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Why measure severity?
WHY MEASURE SEVERITY?
1. K N A U S
There are four major reasons for the measurement of severity of illness in the modem practice of intensive care; the content and reliability of its measurement, its strong influence on patient outcome and resource use, its usefulness in identifying the amount of treatment benefit for individual patients, and the accuracy and complementary nature of severity based outcome measurements with clinical judgment. This presentation will discuss each of these issues. It will conclude with a brief discussion of the next steps required to advance the precision and usefulness of severity measurement.
•
Severity can be reliably measured with medical facts
Over the past 15 years it has become increasingly accepted that acutely ill patients vary greatly in their physiologic status and risk for dying. It is also now recognized that these variations are not represented by traditional methods of disease description. For example, a patient with a clearly defined disease such as pneumococcal pneumonia could be treated as an outpatient with antibiotics or be admitted to an ICU and require high levels of oxygen and mechanical support to maintain life. The difference between the two is the severity of their pneumonia with severity defined predominantly using acute physiologic manifestations. The pneumonia patient treated as an outpatient might have a respiratory rate of 24 and a Pa02 of 92, while the ICU patient might be breathing 40 with a Pa02 of 60. These End other physiologic facts when combined into summary measures of severity provide a unique, objective, and reliable method of describing and classifying patients.
George Washington University Medical Center, 2300 K Street, N.W. - - Washington, D.C. 20037, U.S.A..
The content or nature of this information is also important. Severity measurement is based on analysis of medical facts, specifically the nature and type of the patient's physiologic state at ICU admission and how this state changes over time. Changes in physiology over time have been consistently demonstrated to correlate with patient outcome [1]. These facts are an appropriate basis upon which to base medical decisions. Each day on ICU rounds, medical decisions are made, largely based upon interpretation of these physiologic facts. These physiologic and other related medical facts are also ethically appropriate. They represent the patient's ability to benefit from treatment and therefore are an appropriate measure of a patient's entitlement to care. Certainly, these facts are preferable to other considerations upon which therapeutic decisions might be based, such as the patient's ability to pay for the services [2]. A number of physiologic based severity systems have been developed and are widely used [3]. Measurement of severity using these systems is increasingly becoming an integral part of both research investigations involving severely ill patients and is also part of routine data collection in many intensive care units. The physiologic and related information needed to measure severity is routinely available in all ICUs and is becoming increasingly available through inexpensive electronic links [2]. Progress is also continually being made in the best form for representing the physiologic measures and how to combine them. For example, we are learning when disease or condition specific transformations of physiology are appropriate and when to rely on general measurement [4]. This progress is possible both because of large and growing databases and because many of the sophisticated analytic techniques previously confined to large mainframe computers are now available on desk top models. Therefore, the first argument for why we should measure severity is that we can do so easily, reliably, inexpensively, using medical facts. R6an. Urg., 1994, 3 (2 bis), 159-163
-
1 6 0
-
Why measure severity? * Phv~inlnnv
Severity is the single most important determinant of patient outcome and resource use of ICU patients As the capabilities and success of advanced medical care increase, we find ourselves treating more severely ill patients more frequently. Each of these episodes of care is also becoming increasingly intense and expensive. Determining whether the care provided during these episodes is appropriate, successfully applied, and of reasonable cost is becoming very important. It has been recently demonstrated that severity is the single most important factor that predicts or determines the outcome and cost, both resources and length of stay, of intensive care treatment
[5].
As figures 1A and 1B illustrate, the acute severity of a patient's illness as determined by physiologic measures, is more influential than disease in determining both outcome (1A) and resource (1B) use as measured by length of stay. This means that any evaluation of the outcome from or cost of intensive care treatment must take into consideration the severity of illness of the patients treated. Considering the large and growing interest in outcome evaluation and cost efficiency, increasingly accurate measurement of severity is essential.
•
Physio (73
Other (3.1)
*Chronic HealtP (0.9) *Age (3.4)
her t.9)
(34.1) * As represented in APACHE Ill score
Other Location prior to ICU 6.7 % Region 3.2 % ICU re-admission 1.1% Hospital bed size 0.8 % Emergency surgery 0.2 % Teaching status 0.2 % Disease-- 78 mutually exclusive indications for ICU admission (Reference 5) Fig. l B . - - Relative prognostic information (percentage of chisquare uniquely associated) for variables used to adjust for ICU length of stay.
