BackgroundAn asymptomatic respiratory viral infection during cardiac surgery could lead to pulmonary complications and increased mortality. For elective surgery, testing for respiratory viral... Show moreBackgroundAn asymptomatic respiratory viral infection during cardiac surgery could lead to pulmonary complications and increased mortality. For elective surgery, testing for respiratory viral infection before surgery or vaccination could reduce the number of these pulmonary complications. The aim of this study was to investigate the association between influenzalike illness (ILI) seasons and prolonged mechanical ventilation and inhospital mortality in a Dutch cohort of adult elective cardiac surgery patients.MethodsCardiac surgery patients who were admitted to the intensive care unit between January 1, 2014, and February 1, 2020, were included. The primary endpoint was the duration of invasive mechanical ventilation in the ILI season compared with baseline season. Secondary endpoints were the median Pao2 to fraction of inspired oxygen ratio on days 1, 3, and 7 and postoperative inhospital mortality.ResultsA total of 42,277 patients underwent cardiac surgery, 12,994 (30.7%) in the ILI season, 15,843 (37.5%) in the intermediate season, and 13,440 (31.8%) in the baseline season. No hazard rates indicative of a longer duration of invasive mechanical ventilation during the ILI season were found. No differences were found for the median Pao2 to fraction of inspired oxygen ratio between seasons. However, inhospital mortality was higher in the ILI season compared with baseline season (odds ratio 1.67; 95% CI, 1.14-2.46).ConclusionsPatients undergoing cardiac surgery during the ILI season were at increased risk of inhospital mortality compared with patients in the baseline season. No evidence was found that this difference is caused by direct postoperative pulmonary complications. Show less
Swets, M.C.; Termorshuizen, F.; Keizer, N.F. de; Paassen, J. van; Palmen, M.; Visser, L.G.; ... ; Groeneveld, G.H. 2023
BackgroundAn asymptomatic respiratory viral infection during cardiac surgery could lead to pulmonary complications and increased mortality. For elective surgery, testing for respiratory viral... Show moreBackgroundAn asymptomatic respiratory viral infection during cardiac surgery could lead to pulmonary complications and increased mortality. For elective surgery, testing for respiratory viral infection before surgery or vaccination could reduce the number of these pulmonary complications. The aim of this study was to investigate the association between influenzalike illness (ILI) seasons and prolonged mechanical ventilation and inhospital mortality in a Dutch cohort of adult elective cardiac surgery patients.MethodsCardiac surgery patients who were admitted to the intensive care unit between January 1, 2014, and February 1, 2020, were included. The primary endpoint was the duration of invasive mechanical ventilation in the ILI season compared with baseline season. Secondary endpoints were the median Pao2 to fraction of inspired oxygen ratio on days 1, 3, and 7 and postoperative inhospital mortality.ResultsA total of 42,277 patients underwent cardiac surgery, 12,994 (30.7%) in the ILI season, 15,843 (37.5%) in the intermediate season, and 13,440 (31.8%) in the baseline season. No hazard rates indicative of a longer duration of invasive mechanical ventilation during the ILI season were found. No differences were found for the median Pao2 to fraction of inspired oxygen ratio between seasons. However, inhospital mortality was higher in the ILI season compared with baseline season (odds ratio 1.67; 95% CI, 1.14-2.46).ConclusionsPatients undergoing cardiac surgery during the ILI season were at increased risk of inhospital mortality compared with patients in the baseline season. No evidence was found that this difference is caused by direct postoperative pulmonary complications. Show less
Rationale: Supplemental oxygen is widely administered to ICU patients, but appropriate oxygenation targets remain unclear.Objectives: This study aimed to determine whether a low-oxygenation... Show moreRationale: Supplemental oxygen is widely administered to ICU patients, but appropriate oxygenation targets remain unclear.Objectives: This study aimed to determine whether a low-oxygenation strategy would lower 28-day mortality compared with a high-oxygenation strategy.Methods: This randomized multicenter trial included mechanically ventilated ICU patients with an expected ventilation duration of at least 24 hours. Patients were randomized 1:1 to a low-oxygenation (Pa-O2, 55-80mmHg; or oxygen saturation as measured by pulse oximetry, 91-94%) or high-oxygenation (Pa-O2, 110-150mmHg; or oxygen saturation as measured by pulse oximetry, 96-100%) target until ICU discharge or 28 days after randomization, whichever came first. The primary outcome was 28-day mortality. The study was stopped prematurely