Quality of care and maternal mortality in a tertiary-level hospital in Mozambique: a retrospective study of clinicopathological discrepancies

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Study Justification:
– Maternal mortality remains high in resource-limited areas despite an increasing number of women delivering in health-care facilities.
– Inadequate diagnosis and management of life-threatening conditions contribute to maternal mortality.
– This study aims to analyze clinicopathological discrepancies in maternal deaths in Mozambique and assess changes in the diagnostic process over a 10-year period.
– The goal is to provide data on clinical diagnostic accuracy to improve the quality of care and reduce maternal mortality.
Study Highlights:
– Retrospective analysis of clinicopathological discrepancies in 91 maternal deaths at a tertiary-level hospital in Mozambique.
– Major discrepancies were observed in 38% of cases, indicating significant diagnostic errors.
– Sensitivity of clinical diagnosis for puerperal infections was 17%, non-obstetric infections was 48%, and eclampsia was 100%.
– Performance of clinical diagnosis did not improve over the 10-year period and worsened for some conditions.
– Decreasing maternal mortality requires improvement of the pre-mortem diagnostic process, clinical skills, and availability of quality diagnostic tests.
Study Recommendations:
– Refine clinical skills and increase the availability and quality of diagnostic tests to improve the pre-mortem diagnostic process.
– Monitor reduction of clinical errors by comparing post-mortem information with clinical diagnosis.
– Enhance the quality of care to decrease maternal mortality.
Key Role Players:
– Clinicians (obstetricians, pathologists, microbiologists) for accurate diagnosis and management.
– Multidisciplinary experts for evaluating macroscopic, microscopic, and microbiological findings.
– National Bioethics Committee of Mozambique for ethical oversight.
– Hospital administration for implementing changes and allocating resources.
Cost Items for Planning Recommendations:
– Training programs for clinicians to improve clinical skills.
– Procurement of diagnostic equipment and supplies.
– Laboratory facilities for histological and microbiological examinations.
– Staffing and salaries for healthcare professionals.
– Quality assurance programs for monitoring and evaluation.
– Information systems for data collection and analysis.
Please note that the cost items provided are general examples and may vary based on the specific context and requirements of the hospital in Mozambique.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a retrospective study with a large sample size (91 maternal deaths) and a clear methodology. The study used complete diagnostic autopsies as the gold standard to ascertain cause of death, which enhances the reliability of the findings. The study also compared the findings with a similar study conducted 10 years earlier, providing valuable insights into changes over time. To improve the evidence, the study could have included a control group of non-maternal deaths for comparison and conducted a prospective study to gather real-time data.

Background: Although an increasing number of pregnant women in resource-limited areas deliver in health-care facilities, maternal mortality remains high in these settings. Inadequate diagnosis and management of common life-threatening conditions is an important determinant of maternal mortality. We analysed the clinicopathological discrepancies in a series of maternal deaths from Mozambique and assessed changes over 10 years in the diagnostic process. We aimed to provide data on clinical diagnostic accuracy to be used for improving quality of care and reducing maternal mortality. Methods: We did a retrospective analysis of clinicopathological discrepancies in 91 maternal deaths occurring from Nov 1, 2013, to March 31, 2015 (17 month-long period), at a tertiary-level hospital in Mozambique, using complete diagnostic autopsies as the gold standard to ascertain cause of death. We estimated the performance of the clinical diagnosis and classified clinicopathological discrepancies as major and minor errors. We compared the findings of this analysis with those of a similar study done in the same setting 10 years earlier. Findings: We identified a clinicopathological discrepancy in 35 (38%) of 91 women. All diagnostic errors observed were classified as major discrepancies. The sensitivity of the clinical diagnosis for puerperal infections was 17% and the positive predictive value was 50%. The sensitivity for non-obstetric infections was 48%. The sensitivity for eclampsia was 100% but the positive predictive value was 33%. Over the 10-year period, the performance of clinical diagnosis did not improve, and worsened for some diagnoses, such as puerperal infection. Interpretation: Decreasing maternal mortality requires improvement of the pre-mortem diagnostic process and avoidance of clinical errors by refining clinical skills and increasing the availability and quality of diagnostic tests. Comparison of post-mortem information with clinical diagnosis will help monitor the reduction of clinical errors and thus improve the quality of care. Funding: Bill & Melinda Gates Foundation and Instituto de Salud Carlos III.

