Citations

Objective: Parents frequently purchase and inquire about smartwatch devices to monitor child behaviors and functioning. This pilot study examined the feasibility and accuracy of using smartwatch monitoring for the prediction of disruptive behaviors. Methods: The study enrolled children ( N = 10) aged 7–10 years hospitalized for the treatment of disruptive behaviors. The study team completed continuous behavioral phenotyping during study participation. The machine learning protocol examined severe behavioral outbursts (operationalized as episodes that preceded physical restraint) for preparing the training data. Supervised machine learning methods were trained with cross-validation to predict three behavior states—calm, playful, and disruptive. Results: The participants had a 90% adherence rate for per protocol smartwatch use. Decision trees derived conditional dependencies of heart rate, sleep, and motor activity to predict behavior. A cross-validation demonstrated 80.89% accuracy of predicting the child's behavior state using these conditional dependencies. Conclusion: This study demonstrated the feasibility of 7-day continuous smartwatch monitoring for children with severe disruptive behaviors. A machine learning approach characterized predictive biomarkers of impending disruptive behaviors. Future validation studies will examine smartwatch physiological biomarkers to enhance behavioral interventions, increase parental engagement in treatment, and demonstrate target engagement in clinical trials of pharmacological agents for young children.

ABSTRACT Cefepime exhibits highly variable pharmacokinetics in critically ill patients. The purpose of this study was to develop and qualify a population pharmacokinetic model for use in the critically ill and investigate the impact of various estimated glomerular filtration rate (eGFR) equations using creatinine, cystatin C, or both on model parameters. This was a prospective study of critically ill adults hospitalized at an academic medical center treated with intravenous cefepime. Individuals with acute kidney injury or on kidney replacement therapy or extracorporeal membrane oxygenation were excluded. A nonlinear mixed-effects population pharmacokinetic model was developed using data collected from 2018 to 2022. The 120 included individuals contributed 379 serum samples for analysis. A two-compartment pharmacokinetic model with first-order elimination best described the data. The population mean parameters (standard error) in the final model were 7.84 (0.24) L/h for CL1 and 15.6 (1.45) L for V1. Q was fixed at 7.09 L/h and V2 was fixed at 10.6 L, due to low observed interindividual variation in these parameters. The final model included weight as a covariate for volume of distribution and the eGFR cr-cysC (mL/min) as a predictor of drug clearance. In summary, a population pharmacokinetic model for cefepime was created for critically ill adults. The study demonstrated the importance of cystatin C to prediction of cefepime clearance. Cefepime dosing models which use an eGFR equation inclusive of cystatin C are likely to exhibit improved accuracy and precision compared to dosing models which incorporate an eGFR equation with only creatinine.

Background: Cerebrovascular disease is a life-threatening neurological event and a leading cause of long-term disability and death worldwide. Early detection of characteristic behavioral and physiological changes associated with cerebrovascular disease is critical to improving patient outcomes and quality of life measures. The growing prevalence of remote monitoring tools, from wearable devices to smartphone applications, that facilitate in situ observation of patients and the environments of daily life holds promise for more timely recognition and possible prevention of cerebrovascular accidents (CVA) like stroke. Objective: The goal of this scoping review is to examine and establish categories of innovation with digital sensors that monitor physiological and behavioral variables in situ to augment the current screening and diagnostic processes for patients with cerebrovascular disease. Methods: Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist, a robust search strategy for spanning multiple databases from 2012 to September 30, 2022, excluding review articles, articles including interventions, and articles not published in English, was implemented. Among the databases searched were Web of Science; Scopus; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; and Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily. Results: This search strategy aggregated 689 articles, of which 101 (14.7%) articles met the inclusion criteria for this scoping review. Articles were divided into two categories based on their focus: physiological and behavioral. Articles with a physiological focus were sorted into one of nine subcategories according to the signal(s) monitored: motor function, heart rhythm, heart rate, kinematic analysis, physical activity, blood pressure, sensory deficit, electrodermal activity, and intracranial pressure. Articles focusing on behavioral variables were sorted into two subcategories: mood and fatigue. Most studies used an ECG-enabled smartwatch, like an Apple Watch 3, or passive smartphone sensors. Conclusions: This scoping review identified disparate methods and conclusions associated with the use of digital sensors for in situ physiological and behavioral monitoring of cerebrovascular disease patients. While most articles evaluated pilot validation and feasibility trials, the lack of randomized controlled trials is a critical literature gap specific to this evolving research area.

