Performance Comparison Between SURPAS and ACS NSQIP Surgical Risk Calculator in Pulmonary Resection

被引:10
作者
Chudgar, Neel P. [1 ]
Yan, Shi [1 ,2 ]
Hsu, Meier [1 ]
Tan, Kay See [1 ]
Gray, Katherine D. [3 ]
Molena, Daniela [1 ]
Nobel, Tamar [4 ]
Adusumilli, Prasad S. [1 ]
Bains, Manjit [1 ]
Downey, Robert J. [1 ]
Huang, James [1 ]
Park, Bernard J. [1 ]
Rocco, Gaetano [1 ]
Rusch, Valerie W. [1 ]
Sihag, Smita [1 ]
Jones, David R. [1 ]
Isbell, James M. [1 ]
机构
[1] Mem Sloan Kettering Canc Ctr, Thorac Surg Serv, Dept Surg, 1275 York Ave, New York, NY 10065 USA
[2] Peking Univ Canc Hosp & Inst, Dept Thorac Surg 2, Key Lab Carcinogenesis & Translat Res, Beijing, Peoples R China
[3] Weill Cornell Med, Dept Surg, New York Presbyterian Hosp, New York, NY USA
[4] Mt Sinai Hosp, Dept Surg, New York, NY USA
基金
美国国家卫生研究院;
关键词
ASSESSMENT SYSTEM SURPAS; THORACIC-SURGERY; PREDICT; THORACOSCORE; SOCIETY; QUALITY; MODEL; MORTALITY; DECISION; PROGRAM;
D O I
10.1016/j.athoracsur.2020.08.021
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background. Accurate preoperative risk assessment is critical for informed decision making. The Surgical Risk Preoperative Assessment System (SURPAS) and the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator (SRC) predict risks of common postoperative complications. This study compares observed and predicted outcomes after pulmonary resection between SURPAS and NSQIP SRC. Methods. Between January 2016 and December 2018, 2514 patients underwent pulmonary resection and were included. We entered the requisite patient demographics, preoperative risk factors, and procedural details into the online NSQIP SRC and SURPAS formulas. Performance of the prediction models was assessed by discrimination and calibration. Results. No statistically significant differences were found between the 2 models in discrimination performance for 30-day mortality, urinary tract infection, readmission, and discharge to a nursing or rehabilitation facility. The ability to discriminate between a patient who will develop a complication and a patient who will not was statistically indistinguishable between NSQIP and SURPAS, except for renal failure. With a C index closer to 1.0, the NSQIP performed significantly better than the SURPAS SRC in discriminating risk of renal failure (C index, 0.798 vs 0.694; P = .003). The calibration curves of predicted and observed risk for each model demonstrate similar performance with a tendency toward overestimation of risk, apart from renal failure. Conclusions. Overall, SURPAS and NSQIP SRC performed similarly in predicting outcomes for pulmonary resections in this large, single-center validation study with moderate to good discrimination of outcomes. Notably, SURPAS uses a smaller set of input variables to generate the preoperative risk assessment. The addition of thoracic-specific input variables may improve performance. (C) 2021 by The Society of Thoracic Surgeons
引用
收藏
页码:1643 / 1651
页数:9
相关论文
共 24 条
[1]  
American College of Surgeons, AM COLL SURGEONS NAT
[2]   Development and Evaluation of the Universal ACS NSQIP Surgical Risk Calculator: A Decision Aid and Informed Consent Tool for Patients and Surgeons [J].
Bilimoria, Karl Y. ;
Liu, Yaoming ;
Paruch, Jennifer L. ;
Zhou, Lynn ;
Kmiecik, Thomas E. ;
Ko, Clifford Y. ;
Cohen, Mark E. .
JOURNAL OF THE AMERICAN COLLEGE OF SURGEONS, 2013, 217 (05) :833-+
[3]   Thoracoscore fails to predict complications following elective lung resection [J].
Bradley, Amy ;
Marshall, Andrea ;
Abdelaziz, Mahmoud ;
Hussain, Khalid ;
Agostini, Paula ;
Bishay, Ehab ;
Kalkat, Maninder ;
Steyn, Richard ;
Rajesh, Pala ;
Dunn, Janet ;
Naidu, Babu .
EUROPEAN RESPIRATORY JOURNAL, 2012, 40 (06) :1496-1501
[4]   European risk models for morbidity (EuroLung1) and mortality (EuroLung2) to predict outcome following anatomic lung resections: an analysis from the European Society of Thoracic Surgeons database [J].
Brunelli, Alessandro ;
Salati, Michele ;
Rocco, Gaetano ;
Varela, Gonzalo ;
Van Raemdonck, Dirk ;
Decaluwe, Herbert ;
Falcoz, Pierre Emmanuel .
EUROPEAN JOURNAL OF CARDIO-THORACIC SURGERY, 2017, 51 (03) :490-497
[5]   An Examination of American College of Surgeons NSQIP Surgical Risk Calculator Accuracy [J].
Cohen, Mark E. ;
Liu, Yaoming ;
Ko, Clifford Y. ;
Hall, Bruce L. .
JOURNAL OF THE AMERICAN COLLEGE OF SURGEONS, 2017, 224 (05) :787-+
[6]   A Model to Predict the Use of Surgical Resection for Advanced-Stage Non-Small Cell Lung Cancer Patients [J].
David, Elizabeth A. ;
Andersen, Stina W. ;
Beckett, Laurel A. ;
Melnikow, Joy ;
Kelly, Karen ;
Cooke, David T. ;
Brown, Lisa M. ;
Canter, Robert J. .
ANNALS OF THORACIC SURGERY, 2017, 104 (05) :1665-1672
[7]   COMPARING THE AREAS UNDER 2 OR MORE CORRELATED RECEIVER OPERATING CHARACTERISTIC CURVES - A NONPARAMETRIC APPROACH [J].
DELONG, ER ;
DELONG, DM ;
CLARKEPEARSON, DI .
BIOMETRICS, 1988, 44 (03) :837-845
[8]   The Thoracic Surgery Scoring System (Thoracoscore): Risk model for in-hospital death in 15,183 patients requiring thoracic surgery [J].
Falcoz, Pierre Emmanuel ;
Conti, Massimo ;
Brouchet, Laurent ;
Chocron, Sidney ;
Puyraveau, Marc ;
Mercier, Mariette ;
Etievent, Joseph Philippe ;
Dahan, Marcel .
JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY, 2007, 133 (02) :325-332
[9]   The Society of Thoracic Surgeons Lung Cancer Resection Risk Model: Higher Quality Data and Superior Outcomes [J].
Fernandez, Felix G. ;
Kosinski, Andrzej S. ;
Burfeind, William ;
Park, Bernard ;
DeCamp, Malcolm M. ;
Seder, Christopher ;
Marshall, Blair ;
Magee, Mitchell J. ;
Wright, Cameron D. ;
Kozower, Benjamin D. .
ANNALS OF THORACIC SURGERY, 2016, 102 (02) :370-377
[10]   Accurate preoperative prediction of unplanned 30-day postoperative readmission using 8 predictor variables [J].
Gibula, Douglas R. ;
Singh, Abhinav B. ;
Bronsert, Michael R. ;
Henderson, William G. ;
Battaglia, Catherine ;
Hammermeister, Karl E. ;
Glebova, Natalia O. ;
Meguid, Robert A. .
SURGERY, 2019, 166 (05) :812-819