A recent analysis of 42 ICUs in the U.S. illustrates how such evaluation is both possible and accurate. This survey found that crude or unadjusted hospital death rates varied from 6 to 42 p. 100. Fully 90 p. 100 of this variation in death rates, however, were attributable to variations in patient characteristics (Fig. 2A). That same study found that average length of stay varied from 3.3 to 7.3 days. Again, a combination of physiologic severity and other important OBSERVED HOSPITAL DEATH RATE 45%
sease 13.6) * Age (7.3)
......... Health (2.9) 25%
* As represented in APACHE Ill score Other Length of stay prior to ICU admission Mean duration of hospital stay for survivors Location prior to ICU Emergency surgery
• 1.6 % 1.4 % 0.1 % 0.01%
Disease-- 78 mutually exclusive indications for ICU admission (Reference 5)
f
•
.~BETTER
5,
. 5%
10%
15%
20%
25%
30%
VVORSE 35%
40%
45%
PREDICTED'HOSPITAL DEATH RATE Fig. 1A. - - Relative prognostic information (percentage of chisquare uniquely associated) for variables used to adjust for hospital mortality risk.
ROan. Urg., 1994, 3 ( 2 bis), 159-163
Fig. 2A. - - Distribution of patients and association between first day APACHE III score and hospital mortality rates for 16,622 ICU patients at 40 hospitals [5].
Why measure severity? patient characteristics accounted for 78 p. 100 of the variation in length of stay (LOS) across these 42 units (Fig. 2B). Prospective application of these risk adjusted techniques permits one to evaluate the outcome and cost-effectiveness of ICUs, to compare one unit to another, and to continuously measure the performance of a unit over time. As figures 2_4 and 2B illustrate, it is possible to use these risk adjustments to identify units that are performing better or worse than average, regardless of their unadjusted death rates [5]. OBSERVED ICU LENGTH OF STAY (DAYS) 8
A
NA
3
3.5
4
" 4.5
5
5.5
6
6.5
7
7.5
PREDICTED ICU LENGTH OF STAY (DAYS) Fig. 2B. - - Distribution of patients and association between first day APACHE III score and ICU length of stay for 17,105 ICU admissions at 40 hospitals. (Reference 5)
In some countries, such as the U.S., assessments of hospital and ICU mortality and cost performance are becoming increasingly common. Accurate measurement of severity as the key pre-treatment component of patient risk makes such determinations accurate and useful. Figures 1A and 1B clearly illustrate that reliance on diagnosis alone is insufficient for a description of the patients treated. Figures 2_4 and 2B illustrate that a comprehensive assessment including severity, enables you to evaluate patient outcome and length of stay from intensive care.
Severity measurement can help evaluate new therapies and determine benefits of treatments for individual patients Just as severity measurement is now the major determinant of death rates from intensive care, systematic assessments of severity are proving to be useful in evaluating new therapies and estimating the amount of benefit a treatment may provide to individual patients. This is because severity measurement, as described above, is a comprehensive
-
161
-
assessment that includes many patient characteristics that determine a patient's response to treatment. The best current example of this relationship is found within the area of sepsis. Sepsis is currently a major cause of death for ICU patients and many new therapies are being proposed and tested. These investigations have discovered that traditional diagnostic categories do not adequately adjust for variations in patient presentations. In response, new categorical descriptors, such as sepsis syndrome have been used as entry criteria for clinical trials. Recently it has been recognized that such categorical descriptors may identify a population with an overall predictable mean mortality but with a wide range or severity or risk estimates for individual patients [6]. This has lead to the recommendation that severity measurement and risk prediction become part of clinical evaluations of new drugs for sepsis and systemic inflammatory response syndrome [7]. This recommendation makes sense because the new alternative therapies being evaluated, such as anti-cytokines, are designed to modify the biologic response to sepsis and inflammation, not attack the infecting organism [8]. Many of these inflammatory responses may be protective at low levels of activity but capable of producing end-organ damage at higher exposures. One recent large scale trial of an anticytokine, IL-lra, indicated that a comprehensive measure of patient severity was the best single measure of drug efficacy with the amount of benefit varying directly with the severity level [9]. This finding suggests that not only might severity measurement find a role in the design and analysis of clinical trials involving severely ill patients, but could also be used after the trial was completed as an indicator of potential benefit for individual patients. As figure 3 illustrates, a clinical trial conducted with prospective severity adjustment would be able to provide patients with an individual risk prediction. The reduction in mortality risk anticipated from the use of a new drug could then be estimated. The clinician would then be able to use an actual or revised version of figure 3 to estimate the amount of mortality risk for an individual patient. This information could complement other patient data and clinical judgment when deciding on drug utilization. Considering the likely cost of these new compounds and the fact that patient selection is a major determinant of cost-effectiveness, such risk-benefit information is of great interest and potential value [4].
