Empiric treatment of healthcare-associated central nervous system infections in Denmark: do we need carbapenems?

BACKGROUND: Carbapenems are widely used for empiric treatment of healthcare-associated central nervous system (CNS) infections. We investigated the feasibility of a carbapenem-sparing strategy, utilising a third-generation cephalosporin (ceftriaxone or cefotaxime) (combined with vancomycin) for the empirical treatment of healthcare-associated CNS infections in Eastern Denmark. METHODS: The departments of neurosurgery and neuro-intensive care at Copenhagen University Hospital Rigshospitalet. First, we analysed local microbiological data (1st January 2020-31st August 2022) to identify microorganisms non-susceptible to third-generation cephalosporin. Subsequently, we assessed all carbapenem prescriptions over a three-month period for their indication and justification. RESULTS: In total, 25,247 bacterial cultures were identified, of which 2,563 CNS-related, were included in the analysis. The positivity rate was 10.5% (n = 257/2439) for cerebrospinal-fluid samples and 75.8% (n = 95/124) for brain parenchyma. CNS samples from five individual patients revealed bacteria non-susceptible to third generation cephalosporins (Enterobacter spp. (n = 3), Pseudomonas spp. (n = 2), Klebsiella spp. (n = 2), Citrobacter freundii (n = 1)). All five patients had been hospitalised for ≥10days at the time-point of antibiotic therapy. Out of 11,626 sets of blood cultures, a total of 10 individual patients had Gram-negative blood-stream infections with resistance to ceftriaxone and piperacillin/tazobactam. 140 days-of-therapy (32%) with carbapenem in 18 patients (36%) were definitively or possibly indicated according to guidelines, none were indicated for healthcare-associated CNS-infections. CONCLUSION: An empiric treatment strategy relying on a third-generation cephalosporin appears suitable for healthcare-associated CNS infections at our tertiary hospital, serving a population of 2.6 million. However, in patients with prolonged hospitalization (≥10 days), immunosuppression, prior broad-spectrum antibiotic use, or history of resistant Gram-negative bacteria, empirical prescription of carbapenem may be needed.

Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness.

BACKGROUND: In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations. METHODS: We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome. RESULTS: Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]). CONCLUSIONS: Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study.

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

COVID-19 versus influenza A/B supeRInfectionS in the IntenSive care unit (CRISIS): Protocol for a Danish nationwide cohort study.

BACKGROUND: Superinfection following viral infection is a known complication, which may lead to longer hospitalisation and worse outcome. Empirical antibiotic therapy may prevent bacterial superinfections, but may also lead to overuse, adverse effects and development of resistant pathogens. Knowledge about the incidence of superinfections in intensive care unit (ICU) patients with severe Coronavirus Disease 2019 (COVID-19) is limited. METHODS: We will conduct a nationwide cohort study comparing the incidence of superinfections in patients with severe COVID-19 admitted to the ICU compared with ICU patients with influenza A/B in Denmark. We will include approximately 1000 patients in each group from the time period of 1 October 2014 to 30 April 2019 and from 10 March 2020 to 1 March 2021 for patients with influenza and COVID-19, respectively. The primary outcome is any superinfection within 90 days of admission to the ICU. We will use logistic regression analysis comparing COVID-19 with influenza A/B after adjustment for relevant predefined confounders. Secondarily, we will use unadjusted and adjusted logistic regression analyses to assess six potential risk factors (sex, age, cancer [including haematological], immunosuppression and use of life support on day 1 in the ICU) for superinfections and compare outcomes in patients with COVID-19 with/without superinfections, and present descriptive data regarding the superinfections. CONCLUSION: This study will provide important knowledge about superinfections in ICU patients with severe COVID-19.

Central and peripheral nervous system complications of COVID-19: a prospective tertiary center cohort with 3-month follow-up.

OBJECTIVE: To systematically describe central (CNS) and peripheral (PNS) nervous system complications in hospitalized COVID-19 patients. METHODS: We conducted a prospective, consecutive, observational study of adult patients from a tertiary referral center with confirmed COVID-19. All patients were screened daily for neurological and neuropsychiatric symptoms during admission and discharge. Three-month follow-up data were collected using electronic health records. We classified complications as caused by SARS-CoV-2 neurotropism, immune-mediated or critical illness-related. RESULTS: From April to September 2020, we enrolled 61 consecutively admitted COVID-19 patients, 35 (57%) of whom required intensive care (ICU) management for respiratory failure. Forty-one CNS/PNS complications were identified in 28 of 61 (45.9%) patients and were more frequent in ICU compared to non-ICU patients. The most common CNS complication was encephalopathy (n = 19, 31.1%), which was severe in 13 patients (GCS ≤ 12), including 8 with akinetic mutism. Length of ICU admission was independently associated with encephalopathy (OR = 1.22). Other CNS complications included ischemic stroke, a biopsy-proven acute necrotizing encephalitis, and transverse myelitis. The most common PNS complication was critical illness polyneuromyopathy (13.1%), with prolonged ICU stay as independent predictor (OR = 1.14). Treatment-related PNS complications included meralgia paresthetica. Of 41 complications in total, 3 were para/post-infectious, 34 were secondary to critical illness or other causes, and 4 remained unresolved. Cerebrospinal fluid was negative for SARS-CoV-2 RNA in all 5 patients investigated. CONCLUSION: CNS and PNS complications were common in hospitalized COVID-19 patients, particularly in the ICU, and often attributable to critical illness. When COVID-19 was the primary cause for neurological disease, no signs of viral neurotropism were detected, but laboratory changes suggested autoimmune-mediated mechanisms.