Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse

Fleuren, Lucas M.; Bruin, Daan P. de; Tonutti, Michele; Lalisang, Robbert C. A.; Elbers, Paul

doi:10.1007/s00134-021-06361-x

Cited by 20 publications

(31 citation statements)

References 4 publications

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“…Therefore, we developed an algorithm that determines the start and end of intubation episodes based on other concepts. The overview of this algorithm has been published previously [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

“…This paper presents a full report on the first stable version of the database and addresses the major challenges in the construction of the DDW. Given the crisis, a brief overview of the preliminary dataset was published as a letter [ 11 ]. In the present report, we expand on the methodology underlying the DDW and show the patient population currently included.…”

Section: Introductionmentioning

confidence: 99%

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The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Fleuren

Dam

Tonutti³

et al. 2021

Crit Care

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Background The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. Methods A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract–transform–load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. Results Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. Conclusions In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.

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“…Therefore, we developed an algorithm that determines the start and end of intubation episodes based on other concepts. The overview of this algorithm has been published previously [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Fleuren

Dam

Tonutti³

et al. 2021

Crit Care

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“…Nonetheless, whenever data from the referring and receiving hospital were available, patient data were connected to limit the number of exclusions. In addition, previous analyses have shown that on admission, transferred patients are similar to non-transferred patients [ 9 ]. The DDW represents a relatively unselected sample of patients since all COVID-19 patients from the participating ICUs were included, limiting selection bias.…”

Section: Discussionmentioning

confidence: 99%

“…The highly granular data warehouse is continuously expanding and currently contains over 400 million data points combining electronic health record (EHR) data from 25 hospitals on 2382 critically ill COVID-19 patients throughout their ICU treatment. A more detailed description of the DDW, the data ingestion and the data preprocessing has been published previously [ 9 ]. In brief, data were extracted in the highest frequency available, routine hourly or bihourly measurements, or at least multiple measurements per day.…”

Section: Methodsmentioning

confidence: 99%

“…For these analyses, it is essential to capture all available data throughout ICU admission. We therefore relied on the Dutch Data Warehouse (DDW), a large, observational, multicenter collaboration uniting 66 out of 81 intensive care units in the Netherlands [ 9 ]. Our hypothesis is that ICU treatment characteristics become more important as predictors of outcome throughout the course of IMV.…”

Section: Introductionmentioning

confidence: 99%

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Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Fleuren

Tonutti²,

Bruin³

et al. 2021

ICMx

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Background The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.

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Modern Learning from Big Data in Critical Care: Primum Non Nocere

Gravesteijn

Lingsma

2022

Neurocrit Care

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Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in the field of diagnostics, for example, by identification of radiological anomalies. In other research areas, such as clustering and prediction studies, there is more discussion regarding the benefit and efficiency of ML techniques compared with statistical learning. In this viewpoint, we aim to explain commonly used statistical learning and ML techniques and provide guidance for responsible use in the case of clustering and prediction questions in critical care. Clustering studies have been increasingly popular in critical care research, aiming to inform how patients can be characterized, classified, or treated differently. An important challenge for clustering studies is to ensure and assess generalizability. This limits the application of findings in these studies toward individual patients. In the case of predictive questions, there is much discussion as to what algorithm should be used to most accurately predict outcome. Aspects that determine usefulness of ML, compared with statistical techniques, include the volume of the data, the dimensionality of the preferred model, and the extent of missing data. There are areas in which modern ML methods may be preferred. However, efforts should be made to implement statistical frameworks (e.g., for dealing with missing data or measurement error, both omnipresent in clinical data) in ML methods. To conclude, there are important opportunities but also pitfalls to consider when performing clustering or predictive studies with ML techniques. We advocate careful valuation of new data-driven findings. More interaction is needed between the engineer mindset of experts in ML methods, the insight in bias of epidemiologists, and the probabilistic thinking of statisticians to extract as much information and knowledge from data as possible, while avoiding harm.

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Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse

Cited by 20 publications

References 4 publications

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Modern Learning from Big Data in Critical Care: Primum Non Nocere

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