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This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.

BackgroundAdenosine can be used to reveal dormant pulmonary vein (PV) conduction after PV isolation (PVI). This study presents a subanalysis of real-world 1-year follow-up data from the ESC-EHRA EORP Atrial Fibrillation (AF) Ablation Long-Term registry to analyze the usage of adenosine during PVI treatment in terms of rhythm outcome and safety.MethodsThe registry consists of 104 participating centers in 27 countries within the European Society of Cardiology. The registry data was split into an adenosine group (AG) and no-adenosine group (NAG). Procedure characteristics and patient outcome were compared.ResultsAdenosine was administered in 10.8% of the 3591 PVI patients included in the registry. Spain, the Netherlands, and Italy included the majority of adenosine cases (48.8%). Adenosine was applied more often in combination with open irrigation radiofrequency (RF) energy (74.7%) and less often in combination with nonirrigated RF energy (1.6%). After 1 year, a higher percentage of the AG was free from AF compared with the NAG (68.9% vs 59.1%, p < 0.001). Adenosine was associated with better rhythm outcome in RF ablation procedures, but not in cryo-ablation procedures (freedom from AF: RF: AG: 70.9%, NAG: 58.1%, p < 0.001, cryo: AG: 63.9%, NAG: 63.8%, p = 0.991).ConclusionsThe use of adenosine was associated with a better rhythm outcome after 1 year follow-up and seems more useful in patients treated with RF energy compared with patients treated with cryo energy. Given the improved rhythm outcome at 1-year follow-up, it seems reasonable to encourage the use of adenosine during RF AF ablation.

Data-driven decision support tools have been increasingly recognized to transform health care. However, such tools are often developed on predefined research datasets without adequate knowledge of the origin of this data and how it was selected. How a dataset is extracted from a clinical database can profoundly impact the validity, interpretability and interoperability of the dataset, and downstream analyses, yet is rarely reported. Therefore, we present a case study illustrating how a definitive patient list was extracted from a clinical source database and how this can be reported. A single-center observational study was performed at an academic hospital in the Netherlands to illustrate the impact of selecting a definitive patient list for research from a clinical source database, and the importance of documenting this process. All admissions from the critical care database admitted between January 1, 2013, and January 1, 2023, were used. An interdisciplinary team collaborated to identify and address potential sources of data insufficiency and uncertainty. We demonstrate a stepwise data preparation process, reducing the clinical source database of 54,218 admissions to a definitive patient list of 21,553 admissions. Transparent documentation of the data preparation process improves the quality of the definitive patient list before analysis of the corresponding patient data. This study generated seven important recommendations for preparing observational health-care data for research purposes. Documenting data preparation is essential for understanding a research dataset originating from a clinical source database before analyzing health-care data. The findings contribute to establishing data standards and offer insights into the complexities of preparing health-care data for scientific investigation. Meticulous data preparation and documentation thereof will improve research validity and advance critical care.

Myocardial injury in COVID-19 is associated with in-hospital mortality. However, the development of myocardial injury over time and whether myocardial injury in patients with COVID-19 at the intensive care unit is associated with outcome is unclear. This study prospectively investigates myocardial injury with serial measurements over the full course of intensive care unit admission in mechanically ventilated patients with COVID-19. As part of the prospective Maastricht Intensive Care COVID cohort, predefined myocardial injury markers, including high-sensitivity cardiac troponin T (hs-cTnT), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and electrocardiographic characteristics were serially collected in mechanically ventilated patients with COVID-19. Linear mixed-effects regression was used to compare survivors with nonsurvivors, adjusting for gender, age, APACHE-II score, daily creatinine concentration, hypertension, diabetes mellitus, and obesity. In 90 patients, 57 (63%) were survivors and 33 (37%) nonsurvivors, and a total of 628 serial electrocardiograms, 1,565 hs-cTnT, and 1,559 NT-proBNP concentrations were assessed. Log-hs-cTnT was lower in survivors compared with nonsurvivors at day 1 (β −0.93 [−1.37; −0.49], p <0.001) and did not change over time. Log-NT-proBNP did not differ at day 1 between both groups but decreased over time in the survivor group (β −0.08 [−0.11; −0.04] p <0.001) compared with nonsurvivors. Many electrocardiographic abnormalities were present in the whole population, without significant differences between both groups. In conclusion, baseline hs-cTnT and change in NT-proBNP were strongly associated with mortality. Two-thirds of patients with COVID-19 showed electrocardiographic abnormalities. Our serial assessment suggests that myocardial injury is common in mechanically ventilated patients with COVID-19 and is associated with outcome.

