Predicting in-hospital maternal mortality in Senegal and Mali.
OBJECTIVE: We sought to identify predictors of in-hospital maternal mortality among women attending referral hospitals in Mali and Senegal. METHODS: We conducted a cross-sectional epidemiological survey using data from a cluster randomized controlled trial (QUARITE trial) in 46 referral hospitals in...
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doaj-b79fc0b1f76b41aa9aeb2bc1e9b5725b2020-11-25T01:15:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0185e6415710.1371/journal.pone.0064157Predicting in-hospital maternal mortality in Senegal and Mali.Cheikh NdourSimplice Dossou GbétéNoelle BruMichal AbrahamowiczArnaud FauconnierMamadou TraoréAliou DiopPierre FournierAlexandre DumontOBJECTIVE: We sought to identify predictors of in-hospital maternal mortality among women attending referral hospitals in Mali and Senegal. METHODS: We conducted a cross-sectional epidemiological survey using data from a cluster randomized controlled trial (QUARITE trial) in 46 referral hospitals in Mali and Senegal, during the pre-intervention period of the trial (from October 1st 2007 to October 1st 2008). We included 89,518 women who delivered in the 46 hospitals during this period. Data were collected on women's characteristics, obstetric complications, and vital status until the hospital discharge. We developed a tree-like classification rule (classification rule) to identify patient subgroups at high risk of maternal in-hospital mortality. RESULTS: Our analyses confirm that patients with uterine rupture, hemorrhage or prolonged/obstructed labor, and those who have an emergency ante-partum cesarean delivery have an increased risk of in-hospital mortality, especially if they are referred from another health facility. Twenty relevant patterns, based on fourteen predictors variables, are used to predict in-hospital maternal mortality with 81.41% sensitivity (95% CI = [77.12%-87.70%]) and 81.6% specificity (95% CI = [81.16%-82.02%]). CONCLUSION: The proposed class association rule method will help health care professionals in referral hospitals in Mali and Senegal to identify mothers at high risk of in-hospital death, and can provide scientific evidence on which to base their decisions to manage patients delivering in their health facilities.http://europepmc.org/articles/PMC3667861?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cheikh Ndour Simplice Dossou Gbété Noelle Bru Michal Abrahamowicz Arnaud Fauconnier Mamadou Traoré Aliou Diop Pierre Fournier Alexandre Dumont |
spellingShingle |
Cheikh Ndour Simplice Dossou Gbété Noelle Bru Michal Abrahamowicz Arnaud Fauconnier Mamadou Traoré Aliou Diop Pierre Fournier Alexandre Dumont Predicting in-hospital maternal mortality in Senegal and Mali. PLoS ONE |
author_facet |
Cheikh Ndour Simplice Dossou Gbété Noelle Bru Michal Abrahamowicz Arnaud Fauconnier Mamadou Traoré Aliou Diop Pierre Fournier Alexandre Dumont |
author_sort |
Cheikh Ndour |
title |
Predicting in-hospital maternal mortality in Senegal and Mali. |
title_short |
Predicting in-hospital maternal mortality in Senegal and Mali. |
title_full |
Predicting in-hospital maternal mortality in Senegal and Mali. |
title_fullStr |
Predicting in-hospital maternal mortality in Senegal and Mali. |
title_full_unstemmed |
Predicting in-hospital maternal mortality in Senegal and Mali. |
title_sort |
predicting in-hospital maternal mortality in senegal and mali. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2013-01-01 |
description |
OBJECTIVE: We sought to identify predictors of in-hospital maternal mortality among women attending referral hospitals in Mali and Senegal. METHODS: We conducted a cross-sectional epidemiological survey using data from a cluster randomized controlled trial (QUARITE trial) in 46 referral hospitals in Mali and Senegal, during the pre-intervention period of the trial (from October 1st 2007 to October 1st 2008). We included 89,518 women who delivered in the 46 hospitals during this period. Data were collected on women's characteristics, obstetric complications, and vital status until the hospital discharge. We developed a tree-like classification rule (classification rule) to identify patient subgroups at high risk of maternal in-hospital mortality. RESULTS: Our analyses confirm that patients with uterine rupture, hemorrhage or prolonged/obstructed labor, and those who have an emergency ante-partum cesarean delivery have an increased risk of in-hospital mortality, especially if they are referred from another health facility. Twenty relevant patterns, based on fourteen predictors variables, are used to predict in-hospital maternal mortality with 81.41% sensitivity (95% CI = [77.12%-87.70%]) and 81.6% specificity (95% CI = [81.16%-82.02%]). CONCLUSION: The proposed class association rule method will help health care professionals in referral hospitals in Mali and Senegal to identify mothers at high risk of in-hospital death, and can provide scientific evidence on which to base their decisions to manage patients delivering in their health facilities. |
url |
http://europepmc.org/articles/PMC3667861?pdf=render |
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