Predicting Sepsis with Early Warning Shock Index

Sepsis hospitalizations increased between 2000 & 2008

Between 2000 and 2008, annual hospitalizations for septicimia increased from 326,000 to 727,000, in which 46% of hospitalized septic patients (patients diagnosed with sepsis or septicimia–blood infections) were admitted through the Emergency Department (ED). [Source: Hall MJ, Williams SN, DeFrances CJ, et al. Inpatient Care for Septicemia or Sepsis: A Challenge for Patients and Hospitals. U.S. Department of Health and Human Services – National Center for Health Statistics. 2011; 62:1-7.]

Predicting the future has never been shown to be possible with 100% certainty. It would seem to be a tautology that uncertainty invades every aspect of life. To a large degree, the inability to predict the future is a direct demonstration of the inability to control or, perhaps more correctly, the inability to fully account for all external influencing events that cause deviation from expected behavior. Thus, establishing certainty in causal relationships (that is, cause and effect) can be difficult.

Such is the case in medicine as in other aspects of life. Over the span of the last 150 years a great deal of research into epidemiology has led to great advances in the ability to predict behaviors based on prior causal evidence. The efficacy of drugs is testament to this statement—just look at penicillin to see a simple example of this. Perhaps the most illustrative example of the use of analysis and modeling to predict or assess the correlation between causal events and outcomes is the famous work of Dr. John Snow relative to identifying the root cause of the cholera outbreak in mid-19th century London. This work presented a cause-and-effect relationship surrounding the presence of cholera in a Broad Street well in 1854. Dr. Snow is identified as one of the fathers of epidemiology. In his analysis he identified the occurrence of cholera in Broad Street and the surrounding environs and traced the cause to a contaminated well. Once the pump handle was removed from the well, the epidemic began to subside. One could argue, perhaps, that early warning as to the likelihood of improvement was the removal of the pump handle on the offending well.

In other areas, though, the ability to predict the causal relationships is much less certain. The battle with cancer in its various forms is testament to this in that the complexity of the cause and effect relationship is not so well understood as to claim that any one approach or cause will guarantee an individual will or will not develop cancer in his or her lifetimes. To be clear, there are correlations and associations based upon past history that establish likely relationships assuming large populations (e.g.: smoking is likely to cause lung cancer). Yet, plenty of individual exceptions exist that violate the general population. My father is an example of an individual who smoked first cigarettes and then a pipe for the vast majority of his life and passed away at the age of 90…of old age. On the other hand, my mother, who did not smoke and yet watched her diet her entire life passed away from breast cancer at the age of 54. However, these individual examples, while outliers, do not provide me, as their progeny, with the rationale or an excuse to engage in destructive behavior. In other words, I am not willing to bet on the gene pool superseding the statistics from the larger population.

Other types of early warning measures (Sepsis and Other Syndromes)

In order to reduce uncertainty in predicting causal relationships it is necessary to improve the fidelity of the models with which the relationships are represented. But, the relationships based upon generalities can provide guidance for expected behavior, and this may be enough to do good, particularly when the expected behavior can target specific outcomes that can mean the difference between life and death. Two examples are given below to illustrate this point.

1) Viability for weaning from mechanical ventilation. Yang & Tobin [Yang KL, Tobin MJ: A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation. N Engl J Med 1991; 324:1445-1450] put forth a measure based on the ratio of the respiratory rate to the tidal volume in patients undergoing spontaneous breathing trials. The so-called “rapid shallow breathing index”, or RSBI, is defined as:

RSBI = RR/TV.

In their study, Yang & Tobin determined that this ratio was a good discriminator for weaning success and failure. The threshold RSBI = 105 was identified as the point of demarkation relative to successful weaning (RSBI < 105) and unsuccessful weaning (RSBI > 105). This ratio is used operationally today yet with varying levels of success. However, one of its key benefits is its simplicity. In terms of predicting the causal relationships between successful weaning and failed weaning attempts, the RSBI is, perhaps, moderate to good. Yet, its simplicity lends itself to a general rule-of-thumb that clinicians can opt to use (or ignore) operationally that could motivate the seeking of further, more accurate or telling information that will have a higher, more telling influence on clinical predictability (e.g.: end-tidal CO2, for one).

