摘要：Fluid strategy is the key to the successful management of patients with sepsis. However, previous studies failed to consider individualized treatment strategy, and clinical trials typically included patients with sepsis as a homogeneous study population. We aimed to develop sequential decision rules for managing fluid intake in patients with sepsis by using the dynamic treatment regimen (DTR) model. A retrospective analysis of the eICU Collaborative Research Database comprising highly granular data collected from 335 units at 208 hospitals was performed. The DTR model used a backward induction algorithm to estimate the sequence of optimal rules. 22,868 patients who had sepsis according to the Acute Physiology and Chronic Health Evaluation (APACHE) IV diagnosis group were included. Optimal fluid management (liberal [> 40 ml/kg/d] versus restricted [< 40 ml/kg/d]) strategy were developed on the Day 1, 3 and 5 after ICU admission according to current states and treatment history. Important determinants of optimal fluid strategy included mean blood pressure, heart rate, previous urine output, previous fluid strategy, ICU type and mechanical ventilation. Different functional forms such as quadratic function and interaction terms were used at different stages. The proportion of subjects being inappropriately treated with liberal fluid strategy (i.e. those actually received liberal fluid strategy, but could have longer survival time if they received restricted fluid strategy) increased from day 1 to 5 (19.3% to 29.5%). The survival time could be significantly prolonged had all patients been treated with optimal fluid strategy (5.7 [2.0, 5.9] vs. 4.1 [2.0, 5.0] days; p < 0.001). With a large volume of sepsis data, we successfully computed out a sequence of dynamic fluid management strategy for sepsis patients over the first 5 days after ICU admission. The decision rules generated by the DTR model predicted a longer survival time compared to the true observed strategy, which sheds light for improving patient outcome with the aim from computer-assisted algorithm.
其他摘要：Abstract Fluid strategy is the key to the successful management of patients with sepsis. However, previous studies failed to consider individualized treatment strategy, and clinical trials typically included patients with sepsis as a homogeneous study population. We aimed to develop sequential decision rules for managing fluid intake in patients with sepsis by using the dynamic treatment regimen (DTR) model. A retrospective analysis of the eICU Collaborative Research Database comprising highly granular data collected from 335 units at 208 hospitals was performed. The DTR model used a backward induction algorithm to estimate the sequence of optimal rules. 22,868 patients who had sepsis according to the Acute Physiology and Chronic Health Evaluation (APACHE) IV diagnosis group were included. Optimal fluid management (liberal [> 40 ml/kg/d] versus restricted [< 40 ml/kg/d]) strategy were developed on the Day 1, 3 and 5 after ICU admission according to current states and treatment history. Important determinants of optimal fluid strategy included mean blood pressure, heart rate, previous urine output, previous fluid strategy, ICU type and mechanical ventilation. Different functional forms such as quadratic function and interaction terms were used at different stages. The proportion of subjects being inappropriately treated with liberal fluid strategy (i.e. those actually received liberal fluid strategy, but could have longer survival time if they received restricted fluid strategy) increased from day 1 to 5 (19.3% to 29.5%). The survival time could be significantly prolonged had all patients been treated with optimal fluid strategy (5.7 [2.0, 5.9] vs. 4.1 [2.0, 5.0] days; p < 0.001). With a large volume of sepsis data, we successfully computed out a sequence of dynamic fluid management strategy for sepsis patients over the first 5 days after ICU admission. The decision rules generated by the DTR model predicted a longer survival time compared to the true observed strategy, which sheds light for improving patient outcome with the aim from computer-assisted algorithm.
