One-year outcomes in COVID-19 and non-COVID-19 intensive care unit survivors.

PURPOSE: To determine differences in one-year multi-domain health outcomes in COVID-19 and non-COVID-19 intensive care unit (ICU) survivors. MATERIALS AND METHODS: Adult ICU survivors treated for COVID-19 were compared to a control group consisting of survivors admitted for respiratory distress due to other causes, i.e. non-COVID-19 ARDS or pneumonia. Occurrence of physical (frailty, fatigue, physical symptoms), mental (anxiety, depression, post-traumatic stress) and cognitive symptoms, and quality of life (QoL) scores were measured, using validated questionnaires, before and one year after ICU treatment. RESULTS: In total, 506 COVID-19 survivors could be compared to 228 non-COVID-19 survivors. At one-year follow-up, COVID-19 ICU survivors had less physical (76.2% vs. 86.9%, p = 0.001) and mental symptoms (32.0% vs. 47.1%, p < 0.001) than the control group. Cognitive symptoms were comparable (22.5% vs. 17.2%, p = 0.12). However, compared to pre-ICU health symptoms and scores, COVID-19 survivors experienced an increase in symptom occurrence rates in all domains and a decrease in QoL, whereas the control group only experienced an increase in mental and cognitive symptoms, with a similar QoL at one-year follow-up. CONCLUSIONS: COVID-19 ICU survivors experience equal or less health problems but a greater decline in QoL one year after ICU admission compared to non-COVID-19 ARDS or pneumonia survivors.

Changes in quality of life 1 year after intensive care: a multicenter prospective cohort of ICU survivors.

BACKGROUND: With survival rates of critical illness increasing, quality of life measures are becoming an important outcome of ICU treatment. Therefore, to study the impact of critical illness on quality of life, we explored quality of life before and 1 year after ICU admission in different subgroups of ICU survivors. METHODS: Data from an ongoing prospective multicenter cohort study, the MONITOR-IC, were used. Patients admitted to the ICU in one of eleven participating hospitals between July 2016 and June 2021 were included. Outcome was defined as change in quality of life, measured using the EuroQol five-dimensional (EQ-5D-5L) questionnaire, and calculated by subtracting the EQ-5D-5L score 1 day before hospital admission from the EQ-5D-5L score 1 year post-ICU. Based on the minimal clinically important difference, a change in quality of life was defined as a change in EQ-5D-5L score of ≥ 0.08. Subgroups of patients were based on admission diagnosis. RESULTS: A total of 3913 (50.6%) included patients completed both baseline and follow-up questionnaires. 1 year post-ICU, patients admitted after a cerebrovascular accident, intracerebral hemorrhage, or (neuro)trauma, on average experienced a significant decrease in quality of life. Conversely, 11 other subgroups of ICU survivors reported improvements in quality of life. The largest average increase in quality of life was seen in patients admitted due to respiratory disease (mean 0.17, SD 0.38), whereas the largest average decrease was observed in trauma patients (mean -0.13, SD 0.28). However, in each of the studied 22 subgroups there were survivors who reported a significant increase in QoL and survivors who reported a significant decrease in QoL. CONCLUSIONS:  This large prospective multicenter cohort study demonstrated the diversity in long-term quality of life between, and even within, subgroups of ICU survivors. These findings emphasize the need for personalized information and post-ICU care. TRIAL REGISTRATION: The MONITOR-IC study was registered at ClinicalTrials.gov: NCT03246334 on August 2nd 2017.

Cohort profile of BIGPROMISE: a perioperative biobank of a high-risk surgical population.

