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Record W4410084075 · doi:10.1016/j.ajog.2025.04.060

Frequency and timing of complications within the first postpartum year in the United States and Canada: a systematic review and meta-analysis

2025· review· en· W4410084075 on OpenAlexafffundabout
Janny Xue Chen Ke, Katherine Bilan, Marianne Vidler, Anthony Chau, Qian Zhang, Jeffrey N. Bone, Andrea Sofía López Enríquez, Ronald B. George, Ammar M Lakda, Justine Dol, Lindsay Blake, Brendan Carvalho, Ronald S. Gibbs, Micaela Coombs, E. Tang, Pervez Sultan

Bibliographic record

VenueAmerican Journal of Obstetrics and Gynecology · 2025
Typereview
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsMount Sinai HospitalBC Children's HospitalDalhousie UniversityUniversity of British Columbia HospitalSt. Paul's HospitalIzaak Walton Killam Health CentreProvidence Health Care
FundersNational Institute of Child Health and Human DevelopmentDigital Technology SuperclusterNational Heart, Lung, and Blood InstituteMedtronic CanadaProvidence Health Care
KeywordsMedicineMeta-analysisObstetricsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Understanding the rates and timing of postpartum complications can facilitate timely screening and management to reduce preventable morbidity and mortality. The aim of this study was to summarize the frequency (prevalence or incidence) and timing of complications from hospital delivery to 1 year postpartum in the United States and Canada. DATA SOURCES: PubMed MEDLINE, Web of Science, EBSCO CINAHL, and the Cochrane Central Register of Controlled Trials and Database of Systematic Reviews were reviewed from January 1, 2010 to December 31, 2024. STUDY ELIGIBILITY CRITERIA: Inclusion criteria were studies written in English reporting the frequency and timing of medical, procedural/surgical, and psychosocial complications in adults in the United States and Canada, from hospital delivery to 1 year postpartum. Studies with fewer than 100 patients, those that did not report the timing of evaluation, or those that included only patients with a specific medical condition (e.g., preeclampsia) were excluded. STUDY APPRAISAL AND SYNTHESIS METHODS: Data screening, extraction, and appraisal were performed by 2 reviewers. The appraisal tool was the Joanna Briggs Institute instrument for studies reporting prevalence data. Meta-analysis using random effects modeling was performed if a complication was reported in 2 or more studies. RESULTS: Of 4874 retrieved articles, 117 were included (93 original investigations and 24 reviews). The total sample size from original investigation studies was 246,521,464 patients (median [interquartile range] 6030 [513-327,066] per study). In total, 41 complications and mortality data were extracted, with substantial heterogeneity among definitions and time points of measurements. The 1-year postpartum frequency estimates from meta-analysis (per 10,000, with 95% confidence interval) were anxiety 1380 (845-2174), depression 1008 (749-1343), hypertension 890 (345-2109), obsessive-compulsive disorder 886 (135-4089), hemorrhage 591 (454-763), post-traumatic stress disorder 464 (188-1100), surgical infection 581 (12-7678), postpartum severe maternal morbidity 100 (38-260), venous thromboembolism 17 (13-24), sepsis 11 (8-15), cardiomyopathy 1.9 (0.5-6.8), severe sepsis 1.2 (0.2-9.0), cardiac arrest 0.9 (0.8-1.0), acute myocardial infarction 0.25 (0.06-1.03), and mortality 1.2 (0.3-5.6). CONCLUSION: We report frequencies and timings for 41 complications and mortality from delivery to 1 year postpartum. Of the 14 complications that underwent meta-analysis, anxiety, depression, hypertension, obsessive-compulsive disorder, and hemorrhage were reported to be the most frequent. These results can inform evidence-based resource allocation and guide optimal postpartum monitoring and care pathway development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.834
Threshold uncertainty score0.329

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.029
Bibliometrics0.0050.009
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.336
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2025
Admission routes3
Has abstractno

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