Adverse outcomes in intensive care settings

The role of qSOFA and other biomarkers as an acuity assessment in critical illness

Sepsis, the systemic inflammatory response to a bacterial, viral, fungal or parasitic infectious agent, is a leading cause of mortality and critical illness worldwide 1, 2 It is a major public health concern, accounting for more than $20 billion (5.2%) of total US hospital costs in 2011 3 and is the most common cause of death in patients with infections. Delay in diagnosis and initiation of treatment have been shown to increase mortality in this cohort. Thus, early treatment of sepsis is vital. 4

In 2016, an expert task force convened to evaluate and update the definitions for sepsis and septic shock. In their Third International Consensus for Sepsis and Septic Shock (Sepsis-3), they defined sepsis as a “life-threatening organ dysfunction caused by a dysregulated host response to infection”. They thus highlighted organ dysfunction as the main factor in clinical operationalization in the ICU, represented by an increase in the Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score of 2 points or more, which is associated with an in-hospital mortality greater than 10%. Whereas, septic shock was defined as a subset of sepsis in which particularly profound circulatory, cellular, and metabolic abnormalities are associated with a greater risk of mortality than with sepsis alone. Patients with septic shock can be clinically identified by a vasopressor requirement to maintain a mean arterial pressure of 65mmHg or greater and serum lactate level greater than 2 mmol/L (>18 mg/dL) in the absence of hypovolemia. This combination has been reported in some studies to be associated with hospital mortality rates greater than 40 %. 4

The SOFA score mentioned above was created in 1994 at a European Society of intensive Care Medicine (ESICM) consensus meeting to quantitatively and objectively decide on patients’ degree of organ failure or dysfunction over time. Importantly, it was not designed to predict outcomes but rather to describe complications in the critically ill. “Although any assessment of morbidity must be related to mortality to some degree, the SOFA is not designed just to describe organ dysfunction/failure according to mortality” 5 Thus, unlike other severity scores which were designed primarily to evaluate the risk of mortality, the SOFA can be used as a complementary evaluation of current organ failure in a group of patients or in an individual patient. The relatively new bedside clinical score termed quickSOFA or qSOFA can assist in the early detection of risk for poor outcomes in patients with infection. Namely, if they have at least 2 of the following clinical criteria: a respiratory rate of 22/min or greater, altered mentation, and a systolic blood pressure of 100mmHg or less (Table 1). Although qSOFA is less robust than a SOFA score of 2 or greater in the ICU, it does not require laboratory tests and can be assessed quickly and repeatedly. The task force suggested that the qSOFA criteria be used to prompt clinicians to further investigate for organ dysfunction, to initiate or escalate therapy as appropriate, and to consider referral for ICU admission, or increase the frequency of monitoring, if such actions have not already been undertaken. The task force considered that positive qSOFA score should also prompt consideration of infection in patients.

System Assessment Criterias Cut-off value of Criterias*
Mental Status Glasgow Coma Scale ≤ 13
Respiratory Respiratory rate ≥ 22/min
Cardiac Systolic Blood Pressure ≤ 100 mmHg

Table 1: Quick Sequential Organ Failure Assessment (qSOFA*) Score for Sepsis
(*Adapted from Singer et al.)
(**2 or more criteria suggest a greater risk of outcome)

Many studies since the Sepsis-3 consensus have observed that qSOFA is a poor predictor of mortality in hospitalized patients with suspected infection compared to other scoring models. 6,7,8,9,10,11 This is not surprising since SOFA was not originally designed to predict mortality but rather as an acuity assessment of the degree of current organ failure/dysfunction. However, simple modifications of the qSOFA by adding biomarker testing, such as procalcitonin (PCT) could greatly improve the suboptimal sensitivity problem of qSOFA and may serve as an enhanced quick screening tool for early identification of sepsis (Yu et al., 2019). PCT, a peptide precursor of the hormone calcitonin, increases in response to microbial toxins and systemic inflammation, and has demonstrated value in discriminating sepsis from noninfectious causes of critical illness. 12, 13, 14 In fact, PCT has emerged as the most promising commercial sepsis biomarker. For diagnostic and prognostic purposes in critical care, PCT is an advance on C-reactive protein compared with other traditional markers of inflammation, but is not accurate enough for clinicians to dispense with clinical judgment. 1, 16

Another common biomarker, the serum lactate level, can also be used to predict mortality in patients with sepsis and has a superior discriminative power to qSOFA. 17 Shetty et al. (2018) indicated that the addition of lactate levels >2 mmol/L to the qSOFA in ED patients with suspected infection is a superior risk predictor of adverse outcomes (prolonged ICU stay or death) to qSOFA alone. These high levels of lactate are associated with in-hospital mortality rates greater than 10% and stay times in the ICU longer than 72 hours in patients with respiratory and abdominal infectious foci. 18 Other studies have reported that high levels of lactic acid are correlated with higher 90-day mortality, higher ICU mortality, higher 30-day mortality, longer ICU length of stay, and higher sepsis severity scores compared to all other ICU patients. 19 ,20 Moreover, serum lactate combined with PCT has been reported to predict 28-day mortality and bacteremia in ED patients with suspected infection. 21 Although lactate was not included in the qSOFA model construction, the Sepsis-3 task force recommended serum lactate levels as a possible substitute for some qSOFA variables. However, a number of non-specific conditions can elevate serum lactate levels and hence, serum lactate levels only cannot be used to predict bacterial sepsis without clinical judgment.

