Detection of Brain Activation in Unresponsive Patients with Acute Brain Injury
Claassen J, Doyle K, Matory A, et al. Detection of brain activation in unresponsive patients with acute brain injury. New Engl Journal of Medicine. 2019;380(26):2497-2505. https://doi.org/10.1056/NEJMoa1812757.
Brain activation in response to spoken motor commands can be detected by electroencephalography (EEG) in clinically unresponsive patients. The prevalence and prognostic importance of a dissociation between commanded motor behavior and brain activation in the first few days after brain injury are not well understood.
We studied a prospective, consecutive series of patients in a single intensive care unit who had acute brain injury from a variety of causes and who were unresponsive to spoken commands, including some patients with the ability to localize painful stimuli or to fixate on or track visual stimuli. Machine learning was applied to EEG recordings to detect brain activation in response to commands that patients move their hands. The functional outcome at 12 months was determined with the Glasgow Outcome Scale–Extended (GOS-E; levels range from 1 to 8, with higher levels indicating better outcomes).
A total of 16 of 104 unresponsive patients (15%) had brain activation detected by EEG at a median of 4 days after injury. The condition in 8 of these 16 patients (50%) and in 23 of 88 patients (26%) without brain activation improved such that they were able to follow commands before discharge. At 12 months, 7 of 16 patients (44%) with brain activation and 12 of 84 patients (14%) without brain activation had a GOS-E level of 4 or higher, denoting the ability to function independently for 8 hours (odds ratio, 4.6; 95% confidence interval, 1.2 to 17.1).
A dissociation between the absence of behavioral responses to motor commands and the evidence of brain activation in response to these commands in EEG recordings was found in 15% of patients in a consecutive series of patients with acute brain injury.
Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data
Masino AJ, Harris MC, Forsyth D, et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLOS ONE. 2019;14(2):e0212665. https://doi.org/10.1371/journal.pone.0212665.
Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentation. Our objective was to develop a model using readily available electronic health record (EHR) data capable of recognizing infant sepsis at least 4 hours prior to clinical recognition.
Methods and findings
We performed a retrospective case control study of infants hospitalized ≥48 hours in the Neonatal Intensive Care Unit (NICU) at the Children’s Hospital of Philadelphia between September 2014 and November 2017 who received at least one sepsis evaluation before 12 months of age. We considered two evaluation outcomes as cases: culture positive–positive blood culture for a known pathogen (110 evaluations); and clinically positive–negative cultures but antibiotics administered for ≥120 hours (265 evaluations). Case data was taken from the 44-hour window ending 4 hours prior to evaluation. We randomly sampled 1,100 44-hour windows of control data from all times ≥10 days removed from any evaluation. Model inputs consisted of up to 36 features derived from routine EHR data. Using 10-fold nested cross-validation, 8 machine learning models were trained to classify inputs as sepsis positive or negative. When tasked with discriminating culture positive cases from controls, 6 models achieved a mean area under the receiver operating characteristic (AUC) between 0.80–0.82 with no significant differences between them. Including both culture and clinically positive cases, the same 6 models achieved an AUC between 0.85–0.87, again with no significant differences.
Machine learning models can identify infants with sepsis in the NICU hours prior to clinical recognition. Learning curves indicate model improvement may be achieved with additional training examples. Additional input features may also improve performance. Further research is warranted to assess potential performance improvements and clinical efficacy in a prospective trial.
Economics of Palliative Care for Hospitalized Adults With Serious Illness: A Meta-analysis
May P, Normand C, Cassel J, et a. Economics of palliative care for hospitalized adults with serious illness: A meta-analysis. JAMA Internal Medicine. 2018. doi: 10.1001/jamainternmed.2018.0750.
Importance Economics of care for adults with serious illness is a policy priority worldwide. Palliative care may lower costs for hospitalized adults, but the evidence has important limitations.
Objective To estimate the association of palliative care consultation (PCC) with direct hospital costs for adults with serious illness.
