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.”

Author: johnrzaleski_eqbr0v

John R. Zaleski, PhD, CAP, CPHIMS, is Chief Analytics Officer of Bernoulli, a leader in real-time connected healthcare. Dr. Zaleski brings 21 years of experience in researching and ushering to market devices and products to improve healthcare. He received his PhD from the University of Pennsylvania, with a dissertation that describes a novel approach for modeling and prediction of post-operative respiratory behavior in post-surgical cardiac patients. Dr. Zaleski has a particular expertise in designing, developing, and implementing clinical and non-clinical point-of-care applications for hospital enterprises. Dr. Zaleski is the named inventor or co-inventor on seven issued patents related to medical device interoperability. He is the author of numerous peer-reviewed articles on clinical use of medical device data, information technology and medical devices and wrote three seminal books on integrating medical device data into electronic health records and the use of medical device data for clinical decision making, including the #1 best seller of HIMSS 2015 on connected medical devices.

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