{"doi":"10.1097/aln.0b013e31829ce8fd","title":"Development and Validation of an Intraoperative Predictive Model for Unplanned Postoperative Intensive Care","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Background:</jats:title>\n                    <jats:p>The allocation of intensive care unit (ICU) beds for postoperative patients is a challenging daily task that could be assisted by the real-time detection of ICU needs. The goal of this study was to develop and validate an intraoperative predictive model for unplanned postoperative ICU use.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:</jats:title>\n                    <jats:p>With the use of anesthesia information management system, postanesthesia care unit, and scheduling data, a data set was derived from adult in-patient noncardiac surgeries. Unplanned ICU admissions were identified (4,847 of 71,996; 6.7%), and a logistic regression model was developed for predicting unplanned ICU admission. The model performance was tested using bootstrap validation and compared with the Surgical Apgar Score using area under the curve for the receiver operating characteristic.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:</jats:title>\n                    <jats:p>\n                      The logistic regression model included 16 variables: age, American Society of Anesthesiologists physical status, emergency case, surgical service, and 12 intraoperative variables. The area under the curve was 0.905 (95% CI, 0.900–0.909). The bootstrap validation model area under the curves were 0.513 at booking, 0.688 at 3 h before case end, 0.738 at 2 h, 0.791 at 1 h, and 0.809 at case end. The Surgical Apgar Score area under the curve was 0.692. Unplanned ICU admissions had more ICU-free days than planned ICU admissions (5\n                      <jats:italic toggle=\"yes\">vs</jats:italic>\n                      . 4;\n                      <jats:italic toggle=\"yes\">P</jats:italic>\n                      &lt; 0.001) and similar mortality (5.6\n                      <jats:italic toggle=\"yes\">vs</jats:italic>\n                      . 6.0%;\n                      <jats:italic toggle=\"yes\">P</jats:italic>\n                      = 0.248).\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:</jats:title>\n                    <jats:p>The authors have developed and internally validated an intraoperative predictive model for unplanned postoperative ICU use. Incorporation of this model into a real-time data sniffer may improve the process of allocating ICU beds for postoperative patients.</jats:p>\n                  </jats:sec>","journal":"Anesthesiology","year":2013,"id":661949,"datarank":0.5897738449086489,"base_score":3.9318256327243257,"endowment":3.9318256327243257,"self_citation_contribution":0.5897738449086489,"citation_network_contribution":0.0,"self_endowment_contribution":0.5897738449086489,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":50,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1728096,"name":"John Anderson-Dam","orcid":null,"position":1,"is_corresponding":false},{"id":1728098,"name":"Wilton Levine","orcid":null,"position":2,"is_corresponding":false},{"id":317163,"name":"Edward A. Bittner","orcid":"0000-0002-0159-2373","position":3,"is_corresponding":false},{"id":654877,"name":"Jonathan P. Wanderer","orcid":"0000-0002-3119-6873","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Development and Validation of an Intraoperative Predictive Model for Unplanned Postoperative Intensive Care","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Background:</jats:title>\n                    <jats:p>The allocation of intensive care unit (ICU) beds for postoperative patients is a challenging daily task that could be assisted by the real-time detection of ICU needs. The goal of this study was to develop and validate an intraoperative predictive model for unplanned postoperative ICU use.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:</jats:title>\n                    <jats:p>With the use of anesthesia information management system, postanesthesia care unit, and scheduling data, a data set was derived from adult in-patient noncardiac surgeries. Unplanned ICU admissions were identified (4,847 of 71,996; 6.7%), and a logistic regression model was developed for predicting unplanned ICU admission. The model performance was tested using bootstrap validation and compared with the Surgical Apgar Score using area under the curve for the receiver operating characteristic.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:</jats:title>\n                    <jats:p>\n                      The logistic regression model included 16 variables: age, American Society of Anesthesiologists physical status, emergency case, surgical service, and 12 intraoperative variables. The area under the curve was 0.905 (95% CI, 0.900–0.909). The bootstrap validation model area under the curves were 0.513 at booking, 0.688 at 3 h before case end, 0.738 at 2 h, 0.791 at 1 h, and 0.809 at case end. The Surgical Apgar Score area under the curve was 0.692. Unplanned ICU admissions had more ICU-free days than planned ICU admissions (5\n                      <jats:italic toggle=\"yes\">vs</jats:italic>\n                      . 4;\n                      <jats:italic toggle=\"yes\">P</jats:italic>\n                      &lt; 0.001) and similar mortality (5.6\n                      <jats:italic toggle=\"yes\">vs</jats:italic>\n                      . 6.0%;\n                      <jats:italic toggle=\"yes\">P</jats:italic>\n                      = 0.248).\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:</jats:title>\n                    <jats:p>The authors have developed and internally validated an intraoperative predictive model for unplanned postoperative ICU use. Incorporation of this model into a real-time data sniffer may improve the process of allocating ICU beds for postoperative patients.</jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":3.9318256327243257,"endowment":3.9318256327243257,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"23756454","pmcid":null,"openalex_id":"https://openalex.org/W2057472247","authors":[],"funders":[],"total_grants":0,"fwci":2.5193,"citation_percentile":0.89400613,"influential_citations":0,"citation_trend":[{"year":2013,"count":3},{"year":2014,"count":4},{"year":2015,"count":2},{"year":2016,"count":4},{"year":2017,"count":3},{"year":2018,"count":6},{"year":2019,"count":3},{"year":2020,"count":4},{"year":2021,"count":1},{"year":2022,"count":7},{"year":2023,"count":3},{"year":2024,"count":1},{"year":2025,"count":6},{"year":2026,"count":3}],"oa_status":"bronze","license":null,"oa_locations":[{"url":"https://pubs.asahq.org/anesthesiology/article-pdf/119/3/516/261929/20130900_0-00011.pdf","host_type":"journal"},{"url":"https://pubs.asahq.org/anesthesiology/article-pdf/119/3/516/261929/20130900_0-00011.pdf","host_type":"publisher"},{"url":"http://pubs.asahq.org/anesthesiology/article-pdf/119/3/516/261929/20130900_0-00011.pdf","host_type":"publisher"},{"url":"http://anesthesiology.pubs.asahq.org/article.aspx?volume=119&page=516","host_type":"publisher"},{"url":"https://doi.org/10.1097/aln.0b013e31829ce8fd","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/23756454","host_type":"repository"}],"fields_of_study":["Sepsis Diagnosis and Treatment","Cardiac, Anesthesia and Surgical Outcomes","Healthcare Operations and Scheduling Optimization"],"mesh_terms":["Aged","Humans","Intensive Care Units","Length of Stay","Middle Aged","Postoperative Care","ROC Curve","Logistic Models","Hospital Mortality","Area Under Curve"],"keywords":["Medicine","Logistic regression","Receiver operating characteristic","Intensive care unit","American society of anesthesiologists","Emergency medicine","Predictive value of tests","Area under the curve","Anesthesiology","Intensive care medicine","Anesthesia","Internal medicine"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T12:03:53.567129Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}