Citations

BACKGROUND: After hospital discharge, patients who had sepsis have increased mortality. We sought to estimate factors associated with postdischarge mortality and how they vary with time after discharge. METHODS: This was a retrospective study of hospital survivors of sepsis using time-varying Cox proportional hazard models, which produce a baseline hazard ratio (HR) and a second number (δHR) that reflects the amount by which the baseline HR changes with time. RESULTS: Of the 32,244 patients who survived sepsis at hospital discharge, 13,565 patients (42%) died (mean ± standard deviation: 1.41 ± 1.87 years) after discharge from the index hospitalization, while 18,679 patients were still alive at follow-up (4.98 ± 2.86 years). The mortality rate decreased with time after discharge: approximately 8.7% of patients died during the first month after discharge, 1.1% of patients died during the 12th month after discharge, and 0.3%% died during the 60th month; after Kaplan-Meier analysis, survival was 91% (95% confidence interval [CI], 91%–92%) at 1 month, 76% (95% CI, 76%–77%) at 1 year, 57% (95% CI, 56%–58%) at 5 years, and 48% (95% CI, 47%–48%) at 10 years after discharge. Organ dysfunction at discharge was associated with worse survival. In particular, elevated urea nitrogen at discharge (HR, 1.10 per 10 mg/dL, 95% CI, 1.08–1.12, P < .001) was associated with increased mortality, but the HR decreased with time from discharge (δHR, 0.98 per 10 mg/dL per year, 95% CI, 0.98–0.99, P < .001). Higher hemoglobin levels were associated with lower mortality (HR, 0.92 per g/dL, 95% CI, 0.91–0.93, P < .001), but this association increased with increasing time after discharge (δHR, 1.02 per g/dL per year, 95% CI, 1.01–1.02, P < .001). Older age was associated with an increased risk of mortality (HR, 1.29 per decade of age, 95% CI, 1.27–1.31, P < .001) that grew with increasing time after discharge (δHR, 1.01 per year of follow-up per decade of age, 95% CI, 1.00–1.02, P < .001). Compared to private insurances Medicaid as primary insurance was associated with an increased risk of mortality (HR, 1.17, 95% CI, 1.10–1.25, P < .001) that did not change with time after discharge. In contrast, Medicare status was initially associated with a similar risk of mortality as private insurance at discharge (HR, 1), but was associated with greater risk as time after discharge increased (δHR, 1.04 per year of follow-up, 95% CI, 1.03–1.05, P < .001). CONCLUSIONS: Acute physiologic derangements and organ dysfunction were associated with postdischarge mortality with the associations decreasing over time.

Background Preclinical studies showed metformin reduces exhaustion of tumor-infiltrating lymphocytes and potentiates programmed cell death protein-1 (PD-1) blockade. We hypothesized that metformin with nivolumab would elicit potent antitumor and immune modulatory activity in metastatic microsatellite stable (MSS) colorectal cancer (CRC). We evaluated this hypothesis in a phase II study. Methods Nivolumab (480 mg) was administered intravenously every 4 weeks while metformin (1000 mg) was given orally, two times per day following a 14-day metformin only lead-in phase. Patients ≥18 years of age, with previously treated, stage IV MSS CRC, and Eastern Cooperative Oncology Group 0–1, having received no prior anti-PD-1 agent were eligible. The primary endpoint was overall response rate with secondary endpoints of overall survival (OS) and progression-free survival (PFS). Correlative studies using paired pretreatment/on-treatment biopsies and peripheral blood evaluated a series of immune biomarkers in the tumor microenvironment and systemic circulation using ChipCytometry and flow cytometry. Results A total of 24 patients were enrolled, 6 patients were replaced per protocol, 18 patients had evaluable disease. Of the 18 evaluable patients, 11/18 (61%) were women and the median age was 58 (IQR 50–67). Two patients had stable disease, but no patients had objective response, hence the study was stopped for futility. Median OS and PFS was 5.2 months (95% CI (3.2 to 11.7)) and 2.3 months (95% CI (1.7 to 2.3)). Most common grade 3/4 toxicities: Anemia (n=2), diarrhea (n=2), and fever (n=2). Metformin alone failed to increase the infiltration of T-cell subsets in the tumor, but combined metformin and nivolumab increased percentages of tumor-infiltrating leukocytes (p=0.031). Dual treatment also increased Tim3+ levels in patient tissues and decreased naïve CD8+T cells (p=0.0475). Conclusions Nivolumab and metformin were well tolerated in patients with MSS CRC but had no evidence of efficacy. Correlative studies did not reveal an appreciable degree of immune modulation from metformin alone, but showed trends in tumorous T-cell infiltration as a result of dual metformin and PD-1 blockade despite progression in a majority of patients.