•
Outcome estimates are complementary to clinical judgment
The fourth major reason to measure severity is that it is complementary to clinical judgment. The overall results from comparisons of severity based outcome estimates to those obtained by treating RGan. Urg., 1994, 3 (2 bis), 159-163
- 162 - Why measure severity? clinicians is that the accuracy of objective estimates is equivalent to clinical judgment [2]. This has lead some people to question the utility of severity based outcome estimates. The ability of outcome estimates produced using a limited amount of clinical data to equal those obtained from treating physicians, however, is remarkable. Moreover, since such estimates have always been intended to be used as adjuncts to clinical judgments not as replacements for those insights, a more appropriate question is whether such estimates bring anything to clinical judgment. Recent analyses suggest they do. Specifically, we have found that when you have estimates from treating physicians and you combine this estimate with an objective estimate based on severity, the resulting combined estimate is more predictive than either single estimate used alone [10].
data parallels the clinical process of decision making and demonstrates the potential synergy between the two sources of information. The existing clinical evaluations of prognostic estimates also support this synergy. In a controlled study of objective prognostic estimates of mortality derived from the number of organ system failures in 17 French ICUs, when the objective prognostic estimates were provided on a daily basis there was a small but significant increase in decisions to provide comfort care (Table 1) [11]. This change in decision making was limited to those patients with three or more organ system failures (Table I1). These patients have a very high risk of mortality and very small probability of benefit from aggressive therapy. Table I Distribution of decisions to limit treatment according to number of osfs and availability of outcome estimates
28 Day Mortality 100%
DECISION
ControL Study Period Period
Significance of Difference In Comfort Care Decisions
80% Number of Patients 760 No treatment limitation 85 % Treatment limitations 9% Comfort care 6%
Current 60%
//' J
40%
l ~ ~ o t e n t i al Benefit
8% 10 %
p < 0,01
Table II
(simulated)
20%
DECISION
0% 0%
82 %
Knaus et al., Med. Decis. Making, 1990, 10, 163.
/ JNew Drug /-
705
I
~
~
I
20%
40%
60%
80%
100%
Baseline 28 day Mortality Risk Groups
Control Study Period Period
Patients with fewer than three OSFs 725 655 No Limitations 85 % 85 % Limitations 9% 7% Comfort care 6% 6% Patients with three or more OSFs No Limitations Limitations Comfort care
35 66 % 23 % 11%
50 48 % 16 % 36 %
Significance of Differenc~ In Comfort Care Decisions p < 0.10
p < 0,02
Fig. 3. Relationship between baseline estimated risk of 28-day and actual 28-day mortality for ICU admissions meeting sepsis syndrome criteria (Current Therapy). Lower line indicating possible reductions in risk (e.g., potential benefit) with new drug is simulated. Actual relationship can only be determined following analysis of Phase III data. (Reference 4).
Knaus et al., Med. Decis. Making, 1990, 10, 163.
