Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis.
Ontology highlight
ABSTRACT: Introduction
Recent years have witnessed a persistent threat to public mental health, especially during and after the COVID-19 pandemic. Posttraumatic stress disorder (PTSD) has emerged as a pivotal concern amidst this backdrop. Concurrently, machine learning (ML) techniques have progressively applied in the realm of mental health. Therefore, our present undertaking seeks to provide a comprehensive assessment of studies employing ML methods that use diverse data modalities on the classification of people with PTSD.Methods and analysis
In pursuit of pertinent studies, we will search both English and Chinese databases from January 2000 to May 2022. Two researchers will independently conduct screening, extract data and assess study quality. We intend to employ the assessment framework introduced by Luis Francisco Ramos-Lima in 2020 for quality evaluation. Rate, standard error and 95% CIs will be utilized for effect size measurement. A Cochran's Q test will be applied to assess heterogeneity. Subgroup and sensitivity analysis will further elucidate the source of heterogeneity and funnel plots and Egger's test will detect publication bias.Ethics and dissemination
This systematic review and meta-analysis does not encompass patient interactions or engagements with healthcare providers. The outcomes of this research will be disseminated through scholarly channels, including presentations at scientific conferences and publications in peer-reviewed journals.PROSPERO registration number CRD42023342042.
SUBMITTER: Wang J
PROVIDER: S-EPMC10943756 | biostudies-literature | 2024 Jan-Dec
REPOSITORIES: biostudies-literature
Publications
Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis.
Wang Jing J Ouyang Hui H Jiao Runda R Zhang Haiyan H Cheng Suhui S Shang Zhilei Z Jia Yanpu Y Yan Wenjie W Wu Lili L Liu Weizhi W
Digital health 20240101
<h4>Introduction</h4>Recent years have witnessed a persistent threat to public mental health, especially during and after the COVID-19 pandemic. Posttraumatic stress disorder (PTSD) has emerged as a pivotal concern amidst this backdrop. Concurrently, machine learning (ML) techniques have progressively applied in the realm of mental health. Therefore, our present undertaking seeks to provide a comprehensive assessment of studies employing ML methods that use diverse data modalities on the classif ...[more]
Similar Datasets
Project description:BackgroundThe care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD.MethodsML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains.ResultsSeven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable.ConclusionsIn this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.
| S-EPMC5502325 | biostudies-literature
Project description:BackgroundYoga is increasingly used as a therapeutic treatment and seems to improve psychiatric conditions such as anxiety disorders and depression. The aim of this systematic review was to assess the evidence of yoga for reducing symptoms of posttraumatic stress disorder (PTSD).MethodsThe Cochrane Library, Medline/PubMed, PsycINFO, Scopus, and IndMED were searched through July 2017 for randomized controlled trials (RCTs) assessing the effects of yoga on symptoms of PTSD. Mean differences (MD) and standardized mean differences (SMD) with 95% confidence intervals (CI) were computed. The quality of evidence and the strength of recommendation were graded according to the GRADE recommendations.ResultsSeven RCTs (N = 284) were included. Meta-analysis revealed low quality evidence for clinically relevant effects of yoga on PTSD symptoms compared to no treatment (SMD = - 1.10, 95% CI [- 1.72, - 0.47], p < .001, I2 = 72%; MD = - 13.11, 95% CI [- 17.95, - 8.27]); and very low evidence for comparable effects of yoga and attention control interventions (SMD = - 0.31, 95%CI = [- 0.84, 0.22], p = .25; I2 = 43%). Very low evidence was found for comparable retention of patients in the trial for yoga and no treatment (OR = 0.68, 95%CI [0.06, 7.72]) or attention control interventions (OR = 0.66, 95%CI [0.10, 4.46]). No serious adverse events were reported.LimitationsFew RCTs with only limited sample size were available.ConclusionsOnly a weak recommendation for yoga as an adjunctive intervention for PTSD can be made. More high quality research is needed to confirm or disconfirm these findings.
