Association among craniofacial morphology, ethnicity, and risk of pediatric sleep-related breathing disorders: A multicenter study.

INTRODUCTION: Craniofacial morphology and ethnicity may be risk factors for sleep-related breathing disorder (SRBD) in children but have not yet been assessed in an international multicenter study. The objectives of this study were to assess the association among craniofacial features, self-reported ethnicity, and risk of SRBD in children undergoing orthodontic treatment. METHODS: Children aged 5-18 years who presented for orthodontic evaluation were enrolled in the United States, South Africa, South Korea, Saudi Arabia, and Japan. The risk of SRBD was defined as answering ≥0.33 positive responses to the Pediatric Sleep Questionnaire. Craniofacial features included measurements in sagittal and vertical dimensions to evaluate the cranial base, maxillomandibular and dental relationships, and nasopharyngeal airway dimensions. Logistic regression was performed to assess the association among craniofacial features, ethnicity, age, body mass index, and risk of SRBD. RESULTS: Data were obtained from 602 patients from 5 sites. A total of 76 patients (12.6%) had a risk of SRBD. The mean age was 12.5 years. Male gender (odds ratio [OR], 1.9; 95% confidence interval [CI], 1.0-3.4; P = 0.041), Middle Eastern ethnicity (OR, 10.2; 95% CI, 4.1-25.4; P = 0.001), body mass index (OR, 1.1; 95% CI, 1.04-1.10; P = 0.001), gonial angle (OR, 0.91; 95% CI, 0.85-0.98; P = 0.011), and inferiorly positioned hyoid (OR, 1.1; 95% CI, 1.0-1.2; P = 0.002) were significantly associated with the risk of SRBD. CONCLUSIONS: In an ambidirectional cohort study across 5 sites, male gender, Middle Eastern ethnicity, body mass index, gonial angle, and inferiorly positioned hyoid were associated with the risk of SRBD in children undergoing orthodontic treatment.

Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling.

Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate the effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations (PCLR), which creates latent representations of electrocardiograms (ECGs) from a large number of unlabeled examples using contrastive learning. The resulting representations are expressive, performant, and practical across a wide spectrum of clinical tasks. We develop PCLR using a large health care system with over 3.2 million 12-lead ECGs and demonstrate that training linear models on PCLR representations achieves a 51% performance increase, on average, over six training set sizes and four tasks (sex classification, age regression, and the detection of left ventricular hypertrophy and atrial fibrillation), relative to training neural network models from scratch. We also compared PCLR to three other ECG pre-training approaches (supervised pre-training, unsupervised pre-training with an autoencoder, and pre-training using a contrastive multi ECG-segment approach), and show significant performance benefits in three out of four tasks. We found an average performance benefit of 47% over the other models and an average of a 9% performance benefit compared to best model for each task. We release PCLR to enable others to extract ECG representations at https://github.com/broadinstitute/ml4h/tree/master/model_zoo/PCLR.

Late onset atrial fibrillation in patients undergoing surgical aortic valve replacement.

OBJECTIVE: Aortic valve disease is a risk factor for atrial fibrillation (AF), and AF is associated with increased late mortality and morbidity after cardiac surgery. The evolution of alternative approaches to AF prophylaxis, including less invasive technologies and medical therapies, has altered the balance between risk and potential benefit for prophylactic intervention at the time of surgical aortic valve replacement (SAVR). Such interventions impose incremental risk, however, making an understanding of predictors of new onset AF that persists beyond the perioperative episode relevant. METHODS: We conducted a retrospective single-institution cohort analysis of patients undergoing SAVR with no history of preoperative AF (n = 1014). These patients were cross-referenced against an institutional electrocardiogram (ECG) database to identify those with ECGs 3-12 months after surgery. Logistic regression was used to identify predictors of late AF. RESULTS: Among the 401 patients (40%), who had ECGs in our institution 3-12 months after surgery, 16 (4%) had late AF. Patients with late AF were older than patients without late AF (73 vs. 65, p = .025), and underwent procedures that were more urgent/emergent (38% vs. 15%, p = .015), with higher predicted risk of mortality (2.2% vs. 1.3%, p = .012). Predictors associated with the development of late AF were advanced age, higher preoperative creatinine level and urgent/emergent surgery. CONCLUSIONS: The incidence of late AF 3-12 months after SAVR, is low. Prophylactic AF interventions at the time of SAVR may not be warranted.