This retrospective study was done at the Maputo Central Hospital (Maputo, Mozambique), a 1500-bed government-funded tertiary-level health-care facility. Recruitment of maternal deaths was done from Nov 1, 2013, to March 31, 2015 (17-month period). All deceased women who fulfilled the standard WHO definition of a pregnancy-related death,12 and for whom the family had given verbal informed consent for the autopsy requested by the clinician, were included. Accidental or incidental deaths were excluded. Following the guidelines of the Ministry of Health of Mozambique, all maternal deaths occurring at the Maputo Central Hospital undergo a complete diagnostic autopsy unless the family does not provide consent. This study received approval from the National Bioethics Committee of Mozambique (342/CNBS/13) and the Clinical Research Ethics Committee of the Hospital Clinic of Barcelona (Spain; 2013/8677). A complete dissection was done with macroscopic evaluation of all organs according to a standardised protocol.13 Samples of grossly identified lesions and of solid organs, including the uterus, were collected for histological examination; additionally, samples of blood and cerebrospinal fluid were obtained. When available, the placenta was macroscopically evaluated and sampled. Histological evaluation comprised staining with haematoxylin and eosin in all samples and additional histochemical or immunohistochemical stains (eg, Ziehl-Neelsen or Plasmodium falciparum immunohistochemical staining) when needed. The extensive microbiological analysis done has been reported in detail elsewhere.14 Briefly, universal screening was done, which comprised detection of P falciparum by PCR, detection of antibodies against HIV-1 and HIV-2 and HIV viral load, and bacterial or fungal cultures of blood and cerebrospinal fluid. Additional microbiological screening was applied to HIV-positive cases, including real time PCR in cerebrospinal fluid for Toxoplasma gondii, Mycobacterium tuberculosis, and Cryptococcus spp and real-time PCR in lung samples for Pneumocystis jirovecii. Molecular methods were used in cases in which the histological features were discordant with the culture results (eg, pneumonia by histology and no infectious agent identified on culture). Patient data, including demographic information, previous medical history, and inpatient admission process (collected by clinicians in charge, including obstetricians) were extracted from medical records and recorded in a standardised questionnaire by a study medical doctor (QB). Up to five clinical diagnoses registered in medical records by the caring clinicians were selected and abstracted. The first diagnosis listed was regarded as the main diagnosis, and the remaining diagnoses were classified as secondary. Macroscopic, microscopic, and microbiological findings of complete diagnostic autopsies and any available clinical information were evaluated by a panel of multidisciplinary experts that comprised clinical (maternal and child health) and laboratory (pathology and microbiology) specialists, and the final complete diagnostic autopsy diagnosis was assigned. As previously described,14 all morbid conditions directly leading to death, any underlying conditions, and any other clinically significant conditions possibly contributing to death were classified as either direct obstetric or indirect obstetric deaths, and codified according to the International Classification of Diseases, 10th revision.12, 15 Diseases were grouped into the following eight categories: (1) pregnancies with abortive outcome; (2) hypertensive disorders in pregnancy, childbirth, and puerperium; (3) obstetric haemorrhage; (4) pregnancy-related infections; (5) other obstetric complications; (6) unanticipated complications of management; (7) non-obstetric complications; and (8) unexplained deaths. We considered categories 1 to 6 direct obstetric deaths, whereas category 7 was considered to correspond to indirect obstetric deaths. When more than one severe diagnosis was identified, the disease most likely to have caused the death was considered the final complete diagnostic autopsy diagnosis.14 