Longxiang Su, Shengjun Liu, Yingying Yang, Huizhen Jiang, Xiangyang Ye, Li Weng, Weiguo Zhu, Xinlun Tian, Yun Long
{"title":"脓毒症复苏后首次入住重症监护室时体液呈阳性平衡与不良预后:一项回顾性研究。","authors":"Longxiang Su, Shengjun Liu, Yingying Yang, Huizhen Jiang, Xiangyang Ye, Li Weng, Weiguo Zhu, Xinlun Tian, Yun Long","doi":"10.5114/aoms/172160","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Fluid resuscitation of patients with sepsis is crucial. This study explored the role of fluid balance in the early resuscitation of sepsis patients in the intensive care unit (ICU).</p><p><strong>Material and methods: </strong>A retrospective study of patients with sepsis using the Peking Union Medical College Hospital Intensive Care Medical Information System and Database from January 2014 to June 2020 was performed. Based on the survival status on day 28, the training cohort was divided into an alive group (<i>n</i> = 1,803) and a deceased group (<i>n</i> = 429). Univariate and multivariate analyses were used to identify risk factors, and the integrated learning XGBoost algorithm was used to construct a model for predicting outcomes. ROC and Kaplan-Meier survival curves were used to evaluate the effectiveness of the model. A verification cohort (<i>n</i> = 433) was used to verify the model.</p><p><strong>Results: </strong>Univariate analysis showed that fluid balance is an important covariate. Based on the scatterplot distribution, a significant difference in mortality was determined between groups stratified with a balance of 1000 ml. There were associations in the multivariate analysis between poor outcomes and sex, PO<sub>2</sub>/FiO<sub>2</sub>, serum creatinine, FiO<sub>2</sub>, platelets, respiratory rate, SPO<sub>2</sub>, temperature, and total fluid volume (1000 ml). Among these variables, total fluid balance (1000 ml) had an OR of 1.98 (CI: 1.41-2.77, <i>p</i> < 0.001). Therefore, the model was built with these nine factors using XGBoost. Cross validation was used to verify generalizability. This model performed better than the SOFA and APACHE II models. The result was well verified in the verification cohort. A causal forest model suggested that patients with hypoxemia may suffer from positive fluid balance.</p><p><strong>Conclusions: </strong>Sepsis fluid resuscitation in the ICU should be a targeted and goal-oriented treatment. A new prognostic prediction model was constructed and indicated that a 6-hour positive fluid balance after ICU initial admission is a risk factor for poor outcomes in sepsis patients. A 6-hour fluid balance above 1000 ml should be performed with caution.</p>","PeriodicalId":8278,"journal":{"name":"Archives of Medical Science","volume":"20 2","pages":"464-475"},"PeriodicalIF":3.0000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11094850/pdf/","citationCount":"0","resultStr":"{\"title\":\"Positive fluid balance and poor outcomes after initial intensive care unit admission in sepsis resuscitation: a retrospective study.\",\"authors\":\"Longxiang Su, Shengjun Liu, Yingying Yang, Huizhen Jiang, Xiangyang Ye, Li Weng, Weiguo Zhu, Xinlun Tian, Yun Long\",\"doi\":\"10.5114/aoms/172160\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Fluid resuscitation of patients with sepsis is crucial. This study explored the role of fluid balance in the early resuscitation of sepsis patients in the intensive care unit (ICU).</p><p><strong>Material and methods: </strong>A retrospective study of patients with sepsis using the Peking Union Medical College Hospital Intensive Care Medical Information System and Database from January 2014 to June 2020 was performed. Based on the survival status on day 28, the training cohort was divided into an alive group (<i>n</i> = 1,803) and a deceased group (<i>n</i> = 429). Univariate and multivariate analyses were used to identify risk factors, and the integrated learning XGBoost algorithm was used to construct a model for predicting outcomes. ROC and Kaplan-Meier survival curves were used to evaluate the effectiveness of the model. A verification cohort (<i>n</i> = 433) was used to verify the model.</p><p><strong>Results: </strong>Univariate analysis showed that fluid balance is an important covariate. Based on the scatterplot distribution, a significant difference in mortality was determined between groups stratified with a balance of 1000 ml. There were associations in the multivariate analysis between poor outcomes and sex, PO<sub>2</sub>/FiO<sub>2</sub>, serum creatinine, FiO<sub>2</sub>, platelets, respiratory rate, SPO<sub>2</sub>, temperature, and total fluid volume (1000 ml). Among these variables, total fluid balance (1000 ml) had an OR of 1.98 (CI: 1.41-2.77, <i>p</i> < 0.001). Therefore, the model was built with these nine factors using XGBoost. Cross validation was used to verify generalizability. This model performed better than the SOFA and APACHE II models. The result was well verified in the verification cohort. A causal forest model suggested that patients with hypoxemia may suffer from positive fluid balance.</p><p><strong>Conclusions: </strong>Sepsis fluid resuscitation in the ICU should be a targeted and goal-oriented treatment. A new prognostic prediction model was constructed and indicated that a 6-hour positive fluid balance after ICU initial admission is a risk factor for poor outcomes in sepsis patients. 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Positive fluid balance and poor outcomes after initial intensive care unit admission in sepsis resuscitation: a retrospective study.
Introduction: Fluid resuscitation of patients with sepsis is crucial. This study explored the role of fluid balance in the early resuscitation of sepsis patients in the intensive care unit (ICU).
Material and methods: A retrospective study of patients with sepsis using the Peking Union Medical College Hospital Intensive Care Medical Information System and Database from January 2014 to June 2020 was performed. Based on the survival status on day 28, the training cohort was divided into an alive group (n = 1,803) and a deceased group (n = 429). Univariate and multivariate analyses were used to identify risk factors, and the integrated learning XGBoost algorithm was used to construct a model for predicting outcomes. ROC and Kaplan-Meier survival curves were used to evaluate the effectiveness of the model. A verification cohort (n = 433) was used to verify the model.
Results: Univariate analysis showed that fluid balance is an important covariate. Based on the scatterplot distribution, a significant difference in mortality was determined between groups stratified with a balance of 1000 ml. There were associations in the multivariate analysis between poor outcomes and sex, PO2/FiO2, serum creatinine, FiO2, platelets, respiratory rate, SPO2, temperature, and total fluid volume (1000 ml). Among these variables, total fluid balance (1000 ml) had an OR of 1.98 (CI: 1.41-2.77, p < 0.001). Therefore, the model was built with these nine factors using XGBoost. Cross validation was used to verify generalizability. This model performed better than the SOFA and APACHE II models. The result was well verified in the verification cohort. A causal forest model suggested that patients with hypoxemia may suffer from positive fluid balance.
Conclusions: Sepsis fluid resuscitation in the ICU should be a targeted and goal-oriented treatment. A new prognostic prediction model was constructed and indicated that a 6-hour positive fluid balance after ICU initial admission is a risk factor for poor outcomes in sepsis patients. A 6-hour fluid balance above 1000 ml should be performed with caution.
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