Outcome Prediction in Infectious Disease
Sepsis is a critical, life-threatening condition that demands precise prediction to mitigate adverse outcomes. The heterogeneity of sepsis leads to variable prognoses, making early and accurate identification increasingly difficult. Despite ongoing advancements, no single gold standard has emerged f...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Interna Publishing
2024-10-01
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| Series: | Acta Medica Indonesiana |
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| Online Access: | https://actamedindones.org/index.php/ijim/article/view/2910 |
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| _version_ | 1832585121652277248 |
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| author | Khie Chen Lie Yosia Yonggara Adeline Pasaribu Sharifah Shakinah Leonard Nainggolan |
| author_facet | Khie Chen Lie Yosia Yonggara Adeline Pasaribu Sharifah Shakinah Leonard Nainggolan |
| author_sort | Khie Chen Lie |
| collection | DOAJ |
| description | Sepsis is a critical, life-threatening condition that demands precise prediction to mitigate adverse outcomes. The heterogeneity of sepsis leads to variable prognoses, making early and accurate identification increasingly difficult. Despite ongoing advancements, no single gold standard has emerged for sepsis prediction. Current research explores a range of prognostic tools, from traditional scoring systems and biomarkers to cutting-edge omics technologies and artificial intelligence. These tools can differ significantly across patient populations and clinical settings, such as the emergency department (ED) and intensive care unit (ICU). This review aims to critically evaluate the development and application of outcome prediction modalities in sepsis and other infectious diseases, highlighting the progress made and identifying areas for further research. |
| format | Article |
| id | doaj-art-3a2660dc75114a21997c1c7d01fd35a7 |
| institution | Kabale University |
| issn | 0125-9326 2338-2732 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Interna Publishing |
| record_format | Article |
| series | Acta Medica Indonesiana |
| spelling | doaj-art-3a2660dc75114a21997c1c7d01fd35a72025-01-27T04:12:06ZengInterna PublishingActa Medica Indonesiana0125-93262338-27322024-10-01564703Outcome Prediction in Infectious DiseaseKhie Chen Lie0Yosia Yonggara1Adeline Pasaribu2Sharifah Shakinah3Leonard Nainggolan4Division of Tropical and Infectious Diseases, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Dr. Cipto Mangunkusumo National General Hospital, Jakarta, IndonesiaDivision of Tropical and Infectious Diseases, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Dr. Cipto Mangunkusumo National General Hospital, Jakarta, IndonesiaDivision of Tropical and Infectious Diseases, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Dr. Cipto Mangunkusumo National General Hospital, Jakarta, IndonesiaDivision of Tropical and Infectious Diseases, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Dr. Cipto Mangunkusumo National General Hospital, Jakarta, IndonesiaDivision of Tropical and Infectious Diseases, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Dr. Cipto Mangunkusumo National General Hospital, Jakarta, IndonesiaSepsis is a critical, life-threatening condition that demands precise prediction to mitigate adverse outcomes. The heterogeneity of sepsis leads to variable prognoses, making early and accurate identification increasingly difficult. Despite ongoing advancements, no single gold standard has emerged for sepsis prediction. Current research explores a range of prognostic tools, from traditional scoring systems and biomarkers to cutting-edge omics technologies and artificial intelligence. These tools can differ significantly across patient populations and clinical settings, such as the emergency department (ED) and intensive care unit (ICU). This review aims to critically evaluate the development and application of outcome prediction modalities in sepsis and other infectious diseases, highlighting the progress made and identifying areas for further research.https://actamedindones.org/index.php/ijim/article/view/2910outcome predictioninfectious diseasesepsis |
| spellingShingle | Khie Chen Lie Yosia Yonggara Adeline Pasaribu Sharifah Shakinah Leonard Nainggolan Outcome Prediction in Infectious Disease Acta Medica Indonesiana outcome prediction infectious disease sepsis |
| title | Outcome Prediction in Infectious Disease |
| title_full | Outcome Prediction in Infectious Disease |
| title_fullStr | Outcome Prediction in Infectious Disease |
| title_full_unstemmed | Outcome Prediction in Infectious Disease |
| title_short | Outcome Prediction in Infectious Disease |
| title_sort | outcome prediction in infectious disease |
| topic | outcome prediction infectious disease sepsis |
| url | https://actamedindones.org/index.php/ijim/article/view/2910 |
| work_keys_str_mv | AT khiechenlie outcomepredictionininfectiousdisease AT yosiayonggara outcomepredictionininfectiousdisease AT adelinepasaribu outcomepredictionininfectiousdisease AT sharifahshakinah outcomepredictionininfectiousdisease AT leonardnainggolan outcomepredictionininfectiousdisease |