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BACKGROUND Post-mastectomy free-flap breast reconstruction is becoming increasingly common in the United States. However, predicting which patients may suffer complications remains challenging. We sought to apply the validated modified frailty index (mFI) to free-flap breast reconstruction in breast cancer patients and determine its utility in predicting negative outcomes. METHODS We conducted a retrospective study using National Surgical Quality Improvement Project (NSQIP). All patients who had a CPT code of 19364, indicative of free tissue transfer for breast cancer reconstruction, were included. Data on preoperative characteristics and postoperative outcomes were collected. Patients were separated based on the number of mFI factors present into three categories: 0, 1, and > 2 factors. Preoperative demographics, clinical status, and other comorbidities were also studied. Negative outcomes were compared using multivariate logistic regression. RESULTS 11,852 patients (mean age 50.9 ± 9.5) were found; 24.2% had complications, comparable to previous literature. mFI is predictive of all types of negative outcomes. 22.5% of all patients with 0 mFI, 27.7% of patients with 1 mFI and 34.2% of patients with at least two mFI had a negative outcome. The most common factors contributing to the mFI were history of hypertension (24.8%) and diabetes (6.1%). mFI was found to be an isolated risk factor for negative outcomes, along with steroid use, American Society of Anesthesiology (ASA) classification, body mass index, and immediate, and bilateral operations. CONCLUSIONS This NSQIP-based study for patients undergoing free flap breast reconstruction shows that the mFI holds predictive value regarding negative outcomes. This provides more information to properly counsel patients before free flap breast reconstruction surgery.

Objective Functional seizures often are managed incorrectly as a diagnosis of exclusion. However, a significant minority of patients with functional seizures may have abnormalities on neuroimaging that typically are associated with epilepsy, leading to diagnostic confusion. We evaluated the rate of epilepsy-associated findings on MRI, FDG-PET, and CT in patients with functional seizures. Methods We studied radiologists' reports from neuroimages at our comprehensive epilepsy center from a consecutive series of patients diagnosed with functional seizures without comorbid epilepsy from 2006 to 2019. We summarized the MRI, FDG-PET, and CT results as follows: within normal limits, incidental findings, unrelated findings, non-specific abnormalities, post-operative study, epilepsy risk factors (ERF), borderline epilepsy-associated findings (EAF), and definitive EAF. Results Of the 256 MRIs, 23% demonstrated ERF (5%), borderline EAF (8%), or definitive EAF (10%). The most common EAF was hippocampal sclerosis, with the majority of borderline EAF comprising hippocampal atrophy without T2 hyperintensity or vice versa. Of the 87 FDG-PETs, 26% demonstrated borderline EAF (17%) or definitive EAF (8%). Epilepsy-associated findings primarily included focal hypometabolism, especially of the temporal lobes, with borderline findings including subtle or questionable hypometabolism. Of the 51 CTs, only 2% had definitive EAF. Significance This large case series provides further evidence that, while uncommon, EAF are seen in patients with functional seizures. A significant portion of these abnormal findings are borderline. The moderately high rate of these abnormalities may represent framing bias from the indication of the study being “seizures,” the relative subtlety of EAF, or effects of antiseizure medications.

IntroductionDelirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome.Methods and analysisP-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1–2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time.Ethics and disseminationP-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media.Trial registration numberNCT03291626.

