Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach
Abstract Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strategies using normal saline, Ringer’s lactate, and albumin, and to determine which patient factors are associated with improved outcomes. We an...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-03141-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850231262065721344 |
|---|---|
| author | Yoonjin Kang Shin Young Ahn Min Woo Kang |
| author_facet | Yoonjin Kang Shin Young Ahn Min Woo Kang |
| author_sort | Yoonjin Kang |
| collection | DOAJ |
| description | Abstract Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strategies using normal saline, Ringer’s lactate, and albumin, and to determine which patient factors are associated with improved outcomes. We analyzed septic shock patients from the MIMIC-IV database, categorizing them by the fluid administered: normal saline, Ringer’s lactate, albumin, or their combinations. A deep learning-based causal inference model estimated treatment effects on in-hospital mortality and kidney outcomes (defined as a doubling of creatinine or the initiation of kidney replacement therapy). Multivariable logistic regression was then applied to the individual treatment effects to identify patient characteristics linked to better outcomes for Ringer’s lactate and additional albumin infusion compared to normal saline alone. Among 13,527 patients, 17.8% experienced in-hospital mortality and 16.2% developed kidney injury. Ringer’s lactate reduced mortality by 2.33% and kidney injury by 1.41% compared to normal saline. Adding albumin to normal saline further reduced mortality by 1.20% and kidney outcomes by 0.71%. The combination of Ringer’s lactate and albumin provided the greatest benefit (mortality: −3.07%, kidney injury: −3.00%). Patients with high SOFA scores, low albumin, or high lactate levels benefited more from normal saline, whereas those with low eGFR or on vasopressors were less likely to benefit from albumin. Ringer’s lactate, particularly when combined with albumin, is superior to normal saline in reducing mortality and kidney injury in septic shock patients, underscoring the need for personalized fluid management based on patient-specific factors. |
| format | Article |
| id | doaj-art-a8b8fe30e3c340fe94ef4d54adf3af24 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a8b8fe30e3c340fe94ef4d54adf3af242025-08-20T02:03:35ZengNature PortfolioScientific Reports2045-23222025-05-0115111010.1038/s41598-025-03141-1Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approachYoonjin Kang0Shin Young Ahn1Min Woo Kang2Department of Thoracic and Cardiovascular Surgery, Seoul National University HospitalDepartment of Internal Medicine, Korea University College of MedicineDepartment of Internal Medicine, Korea University Guro HospitalAbstract Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strategies using normal saline, Ringer’s lactate, and albumin, and to determine which patient factors are associated with improved outcomes. We analyzed septic shock patients from the MIMIC-IV database, categorizing them by the fluid administered: normal saline, Ringer’s lactate, albumin, or their combinations. A deep learning-based causal inference model estimated treatment effects on in-hospital mortality and kidney outcomes (defined as a doubling of creatinine or the initiation of kidney replacement therapy). Multivariable logistic regression was then applied to the individual treatment effects to identify patient characteristics linked to better outcomes for Ringer’s lactate and additional albumin infusion compared to normal saline alone. Among 13,527 patients, 17.8% experienced in-hospital mortality and 16.2% developed kidney injury. Ringer’s lactate reduced mortality by 2.33% and kidney injury by 1.41% compared to normal saline. Adding albumin to normal saline further reduced mortality by 1.20% and kidney outcomes by 0.71%. The combination of Ringer’s lactate and albumin provided the greatest benefit (mortality: −3.07%, kidney injury: −3.00%). Patients with high SOFA scores, low albumin, or high lactate levels benefited more from normal saline, whereas those with low eGFR or on vasopressors were less likely to benefit from albumin. Ringer’s lactate, particularly when combined with albumin, is superior to normal saline in reducing mortality and kidney injury in septic shock patients, underscoring the need for personalized fluid management based on patient-specific factors.https://doi.org/10.1038/s41598-025-03141-1Septic shockNormal salineRinger’s lactateAlbuminDeep learningCausal inference |
| spellingShingle | Yoonjin Kang Shin Young Ahn Min Woo Kang Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach Scientific Reports Septic shock Normal saline Ringer’s lactate Albumin Deep learning Causal inference |
| title | Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach |
| title_full | Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach |
| title_fullStr | Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach |
| title_full_unstemmed | Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach |
| title_short | Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach |
| title_sort | exploring treatment effects and fluid resuscitation strategies in septic shock a deep learning based causal inference approach |
| topic | Septic shock Normal saline Ringer’s lactate Albumin Deep learning Causal inference |
| url | https://doi.org/10.1038/s41598-025-03141-1 |
| work_keys_str_mv | AT yoonjinkang exploringtreatmenteffectsandfluidresuscitationstrategiesinsepticshockadeeplearningbasedcausalinferenceapproach AT shinyoungahn exploringtreatmenteffectsandfluidresuscitationstrategiesinsepticshockadeeplearningbasedcausalinferenceapproach AT minwookang exploringtreatmenteffectsandfluidresuscitationstrategiesinsepticshockadeeplearningbasedcausalinferenceapproach |