This suggests that the objective estimates are providing superior deductive reasoning, remembering and recalling thousands of previous patient records with a precision impossible to achieve with human memory. The clinician is also bringing his experience and specifically his intuition to bear on the estimate, incorporating distinct patient characteristics and other information not available to the objective estimate. This combination of objective and subjective
A similar result was recently reported by Murray and colleaques in a trial which tested the impact of severity based prognostic estimates in decision making for patients with head trauma [12]. Using a cross over design this study was able to demonstrate a significant decrease in the use of two forms of aggressive therapy, osmotic diuretics and intubation/ventilation in patients with poor prognoses, a mortality risk equal or greater than 80 p. 100
-
-
R~an. Urg., 1994, 3 ( 2 bis), 159-163
Why measure severity? (Tables llI and IV). These two studies suggest that objective probability estimates based on severity may be capable of influencing decision making and directing aggressive therapy toward patients able to benefit. Table III Treatment of head injury by prognosis and availability of outcome estimates
Mortality Risk Low ( < . 4 ) Moderate (.4-.8) High ( > .8)
Before
Osmotic Diuretics* During N (%)
After
264 (23) . 98 (28) 47 (45)
253 (26) 125 (41) 42 (21)
108 (18) 58 (32) 14 (50)
-
163
-
current capabilities and will extend severity measurement to new settings with greater flexibility. We will also discover new ways of combining and analyzing physiologic and other patient data to produce accurate estimates of a patient's risk for various outcomes beyond hospital mortality such as the expected duration of survival and the level of functional status anticipated. These risk estimates will be hepful in evaluating the quality and efficacy of therapy and directing therapy toward patients most likely to benefit. Clinicians will need to learn more about the derivation and interpretation of probability estimates, but when used in conjunction with their clinical judgment, such estimates have the potential to improve their ability to make decisions in the increasingly complex world of clinical medicine.
p < .01 Murray et aL, Lancet, 1993, 341, 1487.
References
Table IV Treatment of head injury by prognosis and availability of outcome estimates
Before Mortality Risk Low ( < . 4 ) Moderate (.4-.8) High ( > .8)
Intubation/Ventilation* During After N (%)
264 (52) 98 (85) 47 (89)
253 (58) 125 (82) 42 (57)
108 (41) 58 (76) 14 (64)
p <.001 Murray et aL, Lancet, 1993, 341, 1487.
•
Conclusion
We are treating more severely ill patients at more advanced stages of disease. These changes have made traditional description classifications of disease inadequate to accurately describe a patient's risk for important outcomes. Severity is now the most important single characteristic influencing a patient's outcome and their potential to benefit from treatment. Unless we measure severity we are unable to describe the patients, measure the quality of the treatments provided, determine the benefits from specific treatments, or assist physicians in deciding on the appropriateri6ss of therapy. Fortunately, progress over the recent few years have provided us the ability to accurately and reliably measure severity using readily available medical facts. In the future, we will be able to use new techniques to improve the precision of severity measurement. Such advances include the continuous measure of physiologic changes instead of categorical scoring systems. These approaches will add precision to our
[1] WAGNERD.P, KNAUSW.A., HARRELF.F., ZIMMERMANJ.E., WA'n-s C. - - Daily, prognostic estimates for critically ill adults in intensive care units. Crit. Care Med., 1994, [in press]. [2] KNAUSW.A., WAGNERD.P., LYNNJ. - - Short-term mortality predictions for critically ill hospitalized adults: Science and ethics. Science, 1991, 254, 389-394. [3] SENEFFM., KNAUSW.A. - - Predicting patient outcome from intensive care: A guide to APACHE, MPM,SAPS,PRISM,and other prognostic systems. J. Intens. Care Med., 1990, 5, 33-52. [4] KNAUSW.A., HARRELLF.E., FISHERC.J., WAGNERD.P., OPAL S.M., SADOFFJ.C. et aL ~ The clinical evaluation of new drugs for sepsis: a prospective study design based on survival analysis. Jam& 1993, 270, 1233-1241. [5] KNAUSW.A., WAGNERD.P., ZIMMERMANJ.E., DRAPERE.A. -Variations in hospital mortality and length of stay from intensive care. Ann. Intern., Med., 1993, 118, 753-761. [6] KNAUSW.A., SUN X., NYSTROMP.O., WAGNERD.P. - - Evaluation of definitions for sepsis. Chest., 1992, 101, 1656-1662. [7] BONER.C., BALKR.A., CERRAF.B., DELLINGERR.P. et aL - Definitions of sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest., 1992, 101, 1644-1655. [8] DINARELLOG.A., GELFANOJ.A., WOLFFS.M. - - Anticytokine Strategies in the Treatment of the Systemic Inflammatory Response Syndrome. Jama, 1993, 269, 1829-1835. [9] FISHERC.J., Jr DHAINAUTJ.F., PRIBBLEJ.P., KNAUSW.A. and the IL-1 - - Receptor Antagonist Study Group, - - A study evaluating the safety and efficacy of human recombinant interleukin-1 receptor antagonist in the treatment of patients with sepsis syndrome. Presented at the 13th International Symposium on Intensive Care and Emergency Medicine, 1993. Brussels, Belgium. [10] KNAUsW.A., HARRELF.E., LYNNJ., CONNORSA., DAWSONN. et aL - - The SUPPORT Prognostic Model. Clin. Res., 1992, 40, 253 A. [11 ] KNAUSW.A., RAUSSA., ALPEROVITCRA., LE GALLJ., LOIRATP., PATOIS E. et aL - - Do objective estimates for survival influence decisions to withhold or withdraw treatment? Med. Decis. Making, 1990, 10, 163-171. [12] MURRAYL.S., TEASDALEG.M., MURRAYG.D., JENNET]"B., MILLERJ.D., PICKARDJ.D. et a L - Does prediction of outcome alter patient management? Lancet, 1993, 341, 1487-1491. R~an. Urg., 1994, 3(2 bis), 159-163
