The coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 that has significant implications for the cardiovascular care of patients. ...First, those with COVID-19 and pre-existing cardiovascular disease have an increased risk of severe disease and death. Second, infection has been associated with multiple direct and indirect cardiovascular complications including acute myocardial injury, myocarditis, arrhythmias, and venous thromboembolism. Third, therapies under investigation for COVID-19 may have cardiovascular side effects. Fourth, the response to COVID-19 can compromise the rapid triage of non-COVID-19 patients with cardiovascular conditions. Finally, the provision of cardiovascular care may place health care workers in a position of vulnerability as they become hosts or vectors of virus transmission. We hereby review the peer-reviewed and pre-print reports pertaining to cardiovascular considerations related to COVID-19 and highlight gaps in knowledge that require further study pertinent to patients, health care workers, and health systems.
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•Patients with pre-existing CVD appear to have worse outcomes with COVID-19.•CV complications include biomarker elevations, myocarditis, heart failure, and venous thromboembolism, which may be exacerbated by delays in care.•Therapies under investigation for COVID-19 may have significant drug-drug interactions with CV medications.•Health care workers and health systems should take measures to ensure safety while providing high-quality care for COVID-19 patients.
Ketamine to treat super-refractory status epilepticus Alkhachroum, Ayham; Der-Nigoghossian, Caroline A; Mathews, Elizabeth ...
Neurology,
2020-October-20, 2020-10-20, 20201020, Letnik:
95, Številka:
16
Journal Article
Recenzirano
Odprti dostop
OBJECTIVETo test ketamine infusion efficacy in the treatment of super-refractory status epilepticus (SRSE), we studied patients with SRSE who were treated with ketamine retrospectively. We also ...studied the effect of high doses of ketamine on brain physiology as reflected by invasive multimodality monitoring (MMM).
METHODSWe studied a consecutive series of 68 patients with SRSE who were admitted between 2009 and 2018, treated with ketamine, and monitored with scalp EEG. Eleven of these patients underwent MMM at the time of ketamine administration. We compared patients who had seizure cessation after ketamine initiation to those who did not.
RESULTSMean age was 53 ± 18 years and 46% of patients were female. Seizure burden decreased by at least 50% within 24 hours of starting ketamine in 55 (81%) patients, with complete cessation in 43 (63%). Average dose of ketamine infusion was 2.2 ± 1.8 mg/kg/h, with median duration of 2 (1–4) days. Average dose of midazolam was 1.0 ± 0.8 mg/kg/h at the time of ketamine initiation and was started at a median of 0.4 (0.1–1.0) days before ketamine. Using a generalized linear mixed effect model, ketamine was associated with stable mean arterial pressure (odds ratio 1.39, 95% confidence interval 1.38–1.40) and with decreased vasopressor requirements over time. We found no effect on intracranial pressure, cerebral blood flow, or cerebral perfusion pressure.
CONCLUSIONKetamine treatment was associated with a decrease in seizure burden in patients with SRSE. Our data support the notion that high-dose ketamine infusions are associated with decreased vasopressor requirements without increased intracranial pressure.
CLASSIFICATION OF EVIDENCEThis study provides Class IV evidence that ketamine decreases seizures in patients with SRSE.
Recovery trajectories of clinically unresponsive patients with acute brain injury are largely uncertain. Brain activation in the absence of a behavioural response to spoken motor commands can be ...detected by EEG, also known as cognitive-motor dissociation. We aimed to explore the role of cognitive-motor dissociation in predicting time to recovery in patients with acute brain injury.
In this observational cohort study, we prospectively studied two independent cohorts of clinically unresponsive patients (aged ≥18 years) with acute brain injury. Machine learning was applied to EEG recordings to diagnose cognitive-motor dissociation by detecting brain activation in response to verbal commands. Survival statistics and shift analyses were applied to the data to identify an association between cognitive-motor dissociation and time to and magnitude of recovery. The prediction accuracy of the model that was built using the derivation cohort was assessed using the validation cohort. Functional outcomes of all patients were assessed with the Glasgow Outcome Scale–Extended (GOS-E) at hospital discharge and at 3, 6, and 12 months after injury. Patients who underwent withdrawal of life-sustaining therapies were censored, and death was treated as a competing risk.
Between July 1, 2014, and Sept 30, 2021, we screened 598 patients with acute brain injury and included 193 (32%) patients, of whom 100 were in the derivation cohort and 93 were in the validation cohort. At 12 months, 28 (15%) of 193 unresponsive patients had a GOS-E score of 4 or above. Cognitive-motor dissociation was seen in 27 (14%) patients and was an independent predictor of shorter time to good recovery (hazard ratio 5·6 95% CI 2·5–12·5), as was underlying traumatic brain injury or subdural haematoma (4·4 1·4–14·0), a Glasgow Coma Scale score on admission of greater than or equal to 8 (2·2 1·0–4·7), and younger age (1·0 1·0–1·1). Among patients discharged home or to a rehabilitation setting, those diagnosed with cognitive-motor dissociation consistently had higher scores on GOS-E indicating better functional recovery compared with those without cognitive-motor dissociation, which was seen as early as 3 months after the injury (odds ratio 4·5 95% CI 2·0–33·6).
Recovery trajectories of clinically unresponsive patients diagnosed with cognitive-motor dissociation early after brain injury are distinctly different from those without cognitive-motor dissociation. A diagnosis of cognitive-motor dissociation could inform the counselling of families of clinically unresponsive patients, and it could help clinicians to identify patients who will benefit from rehabilitation.
US National Institutes of Health.
Markers in Status Epilepticus Prognosis Alkhachroum, Ayham; Der-Nigoghossian, Caroline A; Rubinos, Clio ...