Abstract Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications, termed “long COVID”. It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery outcomes. We examined post-acute neuropsychological profiles following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits, although mild subjective attention and memory complaints were reported. Vaccination was associated with membership in this “normal cognition” phenotype. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processing speed, and fatigue were predominant. Risk factors for membership in the “memory-speed impaired” neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder “dysexecutive” neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery outcomes at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing improvement in verbal memory and psychomotor speed, the dysexecutive group showing improvement in cognitive flexibility, and the memory-speed impaired group showing no objective improvement and relatively worse functional outcomes compared to the other two clusters. These results indicate that there are multiple post-acute neurophenotypes of long COVID, with different etiological pathways and recovery outcomes. This information may inform phenotype-specific approaches to treatment.

Abstract Primary sclerosing cholangitis (PSC) is a complex bile duct disorder. Its etiology is incompletely understood, but environmental chemicals likely contribute to risk. Patients with PSC have an altered bile metabolome, which may be influenced by environmental chemicals. This novel study utilized state-of-the-art high-resolution mass spectrometry (HRMS) with bile samples to provide the first characterization of environmental chemicals and metabolomics (collectively, the exposome) in PSC patients located in the United States of America (USA) (n = 24) and Norway (n = 30). First, environmental chemical- and metabolome-wide association studies were conducted to assess geographic-based similarities and differences in the bile of PSC patients. Nine environmental chemicals (false discovery rate, FDR < 0.20) and 3143 metabolic features (FDR < 0.05) differed by site. Next, pathway analysis was performed to identify metabolomic pathways that were similarly and differentially enriched by the site. Fifteen pathways were differentially enriched (P < .05) in the categories of amino acid, glycan, carbohydrate, energy, and vitamin/cofactor metabolism. Finally, chemicals and pathways were integrated to derive exposure–effect correlation networks by site. These networks demonstrate the shared and differential chemical–metabolome associations by site and highlight important pathways that are likely relevant to PSC. The USA patients demonstrated higher environmental chemical bile content and increased associations between chemicals and metabolic pathways than those in Norway. Polychlorinated biphenyl (PCB)-118 and PCB-101 were identified as chemicals of interest for additional investigation in PSC given broad associations with metabolomic pathways in both the USA and Norway patients. Associated pathways include glycan degradation pathways, which play a key role in microbiome regulation and thus may be implicated in PSC pathophysiology.

Abstract Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications, termed “long COVID”. It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery trajectories. We examined post-acute outcomes following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective neuropsychological measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits (“normal cognition” neurophenotype), although mild subjective attention and memory complaints were reported. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processed speed, and fatigue were predominant. Risk factors for membership in the “memory-speed impaired” neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder “dysexecutive” neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery trajectories at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing stability, the dysexecutive group showing improvement, and the memory-speed impaired group showing persistent processing speed deficits and fatigue, as well as worse functional outcomes. These results indicate that there are multiple post-acute neurophenotypes of long COVID, with different etiological pathways and recovery trajectories. This information may inform phenotype-specific approaches to treatment.

