2025-11-19 ジョンズ・ホプキンス大学 (JHU)
<関連情報>
- https://hub.jhu.edu/2025/11/19/researchers-demonstrate-advantages-of-machine-learning-in-medicine/
- https://jamanetwork.com/journals/jama/fullarticle/2831858
敗血症性ショックにおける最適なバソプレシン投与開始 OVISS強化学習研究 Optimal Vasopressin Initiation in Septic Shock The OVISS Reinforcement Learning Study
Alexandre Kalimouttou, MD; Jason N. Kennedy, MS; Jean Feng, PhD;et al
Journal of the American Medical Association Published:March 18, 2025
DOI:10.1001/jama.2025.3046
Key Points
Question Does a reinforcement learning model identify the optimal initiation rule for vasopressin in patients with septic shock who are receiving norepinephrine?
Findings Among 14 453 critically ill patients with septic shock from 232 hospitals in 4 independent datasets, a reinforcement learning model recommended the initiation of vasopressin more frequently, sooner, at a lower norepinephrine dose, and at a lower organ failure score compared with the average observed actions of clinicians. Patients in whom vasopressin was initiated similarly, vs differently, to that recommended by the reinforcement learning model had statistically significantly reduced in-hospital mortality (adjusted odds ratio, 0.81).
Meaning The initiation of vasopressin following recommendations by a reinforcement learning model was associated with decreased mortality in patients with septic shock already receiving norepinephrine.
Abstract
Importance Norepinephrine is the first-line vasopressor for patients with septic shock. When and whether a second agent, such as vasopressin, should be added is unknown.
Objective To derive and validate a reinforcement learning model to determine the optimal initiation rule for vasopressin in adult, critically ill patients receiving norepinephrine for septic shock.
Design, Setting, and Participants Reinforcement learning was used to generate the optimal rule for vasopressin initiation to improve short-term and hospital outcomes, using electronic health record data from 3608 patients who met the Sepsis-3 shock criteria at 5 California hospitals from 2012 to 2023. The rule was evaluated in 628 patients from the California dataset and 3 external datasets comprising 10 217 patients from 227 US hospitals, using weighted importance sampling and pooled logistic regression with inverse probability weighting.
Exposures Clinical, laboratory, and treatment variables grouped hourly for 120 hours in the electronic health record.
Main Outcome and Measure The primary outcome was in-hospital mortality.
Results The derivation cohort (n = 3608) included 2075 men (57%) and had a median (IQR) age of 63 (56-70) years and Sequential Organ Failure Assessment (SOFA) score at shock onset of 5 (3-7 [range, 0-24, with higher scores associated with greater mortality]). The validation cohorts (n = 10 217) were 56% male (n = 5743) with a median (IQR) age of 67 (57-75) years and a SOFA score of 6 (4-9). In validation data, the model suggested vasopressin initiation in more patients (87% vs 31%), earlier relative to shock onset (median [IQR], 4 [1-8] vs 5 [1-14] hours), and at lower norepinephrine doses (median [IQR], 0.20 [0.08-0.45] vs 0.37 [0.17-0.69] µg/kg/min) compared with clinicians’ actions. The rule was associated with a larger expected reward in validation data compared with clinician actions (weighted importance sampling difference, 31 [95% CI, 15-52]). The adjusted odds of hospital mortality were lower if vasopressin initiation was similar to the rule compared with different (odds ratio, 0.81 [95% CI, 0.73-0.91]), a finding consistent across external validation sets.
Conclusions and Relevance In adult patients with septic shock receiving norepinephrine, the use of vasopressin was variable. A reinforcement learning model developed and validated in several observational datasets recommended more frequent and earlier use of vasopressin than average care patterns and was associated with reduced mortality.