Journal of clinical neurophysiology
37, Številka:
5
Journal Article
Recenzirano
Odprti dostop
Status epilepticus (SE) is a neurologic emergency with high morbidity and mortality. The assessment of a patient's prognosis is crucial in making treatment decisions. In this review, we discuss ...various markers that have been used to prognosticate SE in terms of recurrence, mortality, and functional outcome. These markers include demographic, clinical, electrophysiological, biochemical, and structural data. The heterogeneity of SE etiology and semiology renders development of prognostic markers challenging. Currently, prognostication in SE is limited to a few clinical scores. Future research should integrate clinical, genetic and epigenetic, metabolic, inflammatory, and structural biomarkers into prognostication models to approach "personalized medicine" in prognostication of outcomes after SE.
Optimal blood pressure (BP) management is controversial in neurocritically ill patients due to conflicting concerns of worsening ischemia with decreased BP versus cerebral edema and increased ...intracranial pressure with elevated BP. In addition, high‐quality evidence is lacking regarding optimal BP goals in patients with most of these conditions. This review summarizes guideline recommendations and examines the literature for BP management in patients with ischemic stroke, intracerebral hemorrhage, aneurysmal subarachnoid hemorrhage, traumatic brain injury, and spinal cord injury.
Study Objective
To describe the effectiveness and tolerability of conivaptan and tolvaptan for the correction of hyponatremia in neurocritically ill patients.
Design
Retrospective cohort study.
...Setting
Neurointensive care units at two academic medical centers.
Patients
Thirty‐six adults admitted to the neurocritical care unit who received at least one dose of conivaptan (5 patients) or tolvaptan (31 patients) between June 2012 and May 2013.
Measurements and Main Results
A single oral dose or intravenous bolus was administered to 23 (74%) patients who received tolvaptan and 2 (40%) patients who received conivaptan, respectively. The mean maximal increase in serum sodium level at 24 hours following the last dose compared with baseline was 5.2 mEq/L for conivaptan (p=0.05) and 7.9 mEq/L for tolvaptan (p<0.001). The mean ± SD maximal increases in serum sodium level at 48, 72, and 96 hours following the last dose of vaptan therapy compared with baseline were 5.5 ± 2.2 mEq/L (p=0.01), 5.6 ± 2.0 mEq/L (p=0.005), and 4.8 ± 2.2 mEq/L (p=0.03), respectively. Sodium overcorrection occurred in six patients (19%) receiving tolvaptan and none of the patients receiving conivaptan. Hypotension occurred in 20% of patients receiving conivaptan and 52% of patients receiving tolvaptan, whereas hypokalemia was observed in 40% of patients receiving conivaptan.
Conclusion
Use of vaptans in neurocritically ill patients led to a significant increase in serum sodium level at 24 hours after the last dose, which was sustained for 96 hours, with the majority of patients receiving a single dose. Risk of sodium overcorrection was high and necessitates appropriate patient selection and frequent monitoring.
Objective
The purpose of this study was to estimate the time to recovery of command‐following and associations between hypoxemia with time to recovery of command‐following.
Methods
In this ...multicenter, retrospective, cohort study during the initial surge of the United Statesʼ pandemic (March–July 2020) we estimate the time from intubation to recovery of command‐following, using Kaplan Meier cumulative‐incidence curves and Cox proportional hazard models. Patients were included if they were admitted to 1 of 3 hospitals because of severe coronavirus disease 2019 (COVID‐19), required endotracheal intubation for at least 7 days, and experienced impairment of consciousness (Glasgow Coma Scale motor score <6).
Results
Five hundred seventy‐one patients of the 795 patients recovered command‐following. The median time to recovery of command‐following was 30 days (95% confidence interval CI = 27–32 days). Median time to recovery of command‐following increased by 16 days for patients with at least one episode of an arterial partial pressure of oxygen (PaO2) value ≤55 mmHg (p < 0.001), and 25% recovered ≥10 days after cessation of mechanical ventilation. The time to recovery of command‐following was associated with hypoxemia (PaO2 ≤55 mmHg hazard ratio HR = 0.56, 95% CI = 0.46–0.68; PaO2 ≤70 HR = 0.88, 95% CI = 0.85–0.91), and each additional day of hypoxemia decreased the likelihood of recovery, accounting for confounders including sedation. These findings were confirmed among patients without any imagining evidence of structural brain injury (n = 199), and in a non‐overlapping second surge cohort (N = 427, October 2020 to April 2021).
Interpretation
Survivors of severe COVID‐19 commonly recover consciousness weeks after cessation of mechanical ventilation. Long recovery periods are associated with more severe hypoxemia. This relationship is not explained by sedation or brain injury identified on clinical imaging and should inform decisions about life‐sustaining therapies. ANN NEUROL 2022;91:740–755
Sleep plays an important role in the recovery of critically ill patients. However, patients in the intensive care unit (ICU) often suffer sleep disturbances and abnormal circadian rhythms, which may ...increase delirium and lengthen ICU stay. Nonpharmacologic strategies for preventing and treating sleep disturbances and delirium, such as overnight eye masks and ear plugs, are usually employed first, given the lack of adverse effects. However, a multimodal approach to care including pharmacotherapy may be necessary. Despite the limited available data supporting their use, medications such as melatonin, ramelteon, suvorexant, and dexmedetomidine may promote sleep and improve a variety of patient-centric outcomes such as delirium. This narrative review focuses on these nonbenzodiazepine agents used for sleep in the ICU. Practical application of each of these agents is described for when providers choose to utilize one of these pharmacotherapies to promote sleep or prevent delirium.
Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and ...healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR).
A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation.
The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%.
The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.
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