What about qSOFA in specialized intensive care settings?

Scoring systems for profiling patients’ risk of mortality were initially validated in general ICU populations and not in special units or subgroups of patients. For example, cardiac surgery patients have been excluded from the development studies of general predictive scoring systems. The acute pathophysiological consequences of cardiopulmonary bypass influence the values of the variables used by qSOFA. Moreover, several pathophysiological changes may be obscured by the influence of invasive support devices, such as intra-aortic balloon pumps, ventricular assist devices, hemofiltration, and mechanical ventilation. 22  Large cohort studies on patients in cardiac ICUs found that the SOFA, especially day 1 SOFA, was a reliable ICU mortality risk stratification model 23 ; 24 , or alternatively, the ‘Daily-Mean-SOFA’ i.e. the sum of SOFA from day 1 until day-n/n, was a reliable derivative for daily risk stratification due to its accuracy and daily availability, even in patients without sepsis. 25 These results have been reported to be comparable to APACHE III and APACHE IV, but SOFA has the advantage of its simplicity, improved discrimination using serial scores, and prediction of long-term mortality. 24 Furthermore, other biomarkers discussed above have been shown to be useful as a mortality risk predictor in cardiac ICUs. A five category, blood lactate-based scale (LacScale) was reported to be significantly more accurate than other widely used postoperative models in cardiac surgical ICUs (SOFA, SAPS II, and APACHE II) in a prospective study of more than 4500 patients in a cardiac ICU. The advantage here is that LacScale is a simple model that can be used at the bedside without electronic calculation as required by other ‘complex’ scoring models while being highly reliable. 26

Neurocritical care is another special unit that has not been considered in the development of SOFA and qSOFA. Neuro ICUs provide specialized interventions aiming to improve severe neurological injuries and neurosurgery. Predictive mortality models are important since these ICUs require substantial resources and continuous advancement of care. SOFA was reported to have good discrimination in the neuro ICU and thus is a good model for mortality prediction in these patients 27 but not as good as the APACHE II 28, and thus may mainly help for objective evaluation of organ dysfunction and benchmarking, as was its original design purpose. 27

The Sepsis-3 definitions are simple with clear associations with adverse outcomes. Nevertheless, there has been some criticism that their utility in identifying patients with serious infections before sepsis develops is not yet clear. 29 This is particularly true when using organ dysfunction models, such as the qSOFA and SOFA, in determining clinical risk outcomes in specialized sub-groups of patients such as cardiac and neurological patients. As with all objective measures, clinical judgment should not be foregone.

Continuous patient monitoring together with AI & Machine Learning are being deployed in critical care settings to predict adverse outcomes. To learn more about AI powered predictive analytics, please contact CLEW for more information.