because of the COVID-19 pandemic when 664 of the planned 1,512 patients were included.Measurements and Main Results: Between November 2018 and November 2021, a total of 664 patients were included in the trial: 335 in the low-oxygenation group and 329 in the high-oxygenation group. The median achieved Pa-O2 was 75mmHg (interquartile range, 70-84) and 115mmHg (interquartile range, 100-129) in the low- and high-oxygenation groups, respectively. At Day 28, 129 (38.5%) and 114 (34.7%) patients had died in the low- and high-oxygenation groups, respectively (risk ratio, 1.11; 95% confidence interval, 0.9-1.4; P = 0.30). At least one serious adverse event was reported in 12 (3.6%) and 17 (5.2%) patients in the low- and high-oxygenation groups, respectively.Conclusions: Among mechanically ventilated ICU patients with an expected mechanical ventilation duration of at least 24 hours, using a low-oxygenation strategy did not result in a reduction of 28-day mortality compared with a high-oxygenation strategy. Show less
Background Large clinical trials on drugs for hospitalized coronavirus disease 2019 (COVID-19) patients have shown significant effects on mortality. There may be a discrepancy with the observed... Show moreBackground Large clinical trials on drugs for hospitalized coronavirus disease 2019 (COVID-19) patients have shown significant effects on mortality. There may be a discrepancy with the observed real-world effect. We describe the clinical characteristics and outcomes of hospitalized COVID-19 patients in the Netherlands during 4 pandemic waves and analyze the association of the newly introduced treatments with mortality, intensive care unit (ICU) admission, and discharge alive. Methods We conducted a nationwide retrospective analysis of hospitalized COVID-19 patients between February 27, 2020, and December 31, 2021. Patients were categorized into waves and into treatment groups (hydroxychloroquine, remdesivir, neutralizing severe acute respiratory syndrome coronavirus 2 monoclonal antibodies, corticosteroids, and interleukin [IL]-6 antagonists). Four types of Cox regression analyses were used: unadjusted, adjusted, propensity matched, and propensity weighted. Results Among 5643 patients from 11 hospitals, we observed a changing epidemiology during 4 pandemic waves, with a decrease in median age (67-64 years; P < .001), in in-hospital mortality on the ward (21%-15%; P < .001), and a trend in the ICU (24%-16%; P = .148). In ward patients, hydroxychloroquine was associated with increased mortality (1.54; 95% CI, 1.22-1.96), and remdesivir was associated with a higher rate of discharge alive within 29 days (1.16; 95% CI, 1.03-1.31). Corticosteroids were associated with a decrease in mortality (0.82; 95% CI, 0.69-0.96); the results of IL-6 antagonists were inconclusive. In patients directly admitted to the ICU, hydroxychloroquine, corticosteroids, and IL-6 antagonists were not associated with decreased mortality. Conclusions Both remdesivir and corticosteroids were associated with better outcomes in ward patients with COVID-19. Continuous evaluation of real-world treatment effects is needed. Show less
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
Candel, B.G.J.; Raven, W.; Lameijer, H.; Thijssen, W.A.M.H.; Temorshuizen, F.; Boerma, C.; ... ; Groot, B. de 2022
Background Treatment and the clinical course during Emergency Department (ED) stay before Intensive Care Unit (ICU) admission may affect predicted mortality risk calculated by the Acute Physiology... Show moreBackground Treatment and the clinical course during Emergency Department (ED) stay before Intensive Care Unit (ICU) admission may affect predicted mortality risk calculated by the Acute Physiology and Chronic Health Evaluation (APACHE)-IV, causing lead-time bias. As a result, comparing standardized mortality ratios (SMRs) among hospitals may be difficult if they differ in the location where initial stabilization takes place. The aim of this study was to assess to what extent predicted mortality risk would be affected if the APACHE-IV score was recalculated with the initial physiological variables from the ED. Secondly, to evaluate whether ED Length of Stay (LOS) was associated with a change (delta) in these APACHE-IV scores. Methods An observational multicenter cohort study including ICU patients admitted from the ED. Data from two Dutch quality registries were linked: the Netherlands Emergency department Evaluation Database (NEED) and the National Intensive Care Evaluation (NICE) registry. The ICU APACHE-IV, predicted mortality, and SMR based on data of the first 24 h of ICU admission were compared with an ED APACHE-IV model, using the most deviating physiological variables from the ED or ICU. Results A total of 1398 patients were included. The predicted mortality from the ICU APACHE-IV (median 0.10; IQR 0.03-0.30) was significantly lower compared to the ED APACHE-IV model (median 0.13; 0.04-0.36; p < 0.01). The SMR changed from 0.63 (95%CI 0.54-0.72) to 0.55 (95%CI 0.47-0.63) based on ED APACHE-IV. Predicted mortality risk changed more than 5% in 321 (23.2%) patients by using the ED APACHE-IV. ED LOS > 3.9 h was associated with a slight increase in delta APACHE-IV of 1.6 (95% CI 0.4-2.8) compared to ED LOS < 1.7 h. Conclusion Predicted mortality risks and SMRs calculated by the APACHE IV scores are not directly comparable in patients admitted from the ED if hospitals differ in their policy to stabilize patients in the ED before ICU admission. Future research should focus on developing models to adjust for these differences. Show less
