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The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections

BACKGROUND: Surgical site infections (SSI) are an important cause of peri-surgical morbidity with risks that vary extensively between patients and surgeries. Quantifying SSI risk would help identify candidates most likely to benefit from interventions to decrease the risk of SSI. METHODS: We randoml...

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Autores principales: van Walraven, Carl, Musselman, Reilly
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694979/
https://www.ncbi.nlm.nih.gov/pubmed/23826224
http://dx.doi.org/10.1371/journal.pone.0067167
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author van Walraven, Carl
Musselman, Reilly
author_facet van Walraven, Carl
Musselman, Reilly
author_sort van Walraven, Carl
collection PubMed
description BACKGROUND: Surgical site infections (SSI) are an important cause of peri-surgical morbidity with risks that vary extensively between patients and surgeries. Quantifying SSI risk would help identify candidates most likely to benefit from interventions to decrease the risk of SSI. METHODS: We randomly divided all surgeries recorded in the National Surgical Quality Improvement Program from 2010 into a derivation and validation population. We used multivariate logistic regression to determine the independent association of patient and surgical covariates with the risk of any SSI (including superficial, deep, and organ space SSI) within 30 days of surgery. To capture factors particular to specific surgeries, we developed a surgical risk score specific to all surgeries having a common first 3 numbers of their CPT code. RESULTS: Derivation (n = 181 894) and validation (n = 181 146) patients were similar for all demographics, past medical history, and surgical factors. Overall SSI risk was 3.9%. The SSI Risk Score (SSIRS) found that risk increased with patient factors (smoking, increased body mass index), certain comorbidities (peripheral vascular disease, metastatic cancer, chronic steroid use, recent sepsis), and operative characteristics (surgical urgency; increased ASA class; longer operation duration; infected wounds; general anaesthesia; performance of more than one procedure; and CPT score). In the validation population, the SSIRS had good discrimination (c-statistic 0.800, 95% CI 0.795–0.805) and calibration. CONCLUSION: SSIRS can be calculated using patient and surgery information to estimate individual risk of SSI for a broad range of surgery types.
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spelling pubmed-36949792013-07-03 The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections van Walraven, Carl Musselman, Reilly PLoS One Research Article BACKGROUND: Surgical site infections (SSI) are an important cause of peri-surgical morbidity with risks that vary extensively between patients and surgeries. Quantifying SSI risk would help identify candidates most likely to benefit from interventions to decrease the risk of SSI. METHODS: We randomly divided all surgeries recorded in the National Surgical Quality Improvement Program from 2010 into a derivation and validation population. We used multivariate logistic regression to determine the independent association of patient and surgical covariates with the risk of any SSI (including superficial, deep, and organ space SSI) within 30 days of surgery. To capture factors particular to specific surgeries, we developed a surgical risk score specific to all surgeries having a common first 3 numbers of their CPT code. RESULTS: Derivation (n = 181 894) and validation (n = 181 146) patients were similar for all demographics, past medical history, and surgical factors. Overall SSI risk was 3.9%. The SSI Risk Score (SSIRS) found that risk increased with patient factors (smoking, increased body mass index), certain comorbidities (peripheral vascular disease, metastatic cancer, chronic steroid use, recent sepsis), and operative characteristics (surgical urgency; increased ASA class; longer operation duration; infected wounds; general anaesthesia; performance of more than one procedure; and CPT score). In the validation population, the SSIRS had good discrimination (c-statistic 0.800, 95% CI 0.795–0.805) and calibration. CONCLUSION: SSIRS can be calculated using patient and surgery information to estimate individual risk of SSI for a broad range of surgery types. Public Library of Science 2013-06-27 /pmc/articles/PMC3694979/ /pubmed/23826224 http://dx.doi.org/10.1371/journal.pone.0067167 Text en © 2013 van Walraven, Musselman http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
van Walraven, Carl
Musselman, Reilly
The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections
title The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections
title_full The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections
title_fullStr The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections
title_full_unstemmed The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections
title_short The Surgical Site Infection Risk Score (SSIRS): A Model to Predict the Risk of Surgical Site Infections
title_sort surgical site infection risk score (ssirs): a model to predict the risk of surgical site infections
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694979/
https://www.ncbi.nlm.nih.gov/pubmed/23826224
http://dx.doi.org/10.1371/journal.pone.0067167
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