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.

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.

Preventive Antibiotics and Delayed Cerebral Ischaemia in Patients with Aneurysmal Subarachnoid Haemorrhage Admitted to the Intensive Care Unit.

INTRODUCTION: Delayed cerebral ischemia (DCI) is an important contributor to poor outcome after aneurysmal subarachnoid haemorrhage (aSAH). Development of DCI is multifactorial, and inflammation, with or without infection, is one of the factors independently associated with development of DCI and poor outcome. We thus postulated that preventive antibiotics might be associated with a reduced risk of DCI and subsequent poor outcome in aSAH patients. METHODS: We performed a retrospective cohort-study in intensive care units (ICU) of three university hospitals in The Netherlands. We included consecutive aSAH patients with minimal ICU stay of 72 h who received either preventive antibiotics (SDD: selective digestive tract decontamination including systemic cefotaxime or SOD: selective oropharyngeal decontamination) or no preventive antibiotics. DCI was defined as a new hypodensity on CT with no other explanation than DCI. Hazard ratio's (HR) for DCI and risk ratio's (RR) for 28-day case-fatality and poor outcome at 3 months were calculated, with adjustment (aHR/aRR) for clinical condition on admission, recurrent bleeding, aneurysm treatment modality and treatment site. RESULTS: Of 459 included patients, 274 received preventive antibiotics (SOD or SDD) and 185 did not. With preventive antibiotics, the aHR for DCI was 1.0 (95% CI 0.6-1.8), the aRR for 28-day case-fatality was 1.1 (95% CI 0.7-1.9) and the aRR for poor functional outcome 1.2 (95% CI 1.0-1.4). CONCLUSIONS: Preventive antibiotics were not associated with reduced risk of DCI or poor outcome in aSAH patients in the ICU.

Antibiotic-Induced Within-Host Resistance Development of Gram-Negative Bacteria in Patients Receiving Selective Decontamination or Standard Care.

OBJECTIVE: To quantify antibiotic-associated within-host antibiotic resistance acquisition rates in Pseudomonas aeruginosa, Klebsiella species, and Enterobacter species from lower respiratory tract samples of ICU patients receiving selective digestive decontamination, selective oropharyngeal decontamination, or standard care. DESIGN: Prospective cohort. SETTING: This study was nested within a cluster-randomized crossover study of selective digestive decontamination and selective oropharyngeal decontamination in 16 ICUs in The Netherlands. PATIENTS: Eligible patients were those colonized in the respiratory tract with P. aeruginosa, Klebsiella species, or Enterobacter species susceptible to one of the marker antibiotics and with at least two subsequent microbiological culture results from respiratory tract samples available. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Antibiotic resistance acquisition rates were defined as the number of conversions from susceptible to resistant for a specific antibiotic per 100 patient-days or 100 days of antibiotic exposure within an individual patient. The hazard of antibiotic use for resistance development in P. aeruginosa was based on time-dependent Cox regression analysis. Findings of this study cohort were compared with those of a previous cohort of patients not receiving selective digestive decontamination/selective oropharyngeal decontamination. Numbers of eligible patients were 277 for P. aeruginosa, 174 for Klebsiella species, and 106 for Enterobacter species. Resistance acquisition rates per 100 patient-days ranged from 0.2 (for colistin and ceftazidime in P. aeruginosa and for carbapenems in Klebsiella species) to 3.0 (for piperacillin-tazobactam in P. aeruginosa and Enterobacter species). For P. aeruginosa, the acquisition rates per 100 days of antibiotic exposure ranged from 1.4 for colistin to 4.9 for piperacillin-tazobactam. Acquisition rates were comparable for patients receiving selective digestive decontamination/selective oropharyngeal decontamination and those receiving standard care. Carbapenem exposure had the strongest association with resistance development (adjusted hazard ratio, 4.2; 95% CI, 1.1-15.6). CONCLUSION: Within-host antibiotic resistance acquisition rates for systemically administered antibiotics were comparable between patients receiving selective decontamination and those receiving standard care and were highest during carbapenem use.

Colistin and tobramycin resistance during long- term use of selective decontamination strategies in the intensive care unit: a post hoc analysis.

