Perfusion-Dependent Focal Neurologic Deficits in a Critically Ill Heart Transplant Recipient: A Case of Tacrolimus-Associated Reversible Cerebral Vasospasm Syndrome?

TACROLIMUS, a mainstay of immunosuppression after orthotopic heart transplantation (OHT), is associated with a broad range of side effects. Vasoconstriction caused by tacrolimus has been proposed as a mechanism underlying common side effects such as hypertension and renal injury. Neurologic side effects attributed to tacrolimus include headaches, posterior reversible encephalopathy syndrome (PRES), or reversible cerebral vasospasm syndrome (RCVS). Six case reports have been published describing RCVS in the setting of tacrolimus administration after OHT. The authors report a case of perfusion-dependent focal neurologic deficits attributed to tacrolimus-induced RCVS in an OHT recipient.

Recommendations for Defining and Reporting Adherence Measured by Biometric Monitoring Technologies: Systematic Review.

BACKGROUND: Suboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence. OBJECTIVE: We aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data. METHODS: We conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution). RESULTS: Our PubMed search terms identified 940 manuscripts; 100 (10.6%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools. CONCLUSIONS: We recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports.

Immunophenotypic Differences in Cerebrospinal Fluid and Peripheral Blood Demonstrating Cancer Heterogenicity in Acute Myeloid Leukemia Patient.

A diagnosis of acute myeloid leukemia involving the central nervous system (CNS) can be confirmed through cerebrospinal fluid (CSF) and serum flow cytometry. These two detection methods should demonstrate the same immunophenotype due to hematogenous dissemination. Here, we reported a 65-year-old male diagnosed with CNS leukemia with differing immunophenotypes between CSF and peripheral blood. This immunophenotypic shift may suggest leukemic migration within the blood-brain barrier. In addition, the case highlights the concept of leukemic heterogeneity and the importance of considering cancer heterogeneity when analyzing a tumor's genetic profile and selecting therapy for patients.

Understanding Heart Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions.

Heart failure (HF) is a major cause of mortality. Accurately monitoring HF progress and adjusting therapies are critical for improving patient outcomes. An experienced cardiologist can make accurate HF stage diagnoses based on combination of symptoms, signs, and lab results from the electronic health records (EHR) of a patient, without directly measuring heart function. We examined whether machine learning models, more specifically the XGBoost model, can accurately predict patient stage based on EHR, and we further applied the SHapley Additive exPlanations (SHAP) framework to identify informative features and their interpretations. Our results indicate that based on structured data from EHR, our models could predict patients' ejection fraction (EF) scores with moderate accuracy. SHAP analyses identified informative features and revealed potential clinical subtypes of HF. Our findings provide insights on how to design computing systems to accurately monitor disease progression of HF patients through continuously mining patients' EHR data.