Validation of childbirth-related posttraumatic stress disorder using psychophysiological assessment

AI and narrative embeddings detect PTSD following childbirth via birth stories

Free-text analysis using machine learning (ML)-based natural language processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility for this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and the text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum mental illness affecting millions of women annually, with no standard screening protocol. Using a sample of 1295 women who gave birth in the last six months and were 18+ years old, recruited through hospital announcements, social media, and professional organizations, we explore ChatGPT's and ADA's potential to screen for CB-PTSD by analyzing maternal childbirth narratives. The PTSD Checklist for DSM-5 (PCL-5; cutoff 31) was used to assess CB-PTSD. By developing an ML model that utilizes numerical vector representation of the ADA model, we identify CB-PTSD via narrative classification. Our model outperformed (F1 score: 0.81) ChatGPT and six previously published large text-embedding models trained on mental health or clinical domains data, suggesting that the ADA model can be harnessed to identify CB-PTSD. Our modeling approach could be generalized to assess other mental health disorders.

Screening for post-traumatic stress disorder following childbirth using the Peritraumatic Distress Inventory

BACKGROUND

Post-traumatic stress disorder (PTSD) following traumatic childbirth may undermine maternal and infant health, but screening for maternal childbirth-related PTSD (CB-PTSD) remains lacking. Acute emotional distress in response to a traumatic experience strongly associates with PTSD. The Peritraumatic Distress Inventory (PDI) assesses acute distress in non-postpartum individuals, but its use to classify women likely to endorse CB-PTSD is unknown.

METHODS

3039 women provided information about their mental health and childbirth experience. They completed the PDI regarding their recent childbirth event, and a PTSD symptom screen to determine CB-PTSD. We employed Exploratory Graph Analysis and bootstrapping to reveal the PDI's factorial structure and optimal cutoff value for CB-PTSD classification.

RESULTS

Factor analysis revealed two strongly correlated stable factors based on a modified version of the PDI: (1) negative emotions and (2) bodily arousal and threat appraisal. A score of 15+ on the modified PDI produced high sensitivity and specificity: 88 % with a positive CB-PTSD screen in the first postpartum months and 93 % with a negative screen.

LIMITATIONS

In this cross-sectional study, the PDI was administered at different timepoints postpartum. Future work should examine the PDI's predictive utility for screening women as closely as possible to the time of childbirth, and establish clinical cutoffs in populations after complicated deliveries.

CONCLUSIONS

Brief self-report screening concerning a woman's emotional reactions to childbirth using our modified PDI tool can detect those likely to endorse CB-PTSD in the early postpartum. This may serve as the initial step of managing symptoms to ultimately prevent chronic manifestations.

Childbirth-related posttraumatic stress disorder: definition, risk factors, pathophysiology, diagnosis, prevention, and treatment

