Classification of postoperative fever patients in the intensive care unit following intra-abdominal surgery: a machine learning-based cluster analysis using the Medical Information Mart for Intensive Care (MIMIC)-IV database, developed in the United States
Background Postoperative fever is common. However, it can sometimes indicate severe complications such as sepsis or pneumonia. Intensive care unit (ICU) patients who have undergone abdominal surgery have a higher risk of postoperative fever due the physical severity of this type of surgery. Neverthe...
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| Format: | Article |
| Language: | English |
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Korean Society of Critical Care Medicine
2025-05-01
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| Series: | Acute and Critical Care |
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| Online Access: | http://accjournal.org/upload/pdf/acc-004464.pdf |
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| author | Sang Mok Lee Hongjin Shim |
| author_facet | Sang Mok Lee Hongjin Shim |
| author_sort | Sang Mok Lee |
| collection | DOAJ |
| description | Background Postoperative fever is common. However, it can sometimes indicate severe complications such as sepsis or pneumonia. Intensive care unit (ICU) patients who have undergone abdominal surgery have a higher risk of postoperative fever due the physical severity of this type of surgery. Nevertheless, determining when more aggressive or invasive management of fever is necessary remains a challenge. Methods We analyzed the Medical Information Mart for Intensive Care (MIMIC)-IV and MIMIC-IV-Note databases, which are open critical care big databases from a single institute in the United States. From this, we selected ICU patients who developed fever after intra-abdominal surgery and classified these patients into two groups using cluster analysis based on diverse variables from the MIMIC-IV databases. Following this cluster analysis, we assessed differences among the identified groups. Results Of 2,858 ICU stays after intra-abdominal surgery, 331 postoperative fever cases were identified. These patients were clustered into two groups. Group A included older patients with a higher mortality rate, while group B consisted of younger patients with a lower mortality rate. Conclusions Postoperative ICU patients with a fever could be classified into two distinct groups, a high-risk group and low-risk group. The high-risk patient group was characterized by older age, higher Sequential Organ Failure Assessment (SOFA) score, and more unstable hemodynamic status, indicating the need for aggressive management. Clustering postoperative fever patients by clinical variables can support medical decision-making and targeted treatment to improve patient outcomes. |
| format | Article |
| id | doaj-art-d023225a1cba45008f02f237fc8ca948 |
| institution | Kabale University |
| issn | 2586-6052 2586-6060 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Korean Society of Critical Care Medicine |
| record_format | Article |
| series | Acute and Critical Care |
| spelling | doaj-art-d023225a1cba45008f02f237fc8ca9482025-08-20T03:30:44ZengKorean Society of Critical Care MedicineAcute and Critical Care2586-60522586-60602025-05-0140229330310.4266/acc.0044641592Classification of postoperative fever patients in the intensive care unit following intra-abdominal surgery: a machine learning-based cluster analysis using the Medical Information Mart for Intensive Care (MIMIC)-IV database, developed in the United StatesSang Mok Lee0Hongjin Shim1 Department of Acute Care Surgery, Korea University Guro Hospital, Seoul, Korea Department of Acute Care Surgery, Korea University Guro Hospital, Seoul, KoreaBackground Postoperative fever is common. However, it can sometimes indicate severe complications such as sepsis or pneumonia. Intensive care unit (ICU) patients who have undergone abdominal surgery have a higher risk of postoperative fever due the physical severity of this type of surgery. Nevertheless, determining when more aggressive or invasive management of fever is necessary remains a challenge. Methods We analyzed the Medical Information Mart for Intensive Care (MIMIC)-IV and MIMIC-IV-Note databases, which are open critical care big databases from a single institute in the United States. From this, we selected ICU patients who developed fever after intra-abdominal surgery and classified these patients into two groups using cluster analysis based on diverse variables from the MIMIC-IV databases. Following this cluster analysis, we assessed differences among the identified groups. Results Of 2,858 ICU stays after intra-abdominal surgery, 331 postoperative fever cases were identified. These patients were clustered into two groups. Group A included older patients with a higher mortality rate, while group B consisted of younger patients with a lower mortality rate. Conclusions Postoperative ICU patients with a fever could be classified into two distinct groups, a high-risk group and low-risk group. The high-risk patient group was characterized by older age, higher Sequential Organ Failure Assessment (SOFA) score, and more unstable hemodynamic status, indicating the need for aggressive management. Clustering postoperative fever patients by clinical variables can support medical decision-making and targeted treatment to improve patient outcomes.http://accjournal.org/upload/pdf/acc-004464.pdfcluster analysiscritical carefeverpostoperative caresurgery |
| spellingShingle | Sang Mok Lee Hongjin Shim Classification of postoperative fever patients in the intensive care unit following intra-abdominal surgery: a machine learning-based cluster analysis using the Medical Information Mart for Intensive Care (MIMIC)-IV database, developed in the United States Acute and Critical Care cluster analysis critical care fever postoperative care surgery |
| title | Classification of postoperative fever patients in the intensive care unit following intra-abdominal surgery: a machine learning-based cluster analysis using the Medical Information Mart for Intensive Care (MIMIC)-IV database, developed in the United States |
| title_full | Classification of postoperative fever patients in the intensive care unit following intra-abdominal surgery: a machine learning-based cluster analysis using the Medical Information Mart for Intensive Care (MIMIC)-IV database, developed in the United States |
| title_fullStr | Classification of postoperative fever patients in the intensive care unit following intra-abdominal surgery: a machine learning-based cluster analysis using the Medical Information Mart for Intensive Care (MIMIC)-IV database, developed in the United States |
| title_full_unstemmed | Classification of postoperative fever patients in the intensive care unit following intra-abdominal surgery: a machine learning-based cluster analysis using the Medical Information Mart for Intensive Care (MIMIC)-IV database, developed in the United States |
| title_short | Classification of postoperative fever patients in the intensive care unit following intra-abdominal surgery: a machine learning-based cluster analysis using the Medical Information Mart for Intensive Care (MIMIC)-IV database, developed in the United States |
| title_sort | classification of postoperative fever patients in the intensive care unit following intra abdominal surgery a machine learning based cluster analysis using the medical information mart for intensive care mimic iv database developed in the united states |
| topic | cluster analysis critical care fever postoperative care surgery |
| url | http://accjournal.org/upload/pdf/acc-004464.pdf |
| work_keys_str_mv | AT sangmoklee classificationofpostoperativefeverpatientsintheintensivecareunitfollowingintraabdominalsurgeryamachinelearningbasedclusteranalysisusingthemedicalinformationmartforintensivecaremimicivdatabasedevelopedintheunitedstates AT hongjinshim classificationofpostoperativefeverpatientsintheintensivecareunitfollowingintraabdominalsurgeryamachinelearningbasedclusteranalysisusingthemedicalinformationmartforintensivecaremimicivdatabasedevelopedintheunitedstates |