Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 332

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

The Surgical Mortality Probability Model: Derivation and Validation of a Simple Risk Prediction Rule for Noncardiac Surgery

Glance, Laurent G., Lustik, Stewart J., Hannan, Edward L., Osler, Turner M., Mukamel, Dana B., Qian, Feng, Dick, Andrew W.·2012·Annals of Surgery

Abstract

In Brief Objective: To develop a 30-day mortality risk index for noncardiac surgery that can be used to communicate risk information to patients and guide clinical management at the “point-of-care,” and that can be used by surgeons and hospitals to internally audit their quality of care. Background: Clinicians rely on the Revised Cardiac Risk Index to quantify the risk of cardiac complications in patients undergoing noncardiac surgery. Because mortality from noncardiac causes accounts for many perioperative deaths, there is also a need for a simple bedside risk index to predict 30-day all-cause mortality after noncardiac surgery. Methods: Retrospective cohort study of 298,772 patients undergoing noncardiac surgery during 2005 to 2007 using the American College of Surgeons National Surgical Quality Improvement Program database. Results: The 9-point S-MPM (Surgical Mortality Probability Model) 30-day mortality risk index was derived empirically and includes three risk factors: ASA (American Society of Anesthesiologists) physical status, emergency status, and surgery risk class. Patients with ASA physical status I, II, III, IV or V were assigned either 0, 2, 4, 5, or 6 points, respectively; intermediate- or high-risk procedures were assigned 1 or 2 points, respectively; and emergency procedures were assigned 1 point. Patients with risk scores less than 5 had a predicted risk of mortality less than 0.50%, whereas patients with a risk score of 5 to 6 had a risk of mortality between 1.5% and 4.0%. Patients with a risk score greater than 6 had risk of mortality more than 10%. S-MPM exhibited excellent discrimination (C statistic, 0.897) and acceptable calibration (Hosmer-Lemeshow statistic 13.0, P = 0.023) in the validation data set. Conclusions: Thirty-day mortality after noncardiac surgery can be accurately predicted using a simple and accurate risk score based on information readily available at the bedside. This risk index may play a useful role in facilitating shared decision making, developing and implementing risk-reduction strategies, and guiding quality improvement efforts. Using data from the American College of Surgeons National Surgical Quality Improvement Program database, we developed a 30-day mortality risk index for noncardiac surgery. The 9-point Surgical Mortality Probability Model (S-MPM) 30-day mortality risk index was derived empirically and includes 3 risk factors: ASA (American Society of Anesthesiologists) physical status, emergency status, and surgery risk class. Patients with ASA physical status I, II, III, IV, or V were assigned either 0, 2, 4, 5, or 6 points, respectively; intermediate- or high-risk procedures were assigned 1 or 2 points, respectively; and emergency procedures were assigned 1 point. S-MPM exhibited excellent discrimination (C statistic, 0.897) and acceptable calibration (Hosmer-Lemeshow statistic 13.0, P = 0.023) in the validation data set.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1097/sla.0b013e31824b45af

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Cited by

See all 332

Metadata

Authors
Glance, L. G., Lustik, S. J., Hannan, E. L., Osler, T. M., Mukamel, D. B., Qian, F., & Dick, A. W.
Venue
Annals of Surgery
Published
2012-04
Volume
255
Issue
4
Pages
696-702
ISSN
0003-4932 (ISSN-L)
OpenAlex
W2166597341
Cited by
332
References
3
FWCI
10.36
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The Surgical Mortality Probability Model: Derivation and Validation of a Simple Risk Prediction Rule for Noncardiac Surgery. https://app.cochat.ai/papers/10.1097/sla.0b013e31824b45af
The paper
Glance, L. G., Lustik, S. J., Hannan, E. L., Osler, T. M., Mukamel, D. B., Qian, F., & Dick, A. W. (2012). The Surgical Mortality Probability Model: Derivation and Validation of a Simple Risk Prediction Rule for Noncardiac Surgery. Annals of Surgery. https://doi.org/10.1097/sla.0b013e31824b45af

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.