Risk prediction models for maternal mortality: A systematic review and meta-analysis

Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

Despite significant technical advances in machine learning (ML) over the past several years, the tangible impact of this technology in healthcare has been limited. This is due not only to the particular complexities of healthcare, but also due to structural issues in the machine learning for healthcare (MLHC) community which broadly reward technical novelty over tangible, equitable impact. We structure our work as a healthcare-focused echo of the 2012 paper "Machine Learning that Matters", which highlighted such structural issues in the ML community at large, and offered a series of clearly defined "Impact Challenges" to which the field should orient itself. Drawing on the expertise of a diverse and international group of authors, we engage in a narrative review and examine issues in the research background environment, training processes, evaluation metrics, and deployment protocols which act to limit the real-world applicability of MLHC. Broadly, we seek to distinguish between machine learning ON healthcare data and machine learning FOR healthcare-the former of which sees healthcare as merely a source of interesting technical challenges, and the latter of which regards ML as a tool in service of meeting tangible clinical needs. We offer specific recommendations for a series of stakeholders in the field, from ML researchers and clinicians, to the institutions in which they work, and the governments which regulate their data access.

Adverse birth outcomes among women with 'low-risk' pregnancies in India: findings from the Fifth National Family Health Survey, 2019-21

Background

Despite substantial progress in improving maternal and newborn health, India continues to experience high rates of newborn mortality and stillbirths. One reason may be that many births happen in health facilities that lack advanced services-such as Caesarean section, blood transfusion, or newborn intensive care. Stratification based on pregnancy risk factors is used to guide 'high-risk' women to advanced facilities. To assess the utility of risk stratification for guiding the choice of facility, we estimated the frequency of adverse newborn outcomes among women classified as 'low risk' in India.

Methods

We used the 2019-21 Fifth National Family Health Survey (NFHS-5)-India's Demographic and Health Survey-which includes modules administered to women aged 15-49 years. In addition to pregnancy history and outcomes, the survey collected a range of risk factors, including biomarkers. We used national obstetric risk guidelines to classify women as 'high risk' versus 'low risk' and assessed the frequency of stillbirths, newborn deaths, and unplanned Caesarean sections for the respondent's last pregnancy lasting 7 or more months in the past five years. We calculated the proportion of deliveries occurring at non-hospital facilities in all the Indian states.

Findings

Using data from nearly 176,699 recent pregnancies, we found that 46.6% of India's newborn deaths and 56.3% of stillbirths were among women who were 'low risk' according to national guidelines. Women classified as 'low risk' had a Caesarean section rate of 8.4% (95% CI 8.1-8.7%), marginally lower than the national average of 10.0% (95% CI 9.8-10.3%). In India as a whole, 32.0% (95% CI 31.5-32.5%) of deliveries occurred in facilities that were likely to lack advanced services. There was substantial variation across the country, with less than 5% non-hospital public facility deliveries in Punjab, Kerala, and Delhi compared to more than 40% in Odisha, Madhya Pradesh, and Rajasthan. Newborn mortality tended to be lower in states with highest hospital delivery rates.

Interpretation

Individual risk stratification based on factors identified in pregnancy fails to accurately predict which women will have delivery complications and experience stillbirth and newborn death in India. Thus a determination of 'low risk' should not be used to guide women to health facilities lacking key life saving services, including Caesarean section, blood transfusion, and advanced newborn resuscitation and care.

Funding

Bill and Melinda Gates Foundation and the World Bank. The findings, interpretations and conclusions expressed in the paper are entirely those of the authors, and do not represent the views of the Gates Foundation or of the World Bank, its Executive Directors, or the countries they represent.

Machine Learning Methods for Pregnancy and Childbirth Risk Management

Machine learning methods enable medical systems to automatically generate data-driven decision support models using real-world data inputs, eliminating the need for explicit rule design. In this research, we investigated the application of machine learning methods in healthcare, specifically focusing on pregnancy and childbirth risks. The timely identification of risk factors during early pregnancy, along with risk management, mitigation, prevention, and adherence management, can significantly reduce adverse perinatal outcomes and complications for both mother and child. Given the existing burden on medical professionals, clinical decision support systems (CDSSs) can play a role in risk management. However, these systems require high-quality decision support models based on validated medical data that are also clinically interpretable. To develop models for predicting childbirth risks and due dates, we conducted a retrospective analysis of electronic health records from the perinatal Center of the Almazov Specialized Medical Center in Saint-Petersburg, Russia. The dataset, which was exported from the medical information system, consisted of structured and semi-structured data, encompassing a total of 73,115 lines for 12,989 female patients. Our proposed approach, which includes a detailed analysis of predictive model performance and interpretability, offers numerous opportunities for decision support in perinatal care provision. The high predictive performance achieved by our models ensures precise support for both individual patient care and overall health organization management.

