Predicting COVID-19 prognosis in the ICU remained challenging: external validation in a multinational regional cohort

Objective: Many prediction models for Coronavirus Disease 2019 (COVID-19) have been developed. External validation is mandatory before implementation in the Intensive Care Unit (ICU). We selected and validated prognostic models in the Euregio Intensive Care COVID (EICC) cohort. Study design and setting: In this multinational cohort study, routine data from COVID-19 patients admitted to ICUs within the Euregio Meuse-Rhine were collected from March to August 2020. COVID-19 models were selected based on model type, predictors, outcomes, and reporting. Furthermore, general ICU scores were assessed. Discrimination was assessed by area under the receiver operating characteristic curves (AUCs) and calibration by calibration-in-the-large and calibration plots. A random-effects meta-analysis was used to pool results. Results: 551 patients were admitted. Mean age was 65.4±11.2 years, 29% were female, and ICU mortality was 36%. Nine out of 238 published models were externally validated. Pooled AUCs were between 0.53 and 0.70 and calibration-in-the-large between -9% and 6%. Calibration plots showed generally poor but, for the 4C Mortality score and SEIMC score, moderate calibration. Conclusion: Of the nine prognostic models that were externally validated in the EICC cohort, only two showed reasonable discrimination and moderate calibration. For future pandemics, better models based on routine data are needed to support admission decision-making

Delirium in older COVID-19 patients:Evaluating risk factors and outcomes

Objectives: A high incidence of delirium has been reported in older patients with Coronavirus disease 2019 (COVID-19). We aimed to identify determinants of delirium, including the Clinical Frailty Scale, in hospitalized older patients with COVID-19. Furthermore, we aimed to study the association of delirium independent of frailty with in-hospital outcomes in older COVID-19 patients. Methods: This study was performed within the framework of the multi-center COVID-OLD cohort study and included patients aged ≥60 years who were admitted to the general ward because of COVID-19 in the Netherlands between February and May 2020. Data were collected on demographics, co-morbidity, disease severity, and geriatric parameters. Prevalence of delirium during hospital admission was recorded based on delirium screening using the Delirium Observation Screening Scale (DOSS) which was scored three times daily. A DOSS score ≥3 was followed by a delirium assessment by the ward physician In-hospital outcomes included length of stay, discharge destination, and mortality. Results: A total of 412 patients were included (median age 76, 58% male). Delirium was present in 82 patients. In multivariable analysis, previous episode of delirium (Odds ratio [OR] 8.9 [95% CI 2.3–33.6] p = 0.001), and pre-existent memory problems (OR 7.6 [95% CI 3.1–22.5] p < 0.001) were associated with increased delirium risk. Clinical Frailty Scale was associated with increased delirium risk (OR 1.63 [95%CI 1.40–1.90] p < 0.001) in univariable analysis, but not in multivariable analysis. Patients who developed delirium had a shorter symptom duration and lower levels of C-reactive protein upon presentation, whereas vital parameters did not differ. Patients who developed a delirium had a longer hospital stay and were more often discharged to a nursing home. Delirium was associated with mortality (OR 2.84 [95% CI1.71–4.72] p < 0.001), but not in multivariable analyses. Conclusions: A previous delirium and pre-existent memory problems were associated with delirium risk in COVID-19. Delirium was not an independent predictor of mortality after adjustment for frailty

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

Pulmonary function three to five months after hospital discharge for COVID-19: a single centre cohort study

Abstract Some COVID-19 survivors suffer from persistent pulmonary function impairment, but the extent and associated factors are unclear. This study aimed to characterize pulmonary function impairment three to five months after hospital discharge and the association with disease severity. Survivors of COVID-19 after hospitalization to the VieCuri Medical Centre between February and December 2020 were invited for follow-up, three to five months after discharge. Dynamic and static lung volumes, respiratory muscle strength and diffusion capacity were measured. The cohort comprised 257 patients after a moderate (n = 33), severe (n = 151) or critical (n = 73) COVID-19 infection with a median follow-up of 112 days (interquartile range 96–134 days). The main sequelae included reduced diffusion capacity (36%) and reduced maximal expiratory pressure (24%). Critically ill patients were more likely to have reduced diffusion capacity than moderate (OR 8.00, 95% CI 2.46–26.01) and severe cases (OR 3.74, 95% CI 1.88–7.44) and lower forced vital capacity (OR 3.29, 95% CI 1.20–9.06) compared to severe cases. Many COVID-19 survivors, especially after a critical disease course, showed pulmonary function sequelae, mainly DLCO impairments, three to five months after discharge. Monitoring is needed to investigate the persistence of these symptoms and the longer-term implications of the COVID-19 burden

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH(2)O. Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes

Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning

Background: For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources. Methods: From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and Extreme Gradient Boosting on readily available and clinically relevant features to predict success of prone positioning after 4 h (window of 1 to 7 h) based on various possible outcomes. These outcomes were defined as improvements of at least 10% in PaO2/FiO2 ratio, ventilatory ratio, respiratory system compliance, or mechanical power. Separate models were created for each of these outcomes. Re-supination within 4 h after pronation was labeled as failure. We also developed models using a 20 mmHg improvement cut-off for PaO2/FiO2 ratio and using a combined outcome parameter. For all models, we evaluated feature importance expressed as contribution to predictive performance based on their relative ranking. Results: The median duration of prone episodes was 17 h (11–20, median and IQR, N = 2632). Despite extensive modeling using a plethora of machine learning techniques and a large number of potentially clinically relevant features, discrimination between responders and non-responders remained poor with an area under the receiver operator characteristic curve of 0.62 for PaO2/FiO2 ratio using Logistic Regression, Random Forest and XGBoost. Feature importance was inconsistent between models for different outcomes. Notably, not even being a previous responder to prone positioning, or PEEP-levels before prone positioning, provided any meaningful contribution to predicting a successful next proning episode. Conclusions: In mechanically ventilated COVID-19 patients, predicting the success of prone positioning using clinically relevant and readily available parameters from electronic health records is currently not feasible. Given the current evidence base, a liberal approach to proning in all patients with severe COVID-19 ARDS is therefore justified and in particular regardless of previous results of proning

Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality

Background The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction. Methods This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores. Results Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/−24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71–0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64–0.71], 0.61 [CI 0.58–0.66], 0.67 [CI 0.63–0.70], 0.70 [CI 0.67–0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively). Conclusions Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far.ISSN:0001-5172ISSN:1399-657