Dose optimization of cefotaxime as pre-emptive treatment in critically ill adult patients: A population pharmacokinetic study.

AIMS: To describe the pharmacokinetics (PK) of cefotaxime as pre-emptive treatment in critically ill adult patients, including covariates and to determine the probability of target attainment (PTA) of different dosage regimens for Enterobacterales and Staphylococcus aureus. METHODS: Five samples were drawn during 1 dosage interval in critically ill patients treated with cefotaxime 1 g q6h or q4h. PK parameters were estimated using NONMEM (v7.4.2). The percentage of patients reaching 100% fT>MICECOFF was used to compare different dosage regimens for Enterobacterales and S. aureus. RESULTS: This study included 92 patients (437 samples). The best structural model was a 2-compartment model with a combined error, interindividual variability on clearance, central volume and intercompartmental clearance. Correlations between interindividual variability were included. Clearance increased with higher estimated glomerular filtration rate (eGFR; creatinine clearance) and albumin concentration. For Enterobacterales, 1 g q8h reached 95% PTA and continuous infusion (CI) of 4 g 24 h-1 100% PTA at the highest eGFR and albumin concentration. For S. aureus the predefined target of 95% PTA was not reached with higher eGFR and/or albumin concentrations. CI of 6 g 24 h-1 for S. aureus resulted in a minimum of 99% PTA. CONCLUSION: Cefotaxime PK in critically ill patients was best described by a 2-compartment model with eGFR and albumin concentration as covariates influencing clearance. For Enterobacterales 1 g q8h or CI of 4 g 24 h-1 was adequate for all combinations of eGFR and albumin concentration. For S. aureus CI of 6 g 24 h-1 would be preferred if eGFR and albumin concentration exceed 80 mL min-1 and 40 g L-1 respectively.

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.

Health Care Costs of Target Attainment for Beta-Lactam Antibiotics in Critically Ill Patients: A Retrospective Analysis of the EXPAT Study.

BACKGROUND: Optimizing beta-lactam antibiotic treatment is a promising method to reduce the length of intensive care unit (ICU) stay and therefore reduce ICU costs. We used data from the EXPAT trial to determine whether beta-lactam antibiotic target attainment is a cost determinant in the ICU. METHODS: Patients included in the EXPAT trial were divided into target attainment and target nonattainment based on serum antibiotic levels. All hospital costs were extracted from the hospital administration system and categorized. RESULTS: In total, 79 patients were included in the analysis. Target attainment showed a trend toward higher total ICU costs (€44,600 versus €28,200, P = 0.103). This trend disappeared when correcting for ICU length of stay (€2680 versus €2700). Renal replacement therapy was the most important cost driver. CONCLUSIONS: Target attainment for beta-lactam antibiotics shows a trend toward higher total costs in ICU patients, which can be attributed to the high costs of a long stay in the ICU and renal replacement therapy.

Predicting need for hospital-specific interventional care after surgery using electronic health record data.

BACKGROUND: A significant proportion of surgical inpatients is often admitted longer than necessary. Early identification of patients who do not need care that is strictly provided within hospitals would allow timely discharge of patients to a postoperative nursing home for further recovery. We aimed to develop a model to predict whether a patient needs hospital-specific interventional care beyond the second postoperative day. METHODS: This study included all adult patients discharged from surgical care in the surgical oncology department from June 2017 to February 2020. The primary outcome was to predict whether a patient still needs hospital-specific interventional care beyond the second postoperative day. Hospital-specific care was defined as unplanned reoperations, radiological interventions, and intravenous antibiotics administration. Different analytical methods were compared with respect to the area under the receiver-operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: Each model was trained on 1,174 episodes. In total, 847 (50.5%) patients required an intervention during postoperative admission. A random forest model performed best with an area under the receiver-operating characteristics curve of 0.88 (95% confidence interval 0.83-0.93), sensitivity of 79.1% (95% confidence interval 0.67-0.92), specificity of 80.0% (0.73-0.87), positive predictive value of 57.6% (0.45-0.70) and negative predictive value of 91.7% (0.87-0.97). CONCLUSION: This proof-of-concept study found that a random forest model could successfully predict whether a patient could be safely discharged to a nursing home and does not need hospital care anymore. Such a model could aid hospitals in addressing capacity challenges and improve patient flow, allowing for timely surgical care.

Mortality related to Verona Integron-encoded Metallo-ß-lactamase-positive Pseudomonas aeruginosa: assessment by a novel clinical tool.

