Postpartum depression: a developed and validated model predicting individual risk in new mothers

Abstract

Postpartum depression (PPD) is a serious condition associated with potentially tragic outcomes, and in an ideal world PPDs should be prevented. Risk prediction models have been developed in psychiatry estimating an individual’s probability of developing a specific condition, and recently a few models have also emerged within the field of PPD research, although none are implemented in clinical care. For the present study we aimed to develop and validate a prediction model to assess individualized risk of PPD and provide a tentative template for individualized risk calculation offering opportunities for additional external validation of this tool. Danish population registers served as our data sources and PPD was defined as recorded contact to a psychiatric treatment facility (ICD-10 code DF32-33) or redeemed antidepressant prescriptions (ATC code N06A), resulting in a sample of 6,402 PPD cases (development sample) and 2,379 (validation sample). Candidate predictors covered background information including cohabitating status, age, education, and previous psychiatric episodes in index mother (Core model), additional variables related to pregnancy and childbirth (Extended model), and further health information about the mother and her family (Extended+ model). Results indicated our recalibrated Extended model with 14 variables achieved highest performance with satisfying calibration and discrimination. Previous psychiatric history, maternal age, low education, and hyperemesis gravidarum were the most important predictors. Moving forward, external validation of the model represents the next step, while considering who will benefit from preventive PPD interventions, as well as considering potential consequences from false positive and negative test results, defined through different threshold values.

Survivors' Experiences of Hyperemesis Gravidarum

“…Postpartum depression, panic attacks, and PTSD due to their traumatic pregnancies were reported by some mothers in their blogs, which confirmed results from multiple previous research studies. 18,22,24 Using Danish population registers, Munk-Olsen et al 21 reported that HG was one of the 4 most significant predictors of postpartum depression.…”

“…Postnatally, 29% of the mothers with HG had probable depression compared to 7% of the control subjects (P = .002). Munk-Olsen et al 21 developed a prediction model for postpartum depression using data from the Danish population registers for 6402 postpartum depression cases and 2379 validation samples. HG was 1 of the 4 most significant predictors of postpartum depression.…”

Interventions to treat and prevent postpartum depression: a protocol for systematic review of the literature and parallel network meta-analyses

“…For each comparison, we shall also look at the node split model, thus helping us to quantify comparison-specific inconsistency to estimate deficiencies in transitivity. We have identified potential effect modifiers (Table 3) from the literature [15][16][17][18] and will assess the distribution of effect modifiers to judge if the transitivity assumption holds. For those that prove to be a study-level parameter, then we plan to explore the impact of this on our network estimates by employing network meta-regression.…”

“…Previous depression diagnosis [15][16][17] b. Lower education status [15] c. Unemployment [15] d. Low socioeconomic status [16] e. Low levels of social support [16] f. Advanced maternal age [17] g.…”

Predicting Patients Requiring Treatment for Depression in the Postpartum Period Using Common Electronic Medical Record Data Available Antepartum

“…The existing early intervention paradigm is based on a USPSTF Grade B recommendation wherein counseling is provided to women with 1 or more established risk factors for PPD, such as a history of depression or recent intimate partner violence. 6 The current approach for identifying at-risk patients is crude at best and represents an opportunity to mobilize available technologies such as machine learning (ML) in a way that can improve health outcomes. The lack of effective screening tools is a public health issue because early interventions in at-risk people can minimize the morbidity and mortality of PPD.…”