PURPOSE: Postoperative complications increase mortality, disability and costs. Advanced understanding of the risk factors for postoperative complications is needed to improve surgical outcomes. This paper discusses the rationale and profile of the BIGPROMISE (biomarkers to guide perioperative management and improve outcome in high-risk surgery) cohort, that aims to investigate risk factors, pathophysiology and outcomes related to postoperative complications. PARTICIPANTS: Adult patients undergoing major surgery in two tertiary teaching hospitals. Clinical data and blood samples are collected before surgery, at the end of surgery and on the first, second and third postoperative day. At each time point a panel of cardiovascular, inflammatory, renal, haematological and metabolic biomarkers is assessed. Aliquots of plasma, serum and whole blood of each time point are frozen and stored. Data on severe complications are prospectively collected during 30 days after surgery. Functional status is assessed before surgery and after 120 days using the WHO Disability Assessment Schedule (WHODAS) 2.0. Mortality is followed up until 2 years after surgery. FINDINGS TO DATE: The first patient was enrolled on 8 October 2021. Currently (1 January 2024) 3086 patients were screened for eligibility, of whom 1750 (57%) provided informed consent for study participation. Median age was 66 years (60; 73), 28% were female, and 68% of all patients were American Society of Anaesthesiologists (ASA) physical status class 3. Most common types of major surgery were cardiac (49%) and gastro-intestinal procedures (26%). The overall incidence of 30-day severe postoperative complications was 16%. FUTURE PLANS: By the end of the recruitment phase, expected in 2026, approximately 3000 patients with major surgery will have been enrolled. This cohort allows us to investigate the role of pathophysiological perioperative processes in the cause of postoperative complications, and to discover and develop new biomarkers to improve risk stratification for adverse postoperative outcomes. TRIAL REGISTRATION NUMBER: NCT05199025.

Abnormal Iron Status and Adverse Outcome After Elective Cardiac Surgery: A Prospective, Observational Multicenter Study.

OBJECTIVES: To investigate the incidence of preoperative abnormal iron status and its association with packed red blood cell (PRBC) transfusion, postoperative major complications, and new onset of clinically significant disability in patients undergoing elective cardiac surgery. DESIGN: A prospective, observational multicenter cohort study. SETTING: Three cardiac surgical centers in the Netherlands between 2019 and 2021. Recruitment was on hold between March and May 2020 due to COVID-19. PATIENTS: A total of 427 patients aged 60 years and older who underwent elective on-pump cardiac surgery. MEASUREMENTS AND MAIN RESULTS: The primary endpoint was a 30-day PRBC transfusion. Secondary endpoints were postoperative major complications within 30 days (eg, acute kidney injury, sepsis), and new onset of clinically significant disability within 120 days of surgery. Iron status was evaluated before surgery. Abnormal iron status was present in 45.2% of patients (n = 193), and most frequently the result of iron deficiency (27.4%, n = 117). An abnormal iron status was not associated with PRBC transfusion (adjusted relative risk [ARR] 1.2; 95% CI 0.9-1.8: p = 0.227) or new onset of clinically significant disability (ARR 2.0; 95% CI 0.9-4.6: p = 0.098). However, the risk of postoperative major complications was increased in patients with an abnormal iron status (ARR 1.7; 95% CI 1.1-2.5: p = 0.012). CONCLUSIONS: An abnormal iron status before elective cardiac surgery was associated with an increased risk of postoperative major complications but not with PRBC transfusion or a new onset of clinically significant disability.

Prediction of Long-Term Physical, Mental, and Cognitive Problems Following Critical Illness: Development and External Validation of the PROSPECT Prediction Model.

OBJECTIVES: ICU survivors often suffer from long-lasting physical, mental, and cognitive health problems after hospital discharge. As several interventions that treat or prevent these problems already start during ICU stay, patients at high risk should be identified early. This study aimed to develop a model for early prediction of post-ICU health problems within 48 hours after ICU admission. DESIGN: Prospective cohort study in seven Dutch ICUs. SETTING/PATIENTS: ICU patients older than 16 years and admitted for greater than or equal to 12 hours between July 2016 and March 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Outcomes were physical problems (fatigue or ≥ 3 new physical symptoms), mental problems (anxiety, depression, or post-traumatic stress disorder), and cognitive impairment. Patient record data and questionnaire data were collected at ICU admission, and after 3 and 12 months, of 2,476 patients. Several models predicting physical, mental, or cognitive problems and a composite score at 3 and 12 months were developed using variables collected within 48 hours after ICU admission. Based on performance and clinical feasibility, a model, PROSPECT, predicting post-ICU health problems at 3 months was chosen, including the predictors of chronic obstructive pulmonary disease, admission type, expected length of ICU stay greater than or equal to 2 days, and preadmission anxiety and fatigue. Internal validation using bootstrapping on data of the largest hospital ( n = 1,244) yielded a C -statistic of 0.73 (95% CI, 0.70-0.76). External validation was performed on data ( n = 864) from the other six hospitals with a C -statistic of 0.77 (95% CI, 0.73-0.80). CONCLUSIONS: The developed and externally validated PROSPECT model can be used within 48 hours after ICU admission for identifying patients with an increased risk of post-ICU problems 3 months after ICU admission. Timely preventive interventions starting during ICU admission and follow-up care can prevent or mitigate post-ICU problems in these high-risk patients.

