Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE)
<b>Background/Objectives</b>: Serious infections in older adults are associated with substantial mortality and morbidity. Diagnosis is challenging because of the non-specific presentation and overlap with pre-existing comorbidities. The objective of this study was to develop a clinical p...
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MDPI AG
2025-04-01
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| Series: | Geriatrics |
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| author | Thomas Struyf Lisa Powaga Marc Sabbe Nicolas Léonard Ivan Myatchin Ben Van Calster Jos Tournoy Frank Buntinx Laurens Liesenborghs Jan Y. Verbakel Ann Van den Bruel |
| author_facet | Thomas Struyf Lisa Powaga Marc Sabbe Nicolas Léonard Ivan Myatchin Ben Van Calster Jos Tournoy Frank Buntinx Laurens Liesenborghs Jan Y. Verbakel Ann Van den Bruel |
| author_sort | Thomas Struyf |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: Serious infections in older adults are associated with substantial mortality and morbidity. Diagnosis is challenging because of the non-specific presentation and overlap with pre-existing comorbidities. The objective of this study was to develop a clinical prediction model using clinical features and biomarkers to support emergency care physicians in diagnosing serious infections in acutely ill older adults. <b>Methods</b>: We conducted a prospective cross-sectional diagnostic study, consecutively including acutely ill patients (≥65 year) presenting to the emergency department. Clinical information and blood samples were collected at inclusion by a trained study nurse. A prediction model for <i>any serious infection</i> was developed based on ten candidate predictors that were further reduced to four ad interim using a penalized Firth multivariable logistic regression model. We assessed discrimination and calibration of the model after internal validation using bootstrapping. <b>Results</b>: We included 425 participants at three emergency departments, of whom 215 were diagnosed with a serious infection (51%). In the final model, we retained systolic blood pressure, oxygen saturation, and C-reactive protein as predictors. This model had good discriminatory value with an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.82 (95% CI: 0.78 to 0.86) and a calibration slope of 0.96 (95% CI: 0.76 to 1.16) after internal validation. Addition of procalcitonin did not improve the discrimination of the model. <b>Conclusions</b>: The ROSIE model uses three predictors that can be easily and quickly measured in the emergency department. It provides good discriminatory power after internal validation. Next steps should include external validation and an impact assessment. |
| format | Article |
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| institution | Matheson Library |
| issn | 2308-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geriatrics |
| spelling | doaj-art-a35f7dce0d9a4ee49b0c20c8e8f19aa52025-06-25T13:54:28ZengMDPI AGGeriatrics2308-34172025-04-011036010.3390/geriatrics10030060Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE)Thomas Struyf0Lisa Powaga1Marc Sabbe2Nicolas Léonard3Ivan Myatchin4Ben Van Calster5Jos Tournoy6Frank Buntinx7Laurens Liesenborghs8Jan Y. Verbakel9Ann Van den Bruel10Epi-Centre, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, 3000 Leuven, BelgiumDepartment of General Practice, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The NetherlandsDepartment of Emergency Medicine, University Hospitals, Herestraat 49, 3000 Leuven, BelgiumDepartment of Emergency Medicine, AZ Voorkempen Hospital, Oude Liersebaan 4, 2390 Malle, BelgiumDepartment of Emergency Medicine, Heilig Hart Hospital, Naamsestraat 105, 3000 Leuven, BelgiumDepartment of Development and Regeneration, KU Leuven, Herestraat 49, 3000 Leuven, BelgiumDepartment of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, 3000 Leuven, BelgiumDepartment of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, 3000 Leuven, BelgiumDepartment of Clinical Sciences, Institute of Tropical Medicine, Kronenburgstraat 43, 2000 Antwerp, BelgiumEpi-Centre, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, 3000 Leuven, BelgiumEpi-Centre, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, 3000 Leuven, Belgium<b>Background/Objectives</b>: Serious infections in older adults are associated with substantial mortality and morbidity. Diagnosis is challenging because of the non-specific presentation and overlap with pre-existing comorbidities. The objective of this study was to develop a clinical prediction model using clinical features and biomarkers to support emergency care physicians in diagnosing serious infections in acutely ill older adults. <b>Methods</b>: We conducted a prospective cross-sectional diagnostic study, consecutively including acutely ill patients (≥65 year) presenting to the emergency department. Clinical information and blood samples were collected at inclusion by a trained study nurse. A prediction model for <i>any serious infection</i> was developed based on ten candidate predictors that were further reduced to four ad interim using a penalized Firth multivariable logistic regression model. We assessed discrimination and calibration of the model after internal validation using bootstrapping. <b>Results</b>: We included 425 participants at three emergency departments, of whom 215 were diagnosed with a serious infection (51%). In the final model, we retained systolic blood pressure, oxygen saturation, and C-reactive protein as predictors. This model had good discriminatory value with an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.82 (95% CI: 0.78 to 0.86) and a calibration slope of 0.96 (95% CI: 0.76 to 1.16) after internal validation. Addition of procalcitonin did not improve the discrimination of the model. <b>Conclusions</b>: The ROSIE model uses three predictors that can be easily and quickly measured in the emergency department. It provides good discriminatory power after internal validation. Next steps should include external validation and an impact assessment.https://www.mdpi.com/2308-3417/10/3/60diagnosisserious infectiongeriatricsemergency careprediction model |
| spellingShingle | Thomas Struyf Lisa Powaga Marc Sabbe Nicolas Léonard Ivan Myatchin Ben Van Calster Jos Tournoy Frank Buntinx Laurens Liesenborghs Jan Y. Verbakel Ann Van den Bruel Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE) Geriatrics diagnosis serious infection geriatrics emergency care prediction model |
| title | Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE) |
| title_full | Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE) |
| title_fullStr | Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE) |
| title_full_unstemmed | Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE) |
| title_short | Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE) |
| title_sort | recognition of serious infections in the elderly visiting the emergency department the development of a diagnostic prediction model rosie |
| topic | diagnosis serious infection geriatrics emergency care prediction model |
| url | https://www.mdpi.com/2308-3417/10/3/60 |
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