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Original Research: Critical Care |

Modified Criteria for the Systemic Inflammatory Response Syndrome Improves Their Utility Following Cardiac SurgerySystemic Inflammatory Response and Cardiac Surgery FREE TO VIEW

Niall S. MacCallum, PhD; Simon J. Finney, PhD; Sarah E. Gordon, MD; Gregory J. Quinlan, PhD; Timothy W. Evans, MD, PhD
Author and Funding Information

From the Unit of Critical Care, Biomedical Research Unit, Imperial College London, Royal Brompton Hospital, Royal Brompton & Harefield NHS Foundation Trust, London, England.

Correspondence to: Timothy W. Evans, MD, PhD, Adult Intensive Care Unit, Royal Brompton Hospital, Sydney St, London, SW3 6NP, England; e-mail: t.evans@rbht.nhs.uk


For editorial comment see page 1181

Drs MacCallum and Finney contributed equally to this work.

Funding/Support: This project was funded by the British Heart Foundation and supported by the National Institute of Health Research Respiratory Disease Biomedical Research Unit of the Royal Brompton & Harefield NHS Foundation Trust and Imperial College London.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2014;145(6):1197-1203. doi:10.1378/chest.13-1023
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Background:  Debate remains regarding whether the systemic inflammatory response syndrome (SIRS) identifies patients with clinically important inflammation. Defining criteria may be disproportionately sensitive and lack specificity. We investigated the incidence and evolution of SIRS in a homogenous population (following cardiac surgery) over 7 days to establish the relationship between SIRS and outcome, modeling alternative permutations of the criteria to increase their discriminatory power for mortality, length of stay, and organ dysfunction.

Methods:  We conducted a retrospective analysis of prospectively collected data from a cardiothoracic ICU. Consecutive patients requiring ICU admission for the first time after cardiac surgery (N = 2,764) admitted over a 41-month period were studied.

Results:  Concurrently, 96.2% of patients met the standard two criterion definition for SIRS within 24 h of ICU admission. Their mortality was 2.78%. By contrast, three or four criteria were more discriminatory of patients with higher mortality (4.21% and 10.2%, respectively). A test dataset suggested that meeting two criteria for at least 6 consecutive h may be the best model. This had a positive and negative predictive value of 7% and 99.5%, respectively, in a validation dataset. It performed well at predicting organ dysfunction and prolonged ICU admission.

Conclusions:  The concept of SIRS remains valid following cardiac surgery. With suitable modification, its specificity can be improved significantly. We propose that meeting two or more defining criteria for 6 h could be used to define better populations with more difficult clinical courses following cardiac surgery. This group may merit a different clinical approach.

Figures in this Article

The systemic inflammatory response syndrome (SIRS) is defined by acute perturbations in clinical, physiologic, and hematologic parameters and is associated with a variety of noninfective insults,1 including cardiac surgery.2,3 At least 30% of patients with SIRS have or develop sepsis.3,4 There is significant associated mortality often due to multiple organ dysfunction.57 SIRS was conceived in part as an inclusion criterion for clinical trials and has been used to stratify patients for potential therapies.8 Nevertheless, debate remains concerning the utility of a definition that may be too sensitive4 and lacks specificity.911

Most patients display some systemic inflammation following cardiac surgery1214 due to operative trauma, ischemia-reperfusion injury,15 mechanical shear stress, hemodilution, hypothermia,16 and cellular activation upon contact with an extracorporeal circulation.17,18 Of such patients, 13% to 67% fulfill the consensus definition of SIRS,1923 although published studies are small and have not evaluated comprehensively the evolution and clinical significance of SIRS. The aims of this study were threefold. We wished to (1) describe the epidemiology and evolution of SIRS developing after cardiac surgery in a large population of patients followed over several days, (2) evaluate the validity of SIRS criteria, and (3) model alternative permutations of the defining criteria to improve their discriminatory power, and, hence, their utility, at defining a cohort of patients who may benefit from earlier or more aggressive clinical interventions.

Prospectively collected, electronically gathered, and archived anonymized data (CareVue; Koninklijke Philips N.V.) for sequential ICU admissions between January 1, 2002 and May 31, 2005, were analyzed. The need for informed consent was waived by the local ethics committee.