Cardiac Surgery in Jehovah's Witnesses: 329 Consecutive Cases.

OBJECTIVES: This study aimed to describe the outcome of Jehovah's Witnesses (JWs) undergoing cardiac surgery at the authors' center. DESIGN: A single-center retrospective cohort study. SETTING: At a cardiovascular center with a tertiary intensive care unit (ICU) and specific experience with cardiac surgery in JWs. The institutional protocol describing all perioperative care in JWs has been applied for 21 years. PARTICIPANTS: All JWs undergoing cardiac surgery in the Amphia Hospital from January 1, 2001 to January 31, 2022. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The study cohort comprised 329 JWs undergoing cardiac surgery. Twenty-three patients (6.8%) were treated preoperatively for anemia. The mean European System for Cardiac Operative Risk Evaluation score was 5.1 (range 0-18). Coronary artery bypass grafting (53.2%) was performed most frequently, followed by aortic valve replacement (13.4%). Mean preoperative hemoglobin levels were 14.5 g/dL (range 9.8-18.5 g/dL), dropping to 11.6 g/dL (range 6.6-15.6 g/dL) at hospital discharge. Mean blood loss was 439 ± 349 mL in the first 12 hours postsurgery. Maximum mean postoperative troponin levels were 431 ± 424 ng/L. Resternotomy and postoperative myocardial infarction occurred in 3.6% and 4.2% of patients, respectively. On average, patients had an ICU stay of 1.4 ± 1.8 days and a hospital stay of 6.8 ± 4.2 days. Hospital mortality was 0.6% and was related to cardiac failure. CONCLUSIONS: This study demonstrated that cardiac surgery in JWs is safe when adhering to a strict perioperative patient blood management protocol.

Cohort profile of PLUTO: a perioperative biobank focusing on prediction and early diagnosis of postoperative complications.

PURPOSE: Although elective surgery is generally safe, some procedures remain associated with an increased risk of complications. Improved preoperative risk stratification and earlier recognition of these complications may ameliorate postoperative recovery and improve long-term outcomes. The perioperative longitudinal study of complications and long-term outcomes (PLUTO) cohort aims to establish a comprehensive biorepository that will facilitate research in this field. In this profile paper, we will discuss its design rationale and opportunities for future studies. PARTICIPANTS: Patients undergoing elective intermediate to high-risk non-cardiac surgery are eligible for enrolment. For the first seven postoperative days, participants are subjected to daily bedside visits by dedicated observers, who adjudicate clinical events and perform non-invasive physiological measurements (including handheld spirometry and single-channel electroencephalography). Blood samples and microbiome specimens are collected at preselected time points. Primary study outcomes are the postoperative occurrence of nosocomial infections, major adverse cardiac events, pulmonary complications, acute kidney injury and delirium/acute encephalopathy. Secondary outcomes include mortality and quality of life, as well as the long-term occurrence of psychopathology, cognitive dysfunction and chronic pain. FINDINGS TO DATE: Enrolment of the first participant occurred early 2020. During the inception phase of the project (first 2 years), 431 patients were eligible of whom 297 patients consented to participate (69%). Observed event rate was 42% overall, with the most frequent complication being infection. FUTURE PLANS: The main purpose of the PLUTO biorepository is to provide a framework for research in the field of perioperative medicine and anaesthesiology, by storing high-quality clinical data and biomaterials for future studies. In addition, PLUTO aims to establish a logistical platform for conducting embedded clinical trials. TRIAL REGISTRATION NUMBER: NCT05331118.