2) Modified Shock Index and mortality in emergency department patients. Ye-cheung Liu et al. showed that the modified shock index (MSI) (Modified shock index and mortality rate of emergency patients; World J Emerg Med, Vol 3, No 2, 2012) is a superior measure to Shock Index (SI) alone for determining hemodynamic stability. Shock Index is a common measure to assess hypovolemic shock. Shock Index is given by:

SI = HR / BP-SYS,

this is the ratio of the heart rate (pulse) to the systolic component of blood pressure.

Modified Shock Index is given by:

MSI = HR / MBP,

this is the ratio of heart rate (pulse) to mean blood pressure (MBP), which is given by:

MBP = [(BP-DIAS x 2) + BP-SYS] / 3.

In the referenced study, MSI > 1.3 or MSI < 0.7 is associated with an increased probability of ICU admission and death.

Many more examples exist in which predicting causal relationships can be shown. The absolute predictability of the event is not possible to assess with 100% certainty. But, the ability to identify the likelihood of an event on the basis of the causal relationship is possible to show. In posts to follow we will take a look at the possible uses for these causal relationships in operational care management of patients.

Studies of Shock Index as an Early Warning Marker for Sepsis Onset is Promising

A cross-institution 2011 study presented in The Western Journal of Emergency Medicine (Berger et al., “The Shock Index and Early Recognition of Sepsis in the Emergency Department – A Pilot Study”) conducted by researchers at UC Davis, NY Hospital and Harvard University proffered using the Shock Index (SI) as an early warning marker for sepsis in the emergency department (ED). The objective of the study was to:

“…compare the ability of SI, individual vital signs, and the systemic inflammatory response syndrome (SIRS) criteria to predict the primary outcome of hyperlactatemia (serum lactate ≥ 4.0 mmol/L) as a surrogate for disease severity, and the secondary outcome of 28-day mortality.”

Per the studies presented above, a cohort of adult patients suspected of infection were screened for sepsis using data that included vital signs, laboratory data, and initial serum lactate levels.
Test characteristics were computed for two specific outcomes:
1) hyperlactatemia (identified as a marker for morbidity); and,
2) 28 day mortality (see here and here).
Covariates considered in the analysis for sepsis diagnosis included:
a. heart rate (HR) > 90 beats/min
b. mean arterial pressure (MAP) < 65 mmHg
c. respiratory rate (RR) > 20 breaths/min
Shock Index = HR / SYS-BP
Results of the analysis showed the following causal relationships:
of 2524 patients with complete medical record data considered in the analysis, 290 patients presented with hyperlactatemia and 361 died within 28 days.

Threshold measures for Shock Index as an Early Warning Score

 Patients with SI > 0.7 (representing 15.8%) were 3 times more likely to present with hyperlactatemia than those with normal SI (4.9%). Negative predictive value of SI > 0.7 was 95%. This was identical to that of SIRS. Furthermore, an SI > 1.0 was “the most specific predictor of both outcomes.”

Rapid Response and Earlier Warning of Impending Shock Leads To Better Outcomes Outside the ICU

Rapid Response Teams Intervene Using Early Warning Criteria

Over the past several years Rapid Response Teams (RRTs) have been instituted at a growing number of hospitals to identify patients at risk for rapid decline before patients experience catastrophic events, such as septic or cardiogenic shock, which can lead to death. Ordinarily, if a patient is detected who appears to show signs of increased pulse or breathing difficulty, the RRT can be called for a consult.

Rapid Response Teams arose collaboratively and as a result of empirical study. One study, conducted by Frank Sebat, MD, et al., lead author of a paper in the May 2005 edition of Chest, and featured in the July 2005 issue of Today’s Hospitalist (“How a shock team can detect and treat critical illness earlier” by Edward Doyle; Today’s Hospitalist; July, 2005), showed marked improvement through the use of a shock protocol implemented using several protocols. Sebat et al. stated, at that time, that:

“Most physicians think they recognize critical illness early on but many studies have shown that we really don’t recognize critical illness until it’s in the late stages…the idea of early recognition of critical illness is…not emphasized.”