| S-EPMC5863799 | biostudies-literature
Project description:Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of PTSD risk after trauma exposure is a critical precursor to large-scale deployment of early interventions that may reduce individual suffering and societal costs. Toward this aim, we applied ensemble machine learning to predict PTSD screening status three months after severe injury using cost-effective and minimally invasive data. Participants (N = 271) were recruited at a Level 1 Trauma Center where they provided variables routinely collected at the hospital, including pulse, injury severity, and demographics, as well as psychological variables, including self-reported current depression, psychiatric history, and social support. Participant zip codes were used to extract contextual variables including population total and density, average annual income, and health insurance coverage rates from publicly available U.S. Census data. Machine learning yielded good prediction of PTSD screening status 3 months post-hospitalization, AUC = 0.85 95% CI [0.83, 0.86], and significantly outperformed all benchmark comparison models in a cross-validation procedure designed to yield an unbiased estimate of performance. These results demonstrate that good prediction can be attained from variables that individually have relatively weak predictive value, pointing to the promise of ensemble machine learning approaches that do not rely on strong isolated risk factors.
| S-EPMC6777842 | biostudies-literature
Project description:Posttraumatic stress disorder (PTSD) and tobacco use are prevalent conditions that co-occur at striking rates in the US. Previous reviews examined prevalence and factors associated with cigarette smoking among individuals with PTSD but have not been summarized since 2007. Moreover, none explored rates and factors associated with the use of other tobacco products. This study aimed to systematically review the most recent literature examining the comorbidity of PTSD and tobacco use to provide prevalence rates, as well as summarize the literature exploring other factors associated with tobacco use among individuals with PTSD. Studies were identified using a systematic search of keywords related to tobacco use and PTSD within the following databases: PubMed, PsycINFO, Web of Knowledge, CINAHL, PsycARTICLES, and Cochrane Clinical Trials Library. The studies included in this review (N = 66) showed that the prevalence of current use of tobacco products in individuals with PTSD was 24.0% and the rate of PTSD among users of tobacco products was 20.2%. Additionally, results demonstrated that individuals with PTSD present with high levels of nicotine dependence and heavy use of tobacco products, as well as underscore the importance of negative emotional states as a contributing factor to tobacco use among individuals with PTSD. It is imperative that future studies continue monitoring tobacco use among individuals with PTSD while also assessing factors identified as having a prominent role in tobacco use among individuals with PTSD. These findings also demonstrate the need for more innovative approaches to reduce the pervasive tobacco use among individuals with PTSD.
| S-EPMC7285418 | biostudies-literature
Project description:ObjectiveWomen can develop posttraumatic stress disorder in response to experienced or perceived traumatic, often medically complicated, childbirth; the prevalence of these events remains high in the United States. Currently, no recommended treatment exists in routine care to prevent or mitigate maternal childbirth-related posttraumatic stress disorder. We conducted a systematic review and meta-analysis of clinical trials that evaluated any therapy to prevent or treat childbirth-related posttraumatic stress disorder.Data sourcesPsycInfo, PsycArticles, PubMed (MEDLINE), ClinicalTrials.gov, CINAHL, ProQuest, Sociological Abstracts, Google Scholar, Embase, Web of Science, ScienceDirect, Scopus, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched for eligible trials published through September 2023.Study eligibility criteriaTrials were included if they were interventional, if they evaluated any therapy for childbirth-related posttraumatic stress disorder for the indication of symptoms or before posttraumatic