Emotion-based decision-making as a predictor of alcohol-related consequences in college students.

Student drinking during the college years can result in many adverse outcomes. Emotion-based decision-making (EBDM), or the use of emotional information to influence future plans and behavior, may lead to increased harmful consequences of alcohol. The current study examined both the number of types and total frequency of alcohol consequences as a function of EBDM. Undergraduate students from three large universities (n = 814) were assessed on EBDM and typical weekly drinking during their 2nd year of college, and alcohol-related consequences during their 2nd, 3rd, and 4th years. Alcohol-related consequences were operationalized both as unique types of consequences and total consequences experienced in the previous year. Latent growth modeling used EBDM in year 2 to predict unique and total alcohol consequences in years 2, 3, and 4. Students who endorsed higher levels of EBDM experienced a significantly increased total frequency of consequences over the three years, without differences in trajectory between students high and low on this construct. Participants with higher levels of EBDM experienced a significantly greater number of unique consequences at all time points, but these consequences increased at a significantly lower rate than individuals lower on this construct. Findings of this study indicate Emotion-Based Decision-Making may be a useful predictor of harmful consequences of student drinking over time.

A pilot randomized controlled trial of smartphone-assisted mindfulness-based intervention with contingency management for smokers with mood disorders.

Cigarette smoking disproportionately affects individuals with mood disorders, but smoking cessation interventions have modest effects in this population. Home mindfulness practice during abstinence incentivized via contingency management (CM) may help those in affective distress quit smoking. METHOD: Adult smokers receiving outpatient psychiatric treatment for mood disorders were randomized to receive a smartphone-assisted mindfulness-based smoking cessation intervention with contingency management (SMI-CM, n = 25) or enhanced standard treatment (EST, n = 24) with noncontingent rewards. Participants in SMI-CM were prompted to practice audio-guided mindfulness five times per day for 38 days (vs. no comparison intervention in EST), and received monetary incentives for carbon monoxide (CO) ≤ 6 ppm. The primary outcome was biochemically verified 7-day point prevalence abstinence rates 2, 4, and 13 weeks after a target quit day. RESULTS: Of the 49 participants, 63.3% were Latinx and 30.6% Black; 75.5% reported household incomes < $25,000. Abstinence rates for SMI-CM were 40.0%, 36.0%, and 16.0% versus 4.2%, 8.3%, and 4.2% in EST at weeks 2, 4, and 13. A generalized estimating equations (GEE) model showed significant overall differences in abstinence rates in SMI-CM versus EST (adjusted odds ratio [AOR] = 8.12, 95% CI = 1.42-46.6, p = .019). Those who received SMI-CM reported significantly greater reduction in smoking-specific experiential avoidance from baseline to 3 days prior to quit date (ß = -7.21, 95% CI = -12.1-2.33, p = .006). CONCLUSIONS: SMI-CM may increase cessation rates among smokers with mood disorders, potentially through reduced smoking-specific experiential avoidance. SMI-CM is a promising intervention, and warrants investigation in a fully powered randomized controlled trial (RCT). (PsycInfo Database Record (c) 2022 APA, all rights reserved).

ICU delirium burden predicts functional neurologic outcomes.

BACKGROUND: We investigated the effect of delirium burden in mechanically ventilated patients, beginning in the ICU and continuing throughout hospitalization, on functional neurologic outcomes up to 2.5 years following critical illness. METHODS: Prospective cohort study of enrolling 178 consecutive mechanically ventilated adult medical and surgical ICU patients between October 2013 and May 2016. Altogether, patients were assessed daily for delirium 2941days using the Confusion Assessment Method for the ICU (CAM-ICU). Hospitalization delirium burden (DB) was quantified as number of hospital days with delirium divided by total days at risk. Survival status up to 2.5 years and neurologic outcomes using the Glasgow Outcome Scale were recorded at discharge 3, 6, and 12 months post-discharge. RESULTS: Of 178 patients, 19 (10.7%) were excluded from outcome analyses due to persistent coma. Among the remaining 159, 123 (77.4%) experienced delirium. DB was independently associated with >4-fold increased mortality at 2.5 years following ICU admission (adjusted hazard ratio [aHR], 4.77; 95% CI, 2.10-10.83; P < .001), and worse neurologic outcome at discharge (adjusted odds ratio [aOR], 0.02; 0.01-0.09; P < .001), 3 (aOR, 0.11; 0.04-0.31; P < .001), 6 (aOR, 0.10; 0.04-0.29; P < .001), and 12 months (aOR, 0.19; 0.07-0.52; P = .001). DB in the ICU alone was not associated with mortality (HR, 1.79; 0.93-3.44; P = .082) and predicted neurologic outcome less strongly than entire hospital stay DB. Similarly, the number of delirium days in the ICU and for whole hospitalization were not associated with mortality (HR, 1.00; 0.93-1.08; P = .917 and HR, 0.98; 0.94-1.03, P = .535) nor with neurological outcomes, except for the association between ICU delirium days and neurological outcome at discharge (OR, 0.90; 0.81-0.99, P = .038). CONCLUSIONS: Delirium burden throughout hospitalization independently predicts long term neurologic outcomes and death up to 2.5 years after critical illness, and is more predictive than delirium burden in the ICU alone and number of delirium days.

