{"doi":"10.1001/jamanetworkopen.2020.5191","title":"Comparison of Early Warning Scoring Systems for Hospitalized Patients With and Without Infection at Risk for In-Hospital Mortality and Transfer to the Intensive Care Unit","abstract":"Importance: Risk scores used in early warning systems exist for general inpatients and patients with suspected infection outside the intensive care unit (ICU), but their relative performance is incompletely characterized. Objective: To compare the performance of tools used to determine points-based risk scores among all hospitalized patients, including those with and without suspected infection, for identifying those at risk for death and/or ICU transfer. Design, Setting, and Participants: In a cohort design, a retrospective analysis of prospectively collected data was conducted in 21 California and 7 Illinois hospitals between 2006 and 2018 among adult inpatients outside the ICU using points-based scores from 5 commonly used tools: National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), Between the Flags (BTF), Quick Sequential Sepsis-Related Organ Failure Assessment (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS). Data analysis was conducted from February 2019 to January 2020. Main Outcomes and Measures: Risk model discrimination was assessed in each state for predicting in-hospital mortality and the combined outcome of ICU transfer or mortality with area under the receiver operating characteristic curves (AUCs). Stratified analyses were also conducted based on suspected infection. Results: The study included 773 477 hospitalized patients in California (mean [SD] age, 65.1 [17.6] years; 416 605 women [53.9%]) and 713 786 hospitalized patients in Illinois (mean [SD] age, 61.3 [19.9] years; 384 830 women [53.9%]). The NEWS exhibited the highest discrimination for mortality (AUC, 0.87; 95% CI, 0.87-0.87 in California vs AUC, 0.86; 95% CI, 0.85-0.86 in Illinois), followed by the MEWS (AUC, 0.83; 95% CI, 0.83-0.84 in California vs AUC, 0.84; 95% CI, 0.84-0.85 in Illinois), qSOFA (AUC, 0.78; 95% CI, 0.78-0.79 in California vs AUC, 0.78; 95% CI, 0.77-0.78 in Illinois), SIRS (AUC, 0.76; 95% CI, 0.76-0.76 in California vs AUC, 0.76; 95% CI, 0.75-0.76 in Illinois), and BTF (AUC, 0.73; 95% CI, 0.73-0.73 in California vs AUC, 0.74; 95% CI, 0.73-0.74 in Illinois). At specific decision thresholds, the NEWS outperformed the SIRS and qSOFA at all 28 hospitals either by reducing the percentage of at-risk patients who need to be screened by 5% to 20% or increasing the percentage of adverse outcomes identified by 3% to 25%. Conclusions and Relevance: In all hospitalized patients evaluated in this study, including those meeting criteria for suspected infection, the NEWS appeared to display the highest discrimination. Our results suggest that, among commonly used points-based scoring systems, determining the NEWS for inpatient risk stratification could identify patients with and without infection at high risk of mortality.","journal":"JAMA Network Open","year":2020,"id":50945,"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":173,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9499,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":252283,"name":"Yun Lu","orcid":"0000-0002-0900-4249","position":1,"is_corresponding":false},{"id":252284,"name":"Kyle A. Carey","orcid":"0000-0002-5038-6074","position":2,"is_corresponding":false},{"id":253342,"name":"Emily Gilbert","orcid":null,"position":3,"is_corresponding":false},{"id":252285,"name":"Majid Afshar","orcid":"0000-0002-6368-4652","position":4,"is_corresponding":false},{"id":253343,"name":"Mary Akel","orcid":null,"position":5,"is_corresponding":false},{"id":252286,"name":"Nirav Shah","orcid":"0000-0002-9107-7788","position":6,"is_corresponding":false},{"id":253344,"name":"John Dolan","orcid":null,"position":7,"is_corresponding":false},{"id":253345,"name":"Christopher Winslow","orcid":null,"position":8,"is_corresponding":false},{"id":235810,"name":"Patricia Kipnis","orcid":"0000-0003-4572-0178","position":9,"is_corresponding":false},{"id":253346,"name":"Dana P. Edelson","orcid":null,"position":10,"is_corresponding":false},{"id":226782,"name":"Gabriel J. Escobar","orcid":"0000-0003-2540-3327","position":11,"is_corresponding":false},{"id":252287,"name":"Matthew M. Churpek","orcid":"0000-0002-4030-5250","position":12,"is_corresponding":false},{"id":229526,"name":"Vincent X. Liu","orcid":null,"position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-18T20:39:29.006085Z","pmid":"32427324","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":[]}