stress disorder onset, and if they were written in English.MethodsIndependent coders extracted the sample characteristics and intervention information of the eligible studies and evaluated the trials using the Downs and Black's quality checklist and Cochrane's method for risk of bias evaluation. Meta-analysis was conducted to evaluate pooled effect sizes of secondary and tertiary prevention trials.ResultsA total of 41 studies (32 randomized controlled trials, 9 nonrandomized trials) were reviewed. They evaluated brief psychological therapies including debriefing, trauma-focused therapies (including cognitive behavioral therapy and expressive writing), memory consolidation and reconsolidation blockage, mother-infant-focused therapies, and educational interventions. The trials targeted secondary preventions aimed at buffering childbirth-related posttraumatic stress disorder usually after traumatic childbirth (n=24), tertiary preventions among women with probable childbirth-related posttraumatic stress disorder (n=14), and primary prevention during pregnancy (n=3). A meta-analysis of the combined randomized secondary preventions showed moderate effects in reducing childbirth-related posttraumatic stress disorder symptoms when compared with usual treatment (standardized mean difference, -0.67; 95% confidence interval, -0.92 to -0.42). Single-session therapy within 96 hours of birth was helpful (standardized mean difference, -0.55). Brief, structured, trauma-focused therapies and semi-structured, midwife-led, dialogue-based psychological counseling showed the largest effects (standardized mean difference, -0.95 and -0.91, respectively). Other treatment approaches (eg, the Tetris game, mindfulness, mother-infant-focused treatment) warrant more research. Tertiary preventions produced smaller effects than secondary prevention but are potentially clinically meaningful (standardized mean difference, -0.37; -0.60 to -0.14). Antepartum educational approaches may help, but insufficient empirical evidence exists.ConclusionBrief trauma-focused and non-trauma-focused psychological therapies delivered early in the period following traumatic childbirth offer a critical and feasible opportunity to buffer the symptoms of childbirth-related posttraumatic stress disorder. Future research that integrates diagnostic and biological measures can inform treatment use and the mechanisms at work.
| S-EPMC11168224 | biostudies-literature
Project description:The nature of the recovery process of posttraumatic stress disorder (PTSD) symptoms is multifactorial. The Massive Parallel Limitless-Arity Multiple-testing Procedure (MP-LAMP), which was developed to detect significant combinational risk factors comprehensively, was utilized to reveal hidden combinational risk factors to explain the long-term trajectory of the PTSD symptoms. In 624 population-based subjects severely affected by the Great East Japan Earthquake, 61 potential risk factors encompassing sociodemographics, lifestyle, and traumatic experiences were analyzed by MP-LAMP regarding combinational associations with the trajectory of PTSD symptoms, as evaluated by the Impact of Event Scale-Revised score after eight years adjusted by the baseline score. The comprehensive combinational analysis detected 56 significant combinational risk factors, including 15 independent variables, although the conventional bivariate analysis between single risk factors and the trajectory detected no significant risk factors. The strongest association was observed with the combination of short resting time, short walking time, unemployment, and evacuation without preparation (adjusted P value = 2.2 × 10-4, and raw P value = 3.1 × 10-9). Although short resting time had no association with the poor trajectory, it had a significant interaction with short walking time (P value = 1.2 × 10-3), which was further strengthened by the other two components (P value = 9.7 × 10-5). Likewise, components that were not associated with a poor trajectory in bivariate analysis were included in every observed significant risk combination due to their interactions with other components. Comprehensive combination detection by MP-LAMP is essential for explaining multifactorial psychiatric symptoms by revealing the hidden combinations of risk factors.
