Evidence map›Paper›PMID 33610360›Full record

ArticleThe Journal of thoracic and cardiovascular surgery2022

The American College of Surgeons Surgical Risk Calculator performs well for pulmonary resection: A validation study.

Neel Chudgar, Shi Yan, Meier Hsu, Kay See Tan, Katherine D Gray, Daniela Molena, David R Jones, Valerie W Rusch, Gaetano Rocco, James M Isbell

Open access · greenAbstract readValidation Study
In one paragraph

Article in The Journal of thoracic and cardiovascular surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Predicthor: AI-Powered Predictive Risk Model for 30-Day Mortality and 30-Day Complications in Patients Undergoing Thoracic Surgery for Lung Cancer.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 1 institution in 2 countries.

Neel ChudgarThoracic Service, Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
Shi YanThoracic Service, Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Thoracic Surgery, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China.
Meier HsuThoracic Service, Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
Kay See TanThoracic Service, Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
Katherine D GrayThoracic Service, Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
Daniela MolenaThoracic Service, Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
David R JonesThoracic Service, Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
Valerie W RuschThoracic Service, Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
Gaetano RoccoThoracic Service, Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
James M IsbellThoracic Service, Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY. Electronic address: isbellj@mskcc.org.
Memorial Sloan Kettering Cancer Center · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

objectiveThe American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP SRC) was developed to estimate the risk of postoperative morbidity and mortality within 30 days of an operation. We sought to externally evaluate the performance of the NSQIP SRC for patients undergoing pulmonary resection.

methodsPatients undergoing pulmonary resection at our center between January 2016 and December 2018 were included. Using data from our institution's prospectively maintained Society of Thoracic Surgeons General Thoracic Database, we identified 2514 patients. We entered requisite patient demographic information, preoperative risk factors, and procedural details into the online calculator. Predicted performance of the calculator versus observed outcomes was assessed by discrimination (concordance index [C-index]) and calibration.

resultsThe observed and predicted probabilities of any complication were 8.3% and 9.9%, respectively, and of serious complications were 7.4% and 9.2%, respectively. Observed and predicted 30-day mortality were 0.5% and 0.9%, respectively. The C-index for readmission was 0.644; the C-indices corresponding to all other outcomes in the NSQIP SRC ranged from 0.703 to 0.821. Calibration curves indicated excellent calibration for all binary end points, with the exception of renal failure (predicted underestimated observed probabilities), discharge to a nursing or rehabilitation facility (overestimated), and sepsis (overestimated). Correlation between predicted and observed length of stay was moderate (Spearman coefficient, 0.562), and calibration was good.

conclusionsExcept for readmission, renal failure, discharge to a location other than home, and sepsis, the NSQIP SRC can be used to reasonably predict postoperative complications in patients undergoing pulmonary resection.

Indexed as

PneumonectomyPostoperative ComplicationsRisk AssessmentAcute Kidney InjuryAgedAged, 80 and overFemaleHumansLength of StayMalePatient DischargePatient ReadmissionPrognosislung resectionNSQIPsurgical risk calculatorsurgical risk prediction

Identifiers

PMID33610360
PMCPMC8292429
OpenAlexW3124146951

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.