Stellate ganglion blockade for treating refractory electrical storm: a historical cohort study.

BACKGROUND: Stellate ganglion blockade (SGB) has been used to treat electrical storm (ES) refractory to antiarrhythmic therapy or to stabilize patients before more definitive intervention. Nevertheless, its efficacy is not well understood, with only a few case reports and retrospective case series in the literature. METHODS: We conducted a historical cohort study on patients with drug-refractory ES who underwent ultrasound-guided unilateral SGB from 1 January 2010 until 19 July 2019 at two hospital sites. Stellate ganglion blockade was performed with variable combinations of bupivacaine, lidocaine, ropivacaine, and dexamethasone. We collected data on demographic and procedural characteristics, the number of arrhythmias and defibrillation episodes, antiarrhythmic and anticoagulant medication, left ventricular ejection fraction (EF), and respiratory support requirement. RESULTS: We identified N = 13 patients; their mean (standard deviation [SD]) age was 64 (13) yr, and 10 (77%) were male. The baseline mean (SD) number of overall arrhythmia and defibrillation episodes per day were 9 (6) and 4 (3), respectively; the mean (SD) pre-SGB EF was 23 (7)%. Seven patients (54%) received dexamethasone in addition to local anesthetic for SGB. One patient experienced hypotension after SGB. Arrhythmias and defibrillation episodes significantly decreased at 24, 48, 72, and 96 hr after SGB; at 96 hr, 62% and 92% of patients had no VA and defibrillation episodes, respectively (P < 0.001 for all time points). Ejection fraction and the number of patients receiving antiarrhythmic medications or requiring respiratory support were unchanged. CONCLUSIONS: Unilateral SGB was associated with a reduction in arrhythmias and defibrillation episodes, but did not affect antiarrhythmic medication, respiratory support, or EF. Randomized controlled trials on larger cohorts are needed to confirm these findings.

RéSUMé: CONTEXTE: Le bloc du ganglion stellaire (BGS) a été employé pour traiter les tempêtes électriques réfractaires à la thérapie antiarythmique ou pour stabiliser les patients avant une intervention plus définitive. Néanmoins, son efficacité n'est pas bien comprise, et il n'existe que quelques présentations de cas et séries de cas rétrospectives dans la littérature. MéTHODE: Nous avons mené une étude de cohorte historique auprès de patients souffrant de tempêtes électriques réfractaires aux médicaments qui ont subi un BGS unilatéral échoguidé entre le 1er janvier 2010 et le 19 juillet 2019 dans deux sites hospitaliers. Le bloc du ganglion stellaire a été réalisé à l'aide de combinaisons variables de bupivacaïne, de lidocaïne, de ropivacaïne et de dexaméthasone. Nous avons colligé les données touchant aux caractéristiques démographiques et procédurales, au nombre d'arythmies et d'épisodes de défibrillation, aux traitements antiarythmique et anticoagulant, à la fraction d'éjection (FE) ventriculaire gauche, et au besoin d'assistance respiratoire. RéSULTATS: Nous avons identifié N = 13 patients; leur âge moyen (écart type [ÉT]) était de 64 (13) ans, et 10 (77 %) patients étaient des hommes. Globalement, le nombre moyen (ÉT) d'épisodes d'arythmie et de défibrillation de base par jour était de 9 (6) et 4 (3), respectivement; la FE moyenne (ÉT) pré-BGS était de 23 (7) %. Sept patients (54 %) ont reçu de la dexaméthasone en plus de l'anesthésique local pour le BGS. Un patient a souffert d'hypotension après le BGS. Les arythmies et les épisodes de défibrillation ont diminué de manière significative à 24, 48, 72, et 96 heures après le BGS; à 96 heures, 62 % et 92 % des patients ne subissaient plus aucun épisode d'arythmie ventriculaire et de défibrillation, respectivement (P < 0,001 pour tous les temps). La fraction d'éjection et le nombre de patients recevant des médicaments antiarythmiques ou nécessitant une assistance respiratoire sont demeurés inchangés. CONCLUSION: Un BGS unilatéral a été associé à une réduction des épisodes d'arythmies et de défibrillation, mais n'a pas eu d'impact sur le traitement antiarythmique, l'assistance respiratoire, ou la FE. Des études randomisées contrôlées réalisées avec des cohortes plus importantes sont nécessaires pour confirmer ces résultats.

