A synthesis of clinical and health system bottlenecks to implementing new <scp>WHO</scp> postpartum hemorrhage recommendations: Secondary data analysis of the Kenya Confidential Enquiry into Maternal Deaths 2014–2017

Ameh, Charles; Meka, Ramya Jyothi; West, Florence; Dickinson, Fiona; Allott, Helen; Godia, Pamela

doi:10.1002/ijgo.14270

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…At present, healthcare providers predominantly rely on clinical expertise and nonspecific risk factors to identify high-risk pregnancies. Further, the International Federation of Gynecology and Obstetrics (FIGO) developed a PPH care pathway to integrate WHO PPH guidelines in Sub-Saharan African countries (29). These guidelines are primarily derived from expert opinion and clinical consensus lacking the ability to offer personalized risk predictions.…”

Section: Discussionmentioning

confidence: 99%

Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population

Shah

Saxena

Rani

et al. 2023

Front. Glob. Womens Health

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IntroductionPostpartum hemorrhage (PPH) is a significant cause of maternal mortality worldwide, particularly in low- and middle-income countries. It is essential to develop effective prediction models to identify women at risk of PPH and implement appropriate interventions to reduce maternal morbidity and mortality. This study aims to predict the occurrence of postpartum hemorrhage using machine learning models based on antenatal, intrapartum, and postnatal visit data obtained from the Kenya Antenatal and Postnatal Care Research Collective cohort.MethodFour machine learning models – logistic regression, naïve Bayes, decision tree, and random forest – were constructed using 67% training data (1,056/1,576). The training data was further split into 67% for model building and 33% cross validation. Once the models are built, the remaining 33% (520/1,576) independent test data was used for external validation to confirm the models' performance. Models were fine-tuned using feature selection through extra tree classifier technique. Model performance was assessed using accuracy, sensitivity, and area under the curve (AUC) of the receiver operating characteristics (ROC) curve.ResultThe naïve Bayes model performed best with 0.95 accuracy, 0.97 specificity, and 0.76 AUC. Seven factors (anemia, limited prenatal care, hemoglobin concentrations, signs of pallor at intrapartum, intrapartum systolic blood pressure, intrapartum diastolic blood pressure, and intrapartum respiratory rate) were associated with PPH prediction in Kenyan population.DiscussionThis study demonstrates the potential of machine learning models in predicting PPH in the Kenyan population. Future studies with larger datasets and more PPH cases should be conducted to improve prediction performance of machine learning model. Such prediction algorithms would immensely help to construct a personalized obstetric path for each pregnant patient, improve resource allocation, and reduce maternal mortality and morbidity.

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Section: Discussionmentioning

confidence: 99%

Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population

Shah

Saxena

Rani

et al. 2023

Front. Glob. Womens Health

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“…Knight et al [49] reported a higher average rate of non-maternal deaths in the UK compared to developing countries. Previous studies [50,51] have also highlighted that in developed countries, indirect causes of maternal mortality, particularly non-obstetric complications, are more prevalent than direct causes. This has been attributed to the well-organized antenatal care programs in these countries.…”

Section: Discussionmentioning

confidence: 99%

“…All of the studies examined this group of causes and reported rates ranging from 4.1 to 66.6%. About 62% of maternal mortalities worldwide can be attributed to direct causes, with obstetric hemorrhages accounting for nearly half of them (about 27%) [50]. Postpartum hemorrhages are the most common cause of obstetric hemorrhages (72%) [51] accounting for about 25% of maternal deaths globally [52].…”

Section: Discussionmentioning

confidence: 99%

Prevalence of maternal mortality causes based on ICD-MM: a systematic review and meta-analysis