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... Show morePurpose: 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. (c) 2021 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
Background To assess trends in the quality of care for COVID-19 patients at the ICU over the course of time in the Netherlands. Methods Data from the National Intensive Care Evaluation (NICE)... Show moreBackground To assess trends in the quality of care for COVID-19 patients at the ICU over the course of time in the Netherlands. Methods Data from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and indicators of quality of care during the first two upsurges (N = 4215: October 5, 2020-January 31, 2021) and the final upsurge of the second wave, called the 'third wave' (N = 4602: February 1, 2021-June 30, 2021) were compared with those during the first wave (N = 2733, February-May 24, 2020). Results During the second and third wave, there were less patients treated with mechanical ventilation (58.1 and 58.2%) and vasoactive drugs (48.0 and 44.7%) compared to the first wave (79.1% and 67.2%, respectively). The occupancy rates as fraction of occupancy in 2019 (1.68 and 1.55 vs. 1.83), the numbers of ICU relocations (23.8 and 27.6 vs. 32.3%) and the mean length of stay at the ICU (HRs of ICU discharge = 1.26 and 1.42) were lower during the second and third wave. No difference in adjusted hospital mortality between the second wave and the first wave was found, whereas the mortality during the third wave was considerably lower (OR = 0.80, 95% CI [0.71-0.90]). Conclusions These data show favorable shifts in the treatment of COVID-19 patients at the ICU over time. The adjusted mortality decreased in the third wave. The high ICU occupancy rate early in the pandemic does probably not explain the high mortality associated with COVID-19. Show less
Grim, C.C.A.; Termorshuizen, F.; Bosman, R.J.; Cremer, O.L.; Meinders, A.J.; Nijsten, M.W.N.; ... ; Jonge, E. de 2021
OBJECTIVES: In critically ill patients, dysnatremia is common, and in these patients, in-hospital mortality is higher. It remains unknown whether changes of serum sodium after ICU admission affect... Show moreOBJECTIVES: In critically ill patients, dysnatremia is common, and in these patients, in-hospital mortality is higher. It remains unknown whether changes of serum sodium after ICU admission affect mortality, especially whether normalization of mild hyponatremia improves survival. DESIGN: Retrospective cohort study. SETTING: Ten Dutch ICUs between January 2011 and April 2017. Patients: Adult patients were included if at least one serum sodium measurement within 24 hours of ICU admission and at least one serum sodium measurement 24-48 hours after ICU admission were available. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A logistic regression model adjusted for age, sex, and Acute Physiology and Chronic Health Evaluation-IV-predicted mortality was used to assess the difference between mean of sodium measurements 24-48 hours after ICU admission and first serum sodium measurement at ICU admission (Delta 48 hr-[Na]) and in-hospital mortality. In total, 36,660 patients were included for analysis. An increase in serum sodium was independently associated with a higher risk of in-hospital mortality in patients admitted with normonatremia (Delta 48 hr-[Na] 5-10 mmol/L odds ratio: 1.61 [1.44-1.79], Delta 48 hr-[Na] > 10 mmol/L odds ratio: 4.10 [3.20-5.24]) and hypernatremia (Delta 48 hr-[Na] 5-10 mmol/L odds ratio: 1.47 [1.02-2.14], Delta 48 hr-[Na] > 10 mmol/L odds ratio: 8.46 [3.31-21.64]). In patients admitted with mild hyponatremia and Delta 48 hr-[Na] greater than 5 mmol/L, no significant difference in hospital mortality was found (odds ratio, 1.11 [0.99-1.25]). CONCLUSIONS: An increase in serum sodium in the first 48 hours of ICU admission was associated with higher in-hospital mortality in patients admitted with normonatremia and in patients admitted with hypernatremia. 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
Huijben, J.A.; Wiegers, E.J.A.; Ercole, A.; Keizer, N.F. de; Maas, A.I.R.; Steyerberg, E.W.; ... ; Jagt, M. van der 2020