Development and External Validation of a Prediction Model for Quality of Life of ICU Survivors: A Subanalysis of the MONITOR-IC Prospective Cohort Study.

OBJECTIVES: To develop and externally validate a prediction model for ICU survivors' change in quality of life 1 year after ICU admission that can support ICU physicians in preparing patients for life after ICU and managing their expectations. DESIGN: Data from a prospective multicenter cohort study (MONITOR-IC) were used. SETTING: Seven hospitals in the Netherlands. PATIENTS: ICU survivors greater than or equal to 16 years old. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Outcome was defined as change in quality of life, measured using the EuroQol 5D questionnaire. The developed model was based on data from an academic hospital, using multivariable linear regression analysis. To assist usability, variables were selected using the least absolute shrinkage and selection operator method. External validation was executed using data of six nonacademic hospitals. Of 1,804 patients included in analysis, 1,057 patients (58.6%) were admitted to the academic hospital, and 747 patients (41.4%) were admitted to a nonacademic hospital. Forty-nine variables were entered into a linear regression model, resulting in an explained variance ( R2 ) of 56.6%. Only three variables, baseline quality of life, admission type, and Glasgow Coma Scale, were selected for the final model ( R2 = 52.5%). External validation showed good predictive power ( R2 = 53.2%). CONCLUSIONS: This study developed and externally validated a prediction model for change in quality of life 1 year after ICU admission. Due to the small number of predictors, the model is appealing for use in clinical practice, where it can be implemented to prepare patients for life after ICU. The next step is to evaluate the impact of this prediction model on outcomes and experiences of patients.

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.

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.

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 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.

INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY.

ABSTRACT: Background: 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.

Mental health symptoms in family members of COVID-19 ICU survivors 3 and 12 months after ICU admission: a multicentre prospective cohort study.

PURPOSE: Long-term mental outcomes in family members of coronavirus disease 2019 (COVID-19) intensive care unit (ICU) survivors are unknown. Therefore, we assessed the prevalence of mental health symptoms, including associated risk factors, and quality of life (QoL) in family members of COVID-19 ICU survivors 3 and 12 months post-ICU. METHODS: A prospective multicentre cohort study in ICUs of ten Dutch hospitals, including adult family members of COVID-19 ICU survivors admitted between March 1, and July 1, 2020. Symptom prevalence rates of anxiety, depression (Hospital Anxiety and Depression Scale) and Post-Traumatic Stress Disorder (Impact of Event Scale-6), and QoL (Short Form-12) before ICU admission (baseline), and after 3 and 12 months were measured. Additionally, associations between family and patient characteristics and mental health symptoms were calculated. RESULTS: A total of 166 out of 197 (84.3%) included family members completed the 12-month follow-up of whom 46.1% and 38.3% had mental health symptoms 3 and 12 months post-ICU, respectively; both higher compared to baseline (22.4%) (p < 0.001). The mental component summary score of the SF-12 was lower at 12-month follow-up compared with baseline [mean difference mental component score: - 5.5 (95% confidence interval (CI) - 7.4 to - 3.6)]. Furthermore, 27.9% experienced work-related problems. Symptoms of anxiety (odds ratio (OR) 9.23; 95% CI 2.296-37.24; p = 0.002) and depression (OR 5.96; 95% CI 1.29-27.42; p = 0.02) prior to ICU admission were identified as risk factors for mental health symptoms after 12 months. CONCLUSION: A considerable proportion of family members of COVID-19 survivors reported mental health symptoms 3 and 12 months after ICU admission, disrupting QoL and creating work-related problems.

Preoperative anaemia and outcome after elective cardiac surgery: a Dutch national registry analysis.