Study Protocol

The number of SIRS criteria met simultaneously was calculated for each patient from ICU admission until discharge (or day 7 of ICU stay). Standard definitions were used,1 subject to the following modifications: the proportions of immature (band form) white cells were not considered; WBC count was regarded as valid for 24 h, or until the next available result if that was earlier; and core temperature was assumed to be 0.5°C higher than axillary temperature.24 Data were analyzed in separate 1-h epochs.

Mortality, ICU length of stay (LOS), and organ dysfunction were defined as study end points, as SIRS is associated with an increase in these outcomes.4,25 ICU LOS of > 3 days was selected to define those who required a significant additional period of intensive therapy following cardiac surgery. The degree of organ dysfunction was quantified using the Sequential Organ Failure Assessment (SOFA) score.26,27 The SOFA score is composed of the worst scores (0-4) within a 24-h period for six domains: respiratory, cardiovascular, hepatic, coagulation, renal, and neurologic. Neurologic function was presumed to be normal in sedated patients unless it was abnormal prior to the administration of sedative medications. Total maximum SOFA score (SOFAtmax; the sum of the highest scores for each domain evaluated over the entire ICU stay) quantifies the cumulative insult suffered by the patient and has been demonstrated to correlate with ICU outcome better than the individual domains.28

Data Analysis and Statistical Modeling

Statistical analyses were performed using Prism, v4.03 (GraphPad Software). Parametric and nonparametric data are expressed as mean ± SEM or median and interquartile range, respectively. Differences between nonparametric variables were determined using the Mann-Whitney test.

Alternative permutations of the SIRS-defining criteria were evaluated against a training dataset, generated randomly from 70% of the main dataset, using permuted blocks of 10 over time. The remaining 30% of data constituted a validation dataset. Modifications were formulated that required a minimum threshold of criteria to be met and considered for how long criteria were met. We considered evaluating maximal thresholds or absolute criteria met, but felt neither were true to the original definition1 nor clinically useful as a prospective tool.

The following models were assessed, all for the first 24 h of admission: (1) the cumulative hours in which two or more criteria were met, (2) the maximum consecutive hours in which two or more criteria were met, (3) the cumulative count for the longest period of consecutive hours in which two or more criteria were met, (4) the cumulative hours in which three or more criteria were met, (5) the maximum consecutive hours in which three or more criteria were met, and (6) the cumulative count for the longest period of consecutive hours in which three or more criteria were met. For each model, a receiver operator curve (ROC) was constructed using training data and the sensitivities and specificities calculated for each of the three previously defined outcome measures. The performance of the best model that maximized sensitivity and specificity during training29 was evaluated using the validation data.

There were 3,663 admissions during the study period. One hundred forty-two records were excluded as incomplete when the system was offline during servicing or the archive failed. A further 757 records were excluded as they did not follow an episode of cardiac surgery or represented ICU readmission. Patients expected to be extubated and discharged to the high-dependency unit within a few hours of surgery are not admitted to the ICU and were not included. Therefore, 2,764 first-time admissions were analyzed. Their characteristics are presented in Table 1. The median ICU LOS was 1.1 (0.9-2.7) days. Five hundred twelve patients (18.1%) were admitted for longer than 3 days. ICU mortality was 2.68%. Patients who died did so after a median of 3.8 (1.4-9.7) days.

Table Graphic Jump Location
Table 1 —Baseline Demographics and Outcomes of the Study Population

APACHE = Acute Physiology and Chronic Health Evaluation; CABG = coronary artery bypass grafting; IQR = interquartile range; LOS = length of stay; SOFA = Sequential Organ Failure Assessment.

Regarding organ dysfunction, the mean daily SOFA score decreased from 6.0 (±2.3) on the first ICU day to 4.8 (±3.3) on the seventh day (e-Fig 1). The mean SOFAtmax was 6.8 (±3.0). The evolution of the individual scores over time is presented in e-Appendix 1; respiratory and cardiovascular dysfunction were most prevalent (e-Fig 2).

SIRS Epidemiology After ICU Admission

During the first 24 h following admission some 96.2%, 58.5%, and 12.4% of patients concurrently met at least two, three, or four SIRS criteria, respectively (Table 2). Only one patient never fulfilled any SIRS criteria. By contrast, during the first hour of admission to ICU, some 57.6%, 14.7%, and 1.1% of patients met at least two, three, or four SIRS criteria, respectively; 197 patients met no criteria. Patients who met more criteria concurrently had a greater mortality and a more prolonged stay in the ICU (Table 2).

