The daily updated Dutch national database on COVID-19 epidemiology, vaccination and sewage surveillance.

The Dutch national open database on COVID-19 has been incrementally expanded since its start on 30 April 2020 and now includes datasets on symptoms, tests performed, individual-level positive cases and deaths, cases and deaths among vulnerable populations, settings of transmission, hospital and ICU admissions, SARS-CoV-2 variants, viral loads in sewage, vaccinations and the effective reproduction number. This data is collected by municipal health services, laboratories, hospitals, sewage treatment plants, vaccination providers and citizens and is cleaned, analysed and published, mostly daily, by the National Institute for Public Health and the Environment (RIVM) in the Netherlands, using automated scripts. Because these datasets cover the key aspects of the pandemic and are available at detailed geographical level, they are essential to gain a thorough understanding of the past and current COVID-19 epidemiology in the Netherlands. Future purposes of these datasets include country-level comparative analysis on the effect of non-pharmaceutical interventions against COVID-19 in different contexts, such as different cultural values or levels of socio-economic disparity, and studies on COVID-19 and weather factors.

Comparison of outcome and characteristics between 6343 COVID-19 patients and 2256 other community-acquired viral pneumonia patients admitted to Dutch ICUs.

PURPOSE: Describe the differences in characteristics and outcomes between COVID-19 and other viral pneumonia patients admitted to Dutch ICUs. MATERIALS AND METHODS: Data from the National-Intensive-Care-Evaluation-registry of COVID-19 patients admitted between February 15th and January 1th 2021 and other viral pneumonia patients admitted between January 1st 2017 and January 1st 2020 were used. Patients' characteristics, the unadjusted, and adjusted in-hospital mortality were compared. RESULTS: 6343 COVID-19 and 2256 other viral pneumonia patients from 79 ICUs were included. The COVID-19 patients included more male (71.3 vs 49.8%), had a higher Body-Mass-Index (28.1 vs 25.5), less comorbidities (42.2 vs 72.7%), and a prolonged hospital length of stay (19 vs 9 days). The COVID-19 patients had a significantly higher crude in-hospital mortality rate (Odds ratio (OR) = 1.80), after adjustment for patient characteristics and ICU occupancy rate the OR was respectively 3.62 and 3.58. CONCLUSION: Higher mortality among COVID-19 patients could not be explained by patient characteristics and higher ICU occupancy rates, indicating that COVID-19 is more severe compared to other viral pneumonia. Our findings confirm earlier warnings of a high need of ICU capacity and high mortality rates among relatively healthy COVID-19 patients as this may lead to a higher mental workload for the staff.

Trends in practice of blood glucose control in critically ill patients in the Netherlands.

BACKGROUND: Publication of the Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation (NICE-SUGAR) trial in 2009 and several observational studies caused a change in the recommendations for blood glucose control in intensive care patients. We evaluated local trends in blood glucose control in intensive care units in the Netherlands before and after the publication of the NICE-SUGAR trial and the revised Surviving Sepsis Campaign (SSC) guidelines in 2012. METHODS: Survey focusing on the timing of changes in thresholds in local guidelines for blood glucose control and interrupted time-series analysis of patients admitted to seven intensive care units in the Netherlands from September 2008 through July 2014. Statistical process control was used to visualise and analyse trends in metrics for blood glucose control in association with the moment changes became effective. RESULTS: Overall, the mean blood glucose level increased and the median percentage of blood glucose levels within the normoglycaemic range and in the hypoglycaemic range decreased, while the relative proportion of hyperglycaemic measurements increased. Changes in metrics were notable after publication of the NICE-SUGAR trial and the SSC guidelines but more frequent after changes in local guidelines; some changes seemed to appear independent of changes in local guidelines. CONCLUSION: Local guidelines for blood glucose practice have changed in intensive care units in the Netherlands since the publication of the NICE-SUGAR trial and the revised SSC guidelines. Trends in the metrics for blood glucose control suggest new, higher target ranges for blood glucose control.

Recommendations for intensive care follow-up clinics; report from a survey and conference of Dutch intensive cares.

BACKGROUND: With the increasing awareness of postintensive care syndrome and the unbridled development of post-ICU clinics in the Netherlands, guidelines for ICU after care are needed. The purpose of this study was to develop recommendations for the set-up of post-ICU clinics. METHODS: Recommendations regarding the design of post-ICU clinics were formulated based on a survey among Dutch ICUs and the available literature. Subsequently, in a round table conference stakeholders discussed and voted on a final approval of the recommendations. RESULTS: The response rate of our survey was 70% (57 of 82), 40% of the respondents provided ICU after care. Twenty-one people from 16 ICUs participated in the round table conference. Only two studies are available with information on organization and effectiveness of post-ICU clinics. It is recommended to invite patients who are mechanically ventilated for more than 2 days at a post-ICU clinic between 6 and 12 weeks after hospital discharge and screen for physical, psychological and cognitive impairments by using validated electronic patient-reported questionnaires. The set-up of a national registry for benchmarking and research purposes is suggested. CONCLUSION: This study recommends how to organize post-ICU clinics based on literature and expert opinion. The implementation of the recommendations will facilitate the set-up of post-ICU clinics, research on effectiveness of post-ICU clinics and benchmarking of quality of ICU care.

Outcomes of intensive care unit admissions after elective cancer surgery.

