Purpose: To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of... Show morePurpose: To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular Electronic Health Records (EHR) data versus less-granular registry data. Methods: Observational study of all COVID-19 patients admitted to 19 Dutch ICUs participating in both the national quality registry National Intensive Care Evaluation (NICE) and the EHR-based Dutch Data Warehouse (hereafter EHR). Multiple models were developed on data from the first 24 h of ICU admissions from February to June 2020 (first COVID-19 wave) and validated on prospective patients admitted to the same ICUs between July and December 2020 (second COVID-19 wave). We assessed model discrimination, calibration, and the degree of relatedness between development and validation population. Coefficients were used to identify relevant risk factors. Results: A total of 1533 patients from the EHR and 1563 from the registry were included. With high granular EHR data, the average AUROC was 0.69 (standard deviation of 0.05) for the internal validation, and the AUROC was 0.75 for the temporal validation. The registry model achieved an average AUROC of 0.76 (standard deviation of 0.05) in the internal validation and 0.77 in the temporal validation. In the EHR data, age, and respiratory-system related variables were the most important risk factors identified. In the NICE registry data, age and chronic respiratory insufficiency were the most important risk factors. Conclusion: In our study, prognostic models built on less-granular but readily-available registry data had similar performance to models built on high-granular EHR data and showed similar transportability to a prospective COVID-19 population. Future research is needed to verify whether this finding can be confirmed for upcoming waves. Show less
Introduction: Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post hoc manner: after the research project is conducted and data... Show moreIntroduction: Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post hoc manner: after the research project is conducted and data are collected. De-novo FAIRification, on the other hand, incorporates the FAIRification steps in the process of a research project. In medical research, data is often collected and stored via electronic Case Report Forms (eCRFs) in Electronic Data Capture (EDC) systems. By implementing a de novo FAIRification process in such a system, the reusability and, thus, scalability of FAIRification across research projects can be greatly improved. In this study, we developed and implemented a novel method for de novo FAIRification via an EDC system. We evaluated our method by applying it to the Registry of Vascular Anomalies (VASCA). Methods: Our EDC and research project independent method ensures that eCRF data entered into an EDC system can be transformed into machine-readable, FAIR data using a semantic data model (a canonical representation of the data, based on ontology concepts and semantic web standards) and mappings from the model to questions on the eCRF. The FAIRified data are stored in a triple store and can, together with associated metadata, be accessed and queried through a FAIR Data Point. The method was implemented in Castor EDC, an EDC system, through a data transformation application. The FAIRness of the output of the method, the FAIRified data and metadata, was evaluated using the FAIR Evaluation Services. Results: We successfully applied our FAIRification method to the VASCA registry. Data entered on eCRFs is automatically transformed into machine-readable data and can be accessed and queried using SPARQL queries in the FAIR Data Point. Twenty-one FAIR Evaluator tests pass and one test regarding the metadata persistence policy fails, since this policy is not in place yet. Conclusion: In this study, we developed a novel method for de novo FAIRification via an EDC system. Its application in the VASCA registry and the automated FAIR evaluation show that the method can be used to make clinical research data FAIR when they are entered in an eCRF without any intervention from data management Show less
Boorn, H.G. van den; Abu-Hanna, A.; Mohammad, N.H.; Hulshof, M.C.C.M.; Gisbertz, S.S.; Klarenbeek, B.R.; ... ; Laarhoven, H.W.M. van 2021
The 21st Century Cures Act (Cures Act), signed into law in 2016, was designed to advance new therapies by modernizing clinical trials, funding research initiatives, and accelerating the development... Show moreThe 21st Century Cures Act (Cures Act), signed into law in 2016, was designed to advance new therapies by modernizing clinical trials, funding research initiatives, and accelerating the development and use of health information technology. To analyze the current issues in cancer care related to the implementation and impact of the Cures Act, NCCN convened a multistakeholder working group. Participants discussed the legislation's impact on the oncology community since enactment and identified the remaining gaps and challenges as experienced by stakeholders. In June 2020, the policy recommendations of the working group were presented at the virtual NCCN Policy Summit: Accelerating Advances in Cancer Care Research: A Lookback at the 21st Century Cures Act in 2020. The summit consisted of informative discussions and a multistakeholder panel to explore the recommendations and the future of the Cures Act. This article explores identified policy recommendations from the NCCN Working Group and the NCCN Policy Summit, and analyzes opportunities to advance innovative cancer care and patient access to data. Show less