Table Graphic Jump Location
Table 2 —Characteristics of Patients Meeting a Minimum of One, Two, Three, or Four SIRS Criteria Within 24 h of ICU Admission

SIRS = systemic inflammatory response syndrome; SOFAtmax = total maximum Sequential Organ Failure Assessment score. See Table 1 legend for expansion of other abbreviations.

The characteristics of the patient populations meeting at least one, two, three, or four SIRS criteria within the first 24 h of ICU admission are shown in Table 2. Those patients who met more criteria were younger and more likely to be women. Fulfillment of more criteria was associated with increased day 1 SOFA score, SOFAtmax score, ICU LOS, and mortality. The maximum point score was higher on day 1 for cardiovascular, liver, and renal (cf comparable studies),3032 but not for respiratory and coagulation parameters, when a greater number of SIRS criteria were met (Table 2).

The respiratory criterion was most frequently fulfilled, in 28.8% of patients on admission, increasing to 58.8% at 10 h, and rising to 85.4% of patients remaining on the ICU at day 7. The heart rate criterion was fulfilled by 43.7% of cases on admission, decreasing to 34.1% at 11 h, and with a subsequent increase to 58.6% by day 7. Only 14.2% of patients fulfilled the WBC count criteria on admission. The temperature criterion was least often fulfilled, with 79.5% fulfilling this at admission before falling to < 11% of patients between 12 h and 7 days (Fig 1).

Figure Jump LinkFigure 1. Fraction of admitted study population displaying specific systemic inflammatory response syndrome (SIRS) criteria over the first 7 d of admission. Fraction of admitted population for individual and mean total SIRS criteria met. The number of patients remaining in ICU on each day is shown. Note that initial variation in the fraction of patients fulfilling the WCC criterion is in part due to less frequent and fixed time point evaluation during the daily ICU workflow. HR = heart rate; RR = respiratory rate; Temp = temperature; WCC = white cell count.Grahic Jump Location
Criteria, Organ Dysfunction, and Outcome

A SOFAtmax score of ≥ 9 defined the upper quartile. This threshold was used to define the group with most organ dysfunction and was associated with increased mortality (10.2% vs 0.3%) and LOS (3.0 [1.8-7.3] days vs 0.9 [0.8-1.8] days).

The ability of two, three, or four SIRS criteria met within 24 h of admission to define a group of patients with higher ICU mortality, longer LOS, or with SOFAtmax score ≥ 9 is presented in Table 3. While the standard two-criteria definition of SIRS did not identify these patients, the presence of three or more criteria demonstrated a relative increase in specificity for mortality, prolonged LOS, and SOFAtmax ≥ 9 by 38.5%, 40.2%, and 40.6%, respectively, coupled to a relative decrease in sensitivity by 8.1%, 27.5%, and 28.4%, respectively. Indeed, meeting three or more criteria increased the positive predictive value, with minimal decrease in the negative predictive value, in identifying mortality, longer LOS, and SOFAtmax score ≥ 9 (Table 3).

Table Graphic Jump Location
Table 3 —Ability of Differing Minimum SIRS Criteria Met Within 24 h of ICU Admission to Predict Outcome

Data are shown as %. See Table 2 legend for expansion of abbreviations.

Evaluation of Alternative Criteria Models

Training and validation datasets were well balanced for the type of surgery, age, mortality, and ICU LOS (Table 4). The total, not necessarily consecutive, number of hours within 24 h of ICU admission, where either at least two or at least three SIRS criteria were met, predicted best that a selected patient would die (areas under the ROC of 0.767 and 0.768, respectively; Table 5). The maximum number of consecutive hours where at least two criteria were met and the cumulative score of the maximum number of consecutive hours where at least two criteria were met within 24 h of admission provided the highest probability of LOS > 3 days (ROC areas of 0.634 and 0.633, respectively) and SOFAtmax ≥ 9 (ROC areas of 0.621 and 0.624, respectively).

Table Graphic Jump Location
Table 4 —Characteristics of Training and Testing Sets

Data are given as No. (%) unless otherwise indicated. See Table 1 legend for expansion of abbreviations.

Table Graphic Jump Location
Table 5 —Discriminatory Power of Predictive Models to Determine Outcome on Training Dataset

Count represents the cumulative addition of the number of criteria in each hour. ROC = receiver operator curve. See Table 2 legend for expansion of other abbreviations.

All P < .01 except aP = .06 and bP = .13.