Use of a wearable device to assess sleep and motor function in Duchenne muscular dystrophy.

INTRODUCTION: Rest-activity disruption is an important feature of Duchenne muscular dystrophy (DMD). We sought to describe sleep impairment and its relationship to quality of life (QOL) and to evaluate associations between rest-activity, sleep quality, and 6-minute walk test (6MWT) in DMD. METHODS: Sleep impairment and its relationship to QOL was assessed by questionnaire in 54 children (33 ambulatory, 21 nonambulatory) with DMD. Rest-activity characteristics were calculated for 23 of these children (14 ambulatory, nine nonambulatory) by actigraphy. RESULTS: Pathologic sleep was reported in 11 (20%) participants and correlated with lower QOL but not with ambulatory status. In ambulatory participants who completed actigraphy, rest-activity rhythm fragmentation was associated with subjective sleep impairment. Habitual daytime activity level was associated with 6MWT performance. DISCUSSION: Children with DMD experience substantial sleep impairment that is related to QOL. Wrist actigraphy may be a parsimonious tool for monitoring both sleep and motor impairment in ambulatory children with DMD.

Spinal cord ependymoma: a review of the literature and case series of ten patients.

Spinal cord ependymoma (SCE) is a rare tumor that is most commonly low-grade. Complete surgical resection has been established as first-line treatment and can be curative. However, SCEs tend to recur when complete tumor resection is not possible. Evidence supporting the use of adjuvant radiation and chemotherapy is not definitive. We review the most recent literature on SCE covering a comprehensive range of topics spanning the biology, presentation, clinical management, and outcomes. In addition, we present a case series of ten SCE patients with the goal of contributing to existing knowledge of this rare disease.

Prediction of operative mortality for patients undergoing cardiac surgical procedures without established risk scores.

OBJECTIVE: Current cardiac surgery risk models do not address a substantial fraction of procedures. We sought to create models to predict the risk of operative mortality for an expanded set of cases. METHODS: Four supervised machine learning models were trained using preoperative variables present in the Society of Thoracic Surgeons (STS) data set of the Massachusetts General Hospital to predict and classify operative mortality in procedures without STS risk scores. A total of 424 (5.5%) mortality events occurred out of 7745 cases. Models included logistic regression with elastic net regularization (LogReg), support vector machine, random forest (RF), and extreme gradient boosted trees (XGBoost). Model discrimination was assessed via area under the receiver operating characteristic curve (AUC), and calibration was assessed via calibration slope and expected-to-observed event ratio. External validation was performed using STS data sets from Brigham and Women's Hospital (BWH) and the Johns Hopkins Hospital (JHH). RESULTS: Models performed comparably with the highest mean AUC of 0.83 (RF) and expected-to-observed event ratio of 1.00. On external validation, the AUC was 0.81 in BWH (RF) and 0.79 in JHH (LogReg/RF). Models trained and applied on the same institution's data achieved AUCs of 0.81 (BWH: LogReg/RF/XGBoost) and 0.82 (JHH: LogReg/RF/XGBoost). CONCLUSIONS: Machine learning models trained on preoperative patient data can predict operative mortality at a high level of accuracy for cardiac surgical procedures without established risk scores. Such procedures comprise 23% of all cardiac surgical procedures nationwide. This work also highlights the value of using local institutional data to train new prediction models that account for institution-specific practices.

Research Reputation Rank of US Medical Schools Is Inversely Associated with Increased Competition Amongst Medical Students.

Introduction: We aim to compare the experience of medical students at higher- versus lower-ranked medical schools in terms of levels of competition, feelings of animosity amongst students, emphasis on subspecialty choice, and the influence of financial situation on choice of residency specialty. Methods: Students at randomly chosen medical schools were contacted via email and administered a 10-question survey assessing levels of burn out, peer relations, and school pressures on career choice. Results: Perceptions of adverse competition were higher in the non-top-45 cohort. Pressure to pursue subspecialty training was higher in the top-45 cohort. Discussion: Medical school reputation appears to be inversely correlated with symptoms of animosity amongst peers, and these findings should be evaluated for downstream ramifications.