INTRODUCTION: Selective decontamination of the digestive tract (SDD) and selective oropharyngeal decontamination (SOD) have been shown to improve intensive care unit (ICU) patients' outcomes. The aim of this study was to determine the effects of long-term use of SDD and SOD on colistin and tobramycin resistance among gram-negative bacteria. METHODS: We performed a post hoc analysis of two consecutive multicentre cluster-randomised trials with crossover of interventions. SDD and SOD were alternately but continuously used during 7 years in five Dutch ICUs participating in two consecutive cluster-randomised trials. In both trials, to measure colistin and tobramycin resistance among gram-negative bacteria, rectal and respiratory samples were obtained monthly from all patients present in the ICU. RESULTS: The prevalence of tobramycin resistance in respiratory and rectal samples decreased significantly during long-term use of SOD and SDD. (rectal samples risk ratio (RR) 0.35 (0.23 to 0.53); respiratory samples RR 0.48 (0.32 to 0.73), SDD compared to standard care). Colistin resistance in rectal and respiratory samples did not change (rectal samples RR 0.63 (0.29 to 1.38); respiratory samples RR 1.26 (0.35 to 4.57), SDD compared to standard care). CONCLUSIONS: In this study, in a setting with low antimicrobial resistance rates, the prevalence of resistance against colistin and tobramycin among gram-negative isolates did not increase during a mean of 7 years of SDD or SOD use.

Effects of selective digestive decontamination (SDD) on the gut resistome.

OBJECTIVES: Selective digestive decontamination (SDD) is an infection prevention measure for critically ill patients in intensive care units (ICUs) that aims to eradicate opportunistic pathogens from the oropharynx and intestines, while sparing the anaerobic flora, by the application of non-absorbable antibiotics. Selection for antibiotic-resistant bacteria is still a major concern for SDD. We therefore studied the impact of SDD on the reservoir of antibiotic resistance genes (i.e. the resistome) by culture-independent approaches. METHODS: We evaluated the impact of SDD on the gut microbiota and resistome in a single ICU patient during and after an ICU stay by several metagenomic approaches. We also determined by quantitative PCR the relative abundance of two common aminoglycoside resistance genes in longitudinally collected samples from 12 additional ICU patients who received SDD. RESULTS: The patient microbiota was highly dynamic during the hospital stay. The abundance of antibiotic resistance genes more than doubled during SDD use, mainly due to a 6.7-fold increase in aminoglycoside resistance genes, in particular aph(2″)-Ib and an aadE-like gene. We show that aph(2″)-Ib is harboured by anaerobic gut commensals and is associated with mobile genetic elements. In longitudinal samples of 12 ICU patients, the dynamics of these two genes ranged from a ∼10(4) fold increase to a ∼10(-10) fold decrease in relative abundance during SDD. CONCLUSIONS: ICU hospitalization and the simultaneous application of SDD has large, but highly individualized, effects on the gut resistome of ICU patients. Selection for transferable antibiotic resistance genes in anaerobic commensal bacteria could impact the risk of transfer of antibiotic resistance genes to opportunistic pathogens.

Effects of decontamination of the oropharynx and intestinal tract on antibiotic resistance in ICUs: a randomized clinical trial.

IMPORTANCE: Selective decontamination of the digestive tract (SDD) and selective oropharyngeal decontamination (SOD) are prophylactic antibiotic regimens used in intensive care units (ICUs) and associated with improved patient outcome. Controversy exists regarding the relative effects of both measures on patient outcome and antibiotic resistance. OBJECTIVE: To compare the effects of SDD and SOD, applied as unit-wide interventions, on antibiotic resistance and patient outcome. DESIGN, SETTING, AND PARTICIPANTS: Pragmatic, cluster randomized crossover trial comparing 12 months of SOD with 12 months of SDD in 16 Dutch ICUs between August 1, 2009, and February 1, 2013. Patients with an expected length of ICU stay longer than 48 hours were eligible to receive the regimens, and 5881 and 6116 patients were included in the clinical outcome analysis for SOD and SDD, respectively. INTERVENTIONS: Intensive care units were randomized to administer either SDD or SOD. MAIN OUTCOMES AND MEASURES: Unit-wide prevalence of antibiotic-resistant gram-negative bacteria. Secondary outcomes were day-28 mortality, ICU-acquired bacteremia, and length of ICU stay. RESULTS: In point-prevalence surveys, prevalences of antibiotic-resistant gram-negative bacteria in perianal swabs were significantly lower during SDD compared with SOD; for aminoglycoside resistance, average prevalence was 5.6% (95% CI, 4.6%-6.7%) during SDD and 11.8% (95% CI, 10.3%-13.2%) during SOD (P < .001). During both interventions the prevalence of rectal carriage of aminoglycoside-resistant gram-negative bacteria increased 7% per month (95% CI, 1%-13%) during SDD (P = .02) and 4% per month (95% CI, 0%-8%) during SOD (P = .046; P = .40 for difference). Day 28-mortality was 25.4% and 24.1% during SOD and SDD, respectively (adjusted odds ratio, 0.96 [95% CI, 0.88-1.06]; P = .42), and there were no statistically significant differences in other outcome parameters or between surgical and nonsurgical patients. Intensive care unit-acquired bacteremia occurred in 5.9% and 4.6% of the patients during SOD and SDD, respectively (odds ratio, 0.77 [95% CI, 0.65-0.91]; P = .002; number needed to treat, 77). CONCLUSIONS AND RELEVANCE: Unit-wide application of SDD and SOD was associated with low levels of antibiotic resistance and no differences in day-28 mortality. Compared with SOD, SDD was associated with lower rectal carriage of antibiotic-resistant gram-negative bacteria and ICU-acquired bacteremia but a more pronounced gradual increase in aminoglycoside-resistant gram-negative bacteria. TRIAL REGISTRATION: trialregister.nlIdentifier: NTR1780.

