailey M, Barnato AE, Bellomo R, Canter RR, et al. Early, goal-directed therapy for septic shock—a patient-level meta-analysis. N Engl J Med. 2017;376:2223–34.

7.Cabral L, Afreixo V, Meireles R, Vaz M, Chaves C, Caetano M, et al. Checking procalcitonin suitability for prognosis and antimicrobial therapy monitoring in burn patients. Burns Trauma. 2018;6:10.

8.Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;315:801–10.

9.de Grooth HJ, Geenen IL, Girbes AR, Vincent JL, Parienti JJ, Oudemans-van Straaten HM. SOFA and mortality endpoints in randomized controlled trials: a systematic review and meta-regression analysis. Critical Care. 2017;21:38.

10.Hayashida K, Kondo Y, Hara Y, Aihara M, Yamakawa K. Head-to-head comparison of procalcitonin and presepsin for the diagnosis of sepsis in critically ill adult patients: a protocol for a systematic review and meta-analysis. BMJ Open. 2017;7:e014305.

11.Moran JL, Santamaria J. Reconsidering lactate as a sepsis risk biomarker. PLoS One. 2017;12:e0185320.

12.Vincent JL, De Backer D. Circulatory shock. N Engl J Med. 2013;369:1726–34.

13.García-Gallo JE, Fonseca-Ruiz NJ, Celi LA, Duitama-Muñoz JF. A machine learning-based model for 1-year mortality prediction in patients admitted to an intensive care unit with diagnosis of sepsis. Med Intensiva. 2018; pii: S0210-5691(18)30245-6.

14.Concato J, Peduzzi P, Holford TR, Feinstein AR. Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol. 1995;48:1495–501.

15.Zhang Y, Khalid S, Jiang L. Diagnostic and predictive performance of biomarkers in patients with sepsis in an intensive care unit. J Int Med Res. 2019;47:44–58.

16.Rothman KJ. Epidemiology: An Introduction. New York:Oxford University Press, 2002.

17.Khwannimit B, Bhurayanontachai R, Vattanavanit V. Comparison of the accuracy of three early warning scores with SOFA score for predicting mortality in adult sepsis and septic shock patients admitted to intensive care unit. Heart Lung. 2019;48:240–4.

18.Khwannimit B, Bhurayanontachai R, Vattanavanit V. Comparison of the performance of SOFA, qSOFA and SIRS for predicting mortality and organ failure among sepsis patients admitted to the intensive care unit in a middle-income country. J Crit Care. 2018;44:156–60.

19.Szakmany T, Pugh R, Kopczynska M, Lundin RM, Sharif B, Morgan P, et al. Defining sepsis on the wards: results of a multi-centre point-prevalence study comparing two sepsis definitions. Anaesthesia. 2018;73:195–04.

20.Ferreira FL, Bota DP, Bross A, Mélot C, Vincent JL. Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA. 2001;286:1754–8.

21.Aublanc M, Richard JC. Assessment of clinical criteria for sepsis—was the cart put before the horse? J Thorac Dis. 2016;8:E816–8.

22.Hu C, Zhou Y, Liu C, Kang Y. Pentraxin-3, procalcitonin and lactate as prognostic markers in patients with sepsis and septic shock. Oncotarget. 2018;9:5125–36.

23.Shukeri W, Ralib AM, Abdulah NZ, Mat-nor MB. Sepsis mortality score for the prediction of mortality in septic patients. J Crit Care. 2018;43:163–8.

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