17 research outputs found
Case Report: Sublingual Microcirculatory Alterations in a Covid-19 Patient With Subcutaneous Emphysema, Venous Thrombosis, and Pneumomediastinum
No full textThe Corona virus disease 2019 (Covid-19) has brought a wide range of challenges in intensive care medicine. Understanding of the pathophysiology of Covid-19 relies on interpreting of its impact on the vascular, particularly microcirculatory system. Herein we report on the first use of the latest generation hand-held vital microscope to evaluate the sublingual microcirculation in a Covid-19 patient with subcutaneous emphysema, venous thrombosis and pneumomediastinum. Remarkably, microcirculatory parameters of the patient were increased during the exacerbation period, which is not a usual finding in critically ill patients mostly presenting with a loss of hemodynamic coherence. In contrast, recovery from the disease led to a subsequent amelioration of these parameters. This report clearly shows the importance of microcirculatory monitoring for evaluating the course and the adequacy of therapy in Covid-19 patients
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
No full textPurpose : 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
Effect of a lower vs higher positive end-expiratory pressure strategy on ventilator-free days in ICU patients without ARDS: A randomized clinical trial
No full textIMPORTANCE It is uncertain whether invasive ventilation can use lower positive end-expiratory pressure (PEEP) in critically ill patients without acute respiratory distress syndrome (ARDS). OBJECTIVE To determine whether a lower PEEP strategy is noninferior to a higher PEEP strategy regarding duration of mechanical ventilation at 28 days. DESIGN, SETTING, AND PARTICIPANTS Noninferiority randomized clinical trial conducted from October 26, 2017, through December 17, 2019, in 8 intensive care units (ICUs) in the Netherlands among 980 patients without ARDS expected not to be extubated within 24 hours after start of ventilation. Final follow-up was conducted in March 2020. INTERVENTIONS Participants were randomized to receive invasive ventilation using either lower PEEP, consisting of the lowest PEEP level between 0 and 5 cm H2O (n = 476), or higher PEEP, consisting of a PEEP level of 8 cm H2O (n = 493). MAIN OUTCOMES AND MEASURES The primary outcome was the number of ventilator-free days at day 28, with a noninferiority margin for the difference in ventilator-free days at day 2 of −10%. Secondary outcomes included ICU and hospital lengths of stay; ICU, hospital, and 28- and 90-day mortality; development of ARDS, pneumonia, pneumothorax, severe atelectasis, severe hypoxemia, or need for rescue therapies for hypoxemia; and days with use of vasopressors or sedation. RESULTS Among 980 patients who were randomized, 969 (99%) completed the trial (median age, 66 [interquartile range {IQR}, 56-74] years; 246 [36%] women). At day 28, 476 patients in the lower PEEP group had a median of 18 ventilator-free days (IQR, 0-27 days) an 493 patients in the higher PEEP group had a median of 17 ventilator-free days (IQR, 0-27 days) (mean ratio, 1.04; 95% CI, 0.95-∞; P = .007 for noninferiority), and the lower boundary of the 95% CI was within the noninferiority margin. Occurrence of severe hypoxemia was 20.6% vs 17.6% (risk ratio, 1.17; 95% CI, 0.90-1.51; P = .99) and need for rescue strategy was 19.7% vs 14.6% (risk ratio, 1.35; 95% CI, 1.02-1.79; adjusted P = .54) in patients in the lower and higher PEEP groups, respectively. Mortality at 28 days was 38.4% vs 42.0% (hazard ratio 0.89; 95% CI, 0.73-1.09; P = .99) in patients in the lower and higher PEEP groups, respectively. There were no statistically significant differences in other secondary outcomes. CONCLUSIONS AND RELEVANCE Among patients in the ICU without ARDS who were expected not to be extubated within 24 hours, a lower PEEP strategy was noninferior to a higher PEEP strategy with regard to the number of ventilator-free days at day 28. These findings support the use of lower PEEP in patients without ARDS
The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients
No full textBackground: 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.</p
Some Patients Are More Equal Than Others:Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome
No full textOBJECTIVES: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients.DESIGN: Multicenter retrospective cohort study.SETTING: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020.PATIENTS: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded.MEASUREMENTS AND MAIN RESULTS: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio2, set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao2/Fio2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant (p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves.CONCLUSIONS: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials.</p
Predictors for extubation failure in COVID-19 patients using a machine learning approach
No full textINTRODUCTION: 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
Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in‐hospital mortality
No full textISSN:0001-5172ISSN:1399-6576ISSN:1399-657
Incidence, Risk Factors and Outcome of Suspected Central Venous Catheter-related Infections in Critically Ill COVID-19 Patients: A Multicenter Retrospective Cohort Study
No full textBackground: 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
Additional file 1 of The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients
No full textAdditional file 1. Data sharing agreement
Additional file 1 of Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning
No full textAdditional file 1: Table S1. Parameter bounds. Table S2. Parameter categories. Table S3. Feature derivations. Table S4. Included features. Table S5. Hyper parameter optimization. Table S6. Feature Importance Metrics. Table S7. Patient characteristics. Table S8. ROC AUC score. Table S9. F1-score. Table S10. Sensitivity Analysis Minimal Prone Duration. Table S11. Sensitivity Analysis Cut-off Values. Table S12. Ranked Feature Importance. Table S13. Feature Importance Values. Figure S1. PaO2/FiO2 ratio difference in mmHg over time after starting prone position. Prone positions which continued for longer than 24 hours were filtered out