  1. Vincent J-L, Marshall JC, Namendys-Silva SA, et al; ICON Investigators. Assessment of the worldwide burden of critical illness: the Intensive Care Over Nations (ICON) audit. Lancet Respir Med. 2014;2(5):380-386.
  2. Fleischmann C, Scherag A, Adhikari NK, et al; International Forum of Acute Care Trialists. Assessment of global incidence and mortality of hospital-treated sepsis: current estimates and limitations. Am J Respir Crit Care Med. 2016;193(3):259-272.
  3. Torio CM, Andrews RM. National inpatient hospital costs: the most expensive conditions by payer, 2011. Statistical Brief #160. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. August 2013. /NBK169005/. Accessed October 31, 2015.
  4. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801-810.
  5. Vincent J-L, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med 1996;22: 707-710.
  6. Lo RSL, Leung LY, Barbarand M, et al. qSOFA is a Poor Predictor of Short-Term Mortality in All Patients: A Systematic Review of 410,000 patients. J Clin Med 2019; 8:1-51.
  7. Kim M, Ahn S, Kim WY, et al. Predictive performance of the quick Sequential Organ Failure Assessment score as a screening tool for sepsis, mortality, and intensive care unit admission in patients with febrile neutropenia. Supportive Care in Cancer. 2017; 25:1557–1562. doi: 10.1007/s00520-016-3567-
  8. Raith EP, Udy AA, Bailey M, et al. Prognostic Accuracy of the SOFA Score, SIRS Criteria, and qSOFA Score for In-Hospital Mortality Among Adults With Suspected Infection Admitted to the Intensive Care Unit. 2017;317(3):290-300.
  9. Maitra S, Som A, Bhattacharjee S. Accuracy of quick Sequential Organ Failure Assessment(qSOFA) score and systemic inflammatory response syndrome (SIRS) criteria for predicting mortality in hospitalized patients with suspected infection: a meta-analysis of observational studies. Clin Microbiol Infect 2018;24(11):1123-1129.
  10. Khwannimit B, Bhurayanontachi 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(3):240-244.
  11. Kelly BJ, Lautenbach E, Nachamkin I, et al. Combined Biomarkers Predict Acute Mortality Among Critically Ill Patients with Suspected Sepsis. Crit Care Med. 2018;46(7):1106–1113.
  12. Wacker C, Prkno A, Brunkhorst FM, et al. Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis. Lancet Infect Dis. 2013; 13:426–435
  13. Han, JH., Nachamkin, I., Coffin, SE., et al. Use of a combination biomarker algorithm to identify medical intensive care unit patients with suspected sepsis at very low likelihood of bacterial infection [Internet]. Antimicrob Agents Chemother. 2015. Available from: Last accessed 18 September 2019.
  14. Kelly BJ, Lautenbach E, Nachamkin I, et al. Combined biomarkers discriminate a low likelihood of bacterial infection among surgical intensive care unit patients with suspected sepsis. Diagn Microbiol Infect Dis. 2016; 85:109–115.
  15. Kibe S, Adams K, Barlow G. Diagnostic and prognostic biomarkers of sepsis in critical care. J Antimicrob Chemother 2011;66 Suppl 2: ii33–ii40
  16. Samsudin I, Vasikaran SD. Clinical Utility and Measurement of Procalcitonin. Clin Biochem Rev 2017;38(2):59-68
  17. Liu Z, Meng Z, Li Y, et al. Prognostic accuracy of the serum lactate level, the SOFA score and the qSOFA score for mortality among adults with Sepsis. Scand J Trauma Resusc Emerg Med. 2019;27(1):5
  18. Shetty AL, Thompson K, Byth K, et al. Serum lactate cut-offs as a risk stratification tool for in-hospital adverse outcomes in emergency department patients screened for suspected sepsis. BMJ Open 2018; 5;8(1):e015492. doi: 10.1136/bmjopen-2016-015492.
  19. Varis E, Pettilä¨ V, Poukkanen M, et al for the FINNAKI Study Group. Evolution of blood lactate and 90-day mortality in septic shock. A post hoc analysis of the FINNAKI study. Shock 2017; 47(5):574–581.
  20. Mulholland HG. Understanding Lactate in an Intensive Care Setting. Thesis: M. Eng. in Computer Science and Molecular Biology, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. Available from:  Last accessed 21 August 2019
  21. Shim B-S, Yoon Y-H, Kim JY, et al. Clinical Value of Whole Blood Procalcitonin Using Point of Care Testing, Quick Sequential Organ Failure Assessment Score, C-Reactive Protein and Lactate in Emergency Department Patients with Suspected Infection. Clin. Med. 2019; 12;8(6). pii: E833. doi: 10.3390/jcm8060833
  22. Exarchopoulos T, Charitidou E, Dedeilias P, et al. Scoring Systems for Outcome Prediction in a Cardiac Surgical Intensive Care Unit: A Comparative Study. Am J Crit Care. 2015; 24:327-335
  23. Doerr F, Badreldin AMA, Heldwein MB, et al. A comparative study of four intensive care outcome prediction models in cardiac surgery patients. J Cardiothorac Surg. 2011;6:21 Available from: Last accessed 18 September 2019
  24. Jentzer JC, Bennett C, Wiley BM, et al. Predictive Value of the Sequential Organ Failure Assessment Score for Mortality in a Contemporary Cardiac Intensive Care Unit Population. J Am Heart Assoc. 2018; 7: e008169. DOI: 10.1161/JAHA.117.008169
  25. Badreldin A, Elsobky S, Lehmann T, et al. Daily-Mean-SOFA, a new derivative to increase accuracy of mortality prediction in cardiac surgical intensive care units. Thorac Cardiovasc Surg. 2012;60(1):43-50.
  26. Badreldin AM, Doerr F, Elsobky S, et al. Mortality prediction after cardiac surgery: blood lactate is indispensible. Thorac Cardiovasc Surg. 2013;61(8):708-717.
  27. de Brito MR, de Almeida Barros AG, Valler L, et al. Evaluation of Sequential Organ Failure Assessment (SOFA) Performance in Neurocritical Care Patients Overtime: A Retrospective Cohort Study. J Brain Disord. 2017;1(1):38-43.
  28. de Almeida Barros AG, Mescolotte GM, de Souza OA, et al. Severity Scores Performance in Neurocritical Care: A Retrospective Cohort Study. Open Access J Neurol Neurosurg. 2017;5(3): 555662. DOI: 10.19080/ OAJNN.2017.05.555662
  29. Rhee C, Klompas M. New Sepsis and Septic Shock Definitions: Clinical Implications and Controversies. Infect Dis Clin North Am 2017;31(3):397-413.


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