An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit.

MOTIVATION: Acute kidney injury (AKI) is a syndrome that affects a large fraction of all critically ill patients, and early diagnosis to receive adequate treatment is as imperative as it is challenging to make early. Consequently, machine learning approaches have been developed to predict AKI ahead of time. However, the prevalence of AKI is often underestimated in state-of-the-art approaches, as they rely on an AKI event annotation solely based on creatinine, ignoring urine output.We construct and evaluate early warning systems for AKI in a multi-disciplinary ICU setting, using the complete KDIGO definition of AKI. We propose several variants of gradient-boosted decision tree (GBDT)-based models, including a novel time-stacking based approach. A state-of-the-art LSTM-based model previously proposed for AKI prediction is used as a comparison, which was not specifically evaluated in ICU settings yet. RESULTS: We find that optimal performance is achieved by using GBDT with the time-based stacking technique (AUPRC = 65.7%, compared with the LSTM-based model's AUPRC = 62.6%), which is motivated by the high relevance of time since ICU admission for this task. Both models show mildly reduced performance in the limited training data setting, perform fairly across different subcohorts, and exhibit no issues in gender transfer.Following the official KDIGO definition substantially increases the number of annotated AKI events. In our study GBDTs outperform LSTM models for AKI prediction. Generally, we find that both model types are robust in a variety of challenging settings arising for ICU data. AVAILABILITY AND IMPLEMENTATION: The code to reproduce the findings of our manuscript can be found at: https://github.com/ratschlab/AKI-EWS.

Physiological changes after fluid bolus therapy in cardiac surgery patients: A propensity score matched case-control study.

Objective: Fluid bolus therapy (FBT) is ubiquitous in intensive care units (ICUs) after cardiac surgery. However, its physiological effects remain unclear. Design: : We performed an electronic health record-based quasi-experimental ICU study after cardiac surgery. We applied propensity score matching and compared the physiological changes after FBT episodes to matched control episodes where despite equivalent physiology no fluid bolus was given. Setting: The study was conducted in a multidisciplinary ICU of a tertiary-level academic hospital. Participants: The study included 2,736 patients who underwent Coronary Artery Bypass Grafting and/or heart valve surgery. Main Outcome Measures: Changes in cardiac output (CO) and mean arterial pressure (MAP) during the 60 minutes following FBT. Results: We analysed 3572 matched fluid bolus (FB) episodes. After FBT, but not in control episodes, CO increased within 10 min, with a maximum increase of 0.2 l/min (95%CI 0.1 to 0.2) or 4% above baseline at 40 min (p < 0.0001 vs. controls). CO increased by > 10% from baseline in 60.6% of FBT and 49.1% of control episodes (p < 0.0001). MAP increased by > 10% in 51.7% of FB episodes compared to 53.4% of controls. Finally, FBT was not associated with changes in acid-base status or oxygen delivery. Conclusion: In this quasi-experimental comparative ICU study in cardiac surgery patients, FBT was associated with statistically significant but numerically small increases in CO. Nearly half of FBT failed to induce a positive CO or MAP response.

A guide to sharing open healthcare data under the General Data Protection Regulation.

Sharing healthcare data is increasingly essential for developing data-driven improvements in patient care at the Intensive Care Unit (ICU). However, it is also very challenging under the strict privacy legislation of the European Union (EU). Therefore, we explored four successful open ICU healthcare databases to determine how open healthcare data can be shared appropriately in the EU. A questionnaire was constructed based on the Delphi method. Then, follow-up questions were discussed with experts from the four databases. These experts encountered similar challenges and regarded ethical and legal aspects to be the most challenging. Based on the approaches of the databases, expert opinion, and literature research, we outline four distinct approaches to openly sharing healthcare data, each with varying implications regarding data security, ease of use, sustainability, and implementability. Ultimately, we formulate seven recommendations for sharing open healthcare data to guide future initiatives in sharing open healthcare data to improve patient care and advance healthcare.

Systematic Review and Comparison of Publicly Available ICU Data Sets-A Decision Guide for Clinicians and Data Scientists.

OBJECTIVE: As data science and artificial intelligence continue to rapidly gain traction, the publication of freely available ICU datasets has become invaluable to propel data-driven clinical research. In this guide for clinicians and researchers, we aim to: 1) systematically search and identify all publicly available adult clinical ICU datasets, 2) compare their characteristics, data quality, and richness and critically appraise their strengths and weaknesses, and 3) provide researchers with suggestions, which datasets are appropriate for answering their clinical question. DATA SOURCES: A systematic search was performed in Pubmed, ArXiv, MedRxiv, and BioRxiv. STUDY SELECTION: We selected all studies that reported on publicly available adult patient-level intensive care datasets. DATA EXTRACTION: A total of four publicly available, adult, critical care, patient-level databases were included (Amsterdam University Medical Center data base [AmsterdamUMCdb], eICU Collaborative Research Database eICU CRD], High time-resolution intensive care unit dataset [HiRID], and Medical Information Mart for Intensive Care-IV). Databases were compared using a priori defined categories, including demographics, patient characteristics, and data richness. The study protocol and search strategy were prospectively registered. DATA SYNTHESIS: Four ICU databases fulfilled all criteria for inclusion and were queried using SQL (PostgreSQL version 12; PostgreSQL Global Development Group) and analyzed using R (R Foundation for Statistical Computing, Vienna, Austria). The number of unique patient admissions varied between 23,106 (AmsterdamUMCdb) and 200,859 (eICU-CRD). Frequency of laboratory values and vital signs was highest in HiRID, for example, 5.2 (±3.4) lactate values per day and 29.7 (±10.2) systolic blood pressure values per hour. Treatment intensity varied with vasopressor and ventilatory support in 69.0% and 83.0% of patients in AmsterdamUMCdb versus 12.0% and 21.0% in eICU-CRD, respectively. ICU mortality ranged from 5.5% in eICU-CRD to 9.9% in AmsterdamUMCdb. CONCLUSIONS: We identified four publicly available adult clinical ICU datasets. Sample size, severity of illness, treatment intensity, and frequency of reported parameters differ markedly between the databases. This should guide clinicians and researchers which databases to best answer their clinical questions.

