ICU 死亡率预测研究：生理轨迹特征比观测模式更易跨院迁移
Physiological Trajectories and Observation Patterns in ICU Mortality Prediction: Transportability Under Changing Measurement Conditions
用 MIMIC 和 eICU 超 7 万患者数据拆解了 ICU 死亡率模型里哪类特征能跨医院复用，做医疗 AI 的值得看看
一项基于 MIMIC-III、MIMIC-IV 和 eICU 数据集的研究，将 ICU 死亡率预测特征分为生理轨迹（R2）和观测模式（R3）两类。在 7,762 名 MetaVision 患者上训练的模型迁移到 21,682 名 MIMIC-IV 和 43,878 名 eICU 患者时，生理轨迹特征在 eICU 上将 log-loss 改善 0.00922，而观测模式特征的迁移增益不明确（-0.00012）。研究还发现，在保留相同观测数量的条件下，均匀分布的测量比随机保留降低 log-loss 0.03280；删除最后 12 小时数据则使 R3 的 log-loss 上升 0.01947。结论是临床预测模型除患者人群外，还需在输入数据的测量条件下评估。
Physiological Trajectories and Observation Patterns in ICU Mortality Prediction: Transportability Under Changing Measurement Conditions
Objective: To distinguish the transferable predictive value of physiological trajectories from that of observation patterns in ICU mortality prediction, and to assess whether measurement timing affects performance independently of observation quantity. Materials and Methods: In-hospital mortality was predicted at 48 hours using MIMIC-III, MIMIC-IV, and eICU. Nested representations progressively integrated latest-value snapshots (R1), longitudinal physiological summaries (R2), and measurement frequency and timing features (R3). Models fitted on 7,762 MetaVision patients were evaluated on 21,682 MIMIC-IV patients and 43,878 eICU patients across 204 hospitals. The effect of measurement timing was assessed while holding the number of retained observations constant. Results: Physiological trajectories improved external prediction in eICU (R2-R1 log-loss difference -0.00922, 95% CI -0.01148 to -0.00704). Observation-pattern features improved locally developed eICU models (R3-R2 -0.00257, 95% CI -0.00370 to -0.00146), but not clearly after transfer from MetaVision (-0.00012, 95% CI -0.00206 to +0.00208). R3 was favoured in 10 of 30 source bootstrap refits. At matched observation counts, retaining measurements spread throughout the window reduced log loss relative to random retention by 0.03280 for R3 versus 0.00703 for R2. Removing the final 12 hours increased log loss relative to count-matched random removal by 0.01947 for R3 versus 0.00822 for R2. Under reduced availability, the local R3 increment changed from beneficial to harmful. Discussion: Physiological trajectories transferred more consistently, while observation-pattern features were particularly sensitive to the timing and availability of measurements. Conclusion: Clinical prediction models should be evaluated not only in new patient populations but also under the measurement conditions that supply their inputs.