1. K N A U S
There are four major reasons for the measurement of severity of illness in the modem practice of intensive care; the content and reliability of its measurement, its strong influence on patient outcome and resource use, its usefulness in identifying the amount of treatment benefit for individual patients, and the accuracy and complementary nature of severity based outcome measurements with clinical judgment. This presentation will discuss each of these issues. It will conclude with a brief discussion of the next steps required to advance the precision and usefulness of severity measurement.
•
Severity can be reliably measured with medical facts
Over the past 15 years it has become increasingly accepted that acutely ill patients vary greatly in their physiologic status and risk for dying. It is also now recognized that these variations are not represented by traditional methods of disease description. For example, a patient with a clearly defined disease such as pneumococcal pneumonia could be treated as an outpatient with antibiotics or be admitted to an ICU and require high levels of oxygen and mechanical support to maintain life. The difference between the two is the severity of their pneumonia with severity defined predominantly using acute physiologic manifestations. The pneumonia patient treated as an outpatient might have a respiratory rate of 24 and a Pa02 of 92, while the ICU patient might be breathing 40 with a Pa02 of 60. These End other physiologic facts when combined into summary measures of severity provide a unique, objective, and reliable method of describing and classifying patients.
George Washington University Medical Center, 2300 K Street, N.W. - - Washington, D.C. 20037, U.S.A..
The content or nature of this information is also important. Severity measurement is based on analysis of medical facts, specifically the nature and type of the patient's physiologic state at ICU admission and how this state changes over time. Changes in physiology over time have been consistently demonstrated to correlate with patient outcome [1]. These facts are an appropriate basis upon which to base medical decisions. Each day on ICU rounds, medical decisions are made, largely based upon interpretation of these physiologic facts. These physiologic and other related medical facts are also ethically appropriate. They represent the patient's ability to benefit from treatment and therefore are an appropriate measure of a patient's entitlement to care. Certainly, these facts are preferable to other considerations upon which therapeutic decisions might be based, such as the patient's ability to pay for the services [2]. A number of physiologic based severity systems have been developed and are widely used [3]. Measurement of severity using these systems is increasingly becoming an integral part of both research investigations involving severely ill patients and is also part of routine data collection in many intensive care units. The physiologic and related information needed to measure severity is routinely available in all ICUs and is becoming increasingly available through inexpensive electronic links [2]. Progress is also continually being made in the best form for representing the physiologic measures and how to combine them. For example, we are learning when disease or condition specific transformations of physiology are appropriate and when to rely on general measurement [4]. This progress is possible both because of large and growing databases and because many of the sophisticated analytic techniques previously confined to large mainframe computers are now available on desk top models. Therefore, the first argument for why we should measure severity is that we can do so easily, reliably, inexpensively, using medical facts. R6an. Urg., 1994, 3 (2 bis), 159-163
-
1 6 0
-
Why measure severity? * Phv~inlnnv
Severity is the single most important determinant of patient outcome and resource use of ICU patients As the capabilities and success of advanced medical care increase, we find ourselves treating more severely ill patients more frequently. Each of these episodes of care is also becoming increasingly intense and expensive. Determining whether the care provided during these episodes is appropriate, successfully applied, and of reasonable cost is becoming very important. It has been recently demonstrated that severity is the single most important factor that predicts or determines the outcome and cost, both resources and length of stay, of intensive care treatment
[5].
