Relationship between In-Hospital Sepsis Prediction Score and Prevalence of Community-Onset Sepsis: Triage for Sepsis Risk Management
Early diagnosis of sepsis is crucial in clinical practice. Several studies have proposed sepsis prediction models to forecast the onset of sepsis in hospitals. However, validation of prediction models for community-onset sepsis, which is sepsis developed before admission to the hospital, is insuffi...
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| Format: | Article |
| Language: | English |
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Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca
2024-11-01
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| Series: | Applied Medical Informatics |
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| Online Access: | https://ami.info.umfcluj.ro/index.php/AMI/article/view/1076 |
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| author | Kyung Hyun LEE Hyunwoo CHOO Sungsoo HONG Sungjun HONG Ki-Byung LEE Hochan CHO |
| author_facet | Kyung Hyun LEE Hyunwoo CHOO Sungsoo HONG Sungjun HONG Ki-Byung LEE Hochan CHO |
| author_sort | Kyung Hyun LEE |
| collection | DOAJ |
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Early diagnosis of sepsis is crucial in clinical practice. Several studies have proposed sepsis prediction models to forecast the onset of sepsis in hospitals. However, validation of prediction models for community-onset sepsis, which is sepsis developed before admission to the hospital, is insufficient. This study investigates the relationship between the in-hospital prediction model scores and community-onset sepsis. We used hierarchical logistic regression analysis to explore the relationship between sepsis prevalence and AITRICS-VC SEPS tertile categories while adjusting for potential confounders. The low-SEPS group was used as the reference group. The odds ratio (ORs) of sepsis comparing the moderate versus low SEPS group are 1.198 (95%, 1.075-3.654), and the high versus low VC-SEPS group are 8.683 (95%, 4.995-15.095). Even though the sepsis prediction model was designed to predict in-hospital sepsis, high prediction scores are related to the prevalence of community-onset sepsis. This result implies that SEPS scores can stratify sepsis risks and be considered a patient assessment tool for triage.
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| format | Article |
| id | doaj-art-92ce7f317b5a4c7493f8dd455e813409 |
| institution | Kabale University |
| issn | 2067-7855 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca |
| record_format | Article |
| series | Applied Medical Informatics |
| spelling | doaj-art-92ce7f317b5a4c7493f8dd455e8134092025-01-05T21:07:33ZengIuliu Hatieganu University of Medicine and Pharmacy, Cluj-NapocaApplied Medical Informatics2067-78552024-11-0146Suppl. 2Relationship between In-Hospital Sepsis Prediction Score and Prevalence of Community-Onset Sepsis: Triage for Sepsis Risk ManagementKyung Hyun LEE0Hyunwoo CHOO1Sungsoo HONG2Sungjun HONG3Ki-Byung LEE4Hochan CHO5AITRICS. Inc, 218 Teheran-ro, Gangnam-gu, 06221 Seoul, Republic of KoreaAITRICS. Inc, 218 Teheran-ro, Gangnam-gu, 06221 Seoul, Republic of KoreaAITRICS. Inc, 218 Teheran-ro, Gangnam-gu, 06221 Seoul, Republic of KoreaMedical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, S06351 eoul, Republic of KoreaDivision of Pulmonary, Allergy and Critical Care Medicine, Hallym University Chuncheon Sacred Heart Hospital, 77 Sakju-ro, 24253 Chuncheon, Republic of KoreaDepartment of Internal Medicine, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, 42601 Daegu, Republic of Korea Early diagnosis of sepsis is crucial in clinical practice. Several studies have proposed sepsis prediction models to forecast the onset of sepsis in hospitals. However, validation of prediction models for community-onset sepsis, which is sepsis developed before admission to the hospital, is insufficient. This study investigates the relationship between the in-hospital prediction model scores and community-onset sepsis. We used hierarchical logistic regression analysis to explore the relationship between sepsis prevalence and AITRICS-VC SEPS tertile categories while adjusting for potential confounders. The low-SEPS group was used as the reference group. The odds ratio (ORs) of sepsis comparing the moderate versus low SEPS group are 1.198 (95%, 1.075-3.654), and the high versus low VC-SEPS group are 8.683 (95%, 4.995-15.095). Even though the sepsis prediction model was designed to predict in-hospital sepsis, high prediction scores are related to the prevalence of community-onset sepsis. This result implies that SEPS scores can stratify sepsis risks and be considered a patient assessment tool for triage. https://ami.info.umfcluj.ro/index.php/AMI/article/view/1076SepsisPredictionDeep learningRisk stratificationRegression analysis |
| spellingShingle | Kyung Hyun LEE Hyunwoo CHOO Sungsoo HONG Sungjun HONG Ki-Byung LEE Hochan CHO Relationship between In-Hospital Sepsis Prediction Score and Prevalence of Community-Onset Sepsis: Triage for Sepsis Risk Management Applied Medical Informatics Sepsis Prediction Deep learning Risk stratification Regression analysis |
| title | Relationship between In-Hospital Sepsis Prediction Score and Prevalence of Community-Onset Sepsis: Triage for Sepsis Risk Management |
| title_full | Relationship between In-Hospital Sepsis Prediction Score and Prevalence of Community-Onset Sepsis: Triage for Sepsis Risk Management |
| title_fullStr | Relationship between In-Hospital Sepsis Prediction Score and Prevalence of Community-Onset Sepsis: Triage for Sepsis Risk Management |
| title_full_unstemmed | Relationship between In-Hospital Sepsis Prediction Score and Prevalence of Community-Onset Sepsis: Triage for Sepsis Risk Management |
| title_short | Relationship between In-Hospital Sepsis Prediction Score and Prevalence of Community-Onset Sepsis: Triage for Sepsis Risk Management |
| title_sort | relationship between in hospital sepsis prediction score and prevalence of community onset sepsis triage for sepsis risk management |
| topic | Sepsis Prediction Deep learning Risk stratification Regression analysis |
| url | https://ami.info.umfcluj.ro/index.php/AMI/article/view/1076 |
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