Presence of procoagulant peripheral blood mononuclear cells in severe COVID-19 patients relate to ventilation perfusion mismatch and precede pulmonary embolism.

PURPOSE: Pulmonary emboli (PE) contribute substantially to coronavirus disease 2019 (COVID-19) related mortality and morbidity. Immune cell-mediated hyperinflammation drives the procoagulant state in COVID-19 patients, resulting in immunothrombosis. To study the role of peripheral blood mononuclear cells (PBMC) in the procoagulant state of COVID-19 patients, we performed a functional bioassay and related outcomes to the occurrence of PE. Secondary aims were to relate this functional assay to plasma D-dimer levels, ventilation perfusion mismatch and TF expression on monocyte subsets. METHODS: PBMC from an ICU biobank were obtained from 20 patients with a computed tomography angiograph (CTA) proven PE and compared to 15 COVID-19 controls without a proven PE. Functional procoagulant properties of PBMC were measured using a modified fibrin generation time (MC-FGT) assay. Tissue factor (TF) expression on monocyte subsets were measured by flow cytometry. Additional clinical data were obtained from patient records including end-tidal to arterial carbon dioxide gradient. RESULTS: MC-FGT levels were highest in the samples taken closest to the PE detection, similar to the end-tidal to arterial carbon dioxide gradient (ETCO2 - PaCO2), a measurement to quantify ventilation-perfusion mismatch. In patients without proven PE, peak MC-FGT relates to an increase in end-tidal to arterial carbon dioxide gradient. We identified non-classical, CD16 positive monocytes as the subset with increased TF expression. CONCLUSION: We show that the procoagulant state of PBMC could aid in early detection of PE in COVID-19 ICU patients. Combined with end-tidal to ETCO2 - PaCO2 gradient, these tests could improve early detection of PE on the ICU.

Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice.

This scoping review focuses on the essential role of models for causal inference in shaping actionable artificial intelligence (AI) designed to aid clinicians in decision-making. The objective was to identify and evaluate the reporting quality of studies introducing models for causal inference in intensive care units (ICUs), and to provide recommendations to improve the future landscape of research practices in this domain. To achieve this, we searched various databases including Embase, MEDLINE ALL, Web of Science Core Collection, Google Scholar, medRxiv, bioRxiv, arXiv, and the ACM Digital Library. Studies involving models for causal inference addressing time-varying treatments in the adult ICU were reviewed. Data extraction encompassed the study settings and methodologies applied. Furthermore, we assessed reporting quality of target trial components (i.e., eligibility criteria, treatment strategies, follow-up period, outcome, and analysis plan) and main causal assumptions (i.e., conditional exchangeability, positivity, and consistency). Among the 2184 titles screened, 79 studies met the inclusion criteria. The methodologies used were G methods (61%) and reinforcement learning methods (39%). Studies considered both static (51%) and dynamic treatment regimes (49%). Only 30 (38%) of the studies reported all five target trial components, and only seven (9%) studies mentioned all three causal assumptions. To achieve actionable AI in the ICU, we advocate careful consideration of the causal question of interest, describing this research question as a target trial emulation, usage of appropriate causal inference methods, and acknowledgement (and examination of potential violations of) the causal assumptions.

Starting HMV at home: a reasonable option for many patients?

BACKGROUND AND OBJECTIVE: In the current study, we undertook a more detailed exploration of the reasons why patients undergoing HMV were screened out of a recently published study in order to better understand how applicable home initiation of HMV is under real life conditions. METHODS: All referred patients who had an indication for starting HMV were screened to participate in the Homerun study. In this trial 512 patients were screened out of the study. Those patients not enrolled in the trial were divided into the following 3 groups: (1) those not fulfilling the inclusion criteria; 2) those meeting the exclusion criteria and 3) those excluded on the basis of medical or organisation reasons. Each group was then further divided into those who would likely have been suitable for initiation of HMV at home in real world practice and those who were unsuitable. RESULTS: Based on inclusion criteria (group 1) 116 patients could not start HMV in real life, while this was 245 patients in the study. Based on the exclusion criteria (group 2) 11 patients could not start in real life while this was 79 in the study. One hundred and eighty-eight could not be enrolled in the study due to medical and organisational reasons ( group 3), while in real life this was only 95. CONCLUSION: This study indicates that more than 55% of patients who did not participate in the Homerun study could have started HMV at home in real life.

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.

Home mechanical ventilation: the Dutch approach.

In the Netherlands we have an unique organisation of only 4 centres being responsible for all patients who need Home Mechanical ventilation(HMV). Nationwide criteria for referral and initiation of HMV are stated in our national guideline and recently a unique national learning management system (LMS) for all caregivers and professionals was developed. A nationwide multi-centric research program is running and every centre is participating. In this paper we provide information about the evolution of HMV in the Netherlands during the last 30 years, including details about the number of patients, different diagnose groups, residence and the type of ventilators.