Diagnostic discrepancies were classified as major or minor.16, 17 Major discrepancies involved major diagnoses and were classified as class I or class II. Class I refers to discrepancies in which the knowledge of the correct diagnosis before death would have led to changes in clinical management that could have prolonged survival or cured the patient (eg, pyogenic meningitis treated as eclampsia). In class II errors, patient survival would have not been modified (eg, fulminant hepatitis treated as sepsis). Minor discrepancies involved minor diagnoses and were classified as class III (non-diagnosed diseases with symptoms that should have been treated—eg, mild aspiration pneumonia in a patient with eclampsia) and class IV (non-diagnosed diseases with possible epidemiological or genetic importance—eg, schistosomal infections). Correctly diagnosed patients were classified as class V. Class VI comprised non-classifiable cases (autopsy unsatisfactory or with no clear diagnosis). For analysis of clinicopathological discrepancies, two masked investigators assessed each case; their evaluations were compared and a third rater evaluated any discrepant cases. The following information was provided to each rater: autopsy final diagnosis, antecedent causes, and other significant conditions and clinical diagnoses (main diagnosis, and up to a maximum of four additional diagnoses) extracted from the medical record. Clinicopathological correlation was determined by assessing whether the complete diagnostic autopsy diagnosis was identified among any of the clinical diagnoses. A case was considered discrepant when there was no coincidence between any of the five clinical diagnoses listed by the clinician and the final cause of death identified in the complete diagnostic autopsy. In each case, only the worst diagnostic error was considered. We did a comparative analysis of the performance of the clinical diagnosis of four main maternal death categories between the current findings and those of a study undertaken 10 years earlier in the same hospital and using the same methods to determine cause of death.11 We assessed concordance between raters with the κ statistic.18 We compared proportions by χ2 test and used logistic regression with penalised likelihood to evaluate factors associated with major clinical errors.19, 20 We used penalised likelihood to mitigate the bias caused by rare events in the dataset, as major errors were infrequent or non-existent for some covariates included in the analysis of associations or a combination of them in multivariable analyses (eg, ectopic gravidity, bloody diarrhoea, and choluria). This situation is referred to as separation or monotone likelihood and produces infinite estimates for some coefficients. In such a situation, it can be useful to maximise Firth’s penalised likelihood, rather than the usual likelihood.19 We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for each diagnosis. We defined false-negative diagnoses as discrepancies for which the autopsy diagnosis was in the assessed diagnostic category, but the clinical diagnosis was in another diagnostic category. We defined false-positive diagnoses as discrepancies for which the clinical diagnosis was in the diagnostic category but not the autopsy diagnosis. We estimated a multivariable adjusted model using all covariates with p≤0·15 in the crude analysis. Data were analysed with STATA (version 15). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Based on the information provided, here are some potential innovations that could be used to improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can improve access to maternal health by allowing remote consultations between healthcare providers and pregnant women. This can help address the shortage of healthcare professionals in resource-limited areas and provide timely medical advice and support.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources related to maternal health can empower pregnant women with knowledge and tools to manage their health. These apps can include features such as appointment reminders, medication tracking, and educational content.