A Deep Learning Model for Inferring Elevated Pulmonary Capillary Wedge Pressures From the 12-Lead Electrocardiogram.

Background: Central hemodynamic parameters are typically measured via pulmonary artery catherization-an invasive procedure that involves some risk to the patient and is not routinely available in all settings. Objectives: This study sought to develop a noninvasive method to identify elevated mean pulmonary capillary wedge pressure (mPCWP). Methods: We leveraged data from 248,955 clinical records at the Massachusetts General Hospital to develop a deep learning model that can infer when the mPCWP >15 mmHg using the 12-lead electrocardiogram (ECG). Of these data, 242,216 records were used to pre-train a model that generates useful ECG representations. The remaining 6,739 records contain encounters with direct measurements of the mPCWP. Eighty percent of these data were used for model development and testing (5,390), and the remaining records comprise a holdout set (1,349) that was used to evaluate the model. We developed an associated unreliability score that identifies when model predictions are likely to be untrustworthy. Results: The model achieves an area under the receiver operating characteristic curve (AUC) of 0.80 ± 0.02 (test set) and 0.79 ± 0.01 (holdout set). Model performance varies as a function of the unreliability, where patients with high unreliability scores correspond to a subgroup where model performance is poor: for example, patients in the holdout set with unreliability scores in the highest decile have a reduced AUC of 0.70 ± 0.06. Conclusions: The mPCWP can be inferred from the ECG, and the reliability of this inference can be measured. When invasive monitoring cannot be expeditiously performed, deep learning models may provide information that can inform clinical care.

Physiology as a Lingua Franca for Clinical Machine Learning.

The intersection of medicine and machine learning (ML) has the potential to transform healthcare. We describe how physiology, a foundational discipline of medical training and practice with a rich quantitative history, could serve as a starting point for the development of a common language between clinicians and ML experts, thereby accelerating real-world impact.

Association of Red Blood Cell Distribution Width With Mortality Risk in Hospitalized Adults With SARS-CoV-2 Infection.

Importance: Coronavirus disease 2019 (COVID-19) is an acute respiratory illness with a high rate of hospitalization and mortality. Biomarkers are urgently needed for patient risk stratification. Red blood cell distribution width (RDW), a component of complete blood counts that reflects cellular volume variation, has been shown to be associated with elevated risk for morbidity and mortality in a wide range of diseases. Objective: To investigate whether an association between mortality risk and elevated RDW at hospital admission and during hospitalization exists in patients with COVID-19. Design, Setting, and Participants: This cohort study included adults diagnosed with SARS-CoV-2 infection and admitted to 1 of 4 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital, Brigham and Women's Hospital, North Shore Medical Center, and Newton-Wellesley Hospital) between March 4, 2020, and April 28, 2020. Main Outcomes and Measures: The main outcome was patient survival during hospitalization. Measures included RDW at admission and during hospitalization, with an elevated RDW defined as greater than 14.5%. Relative risk (RR) of mortality was estimated by dividing the mortality of those with an elevated RDW by the mortality of those without an elevated RDW. Mortality hazard ratios (HRs) and 95% CIs were estimated using a Cox proportional hazards model. Results: A total of 1641 patients were included in the study (mean [SD] age, 62[18] years; 886 men [54%]; 740 White individuals [45%] and 497 Hispanic individuals [30%]; 276 nonsurvivors [17%]). Elevated RDW (>14.5%) was associated with an increased mortality risk in patients of all ages. The RR for the entire cohort was 2.73, with a mortality rate of 11% in patients with normal RDW (1173) and 31% in those with an elevated RDW (468). The RR in patients younger than 50 years was 5.25 (normal RDW, 1% [n = 341]; elevated RDW, 8% [n = 65]); 2.90 in the 50- to 59-year age group (normal RDW, 8% [n = 256]; elevated RDW, 24% [n = 63]); 3.96 in the 60- to 69-year age group (normal RDW, 8% [n = 226]; elevated RDW, 30% [104]); 1.45 in the 70- to 79-year age group (normal RDW, 23% [n = 182]; elevated RDW, 33% [n = 113]); and 1.59 in those ≥80 years (normal RDW, 29% [n = 168]; elevated RDW, 46% [n = 123]). RDW was associated with mortality risk in Cox proportional hazards models adjusted for age, D-dimer (dimerized plasmin fragment D) level, absolute lymphocyte count, and common comorbidities such as diabetes and hypertension (hazard ratio of 1.09 per 0.5% RDW increase and 2.01 for an RDW >14.5% vs ≤14.5%; P < .001). Patients whose RDW increased during hospitalization had higher mortality compared with those whose RDW did not change; for those with normal RDW, mortality increased from 6% to 24%, and for those with an elevated RDW at admission, mortality increased from 22% to 40%. Conclusions and Relevance: Elevated RDW at the time of hospital admission and an increase in RDW during hospitalization were associated with increased mortality risk for patients with COVID-19 who received treatment at 4 hospitals in a large academic medical center network.