Overall, the cumulative score for the longest period of consecutive hours in which at least two criteria were met for the first 24 h of admission was most discriminatory for all three outcome measures. The operating points for each measure varied: the hour-criteria thresholds being 16 for mortality, 10 for LOS ≥ 3 days, and 8 for SOFAtmax ≥ 9. However, the maximum number of consecutive hours in which at least two criteria were met for the first 24 h of admission was the second most discriminatory model. Importantly, this model was easy to evaluate, and a better pragmatic choice. The performance of this was evaluated using the validation dataset (Table 6). An operating point of 6 h was selected as this maximized sensitivity and specificity on training data for mortality. The best operating point for both SOFAtmax ≥ 9 and LOS ≥ 3 days was 4 h.

Table Graphic Jump Location
Table 6 —Performance of Final Model (≥ 6 Consecutive Hours Fulfilling ≥ 2 SIRS Criteria)

See Table 1 and 2 legends for expansion of abbreviations.

Comparison of the final model to meeting two or more, or three or more, criteria improved specificity with only a nominal loss in sensitivity for mortality, manifest as an increase in positive predictive value without alteration in the negative predictive value (Tables 3, 6). LOS ≥ 3 days and SOFAtmax ≥ 9 proved to be similar, but with a greater loss in sensitivity, and, thus, a less pronounced increase in positive predictive value.

Our data show that nearly all patients undergoing cardiac surgery fulfilled the standard two criterion definition within 24 h of admission. However, this did not identify those having more organ dysfunction, longer ICU stays, and a greater mortality. Meeting the defining criteria for SIRS should infer some clinical significance. Indeed, the presence of three or more criteria was more discriminatory of these clinically relevant outcomes. The performance of individual criteria following cardiac surgery may be limited by perioperative manipulation (eg, pacing) or drug therapy. Standard anesthetic practice is to ensure patients are normothermic on admission to ICU. Corticosteroids are not routinely administered to patients. Aspirin is given to all patients undergoing coronary artery surgery unless specifically contraindicated. The antipyretic paracetamol is used universally as a supplementary analgesic. The widespread application of these therapies means they are unlikely to confound our results.

Given that our intention was to evaluate the SIRS-defining criteria in a homogenous group of patients undergoing a unified and defined insult leading to a clinically relevant inflammatory response, with the aim of modifying the criteria to reflect the clinical severity of inflammation and its impact on outcome our primary aim in this study was achieved. We did not intend to evaluate the SIRS-defining criteria as a prognostic marker following cardiac surgery, which is clearly a relatively safe procedure. We also did not intend to evaluate the underlying reasons for the development of SIRS. However, defining clinically important inflammation may allow potential interventions to be directed at the population (most likely to benefit) and avoided in those who will recover anyway. The data presented suggest that three criteria outperformed two and that a model requiring patients to fulfill two or more criteria for at least 6 consecutive h provides the best prediction of mortality, greater ICU LOS, and greater organ dysfunction. Such a modification is not cumbersome.

We did not split the population into specific subgroups such as those described by operative procedure. Fewer patients would have been in the subgroups, limiting the generalizability of the study which intended to evaluate whether SIRS criteria could usefully identify patients following a difficult clinical course irrespective of the prior cardiac surgery.

To our knowledge, these data represent the largest study of SIRS in a defined and homogenous population. Despite this, the investigation had some weaknesses. First, the analysis was retrospective, although data were collected prospectively. Second, missing (electronic) data, of which there were few, may have led to an underestimate of the incidence of SIRS. Third, grouping of SIRS-defining criteria into 1-h epochs was necessary as it was the standard charting frequency in ICU; however, whether criteria within each epoch were met at precisely the same moment is impossible to assess, potentially leading to an underestimate in the incidence of SIRS.33 Fourth, patients who met four criteria within 24 h had a higher morbidity, mortality, and APACHE (Acute Physiology and Chronic Health Evaluation) II and SOFA scores, but were of a younger age. This was due to an increased proportion of valve and other surgery. The contribution of redo valve and adult congenital heart disease surgery was not accessed. Fifth, accurate data on the proportion of off-pump cardiac surgical cases were not available, although overall it was < 5% during the period of analysis. Sixth, the contribution of preoperative comorbidities to organ failure scores was not determined. Finally, the patients were recruited from 2002 to 2005. However, clinical practices (operating room or ICU) did not change over this period, and we were early adopters of routine cell salvage.