Purpose: Potential drug-drug interactions (pDDIs) may harm patients admitted to the Intensive Care Unit (ICU). Due to the patient's critical condition and continuous monitoring on the ICU, not all... Show morePurpose: Potential drug-drug interactions (pDDIs) may harm patients admitted to the Intensive Care Unit (ICU). Due to the patient's critical condition and continuous monitoring on the ICU, not all pDDIs are clinically relevant. Clinical decision support systems (CDSSs) warning for irrelevant pDDIs could result in alert fatigue and overlooking important signals. Therefore, our aim was to describe the frequency of clinically relevant pDDIs (crpDDIs) to enable tailoring of CDSSs to the ICU setting. Materials & methods: In this multicenter retrospective observational study, we used medication administration data to identify pDDIs in ICU admissions from 13 ICUs. Clinical relevance was based on a Delphi study in which intensivists and hospital pharmacists assessed the clinical relevance of pDDIs for the ICU setting. Results: The mean number of pDDIs per 1000 medication administrations was 70.1, dropping to 31.0 when con -sidering only crpDDIs. Of 103,871 ICU patients, 38% was exposed to a crpDDI. The most frequently occurring crpDDIs involve QT-prolonging agents, digoxin, or NSAIDs. Conclusions: Considering clinical relevance of pDDIs in the ICU setting is important, as only half of the detected pDDIs were crpDDIs. Therefore, tailoring CDSSs to the ICU may reduce alert fatigue and improve medication safety in ICU patients. ? 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). Show less
Bakker, T.; Klopotowska, J.E.; Keizer, N.F. de; Marum, R. van; Sijs, H. van der; Lange, D.W. de; ... ; Simplify Study Grp 2020
Purpose: Drug-drug interactions (DDIs) may cause adverse outcomes in patients admitted to the Intensive Care Unit (ICU). Computerized decision support systems (CDSSs) may help prevent DDIs by... Show morePurpose: Drug-drug interactions (DDIs) may cause adverse outcomes in patients admitted to the Intensive Care Unit (ICU). Computerized decision support systems (CDSSs) may help prevent DDIs by timely showing relevant warning alerts, but knowledge on which DDIs are clinically relevant in the ICU setting is limited. Therefore, the purpose of this study was to identify DDIs relevant for the ICU. Materials and methods: We conducted a modified Delphi procedure with a Dutch multidisciplinary expert panel consisting of intensivists and hospital pharmacists to assess the clinical relevance of DDIs for the ICU. The procedure consisted of two rounds, each included a questionnaire followed by a live consensus meeting. Results: In total the clinical relevance of 148 DDIs was assessed, of which agreement regarding the relevance was reached for 139 DDIs (94%). Of these 139 DDIs, 53 (38%) were considered not clinically relevant for the ICU setting. Conclusions: A list of clinically relevant DDIs for the ICU setting was established on a national level. The clinical value of CDSSs for medication safety could be improved by focusing on the identified clinically relevant DDIs, thereby avoiding alert fatigue. (c) 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). Show less
Bakker, T.; Klopotowska, J.E.; Eslami, S.; Lange, D.W. de; Marum, R. van; Sijs, H. van der; ... ; Abu-Hanna, A. 2019
PURPOSE\nTo analyze the influence of using mortality 1, 3, and 6 months after intensive care unit (ICU) admission instead of in-hospital mortality on the quality indicator standardized mortality... Show morePURPOSE\nTo analyze the influence of using mortality 1, 3, and 6 months after intensive care unit (ICU) admission instead of in-hospital mortality on the quality indicator standardized mortality ratio (SMR).\nMETHODS\nA cohort study of 77,616 patients admitted to 44 Dutch mixed ICUs between 1 January 2008 and 1 July 2011. Four Acute Physiology and Chronic Health Evaluation (APACHE) IV models were customized to predict in-hospital mortality and mortality 1, 3, and 6 months after ICU admission. Models' performance, the SMR and associated SMR rank position of the ICUs were assessed by bootstrapping.\nRESULTS\nThe customized APACHE IV models can be used for prediction of in-hospital mortality as well as for mortality 1, 3, and 6 months after ICU admission. When SMR based on mortality 1, 3 or 6 months after ICU admission was used instead of in-hospital SMR, 23, 36, and 30% of the ICUs, respectively, received a significantly different SMR. The percentages of patients discharged from ICU to another medical facility outside the hospital or to home had a significant influence on the difference in SMR rank position if mortality 1 month after ICU admission was used instead of in-hospital mortality.\nCONCLUSIONS\nThe SMR and SMR rank position of ICUs were significantly influenced by the chosen endpoint of follow-up. Case-mix-adjusted in-hospital mortality is still influenced by discharge policies, therefore SMR based on mortality at a fixed time point after ICU admission should preferably be used as a quality indicator for benchmarking purposes. Show less