Objective Postictal generalized electroencephalographic suppression (PGES) has been defined as electroencephalographic (EEG) activity of less than 10 microvolts following a generalized seizure. PGES is associated with an increased risk of sudden unexplained death in epilepsy, as well as treatment efficacy of electroconvulsive therapy (ECT). We investigated the impact of anesthetic on PGES expression and temporal characteristics. Methods We recorded postictal EEG in 50 ECT sessions in 11 patients with treatment resistant depression (ClinicalTrials.gov NCT02761330). For each participant, repeated sessions included either ketamine or etomidate general anesthesia during ECT. An automated algorithm was employed to detect PGES within 5 minutes after seizure termination. Results PGES was detected in 31/50 recordings, with intermittent epochs recurring up to five minutes after seizure termination. PGES total duration was greater following ketamine than etomidate anesthesia (p = 0.04). PGES expression declined loglinearly as a function of time (r = -0.89, p Conclusions PGES can occur intermittently for several minutes following seizure termination. Anesthetic effects should be considered when correlating PGES duration to clinical outcomes. Significance Prolonged EEG monitoring several minutes following seizure termination may be necessary to fully evaluate the presence and total duration of PGES.

Electroconvulsive therapy (ECT) relies on the electrical induction of generalized seizures to treat major depressive disorder and other psychiatric illnesses. These planned procedures provide a clinically relevant model system for studying neurophysiologic characteristics of generalized seizures. We recently described novel central-positive complexes (CPCs), which were observed during ECT-induced seizures as generalized, high-amplitude waveforms with maximum positive voltage over the vertex. Here, we performed a systematic characterization of 6,928 CPC ictal waveforms recorded in 11 patients undergoing right unilateral (RUL) ECT. Analyses of high-density 65-electrode EEG recordings during these 50 seizures allowed evaluation of these CPCs across temporal, spatial, and spectral domains. Peak-amplitude CPC scalp topology was consistent across seizures, showing maximal positive polarity over the midline fronto-central region and maximal negative polarity over the suborbital regions. Total duration of CPCs positively correlated with the time required for return of responsiveness after ECT treatment (r = 0.39, p = 0.005). The rate of CPCs showed a frequency decline consistent with an exponential decay (median 0.032 (IQR 0.053) complexes/second). Gamma band (30-80 Hz) oscillations correlated with the peak amplitude of CPCs, which was also reproducible across seizures, with band power declining over time (r = −0.32, p < 10−7). The sources of these peak potentials were localized to the bilateral medial thalamus and cingulate cortical regions. Our findings demonstrated CPC characteristics that were invariant to participant, stimulus charge, time, and agent used to induce general anesthesia during the procedure. Consistent with ictal waveforms of other generalized epilepsy syndromes, CPCs showed topographic distribution over the fronto-central regions, predictable intra-seizure frequency decline, and correlation with gamma-range frequencies. Furthermore, source localization to the medial thalamus was consistent with underlying thalamocortical pathophysiology, as established in generalized epilepsy syndromes. The consistency and reproducibility of CPCs offers a new avenue for studying the dynamics of seizure activity and thalamocortical networks.

Objective Postictal generalized electroencephalographic suppression (PGES) is a pattern of low-voltage scalp electroencephalographic (EEG) activity following termination of generalized seizures. PGES has been associated with both sudden unexplained death in patients with epilepsy and therapeutic efficacy of electroconvulsive therapy (ECT). Automated detection of PGES epochs may aid in reliable quantification of this phenomenon. Methods We developed a voltage-based algorithm for detecting PGES. This algorithm applies existing criteria to simulate expert epileptologist readings. Validation relied on postictal EEG recording from patients undergoing ECT (NCT02761330), assessing concordance among the algorithm and four clinical epileptologists. Results We observed low-to-moderate concordance among epileptologist ratings of PGES. Despite this, the algorithm displayed high discriminability in comparison to individual epileptologists (c-statistic range: 0.86-0.92). The algorithm displayed high discrimination (c-statistic: 0.91) and substantial peak agreement (Cohen’s Kappa: 0.65) in comparison to a consensus of clinical ratings. Interrater agreement between the algorithm and individual epileptologists was on par with that among expert epileptologists. Conclusions An automated voltage-based algorithm can be used to detect PGES following ECT, with discriminability nearing that of experts. Significance Algorithmic detection may support clinical readings of PGES and improve precision when correlating this marker with clinical outcomes following generalized seizures.