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses.

Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.

Multiscale network dynamics between heart rate and locomotor activity are altered in schizophrenia.

OBJECTIVE: Changes in heart rate (HR) and locomotor activity reflect changes in autonomic physiology, behavior, and mood. These systems may involve interrelated neural circuits that are altered in psychiatric illness, yet their interactions are poorly understood. We hypothesized interactions between HR and locomotor activity could be used to discriminate patients with schizophrenia from controls, and would be less able to discriminate non-psychiatric patients from controls. APPROACH: HR and locomotor activity were recorded via wearable patches in 16 patients with schizophrenia and 19 healthy controls. Measures of signal complexity and interactions were calculated over multiple time scales, including sample entropy, mutual information, and transfer entropy. A support vector machine was trained on these features to discriminate patients from controls. Additionally, time series were converted into a network with nodes comprised of HR and locomotor activity states, and edges representing state transitions. Graph properties were used as features. Leave-one-out cross validation was performed. To compare against non-psychiatric illness, the same approach was repeated in 41 patients with atrial fibrillation (AFib) and 53 controls. MAIN RESULTS: Network features enabled perfect discrimination of schizophrenia patients from controls with an areas under the receiver operating characteristic curve (AUC) of 1.00 for training and test data. Other bivariate measures of interaction achieved lower AUCs (train 0.98, test 0.96), and univariate measures of complexity achieved the lowest performance. Conversely, interaction features did not improve discrimination of AFib patients from controls beyond univariate approaches. SIGNIFICANCE: Interactions between HR and locomotor activity enabled perfect discrimination of subjects with schizophrenia from controls, but these features were less performant in a non-psychiatric illness. This is the first quantitative evaluation of interactions between physiology and behavior in patients with psychiatric illness.

Meningioma With Tyrosine-Rich Crystalloids: A Case Report and Review of the Literature.

We report a case of fibrous meningioma with tyrosine-rich crystalloid in the frontal lobe of a middle-aged woman. The patient presented with a history of several years of worsening headaches and blurry vision, which progressed to include syncopal episodes and right-sided weakness. Imaging demonstrated a dural-based extra-axial mass arising from the right orbital roof and extending superiorly along the right frontal convexity causing right-to-left midline shift. The patient underwent a craniotomy and operative resection. Tumor architecture and cytology was similar to that of a Schwannian neoplasm, with spindled cells arranged in a fascicular architecture and displaying focal nuclear palisading. Immunohistochemical stains confirmed a diagnosis of fibrous meningioma. Light microscopy demonstrated extracellular deposits of eosinophilic crystalline material parallel to the spindled tumor cells, reminiscent of "tyrosine-rich" crystals described in salivary gland neoplasms. This is the third meningioma featuring tyrosine-rich crystalloid reported in the literature; we also summarize the previous 2 reports.

A comparison of entropy approaches for AF discrimination.