Models predicting in-hospital mortality for intensive care unit (ICU) patients, like Acute Physiologic and Chronic Health Evaluation (APACHE) IV, depend on correct data about admission diagnoses. Incorrectly recording diagnoses or changes in diagnoses during admission may impact estimating mortality. All deceased patients admitted to the ICU between Jan 1, 2018 and Dec 31, 2020 were included. Up to two discharge diagnoses were assigned using APACHE IV diagnostic codes. These were compared to the up to two diagnoses documented at admission. When differences occurred, these were classified as registration errors or diagnostic change. The APACHE IV predicted mortality was calculated using either admission or discharge diagnoses. The agreement between both methods was expressed as the mean difference and the intra-class correlation coefficient (ICC). 886 (16 %) of 5633 patients died. In 363 (41 %) patients, there was no difference between admission and discharge diagnoses. Registration errors occurred in 138 (16 %) patients. 416 (47 %) patients had diagnostic change. The mean difference between predictions was 0.019 (95 % CI: 0.015–0.024). The ICC was 0.97 (95 % CI: 0.97–0.98). Differences between ICU admission and discharge diagnoses occur frequently, this leads to only a small discrepancy in the overall predicted mortality of deceased ICU patients. •Registration errors of admission diagnoses occur in 16 % of deceased ICU patients.•Diagnostic change of up to two admission diagnoses occurred in nearly half of non-surviving ICU patients.•Registration errors and diagnostic change had a negligible effect on benchmarking using the APACHE IV predicted mortality.

To describe the effect of dexamethasone and tocilizumab on regional lung mechanics over admission in all mechanically ventilated COVID-19 patients. Dynamic compliance, alveolar overdistension and collapse were serially determined using electric impedance tomography (EIT). Patients were categorized into three groups; no anti-inflammatory therapy, dexamethasone therapy, dexamethasone + tocilizumab therapy. The EIT variables were (I) visualized using polynomial regression, (II) evaluated throughout admission using linear mixed-effects models, and (III) average respiratory variables were compared. Visual inspection of EIT variables showed a pattern of decreasing dynamic compliance. Overall, optimal set PEEP was lower in the dexamethasone group (−1.4 cmH2O, −2.6; −0.2). Clinically applied PEEP was lower in the dexamethasone and dexamethasone + tocilizumab group (−1.5 cmH2O, −2.6; −0.2; −2.2 cmH2O, −5.1; 0.6). Dynamic compliance, alveolar overdistension, and alveolar collapse at optimal set PEEP did not significantly differ between the three groups. Optimal and clinically applied PEEP were lower in the dexamethasone and dexamethasone + tocilizumab groups. The results suggest that the potential beneficial effects of these therapies do not affect lung mechanics favorably. However, this study cannot fully rule out any beneficial effect of anti-inflammatory treatment on pulmonary function due to its observational nature. •This study provides further insights into respiratory outcomes in mechanically ventilated COVID-19 patients.•Focus on visualization of alveolar overdistension, -collapse and dynamic compliance over time.•Optimal and clinically applied PEEP were lower in the dexamethasone and dexamethasone + tocilizumab group.

Neurological complications in COVID-19 patients admitted to an intensive care unit (ICU) have been previously reported. As the pandemic progressed, therapeutic strategies were tailored to new insights. This study describes the incidence, outcome, and types of reported neurological complications in invasively mechanically ventilated (IMV) COVID-19 patients in relation to three periods during the pandemic. IMV COVID-19 ICU patients from the Dutch Maastricht Intensive Care COVID (MaastrICCht) cohort were included in a single-center study (March 2020 – October 2021). Demographic, clinical, and follow-up data were collected. Electronic medical records were screened for neurological complications during hospitalization. Three distinct periods (P1, P2, P3) were defined, corresponding to periods with high hospitalization rates. ICU survivors with and without reported neurological complications were compared in an exploratory analysis. IMV COVID-19 ICU patients (n=324; median age 64 [IQR 57–72] years; 238 males (73.5%)) were stratified into P1 (n=94), P2 (n=138), and P3 (n=92). ICU mortality did not significantly change over time (P1=38.3%; P2=41.3%; P3=37.0%; p=.787). The incidence of reported neurological complications during ICU admission gradually decreased over the periods (P1=29.8%; P2=24.6%; P3=18.5%; p=.028). Encephalopathy/delirium (48/324 (14.8%)) and ICU-acquired weakness (32/324 (9.9%)) were most frequently reported and associated with ICU treatment intensity. ICU survivors with neurological complications (n=53) were older (p=.025), predominantly male (p=.037), and had a longer duration of IMV (p<.001) and ICU stay (p<.001), compared to survivors without neurological complications (n=132). A multivariable analysis revealed that only age was independently associated with the occurrence of neurological complications (ORadj=1.0541; 95% CI=1.0171–1.0925; p=.004). Health-related quality-of-life at follow-up was not significantly different between survivors with and without neurological complications (n = 82, p=.054). A high but decreasing incidence of neurological complications was reported during three consecutive COVID-19 periods in IMV COVID-19 patients. Neurological complications were related to the intensity of ICU support and treatment, and associated with prolonged ICU stay, but did not lead to significantly worse reported health-related quality-of-life at follow-up. •Neurological complications are common in mechanically ventilated COVID-19 patients.•The most common neurological complications are delirium and ICU-acquired weakness.•The incidence of neurological complications was highest early in the pandemic.•Neurological complications were related to the intensity of ICU support/treatment.