Brinkman, S.; Jonge, E. de; Abu-Hanna, A.; Arbous, M.S.; Lange, D.W. de; Keizer, N.F. de 2013
OBJECTIVES\nTo assess the mortality risk of ICU patients after hospital discharge and compare it to mortality of the general Dutch population.\nDESIGN\nCohort study of ICU admissions from a... Show moreOBJECTIVES\nTo assess the mortality risk of ICU patients after hospital discharge and compare it to mortality of the general Dutch population.\nDESIGN\nCohort study of ICU admissions from a national ICU registry linked to administrative records from an insurance claims database.\nSETTING\nEighty-one Dutch ICUs.\nPATIENTS\nICU patients (n = 91,203) who were discharged alive from the hospital between January 1, 2007, and October 1, 2010.\nINTERVENTIONS\nNone.\nMEASUREMENTS AND MAIN RESULTS\nThe unadjusted observed survival was inspected by Kaplan-Meier curves. Mortality risk at 1, 2, and 3 years after hospital discharge was 12.5%, 19.3%, and 27.5%, respectively. The 3-year mortality after hospital discharge in ICU patients was higher than the weighted average of the gender and age-specific death risks of the general Dutch population (27.5% versus 8.2%). The 1-year mortality after hospital discharge was adjusted for case-mix differences by a set of determinants which showed a statistically significant influence on the outcome in a 10-fold cross validation. The elective and cardiac surgical patients have statistically significantly better mortality outcomes (adjusted hazard ratio, 0.73 and 0.28, respectively), whereas medical patients and patients admitted for cancer have statistically significantly worse mortality outcomes (adjusted hazard ratio, 1.41, 1.94, respectively) compared with other ICU patients. Urgent surgery patients and patients with a subarachnoid hemorrhage, trauma, acute renal failure, or severe community-acquired pneumonia did not differ statistically from the other ICU patients after adjustment for case-mix differences.\nCONCLUSIONS\nIn-hospital mortality underestimates the true mortality of ICU patients as the mortality in the first months after hospital discharge is substantial. Most ICU patients still have an increased mortality risk in the subsequent years after hospital discharge compared with the general Dutch population. The mortality after hospital discharge differs widely between ICU subgroups. Future studies should focus on the analysis of mortality after hospital discharge that is attributable to the former ICU admission. Show less
Minne, L.; Toma, T.; Jonge, E. de; Abu-Hanna, A. 2013
OBJECTIVE\nRecently, we devised a method to develop prognostic models incorporating patterns of sequential organ failure to predict the eventual hospital mortality at each day of intensive care... Show moreOBJECTIVE\nRecently, we devised a method to develop prognostic models incorporating patterns of sequential organ failure to predict the eventual hospital mortality at each day of intensive care unit (ICU) stay. In this study, we investigate using a real world setting how these models perform compared to physicians, who are exposed to additional information than the models.\nMETHODS\nWe developed prognostic models for days 2-7 of ICU stay by data-driven discovery of patterns of sequential qualitative organ failure (SOFA) scores and embedding the patterns as binary variables in three types of logistic regression models. Type A models include the severity of illness score at admission (SAPS-II) and the SOFA patterns. Type B models add to these covariates the mean, max and delta (increments) of SOFA scores. Type C models include, in addition, the mean, max and delta in expert opinion (i.e. the physicians' prediction of mortality).\nRESULTS\nPhysicians had a statistically significantly better discriminative ability compared to the models without subjective information (AUC range over days: 0.78-0.79 vs. 0.71-0.74) and comparable accuracy (Brier score range: 0.15-0.18 vs. 0.16-0.18). However when we combined both sources of predictions, in Type C models, we arrived at a significantly superior discrimination as well as accuracy than the objective and subjective models alone (AUC range: 0.80-0.83; Brier score range: 0.13-0.16).\nCONCLUSION\nThe models and the physicians draw on complementary information that can be best harnessed by combining both prediction sources. Extensive external validation and impact studies are imperative to further investigate the ability of the combined model. Show less
Brinkman, S.; Bakhshi-Raiez, F.; Abu-Hanna, A.; Jonge, E. de; Keizer, N.F. de 2013