Decision-making regarding antibiotic therapy duration: An observational study of multidisciplinary meetings in the intensive care unit.

PURPOSE: Antibiotic therapy is commonly prescribed longer than recommended in intensive care patients (ICU). We aimed to provide insight into the decision-making process on antibiotic therapy duration in the ICU. METHODS: A qualitative study was conducted, involving direct observations of antibiotic decision-making during multidisciplinary meetings in four Dutch ICUs. The study used an observation guide, audio recordings, and detailed field notes to gather information about the discussions on antibiotic therapy duration. We described the participants' roles in the decision-making process and focused on arguments contributing to decision-making. RESULTS: We observed 121 discussions on antibiotic therapy duration in sixty multidisciplinary meetings. 24.8% of discussions led to a decision to stop antibiotics immediately. In 37.2%, a prospective stop date was determined. Arguments for decisions were most often brought forward by intensivists (35.5%) and clinical microbiologists (22.3%). In 28.9% of discussions, multiple healthcare professionals participated equally in the decision. We identified 13 main argument categories. While intensivists mostly used arguments based on clinical status, clinical microbiologists used diagnostic results in the discussion. CONCLUSIONS: Multidisciplinary decision-making regarding the duration of antibiotic therapy is a complex but valuable process, involving different healthcare professionals, using a variety of argument-types to determine the duration of antibiotic therapy. To optimize the decision-making process, structured discussions, involvement of relevant specialties, and clear communication and documentation of the antibiotic plan are recommended.

Decontamination of cephalosporin-resistant Enterobacteriaceae during selective digestive tract decontamination in intensive care units.

OBJECTIVES: Prevalences of cephalosporin-resistant Enterobacteriaceae are increasing globally, especially in intensive care units (ICUs). The effect of selective digestive tract decontamination (SDD) on the eradication of cephalosporin-resistant Enterobacteriaceae from the intestinal tract is unknown. We quantified eradication rates of cephalosporin-resistant and cephalosporin-susceptible Enterobacteriaceae during SDD in patients participating in a 13 centre cluster-randomized study and from a single-centre cohort. METHODS: All SDD patients colonized with Enterobacteriaceae in the intestinal tract at ICU admission were included. Cephalosporin resistance was defined as resistance to ceftazidime, cefotaxime or ceftriaxone and aminoglycoside resistance as resistance to tobramycin or gentamicin. Duration of rectal colonization was determined by screening twice weekly during ICU stay. Swabs were inoculated on selective medium supplemented with tobramycin or cefotaxime. RESULTS: Five hundred and seven (17%) of 2959 SDD patients with at least one rectal sample were colonized with Enterobacteriaceae at ICU admission: 77 (15%) with cephalosporin-resistant Enterobacteriaceae and 50 (10%) with aminoglycoside-resistant Enterobacteriaceae. Fifty-six (73%) patients colonized with cephalosporin-resistant Enterobacteriaceae were successfully decontaminated before ICU discharge, as were 343 (80%) patients colonized with cephalosporin-susceptible Enterobacteriaceae (P = 0.17). For aminoglycoside resistance, 31 (62%) patients were decontaminated, as were 368 patients (81%) colonized with aminoglycoside-susceptible Enterobacteriaceae (P < 0.01). On average, decolonization was demonstrated after 4 days if colonized with cephalosporin-susceptible Enterobacteriaceae and aminoglycoside-susceptible Enterobacteriaceae, and after 5 and 5.5 days if colonized with cephalosporin-resistant Enterobacteriaceae and aminoglycoside-resistant Enterobacteriaceae, respectively (log-rank test P = 0.053 for cephalosporin resistance and P = 0.03 for aminoglycoside resistance). If eradication failed, no associations were found with increased resistance in time (P > 0.05 for all comparisons). CONCLUSIONS: SDD can successfully eradicate cephalosporin-resistant Enterobacteriaceae from the intestinal tract.

