Early positive fluid balance is predictive for venous thromboembolism in critically ill surgical patients

Lin, Tsan‐Shiun; Dhillon, Navpreet K.; Conde, Geena; Toscano, Samantha; Margulies, Daniel R.; Barmparas, Galinos; Ley, Eric J.

doi:10.1016/j.amjsurg.2020.08.032

Cited by 2 publications

(3 citation statements)

References 38 publications

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“…27 Furthermore, Lin et al have shown that patients with an early positive fluid balance have an increased risk of developing venous thromboembolism. 30 The weight of net balance, as shown in our SHAP analysis, further emphasized the importance of careful fluid management in critically ill patients. Therefore, we argue that including the net balance in the prediction model may improve not only the performance but also the clinical management in the ICU.…”

Section: Discussionmentioning

confidence: 67%

“…Indeed, positive cumulative fluid balance has been reported to be an independent predictor of ICU mortality (32). Moreover, Lin et al have shown that patients with an early positive fluid balance have an increased risk of developing venous thromboembolism (36). Our SHAP analysis further emphasized the importance of careful fluid management in critically ill patients.…”

Section: Discussionmentioning

confidence: 99%

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Transferability and interpretability of the sepsis prediction models in the intensive care unit

Chen

Lin

et al. 2021

Preprint

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Sepsis is an essential issue in critical care medicine, and early detection and intervention are key for survival. We established the sepsis early warning system based on a data integration platform that can be implemented in ICU. The sepsis early warning module can detect the onset of sepsis 5 hours proceeding, and the data integration platform integrates, standardizes, and stores information from different medical devices, making the inference of the early warning module possible. Our best early warning model got an AUC of 0.9833 in the task of detect sepsis in 4 hours proceeding on the open-source database. Our data integration platform has already been operational in a hospital for months.

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Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Transferability and interpretability of the sepsis prediction models in the intensive care unit

Chen

Lin

et al. 2021

Preprint

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“…Indeed, the positive cumulative fluid balance has been reported to be an independent predictor of ICU mortality [ 32 ]. Moreover, Lin et al [ 39 ] have shown that patients with an early positive fluid balance have an increased risk of developing venous thromboembolism. Our SHAP analysis further emphasized the importance of careful fluid management in critically ill patients.…”

Section: Discussionmentioning

confidence: 99%

Transferability and interpretability of the sepsis prediction models in the intensive care unit

Chen

Lin

et al. 2022

BMC Med Inform Decis Mak

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Background We aimed to develop an early warning system for real-time sepsis prediction in the ICU by machine learning methods, with tools for interpretative analysis of the predictions. In particular, we focus on the deployment of the system in a target medical center with small historical samples. Methods Light Gradient Boosting Machine (LightGBM) and multilayer perceptron (MLP) were trained on Medical Information Mart for Intensive Care (MIMIC-III) dataset and then finetuned on the private Historical Database of local Ruijin Hospital (HDRJH) using transfer learning technique. The Shapley Additive Explanations (SHAP) analysis was employed to characterize the feature importance in the prediction inference. Ultimately, the performance of the sepsis prediction system was further evaluated in the real-world study in the ICU of the target Ruijin Hospital. Results The datasets comprised 6891 patients from MIMIC-III, 453 from HDRJH, and 67 from Ruijin real-world data. The area under the receiver operating characteristic curves (AUCs) for LightGBM and MLP models derived from MIMIC-III were 0.98 − 0.98 and 0.95 − 0.96 respectively on MIMIC-III dataset, and, in comparison, 0.82 − 0.86 and 0.84 − 0.87 respectively on HDRJH, from 1 to 5 h preceding. After transfer learning and ensemble learning, the AUCs of the final ensemble model were enhanced to 0.94 − 0.94 on HDRJH and to 0.86 − 0.9 in the real-world study in the ICU of the target Ruijin Hospital. In addition, the SHAP analysis illustrated the importance of age, antibiotics, net balance, and ventilation for sepsis prediction, making the model interpretable. Conclusions Our machine learning model allows accurate real-time prediction of sepsis within 5-h preceding. Transfer learning can effectively improve the feasibility to deploy the prediction model in the target cohort, and ameliorate the model performance for external validation. SHAP analysis indicates that the role of antibiotic usage and fluid management needs further investigation. We argue that our system and methodology have the potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention. Trial registration: NCT05088850 (retrospectively registered).

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Early positive fluid balance is predictive for venous thromboembolism in critically ill surgical patients

Cited by 2 publications

References 38 publications

Transferability and interpretability of the sepsis prediction models in the intensive care unit

Transferability and interpretability of the sepsis prediction models in the intensive care unit

Transferability and interpretability of the sepsis prediction models in the intensive care unit

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