BACKGROUND: Previous studies have shown that preoperative anaemia in patients undergoing cardiac surgery is associated with adverse outcomes. However, most of these studies were retrospective, had a relatively small sample size, and were from a single centre. The aim of this study was to analyse the relationship between the severity of preoperative anaemia and short- and long-term mortality and morbidity in a large multicentre national cohort of patients undergoing cardiac surgery. METHODS: A nationwide, prospective, multicentre registry (Netherlands Heart Registration) of patients undergoing elective cardiac surgery between January 2013 and January 2019 was used for this observational study. Anaemia was defined according to the WHO criteria, and the main study endpoint was 120-day mortality. The association was investigated using multivariable logistic regression analysis. RESULTS: In total, 35 484 patients were studied, of whom 6802 (19.2%) were anaemic. Preoperative anaemia was associated with an increased risk of 120-day mortality (adjusted odds ratio [aOR] 1.7; 95% confidence interval [CI]: 1.4-1.9; P<0.001). The risk of 120-day mortality increased with anaemia severity (mild anaemia aOR 1.6; 95% CI: 1.3-1.9; P<0.001; and moderate-to-severe anaemia aOR 1.8; 95% CI: 1.4-2.4; P<0.001). Preoperative anaemia was associated with red blood cell transfusion and postoperative morbidity, the causes of which included renal failure, pneumonia, and myocardial infarction. CONCLUSIONS: Preoperative anaemia was associated with mortality and morbidity after cardiac surgery. The risk of adverse outcomes increased with anaemia severity. Preoperative anaemia is a potential target for treatment to improve postoperative outcomes.

Clinical Outcomes Among Patients With 1-Year Survival Following Intensive Care Unit Treatment for COVID-19.

IMPORTANCE: One-year outcomes in patients who have had COVID-19 and who received treatment in the intensive care unit (ICU) are unknown. OBJECTIVE: To assess the occurrence of physical, mental, and cognitive symptoms among patients with COVID-19 at 1 year after ICU treatment. DESIGN, SETTING, AND PARTICIPANTS: An exploratory prospective multicenter cohort study conducted in ICUs of 11 Dutch hospitals. Patients (N = 452) with COVID-19, aged 16 years and older, and alive after hospital discharge following admission to 1 of the 11 ICUs during the first COVID-19 surge (March 1, 2020, until July 1, 2020) were eligible for inclusion. Patients were followed up for 1 year, and the date of final follow-up was June 16, 2021. EXPOSURES: Patients with COVID-19 who received ICU treatment and survived 1 year after ICU admission. MAIN OUTCOMES AND MEASURES: The main outcomes were self-reported occurrence of physical symptoms (frailty [Clinical Frailty Scale score ≥5], fatigue [Checklist Individual Strength-fatigue subscale score ≥27], physical problems), mental symptoms (anxiety [Hospital Anxiety and Depression {HADS} subscale score ≥8], depression [HADS subscale score ≥8], posttraumatic stress disorder [mean Impact of Event Scale score ≥1.75]), and cognitive symptoms (Cognitive Failure Questionnaire-14 score ≥43) 1 year after ICU treatment and measured with validated questionnaires. RESULTS: Of the 452 eligible patients, 301 (66.8%) patients could be included, and 246 (81.5%) patients (mean [SD] age, 61.2 [9.3] years; 176 men [71.5%]; median ICU stay, 18 days [IQR, 11 to 32]) completed the 1-year follow-up questionnaires. At 1 year after ICU treatment for COVID-19, physical symptoms were reported by 182 of 245 patients (74.3% [95% CI, 68.3% to 79.6%]), mental symptoms were reported by 64 of 244 patients (26.2% [95% CI, 20.8% to 32.2%]), and cognitive symptoms were reported by 39 of 241 patients (16.2% [95% CI, 11.8% to 21.5%]). The most frequently reported new physical problems were weakened condition (95/244 patients [38.9%]), joint stiffness (64/243 patients [26.3%]) joint pain (62/243 patients [25.5%]), muscle weakness (60/242 patients [24.8%]) and myalgia (52/244 patients [21.3%]). CONCLUSIONS AND RELEVANCE: In this exploratory study of patients in 11 Dutch hospitals who survived 1 year following ICU treatment for COVID-19, physical, mental, or cognitive symptoms were frequently reported.

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.

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

INTRODUCTION: 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.

Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.

OBJECTIVES: 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.

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.

BACKGROUND: 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.