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

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.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

De invasieve Oost-Amerikaanse kersenboorvlieg Rhagoletis cingulata in Nederland (Diptera: Tephritidae).

The invasive American Eastern Cherry Fruitfly Rhagoletis cingulata in the Netherlands (Diptera: Tephritidae) In 2003 the European Invertebrate Survey - Netherlands, on request of the Plant Protection Service of the Netherlands, conducted a survey of the distribution and phenology of the American Eastern Cherry Fruitfly Rhagoletis cingulata (Loew, 1862). The focus of this survey was the province of Zeeland, because the first records stemmed from this province (Van Aartsen 2001). Three populations were monitored with Pherocon am yellow sticky traps (without attractant). Two of these populations were on the peninsula of Walcheren (‘Zandput’ and ‘Oranjebos’, both near the town of Vrouwenpolder) and one on the island of Schouwen (‘Het Zeepe’, near the town of Burgh). This survey was supplementary to the surveys of the National Plant Protection Service, conducted in commercially grown cherry orchards in 2003-2006. In this paper an overview is given of the results of these surveys, supplemented with a few scattered records of others. Rhagoletis cingulata is a well-known and severe pest in commercially grown cherries in North America. This originally Nearctic species is listed as a quarantine species in the European Community (Annex 1a1 of the Council Directive 2000/29/ec) of which introduction into, and spread within, all member states should be banned. In the Netherlands R. cingulata should be regarded as an established species that has been introduced by man and which has sustained populations for more than ten years. Extermination of this species, as required by eu legislation, is therefore useless. Meanwhile several eu member states have asked the European Committee to remove this species of the quarantine list. Given the distribution of its host Prunus serotina in the Netherlands it is to be expected that the actual distribution of R. cingulata is much wider than presented in the distribution map (fig. 18). Moreover, it is discussed that it is probably able to shift to other hosts, so that it might even spread over a yet larger part of the Netherlands. All given known (potential) hosts are nonindigenous species in the Netherlands and therefore mostly ignored by entomologists, probably causing species like R. cingulata to be overlooked. The phenology of the population at ‘Zandput’ differs greatly from both other populations (fig. 10 versus fig. 9 & 11 ). Possible explanations are an intrinsic (genetic) difference between the populations or the presence of a second species: the American Western Cherry Fruitfly Rhagoletis indifferens Curran, 1932. This has to be investigated further. The Pherocon am yellow sticky traps pose a good method for monitoring the phenology of R. cingulata, provided the host plants in which they are placed are chosen carefully. A welldeveloped population seems to be necessary, and the traps should be placed within the treetop and free from branches. The only host plant of R. cingulata in the Netherlands reported thus far is Prunus serotina. A single specimen was swept from Sorbus aucuparia, but it was probably coming from an adjacent P. serotina. Extensive examination of several other potential hosts yielded no proof of reproduction of R. cingulata on other hosts than P. serotina

Innovatie en duurzaamheid : effecten van het topsectorenbeleid op de kwaliteit van de groene ruimte

Het bedrijvenbeleid is gericht op samenwerking tussen bedrijven, kennisinstellingen en overheid binnen negen topsectoren. Hiervoor zijn in het voorjaar van 2012 innovatiecontracten afgesloten. Voor drie van de negen topsectoren, Agro & Food, Tuinbouw & Uitgangsmaterialen en Chemie is duurzaamheid zeer belangrijk en onderdeel van hun ‘licence to produce’. Bij Energie, Logistiek en Water speelt duurzaamheid ook een grote rol. Voor twee topsectoren, de High Tech en de Creatieve Industrie, is duurzaamheid vooral een toepassingsveld. In de plannen voor de topsector Life Sciences & Health en voor het later toegevoegde topgebied Hoofdkantoren, speelt duurzaamheid geen rol. In de plannen en innovatiecontracten wordt duurzaamheid vooral opgevat als resource efficiency en emissiereductie. Schaal- en locatie-effecten zijn slechts beperkt meegenomen. Voor het vaststellen van de effecten op duurzaamheid in de toekomst is het kiezen van concrete doelen en monitoring essentieel

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

BACKGROUND: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. METHODS: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. RESULTS: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. DISCUSSION: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research

INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY

Background: Aims of this study were to investigate the prevalence and incidence of catheter-related infection, identify risk factors, and determine the relation of catheter-related infection with mortality in critically ill COVID-19 patients. Methods: This was a retrospective cohort study of central venous catheters (CVCs) in critically ill COVID-19 patients. Eligible CVC insertions required an indwelling time of at least 48 hours and were identified using a full-admission electronic health record database. Risk factors were identified using logistic regression. Differences in survival rates at day 28 of follow-up were assessed using a log-rank test and proportional hazard model. Results: In 538 patients, a total of 914 CVCs were included. Prevalence and incidence of suspected catheter-related infection were 7.9% and 9.4 infections per 1,000 catheter indwelling days, respectively. Prone ventilation for more than 5 days was associated with increased risk of suspected catheter-related infection; odds ratio, 5.05 (95% confidence interval 2.12-11.0). Risk of death was significantly higher in patients with suspected catheter-related infection (hazard ratio, 1.78; 95% confidence interval, 1.25-2.53). Conclusions: This study shows that in critically ill patients with COVID-19, prevalence and incidence of suspected catheter-related infection are high, prone ventilation is a risk factor, and mortality is higher in case of catheter-related infection

Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: a retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records

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 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.</p

Predictors for extubation failure in COVID-19 patients using a machine learning approach

Introduction Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. Methods We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. Results A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. Conclusion The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care