Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example*

Thoral, Patrick; Peppink, Jan; Driessen, Ronald H.; Sijbrands, Eric; Kompanje, Erwin J. O.; Kaplan, Lewis J.; Bailey, Heatherlee; Kesecioğlu, Jozef; Cecconi, Maurizio; Churpek, Matthew M; Clermont, Gilles; Schaar, Mihaela van der; Ercole, Ari; Girbes, Armand R.J.; Elbers, Paul

doi:10.1097/ccm.0000000000004916

Cited by 102 publications

(65 citation statements)

References 34 publications

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“…However, the Joint Data Science Task Force of the Society of Critical Care Medicine (SCCM) and the European Society of Intensive Care Medicine (ESICM) recently launched their Data Sharing initiative to ameliorate this situation ( 15 ). AmsterdamUMCdb is the first freely accessible European critical care database released under this initiative ( 16 ).…”

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confidence: 99%

Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists

Thoral

Fornasa²,

Bruin³

et al. 2021

Critical Care Explorations

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Objectives: Unexpected ICU readmission is associated with longer length of stay and increased mortality. To prevent ICU readmission and death after ICU discharge, our team of intensivists and data scientists aimed to use AmsterdamUMCdb to develop an explainable machine learning–based real-time bedside decision support tool. Derivation Cohort: Data from patients admitted to a mixed surgical-medical academic medical center ICU from 2004 to 2016. Validation Cohort: Data from 2016 to 2019 from the same center. Prediction Model: Patient characteristics, clinical observations, physiologic measurements, laboratory studies, and treatment data were considered as model features. Different supervised learning algorithms were trained to predict ICU readmission and/or death, both within 7 days from ICU discharge, using 10-fold cross-validation. Feature importance was determined using SHapley Additive exPlanations, and readmission probability-time curves were constructed to identify subgroups. Explainability was established by presenting individualized risk trends and feature importance. Results: Our final derivation dataset included 14,105 admissions. The combined readmission/mortality rate within 7 days of ICU discharge was 5.3%. Using Gradient Boosting, the model achieved an area under the receiver operating characteristic curve of 0.78 (95% CI, 0.75–0.81) and an area under the precision-recall curve of 0.19 on the validation cohort ( n = 3,929). The most predictive features included common physiologic parameters but also less apparent variables like nutritional support. At a 6% risk threshold, the model showed a sensitivity (recall) of 0.72, specificity of 0.70, and a positive predictive value (precision) of 0.15. Impact analysis using probability-time curves and the 6% risk threshold identified specific patient groups at risk and the potential of a change in discharge management to reduce relative risk by 14%. Conclusions: We developed an explainable machine learning model that may aid in identifying patients at high risk for readmission and mortality after ICU discharge using the first freely available European critical care database, AmsterdamUMCdb. Impact analysis showed that a relative risk reduction of 14% could be achievable, which might have significant impact on patients and society. ICU data sharing facilitates collaboration between intensivists and data scientists to accelerate model development.

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mentioning

confidence: 99%

Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists

Thoral

Fornasa²,

Bruin³

et al. 2021

Critical Care Explorations

Self Cite

View full text Add to dashboard Cite

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“…Vinks 2 Machine learning is now being introduced in medicine, with the idea that the vast multidimensional clinical data and experiences documented in electronic health records can assist in the care of future patients. 1 The ability of a machine learning model to find statistical patterns across millions of features and examples is what enables superhuman performance. 2 By mining the complex and heterogeneous data from electronic health records (referred to as "big data"), patients can benefit from previous collective experience leading to improvements in diagnostic procedures, outcome predictions, and individualized treatments.…”

Section: Machine Learning As a Novel Methods To Support Therapeutic Drug Management And Precision Dosingmentioning

confidence: 99%

“…Machine learning is now being introduced in medicine, with the idea that the vast multidimensional clinical data and experiences documented in electronic health records can assist in the care of future patients 1 . The ability of a machine learning model to find statistical patterns across millions of features and examples is what enables superhuman performance 2 .…”

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confidence: 99%

Machine Learning as a Novel Method to Support Therapeutic Drug Management and Precision Dosing

Gelder

Vinks

2021

Clin Pharma and Therapeutics

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“…17 AUMCdb is the first freely accessible European ICU database and contains data from the Amsterdam University Medical Center ICU, a mixed medical-surgical ICU with data on more than 20 000 patients including admissions between 2003 and 2016 (V.1.0.2). 18 The PLAGH-ICUdb integrates data from nine ICUs in the Chinese People's Liberation Army General Hospital in Beijing, China. 19 PLAGH-ICUdb includes data on more than 74 000 adult patients admitted between 2008 and 2019.…”

Section: Settingmentioning

confidence: 99%

Varying association of laboratory values with reference ranges and outcomes in critically ill patients: an analysis of data from five databases in four countries across Asia, Europe and North America

Agha-Mir-Salim

O’Brien

et al. 2021

BMJ Health Care Inform

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BackgroundDespite wide usage across all areas of medicine, it is uncertain how useful standard reference ranges of laboratory values are for critically ill patients.ObjectivesThe aim of this study is to assess the distributions of standard laboratory measurements in more than 330 selected intensive care units (ICUs) across the USA, Amsterdam, Beijing and Tarragona; compare differences and similarities across different geographical locations and evaluate how they may be associated with differences in length of stay (LOS) and mortality in the ICU.MethodsA multi-centre, retrospective, cross-sectional study of data from five databases for adult patients first admitted to an ICU between 2001 and 2019 was conducted. The included databases contained patient-level data regarding demographics, interventions, clinical outcomes and laboratory results. Kernel density estimation functions were applied to the distributions of laboratory tests, and the overlapping coefficient and Cohen standardised mean difference were used to quantify differences in these distributions.ResultsThe 259 382 patients studied across five databases in four countries showed a high degree of heterogeneity with regard to demographics, case mix, interventions and outcomes. A high level of divergence in the studied laboratory results (creatinine, haemoglobin, lactate, sodium) from the locally used reference ranges was observed, even when stratified by outcome.ConclusionStandardised reference ranges have limited relevance to ICU patients across a range of geographies. The development of context-specific reference ranges, especially as it relates to clinical outcomes like LOS and mortality, may be more useful to clinicians.

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Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example*

Abstract: Supplemental Digital Content is available in the text.

Cited by 102 publications

References 34 publications

Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists

Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists

Machine Learning as a Novel Method to Support Therapeutic Drug Management and Precision Dosing

Varying association of laboratory values with reference ranges and outcomes in critically ill patients: an analysis of data from five databases in four countries across Asia, Europe and North America

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