| S-EPMC7730124 | biostudies-literature
Project description:ImportanceMilitary deployment involves significant risk for life-threatening experiences that can lead to posttraumatic stress disorder (PTSD). Accurate predeployment prediction of PTSD risk may facilitate the development of targeted intervention strategies to enhance resilience.ObjectiveTo develop and validate a machine learning (ML) model to predict postdeployment PTSD.Design, setting, and participantsThis diagnostic/prognostic study included 4771 soldiers from 3 US Army brigade combat teams who completed assessments between January 9, 2012, and May 1, 2014. Predeployment assessments occurred 1 to 2 months before deployment to Afghanistan, and follow-up assessments occurred approximately 3 and 9 months post deployment. Machine learning models to predict postdeployment PTSD were developed in the first 2 recruited cohorts using as many as 801 predeployment predictors from comprehensive self-report assessments. In the development phase, cross-validated performance metrics and predictor parsimony were considered to select an optimal model. Next, the selected model's performance was evaluated with area under the receiver operating characteristics curve and expected calibration error in a temporally and geographically distinct cohort. Data analyses were performed from August 1 to November 30, 2022.Main outcomes and measuresPosttraumatic stress disorder diagnosis was assessed by clinically calibrated self-report measures. Participants were weighted in all analyses to address potential biases related to cohort selection and follow-up nonresponse.ResultsThis study included 4771 participants (mean [SD] age, 26.9 [6.2] years), 4440 (94.7%) of whom were men. In terms of race and ethnicity, 144 participants (2.8%) identified as American Indian or Alaska Native, 242 (4.8%) as Asian, 556 (13.3%) as Black or African American, 885 (18.3%) as Hispanic, 106 (2.1%) as Native Hawaiian or other Pacific Islander, 3474 (72.2%) as White, and 430 (8.9%) as other or unknown race or ethnicity; participants could identify as of more than 1 race or ethnicity. A total of 746 participants (15.4%) met PTSD criteria post deployment. In the development phase, models had comparable performance (log loss range, 0.372-0.375; area under the curve range, 0.75-0.76). A gradient-boosting machine with 58 core predictors was selected over an elastic net with 196 predictors and a stacked ensemble of ML models with 801 predictors. In the independent test cohort, the gradient-boosting machine had an area under the curve of 0.74 (95% CI, 0.71-0.77) and low expected calibration error of 0.032 (95% CI, 0.020-0.046). Approximately one-third of participants with the highest risk accounted for 62.4% (95% CI, 56.5%-67.9%) of the PTSD cases. Core predictors cut across 17 distinct domains: stressful experiences, social network, substance use, childhood or adolescence, unit experiences, health, injuries, irritability or anger, personality, emotional problems, resilience, treatment, anxiety, attention or concentration, family history, mood, and religion.Conclusions and relevanceIn this diagnostic/prognostic study of US Army soldiers, an ML model was developed to predict postdeployment PTSD risk with self-reported information collected before deployment. The optimal model showed good performance in a temporally and geographically distinct validation sample. These results indicate that predeployment stratification of PTSD risk is feasible and may facilitate the development of targeted prevention and early intervention strategies.
| S-EPMC10314304 | biostudies-literature
Project description:BackgroundPosttraumatic stress disorder (PTSD) is a disorder that develops following exposure to severely stressful events. Altered cortisol secretion has been reported in PTSD; however, results have been inconsistent. Previous meta-analyses of cortisol levels in PTSD have combined results of studies that have used different tissue samples (blood, saliva, urine) for cortisol measurement and have not included newer methods of determining cortisol levels (e.g. hair samples). In this systematic review, we will synthesise evidence from studies evaluating basal cortisol levels in PTSD patients versus controls and stratify studies according to tissue type used for cortisol measurement. We will also determine whether results from different tissue types can be pooled and if any specific tissue samples have better utility in research studies on PTSD.MethodsWe will perform a systematic review of the scientific literature including all studies that have evaluated basal or baseline cortisol levels in adults with current PTSD versus controls, with and without trauma exposure. Independent reviewers will conduct searches in electronic databases (Medline, CINAHL, PTSDpubs, Web of Science, Scopus, ProQuest Dissertations & Theses A&I, ClinicalTrials.gov, and ICTRP), and additional studies will be obtained by searching the reference lists of articles. Two reviewers (LLvdH and SW) will independently conduct standardised screening, eligibility assessments, data extraction, and quality assessments before qualitative and, if appropriate, quantitative (meta-analysis and meta-regression) synthesis. Disagreements that arise at any stage will be resolved by a third reviewer (ShS).DiscussionIn line with previous reviews, we expect that cortisol levels will be lower in PTSD patients than in controls, but that patterns may vary somewhat according to the tissue sample in which cortisol is measured. This systematic review will assist in developing a better understanding of the acute and chronic patterns of basal cortisol secretion in PTSD and will inform future research.Systematic review registrationPROSPERO CRD42018091874.