Cardiogenic and septic shock span many departments and patients

The problem of shock and its identification spans intensive care units, step-down wards and medical / surgical wards. Today’s Hospitalist lists what is often published in the literature on the subject of early recognition of events outside of ICU: treatment outside of ICU can be delayed in part because of a general lack of recognition, poor venous access, inadequate fluid resuscitation, and difficulty in finding bed access in the ICU.

Mortality rates for shock and early warning reference criteria

Mortality rates for septic shock can range in the 30%-60% [1]. In the case of cardiogenic shock (CS), mortality rates can be even higher, and is most commonly caused by damage to the left ventricle resulting from lack of oxygenation resulting from myocardial infarction (MI) [2]. Sebat et al. had further observed:

“A team approach to the resuscitation of patients with shock was first described in 1967…This concept reemerged as the medical emergency team, a group of physicians and nurses that can be activated by frontline nonphysician providers to immediately evaluate and treat patients with significant alterations in vital signs or neurologic deterioration.”

It is interesting to consider the specific criteria employed in the screening and confirmation criteria  setup as part of Dr. Sebat’s protocol. Results from his study showed significant improvements in time to identify and confirm diagnosis; reduction in arrival of intensivist; reduction in time to admit to ICU; reduction in time to place a central line catheter or PA catheter; and reduction in time to antibiotic administration between a control group of patients and those within protocol. Furthermore, the mortality rate between the control group and protocol group of patients was 40.7% and 28.2%, respectively.

An interesting article published in the May Edition of Critical Care Medicine (Gaieski et al., “Benchmarking the Incidence and Mortality of Severe Sepsis in the United States.” CCM May 2013. Volume 41. Number 5. DOI: 10.1097/CCM.0b013e31827c09f8) summarized a survey of sepsis reporting based upon ICD-9 codes established for sepsis, severe sepsis, and septic shock established in 2002-2003, and on the variability in reporting and rate of occurrence during the 6-year period from 2004-2009. Between 2002 and 2003, ICD-9 codes for sepsis, severe sepsis, and septic shock (995.91, 995.92, 785.52) were introduced.

Using ICD sepsis codes, the authors surveyed reports associated with these ICD-9 codes. What they found was that the average annual incidence of sepsis (ICD-9 Code 995.91) was 231 cases of sepsis per 100,000 patients; 144 cases of severe sepsis (ICD-9 Code 995.92); and 95 cases of septic shock (ICD-9 Code 785.52) per 100,000 over this 6-year period.

Sepsis was identified as the 11th leading cause of death in US (Reference: CDC, 2009). Severe sepsis, defined as sepsis associated with new organ dysfunction, hypoperfusion or hypotension, was estimated to cost U.S. healthcare system $24.3B in 2007.

Various studies considered:

Angus et al: 750,000 cases (300 / 100,000 population) and in-hospital mortality rate of 28.6% in 1995.

Martin et al.: 256,000 cases in 2000 (81 / 100,000).

Dombrovskiy et al.: 391,000 cases (134 / 100,000) with an in-hospital mortaility rate of 37.7% in 2003.

Wang et al.: 571,000 annual emergency department (ED) cases nationally between 2001 and 2003.

Gaieski et al. estimated that, for all sepsis codes, an annual increase was observed and the rate of this increaase varied by ICD-9 code: 22.3% for sepsis, 25.3% for severe sepsis, 18.2% for septic shock.

Early warning shock criteria for rapid response

Key screening criteria: systolic blood pressure (SBP) < 90 bpm, mean arterial pressure (MAP) < 66 mmHg, with one or more of the following or normotension with three or more of the following: temperature < 36C, respirations > 20 rpm, altered mental status (anxiety, apathy, agitation, lethargy, stupor, coma), cool extremities or skin mottling, oliguria < 30 mL/h, lactic acid > 2 mmol/L or base excess < -5 mmol/L.