Abstract Aim To appraise the current evidence on the efficacy and safety of lamotrigine (LAM) in the treatment of pediatric mood disorders (PMD) (i.e., Major Depressive disorder [MDD], bipolar disorder [BD]). Methods Major databases were searched for randomized controlled trials (RCTs), open‐label trials, and observational studies reporting on pediatric (age < 18 years) patients treated with LAM for mood disorders. Results A total of 3061 abstracts were screened and seven articles were selected for inclusion. Seven studies (319 BD and 43 MDD patients), including one RCT ( n = 173), three prospective ( n = 105), and three retrospective ( n = 84) studies, met the study criteria with a study duration range from 8 to 60.9 weeks. The mean age of this pooled data is 14.6 ± 2.0 years. LAM daily dosage varied from 12.5 to 391.3 mg/day among the studies. In an important finding, the RCT reported favorable outcomes with LAM (HR = 0.46; p = 0.02) in 13‐ to 17‐year‐old age group as compared with 10‐ to 12‐year‐old age group (HR = 0.93; p = 0.88). In addition, time to occurrence of a bipolar event trended toward favoring LAM over placebo. All the studies identified LAM as an effective and safe drug in PMDs especially, BDs. Overall, LAM was well tolerated with no major significant side effects and no cases of Stevens–Johnson syndrome. Conclusions Most studies suggested that LAM was safe and effective in pediatric patients with mood disorders. However, the data regarding the therapeutic range for LAM are lacking. Based on the data, there is inconsistent evidence to make conclusive recommendations on therapeutic LAM dosage for mood improvement in the pediatric population. Further studies including larger sample sizes are required to address this relevant clinical question.

AbstractBackground and ObjectivesSubstance use disorders (SUDs) are chronic relapsing diseases characterized by significant morbidity and mortality. Phenomenologically, patients with SUDs present with a repeating cycle of intoxication, withdrawal, and craving, significantly impacting their diagnosis and treatment. There is a need for better identification and monitoring of these disease states. Remote monitoring chronic illness with wearable devices offers a passive, unobtrusive, constant physiological data assessment. We evaluate the current evidence base for remote monitoring of nonalcohol, nonnicotine SUDs.MethodsWe performed a systematic, comprehensive literature review and screened 1942 papers.ResultsWe found 15 studies that focused mainly on the intoxication stage of SUD. These studies used wearable sensors measuring several physiological parameters (ECG, HR, O2, Accelerometer, EDA, temperature) and implemented study‐specific algorithms to evaluate the data.Discussion and ConclusionsStudies were extracted, organized, and analyzed based on the three SUD disease states. The sample sizes were relatively small, focused primarily on the intoxication stage, had low monitoring compliance, and required significant computational power preventing “real‐time” results. Cardiovascular data was the most consistently valuable data in the predictive algorithms. This review demonstrates that there is currently insufficient evidence to support remote monitoring of SUDs through wearable devices.Scientific SignificanceThis is the first systematic review to show the available data on wearable remote monitoring of SUD symptoms in each stage of the disease cycle. This clinically relevant approach demonstrates what we know and do not know about the remote monitoring of SUDs within disease states.

Abstract The human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) proteins play key roles in the cellular internalization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus responsible for the coronavirus disease of 2019 (COVID-19) pandemic. We set out to functionally characterize the ACE2 and TMPRSS2 protein abundance for variant alleles encoding these proteins that contained non-synonymous single-nucleotide polymorphisms (nsSNPs) in their open reading frames (ORFs). Specifically, a high-throughput assay, deep mutational scanning (DMS), was employed to test the functional implications of nsSNPs, which are variants of uncertain significance in these two genes. Specifically, we used a ‘landing pad’ system designed to quantify the protein expression for 433 nsSNPs that have been observed in the ACE2 and TMPRSS2 ORFs and found that 8 of 127 ACE2, 19 of 157 TMPRSS2 isoform 1 and 13 of 149 TMPRSS2 isoform 2 variant proteins displayed less than ~25% of the wild-type protein expression, whereas 4 ACE2 variants displayed 25% or greater increases in protein expression. As a result, we concluded that nsSNPs in genes encoding ACE2 and TMPRSS2 might potentially influence SARS-CoV-2 infectivity. These results can now be applied to DNA sequence data for patients infected with SARS-CoV-2 to determine the possible impact of patient-based DNA sequence variation on the clinical course of SARS-CoV-2 infection.