Analysis of L-leucine amino acid transporter species activity and gene expression by human blood brain barrier hCMEC/D3 model reveal potential LAT1, LAT4, B0AT2 and y+LAT1 functional cooperation.

In the CNS, amino acid (AA) neurotransmitters and neurotransmitter precursors are subject to tight homeostatic control mediated by blood-brain barrier (BBB) solute carrier amino acid transporters (AATs). Since the BBB is composed of multiple closely apposed cell types and opportunities for human in vivo studies are limited, we used in vitro and computational approaches to investigate human BBB AAT activity and regulation. Quantitative real-time PCR (qPCR) of the human BBB endothelial cell model hCMEC/D3 (D3) was used to determine expression of selected AAT, tight junction (TJ), and signal transduction (ST) genes under various culture conditions. L-leucine uptake data were interrogated with a computational model developed by our group for calculating AAT activity in complex cell cultures. This approach is potentially applicable to in vitro cell culture drug studies where multiple "receptors" may mediate observed responses. Of 7 Leu AAT genes expressed by D3 only the activity of SLC7A5-SLC3A2/LAT1-4F2HC (LAT1), SLC43A2/LAT4 (LAT4) and sodium-dependent AATs, SLC6A15/B0AT2 (B0AT2), and SLC7A7/y+LAT1 (y+LAT1) were calculated to be required for Leu uptake. Therefore, D3 Leu transport may be mediated by a potentially physiologically relevant functional cooperation between the known BBB AAT, LAT1 and obligatory exchange (y+LAT1), facilitative diffusion (LAT4), and sodium symporter (B0AT2) transporters.

An automated retrospective VAE-surveillance tool for future quality improvement studies.

Ventilator-associated pneumonia (VAP) is a frequent complication of mechanical ventilation and is associated with substantial morbidity and mortality. Accurate diagnosis of VAP relies in part on subjective diagnostic criteria. Surveillance according to ventilator-associated event (VAE) criteria may allow quick and objective benchmarking. Our objective was to create an automated surveillance tool for VAE tiers I and II on a large data collection, evaluate its diagnostic accuracy and retrospectively determine the yearly baseline VAE incidence. We included all consecutive intensive care unit admissions of patients with mechanical ventilation at Bern University Hospital, a tertiary referral center, from January 2008 to July 2016. Data was automatically extracted from the patient data management system and automatically processed. We created and implemented an application able to automatically analyze respiratory and relevant medication data according to the Centers for Disease Control protocol for VAE-surveillance. In a subset of patients, we compared the accuracy of automated VAE surveillance according to CDC criteria to a gold standard (a composite of automated and manual evaluation with mediation for discrepancies) and evaluated the evolution of the baseline incidence. The study included 22'442 ventilated admissions with a total of 37'221 ventilator days. 592 ventilator-associated events (tier I) occurred; of these 194 (34%) were of potentially infectious origin (tier II). In our validation sample, automated surveillance had a sensitivity of 98% and specificity of 100% in detecting VAE compared to the gold standard. The yearly VAE incidence rate ranged from 10.1-22.1 per 1000 device days and trend showed a decrease in the yearly incidence rate ratio of 0.96 (95% CI, 0.93-1.00, p = 0.03). This study demonstrated that automated VAE detection is feasible, accurate and reliable and may be applied on a large, retrospective sample and provided insight into long-term institutional VAE incidences. The surveillance tool can be extended to other centres and provides VAE incidences for performing quality control and intervention studies.

Early prediction of circulatory failure in the intensive care unit using machine learning.

Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monitoring alarms lead to alarm fatigue. We used machine learning to develop an early-warning system that integrates measurements from multiple organ systems using a high-resolution database with 240 patient-years of data. It predicts 90% of circulatory-failure events in the test set, with 82% identified more than 2 h in advance, resulting in an area under the receiver operating characteristic curve of 0.94 and an area under the precision-recall curve of 0.63. On average, the system raises 0.05 alarms per patient and hour. The model was externally validated in an independent patient cohort. Our model provides early identification of patients at risk for circulatory failure with a much lower false-alarm rate than conventional threshold-based systems.