Dynamics of ampicillin-resistant Enterococcus faecium clones colonizing hospitalized patients: data from a prospective observational study.

BACKGROUND: Little is known about the dynamics of colonizing Enterococcus faecium clones during hospitalization, invasive infection and after discharge. METHODS: In a prospective observational study we compared intestinal E. faecium colonization in three patient cohorts: 1) Patients from the Hematology Unit at the University Hospital Basel (UHBS), Switzerland, were investigated by weekly rectal swabs (RS) during hospitalization (group 1a, n = 33) and monthly after discharge (group 1b, n = 21). 2) Patients from the Intensive Care Unit (ICU) at the University Medical Center Utrecht, the Netherlands (group 2, n = 25) were swabbed weekly. 3) Patients with invasive E. faecium infection at UHBS were swabbed at the time of infection (group 3, n = 22). From each RS five colonies with typical E. faecium morphology were picked. Species identification was confirmed by PCR and ampicillin-resistant E. faecium (ARE) isolates were typed using Multiple Locus Variable Number Tandem Repeat Analysis (MLVA). The Simpson's Index of Diversity (SID) was calculated. RESULTS: Out of 558 ARE isolates from 354 RS, MT159 was the most prevalent clone (54%, 100%, 52% and 83% of ARE in groups 1a, 1b, 2 and 3, respectively). Among hematological inpatients 13 (40%) had ARE. During hospitalization, the SID of MLVA-typed ARE decreased from 0.745 [95%CI 0.657-0.833] in week 1 to 0.513 [95%CI 0.388-0.637] in week 3. After discharge the only detected ARE was MT159 in 3 patients. In the ICU (group 2) almost all patients (84%) were colonized with ARE. The SID increased significantly from 0.373 [95%CI 0.175-0.572] at week 1 to a maximum of 0.808 [95%CI 0.768-0.849] at week 3 due to acquisition of multiple ARE clones. All 16 patients with invasive ARE were colonized with the same MLVA clone (p < 0.001). CONCLUSIONS: In hospitalized high-risk patients MT159 is the most frequent colonizer and cause of invasive E. faecium infections. During hospitalization, ASE are quickly replaced by ARE. Diversity of ARE increases on units with possible cross-transmission such as ICUs. After hospitalization ARE are lost with the exception of MT159. In invasive infections, the invasive clone is the predominant gut colonizer.

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.

The role of intestinal colonization with gram-negative bacteria as a source for intensive care unit-acquired bacteremia.

OBJECTIVE: Selective digestive tract decontamination aims to eradicate gram-negative bacteria in both the intestinal tract and respiratory tract and is combined with a 4-day course of intravenous cefotaxime. Selective oropharyngeal decontamination only aims to eradicate respiratory tract colonization. In a recent study, selective digestive tract decontamination and selective oropharyngeal decontamination were associated with lower day-28 mortality, when compared to standard care. Furthermore, selective digestive tract decontamination was associated with a lower incidence of intensive care unit-acquired bacteremia caused by gram-negative bacteria. We quantified the role of intestinal tract carriage with gram-negative bacteria and intensive care unit-acquired gram-negative bacteremia. DESIGN: Data from a cluster-randomized and a single-center observational study. SETTING: Intensive care unit in The Netherlands. PATIENTS: Patients with intensive care unit stay of >48 hrs that received selective digestive tract decontamination (n = 2,667), selective oropharyngeal decontamination (n = 2,166) or standard care (n = 1,945). INTERVENTIONS: Selective digestive tract decontamination or selective oropharyngeal decontamination. MEASUREMENTS AND MAIN RESULTS: Incidence densities (episodes/1000 days) of intensive care unit-acquired gram-negative bacteremia were 4.5, 3.0, and 1.4 during standard care, selective oropharyngeal decontamination, and selective digestive tract decontamination, respectively, and the daily risk for developing intensive care unit-acquired gram-negative bacteria bacteremia increased until days 36, 33, and 31 for selective digestive tract decontamination, standard care, and selective oropharyngeal decontamination and was always lowest during selective digestive tract decontamination. Rectal colonization with gram-negative bacteria was present in 26% and 71% of patient days during selective digestive tract decontamination and selective oropharyngeal decontamination, respectively (p < .01). Irrespective of interventions, incidence densities of intensive care unit-acquired gram-negative bacteremia was 4.5 during patient days with both intestinal and respiratory tract gram-negative bacteria carriage. These incidence densities reduced with 33% (to 3.1) during days with intestinal gram-negative bacteria carriage only and with another 45% (to 1.0) during days without gram-negative bacteria carriage at both sites. CONCLUSIONS: Respiratory tract decolonization was associated with a 33% and intestinal tract decolonization was associated with a 45% reduction in the occurrence of intensive care unit-acquired gram-negative bacteremia.

