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Original Investigation |

External Validation of the Estimation of Physiologic Ability and Surgical Stress (E-PASS) Risk Model to Predict Operative Risk in Perihilar Cholangiocarcinoma ONLINE FIRST

Robert J. S. Coelen, MD1; Pim B. Olthof, MD1; Susan van Dieren, PhD1; Marc G. H. Besselink, MD, PhD, MSc1; Olivier R. C. Busch, MD, PhD1; Thomas M. van Gulik, MD, PhD1
[+] Author Affiliations
1Department of Surgery, Academic Medical Center, Amsterdam, the Netherlands
JAMA Surg. Published online August 31, 2016. doi:10.1001/jamasurg.2016.2305
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Importance  Resection of perihilar cholangiocarcinoma (PHC) is high-risk surgery, with reported operative mortality up to 17%. Therefore, preoperative risk assessment is needed to identify high-risk patients and anticipate postoperative adverse outcomes.

Objective  To provide external validation of the Estimation of Physiologic Ability and Surgical Stress (E-PASS) risk model in a Western PHC cohort.

Design, Setting, and Participants  The E-PASS variables were obtained from a database that included 156 consecutive patients who underwent resection for suspected PHC between January 1, 2000, and December 31, 2015, at the Academic Medical Center, Amsterdam, the Netherlands. The accuracy of E-PASS using intraoperative variables and its modified form that can be used before surgery (mE-PASS) in predicting mortality was assessed by area under the curve analysis (discrimination) and by the Hosmer-Lemeshow goodness-of-fit test (calibration).

Main Outcomes and Measures  In-hospital mortality, severe morbidity (Clavien-Dindo grade≥III), and a high Comprehensive Complication Index.

Results  Among 156 patients included in the study, the median age was 63 years, and 62.8% (n = 98) were male. Of them, 85.3% (n = 133) underwent major liver resection. Severe morbidity occurred in 51.3% (n = 80), and in-hospital mortality was 13.5% (n = 21). Both E-PASS and mE-PASS had adequate discriminative performance, with areas under the curve of 0.78 (95% CI, 0.67-0.88) and 0.79 (95% CI, 0.70-0.89), respectively, while E-PASS showed better calibration (P = .33 vs P = .02, Hosmer-Lemeshow goodness-of-fit test). The ratios of observed to expected mortality were 1.31 for E-PASS and 1.24 for mE-PASS. Both models were able to distinguish groups with low risk, intermediate risk, and high risk, with observed mortality rates of 0.0% to 3.6%, 8.3% to 9.0%, and 25.0% to 28.3%, respectively. Severe morbidity and a high Comprehensive Complication Index were more frequently observed among high-risk patients.

Conclusions and Relevance  Both E-PASS models accurately identify patients at high risk of postoperative in-hospital mortality after resection for PHC. The mE-PASS model can be used before surgery in outpatient settings and allows for risk assessment and shared decision making.

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Figure 1.
Area Under the Curve (AUC) Analysis for Estimation of Physiologic Ability and Surgical Stress Models (Comprehensive Risk Score [CRS] and Fixed CRSf [CRSf]) as a Discriminant of Postoperative In-Hospital Mortality
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Figure 2.
Morbidity and Mortality Rates Among Risk Groups of Estimation of Physiologic Ability and Surgical Stress Models

CCI indicates Comprehensive Complication Index; CRS, Comprehensive Risk Score; and CRSf, fixed CRS.

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