Long-term mortality of Dutch COVID-19 patients admitted to the intensive care medicine: a retrospective analysis from a national quality registry.

Objective: To describe the 12-month mortality of Dutch COVID-19 intensive care unit patients, the total COVID-19 population and various subgroups on the basis of the number of comorbidities, age, sex, mechanical ventilation, and vasoactive medication use.

Methods: We included all patients admitted with COVID-19 between March 1, 2020, and March 29, 2022, from the Dutch National Intensive Care (NICE) database. The crude 12-month mortality rate is presented via Kaplan-Meier survival curves for each patient subgroup.

ATC-to-RxNorm mappings - A comparison between OHDSI Standardized Vocabularies and UMLS Metathesaurus.

Introduction: The World Health Organization global standard for representing drug data is the Anatomical Therapeutic Chemical (ATC) classification. However, it does not represent ingredients and other drug properties required by clinical decision support systems. A mapping to a terminology system that contains this information, like RxNorm, may help fill this gap.

Implementing Findable, Accessible, Interoperable, Reusable (FAIR) Principles in Child and Adolescent Mental Health Research: Mixed Methods Approach.

Background: The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are a guideline to improve the reusability of data. However, properly implementing these principles is challenging due to a wide range of barriers.

Objectives: To further the field of FAIR data, this study aimed to systematically identify barriers regarding implementing the FAIR principles in the area of child and adolescent mental health research, define the most challenging barriers, and provide recommendations for these barriers.

Three-year mortality of ICU survivors with sepsis, an infection or an inflammatory illness: an individually matched cohort study of ICU patients in the Netherlands from 2007 to 2019.

Background: Sepsis is a frequent reason for ICU admission and a leading cause of death. Its incidence has been increasing over the past decades. While hospital mortality is decreasing, it is recognized that the sequelae of sepsis extend well beyond hospitalization and are associated with a high mortality rate that persists years after hospitalization.

The use of outcome data from quality registries to learn and improve; a Dutch nationwide quantitative analysis in five disease areas.

Background: Clinical quality registries (CQR) aid in measuring, collecting and monitoring outcome data but it is still unknown how these data are used by hospitals to improve the quality of care. This study assessed the current state of outcome-based quality improvement in the Netherlands in 2022 based on data from multiple disease areas and CQRs; cardiothoracic surgery (Netherlands Heart Registration [NHR]), cardiology (NHR), nephrology, (Nefrovision), intensive care (National Intensive Care Evaluation [NICE]), and orthopaedic surgery (Dutch Arthroplasty Register [LROI]).

Methods & Results: The Health Outcomes Management Evaluation (HOME) model was used to assess the current state of outcome-based quality improvement.

Clinical phenotyping uncovers heterogeneous associations between corticosteroid treatment and survival in critically ill COVID-19 patients.

Purpose: Disease heterogeneity in coronavirus disease 2019 (COVID-19) may render the current one-size-fits-all treatment approach suboptimal. We aimed to identify and immunologically characterize clinical phenotypes among critically ill COVID-19 patients, and to assess heterogeneity of corticosteroid treatment effect.

Methods: We applied consensus k-means clustering on 21 clinical parameters obtained within 24 h after admission to the intensive care unit (ICU) from 13,279 COVID-19 patients admitted to 82 Dutch ICUs from February 2020 to February 2022.

Linking Dutch Intensive Care Registry Data to Socioeconomic Status Scores.

This study aimed to gain insight into the success rate of linking the NICE registry with SES data from CBS and to examine whether the characteristics of linked and non-linked patients differ. Although clinically relevant differences were found, in total 93,4% of the admissions were successfully linked.

Feasibility of Calculating Indicators for Intensive Care Unit Antibiotic Use Based on Routine Electronic Health Record Data.

This study evaluated the feasibility of utilizing routinely collected EHR data to calculate pre-developed quality indicators on antibiotic use. Three out of four indicators were found feasible. Main barriers included local codes for lab tests and surveillance cultures and lack of data on empirical prescription.

Emergency Department Triage, Transfer Times, and Hospital Mortality of Patients Admitted to the ICU: A Retrospective Replication and Continuation Study.

Objectives: This study aimed to provide new insights into the impact of emergency department (ED) to ICU time on hospital mortality, stratifying patients by academic and nonacademic teaching (NACT) hospitals, and considering Acute Physiology and Chronic Health Evaluation (APACHE)-IV probability and ED-triage scores.

Design, Setting, And Patients: We conducted a retrospective cohort study (2009-2020) using data from the Dutch National Intensive Care Evaluation registry. Patients directly admitted from the ED to the ICU were included from four academic and eight NACT hospitals.

Comparing causal random forest and linear regression to estimate the independent association of organisational factors with ICU efficiency.