| S-EPMC6322257 | biostudies-literature
Project description:Background: Posttraumatic stress disorder (PTSD) is considered an independent risk factor for dementia. Despite the (clinical) evidence that PTSD is associated with neuropsychiatric symptoms in people with dementia, studies on its prevalence and clinical manifestation are limited, and their quality is affected by the lack of a structured method to diagnose PTSD in this population. The primary aim of the current study is to validate the 'TRAuma and DEmentia' interview as a diagnostic tool for PTSD in people with dementia and to test feasibility of EMDR treatment for people with PTSD and dementia.Methods: This prospective multi-centre study is divided into two parts. In study A, 90 participants with dementia will be included to test the criterion validity, inter-rater reliability and feasibility of the 'TRAuma and DEmentia' interview. In study B, 29 participants with dementia and PTSD will receive eye movement desensitisation and reprocessing therapy by a trained psychologist, and 29 participants with dementia and PTSD will be placed on the waiting list control group.Conclusion: This study aims to improve the diagnostic process of PTSD and to assess the effects of eye movement desensitisation and reprocessing treatment in people with dementia living in Dutch care facilities.Trial registration: NL70479.068.20 / METC 20-063 / OSF registration: https://doi.org/10.17605/OSF.IO/AKW4F.
| S-EPMC10946268 | biostudies-literature
Project description:BackgroundTo date, only one systematic review and meta-analysis of randomized controlled trials (RCTs) has evaluated the effect of neurofeedback in PTSD, which included only four studies and found an uncertainty of the effect of EEG-NF on PTSD symptoms. This meta-analysis is an update considering that numerous studies have since been published. Additionally, more recent studies have included fMRI-NF as well as fMRI-guided or -inspired EEG NF.MethodsSystematic literature searches for RCTs were conducted in three online databases. Additional hand searches of each study identified and of systematic reviews and meta-analyses published were also undertaken. Outcomes evaluated the effect of neurofeedback vs. a control (active, sham, and waiting list) on their effects in reducing PTSD symptoms using various health instruments. Meta-analytical methods used were inverse variance random-effects models measuring both mean and standardized mean differences. Quality and certainty of the evidence were assessed using GRADE. Adverse events were also evaluated.ResultsA total of 17 studies were identified evaluating a total of 628 patients. There were 10 studies used in the meta-analysis. Results from all studies identified favored neurofeedback's effect on reducing PTSD symptoms including BDI pretest-posttest [mean difference (MD): 8.30 (95% CI: 3.09 to 13.52; P = 0.002; I 2 = 0%)]; BDI pretest-follow-up (MD: 8.75 (95% CI: 3.53 to 13.97; P < 0.00001; I 2 = 0%); CAPS-5 pretest-posttest [MD: 7.01 (95% CI: 1.36 to 12.66; P = 0.02; I 2 = 86%)]; CAPS-5 pretest-follow-up (MD: 10 (95% CI: 1.29 to 21.29; P = 0.006; I 2 = 77%); PCL-5 pretest-posttest (MD: 7.14 (95% CI: 3.08 to 11.2; P = 0.0006; I 2 = 0%); PCL-5 pretest-follow-up (MD: 14.95 (95% CI: 7.95 to 21.96; P < 0.0001; I 2 = 0%). Other studies reported improvements using various other instruments. GRADE assessments of CAPS, PCL, and BDI demonstrated a moderate/high level in the quality of the evidence that NF has a positive clinical effect.ConclusionBased on newer published studies and the outcomes measured, NF has demonstrated a clinically meaningful effect size, with an increased effect size at follow-up. This clinically meaningful effect appears to be driven by newer fMRI-guided NF and deeper brain derivates of it.
| S-EPMC10993781 | biostudies-literature