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Can passive measurement of physiological distress help better predict suicidal thinking?

ABSTRACT: There has been growing interest in using wearable physiological monitors to passively detect the signals of distress (i.e., increases in autonomic arousal measured through increased electrodermal activity [EDA]) that may be imminently associated with suicidal thoughts. Before using these monitors in advanced applications such as creating suicide risk detection algorithms or just-in-time interventions, several preliminary questions must be answered. Specifically, we lack information about whether: (1) EDA concurrently and prospectively predicts suicidal thinking and (2) data on EDA adds to the ability to predict the presence and severity of suicidal thinking over and above self-reports of emotional distress. Participants were suicidal psychiatric inpatients (n = 25, 56% female, M age = 33.48 years) who completed six daily assessments of negative affect and suicidal thinking duration of their psychiatric inpatient stay and 28 days post-discharge, and wore on their wrist a physiological monitor (Empatica Embrace) that passively detects autonomic activity. We found that physiological data alone both concurrently and prospectively predicted periods of suicidal thinking, but models with physiological data alone had the poorest fit. Adding physiological data to self-report models improved fit when the outcome variable was severity of suicidal thinking, but worsened model fit when the outcome was presence of suicidal thinking. When predicting severity of suicidal thinking, physiological data improved model fit more for models with non-overlapping self-report data (i.e., low arousal negative affect) than for overlapping self-report data (i.e., high arousal negative affect). These findings suggest that physiological data, under certain contexts (e.g., when combined with self-report data), may be useful in better predicting-and ultimately, preventing-acute increases in suicide risk. However, some cautious optimism is warranted since physiological data do not always improve our ability to predict suicidal thinking.

SUBMITTER: Kleiman EM

PROVIDER: S-EPMC8640041 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceThe weeks following discharge from psychiatric hospitalization are the highest-risk period for suicide attempts. Real-time monitoring of suicidal thoughts via smartphone prompts may be more indicative of short-term risk than a single, cross-sectional assessment.ObjectiveTo test whether modeling dynamic changes in real-time suicidal thoughts during psychiatric hospitalization can improve predictions of postdischarge suicide attempts vs using only baseline (ie, admission) data or using the mean level of real-time suicidal thoughts during hospitalization.Design, setting, and participantsIn this prognostic study, 83 adults recruited from the inpatient psychiatric unit at Massachusetts General Hospital completed ecological momentary assessment surveys of suicidal thinking 4 to 6 times per day during hospitalization as well as brief follow-up surveys assessing suicide attempts at 2 and 4 weeks after discharge. Participants completed at least 3 real-time monitoring surveys. Inclusion criteria included hospitalization for suicidal thoughts and/or behaviors and English fluency. Data were collected from January 2016 to December 2018 and analyzed from January to December 2020.Main outcomes and measuresThe primary outcome was suicide attempt in the month after discharge.ResultsOf 83 participants (mean [SD] age, 38.4 [13.6] years; 43 [51.8%] male participants; 69 [83.1%] White individuals), 9 (10.8%) made a suicide attempt in the month after discharge. Mean cross-validated AUC for elastic net models revealed predictive accuracy was fair for the model using baseline data (area under the curve [AUC], 0.71; first to third quartile, 0.55-0.88), good for the model using the mean level of real-time suicidal thoughts during hospitalization (AUC, 0.81; first to third quartile, 0.67-0.91), and best for the model using dynamic changes in real-time suicidal thoughts during hospitalization (AUC, 0.89; first to third quartile, 0.81-0.97); this pattern of results held for other classification metrics (eg, accuracy, positive predictive value, Brier score) and when using different cross-validation procedures. Features assessing rapid fluctuations in suicidal thinking emerged as the strongest predictors of posthospital suicide attempts. A final set of models incorporating percentage missingness further improved both the mean (mean AUC, 0.93; first to third quartile, 0.90-1.00) and dynamic feature (mean AUC, 0.93; first to third quartile, 0.88-1.00) models.Conclusions and relevanceIn this study, collecting real-time data about suicidal thinking during the course of hospitalization significantly improved short-term prediction of posthospitalization suicide attempts. Models including dynamic changes in suicidal thinking over time yielded the best prediction; features that captured rapid changes in suicidal thoughts were particularly strong predictors. Survey noncompletion also emerged as an important predictor of posthospitalization suicide attempts.

Project description:BackgroundDisease severity may change in the first week after acute respiratory distress syndrome (ARDS) onset. The aim of this study was to evaluate whether the reclassification of disease severity after 48 h (i.e. day 3) of ARDS onset could help in predicting mortality and determine factors associated with ARDS persistence and mortality.MethodsWe performed a secondary analysis of a 3-year prospective, observational cohort study of ARDS in a tertiary care referral center. Disease severity was reclassified after 48 h of enrollment, and cases that still fulfilled the Berlin criteria were regarded as nonresolving ARDS.ResultsA total of 1034 ARDS patients were analyzed. Overall hospital mortality was 57.7% (56.7%, 57.5%, and 58.6% for patients with initial mild, moderate, and severe ARDS, respectively, p = 0.189). On day 3 reclassification, the hospital mortality rates were as follows: resolved (42.1%), mild (47.9%), moderate (62.4%), and severe ARDS (76.1%) (p < 0.001). Patients with improving severity on day 3 had lower mortality (48.8%), whereas patients with the same or worsening severity on day 3 had higher mortality (62.7% and 76.3%, respectively). Patients who were older, had lower PaO2/FiO2, or higher positive end-expiratory pressure on day 1 were significantly associated with nonresolving ARDS on day 3. A Cox regression model with ARDS severity as a time-dependent covariate and competing risk analysis demonstrated that ARDS severity was independently associated with hospital mortality, and nonresolving ARDS had significantly increased hazard of death than resolved ARDS (p < 0.0001). Cumulative mortality curve for ARDS severity comparisons demonstrated significantly different (overall comparison, p < 0.001).ConclusionsReclassification of disease severity after 48 h of ARDS onset could help to divide patients into subgroups with greater separation in terms of mortality. The reviews of this paper are available via the supplemental material section.

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Can passive measurement of physiological distress help better predict suicidal thinking?

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