Recent trends in antibiotic resistance in European ICUs.

PURPOSE OF REVIEW: Antimicrobial resistance is an emerging problem in ICUs worldwide. As numbers of published results from national/international surveillance studies rise rapidly, the amount of new information may be overwhelming. Therefore, we reviewed recent trends in antibiotic resistance in ICUs across Europe in the past 18 months. RECENT FINDINGS: In this period, infections caused by methicillin-resistant Staphylococcus aureus appeared to stabilize (and even decrease) in some countries, and infection rates due to Gram-positive bacteria resistant to vancomycin, linezolid or daptomycin have remained low. In contrast, we are witnessing a continent-wide emergence of infections caused by multiresistant Gram-negative bacteria, especially Escherichia coli and Klebsiella pneumoniae, with easily exchangeable resistance genes located on plasmids, producing enzymes such as extended spectrum ß-lactamases and carbapenamases. In the absence of new antibiotics, prevention of infections, reducing unnecessary antibiotic use, optimizing adherence to universal hygienic and infection control measures, and improving implementation of diagnostic tests are our only tools to combat this threat. SUMMARY: As the epidemiology of antibiotic resistance in ICUs is rapidly changing toward more frequently occurring epidemics and endemicity of multi and panresistant Gram-negative pathogens, better infection control and improved diagnostics will become even more important than before.

Ecological effects of selective decontamination on resistant gram-negative bacterial colonization.

RATIONALE: Selective digestive tract decontamination (SDD) and selective oropharyngeal decontamination (SOD) eradicate gram-negative bacteria (GNB) from the intestinal and respiratory tract in intensive care unit (ICU) patients, but their effect on antibiotic resistance remains controversial. OBJECTIVES: We quantified the effects of SDD and SOD on bacterial ecology in 13 ICUs that participated in a study, in which SDD, SOD, or standard care was used during consecutive periods of 6 months (de Smet AM, Kluytmans JA, Cooper BS, Mascini EM, Benus RF, van der Werf TS, van der Hoeven JG, Pickkers P, Bogaers-Hofman D, van der Meer NJ, et al. N Engl J Med 2009;360:20-31). METHODS: Point prevalence surveys of rectal and respiratory samples were performed once monthly in all ICU patients (receiving or not receiving SOD/SDD). Effects of SDD on rectal, and of SDD/SOD on respiratory tract, carriage of GNB were determined by comparing results from consecutive point prevalence surveys during intervention (6 mo for SDD and 12 mo for SDD/SOD) with consecutive point prevalence data in the pre- and postintervention periods. MEASUREMENTS AND MAIN RESULTS: During SDD, average proportions of patients with intestinal colonization with GNB resistant to either ceftazidime, tobramycin, or ciprofloxacin were 5, 7, and 7%, and increased to 15, 13, and 13% postintervention (P < 0.05). During SDD/SOD resistance levels in the respiratory tract were not more than 6% for all three antibiotics but increased gradually (for ceftazidime; P < 0.05 for trend) during intervention and to levels of 10% or more for all three antibiotics postintervention (P < 0.05). CONCLUSIONS: SOD and SDD have marked effects on the bacterial ecology in an ICU, with rising ceftazidime resistance prevalence rates in the respiratory tract during intervention and a considerable rebound effect of ceftazidime resistance in the intestinal tract after discontinuation of SDD.

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.

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 cmH2O. 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.