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Terhalle L, Arntz L, Hoffmann F, Arnold I, Hafner L, Picking-Pitasch L, Zuppinger J, Delport Lehnen K, Leuppi J, Somasundaram R, Nickel CH, Bingisser R. Nonspecific stress biomarkers for mortality prediction in older emergency department patients presenting with falls: a prospective multicenter observational study. Intern Emerg Med 2024:10.1007/s11739-024-03693-6. [PMID: 38960969 DOI: 10.1007/s11739-024-03693-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 06/21/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Older patients presenting to the emergency department (ED) after falling are increasingly prevalent. Falls are associated with functional decline and death. Biomarkers predicting short-term mortality might facilitate decisions regarding resource allocation and disposition. D-dimer levels are used to rule out thromboembolic disease, while copeptin and adrenomedullin (MR-proADM) may be used as measures of the patient`s stress level. These nonspecific biomarkers were selected as potential predictors for mortality. METHODS Prospective, international, multicenter, cross-sectional observation was performed in two tertiary and two regional hospitals in Germany and Switzerland. Patients aged 65 years or older presenting to the ED after a fall were enrolled. Demographic data, Activities of Daily Living (ADL), and D-dimers were collected upon presentation. Copeptin and MR-proADM levels were determined from frozen samples. Primary outcome was 30-day mortality; and secondary outcomes were mortality at 90, 180, and 365 days. RESULTS Five hundred and seventy-two patients were included. Median age was 83 [IQR 78, 89] years, 236 (67.7%) were female. Mortality overall was 3.1% (30 d), 5.4% (90 d), 7.5% (180 d), and 13.8% (365 d), respectively. Non-survivors were older, had a lower ADL index and higher levels of all three biomarkers. Elevated levels of MR-proADM and D-dimer were associated with higher risk of mortality. MR-proADM and D-dimer showed high sensitivity and low negative likelihood ratio regarding short-term mortality, whereas copeptin did not. CONCLUSION D-dimer and MR-proADM levels might be useful as prognostic markers in older patients presenting to the ED after a fall, by identifying patients at low risk of short-term mortality. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02244983.
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Affiliation(s)
- Lukas Terhalle
- Emergency Department, University Hospital Basel, University of Basel, Basel, Switzerland.
| | - Laura Arntz
- Emergency Department, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Hoffmann
- Emergency Department, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Isabelle Arnold
- Emergency Department, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Livia Hafner
- Emergency Department, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Joanna Zuppinger
- Emergency Department, University Hospital Basel, University of Basel, Basel, Switzerland
- Emergency Department, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland
| | - Karen Delport Lehnen
- Emergency Department, Cantonal Hospital Basel-Landschaft Campus Bruderholz, Binningen, Switzerland
| | - Jörg Leuppi
- Medical Faculty, University of Basel and Cantonal Hospital Baselland, Liestal, Switzerland
| | - Rajan Somasundaram
- Emergency Department, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christian H Nickel
- Emergency Department, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Roland Bingisser
- Emergency Department, University Hospital Basel, University of Basel, Basel, Switzerland
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Barash Y, Soffer S, Grossman E, Tau N, Sorin V, BenDavid E, Irony A, Konen E, Zimlichman E, Klang E. Alerting on mortality among patients discharged from the emergency department: a machine learning model. Postgrad Med J 2020; 98:166-171. [PMID: 33273105 DOI: 10.1136/postgradmedj-2020-138899] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 11/05/2020] [Accepted: 11/09/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVES Physicians continuously make tough decisions when discharging patients. Alerting on poor outcomes may help in this decision. This study evaluates a machine learning model for predicting 30-day mortality in emergency department (ED) discharged patients. METHODS We retrospectively analysed visits of adult patients discharged from a single ED (1/2014-12/2018). Data included demographics, evaluation and treatment in the ED, and discharge diagnosis. The data comprised of both structured and free-text fields. A gradient boosting model was trained to predict mortality within 30 days of release from the ED. The model was trained on data from the years 2014-2017 and validated on data from the year 2018. In order to reduce potential end-of-life bias, a subgroup analysis was performed for non-oncological patients. RESULTS Overall, 363 635 ED visits of discharged patients were analysed. The 30-day mortality rate was 0.8%. A majority of the mortality cases (65.3%) had a known oncological disease. The model yielded an area under the curve (AUC) of 0.97 (95% CI 0.96 to 0.97) for predicting 30-day mortality. For a sensitivity of 84% (95% CI 0.81 to 0.86), this model had a false positive rate of 1:20. For patients without a known malignancy, the model yielded an AUC of 0.94 (95% CI 0.92 to 0.95). CONCLUSIONS Although not frequent, patients may die following ED discharge. Machine learning-based tools may help ED physicians identify patients at risk. An optimised decision for hospitalisation or palliative management may improve patient care and system resource allocation.
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Affiliation(s)
- Yiftach Barash
- Department of Diagnostic Imaging, The Chaim Sheba Medical Center, Ramat Gan, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,DeepVision lab, The Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Shelly Soffer
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,DeepVision lab, The Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Ehud Grossman
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Internal Medicine, The Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Noam Tau
- Department of Diagnostic Imaging, The Chaim Sheba Medical Center, Ramat Gan, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Vera Sorin
- Department of Diagnostic Imaging, The Chaim Sheba Medical Center, Ramat Gan, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eyal BenDavid
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Avinoah Irony
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Emergency Department, The Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Eli Konen
- Department of Diagnostic Imaging, The Chaim Sheba Medical Center, Ramat Gan, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eyal Zimlichman
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Hospital Management, The Chaim Sheba Medcical Center, Ramat Gan, Israel
| | - Eyal Klang
- Department of Diagnostic Imaging, The Chaim Sheba Medical Center, Ramat Gan, Israel .,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,DeepVision lab, The Chaim Sheba Medical Center, Ramat Gan, Israel
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