External validation of a predictive model for reintubation after cardiac surgery: A retrospective, observational study
Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation. We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surge...
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| Vydáno v: | Journal of clinical anesthesia Ročník 92; s. 111295 |
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| Hlavní autoři: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Elsevier Inc
01.02.2024
Elsevier Limited |
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| ISSN: | 0952-8180, 1873-4529, 1873-4529 |
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| Abstract | Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation.
We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed.
Three academic medical centers in the United States.
Adult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery.
Receiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability.
Temporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13).
The reintubation model displayed strong discriminative ability and low predictive error within each validation cohort.
Future work is needed to explore how to optimize models before local implementation.
•Predictive model validation is frequently proposed but rarely done in anesthesiology.•Multicenter validation has regulatory and logistical barriers.•A federated learning approach, as we demonstrate, helps to overcome these barriers. |
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| AbstractList | Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation.
We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed.
Three academic medical centers in the United States.
Adult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery.
Receiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability.
Temporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13).
The reintubation model displayed strong discriminative ability and low predictive error within each validation cohort.
Future work is needed to explore how to optimize models before local implementation.
•Predictive model validation is frequently proposed but rarely done in anesthesiology.•Multicenter validation has regulatory and logistical barriers.•A federated learning approach, as we demonstrate, helps to overcome these barriers. Study objectiveExplore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation.DesignWe performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed.SettingThree academic medical centers in the United States.PatientsAdult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery.InterventionsReceiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability.MeasurementsTemporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13).Main resultsThe reintubation model displayed strong discriminative ability and low predictive error within each validation cohort.ConclusionsFuture work is needed to explore how to optimize models before local implementation. Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation. We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed. Three academic medical centers in the United States. Adult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery. Receiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability. Temporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13). The reintubation model displayed strong discriminative ability and low predictive error within each validation cohort. Future work is needed to explore how to optimize models before local implementation. Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation.STUDY OBJECTIVEExplore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation.We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed.DESIGNWe performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed.Three academic medical centers in the United States.SETTINGThree academic medical centers in the United States.Adult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery.PATIENTSAdult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery.Receiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability.INTERVENTIONSReceiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability.Temporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13).MEASUREMENTSTemporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13).The reintubation model displayed strong discriminative ability and low predictive error within each validation cohort.MAIN RESULTSThe reintubation model displayed strong discriminative ability and low predictive error within each validation cohort.Future work is needed to explore how to optimize models before local implementation.CONCLUSIONSFuture work is needed to explore how to optimize models before local implementation. AbstractStudy objectiveExplore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation. DesignWe performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed. SettingThree academic medical centers in the United States. PatientsAdult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery. InterventionsReceiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability. MeasurementsTemporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13). Main resultsThe reintubation model displayed strong discriminative ability and low predictive error within each validation cohort. ConclusionsFuture work is needed to explore how to optimize models before local implementation. |