Incidence of thrombotic complications and overall survival in hospitalized patients with COVID-19 in the second and first wave.

INTRODUCTION: In the first wave, thrombotic complications were common in COVID-19 patients. It is unknown whether state-of-the-art treatment has resulted in less thrombotic complications in the second wave. METHODS: We assessed the incidence of thrombotic complications and overall mortality in COVID-19 patients admitted to eight Dutch hospitals between September 1st and November 30th 2020. Follow-up ended at discharge, transfer to another hospital, when they died, or on November 30th 2020, whichever came first. Cumulative incidences were estimated, adjusted for competing risk of death. These were compared to those observed in 579 patients admitted in the first wave, between February 24th and April 26th 2020, by means of Cox regression techniques adjusted for age, sex and weight. RESULTS: In total 947 patients with COVID-19 were included in this analysis, of whom 358 patients were admitted to the ICU; 144 patients died (15%). The adjusted cumulative incidence of all thrombotic complications after 10, 20 and 30 days was 12% (95% confidence interval (CI) 9.8-15%), 16% (13-19%) and 21% (17-25%), respectively. Patient characteristics between the first and second wave were comparable. The adjusted hazard ratio (HR) for overall mortality in the second wave versus the first wave was 0.53 (95%CI 0.41-0.70). The adjusted HR for any thrombotic complication in the second versus the first wave was 0.89 (95%CI 0.65-1.2). CONCLUSIONS: Mortality was reduced by 47% in the second wave, but the thrombotic complication rate remained high, and comparable to the first wave. Careful attention to provision of adequate thromboprophylaxis is invariably warranted.

Incidence of thrombotic complications in critically ill ICU patients with COVID-19.

INTRODUCTION: COVID-19 may predispose to both venous and arterial thromboembolism due to excessive inflammation, hypoxia, immobilisation and diffuse intravascular coagulation. Reports on the incidence of thrombotic complications are however not available. METHODS: We evaluated the incidence of the composite outcome of symptomatic acute pulmonary embolism (PE), deep-vein thrombosis, ischemic stroke, myocardial infarction or systemic arterial embolism in all COVID-19 patients admitted to the ICU of 2 Dutch university hospitals and 1 Dutch teaching hospital. RESULTS: We studied 184 ICU patients with proven COVID-19 pneumonia of whom 23 died (13%), 22 were discharged alive (12%) and 139 (76%) were still on the ICU on April 5th 2020. All patients received at least standard doses thromboprophylaxis. The cumulative incidence of the composite outcome was 31% (95%CI 20-41), of which CTPA and/or ultrasonography confirmed VTE in 27% (95%CI 17-37%) and arterial thrombotic events in 3.7% (95%CI 0-8.2%). PE was the most frequent thrombotic complication (n = 25, 81%). Age (adjusted hazard ratio (aHR) 1.05/per year, 95%CI 1.004-1.01) and coagulopathy, defined as spontaneous prolongation of the prothrombin time > 3 s or activated partial thromboplastin time > 5 s (aHR 4.1, 95%CI 1.9-9.1), were independent predictors of thrombotic complications. CONCLUSION: The 31% incidence of thrombotic complications in ICU patients with COVID-19 infections is remarkably high. Our findings reinforce the recommendation to strictly apply pharmacological thrombosis prophylaxis in all COVID-19 patients admitted to the ICU, and are strongly suggestive of increasing the prophylaxis towards high-prophylactic doses, even in the absence of randomized evidence.

Intensive care unit patients with lower respiratory tract nosocomial infections: the ENIRRIs project.

The clinical course of intensive care unit (ICU) patients may be complicated by a large spectrum of lower respiratory tract infections (LRTI), defined by specific epidemiological, clinical and microbiological aspects. A European network for ICU-related respiratory infections (ENIRRIs), supported by the European Respiratory Society, has been recently established, with the aim at studying all respiratory tract infective episodes except community-acquired ones. A multicentre, observational study is in progress, enrolling more than 1000 patients fulfilling the clinical, biochemical and radiological findings consistent with a LRTI. This article describes the methodology of this study. A specific interest is the clinical impact of non-ICU-acquired nosocomial pneumonia requiring ICU admission, non-ventilator-associated LRTIs occurring in the ICU, and ventilator-associated tracheobronchitis. The clinical meaning of microbiologically negative infectious episodes and specific details on antibiotic administration modalities, dosages and duration are also highlighted. Recently released guidelines address many unresolved questions which might be answered by such large-scale observational investigations. In light of the paucity of data regarding such topics, new interesting information is expected to be obtained from our network research activities, contributing to optimisation of care for critically ill patients in the ICU.