OBJECTIVE: This study focuses on the comparison of single entropy measures for ventricular response analysis-based AF detection. APPROACH: To enhance the performance of entropy-based AF detectors, we developed a normalized fuzzy entropy, [Formula: see text], a novel metric that (1) uses a fuzzy function to determine vector similarity, (2) replaces probability estimation with density estimation for entropy approximation, (3) utilizes a flexible distance threshold parameter, and (4) adjusts for heart rate by subtracting the natural log value of the mean RR interval. An AF detector based on [Formula: see text] was trained using the MIT-BIH atrial fibrillation (AF) database, and tested on the MIT-BIH normal sinus rhythm (NSR) and MIT-BIH arrhythmia databases. The [Formula: see text]-based AF detector was compared to AF detectors based on three other entropy measures: sample entropy ([Formula: see text]), fuzzy measure entropy ([Formula: see text]) and coefficient of sample entropy ([Formula: see text]), over three standard window sizes. MAIN RESULTS: To classify AF and non-AF rhythms, [Formula: see text] achieved the highest area under receiver operating characteristic curve (AUC) values of 92.72%, 95.27% and 96.76% for 12-, 30- and 60-beat window lengths respectively. This was higher than the performance of the next best technique, [Formula: see text], over all windows sizes, which provided respective AUCs of 91.12%, 91.86% and 90.55%. [Formula: see text] and [Formula: see text] resulted in lower AUCs (below 90%) over all window sizes. [Formula: see text] also provided superior performance for all other tested statistics, including the Youden index, sensitivity, specificity, accuracy, positive predictivity and negative predictivity. In conclusion, we show that [Formula: see text] can be used to accurately identify AF from RR interval time series. Furthermore, longer window lengths (up to one minute) increase the performance of all entropy-based AF detectors under evaluation except the [Formula: see text]-based method. SIGNIFICANCE: Our results demonstrate that the new developed normalized fuzzy entropy is an accurate measure for detecting AF.

Encouraging Student Interest in Teaching Through a Medical Student Teaching Competition.

PROBLEM: Clinician educators have realized the value not only of assigning teaching roles to medical students but also of offering explicit training in how to teach effectively. Despite this interest in the development of medical students' teaching skills, formal teaching instruction and opportunities for practice are lacking. APPROACH: To encourage medical student interest in teaching, the authors developed and implemented a medical student teaching competition (MSTC) at Emory University School of Medicine during the summers of 2014, 2015, and 2016. Each year, eight student finalists were each paired with a physician "teaching coach" and given one month to prepare for the MSTC. During the competition, each finalist delivered an eight-minute presentation to a panel of seven physician and resident judges. The authors describe the development, implementation, and assessment of the MSTC. OUTCOMES: Approximately 150 medical students and faculty members attended the MSTC each year. The students in attendance felt that the MSTC made them more likely to seek out opportunities to learn how to teach effectively and to practice teaching. Additionally, some students are now more interested in learning about a career in academic medicine than they were before the MSTC. NEXT STEPS: Given the need for more formal initiatives dedicated to improving the teaching skills of doctors-in-training, including medical students, innovative solutions such as the MSTC may enhance a medical school's existing curriculum and encourage student interest in teaching. The MSTC model may be generalizable to other medical schools.

Continuous assessment of schizophrenia using heart rate and accelerometer data.

OBJECTIVE: Schizophrenia has been associated with changes in heart rate (HR) and physical activity measures. However, the relationship between analysis window length and classifier accuracy using these features has yet to be quantified. APPROACH: Here we used objective HR and activity data to classify contiguous days of data as belonging to a schizophrenia patient or a healthy control. HR and physical activity recordings were made on 12 medicated subjects with schizophrenia and 12 healthy controls. Features derived from these data included classical statistical characteristics, rest-activity metrics, transfer entropy, and multiscale fuzzy entropy. We varied the analysis window length from two to eight days, and selected features via minimal-redundancy-maximal-relevance. A support vector machine was trained to classify schizophrenia from control windows on a daily basis. Model performance was assessed via subject-wise leave-one-out-crossfold-validation. MAIN RESULTS: An analysis window length of eight days resulted in an area under a receiver operating characteristic curve (AUC) of 0.96. Reducing the analysis window length to two days only lowered the AUC to 0.91. The type of most predictive features varied with analysis window length. SIGNIFICANCE: Our results suggest continuous tracking of subjects with schizophrenia over short time scales may be sufficient to estimate illness severity on a daily basis.

Heart rate-based window segmentation improves accuracy of classifying posttraumatic stress disorder using heart rate variability measures.