As figures 1A and 1B illustrate, the acute severity of a patient's illness as determined by physiologic measures, is more influential than disease in determining both outcome (1A) and resource (1B) use as measured by length of stay. This means that any evaluation of the outcome from or cost of intensive care treatment must take into consideration the severity of illness of the patients treated. Considering the large and growing interest in outcome evaluation and cost efficiency, increasingly accurate measurement of severity is essential.
•
Physio (73
Other (3.1)
*Chronic HealtP (0.9) *Age (3.4)
her t.9)
(34.1) * As represented in APACHE Ill score
Other Location prior to ICU 6.7 % Region 3.2 % ICU re-admission 1.1% Hospital bed size 0.8 % Emergency surgery 0.2 % Teaching status 0.2 % Disease-- 78 mutually exclusive indications for ICU admission (Reference 5) Fig. l B . - - Relative prognostic information (percentage of chisquare uniquely associated) for variables used to adjust for ICU length of stay.
A recent analysis of 42 ICUs in the U.S. illustrates how such evaluation is both possible and accurate. This survey found that crude or unadjusted hospital death rates varied from 6 to 42 p. 100. Fully 90 p. 100 of this variation in death rates, however, were attributable to variations in patient characteristics (Fig. 2A). That same study found that average length of stay varied from 3.3 to 7.3 days. Again, a combination of physiologic severity and other important OBSERVED HOSPITAL DEATH RATE 45%
sease 13.6) * Age (7.3)
......... Health (2.9) 25%
* As represented in APACHE Ill score Other Length of stay prior to ICU admission Mean duration of hospital stay for survivors Location prior to ICU Emergency surgery
• 1.6 % 1.4 % 0.1 % 0.01%
Disease-- 78 mutually exclusive indications for ICU admission (Reference 5)
f
•
.~BETTER
5,
. 5%
10%
15%
20%
25%
30%
VVORSE 35%
40%
45%
PREDICTED'HOSPITAL DEATH RATE Fig. 1A. - - Relative prognostic information (percentage of chisquare uniquely associated) for variables used to adjust for hospital mortality risk.
ROan. Urg., 1994, 3 ( 2 bis), 159-163
Fig. 2A. - - Distribution of patients and association between first day APACHE III score and hospital mortality rates for 16,622 ICU patients at 40 hospitals [5].
Why measure severity? patient characteristics accounted for 78 p. 100 of the variation in length of stay (LOS) across these 42 units (Fig. 2B). Prospective application of these risk adjusted techniques permits one to evaluate the outcome and cost-effectiveness of ICUs, to compare one unit to another, and to continuously measure the performance of a unit over time. As figures 2_4 and 2B illustrate, it is possible to use these risk adjustments to identify units that are performing better or worse than average, regardless of their unadjusted death rates [5]. OBSERVED ICU LENGTH OF STAY (DAYS) 8
A
NA
3
3.5
4
" 4.5
5
5.5
6
6.5
7
7.5
PREDICTED ICU LENGTH OF STAY (DAYS) Fig. 2B. - - Distribution of patients and association between first day APACHE III score and ICU length of stay for 17,105 ICU admissions at 40 hospitals. (Reference 5)
In some countries, such as the U.S., assessments of hospital and ICU mortality and cost performance are becoming increasingly common. Accurate measurement of severity as the key pre-treatment component of patient risk makes such determinations accurate and useful. Figures 1A and 1B clearly illustrate that reliance on diagnosis alone is insufficient for a description of the patients treated. Figures 2_4 and 2B illustrate that a comprehensive assessment including severity, enables you to evaluate patient outcome and length of stay from intensive care.