3. Point-of-care diagnostic tests: Increasing the availability and quality of diagnostic tests can help improve the accuracy of clinical diagnoses. Developing affordable and portable point-of-care diagnostic tests for common maternal health conditions can enable healthcare providers to make more informed decisions and provide appropriate treatment.

4. Training and capacity building: Investing in training programs for healthcare providers can enhance their clinical skills and improve the quality of care. This can include training on diagnosing and managing common life-threatening conditions related to pregnancy, as well as improving communication and decision-making skills.

5. Community outreach programs: Implementing community-based programs that focus on maternal health education and awareness can help reach pregnant women in remote areas. These programs can provide information on prenatal care, nutrition, hygiene practices, and the importance of seeking timely medical attention.

6. Strengthening referral systems: Establishing effective referral systems between primary healthcare centers and tertiary-level hospitals can ensure that pregnant women receive appropriate care at the right level of the healthcare system. This can help reduce delays in accessing emergency obstetric care and improve maternal outcomes.

7. Public-private partnerships: Collaborating with private sector organizations can help leverage their resources and expertise to improve access to maternal health. This can involve initiatives such as public-private partnerships for the provision of healthcare services, technology transfer, and capacity building.

It’s important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of Mozambique.
AI Innovations Description
The recommendation that can be used to develop an innovation to improve access to maternal health based on the study is to focus on improving the pre-mortem diagnostic process and reducing clinical errors. This can be achieved by refining clinical skills and increasing the availability and quality of diagnostic tests.

Specifically, the study suggests the following actions to improve the quality of care and reduce maternal mortality:

1. Enhance clinical skills: Healthcare providers should receive training and continuous education to improve their diagnostic abilities. This includes improving their knowledge and skills in identifying and managing common life-threatening conditions related to pregnancy.

2. Increase availability and quality of diagnostic tests: There is a need to ensure that healthcare facilities have access to reliable and accurate diagnostic tests. This includes improving laboratory infrastructure, ensuring the availability of necessary equipment and supplies, and implementing quality control measures to ensure the accuracy of test results.

3. Monitor and evaluate clinical errors: Regular monitoring and evaluation of clinical errors can help identify areas of improvement and track progress over time. This can be done by comparing post-mortem information with clinical diagnoses to identify discrepancies and analyze the reasons behind them.

4. Strengthen collaboration and multidisciplinary approach: A panel of multidisciplinary experts, including clinical and laboratory specialists, can be established to evaluate diagnostic discrepancies and provide recommendations for improvement. This collaborative approach can help ensure a comprehensive assessment of each case and improve the accuracy of diagnoses.

5. Implement evidence-based guidelines: Healthcare facilities should adopt evidence-based guidelines and protocols for the diagnosis and management of maternal health conditions. These guidelines should be regularly updated based on the latest research and best practices.

By implementing these recommendations, healthcare providers can improve the accuracy of diagnoses, reduce clinical errors, and ultimately improve the quality of care and access to maternal health services.
AI Innovations Methodology
Based on the provided study, here are some potential recommendations for improving access to maternal health:

1. Strengthening diagnostic skills: The study highlights the importance of accurate diagnosis in reducing maternal mortality. Therefore, investing in training programs and workshops to enhance the diagnostic skills of healthcare providers can significantly improve access to maternal health.

2. Increasing availability of diagnostic tests: The study suggests that the availability and quality of diagnostic tests need to be improved. Investing in infrastructure and resources to ensure that healthcare facilities have access to reliable diagnostic tools can help in early detection and appropriate management of maternal health conditions.

3. Implementing regular clinical audits: Regular clinical audits can help identify gaps and discrepancies in the diagnostic process. By reviewing clinical diagnoses and comparing them with post-mortem findings, healthcare facilities can identify areas for improvement and take corrective actions to reduce clinical errors.

4. Strengthening referral systems: Improving access to maternal health requires a well-functioning referral system. Enhancing communication and coordination between primary healthcare centers, secondary hospitals, and tertiary-level facilities can ensure timely and appropriate care for pregnant women, especially those with high-risk pregnancies.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Collect baseline data: Gather data on the current state of maternal health access, including maternal mortality rates, diagnostic accuracy, availability of diagnostic tests, and referral patterns.

2. Define indicators: Identify key indicators that reflect the impact of the recommendations, such as improved diagnostic accuracy, reduced maternal mortality rates, increased availability of diagnostic tests, and improved referral rates.

3. Develop a simulation model: Build a simulation model that incorporates the baseline data and the potential impact of the recommendations. This model should consider various factors, such as population demographics, healthcare infrastructure, and resource allocation.

4. Simulate scenarios: Run simulations using the model to assess the potential impact of implementing the recommendations. Explore different scenarios by adjusting variables such as the percentage of healthcare providers trained, the availability of diagnostic tests, and the effectiveness of the referral system.

5. Analyze results: Evaluate the outcomes of the simulations and analyze the impact of the recommendations on improving access to maternal health. Assess the changes in key indicators and compare them to the baseline data.

6. Refine and iterate: Based on the simulation results, refine the recommendations and the simulation model if necessary. Repeat the simulation process to further optimize the strategies for improving access to maternal health.

By using this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of different interventions and make informed decisions to improve access to maternal health.

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