Psychological birth trauma and childbirth-related posttraumatic stress disorder represent a substantial burden of disease with 6.6 million mothers and 1.7 million fathers or co-parents affected by childbirth-related posttraumatic stress disorder worldwide each year. There is mounting evidence to indicate that parents who develop childbirth-related posttraumatic stress disorder do so as a direct consequence of a traumatic childbirth experience. High-risk groups, such as those who experience preterm birth, stillbirth, or preeclampsia, have higher prevalence rates. The main risks include antenatal factors (eg, depression in pregnancy, fear of childbirth, poor health or complications in pregnancy, history of trauma or sexual abuse, or mental health problems), perinatal factors (eg, negative subjective birth experience, operative birth, obstetrical complications, and severe maternal morbidity, as well as maternal near misses, lack of support, dissociation), and postpartum factors (eg, depression, postpartum physical complications, and poor coping and stress). The link between birth events and childbirth-related posttraumatic stress disorder provides a valuable opportunity to prevent traumatic childbirths and childbirth-related posttraumatic stress disorder from occurring in the first place. Childbirth-related posttraumatic stress disorder is an extremely distressing mental disorder and has a substantial negative impact on those who give birth, fathers or co-parents, and, potentially, the whole family. Still, a traumatic childbirth experience and childbirth-related posttraumatic stress disorder remain largely unrecognized in maternity services and are not routinely screened for during pregnancy and the postpartum period. In fact, there are gaps in the evidence on how, when, and who to screen. Similarly, there is a lack of evidence on how best to treat those affected. Primary prevention efforts (eg, screening for antenatal risk factors, use of trauma-informed care) are aimed at preventing a traumatic childbirth experience and childbirth-related posttraumatic stress disorder in the first place by eliminating or reducing risk factors for childbirth-related posttraumatic stress disorder. Secondary prevention approaches (eg, trauma-focused psychological therapies, early psychological interventions) aim to identify those who have had a traumatic childbirth experience and to intervene to prevent the development of childbirth-related posttraumatic stress disorder. Tertiary prevention (eg, trauma-focused cognitive behavioural therapy and eye movement desensitization and reprocessing) seeks to ensure that people with childbirth-related posttraumatic stress disorder are identified and treated to recovery so that childbirth-related posttraumatic stress disorder does not become chronic. Adequate prevention, screening, and intervention could alleviate a considerable amount of suffering in affected families. In light of the available research on the impact of childbirth-related posttraumatic stress disorder on families, it is important to develop and evaluate assessment, prevention, and treatment interventions that target the birthing person, the couple dyad, the parent-infant dyad, and the family as a whole. Further research should focus on the inclusion of couples in different constellations and, more generally, on the inclusion of more diverse populations in diverse settings. The paucity of national and international policy guidance on the prevention, care, and treatment of psychological birth trauma and the lack of formal psychological birth trauma services and training, highlight the need to engage with service managers and policy makers.

Obstetric violence is a misnomer

The term "obstetric violence" has been used in the legislative language of several countries to protect mothers from abuse during pregnancy. Subsequently, it has been expanded to include a spectrum of obstetric procedures, such as induction of labor, episiotomy, and cesarean delivery, and has surfaced in the peer-reviewed literature. The term "obstetric violence" can be seen as quite strong and emotionally charged, which may lead to misunderstandings or misconceptions. It might be interpreted as implying a deliberate act of violence by healthcare providers when mistreatment can sometimes result from systemic issues, lack of training, or misunderstandings rather than intentional violence. "Obstetric mistreatment" is a more comprehensive term that can encompass a broader range of behaviors and actions. "Violence" generally refers to the intentional use of physical force to cause harm, injury, or damage to another person (eg, physical assault, domestic violence, street fights, or acts of terrorism), whereas "mistreatment" is a more general term and refers to the abuse, harm, or control exerted over another person (such as nonconsensual medical procedures, verbal abuse, disrespect, discrimination and stigmatization, or neglect, to name a few examples). There may be cases where unprofessional personnel may commit mistreatment and violence against pregnant patients, but as obstetrics is dedicated to the health and well-being of pregnant and fetal patients, mistreatment of obstetric patients should never be an intended component of professional obstetric care. It is necessary to move beyond the term "obstetric violence" in discourse and acknowledge and address the structural dimensions of abusive reproductive practices. Similarly, we do not use the term "psychiatric violence" for appropriately used professional procedures in psychiatry, such as electroshock therapy, or use the term "neurosurgical violence" when drilling a burr hole. There is an ongoing need to raise awareness about the potential mistreatment of obstetric patients within the context of abuse against women in general. Using the term "mistreatment in healthcare" instead of the more limited term "obstetric violence" is more appropriate and applies to all specialties when there is unprofessional abuse and mistreatment, such as biased care, neglect, emotional abuse (verbal), or physical abuse, including performing procedures that are unnecessary, unindicated, or without informed patient consent. Healthcare providers must promote unbiased, respectful, and patient-centered professional care; provide an ethical framework for all healthcare personnel; and work toward systemic change to prevent any mistreatment or abuse in our specialty.