Severity measurement can help evaluate new therapies and determine benefits of treatments for individual patients Just as severity measurement is now the major determinant of death rates from intensive care, systematic assessments of severity are proving to be useful in evaluating new therapies and estimating the amount of benefit a treatment may provide to individual patients. This is because severity measurement, as described above, is a comprehensive
-
161
-
assessment that includes many patient characteristics that determine a patient's response to treatment. The best current example of this relationship is found within the area of sepsis. Sepsis is currently a major cause of death for ICU patients and many new therapies are being proposed and tested. These investigations have discovered that traditional diagnostic categories do not adequately adjust for variations in patient presentations. In response, new categorical descriptors, such as sepsis syndrome have been used as entry criteria for clinical trials. Recently it has been recognized that such categorical descriptors may identify a population with an overall predictable mean mortality but with a wide range or severity or risk estimates for individual patients [6]. This has lead to the recommendation that severity measurement and risk prediction become part of clinical evaluations of new drugs for sepsis and systemic inflammatory response syndrome [7]. This recommendation makes sense because the new alternative therapies being evaluated, such as anti-cytokines, are designed to modify the biologic response to sepsis and inflammation, not attack the infecting organism [8]. Many of these inflammatory responses may be protective at low levels of activity but capable of producing end-organ damage at higher exposures. One recent large scale trial of an anticytokine, IL-lra, indicated that a comprehensive measure of patient severity was the best single measure of drug efficacy with the amount of benefit varying directly with the severity level [9]. This finding suggests that not only might severity measurement find a role in the design and analysis of clinical trials involving severely ill patients, but could also be used after the trial was completed as an indicator of potential benefit for individual patients. As figure 3 illustrates, a clinical trial conducted with prospective severity adjustment would be able to provide patients with an individual risk prediction. The reduction in mortality risk anticipated from the use of a new drug could then be estimated. The clinician would then be able to use an actual or revised version of figure 3 to estimate the amount of mortality risk for an individual patient. This information could complement other patient data and clinical judgment when deciding on drug utilization. Considering the likely cost of these new compounds and the fact that patient selection is a major determinant of cost-effectiveness, such risk-benefit information is of great interest and potential value [4].
•
Outcome estimates are complementary to clinical judgment
The fourth major reason to measure severity is that it is complementary to clinical judgment. The overall results from comparisons of severity based outcome estimates to those obtained by treating RGan. Urg., 1994, 3 (2 bis), 159-163
- 162 - Why measure severity? clinicians is that the accuracy of objective estimates is equivalent to clinical judgment [2]. This has lead some people to question the utility of severity based outcome estimates. The ability of outcome estimates produced using a limited amount of clinical data to equal those obtained from treating physicians, however, is remarkable. Moreover, since such estimates have always been intended to be used as adjuncts to clinical judgments not as replacements for those insights, a more appropriate question is whether such estimates bring anything to clinical judgment. Recent analyses suggest they do. Specifically, we have found that when you have estimates from treating physicians and you combine this estimate with an objective estimate based on severity, the resulting combined estimate is more predictive than either single estimate used alone [10].
data parallels the clinical process of decision making and demonstrates the potential synergy between the two sources of information. The existing clinical evaluations of prognostic estimates also support this synergy. In a controlled study of objective prognostic estimates of mortality derived from the number of organ system failures in 17 French ICUs, when the objective prognostic estimates were provided on a daily basis there was a small but significant increase in decisions to provide comfort care (Table 1) [11]. This change in decision making was limited to those patients with three or more organ system failures (Table I1). These patients have a very high risk of mortality and very small probability of benefit from aggressive therapy. Table I Distribution of decisions to limit treatment according to number of osfs and availability of outcome estimates
28 Day Mortality 100%
DECISION
ControL Study Period Period
Significance of Difference In Comfort Care Decisions
80% Number of Patients 760 No treatment limitation 85 % Treatment limitations 9% Comfort care 6%
Current 60%
//' J
40%
l ~ ~ o t e n t i al Benefit
8% 10 %
p < 0,01
Table II
(simulated)
20%
DECISION
0% 0%
82 %
Knaus et al., Med. Decis. Making, 1990, 10, 163.
