The document describes the Estimation of Physiologic Ability and Surgical Stress (E-PASS) scoring system, which uses a pre-operative risk score (PRS), surgical stress score (SSS), and comprehensive risk score (CRS) to predict postoperative outcomes. The PRS and SSS are calculated using equations that incorporate patient characteristics and surgical details. The CRS is calculated from the PRS and SSS, and can be used to predict mortality and morbidity risks after surgery. E-PASS provides a simple, accurate means of risk assessment that does not require as many variables as other systems like POSSUM.

1. E-PASS scoring
Estimation of physiologic ability
and surgical stress

2. • Estimation of Physiologic Ability and
Surgical Stress (E-PASS) scoring
system:
• E-PASS=a pre-operative risk score (PRS), a
surgical stress score (SSS), and a
comprehensive risk score (CRS), which is
calculated from the PRS and SSS.
• CRS=PRS+SSS
• E-PASS=K*CRS

3. equations of the E-PASS scoring
system
• The equations of the E-PASS scoring system are as follows (data from Haga et al1):
(1) Estimation of physiologic ability and surgical stress (E-PASS)
as a predictor of immediate outcome after elective
abdominal aortic aneurysm surgery

4. equations of the E-PASS scoring system are as follows (data from
Haga et al1):
• (1) PRS = -0.0686 + 0.00345X1 +0.323X2
+0.205X3 +0.153X4 +0.148X5 +0.0666X6,
where X1 is age; X2, the presence (1) or
absence (0) of severe heart disease; X3, the
presence (1) or absence (0) of severe
pulmonary disease; X4, the presence (1) or
absence (0) of diabetes mellitus; X5, the
performance status index (range, 0-4); and X6,
the American Society of Anesthesiologists'
physiological status classification (range, 1-5).

5. • (1) PRS = -0.0686 + 0.00345X1 +0.323X2
+0.205X3 +0.153X4 +0.148X5 +0.0666X6,
dove: X1 è etò, X2,la presenza (1) o assenza (0)
di malattia cardiaca severa; X3 la presenza (1)
o assenza (0)di malattia polmonare severa; X4,
la presenza (1) o assenza (0) di diabete
mellitus; X5, il performance status index
(range, 0-4); X6, la classificazione di stato fisico
della American Society of Anesthesiologists
(ASA Ps) (range, 1-5).

6. • Severe heart disease is defined as heart failure of New York Heart
Association class III or IV or severe arrhythmia requiringmechanical
support.
• Severe pulmonary disease is defined as any condition with a percentage
vital capacity of less than 60%and/or a percentage forced expiratory
volume in 1 second of less than 50%.
• Diabetes mellitus is defined according to theWorld Health Organization
criteria.
• Performance status index is defined by the Japanese Society for Cancer
Therapy.

7. SSS:surgical stress core
• (2) SSS = -0.342 + 0.0139X1 +0.0392X2 +0.352X3,
where X1 is blood loss (in grams) divided by
body weight (in kilograms); X2, the operating
time (in hours); and X3, the extent of the skin
incision (0 indicates a minor incision for
laparoscopic or thoracoscopic surgery,
including laparoscopic- or thoracoscopic-assisted
surgery; 1, laparotomy or
thoracotomy alone; and 2, laparotomy and
thoracotomy).
(

8. • 2) SSS = -0.342 + 0.0139X1 +0.0392X2 +0.352X3,
dove X1 è la perdita ematica (in grammi)
diviso per il peso corporeo (in kg); X2, tempo
operatorio ( h); X3, l’estensione della incisione
cutanea: (0 indica una incisione minore
laparoscopica o toracoscopica; 1, laparotomia
o toracototomia da sole ; 2, laparotomia e
toracotomia

9. comprehensive risk score (CRS)
• 3) CRS = -0.328 + (0.936 x PRS) + (0.976 x SSS).

10. Esempio di di EPass
• 70 anni
• Copd
• Iperteso
• Gastrtect 5 h,perdite 800 ml stimate…….
• PRS = -0.0686 + 0.00345*70+0.323*0 +0.205*1
+0.153X4 +0.148*??X5 +0.0666*3,assumiamo X5=1…
• PRS=3,49
• SSS =0,4345
• CRS = -0.328 + (3,26) + (0,4240).=3,35 ,cioè mortalità
0-5%,morbilità 44%

11. Incidence of mortality and morbidity accordingto CRS. The graph appears to
demonstrate that patients in the ≥1.0 categoryare at particularly high risk of
mortality, and in the .5 to <1.0 and ≥1.0categories at particularly high risk of
morbidity. Bars show 95% confidence intervals Estimation of physiologic ability and
surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm
surgery

12. Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after
elective abdominal aortic aneurysm surgery

13. American Journal of Surgery - Volume 194, Issue 2 (August 2007) -
Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate
outcome after elective abdominal aortic aneurysm surgery
Tjun Tang,Stewart R. Walsh,Thomas R. Fanshawe, Jonathan H. Gillard,Umar Sadat,
Kevin Varty, Michael E. Gaunt, Jonathan R. Boyle.
• Haga et al [10] derived and validated the Estimation of Physiologic Ability and
Surgical Stress (E-PASS) scoring system for risk stratification of patients undergoing
elective general gastrointestinal (GI) surgery. Furthermore, it has been externally
validated in a different geographical setting from where it was originally developed
and has been shown to be reproducible in accurately predicting outcome following
elective GI surgery [11]. This system comprises a pre-operative risk score (PRS), a
surgical stress score (SSS), and a comprehensive risk score (CRS), which is
calculated from the PRS and SSS. E-PASS was based on the premise that morbidity
and mortality rates can be correlated with the patient’s physiologic risk and the
surgical stress applied. Surgical stress can be estimated, in general, because tissue
destruction, bleeding and ischemia caused by basic surgical techniques produce
inflammatory cytokines, which are thought to be an underlying mechanism in the
development of organ failure following a surgical insult [12].