OpenAI's Narrative Embeddings Can Be Used for Detecting Post-Traumatic Stress Following Childbirth Via Birth Stories

Free-text analysis using Machine Learning (ML)-based Natural Language Processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility for this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and the text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum mental illness affecting millions of women annually, with no standard screening protocol. Using a sample of 1,295 women who gave birth in the last six months and were 18+ years old, recruited through hospital announcements, social media, and professional organizations, we explore ChatGPT's and ADA's potential to screen for CB-PTSD by analyzing maternal childbirth narratives. The PTSD Checklist for DSM-5 (PCL-5; cutoff 31) was used to assess CB-PTSD. By developing an ML model that utilizes numerical vector representation of the ADA model, we identify CB-PTSD via narrative classification. Our model outperformed (F1 score: 0.82) ChatGPT and six previously published large language models (LLMs) trained on mental health or clinical domains data, suggesting that the ADA model can be harnessed to identify CB-PTSD. Our modeling approach could be generalized to assess other mental health disorders.

OpenAI's Narrative Embeddings Can Be Used for Detecting Post-Traumatic Stress Following Childbirth Via Birth Stories

Free-text analysis using Machine Learning (ML)-based Natural Language Processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility for this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and the text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum mental illness affecting millions of women annually, with no standard screening protocol. Using a sample of 1,295 women who gave birth in the last six months and were 18+ years old, recruited through hospital announcements, social media, and professional organizations, we explore ChatGPT's and ADA's potential to screen for CB-PTSD by analyzing maternal childbirth narratives. The PTSD Checklist for DSM-5 (PCL-5; cutoff 31) was used to assess CB-PTSD. By developing an ML model that utilizes numerical vector representation of the ADA model, we identify CB-PTSD via narrative classification. Our model outperformed (F1 score: 0.82) ChatGPT and six previously published large language models (LLMs) trained on mental health or clinical domains data, suggesting that the ADA model can be harnessed to identify CB-PTSD. Our modeling approach could be generalized to assess other mental health disorders.

Preventing posttraumatic stress disorder following childbirth: a systematic review and meta-analysis

OBJECTIVE

Women 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 SOURCES

PsycInfo, 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 were searched for eligible trials published through September 2023.

STUDY ELIGIBILITY CRITERIA

Trials 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.

METHODS

Independent 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.

RESULTS

A 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.

CONCLUSION

Brief 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 biologic measures can inform treatment use and the mechanisms at work.

Establishing the validity of a diagnostic questionnaire for childbirth-related posttraumatic stress disorder

BACKGROUND

Labor and delivery can entail complications and severe maternal morbidities that threaten a woman's life or cause her to believe that her life is in danger. Women with these experiences are at risk for developing posttraumatic stress disorder. Postpartum posttraumatic stress disorder, or childbirth-related posttraumatic stress disorder, can become an enduring and debilitating condition. At present, validated tools for a rapid and efficient screen for childbirth-related posttraumatic stress disorder are lacking.

OBJECTIVE

We examined the diagnostic validity of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, for detecting posttraumatic stress disorder among women who have had a traumatic childbirth. This Checklist assesses the 20 Diagnostic and Statistical Manual of Mental Disorders, posttraumatic stress disorder symptoms and is a commonly used patient-administrated screening instrument. Its diagnostic accuracy for detecting childbirth-related posttraumatic stress disorder is unknown.

STUDY DESIGN

The sample included 59 patients who reported a traumatic childbirth experience determined in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, posttraumatic stress disorder criterion A for exposure involving a threat or potential threat to the life of the mother or infant, experienced or perceived, or physical injury. The majority (66%) of the participants were less than 1 year postpartum (for full sample: median, 4.67 months; mean, 1.5 years) and were recruited via the Mass General Brigham's online platform, during the postpartum unit hospitalization or after discharge. Patients were instructed to complete the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, concerning posttraumatic stress disorder symptoms related to childbirth. Other comorbid conditions (ie, depression and anxiety) were also assessed. They also underwent a clinician interview for posttraumatic stress disorder using the gold-standard Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. A second administration of the checklist was performed in a subgroup (n=43), altogether allowing an assessment of internal consistency, test-retest reliability, and convergent and diagnostic validity of the Checklist. The diagnostic accuracy of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, in reference to the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, was determined using the area under the receiver operating characteristic curve; an optimal cutoff score was identified using the Youden's J index.

