Predicting postoperative surgical site infection with administrative data: a random forests algorithm.
Background: Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health...
| Publicado en: | BMC Medical Research Methodology Vol. 21; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
8/28/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152168321&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152168321 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712288 1CI1 jtl: BMC Medical Research Methodology issn: 14712288 maglogo: N pubinfo: dt: 8/28/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 152168321 152168321 NLM34454414 152168321 10.1186/s12874-021-01369-9 NLM34454414 152168321 ppf: 1 ppct: 11 formats: tig: atl: Predicting postoperative surgical site infection with administrative data: a random forests algorithm. aug: au: Petrosyan, Yelena Thavorn, Kednapa Smith, Glenys Maclure, Malcolm Preston, Roanne van Walravan, Carl Forster, Alan J. affil: Clinical Epidemiology, Ottawa Hospital Research Institute, 1053 Carling Ave, K1Y 4E9, Ottawa, Ontario, Canada sug: subj: Algorithms Surgical Wound Infection Epidemiology Surgical Wound Infection Diagnosis Human Ontario Risk Factors Logistic Regression Comparative Studies Multicenter Studies Evaluation Research Validation Studies Social Support Index Ferrans and Powers Quality of Life Index ab: Background: Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health administrative data, using Machine Learning algorithms.Methods: All patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk score system. The SSI risk models were validated in the validation datasets.Results: Of 14,351 patients, 795 (5.5%) had an SSI. First, separate predictive models were built for three distinct administrative datasets. The final model, including hospitalization diagnostic, physician diagnostic and procedure codes, demonstrated excellent discrimination (C statistics, 0.91, 95% CI, 0.90-0.92) and calibration (Hosmer-Lemeshow χ2 statistics, 4.531, p = 0.402).Conclusion: We demonstrated that health administrative data can be effectively used to identify SSIs. Machine learning algorithms have shown a high degree of accuracy in predicting postoperative SSIs and can integrate and utilize a large amount of administrative data. External validation of this model is required before it can be routinely used to identify SSIs. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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