| ArticleNumber | 111295 |
| Author | Fabbro, Michael Hofer, Ira S. Clifton, Jacob C. Epstein, Richard H. Grogan, Tristan R. Freundlich, Robert E. Byrne, Daniel W. Pandharipande, Pratik P. Moore, Ryan P. |
| AuthorAffiliation | f) Vanderbilt University Medical Center, Department of Biostatistics, Nashville, TN, USA i) University of California, Los Angeles, Department of Anesthesiology, Los Angeles, CA, USA a) Vanderbilt University Medical Center, Departments of Anesthesiology and Biomedical Informatics, 1211 21 st Avenue South, Nashville, TN, 37212 USA c) University of Miami, Department of Anesthesiology, Miami, FL, USA e) University of California, Los Angeles, Department of Anesthesiology, Los Angeles, CA, USA g) Vanderbilt University Medical Center, Department of Biostatistics, Nashville, TN, USA d) Vanderbilt University Medical Center, Departments of Anesthesiology and Surgery, 1211 21 st Avenue South, Nashville, TN, 37212 USA h) University of Miami, Department of Anesthesiology, Miami, FL, USA b) Vanderbilt University Medical Center, Department of Anesthesiology, 1211 21 st Avenue South, Nashville, TN, 37212 USA |
| AuthorAffiliation_xml | – name: c) University of Miami, Department of Anesthesiology, Miami, FL, USA – name: d) Vanderbilt University Medical Center, Departments of Anesthesiology and Surgery, 1211 21 st Avenue South, Nashville, TN, 37212 USA – name: a) Vanderbilt University Medical Center, Departments of Anesthesiology and Biomedical Informatics, 1211 21 st Avenue South, Nashville, TN, 37212 USA – name: b) Vanderbilt University Medical Center, Department of Anesthesiology, 1211 21 st Avenue South, Nashville, TN, 37212 USA – name: g) Vanderbilt University Medical Center, Department of Biostatistics, Nashville, TN, USA – name: h) University of Miami, Department of Anesthesiology, Miami, FL, USA – name: e) University of California, Los Angeles, Department of Anesthesiology, Los Angeles, CA, USA – name: f) Vanderbilt University Medical Center, Department of Biostatistics, Nashville, TN, USA – name: i) University of California, Los Angeles, Department of Anesthesiology, Los Angeles, CA, USA |
| Author_xml | – sequence: 1 givenname: Robert E. surname: Freundlich fullname: Freundlich, Robert E. email: robert.e.freundlich@vumc.org organization: Vanderbilt University Medical Center, Departments of Anesthesiology and Biomedical Informatics, 1211 21st Avenue South, Nashville, TN 37212, USA – sequence: 2 givenname: Jacob C. surname: Clifton fullname: Clifton, Jacob C. email: jacob.c.clifton@vumc.org organization: Vanderbilt University Medical Center, Department of Anesthesiology, 1211 21st Avenue South, Nashville, TN 37212, USA – sequence: 3 givenname: Richard H. surname: Epstein fullname: Epstein, Richard H. organization: University of Miami, Department of Anesthesiology, Miami, FL, USA – sequence: 4 givenname: Pratik P. surname: Pandharipande fullname: Pandharipande, Pratik P. email: pratik.pandharipande@vumc.org organization: Vanderbilt University Medical Center, Departments of Anesthesiology and Surgery, 1211 21st Avenue South, Nashville, TN 37212, USA – sequence: 5 givenname: Tristan R. surname: Grogan fullname: Grogan, Tristan R. organization: University of California, Los Angeles, Department of Anesthesiology, Los Angeles, CA, USA – sequence: 6 givenname: Ryan P. surname: Moore fullname: Moore, Ryan P. email: ryan.moore@vumc.org organization: Vanderbilt University Medical Center, Department of Biostatistics, Nashville, TN, USA – sequence: 7 givenname: Daniel W. surname: Byrne fullname: Byrne, Daniel W. email: daniel.byrne@vumc.org organization: Vanderbilt University Medical Center, Department of Biostatistics, Nashville, TN, USA – sequence: 8 givenname: Michael surname: Fabbro fullname: Fabbro, Michael organization: University of Miami, Department of Anesthesiology, Miami, FL, USA – sequence: 9 givenname: Ira S. surname: Hofer fullname: Hofer, Ira S. organization: University of California, Los Angeles, Department of Anesthesiology, Los Angeles, CA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37883900$$D View this record in MEDLINE/PubMed |
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| Snippet | Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal... AbstractStudy objectiveExplore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions... Study objectiveExplore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding... |
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| SubjectTerms | Accuracy Adult Anesthesia Calibration Cardiac surgery Cardiac Surgical Procedures - adverse effects Electronic health records External validation Extubation Heart failure Heart surgery Hospitals Human subjects Humans Intensive Care Units Intubation, Intratracheal - adverse effects Model validation Observational studies Pain Medicine Patients Predictive modeling Probability Reintubation Retrospective Studies Statistical analysis Structured Query Language-SQL Transplants & implants Variance analysis |
| Title | External validation of a predictive model for reintubation after cardiac surgery: A retrospective, observational study |
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