RESULTS

One-third of the sample (35.59%) met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria for a posttraumatic stress disorder diagnosis stemming from childbirth. The Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, symptom severity score was strongly correlated with the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, total score (ρ=0.82; P<.001). The area under the receiver operating characteristic curve was 0.93 (95% confidence interval, 0.87-0.99), indicating excellent diagnostic performance of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. A cutoff value of 28 maximized the sensitivity (0.81) and specificity (0.90) and correctly diagnosed 86% of women. A higher value (32) identified individuals with more severe posttraumatic stress disorder symptoms (specificity, 0.95), but with lower sensitivity (0.62). Checklist scores were also stable over time (intraclass correlation coefficient, 0.73), indicating good test-retest reliability. Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, scores were moderately correlated with the depression and anxiety symptom scores (Edinburgh Postnatal Depression Scale: ρ=0.58; P<.001 and the Brief Symptom Inventory, anxiety subscale: ρ=0.51; P<.001).

CONCLUSION

This study demonstrates the validity of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, as a screening tool for posttraumatic stress disorder among women who had a traumatic childbirth experience. The instrument may facilitate screening for childbirth-related posttraumatic stress disorder on a large scale and help identify women who might benefit from further diagnostics and services. Replication of the findings in larger, postpartum samples is needed.

A Systematic Review of Interventions for Prevention and Treatment of Post-Traumatic Stress Disorder Following Childbirth

Objective

Postpartum women can develop post-traumatic stress disorder (PTSD) in response to complicated, traumatic childbirth; prevalence of these events remains high in the U.S. Currently, there is no recommended treatment approach in routine peripartum care for preventing maternal childbirth-related PTSD (CB-PTSD) and lessening its severity. Here, we provide a systematic review of available clinical trials testing interventions for the prevention and indication of CB-PTSD.

Data Sources

We conducted a systematic review of PsycInfo, PsycArticles, PubMed (MEDLINE), ClinicalTrials.gov, CINAHL, ProQuest, Sociological Abstracts, Google Scholar, Embase, Web of Science, ScienceDirect, and Scopus through December 2022 to identify clinical trials involving CB-PTSD prevention and treatment.

Study Eligibility Criteria

Trials were included if they were interventional, evaluated CB-PTSD preventive strategies or treatments, and reported outcomes assessing CB-PTSD symptoms. Duplicate studies, case reports, protocols, active clinical trials, and studies of CB-PTSD following stillbirth were excluded.

Study Appraisal and Synthesis Methods

Two independent coders evaluated trials using a modified Downs and Black methodological quality assessment checklist. Sample characteristics and related intervention information were extracted via an Excel-based form.

Results

A total of 33 studies, including 25 randomized controlled trials (RCTs) and 8 non-RCTs, were included. Trial quality ranged from Poor to Excellent. Trials tested psychological therapies most often delivered as secondary prevention against CB-PTSD onset (n=21); some examined primary (n=3) and tertiary (n=9) therapies. Positive treatment effects were found for early interventions employing conventional trauma-focused therapies, psychological counseling, and mother-infant dyadic focused strategies. Therapies' utility to aid women with severe acute traumatic stress symptoms or reduce incidence of CB-PTSD diagnosis is unclear, as is whether they are effective as tertiary intervention. Educational birth plan-focused interventions during pregnancy may improve maternal health outcomes, but studies remain scarce.

Conclusions

An array of early psychological therapies delivered in response to traumatic childbirth, rather than universally, in the first postpartum days and weeks, may potentially buffer CB-PTSD development. Rather than one treatment being suitable for all, effective therapy should consider individual-specific factors. As additional RCTs generate critical information and guide recommendations for first-line preventive treatments for CB-PTSD, the psychiatric consequences associated with traumatic childbirth could be lessened.

