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.

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.

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.

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.

Covid-19 Pneumonia and Ventilation-induced Lung Injury: A Case Report.

We present the case of a 67-year-old male patient, who was admitted to the intensive care unit for hypoxemic respiratory failure due to severe COVID-19 pneumonitis, requiring mechanical ventilation. Despite close monitoring using transpulmonary pressure measurements and interventions to pursue lung-protective ventilation, the patient developed extensive barotrauma including a right-sided pneumothorax, subcutaneous emphysema and pneumomediastinum while on pressure support ventilation. We hypothesize that the high respiratory drive that COVID-19 patients seem to exhibit, combined with diffuse alveolar injury and increased alveolar pressure, resulted in gross barotrauma. CONCLUSION: The respiratory characteristics that COVID-19 patients seem to exhibit might expose those on mechanical ventilation to an increased risk of developing ventilation-induced lung injury. This case emphasizes that caution should be taken in the respiratory treatment of patients with COVID-19 pneumonitis.

Selective decontamination of the digestive tract (SDD) in critically ill patients: a narrative review.

Selective decontamination of the digestive tract (SDD) is an infection prevention measure for intensive care unit (ICU) patients that was proposed more than 30 years ago, and that is currently considered standard of care in the Netherlands, but only used sporadically in ICUs in other countries. In this narrative review, we first describe the rationale of the individual components of SDD and then review the evidence base for patient-centered outcomes, where we distinguish ICUs with low prevalence of antibiotic resistance from ICUs with moderate-high prevalence of resistance. In settings with low prevalence of antibiotic resistance, SDD has been associated with improved patient outcome in three cluster-randomized studies. These benefits were not confirmed in a large international cluster-randomized study in settings with moderate-to-high prevalence of antibiotic resistance. There is no evidence that SDD increases antibiotic resistance. We end with future directions for research.

Cost-effectiveness of selective digestive decontamination (SDD) versus selective oropharyngeal decontamination (SOD) in intensive care units with low levels of antimicrobial resistance: an individual patient data meta-analysis.

OBJECTIVE: To determine the cost-effectiveness of selective digestive decontamination (SDD) as compared to selective oropharyngeal decontamination (SOD) in intensive care units (ICUs) with low levels of antimicrobial resistance. DESIGN: Post-hoc analysis of a previously performed individual patient data meta-analysis of two cluster-randomised cross-over trials. SETTING: 24 ICUs in the Netherlands. PARTICIPANTS: 12 952 ICU patients who were treated with ≥1 dose of SDD (n=6720) or SOD (n=6232). INTERVENTIONS: SDD versus SOD. PRIMARY AND SECONDARY OUTCOME MEASURES: The incremental cost-effectiveness ratio (ICER; ie, costs to prevent one in-hospital death) was calculated by comparing differences in direct healthcare costs and in-hospital mortality of patients treated with SDD versus SOD. A willingness-to-pay curve was plotted to reflect the probability of cost-effectiveness of SDD for a range of different values of maximum costs per prevented in-hospital death. RESULTS: The ICER resulting from the fixed-effect meta-analysis, adjusted for clustering and differences in baseline characteristics, showed that SDD significantly reduced in-hospital mortality (adjusted absolute risk reduction 0.0195, 95% CI 0.0050 to 0.0338) with no difference in costs (adjusted cost difference €62 in favour of SDD, 95% CI -€1079 to €935). Thus, SDD yielded significantly lower in-hospital mortality and comparable costs as compared with SOD. At a willingness-to-pay value of €33 633 per one prevented in-hospital death, SDD had a probability of 90.0% to be cost-effective as compared with SOD. CONCLUSION: In Dutch ICUs, SDD has a very high probability of cost-effectiveness as compared to SOD. These data support the implementation of SDD in settings with low levels of antimicrobial resistance.

Comparative gut microbiota and resistome profiling of intensive care patients receiving selective digestive tract decontamination and healthy subjects.

BACKGROUND: The gut microbiota is a reservoir of opportunistic pathogens that can cause life-threatening infections in critically ill patients during their stay in an intensive care unit (ICU). To suppress gut colonization with opportunistic pathogens, a prophylactic antibiotic regimen, termed "selective decontamination of the digestive tract" (SDD), is used in some countries where it improves clinical outcome in ICU patients. Yet, the impact of ICU hospitalization and SDD on the gut microbiota remains largely unknown. Here, we characterize the composition of the gut microbiota and its antimicrobial resistance genes ("the resistome") of ICU patients during SDD and of healthy subjects. RESULTS: From ten patients that were acutely admitted to the ICU, 30 fecal samples were collected during ICU stay. Additionally, feces were collected from five of these patients after transfer to a medium-care ward and cessation of SDD. Feces from ten healthy subjects were collected twice, with a 1-year interval. Gut microbiota and resistome composition were determined using 16S rRNA gene phylogenetic profiling and nanolitre-scale quantitative PCRs. The microbiota of the ICU patients differed from the microbiota of healthy subjects and was characterized by lower microbial diversity, decreased levels of Escherichia coli and of anaerobic Gram-positive, butyrate-producing bacteria of the Clostridium clusters IV and XIVa, and an increased abundance of Bacteroidetes and enterococci. Four resistance genes (aac(6')-Ii, ermC, qacA, tetQ), providing resistance to aminoglycosides, macrolides, disinfectants, and tetracyclines, respectively, were significantly more abundant among ICU patients than in healthy subjects, while a chloramphenicol resistance gene (catA) and a tetracycline resistance gene (tetW) were more abundant in healthy subjects. CONCLUSIONS: The gut microbiota of SDD-treated ICU patients deviated strongly from the gut microbiota of healthy subjects. The negative effects on the resistome were limited to selection for four resistance genes. While it was not possible to disentangle the effects of SDD from confounding variables in the patient cohort, our data suggest that the risks associated with ICU hospitalization and SDD on selection for antibiotic resistance are limited. However, we found evidence indicating that recolonization of the gut by antibiotic-resistant bacteria may occur upon ICU discharge and cessation of SDD.

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 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.

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.