Background: Verona Integron-encoded Metallo-ß-lactamase-positive Pseudomonas aeruginosa (VIM-PA) can cause nosocomial infections and may be responsible for increased mortality. Multidrug resistance in VIM-PA complicates treatment. We aimed to assess the contribution of VIM-PA to mortality in patients in a large tertiary care hospital in the Netherlands. Methods: A focus group of five members created a scheme to define related mortality based on clinical and diagnostic findings. Contribution to mortality was categorized as "definitely", "probably", "possibly", or "not" related to infection with VIM-PA, or as "unknown". Patients were included when infected with or carrier of VIM-PA between January 2008 and January 2016. Patient-related data and specific data on VIM-PA cultures were retrieved from the electronic laboratory information system. For patients who died in our hospital, medical records were independently reviewed and thereafter discussed by three physicians. Results: A total of 198 patients with any positive culture with VIM-PA were identified, of whom 95 (48.0%) died. Sixty-seven patients died in our hospital and could be included in the analysis. The death of 15 patients (22.4%) was judged by all reviewers to be definitely related to infection with VIM-PA. In 17 additional patients (25.4%), death was probably or possibly related to an infection with VIM-PA. The level of agreement was 65.7% after the first evaluation, and 98.5% after one session of discussion. Conclusion: Using our assessment tool, infections with VIM-PA were shown to have an important influence on mortality in our complex and severely ill patients. The tool may be used for other (resistant) bacteria as well but this needs further exploration.

Microcirculatory assessment of patients under VA-ECMO.

BACKGROUND: Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is an effective technique for providing emergency mechanical circulatory support for patients with cardiogenic shock. VA-ECMO enables a rapid restoration of global systemic organ perfusion, but it has not been found to always show a parallel improvement in the microcirculation. We hypothesized in this study that the response of the microcirculation to the initiation of VA-ECMO might identify patients with increased chances of intensive care unit (ICU) survival. METHODS: Twenty-four patients were included in this study. Sublingual microcirculation measurements were performed using the CytoCam-IDF (incident dark field) imaging device. Microcirculatory measurements were performed at baseline, after VA-ECMO insertion (T1), 48-72 h after initiation of VA-ECMO (T2), 5-6 days after (T3), 9-10 days after (T4), and within 24 h of VA-ECMO removal. RESULTS: Of the 24 patients included in the study population, 15 survived and 9 died while on VA-ECMO. There was no significant difference between the systemic global hemodynamic variables at initiation of VA-ECMO between the survivors and non-survivors. There was, however, a significant difference in the microcirculatory parameters of both small and large vessels at all time points between the survivors and non-survivors. Perfused vessel density (PVD) at baseline (survivor versus non-survivor, 19.21 versus 13.78 mm/mm2, p = 0.001) was able to predict ICU survival on initiation of VA-ECMO; the area under the receiver operating characteristic curve (ROC) was 0.908 (95 % confidence interval 0.772-1.0). CONCLUSION: PVD of the sublingual microcirculation at initiation of VA-ECMO can be used to predict ICU mortality in patients with cardiogenic shock.

Assessment of ventilation inhomogeneity during mechanical ventilation using a rapid-response oxygen sensor-based oxygen washout method.

PURPOSE: Ventilatory inhomogeneity indexes in critically ill mechanically ventilated patients could be of importance to optimize ventilator settings in order to reduce additional lung injury. The present study compared six inhomogeneity indexes calculated from the oxygen washout curves provided by the rapid oxygen sensor of the LUFU end-expiratory lung volume measurement system. METHODS: Inhomogeneity was tested in a porcine model before and after induction of acute lung injury (ALI) at four different levels of positive end-expiratory pressure (PEEP; 15, 10, 5 and 0 cm H2O). The following indexes were assessed: lung clearance index (LCI), mixing ratio, Becklake index, multiple breath alveolar mixing inefficiency, moment ratio and pulmonary clearance delay. RESULTS: LCI, mixing ratio, Becklake index and moment ratio were comparable with previous reported values and showed acceptable variation coefficients at baseline with and without ALI. Moment ratio had the highest precision, as calculated by the variation coefficients. LCI, Becklake index and moment ratio showed comparable increases in inhomogeneity during decremental PEEP steps before and after ALI. CONCLUSIONS: The advantage of the method we introduce is the combined measurement of end-expiratory lung volume (EELV) and inhomogeneity of lung ventilation with the LUFU fast-response medical-grade oxygen sensor, without the need for external tracer gases. This can be combined with conventional breathing systems. The moment ratio and LCI index appeared to be the most favourable for integration with oxygen washout curves as judged by high precision and agreement with previous reported findings. Studies are under way to evaluate the indexes in critically ill patients.