{"doi":"10.1101/2022.03.06.22271982","title":"Risk Factor Stratification for Postoperative Delirium: A Retrospective Database Study","abstract":"Abstract Background Postoperative Delirium (POD) is a disturbing reality for patients and their families. Absence of easy-to-use and accurate risk scores prompted us to retrospectively extract data from the electronic health records (EHR) to identify clinical factors associated with POD. We seek to create a multivariate nomogram to predict the risk of POD based upon the most significant clinical factors. Methods The EHR of patients >18 years of age undergoing surgery and had POD assessment were reviewed. Patient characteristics and study variables were summarized between delirium groups. We constructed univariate logistic regression models for POD using each study variable to estimate odds ratios (OR) and constructed a multivariable logistic regression model with stepwise variable selection. In order to create a clinically useful/implementable tool we created a nomogram to predict risk of delirium. Results Overall, we found a rate of POD of 3.7% across our study population. The Model achieved an AUC of the ROC curve of 0.83 (95% CI 0.82-0.84). We found that age, increased ASA score (ASA 3-4 OR 2.81, CI1.49-5.28, p<0.001), depression (OR 1.28, CI1.12-1.47, p<0.001), postoperative benzodiazepine use (OR 3.52, CI3.06-4.06, p<0.001) and urgent cases (Urgent OR 3.51, CI2.92-4.21, p<0.001; Emergent OR 3.99, CI3.21-4.96, p<0.001; Critically Emergent OR 5.30, CI3.53-7.96, p<0.001) were associated with POD. Conclusions We were able to distinguish the contribution of individual risk factors to the development of POD. We created a clinically useful easy-to-use tool that has the potential to accurately identify those at high-risk of delirium, a first step to prevent POD.","journal":"medRxiv","year":2022,"id":306279,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6815,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":58604,"name":"Tristan Grogan","orcid":"0000-0001-9471-2938","position":1,"is_corresponding":false},{"id":1000427,"name":"Drew Cheng","orcid":"0000-0002-1141-8694","position":2,"is_corresponding":false},{"id":298002,"name":"Ira Hofer","orcid":"0000-0001-7456-6697","position":3,"is_corresponding":false},{"id":745950,"name":"Susana Vacas","orcid":"0000-0002-4099-3946","position":0,"is_corresponding":true}],"reference_count":36,"raw_metadata":null,"created_at":"2026-07-19T00:32:48.984902Z","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":[]}