BACKGROUND: Postoperative care for major elective cancer surgery is frequently provided on the Intensive Care Unit (ICU). OBJECTIVE: To analyze the characteristics and outcome of patients after ICU admission following elective surgery for different cancer diagnoses. METHODS: We analyzed all ICU admissions following elective cancer surgery in the Netherlands collected in the National Intensive Care Evaluation registry between January 2007 and January 2012. RESULTS: 28,973 patients (9.0% of all ICU admissions; 40% female) were admitted to the ICU after elective cancer surgery. Of these admissions 77% were planned; in 23% of cases the decision for ICU admission was made during or directly after surgery. The most frequent malignancies were colorectal cancer (25.6%), lung cancer (18.5%) and tumors of the central nervous system (14.3%). Mechanical ventilation was necessary in 24.8% of all patients, most frequently after surgery for esophageal (62.5%) and head and neck cancer (50.2%); 20.7% of patients were treated with vasopressors in the acute postoperative phase, in particular after surgery for esophageal cancer (41.8%). The median length of stay on the ICU was 0.9 days (interquartile ranges [IQR] 0.8-1.5); surgery for esophageal cancer was associated with the longest ICU length of stay (median 2.0 days) with the largest variation (IQR 1.0-4.8 days). ICU mortality was 1.4%; surgery for gastrointestinal cancer was associated with the highest ICU mortality (colorectal cancer 2.2%, pancreatico-cholangiocarcinoma 2.0%). CONCLUSION: Elective cancer surgery represents a significant part of all ICU admissions, with a short length of stay and low mortality.

A usability evaluation of a SNOMED CT based compositional interface terminology for intensive care.

OBJECTIVE: To evaluate the usability of a large compositional interface terminology based on SNOMED CT and the terminology application for registration of the reasons for intensive care admission in a Patient Data Management System. DESIGN: Observational study with user-based usability evaluations before and 3 months after the system was implemented and routinely used. MEASUREMENTS: Usability was defined by five aspects: effectiveness, efficiency, learnability, overall user satisfaction, and experienced usability problems. Qualitative (the Think-Aloud user testing method) and quantitative (the System Usability Scale questionnaire and Time-on-Task analyses) methods were used to examine these usability aspects. RESULTS: The results of the evaluation study revealed that the usability of the interface terminology fell short (SUS scores before and after implementation of 47.2 out of 100 and 37.5 respectively out of 100). The qualitative measurements revealed a high number (n=35) of distinct usability problems, leading to ineffective and inefficient registration of reasons for admission. The effectiveness and efficiency of the system did not change over time. About 14% (n=5) of the revealed usability problems were related to the terminology content based on SNOMED CT, while the remaining 86% (n=30) was related to the terminology application. The problems related to the terminology content were more severe than the problems related to the terminology application. CONCLUSIONS: This study provides a detailed insight into how clinicians interact with a controlled compositional terminology through a terminology application. The extensiveness, complexity of the hierarchy, and the language usage of an interface terminology are defining for its usability. Carefully crafted domain-specific subsets and a well-designed terminology application are needed to facilitate the use of a complex compositional interface terminology based on SNOMED CT.

Construction of an interface terminology on SNOMED CT. Generic approach and its application in intensive care.

OBJECTIVE: To provide a generic approach for developing a domain-specific interface terminology on SNOMED CT and to apply this approach to the domain of intensive care. METHODS: The process of developing an interface terminology on SNOMED CT can be regarded as six sequential phases: domain analysis, mapping from the domain concepts to SNOMED CT concepts, creating the SNOMED CT subset guided by the mapping, extending the subset with non-covered concepts, constraining the subset by removing irrelevant content, and deploying the subset in a terminology server. RESULTS: The APACHE IV classification, a standard in the intensive care with 445 diagnostic categories, served as the starting point for designing the interface terminology. The majority (89.2%) of the diagnostic categories from APACHE IV could be mapped to SNOMED CT concepts and for the remaining concepts a partial match was identified. The resulting initial set of mapped concepts consisted of 404 SNOMED CT concepts. This set could be extended to 83,125 concepts if all taxonomic children of these concepts were included. Also including all concepts that are referred to in the definition of other concepts lead to a subset of 233,782 concepts. An evaluation of the interface terminology should reveal what level of detail in the subset is suitable for the intensive care domain and whether parts need further constraining. In the final phase, the interface terminology is implemented in the intensive care in a locally developed terminology server to collect the reasons for intensive care admission. CONCLUSIONS: We provide a structure for the process of identifying a domain-specific interface terminology on SNOMED CT. We use this approach to design an interface terminology on SNOMED CT for the intensive care domain. This work is of value for other researchers who intend to build a domain-specific interface terminology on SNOMED CT.

Cross-mapping APACHE IV "reasons for intensive care admission" classification to SNOMED CT.

The APACHE IV classification is used to capture diagnostic information for calculation of mortality risks in intensive care (IC). The lack of structured and formal definitions for concepts in APACHE IV classification as in any classification results in shortcomings when scaling up for re-use. The use of SNOMED CT as a reference terminology can address these shortcomings. However, all of SNOMED CT contains large amounts of information that is irrelevant for IC. By building an interface terminology (IfT) based on SNOMED CT and APACHE IV, it is possible to isolate the IC users from the complexity of SNOMED CT while enabling standardized data registration. Within this study, a mapping is realized from the APACHE IV classification to SNOMED CT. The results of the mapping will be used to identify a relevant SNOMED CT subset for the development of an IC-specific IfT. The vast majority of the diagnostic categories in APACHE IV could be mapped to one or more SNOMED CT concepts (83.8%) and for the remaining concept a partial match was identified (16%). The good mapping results will provide a SNOMED CT subset sufficient for developing an IC-specific IfT. Finally, lessons learned in this study are valuable for other researchers who intend to realize a mapping from a classification to SNOMED CT.