Purpose: Parametric regression models have been the main statistical method for identifying average treatment effects. Causal machine learning models showed promising results in estimating heterogeneous treatment effects in causal inference. Here we aimed to compare the application of causal random forest (CRF) and linear regression modelling (LRM) to estimate the effects of organisational factors on ICU efficiency.

Evaluating Artificial Intelligence in Clinical Settings-Let Us Not Reinvent the Wheel.

Given the requirement to minimize the risks and maximize the benefits of technology applications in health care provision, there is an urgent need to incorporate theory-informed health IT (HIT) evaluation frameworks into existing and emerging guidelines for the evaluation of artificial intelligence (AI). Such frameworks can help developers, implementers, and strategic decision makers to build on experience and the existing empirical evidence base. We provide a pragmatic conceptual overview of selected concrete examples of how existing theory-informed HIT evaluation frameworks may be used to inform the safe development and implementation of AI in health care settings.

Comparative performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data.

Introduction: Benchmarking intensive care units for audit and feedback is frequently based on comparing actual mortality versus predicted mortality. Traditionally, mortality prediction models rely on a limited number of input variables and significant manual data entry and curation. Using automatically extracted electronic health record data may be a promising alternative.

One-year transplant-free survival following hospital discharge after ICU admission for ACLF in the Netherlands.

Background & Aims: Patients with acute decompensation of cirrhosis or acute-on-chronic liver failure (ACLF) often require intensive care unit (ICU) admission for organ support. Existing research, mostly from specialized liver transplant centers, largely addresses short-term outcomes. Our aim was to evaluate in-hospital mortality and 1-year transplant-free survival after hospital discharge in the Netherlands.

Projecting COVID-19 intensive care admissions for policy advice, the Netherlands, February 2020 to January 2021.

BackgroundModel projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.

Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: a case study of the MIMIC-IV Emergency Department module.

Objectives: To provide a real-world example on how and to what extent Health Level Seven Fast Healthcare Interoperability Resources (FHIR) implements the Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles for scientific data. Additionally, presents a list of FAIR implementation choices for supporting future FAIR implementations that use FHIR.

Materials And Methods: A case study was conducted on the Medical Information Mart for Intensive Care-IV Emergency Department (MIMIC-ED) dataset, a deidentified clinical dataset converted into FHIR.

The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring: a cluster randomised stepped-wedge trial.

Background: Drug-drug interactions (DDIs) can harm patients admitted to the intensive care unit (ICU). Yet, clinical decision support systems (CDSSs) aimed at helping physicians prevent DDIs are plagued by low-yield alerts, causing alert fatigue and compromising patient safety. The aim of this multicentre study was to evaluate the effect of tailoring potential DDI alerts to the ICU setting on the frequency of administered high-risk drug combinations.

Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the consecutive stages of the COVID-19 pandemic in the Netherlands, an update.

Background: Previously, we reported a decreased mortality rate among patients with COVID-19 who were admitted at the ICU during the final upsurge of the second wave (February-June 2021) in the Netherlands. We examined whether this decrease persisted during the third wave and the phases with decreasing incidence of COVID-19 thereafter and brought up to date the information on patient characteristics.

Methods: Data from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used.

Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges.

Background: Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response.

Strain on Scarce Intensive Care Beds Drives Reduced Patient Volumes, Patient Selection, and Worse Outcome: A National Cohort Study.

Objectives: Strain on ICUs during the COVID-19 pandemic required stringent triage at the ICU to distribute resources appropriately. This could have resulted in reduced patient volumes, patient selection, and worse outcome of non-COVID-19 patients, especially during the pandemic peaks when the strain on ICUs was extreme. We analyzed this potential impact on the non-COVID-19 patients.

Quality improvement of Dutch ICUs from 2009 to 2021: A registry based observational study.

Purpose: To investigate the development in quality of ICU care over time using the Dutch National Intensive Care Evaluation (NICE) registry.

Materials And Methods: We included data from all ICU admissions in the Netherlands from those ICUs that submitted complete data between 2009 and 2021 to the NICE registry. We determined median and interquartile range for eight quality indicators.

Correctly structured problem lists lead to better and faster clinical decision-making in electronic health records compared to non-curated problem lists: A single-blinded crossover randomized controlled trial.

Background: Correctly structured problem lists in electronic health records (EHRs) offer major benefits to patient care. Without structured lists, diagnosis information is often scatteredly documented in free text, which may contribute to errors and inefficient information retrieval. This study aims to assess whether EHRs with correctly structured problem lists result in better and faster clinical decision-making compared to non-curated problem lists.

Determining and assessing characteristics of data element names impacting the performance of annotation using Usagi.

Introduction: Hospitals generate large amounts of data and this data is generally modeled and labeled in a proprietary way, hampering its exchange and integration. Manually annotating data element names to internationally standardized data element identifiers is a time-consuming effort. Tools can support performing this task automatically.