Data Sources Systematic searches of the Embase, PsycINFO, CENTRAL, PubMed, CINAHL, and EconLit databases were performed for English-language journal articles using keywords in the domains of palliative care (eg, palliative, terminal) and economics (eg, cost, utilization), with limiters for hospital and consultation. For Embase, PsycINFO, and CENTRAL, we searched without a time limitation. For PubMed, CINAHL, and EconLit, we searched for articles published after August 1, 2013. Data analysis was performed from April 8, 2017, to September 16, 2017.
Study Selection Economic evaluations of interdisciplinary PCC for hospitalized adults with at least 1 of 7 illnesses (cancer; heart, liver, or kidney failure; chronic obstructive pulmonary disease; AIDS/HIV; or selected neurodegenerative conditions) in the hospital inpatient setting vs usual care only, controlling for a minimum list of confounders.
Data Extraction and Synthesis Eight eligible studies were identified, all cohort studies, of which 6 provided sufficient information for inclusion. The study estimated the association of PCC within 3 days of admission with direct hospital costs for each sample and for subsamples defined by primary diagnoses and number of comorbidities at admission, controlling for confounding with an instrumental variable when available and otherwise propensity score weighting. Treatment effect estimates were pooled in the meta-analysis.
Main Outcomes and Measures Total direct hospital costs.
Results This study included 6 samples with a total 133 118 patients (range, 1020-82 273), of whom 93.2% were discharged alive (range, 89.0%-98.4%), 40.8% had a primary diagnosis of cancer (range, 15.7%-100.0%), and 3.6% received a PCC (range, 2.2%-22.3%). Mean Elixhauser index scores ranged from 2.2 to 3.5 among the studies. When patients were pooled irrespective of diagnosis, there was a statistically significant reduction in costs (−$3237; 95% CI, −$3581 to −$2893; P < .001). In the stratified analyses, there was a reduction in costs for the cancer (−$4251; 95% CI, −$4664 to −$3837; P < .001) and noncancer (−$2105; 95% CI, −$2698 to −$1511; P < .001) subsamples. The reduction in cost was greater in those with 4 or more comorbidities than for those with 2 or fewer.
TryCYCLE: A Prospective Study of the Safety and Feasibility of Early In-Bed Cycling in Mechanically Ventilated Patients
Kho ME, Molloy AJ, Clarke FJ, et al. TryCYCLE: A prospective study of the safety and feasibility of early in-bed cycling in mechanically ventilated patients. PLOS ONE. 2016;11(12):e0167561.
The objective of this study was to assess the safety and feasibility of in-bed cycling started within the first 4 days of mechanical ventilation (MV) to inform a future randomized clinical trial.
We conducted a 33-patient prospective cohort study in a 21-bed adult academic medical-surgical intensive care unit (ICU) in Hamilton, ON, Canada. We included adult patients (≥ 18 years) receiving MV who walked independently pre-ICU. Our intervention was 30 minutes of in-bed supine cycling 6 days/week in the ICU. Our primary outcome was Safety (termination), measured as events prompting cycling termination; secondary Safety (disconnection or dislodgement) outcomes included catheter/tube dislodgements. Feasibility was measured as consent rate and fidelity to intervention. For our primary outcome, we calculated the binary proportion and 95% confidence interval (CI).
From 10/2013-8/2014, we obtained consent from 34 of 37 patients approached (91.9%), 33 of whom received in-bed cycling. Of those who cycled, 16(48.4%) were female, the mean (SD) age was 65.8(12.2) years, and APACHE II score was 24.3(6.7); 29(87.9%) had medical admitting diagnoses. Cycling termination was infrequent (2.0%, 95% CI: 0.8%-4.9%) and no device dislodgements occurred. Cycling began a median [IQR] of 3 [2, 4] days after ICU admission; patients received 5 [3, 8] cycling sessions with a median duration of 30.7 [21.6, 30.8] minutes per session. During 205 total cycling sessions, patients were receiving invasive MV (150 [73.1%]), vasopressors (6 [2.9%]), sedative or analgesic infusions (77 [37.6%]) and dialysis (4 [2.0%]).
Early cycling within the first 4 days of MV among hemodynamically stable patients is safe and feasible. Research to evaluate the effect of early cycling on patient function is warranted.