The Prevalence, Incidence, Indications and Outcomes of Peripartum Hysterectomy in Kazakhstan: Data from Unified Nationwide Electronic Healthcare System 2014–2018
Bibliographic record
Abstract
Gulzhanat Aimagambetova,1,* Yesbolat Sakko,2,* Arnur Gusmanov,2 Alpamys Issanov,2,3 Talshyn Ukybassova,4 Gauri Bapayeva,4 Aizada Marat,5 Aiymzhan Nurpeissova,6 Abduzhappar Gaipov2 1Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Nur-Sultan, Kazakhstan; 2Department of Medicine, School of Medicine, Nazarbayev University, Nur-Sultan, Kazakhstan; 3School of Population and Public Health, University of British Columbia, Vancouver, Canada; 4Clinical Academic Department of Women’s Health, National Research Center of Mother and Child Health, University Medical Center, Nur-Sultan, Kazakhstan; 5Department of Obstetrics and Gynecology #1, NJSC “Astana Medical University”, Nur-Sultan, Kazakhstan; 6Department of Medical Information Analysis of Outpatient and Polyclinic Care, The Republican Center of Electronic Healthcare, The Ministry of Healthcare of the Republic of Kazakhstan, Nur-Sultan, Kazakhstan*These authors contributed equally to this workCorrespondence: Gulzhanat Aimagambetova, Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Kerey and Zhanibek Khan’s Street 5/1, Nur-Sultan, 010000, Kazakhstan, Tel +7 7172 694645, Email gulzhanat.aimagambetova@nu.edu.kzPurpose: Peripartum hysterectomy is a surgical procedure performed as a life-saving surgery to manage severe postpartum hemorrhage. The prevalence of peripartum hysterectomy in high‐resource settings is relatively low. However, maternal mortality due to postpartum hemorrhage and after peripartum hysterectomy remains high in developing countries. To date, there is a lack of information about the rates of peripartum hysterectomy and its common indications in Kazakhstan. Objectives were to study the prevalence, indications, and outcomes of peripartum hysterectomy using nationwide large-scale health-care data from the national registry.Patients and Methods: We performed a descriptive, population‐based study among women who underwent a peripartum hysterectomy in any health-care setting of the Republic of Kazakhstan during the period of 2014– 2018. Data were collected from the Unified Nationwide Electronic Health System (UNEHS).Results: Data included 3838 medical records of women who had a peripartum hysterectomy performed due to specific indications for the period of 5 years (2014– 2018). The median age of the participants was 33 years old, with 60.7% of women aged between 18 and 34 years. The leading indications for peripartum hysterectomy were intrapartum hemorrhage (IPH) and postpartum hemorrhage (PPH) reported in 60% of the cases analyzed. The second most common indication was placental pathology – placental abruption and placenta previa in 9.6% and 7.9% of cases, respectively. In 1633 cases (42.4%), total abdominal hysterectomy was performed, while subtotal hysterectomy was done in 2195 cases (57.0%). Based on these data, the estimated prevalence of peripartum hysterectomies was calculated: overall weighted mean prevalence 1.93 per 1000 deliveries.Conclusion: IPH and PPH are the commonest indications for peripartum hysterectomy followed by placental pathology. Appropriate maternal care during labor and delivery should be reinforced to decrease the incidence of peripartum hysterectomy in Kazakhstan.Keywords: postpartum hemorrhage, maternal mortality, peripartum hysterectomy, epidemiology, Kazakhstan
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".