/ JNew Drug /-
705
I
~
~
I
20%
40%
60%
80%
100%
Baseline 28 day Mortality Risk Groups
Control Study Period Period
Patients with fewer than three OSFs 725 655 No Limitations 85 % 85 % Limitations 9% 7% Comfort care 6% 6% Patients with three or more OSFs No Limitations Limitations Comfort care
35 66 % 23 % 11%
50 48 % 16 % 36 %
Significance of Differenc~ In Comfort Care Decisions p < 0.10
p < 0,02
Fig. 3. Relationship between baseline estimated risk of 28-day and actual 28-day mortality for ICU admissions meeting sepsis syndrome criteria (Current Therapy). Lower line indicating possible reductions in risk (e.g., potential benefit) with new drug is simulated. Actual relationship can only be determined following analysis of Phase III data. (Reference 4).
Knaus et al., Med. Decis. Making, 1990, 10, 163.
This suggests that the objective estimates are providing superior deductive reasoning, remembering and recalling thousands of previous patient records with a precision impossible to achieve with human memory. The clinician is also bringing his experience and specifically his intuition to bear on the estimate, incorporating distinct patient characteristics and other information not available to the objective estimate. This combination of objective and subjective
A similar result was recently reported by Murray and colleaques in a trial which tested the impact of severity based prognostic estimates in decision making for patients with head trauma [12]. Using a cross over design this study was able to demonstrate a significant decrease in the use of two forms of aggressive therapy, osmotic diuretics and intubation/ventilation in patients with poor prognoses, a mortality risk equal or greater than 80 p. 100
-
-
R~an. Urg., 1994, 3 ( 2 bis), 159-163
Why measure severity? (Tables llI and IV). These two studies suggest that objective probability estimates based on severity may be capable of influencing decision making and directing aggressive therapy toward patients able to benefit. Table III Treatment of head injury by prognosis and availability of outcome estimates
Mortality Risk Low ( < . 4 ) Moderate (.4-.8) High ( > .8)
Before
Osmotic Diuretics* During N (%)
After
264 (23) . 98 (28) 47 (45)
253 (26) 125 (41) 42 (21)
108 (18) 58 (32) 14 (50)
-
163
-
current capabilities and will extend severity measurement to new settings with greater flexibility. We will also discover new ways of combining and analyzing physiologic and other patient data to produce accurate estimates of a patient's risk for various outcomes beyond hospital mortality such as the expected duration of survival and the level of functional status anticipated. These risk estimates will be hepful in evaluating the quality and efficacy of therapy and directing therapy toward patients most likely to benefit. Clinicians will need to learn more about the derivation and interpretation of probability estimates, but when used in conjunction with their clinical judgment, such estimates have the potential to improve their ability to make decisions in the increasingly complex world of clinical medicine.
p < .01 Murray et aL, Lancet, 1993, 341, 1487.
References
Table IV Treatment of head injury by prognosis and availability of outcome estimates
Before Mortality Risk Low ( < . 4 ) Moderate (.4-.8) High ( > .8)
Intubation/Ventilation* During After N (%)
264 (52) 98 (85) 47 (89)
253 (58) 125 (82) 42 (57)
108 (41) 58 (76) 14 (64)
p <.001 Murray et aL, Lancet, 1993, 341, 1487.
•
Conclusion
We are treating more severely ill patients at more advanced stages of disease. These changes have made traditional description classifications of disease inadequate to accurately describe a patient's risk for important outcomes. Severity is now the most important single characteristic influencing a patient's outcome and their potential to benefit from treatment. Unless we measure severity we are unable to describe the patients, measure the quality of the treatments provided, determine the benefits from specific treatments, or assist physicians in deciding on the appropriateri6ss of therapy. Fortunately, progress over the recent few years have provided us the ability to accurately and reliably measure severity using readily available medical facts. In the future, we will be able to use new techniques to improve the precision of severity measurement. Such advances include the continuous measure of physiologic changes instead of categorical scoring systems. These approaches will add precision to our
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