14. • [10] Haga Y., Ikei S., Ogawa M.: Estimation of Physiologic Ability and
Surgical Stress (E-PASS) as a new prediction scoring system for post-operative
morbidity and mortality following gastrointestinal
surgery. Surg Today 29. 219-225.1999;
[11] Oka Y., Nishijima J., Oku K., et al: Usefulness of an Estimation of
Physiologic Ability and Surgical Stress (E-PASS) scoring system to predict
the incidence of postoperative complications in gastrointestinal
surgery. World J Surg 29. 1029-1033.2005;
[12] Ogawa M.: Mechanisms of the development of organ failure
following surgical insult: the “second attack” theory. Clin Intens
Care 7. 34-38.1996;
[13] Haga Y., Ikei S., Wada Y., et al: Evaluation of an Estimation of
Physiologic Ability and Surgical Stress (E-PASS) scoring system to predict
postoperative risk: a multicenter prospective study. Surg Today 31. 569-
574.2001;

15. Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of
immediate outcome after elective abdominal aortic aneurysm surgery
• Risk adjustment is important in comparative audit and in general, models of adverse outcome are
formed using logistic regression as the statistical technique. Unfortunately, the current scoring
systems that have been developed to assess postoperative mortality and morbidity involve collection
of numerous variables and therefore databases are likely to be incomplete [22], [23]. The Physiological
and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) [24] has
been proposed as a predictor equation of complications and mortality taking into account differences
in case-mix. A major drawback of the POSSUM approach is that it requires up to 19 perioperative
physiologic data items per patient, which are not necessarily collected as part of routine clinical care.
Furthermore, it was criticized because it overpredicted the mortality rate of patients at low risk [25].
Portsmouth-POSSUM and Vascular-POSSUM, although more accurate predictors of death than
POSSUM in vascular patients, have not been shown to be robust in different geographic locations [26],
[27]. E-PASS has also been compared to POSSUM and P-POSSUM in elective GI surgery, which revealed
that although both systems had significant correlations with the observed rates of postoperative
complications, the POSSUM equations overpredicted mortality [28].

16. Estimation of physiologic ability and surgical stress (E-PASS) as
a predictor of immediate outcome after elective abdominal
aortic aneurysm surgery
• We have started to prospectively compare E-PASS with the different POSSUM predictor
equations in vascular surgery to evaluate its usefulness in defining quality of care.
Undoubtedly, the practical logistics associated with collecting such a large dataset in the
POSSUM models have been one of the main factors inhibiting their universal adoption
by vascular surgeons. E-PASS uses far fewer variables and therefore has obvious
advantages over POSSUM in amount of data entry needed and the complexity of the
analysis. We have found that the CRS can be quickly calculated immediately after the
operation and the different parameters to calculate PRS and SSS were relatively easy to
collect, as demonstrated by the low number of cases excluded. The POSSUM scoring
system can only be used as a prediction guideline if the physiology-only equations are
used. Generally, the estimated mortality rates can be determined only after the
pathologic results are known [24]. Moreover POSSUM devised for exponential analysis
does not provide accurate predicted mortality rates for individual patients. The E-PASS
model was developed originally as a prediction guideline for decision-making and
therefore the estimated mortality rates can be computed easily after an operation. It
was previously reported that E-PASS was useful in estimating surgical costs in GI surgery
[29]. CRS had a significant positive correlation to the duration and costs of hospital stay.
They showed an equation for estimating surgical costs and compared a real to estimated
costs among hospitals, proposing a risk-based payment system because hospitals that
treat more high-risk patients would not only show higher mortality and morbidity rates

17. Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of
immediate outcome after elective abdominal aortic aneurysm surgery
• The strong correlation between PRS and outcome (P < .0001 for mortality and
morbidity) may allow the vascular surgeon to predict risk in an individual patient
before surgery. Furthermore, this risk can be discussed confidently with both patient
and relatives while gaining informed consent. If the risk predicted by PRS is too high
for a patient, a less invasive procedure such as endovascular stenting or conservative
management may be considered. The fact that PRS, on an individual basis, was
extremely powerful in predicting mortality and morbidity ranges may allow for the
reduction of data required for a national vascular database without compromising
the statistical basis of comparative audit. Prytherch et al were able to successfully
model surgical outcomes in arterial surgery using a minimal dataset of blood tests
known as “VBHOM” (vascular biochemistry and hematology outcome models) [3].
This has the advantage that it is universal in its application and does not require
operative data. Many models like POSSUM suffer from the same weakness, which is,
by definition, that they exclude patients who were either not offered or refused
surgery. The PRS component of E-PASS, in the future, may be developed and
validated like VBHOM to overcome this problem.