Alipour,

Payandeh,

Karimi

2023

BMC Pregnancy Childbirth

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Background Maternal mortality is a universal public health challenge. ICD-Maternal Mortality (ICD-MM) was introduced in 2012 to facilitate the gathering, analysis, and interpretation of data on maternal deaths worldwide. The present study aimed to estimate the global prevalence of maternal death causes through a systematic review and meta-analysis. Methods A systematic literature search was conducted using various databases, including Web of Science, PubMed, Scopus, ScienceDirect, Cochrane Library, as well as Persian databases such as MagIran and Scientific Information Database (SID). The search encompassed articles published until August 21, 2022. Thirty-four eligible articles were included in the final analysis. Analysis was performed using a meta-analysis approach. The exact Clopper-Pearson confidence intervals, heterogeneity assessment, and random effects models with Mantel-Haenszel methods were employed using the STATA software version 14.2. Results The most prevalent causes of maternal deaths, listed in descending order from highest to lowest prevalence, were non-obstetric complications (48.32%), obstetric hemorrhage (17.63%), hypertensive disorders of pregnancy, childbirth, and the puerperium (14.01%), other obstetric complications (7.11%), pregnancy with abortive outcome (5.41%), pregnancy-related infection (5.26%), unanticipated complications of management (2.25%), unknown/undetermined causes (2.01%), and coincidental causes (1.59%), respectively. Conclusion Non-obstetric complications, obstetric hemorrhage, and hypertensive disorders of pregnancy, childbirth, and puerperium were the most common causes of maternal deaths. To reduce the burden of maternal mortality causes, increasing awareness and promoting self-care management among women of reproductive age, and implementing effective screening mechanisms for high-risk mothers during pregnancy, childbirth, and the puerperium can play a significant role. ICD-MM enables the uniform collection and comparison of maternal death information at different levels (local, national, and international) by facilitating the consistent collection, analysis, and interpretation of data on maternal deaths. Our findings can be utilized by policymakers and managers at various levels to facilitate necessary planning aimed at reducing the burden of maternal mortality causes.

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“…While several challenges have been reported with these programs -including a lack of support for the costs of referrals, challenges for patients trying to access the services to which they were entitled [25], and persistent socioeconomic disparities in access to care [26]-these changes have been associated with improvements in the continuity of care [26]. However, alongside these improvements, several studies have documented important gaps in the quality of maternity care in Kenya [27][28][29].…”

Section: Study Setting and Designmentioning

confidence: 99%

Health care providers’ knowledge of clinical protocols for postpartum hemorrhage care in Kenya: a cross-sectional study

Henry

Clarke-Deelder

Han

et al. 2022

BMC Pregnancy Childbirth

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Background Postpartum hemorrhage (PPH) remains the leading cause of maternal death worldwide despite its often-preventable nature. Understanding health care providers’ knowledge of clinical protocols is imperative for improving quality of care and reducing mortality. This is especially pertinent in referral and teaching hospitals that train nursing and medical students and interns in addition to managing emergency and referral cases. Methods This study aimed to (1) measure health care providers’ knowledge of clinical protocols for risk assessment, prevention, and management of PPH in 3 referral hospitals in Kenya and (2) examine factors associated with providers’ knowledge. We developed a knowledge assessment tool based on past studies and clinical guidelines from the World Health Organization and the Kenyan Ministry of Health. We conducted in-person surveys with health care providers in three high-volume maternity facilities in Nairobi and western Kenya from October 2018-February 2019. We measured gaps in knowledge using a summative index and examined factors associated with knowledge (such as age, gender, qualification, experience, in-service training attendance, and a self-reported measure of peer-closeness) using linear regression. Results We interviewed 172 providers including consultants, medical officers, clinical officers, nurse-midwives, and students. Overall, knowledge was lowest for prevention-related protocols (an average of 0.71 out of 1.00; 95% CI 0.69–0.73) and highest for assessment-related protocols (0.81; 95% CI 0.79–0.83). Average knowledge scores did not differ significantly between qualified providers and students. Finally, we found that being a qualified nurse, having a specialization, being female, having a bachelor's degree and self-reported closer relationships with colleagues were statistically significantly associated with higher knowledge scores. Conclusion We found gaps in knowledge of PPH care clinical protocols in Kenya. There is a clear need for innovations in clinical training to ensure that providers in teaching referral hospitals are prepared to prevent, assess, and manage PPH. It is possible that training interventions focused on learning by doing and teamwork may be beneficial.

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A synthesis of clinical and health system bottlenecks to implementing new WHO postpartum hemorrhage recommendations: Secondary data analysis of the Kenya Confidential Enquiry into Maternal Deaths 2014–2017

Cited by 10 publications

References 24 publications

Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population

Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population

Prevalence of maternal mortality causes based on ICD-MM: a systematic review and meta-analysis

Health care providers’ knowledge of clinical protocols for postpartum hemorrhage care in Kenya: a cross-sectional study

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