OBJECTIVE: Heart rate variability (HRV) characterizes changes in autonomic nervous system function and varies with posttraumatic stress disorder (PTSD). In this study we developed a classifier based on heart rate (HR) and HRV measures, and improved classifier performance using a novel HR-based window segmentation. APPROACH: Single-channel ECG data were collected from 23 subjects with current PTSD, and 25 control subjects with no history of PTSD over 24 h. RR intervals were derived from these data, cleaned, and used to calculate HR and HRV metrics. These metrics were used as features in a logistic regression classifier. Performance was assessed via repeated random sub-sampling validation. To reduce noise and activity-related effects, we calculated features from five non-overlapping ten-minute quiescent segments of RR intervals defined by lowest HR, as well as random ten-minute segments as a control. MAIN RESULTS: Using a combination of the four most predictive features derived from quiescent segments we achieved a median area under the receiver operating curve (AUC) of 0.86 on out-of-sample test set data. This was significantly higher than the AUC using 24 h of data (0.72) or random segments (0.67). SIGNIFICANCE: These results demonstrate our segmentation approach improves the classification of PTSD from HR and HRV measures, and suggest the potential for tracking PTSD illness severity via objective physiological monitoring. Future studies should prospectively evaluate if classifier output changes significantly with worsening or effective treatment of PTSD.

Surgeon motivations behind the timing of breast reconstruction in patients requiring postmastectomy radiation therapy.

OBJECTIVES: Although postmastectomy radiation therapy (PMRT) has been shown to reduce breast cancer burden and improve survival, PMRT may negatively influence outcomes after reconstruction. The goal of this study was to compare current opinions of plastic and reconstructive surgeons (PRS) and surgical oncologists (SO) regarding the optimal timing of breast reconstruction for patients requiring PMRT. METHODS: Members of the American Society of Plastic Surgeons (ASPS), the American Society of Breast Surgeons (ASBS), and the Society of Surgical Oncology (SSO) were asked to participate in an anonymous web-based survey. Responses were solicited in accordance to the Dillman method, and they were analyzed using standard descriptive statistics. RESULTS: A total of 330 members of the ASPS and 348 members of the ASBS and SSO participated in our survey. PRS and SO differed in patient-payor mix (p < 0.01) and practice setting (p < 0.01), but they did not differ by urban versus rural setting (p = 0.65) or geographic location (p = 0.30). Although PRS favored immediate reconstruction versus SO, overall timing did not significantly differ between the two specialists (p = 0.14). The primary rationale behind delayed breast reconstruction differed significantly between PRS and SO (p < 0.01), with more PRS believing that the reconstructive outcome is significantly and adversely affected by radiation. Both PRS and SO cited "patient-driven desire to have immediate reconstruction" (p = 0.86) as the primary motivation for immediate reconstruction. CONCLUSIONS: Although the optimal timing of reconstruction is controversial between PRS and SO, our study suggests that the timing of reconstruction in PMRT patients is ultimately driven by patient preferences and the desire of PRS to optimize aesthetic outcomes.

Concentration of fibrin and presence of plasminogen affect proliferation, fibrinolytic activity, and morphology of human fibroblasts and keratinocytes in 3D fibrin constructs.

Fibrin is a hemostatic protein found in the clotting cascade. It is used in the operating room to stop bleeding and deliver cells and growth factors to heal wounds. However, formulations of clinically approved fibrin are optimized for hemostasis, and the extent to which biochemical and physical cues in fibrin mediate skin cell behavior is not fully understood nor utilized in the design of biomaterials. To determine if the concentration of fibrinogen and the presence of plasminogen affect cell behavior relevant to wound healing, we fabricated three-dimensional fibrin constructs made from 5, 10, or 20 mg/mL of clinical fibrin or plasminogen-depleted (PD) fibrin. We cultured dermal fibroblasts or epidermal keratinocytes in these constructs. Fibroblasts proliferated similarly in both types of fibrin, but keratinocytes proliferated more in low concentrations of clinical fibrin and less in PD fibrin. Clinical fibrin constructs with fibroblasts were less stiff and degraded faster than PD fibrin constructs with fibroblasts. Similarly, keratinocytes degraded clinical fibrin, but not PD fibrin. Fibroblast spreading varied with fibrin concentration in both types of fibrin. In conclusion, the concentration of fibrinogen and the presence of plasminogen affect fibroblast and keratinocyte proliferation, morphology, and fibrin degradation. Creating materials with heterogeneous regions of fibrin formulations and concentrations could be a novel strategy for controlling the phenotype of encapsulated fibroblasts and keratinocytes, and the subsequent biomechanical properties of the construct. However, other well-investigated aspects of wound healing remain to be utilized in the design of fibrin biomaterials, such as autocrine and paracrine signaling between fibroblasts, keratinocytes, and immune cells.