E-PASS scoring

Estimation of physiologic ability and surgical stress

• 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

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

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).

• (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).

• Severe heart disease is defined as heart failure of New York Heart Association class III or IV or severe arrhythmia requiring mechanical 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 the World Health Organization criteria.

• Performance status index is defined by the Japanese Society for Cancer Therapy.

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).(

• 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

comprehensive risk score (CRS)

• 3) CRS = -0.328 + (0.936 x PRS) + (0.976 x SSS).

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%

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

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

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 surgeryTjun 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].

• [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;

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].

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 but also surgical costs of hospital stay. Although not performed in this study, this may be

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.