Key confirmation criteria: Required one of the following: administration of >4 L fluid in first 24 hours, use of vasoconstrictors, lactic acid > 2 mmol/L, death (due to hemodynamic instability).

In the aforementioned study, approximately 66% of patients were admitted through the emergency department (ED); 24% were admitted from general wards; remaining 10% were admitted from other locations.

References

[1] (Sandra Christina Pereira Lima Shiramizo et al.,”Decreasing Mortality in Severe Sepsis and Septic Shock Patients by Implementing a Sepsis Bundle in a Hospital Setting”, http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026790; Nov. 3rd 2011)
[2] The Mayo Clinic. http://www.mayoclinic.com/health/cardiogenic-shock/DS01152/DSECTION=causes

Early Warning of Impending Events Through Physiologic Surveillance

Can Data Obtained Through Physiologic Surveillance Help Predict Their Onset?

In the October 2006 issue of Resuscitation, Smith etal. [1] published an article on the benefits of early warning associated with the monitoring and physiologic surveillance of patients in the hospital (ICU, principally). From the abstract of that publication:

“Hospitalised patients, who suffer cardiac arrest and require unanticipated intensive care unit (ICU) admission or die, often exhibit premonitory abnormalities in vital signs…”

“…It is possible for raw physiology data, early warning scores (EWS), vital signs charts and oxygen therapy records to be made instantaneously available to any member of the hospital healthcare team via the W-LAN or hospital intranet…”

From another source [2]:

“Physiologic monitoring systems measure [pulse], blood pressure, …other vitals…Data about adverse events in hospitalized patients indicate…a majority of physiologic abnormalities are not detected early enough to prevent the event, even when some…abnormalities are present for hours before…[occurrence].”

Early Warning Physiologic Surveillance Research

Early warning and physiologic surveillance are not new concepts, whether in the ICU or elsewhere. What, perhaps, has evolved over the past 10 years or so since the formal introduction of the electronic medical record (EMR) is that the automated and complete collection of data normally charted within the EMR is necessary to support such early warning protocols (particularly outside of the ICU environment) so long as the data available are part of an integrated delivery system [3]. That is, complete and contextual data are necessary to promote accurate early warning notifications that can be developed from multiple sources of data, inclusive of physiologic, laboratory, and demographic.

Early warning score predictors can include:

a. vital signs

b. laboratory test results

c. severity of illness scores

d. longitudinal chronic illness burden scores

e. transpired length of hospital stay, and

f. care directives

Escobar [3] reported that of 4,036 events from a cohort of 102,422 patients, modified early warning score c-statistics of 0.709 at 95% confidence. In comparison to EMR-based models, which had a c-statistic of 0.845. Best early warning performance was detected amongst those patients with gastrointestinal (GI) diagnoses (0.841) and worst amongst those with congestive heart failure (CHF) (0.683).

While performance was less robust among the modified early warning models compared with EMR-based models, the performance correlation between the two is encouraging. Perhaps a place exists for the use of early warning protocols and methods which can be based less on the availability of sophisticated information and more on the availability of data readily collected from the bedside.

Improved Physiologic Surveillance through Medical Device Integration

Data required for these models can be obtained automatically through the use of medical device integration from bedside physiologic monitors and other devices at the point of care. The benefits of automated data collection are ensuring complete data collection, mitigation of error due to manual transcription, and regular data updates at pre-defined intervals.

References

[1] Smith GB, etal., “Hospital-wide physiological surveillance–a new approach to the early identification and management of the sick patient.” Resuscitation. 2006 Oct;71(1):19-28. Epub 2006 Aug 30.

[2] Yoder-Wise, Patricia S., Leading and Managing in Nursing: fifth Edition. Elsevier-Mosby. 2014. ISBN: 978-0-323-24183-0. Page 201

[3] Escobar etal., Early Detection of Impending Physiologic Deterioration Among Patients Who are Not in Intensive Care: Development of Predictive Models Using Data From an Automated Electronic Medical Record.” Journal of Hospital Medicine. 2012 Society of Hospital Medicine. DOI 10.1002/jhm.1929. Wileyonlinelibrary.com