Deleterious and Protective Psychosocial and Stress-Related Factors Predict Risk of Spontaneous Preterm Birth

Martin Becker; Jonathan A. Mayo; Nisha K. Phogat; Cecele C. Quaintance; Ana Laborde; Lucy King; Ian H. Gotlib; Brice Gaudilliere; Martin S. Angst; Gary M. Shaw; David K. Stevenson; Nima Aghaeepour; Firdaus S. Dhabhar

doi:10.1055/s-0041-1729162

American Journal of Perinatology, Inhaltsverzeichnis

Am J Perinatol 2023; 40(01): 074-088
DOI: 10.1055/s-0041-1729162

Original Article

Deleterious and Protective Psychosocial and Stress-Related Factors Predict Risk of Spontaneous Preterm Birth

Martin Becker‡^*

¹Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California

,

Jonathan A. Mayo‡^*

²Department of Pediatrics, Stanford University School of Medicine, Stanford, California

,

Nisha K. Phogat

³Department of Psychiatry and Behavioral Sciences and Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, Florida

,

Cecele C. Quaintance

²Department of Pediatrics, Stanford University School of Medicine, Stanford, California

,

Ana Laborde

²Department of Pediatrics, Stanford University School of Medicine, Stanford, California

,

Lucy King

⁴Department of Psychology, Stanford University, Stanford, California

,

Ian H. Gotlib

⁴Department of Psychology, Stanford University, Stanford, California

,

Brice Gaudilliere

¹Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California

,

Martin S. Angst

¹Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California

,

Gary M. Shaw

²Department of Pediatrics, Stanford University School of Medicine, Stanford, California

,

David K. Stevenson

²Department of Pediatrics, Stanford University School of Medicine, Stanford, California

,

Nima Aghaeepour

¹Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California

,

Firdaus S. Dhabhar

³Department of Psychiatry and Behavioral Sciences and Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, Florida

⁵Department of Microbiology & Immunology, Miller School of Medicine, Univ. of Miami, Miami, Florida

› Institutsangaben

Abstract

Objectives The aim of the study was to: (1) Identify (early in pregnancy) psychosocial and stress-related factors that predict risk of spontaneous preterm birth (PTB, gestational age <37 weeks); (2) Investigate whether “protective” factors (e.g., happiness/social support) decrease risk; (3) Use the Dhabhar Quick-Assessment Questionnaire for Stress and Psychosocial Factors (DQAQ-SPF) to rapidly quantify harmful or protective factors that predict increased or decreased risk respectively, of PTB.

Study Design This is a prospective cohort study. Relative risk (RR) analyses investigated association between individual factors and PTB. Machine learning-based interdependency analysis (IDPA) identified factor clusters, strength, and direction of association with PTB. A nonlinear model based on support vector machines was built for predicting PTB and identifying factors that most strongly predicted PTB.

Results Higher levels of deleterious factors were associated with increased RR for PTB: General anxiety (RR = 8.9; 95% confidence interval [CI] = 2.0,39.6), pain (RR = 5.7; CI = 1.7,17.0); tiredness/fatigue (RR = 3.7; CI = 1.09,13.5); perceived risk of birth complications (RR = 4; CI = 1.6,10.01); self-rated health current (RR = 2.6; CI = 1.0,6.7) and previous 3 years (RR = 2.9; CI = 1.1,7.7); and divorce (RR = 2.9; CI = 1.1,7.8). Lower levels of protective factors were also associated with increased RR for PTB: low happiness (RR = 9.1; CI = 1.25,71.5); low support from parents/siblings (RR = 3.5; CI = 0.9,12.9), and father-of-baby (RR = 3; CI = 1.1,9.9). These factors were also components of the clusters identified by the IDPA: perceived risk of birth complications (p < 0.05 after FDR correction), and general anxiety, happiness, tiredness/fatigue, self-rated health, social support, pain, and sleep (p < 0.05 without FDR correction). Supervised analysis of all factors, subject to cross-validation, produced a model highly predictive of PTB (AUROC or area under the receiver operating characteristic = 0.73). Model reduction through forward selection revealed that even a small set of factors (including those identified by RR and IDPA) predicted PTB.

Conclusion These findings represent an important step toward identifying key factors, which can be assessed rapidly before/after conception, to predict risk of PTB, and perhaps other adverse pregnancy outcomes. Quantifying these factors, before, or early in pregnancy, could identify women at risk of delivering preterm, pinpoint mechanisms/targets for intervention, and facilitate the development of interventions to prevent PTB.

Key Points

Newly designed questionnaire used for rapid quantification of stress and psychosocial factors early during pregnancy.
Deleterious factors predict increased preterm birth (PTB) risk.
Protective factors predict decreased PTB risk.

Keywords

chronic stress - psychosocial factors - predicting preterm birth - anxiety - sleep - social support - life events - pregnancy - machine learning

Volltext

Referenzen

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