Automated tracking of level of consciousness and delirium in critical illness using deep learning.

Over- and under-sedation are common in the ICU, and contribute to poor ICU outcomes including delirium. Behavioral assessments, such as Richmond Agitation-Sedation Scale (RASS) for monitoring levels of sedation and Confusion Assessment Method for the ICU (CAM-ICU) for detecting signs of delirium, are often used. As an alternative, brain monitoring with electroencephalography (EEG) has been proposed in the operating room, but is challenging to implement in ICU due to the differences between critical illness and elective surgery, as well as the duration of sedation. Here we present a deep learning model based on a combination of convolutional and recurrent neural networks that automatically tracks both the level of consciousness and delirium using frontal EEG signals in the ICU. For level of consciousness, the system achieves a median accuracy of 70% when allowing prediction to be within one RASS level difference across all patients, which is comparable or higher than the median technician-nurse agreement at 59%. For delirium, the system achieves an AUC of 0.80 with 69% sensitivity and 83% specificity at the optimal operating point. The results show it is feasible to continuously track level of consciousness and delirium in the ICU.

Electroencephalogram Based Detection of Deep Sedation in ICU Patients Using Atomic Decomposition.

OBJECTIVE: This study was performed to evaluate how well states of deep sedation in ICU patients can be detected from the frontal electroencephalogram (EEG) using features based on the method of atomic decomposition (AD). METHODS: We analyzed a clinical dataset of 20 min of EEG recordings per patient from 44 mechanically ventilated adult patients receiving sedatives in an intensive care unit (ICU) setting. Several features derived from AD of the EEG signal were used to discriminate between awake and sedated states. We trained support vector machine (SVM) classifiers using AD features and compared the classification performance with SVM classifiers trained using standard spectral and entropy features using leave-one-subject-out validation. The potential of each feature to discriminate between awake and sedated states was quantified using area under the receiver operating characteristic curve (AUC). RESULTS: The sedation level classification system using AD was able to reliably discriminate between sedated and awake states achieving an average AUC of 0.90, which was significantly better () than performance achieved using spectral (AUC = 0.86) and entropy (AUC = 0.81) domain features. A combined feature set consisting of AD, entropy, and spectral features provided better discrimination (AUC = 0.91, ) than any individual feature set. CONCLUSIONS: Features derived from the atomic decomposition of EEG signals provide useful discriminative information about the depth of sedation in ICU patients. SIGNIFICANCE: With further refinement and external validation, the proposed system may be able to assist clinical staff with continuous surveillance of sedation levels in mechanically ventilated critically ill ICU patients.

Rationale, design and pilot feasibility results of a smartphone-assisted, mindfulness-based intervention for smokers with mood disorders: Project mSMART MIND.

BACKGROUND: Although individuals with psychiatric disorders are disproportionately affected by cigarette smoking, few outpatient mental health treatment facilities offer smoking cessation services. In this paper, we describe the development of a smartphone-assisted mindfulness smoking cessation intervention with contingency management (SMI-CM), as well as the design and methods of an ongoing pilot randomized controlled trial (RCT) targeting smokers receiving outpatient psychiatric treatment. We also report the results of an open-label pilot feasibility study. METHODS: In phase 1, we developed and pilot-tested SMI-CM, which includes a smartphone intervention app that prompts participants to practice mindfulness, complete ecological momentary assessment (EMA) reports 5 times per day, and submit carbon monoxide (CO) videos twice per day. Participants earned incentives if submitted videos showed CO≤6ppm. In phase 2, smokers receiving outpatient treatment for mood disorders are randomized to receive SMI-CM or enhanced standard treatment plus non-contingent CM (EST). RESULTS: The results from the pilot feasibility study (N=8) showed that participants practiced mindfulness an average of 3.4times/day (≥3min), completed 72.3% of prompted EMA reports, and submitted 68.0% of requested CO videos. Participants reported that the program was helpful overall (M=4.85/5) and that daily mindfulness practice was helpful for both managing mood and quitting smoking (Ms=4.50/5). CONCLUSIONS: The results from the feasibility study indicated high levels of acceptability and satisfaction with SMI-CM. The ongoing RCT will allow evaluation of the efficacy and mechanisms of action underlying SMI-CM for improving cessation rates among smokers with mood disorders.

Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability.

OBJECTIVE: To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability. DESIGN: Multicenter, pilot study. SETTING: Several ICUs at Massachusetts General Hospital, Boston, MA. PATIENTS: We gathered 21,912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU patients. All patients included in the study were mechanically ventilated and were receiving sedatives. MEASUREMENTS AND MAIN RESULTS: As "ground truth" for developing our method, we used Richmond Agitation Sedation Scale scores grouped into four levels denoted "comatose" (-5), "deeply sedated" (-4 to -3), "lightly sedated" (-2 to 0), and "agitated" (+1 to +4). We trained a support vector machine learning algorithm to calculate the probability of each sedation level from heart rate variability measures derived from the electrocardiogram. To estimate algorithm performance, we calculated leave-one-subject out cross-validated accuracy. The patient-independent version of the proposed system discriminated between the four sedation levels with an overall accuracy of 59%. Upon personalizing the system supplementing the training data with patient-specific calibration data, consisting of an individual's labeled heart rate variability epochs from the preceding 24 hours, accuracy improved to 67%. The personalized system discriminated between light- and deep-sedation states with an average accuracy of 75%. CONCLUSIONS: With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and under sedation.

Automatic Classification of Sedation Levels in ICU Patients Using Heart Rate Variability.

OBJECTIVE: To explore the potential value of heart rate variability features for automated monitoring of sedation levels in mechanically ventilated ICU patients. DESIGN: Multicenter, pilot study. SETTING: Several ICUs at Massachusetts General Hospital, Boston, MA. PATIENTS: Electrocardiogram recordings from 40 mechanically ventilated adult patients receiving sedatives in an ICU setting were used to develop and test the proposed automated system. MEASUREMENTS AND MAIN RESULTS: Richmond Agitation-Sedation Scale scores were acquired prospectively to assess patient sedation levels and were used as ground truth. Richmond Agitation-Sedation Scale scores were grouped into four levels, denoted "unarousable" (Richmond Agitation- Sedation Scale = -5, -4), "sedated" (-3, -2, -1), "awake" (0), "agitated" (+1, +2, +3, +4). A multiclass support vector machine algorithm was used for classification. Classifier training and performance evaluations were carried out using leave-oneout cross validation. An overall accuracy of 69% was achieved for discriminating between the four levels of sedation. The proposed system was able to reliably discriminate (accuracy = 79%) between sedated (Richmond Agitation-Sedation Scale < 0) and nonsedated states (Richmond Agitation-Sedation Scale > 0). CONCLUSIONS: With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and undersedation.

Heart rate variability as a biomarker for sedation depth estimation in ICU patients.

An automated patient-specific system to classify the level of sedation in ICU patients using heart rate variability signal is presented in this paper. ECG from 70 mechanically ventilated adult patients with administered sedatives in an ICU setting were used to develop a support vector machine based system for sedation depth monitoring using several heart rate variability measures. A leave-one-subject-out cross validation was used for classifier training and performance evaluations. The proposed patient-specific system provided a sensitivity, specificity and an AUC of 64%, 84.8% and 0.72, respectively. It is hoped that with the help of additional physiological signals the proposed patient-specific sedation level prediction system could lead to a fully automated multimodal system to assist clinical staff in ICUs to interpret the sedation level of the patient.

Clustering analysis to identify distinct spectral components of encephalogram burst suppression in critically ill patients.

Millions of patients are admitted each year to intensive care units (ICUs) in the United States. A significant fraction of ICU survivors develop life-long cognitive impairment, incurring tremendous financial and societal costs. Delirium, a state of impaired awareness, attention and cognition that frequently develops during ICU care, is a major risk factor for post-ICU cognitive impairment. Recent studies suggest that patients experiencing electroencephalogram (EEG) burst suppression have higher rates of mortality and are more likely to develop delirium than patients who do not experience burst suppression. Burst suppression is typically associated with coma and deep levels of anesthesia or hypothermia, and is defined clinically as an alternating pattern of high-amplitude "burst" periods interrupted by sustained low-amplitude "suppression" periods. Here we describe a clustering method to analyze EEG spectra during burst and suppression periods. We used this method to identify a set of distinct spectral patterns in the EEG during burst and suppression periods in critically ill patients. These patterns correlate with level of patient sedation, quantified in terms of sedative infusion rates and clinical sedation scores. This analysis suggests that EEG burst suppression in critically ill patients may not be a single state, but instead may reflect a plurality of states whose specific dynamics relate to a patient's underlying brain function.