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

Background: Time-lapse incubators allow for ongoing evaluation of embryos without culture condition disruption. The use of time-lapse incubation has been shown to improve outcomes either by improving overall conditions or providing additional information to aid in embryo selection for transfer. Time-lapse incubators can also utilise morphokinetic models to rank embryos based on morphokinetic parameters. We sought to compare a morphokinetic model for embryo comparison to traditional morphologic evaluation. Aims: The aim of the study is to compare a morphokinetic model for embryo comparison to traditional morphologic evaluation. Settings and Design: This is a retrospective cohort design. Materials and Methods: Embryos cultured in a time-lapse culture system that had traditional morphologic evaluation, morphokinetic modelling and known live birth outcomes were included in this study. Embryos with unknown competence were excluded, including when two embryos were transferred with a single live birth resulted. Statistical Analysis Used: Receiver operating characteristic (ROC) curves were determined for both the morphologic analysis and the morphokinetic model on culture day 3 and day 5. Using the ROC-determined cutoff that optimised both sensitivity and specificity, a binary outcome for each test was analysed using agreement statistics to determine if one method of embryo evaluation was superior to the other. Results: Morphological and morphokinetic grading were both predictive of embryo competence on days 3 and 5. However, on day 3, morphologic grading was superior to morphokinetic grading with area under the curve (AUC) of 0.66 ( P < 0.001) and 0.58 ( P = 0.009), respectively. Contrarily, on day 5, the morphokinetic model had a higher AUC of 0.65 ( P = 0.03) compared to the morphologic grading, AUC 0.56 ( P = 0.02). Conclusion: Traditional morphology was noted to be a better diagnostic tool (higher AUC) on culture day 3 while a morphokinetic model was superior on day 5.

Abstract INTRODUCTION Magnetic resonance imaging (MRI) research has advanced our understanding of neurodegeneration in sporadic early‐onset Alzheimer's disease (EOAD) but studies include small samples, mostly amnestic EOAD, and have not focused on developing an MRI biomarker. METHODS We analyzed MRI scans to define the sporadic EOAD‐signature atrophy in a small sample (n = 25) of Massachusetts General Hospital (MGH) EOAD patients, investigated its reproducibility in the large longitudinal early‐onset Alzheimer's disease study (LEADS) sample (n = 211), and investigated the relationship of the magnitude of atrophy with cognitive impairment. RESULTS The EOAD‐signature atrophy was replicated across the two cohorts, with prominent atrophy in the caudal lateral temporal cortex, inferior parietal lobule, and posterior cingulate and precuneus cortices, and with relative sparing of the medial temporal lobe. The magnitude of EOAD‐signature atrophy was associated with the severity of cognitive impairment. DISCUSSION The EOAD‐signature atrophy is a reliable and clinically valid biomarker of AD‐related neurodegeneration that could be used in clinical trials for EOAD. Highlights We developed an early‐onset Alzheimer's disease (EOAD)–signature of atrophy based on magnetic resonance imaging (MRI) scans. EOAD signature was robustly reproducible across two independent patient cohorts. EOAD signature included prominent atrophy in parietal and posterior temporal cortex. The EOAD‐signature atrophy was associated with the severity of cognitive impairment. EOAD signature is a reliable and clinically valid biomarker of neurodegeneration.

Antiseizure medications (ASMs) are the mainstay of symptomatic epilepsy treatment. The primary goal of pharmacotherapy with ASMs in epilepsy is to achieve complete seizure remission while minimizing therapy-related adverse events. Over the years, more ASMs have been introduced, with approximately 30 now in everyday use. With such a wide variety, much guidance is needed in choosing ASMs for initial therapy, subsequent replacement monotherapy, or adjunctive therapy. The specific ASMs are typically tailored by the patient’s related factors, including epilepsy syndrome, age, sex, comorbidities, and ASM characteristics, including the spectrum of efficacy, pharmacokinetic properties, safety, and tolerability. Weighing these key clinical variables requires experience and expertise that may be limited. Furthermore, with this approach, patients may endure multiple trials of ineffective treatments before the most appropriate ASM is found. A more reliable way to predict response to different ASMs is needed so that the most effective and tolerated ASM can be selected. Soon, alternative approaches, such as deep machine learning (ML), could aid the individualized selection of the first and subsequent ASMs. The recognition of epilepsy as a network disorder and the integration of personalized epilepsy networks in future ML platforms can also facilitate the prediction of ASM response. Augmenting the conventional approach with artificial intelligence (AI) opens the door to personalized pharmacotherapy in epilepsy. However, more work is needed before these models are ready for primetime clinical practice.