Background Data on hyperglycemia and glucose variability in relation to diabetes mellitus, either known or unknown in ICU-setting in COVID-19, are scarce. We prospectively studied daily glucose variables and mortality in strata of diabetes mellitus and glycosylated hemoglobin among mechanically ventilated COVID-19 patients. Methods We used linear-mixed effect models in mechanically ventilated COVID-19 patients to investigate mean and maximum difference in glucose concentration per day over time. We compared ICU survivors and non-survivors and tested for effect-modification by pandemic wave 1 and 2, diabetes mellitus, and admission HbA1c. Results Among 232 mechanically ventilated COVID-19 patients, 21.1% had known diabetes mellitus, whereas 16.9% in wave 2 had unknown diabetes mellitus. Non-survivors had higher mean glucose concentrations (ß 0.62 mmol/l; 95%CI 0.20–1.06; ß 11.2 mg/dl; 95% CI 3.6–19.1; P  = 0.004) and higher maximum differences in glucose concentrations per day (ß 0.85 mmol/l; 95%CI 0.37–1.33; ß 15.3; 95%CI 6.7–23.9; P  = 0.001). Effect modification by wave, history of diabetes mellitus and admission HbA1c in associations between glucose and survival was not present. Effect of higher mean glucose concentrations was modified by pandemic wave (wave 1 (ß 0.74; 95% CI 0.24–1.23 mmol/l) ; (ß 13.3; 95%CI 4.3–22.1 mg/dl)) vs. (wave 2 (ß 0.37 (95%CI 0.25–0.98) mmol/l) (ß 6.7 (95% ci 4.5–17.6) mg/dl)). Conclusions Hyperglycemia and glucose variability are associated with mortality in mechanically ventilated COVID-19 patients irrespective of the presence of diabetes mellitus.

BackgroundCoronary artery calcification (CAC) is associated with poor outcome in critically ill patients. A deterioration in cardiac conduction and loss of myocardial tissue could be an underlying cause. Vectorcardiography (VCG) and cardiac biomarkers provide insight into these underlying causes. The aim of this study was to investigate whether a high degree of CAC is associated with VCG-derived variables and biomarkers, including high-sensitivity troponin-T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP).MethodsMechanically ventilated coronavirus-19 (COVID-19) patients with an available chest computed tomography (CT) and 12-lead electrocardiogram (ECG) were studied. CAC scores were determined using chest CT scans. Patients were categorized into 3 sex-specific tertiles: low, intermediate, and high CAC. Daily 12 leads-ECGs were converted to VCGs. Daily hs-cTnT and NT-proBNP levels were determined. Linear mixed-effects regression models examined the associations between CAC tertiles and VCG variables, and between CAC tertiles and hs-cTnT or NT-proBNP levels.ResultsIn this study, 205 patients (73.2% men, median age 65 years [IQR 57.0; 71.0]) were included. Compared to the lowest CAC tertile, the highest CAC tertile had a larger QRS area at baseline (6.65 µVs larger [1.50; 11.81], p = 0.012), which decreased during admission (− 0.27 µVs per day [− 0.43; − 0.11], p = 0.001). Patients with the highest CAC tertile also had a longer QRS duration (12.02 ms longer [4.74; 19.30], p = 0.001), higher levels of log hs-cTnT (0.79 ng/L higher [0.40; 1.19], p < 0.001) and log NT-proBNP (0.83 pmol/L higher [0.30; 1.37], p = 0.002).ConclusionPatients with a high degree of CAC had the largest QRS area and higher QRS amplitude, which decreased more over time when compared to patients with a low degree of CAC. These results suggest that CAC might contribute to loss of myocardial tissue during critical illness. These insights could improve risk stratification and prognostication of patients with critical illness.