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INTENSIVE CARE MEDICINE / CLINICAL RESEARCH
Positive fluid balance and poor outcomes after initial intensive care unit admission in sepsis resuscitation: a retrospective study
1
Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
2
Information Center, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
3
Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
Submission date: 2023-05-23
Final revision date: 2023-08-16
Acceptance date: 2023-09-12
Online publication date: 2024-03-30
Corresponding author
Yun Long
Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, China
Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, China
Arch Med Sci 2024;20(2):464-475
KEYWORDS
TOPICS
ABSTRACT
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.
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.
REFERENCES (35)
1.
Rivers E, Nguyen B, Havstad S, et al. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med 2001; 345: 1368-77.
2.
Dellinger RP, Carlet JM, Masur H, et al. Surviving Sepsis Campaign guidelines for management of severe sepsis and septic shock. Crit Care Med 2004; 32: 858-73.
3.
Dellinger RP, Levy MM, Carlet JM, et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med 2008; 36: 296-327.
4.
Dellinger RP, Levy MM, Rhodes A, et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med 2013; 41: 580-637.
5.
Rhodes A, Evans LE, Alhazzani W, et al. Surviving Sepsis Campaign: international guidelines for management of sepsis and septic shock: 2016. Crit Care Med 2017; 45: 486-552.
6.
Levy MM, Evans LE, Rhodes A. The surviving sepsis campaign bundle: 2018 update. Crit Care Med 2018; 46: 997-1000.
7.
Guo Q, Li HY, Li YM, et al. Compliance with severe sepsis bundles and its effect on patient outcomes of severe community-acquired pneumonia in a limited resources country. Arch Med Sci 2014; 10: 970-8.
9.
Pro CI, Yealy DM, Kellum JA, et al. A randomized trial of protocol-based care for early septic shock. N Engl J Med 2014; 370: 1683-93.
10.
Investigators A, Group ACT, Peake SL, Delaney A, et al. Goal-directed resuscitation for patients with early septic shock. N Engl J Med 2014; 371: 1496-506.
11.
Mouncey PR, Osborn TM, Power GS, et al. Trial of early, goal-directed resuscitation for septic shock. N Engl J Med 2015; 372: 1301-11.
12.
Maitland K, Kiguli S, Opoka RO, et al. Mortality after fluid bolus in African children with severe infection. N Engl J Med 2011; 364: 2483-95.
13.
Andrews B, Semler MW, Muchemwa L, et al. Effect of an early resuscitation protocol on in-hospital mortality among adults with sepsis and hypotension: a randomized clinical trial. JAMA 2017; 318: 1233-40.
14.
Sirvent JM, Ferri C, Baro A, Murcia C, Lorencio C. Fluid balance in sepsis and septic shock as a determining factor of mortality. Am J Emerg Med 2015; 33: 186-9.
15.
Kelm DJ, Perrin JT, Cartin-Ceba R, Gajic O, Schenck L, Kennedy CC. Fluid overload in patients with severe sepsis and septic shock treated with early goal-directed therapy is associated with increased acute need for fluid-related medical interventions and hospital death. Shock 2015; 43: 68-73.
16.
Acheampong A, Vincent JL. A positive fluid balance is an independent prognostic factor in patients with sepsis. Crit Care 2015; 19: 251.
17.
Investigators P, Rowan KM, Angus DC, Bailey M, et al. Early, goal-directed therapy for septic shock – a patient-level meta-analysis. N Engl J Med 2017; 376: 2223-34.
18.
Batista GEAPA MM. An analysis of four missing data treatment methods for supervised learning. Appl Artif Intel 2003; 17: 519-33.
19.
Kalil AC, Johnson DW, Lisco SJ, Sun J. Early goal-directed therapy for sepsis: a novel solution for discordant survival outcomes in clinical trials. Crit Care Med 2017; 45: 607-14.
20.
Malbrain M, Van Regenmortel N, Saugel B, et al. Principles of fluid management and stewardship in septic shock: it is time to consider the four D’s and the four phases of fluid therapy. Ann Intensive Care 2018; 8: 66.
21.
Boyd JH, Forbes J, Nakada TA, Walley KR, Russell JA. Fluid resuscitation in septic shock: a positive fluid balance and elevated central venous pressure are associated with increased mortality. Crit Care Med 2011; 39: 259-65.
22.
National Heart L, Blood Institute Acute Respiratory Distress Syndrome Clinical Trials N, Wiedemann HP, Wheeler AP, Bernard GR, Thompson BT, et al. Comparison of two fluid-management strategies in acute lung injury. N Engl J Med 2006; 354: 2564-75.
23.
Murphy CV, Schramm GE, Doherty JA, et al. The importance of fluid management in acute lung injury secondary to septic shock. Chest 2009; 136: 102-9.
24.
Upadya A, Tilluckdharry L, Muralidharan V, Amoateng-Adjepong Y, Manthous CA. Fluid balance and weaning outcomes. Intensive Care Med 2005; 31: 1643-7.
25.
Payen D, de Pont AC, Sakr Y, et al. A positive fluid balance is associated with a worse outcome in patients with acute renal failure. Crit Care 2008; 12: R74.
26.
Vaara ST, Korhonen AM, Kaukonen KM, et al. Fluid overload is associated with an increased risk for 90-day mortality in critically ill patients with renal replacement therapy: data from the prospective FINNAKI study. Crit Care 2012; 16: R197.
27.
Malbrain ML, Chiumello D, Pelosi P, et al. Incidence and prognosis of intraabdominal hypertension in a mixed population of critically ill patients: a multiple-center epidemiological study. Crit Care Med 2005; 33: 315-22.
28.
Ogbu OC, Murphy DJ, Martin GS. How to avoid fluid overload. Curr Opin Crit Care 2015; 21: 315-21.
29.
Hoste EA, Maitland K, Brudney CS, et al. Four phases of intravenous fluid therapy: a conceptual model. Br J Anaesth 2014; 113: 740-7.
30.
Vincent JL, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med 1996; 22: 707-10.
31.
Jones AE, Trzeciak S, Kline JA. The Sequential Organ Failure Assessment score for predicting outcome in patients with severe sepsis and evidence of hypoperfusion at the time of emergency department presentation. Crit Care Med 2009; 37: 1649-54.
32.
Safari S, Shojaee M, Rahmati F, et al. Accuracy of SOFA score in prediction of 30-day outcome of critically ill patients. Turk J Emerg Med 2016; 16: 146-50.
33.
Lie KC, Lau CY, Van Vinh Chau N, West TE, Limmathurotsakul D, for Southeast Asia Infectious Disease Clinical Research N. Utility of SOFA score, management and outcomes of sepsis in Southeast Asia: a multinational multicenter prospective observational study. J Intensive Care 2018; 6: 9.
34.
de Grooth HJ, Geenen IL, Girbes AR, Vincent JL, Parienti JJ, Oudemans-van Straaten HM. SOFA and mortality endpoints in randomized controlled trials: a systematic review and meta-regression analysis. Crit Care 2017; 21: 38.
35.
Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med 2018; 24: 1716-20.
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