Screening for Post-Traumatic Stress Disorder following Childbirth using the Peritraumatic Distress Inventory

Background

Maternal psychiatric morbidities include a range of psychopathologies; one condition is post-traumatic stress disorder (PTSD) that develops following a traumatic childbirth experience and may undermine maternal and infant health. Although assessment for maternal mental health problems is integrated in routine perinatal care, screening for maternal childbirth-related PTSD (CB-PTSD) remains lacking. Acute emotional distress in response to a traumatic event strongly associates with PTSD. The brief 13-item Peritraumatic Distress Inventory (PDI) is a common tool to assess acute distress in non-postpartum individuals. How well the PDI specified to childbirth can classify women likely to endorse CB-PTSD is unknown.

Objectives

We sought to determine the utility of the PDI to detect CB-PTSD in the early postpartum period. This involved examining the psychometric properties of the PDI specified to childbirth, pertaining to its factorial structure, and establishing an optimal cutoff point for the classification of women with high vs. low likelihood of endorsing CB-PTSD.

Study Design

A sample of 3,039 eligible women who had recently given birth provided information about their mental health and childbirth experience. They completed the PDI regarding their recent childbirth event, and a PTSD symptom screen to determine CB-PTSD. We employed Exploratory Graph Analysis (EGA) and bootstrapping analysis to reveal the factorial structure of the PDI and the optimal PDI cutoff value for CB-PTSD classification.

Results

Factor analysis of the PDI shows two strongly correlated stable factors based on a modified 12-item version of the PDI consisting of (1) negative emotions and (2) bodily arousal and threat appraisal in regard to recent childbirth. This structure largely accords with prior studies of individuals who experienced acute distress resulting from other forms of trauma. We report that a score of 15 or higher on the modified PDI produces strong sensitivity and specificity. 88% of women with a positive CB-PTSD screen in the first postpartum months and 93% with a negative screen are identified as such using the established cutoff.

Conclusions

Our work reveals that a brief self-report screening concerning a woman's immediate emotional reactions to childbirth that uses our modified PDI tool can detect women likely to endorse CB-PTSD in the early postpartum period. This form of maternal mental health assessment may serve as the initial step of managing symptoms to ultimately prevent chronic symptom manifestation. Future research is needed to examine the utility of employing the PDI as an assessment performed during maternity hospitalization stay in women following complicated deliveries to further guide recommendations to implement maternal mental health screening for women at high risk for developing CB-PTSD.

Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives

BACKGROUND

Maternal mental disorders are considered a leading complication of childbirth and a common contributor to maternal death. In addition to undermining maternal welfare, untreated postpartum psychopathology can result in child emotional and physical neglect and associated significant pediatric health costs. Some women may experience traumatic childbirth and develop posttraumatic stress disorder symptoms after delivery (childbirth-related posttraumatic stress disorder). Although women are routinely screened for postpartum depression in the United States, there is no recommended protocol to inform the identification of women who are likely to experience childbirth-related posttraumatic stress disorder. Advancements in computational methods of free text have shown promise in informing the diagnosis of psychiatric conditions. Although the language in narratives of stressful events has been associated with posttrauma outcomes, whether the narratives of childbirth processed via machine learning can be useful for childbirth-related posttraumatic stress disorder screening is unknown.

OBJECTIVE

This study aimed to examine the use of written narrative accounts of personal childbirth experiences for the identification of women with childbirth-related posttraumatic stress disorder. To this end, we developed a model based on natural language processing and machine learning algorithms to identify childbirth-related posttraumatic stress disorder via the classification of birth narratives.

STUDY DESIGN

Overall, 1127 eligible postpartum women who enrolled in a study survey during the COVID-19 pandemic provided short written childbirth narrative accounts in which they were instructed to focus on the most distressing aspects of their childbirth experience. They also completed a posttraumatic stress disorder symptom screen to determine childbirth-related posttraumatic stress disorder. After the exclusion criteria were applied, data from 995 participants were analyzed. A machine learning-based Sentence-Transformers natural language processing model was used to represent narratives as vectors that served as inputs for a neural network machine learning model developed in this study to identify participants with childbirth-related posttraumatic stress disorder.