Our findings may not be generalizable to other centers where case mix or patient management may differ. Indeed, the ratio of valve, combined, and other surgery to coronary artery bypass grafting (CABG) was greater than the UK national average for 2003.34 In comparison with case series from other European cardiac centers, the case mix may be similar with higher APACHE II and SOFA scores, but lower age and mortality.3538 Finally, a small proportion of patients deemed low risk were absent from this dataset, following a different, non-ICU-based care pathway.

In conclusion, during the first 24 h following cardiac surgery, meeting at least three defining criteria for SIRS, or requiring that at least two criteria are met for 6 consecutive hours, is more discriminatory in defining a cohort of patients with adverse clinical outcomes. These data are routinely available. We propose that these modifications may be used as investigative tools in clinical trials of systemic inflammation after cardiac surgery.

Author contributions: Drs MacCallum and Finney take responsibility for the content of the manuscript.

Dr MacCallum: contributed to study conception and design; fulfilled authorship criteria; acquired, analyzed, and interpreted data; and contributed to article drafting and revision for intellectual content and final approval of the manuscript.

Dr Finney: contributed to study conception and design; fulfilled authorship criteria; acquired, analyzed, and interpreted data; and contributed to article drafting and revision for intellectual content and final approval of the manuscript.

Dr Gordon: contributed to study conception and design, fulfilled authorship criteria, analyzed and interpreted data; and contributed to article drafting and revision for intellectual content and final approval of the manuscript.

Dr Quinlan: contributed to study conception and design, fulfilled authorship criteria, and contributed to article drafting and revision for intellectual content and final approval of the manuscript.

Dr Evans: contributed to study conception and design, fulfilled authorship criteria, and contributed to article drafting and revision for intellectual content and final approval of the manuscript.

Financial/nonfinancial disclosures: The authors have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Role of sponsors: The sponsor had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript.

Additional information: The e-Appendix and e-Figures can be found in the “Supplemental Materials” area of the online article.

APACHE

Acute Physiology and Chronic Health Evaluation

CABG

coronary artery bypass grafting

LOS

length of stay

ROC

receiver operator curve

SIRS

systemic inflammatory response syndrome

SOFA

sequential organ failure assessment

SOFAtmax

total maximum Sequential Organ Failure Assessment score

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Figures

Figure Jump LinkFigure 1. Fraction of admitted study population displaying specific systemic inflammatory response syndrome (SIRS) criteria over the first 7 d of admission. Fraction of admitted population for individual and mean total SIRS criteria met. The number of patients remaining in ICU on each day is shown. Note that initial variation in the fraction of patients fulfilling the WCC criterion is in part due to less frequent and fixed time point evaluation during the daily ICU workflow. HR = heart rate; RR = respiratory rate; Temp = temperature; WCC = white cell count.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Baseline Demographics and Outcomes of the Study Population

APACHE = Acute Physiology and Chronic Health Evaluation; CABG = coronary artery bypass grafting; IQR = interquartile range; LOS = length of stay; SOFA = Sequential Organ Failure Assessment.

Table Graphic Jump Location
Table 2 —Characteristics of Patients Meeting a Minimum of One, Two, Three, or Four SIRS Criteria Within 24 h of ICU Admission

SIRS = systemic inflammatory response syndrome; SOFAtmax = total maximum Sequential Organ Failure Assessment score. See Table 1 legend for expansion of other abbreviations.

Table Graphic Jump Location
Table 3 —Ability of Differing Minimum SIRS Criteria Met Within 24 h of ICU Admission to Predict Outcome

Data are shown as %. See Table 2 legend for expansion of abbreviations.

Table Graphic Jump Location
Table 4 —Characteristics of Training and Testing Sets

Data are given as No. (%) unless otherwise indicated. See Table 1 legend for expansion of abbreviations.

Table Graphic Jump Location
Table 5 —Discriminatory Power of Predictive Models to Determine Outcome on Training Dataset

Count represents the cumulative addition of the number of criteria in each hour. ROC = receiver operator curve. See Table 2 legend for expansion of other abbreviations.

All P < .01 except aP = .06 and bP = .13.

Table Graphic Jump Location
Table 6 —Performance of Final Model (≥ 6 Consecutive Hours Fulfilling ≥ 2 SIRS Criteria)

See Table 1 and 2 legends for expansion of abbreviations.

References

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