{
  "abstract": "Background: Antenatal depression is common, under-detected, and harms mothers and infants. Pakistan has Asia’s highest perinatal depression prevalence and an estimated national economic burden of US$16.5 billion. About 9.7 million Pakistani women become pregnant each year, and roughly 3.6 million may experience antenatal depression. Routine detection relies on self-report scales (e.g., Edinburgh Postnatal Depression Scale [EPDS]); whether biomarkers add prognostic value is largely untested. Objective: Develop and validate a multivariable prognostic tool (MPT) to predict clinically significant depressive symptoms (EPDS ≥ 10) and assess the added value of biomarker data. Methods: We will use secondary data (N = 1,621) from the Pakistani Pregnancy Cohort at the Aga Khan Hospital for Women and Children, Sindh. Self-reported predictors across eight domains (demographic, socioeconomic, behavioural, mental health, clinical history, pregnancy-related, and social network) plus biomarkers will inform MPT development. In Stage 1 we will compare supervised classifiers (regularized regression, decision trees, support vector machines, and neural networks) to logistic regression. Models will be trained with stratified five-fold cross-validation and nested L1 selection within folds to retain the smallest predictor set within 0.01 area under the receiver operating characteristic curve (ROC-AUC) of the full mode. External cross-validation on an independent hold-out sample will report ROC-AUC, area under the precision-recall curve (relative to prevalence), calibration, sensitivity, specificity, and predictive values. Decision thresholds will be set on training data and applied unchanged. In Stage 2 we will build a biomarker-augmented model and compare it to the optimized self-report model using paired AUC tests and reclassification metrics; exploratory clustering will identify participant subgroups. Pre-specified criteria (ROC-AUC ≥ 0.70; calibration slope 0.8–1.2; sensitivity ≥ 0.70; positive net benefit) will determine progression to prospective validation studies in new samples. Expected Outcomes: A pragmatic self-report prognostic tool and evidence on whether biomarkers meaningfully improve prediction and justify added complexity.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Sheriff Ibrahim"
    },
    {
      "name": "Dr. Shahirose Sadrudin Premji"
    },
    {
      "name": "Shehzad Ali"
    },
    {
      "name": "Jaime Delgadillo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ae3qxm8zn5vbk.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/hcgfx_v1",
  "fulltext_url": null,
  "id": "ae3qxm8zn5vbk",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/hcgfx_v1/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Computational Modeling"
    ],
    [
      "bepress",
      "Health Psychology"
    ],
    [
      "bepress",
      "Mental Health"
    ],
    [
      "bepress",
      "Psychiatry"
    ],
    [
      "bepress",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "openalex",
      "2739|Maternal Mental Health During Pregnancy and Postpartum"
    ]
  ],
  "openalex": "W7225117554",
  "pdf_url": "https://osf.io/hcgfx/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ae3qxm8zn5vbk.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "hcgfx",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/hcgfx_v1/"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "bepress:Psychiatry",
      "subfield": "psychiatry"
    }
  ],
  "title": "Development and validation of a prognostic tool for antenatal depressive symptoms: Statistical analysis plan for a two-stage model integrating self-reported and biomarker measures",
  "title_text": "Development and validation of a prognostic tool for antenatal depressive symptoms: Statistical analysis plan for a two-stage model integrating self-reported and biomarker measures",
  "type": "preprint",
  "updated_at": "2026-11-04T12:21:42Z",
  "url": "https://research.draconitic.com/paper/ae3qxm8zn5vbk",
  "venue": "PsyArXiv",
  "versions": []
}