RESULTS

The machine learning model derived from natural language processing of childbirth narratives achieved good performance (area under the curve, 0.75; F1 score, 0.76; sensitivity, 0.8; specificity, 0.70). Moreover, women with childbirth-related posttraumatic stress disorder generated longer narratives (t test results: t=2.30; p=.02) and used more negative emotional expressions (Wilcoxon test: sadness: p=8.90e-04; W=31,017; anger: p=1.32e-02; W=35,005.50) and death-related words (Wilcoxon test: p=3.48e-05; W=34,538) in describing their childbirth experience than those with no childbirth-related posttraumatic stress disorder.

CONCLUSION

This study provided proof of concept that personal childbirth narrative accounts generated in the early postpartum period and analyzed via advanced computational methods can detect with relatively high accuracy women who are likely to endorse childbirth-related posttraumatic stress disorder and those at low risk. This suggests that birth narratives could be promising for informing low-cost, noninvasive tools for maternal mental health screening, and more research that used machine learning to predict early signs of maternal psychiatric morbidity is warranted.

Identifying Women with Post-Delivery Posttraumatic Stress Disorder using Natural Language Processing of Personal Childbirth Narratives

Background

Maternal mental disorders are considered a leading complication of childbirth and a common contributor to maternal death. In addition to undermining maternal welfare, untreated postpartum psychopathology can result in child emotional and physical neglect, and associated significant pediatric health costs. Some women may experience a traumatic childbirth and develop posttraumatic stress disorder (PTSD) symptoms following delivery (CB-PTSD). Although women are routinely screened for postpartum depression in the U.S., there is no recommended protocol to inform the identification of women who are likely to experience CB-PTSD. Advancements in computational methods of free text has shown promise in informing diagnosis of psychiatric conditions. Although the language in narratives of stressful events has been associated with post-trauma outcomes, whether the narratives of childbirth processed via machine learning can be useful for CB-PTSD screening is unknown.

Objective

This study examined the utility of written narrative accounts of personal childbirth experience for the identification of women with provisional CB-PTSD. To this end, we developed a model based on natural language processing (NLP) and machine learning (ML) algorithms to identify CB-PTSD via classification of birth narratives.

Study Design

A total of 1,127 eligible postpartum women who enrolled in a study survey during the COVID-19 era provided short written childbirth narrative accounts in which they were instructed to focus on the most distressing aspects of their childbirth experience. They also completed a PTSD symptom screen to determine provisional CB-PTSD. After exclusion criteria were applied, data from 995 participants was analyzed. An ML-based Sentence-Transformer NLP model was used to represent narratives as vectors that served as inputs for a neural network ML model developed in this study to identify participants with provisional CB-PTSD.

Results

The ML model derived from NLP of childbirth narratives achieved good performance: AUC 0.75, F1-score 0.76, sensitivity 0.8, and specificity 0.70. Moreover, women with provisional CB-PTSD generated longer narratives (t-test results: t=2 . 30, p=0 . 02 ) and used more negative emotional expressions (Wilcoxon test: 'sadness': p=8 . 90e- ⁰⁴ , W=31,017 ; 'anger': p=1 . 32e- ⁰² , W=35,005 . 50 ) and death-related words (Wilcoxon test: p=3 . 48e- ⁰⁵ , W=34,538 ) in describing their childbirth experience than those with no CB-PTSD.

Conclusions

This study provides proof of concept that personal childbirth narrative accounts generated in the early postpartum period and analyzed via advanced computational methods can detect with relatively high accuracy women who are likely to endorse CB-PTSD and those at low risk. This suggests that birth narratives could be promising for informing low-cost, non-invasive tools for maternal mental health screening, and more research that utilizes ML to predict early signs of maternal psychiatric morbidity is warranted.