Background The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units... Show moreBackground The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measurement and improvement. Methods Our analysis was based on 2006 adult patients admitted to 54 ICUs between 2014 and 2018, enrolled in the CENTER-TBI study. Indicator scores were calculated as percentage adherence for structure and process indicators and as event rates or median scores for outcome indicators. Feasibility was quantified by the completeness of the variables. Discriminability was determined by the between-centre variation, estimated with a random effect regression model adjusted for case-mix severity and quantified by the median odds ratio (MOR). Statistical uncertainty of outcome indicators was determined by the median number of events per centre, using a cut-off of 10. Results A total of 26/42 indicators could be calculated from the CENTER-TBI database. Most quality indicators proved feasible to obtain with more than 70% completeness. Sub-optimal adherence was found for most quality indicators, ranging from 26 to 93% and 20 to 99% for structure and process indicators. Significant (p < 0.001) between-centre variation was found in seven process and five outcome indicators with MORs ranging from 1.51 to 4.14. Statistical uncertainty of outcome indicators was generally high; five out of seven had less than 10 events per centre. Conclusions Overall, nine structures, five processes, but none of the outcome indicators showed potential for quality improvement purposes for TBI patients in the ICU. Future research should focus on implementation efforts and continuous reevaluation of quality indicators. Show less
Groenland, C.N.; Termorshuizen, F.; Rietdijk, W.J.R.; Brule, J. van den; Dongelmans, D.A.; Jonge, E. de; ... ; Uil, C.A. den 2019
Objectives: To compare methods to adjust for confounding by disease severity during multicenter intervention studies in ICU, when different disease severity measures are collected across centers.... Show moreObjectives: To compare methods to adjust for confounding by disease severity during multicenter intervention studies in ICU, when different disease severity measures are collected across centers. Design: In silico simulation study using national registry data. Setting: Twenty mixed ICUs in The Netherlands. Subjects: Fifty-five-thousand six-hundred fifty-five ICU admissions between January 1, 2011, and January 1, 2016. Interventions: None. Measurements and Main Results: To mimic an intervention study with confounding, a fictitious treatment variable was simulated whose effect on the outcome was confounded by Acute Physiology and Chronic Health Evaluation IV predicted mortality (a common measure for disease severity). Diverse, realistic scenarios were investigated where the availability of disease severity measures (i.e., Acute Physiology and Chronic Health Evaluation IV, Acute Physiology and Chronic Health Evaluation II, and Simplified Acute Physiology Score II scores) varied across centers. For each scenario, eight different methods to adjust for confounding were used to obtain an estimate of the (fictitious) treatment effect. These were compared in terms of relative (%) and absolute (odds ratio) bias to a reference scenario where the treatment effect was estimated following correction for the Acute Physiology and Chronic Health Evaluation IV scores from all centers. Complete neglect of differences in disease severity measures across centers resulted in bias ranging from 10.2% to 173.6% across scenarios, and no commonly used methodology-such as two-stage modeling or score standardization-was able to effectively eliminate bias. In scenarios where some of the included centers had (only) Acute Physiology and Chronic Health Evaluation II or Simplified Acute Physiology Score II available (and not Acute Physiology and Chronic Health Evaluation IV), either restriction of the analysis to Acute Physiology and Chronic Health Evaluation IV centers alone or multiple imputation of Acute Physiology and Chronic Health Evaluation IV scores resulted in the least amount of relative bias (0.0% and 5.1% for Acute Physiology and Chronic Health Evaluation II, respectively, and 0.0% and 4.6% for Simplified Acute Physiology Score II, respectively). In scenarios where some centers used Acute Physiology and Chronic Health Evaluation II, regression calibration yielded low relative bias too (relative bias, 12.4%); this was not true if these same centers only had Simplified Acute Physiology Score II available (relative bias, 54.8%). Conclusions: When different disease severity measures are available across centers, the performance of various methods to control for confounding by disease severity may show important differences. When planning multicenter studies, researchers should make contingency plans to limit the use of or properly incorporate different disease measures across centers in the statistical analysis. Show less
Roos-Blom, M.J.; Gude, W.T.; Spijkstra, J.J.; Jonge, E. de; Dongelmans, D.; Keizer, N.F. de 2019