Mechanical ventilation and death in pregnant patients admitted for COVID-19: a prognostic analysis from the Brazilian COVID-19 registry score

BACKGROUND

The assessment of clinical prognosis of pregnant COVID-19 patients at hospital presentation is challenging, due to physiological adaptations during pregnancy. Our aim was to assess the performance of the ABC₂-SPH score to predict in-hospital mortality and mechanical ventilation support in pregnant patients with COVID-19, to assess the frequency of adverse pregnancy outcomes, and characteristics of pregnant women who died.

METHODS

This multicenter cohort included consecutive pregnant patients with COVID-19 admitted to the participating hospitals, from April/2020 to March/2022. Primary outcomes were in-hospital mortality and the composite outcome of mechanical ventilation support and in-hospital mortality. Secondary endpoints were pregnancy outcomes. The overall discrimination of the model was presented as the area under the receiver operating characteristic curve (AUROC). Overall performance was assessed using the Brier score.

RESULTS

From 350 pregnant patients (median age 30 [interquartile range (25.2, 35.0)] years-old]), 11.1% had hypertensive disorders, 19.7% required mechanical ventilation support and 6.0% died. The AUROC for in-hospital mortality and for the composite outcome were 0.809 (95% IC: 0.641-0.944) and 0.704 (95% IC: 0.617-0.792), respectively, with good overall performance (Brier = 0.0384 and 0.1610, respectively). Calibration was good for the prediction of in-hospital mortality, but poor for the composite outcome. Women who died had a median age 4 years-old higher, higher frequency of hypertensive disorders (38.1% vs. 9.4%, p < 0.001) and obesity (28.6% vs. 10.6%, p = 0.025) than those who were discharged alive, and their newborns had lower birth weight (2000 vs. 2813, p = 0.001) and five-minute Apgar score (3.0 vs. 8.0, p < 0.001).

CONCLUSIONS

The ABC₂-SPH score had good overall performance for in-hospital mortality and the composite outcome mechanical ventilation and in-hospital mortality. Calibration was good for the prediction of in-hospital mortality, but it was poor for the composite outcome. Therefore, the score may be useful to predict in-hospital mortality in pregnant patients with COVID-19, in addition to clinical judgment. Newborns from women who died had lower birth weight and Apgar score than those who were discharged alive.

Quality of care for postpartum hemorrhage: A direct observation study in referral hospitals in Kenya

Postpartum hemorrhage (PPH) is the leading cause of maternal mortality in Kenya. The aim of this study was to measure quality and timeliness of care for PPH in a sample of deliveries in referral hospitals in Kenya. We conducted direct observations of 907 vaginal deliveries in three Kenyan hospitals from October 2018 through February 2019, observing the care women received from admission for labor and delivery through hospital discharge. We identified cases of "suspected PPH", defined as cases in which providers indicated suspicion of and/or took an action to manage abnormal bleeding. We measured adherence to World Health Organization and Kenyan guidelines for PPH risk assessment, prevention, identification, and management and the timeliness of care in each domain. The rate of suspected PPH among the observed vaginal deliveries was 9% (95% Confidence Interval: 7% - 11%). Health care providers followed all guidelines for PPH risk assessment in 7% (5% - 10%) of observed deliveries and all guidelines for PPH prevention in 4% (3% - 6%) of observed deliveries. Lowest adherence was observed for taking vital signs and for timely administration of a prophylactic uterotonic. Providers did not follow guidelines for postpartum monitoring in any of the observed deliveries. When suspected PPH occurred, providers performed all recommended actions in 23% (6% - 40%) of cases. Many of the critical actions for suspected PPH were performed in a timely manner, but, in some cases, substantial delays were observed. In conclusion, we found significant gaps in the quality of risk assessment, prevention, identification, and management of PPH after vaginal deliveries in referral hospitals in Kenya. Efforts to reduce maternal morbidity and mortality from PPH should emphasize improvements in the quality of care, with a particular focus on postpartum monitoring and timely emergency response.

Predicting Postpartum Transition to Primary Care in Community Health Centers

INTRODUCTION

Although the transition to primary care after routine postpartum care has been recommended to mitigate adverse maternal outcomes, little is known about real-world transition patterns. The objective of this study was to describe the patterns and predictors of transition in a postpartum cohort receiving care at federally qualified health centers and a subcohort of clinically high-risk patients.

METHODS

Electronic health record data collected between 2017 and 2019 were analyzed in 2021 for unadjusted analyses and multivariable regression models for both the full and high-risk cohorts. The primary outcome was completion of a primary care visit within 6 months of delivery. Primary predictors in both cohorts were insurance loss, postpartum visit, first-trimester visit, and medical visit within the year prepregnancy; for the full cohort, high-risk status was also studied.

RESULTS

The full cohort (N=7,926) analysis showed that 17.3% completed a primary care visit. In unadjusted and adjusted analysis, all 5 predictors were significantly associated with primary care visit completion; 25.0% of high-risk patients completed a primary care visit, and patients who lost insurance had 66% lower odds of primary care visit completion (95% CI=0.24, 0.48). In unadjusted and adjusted analysis for the high-risk cohort (n=1,956, 24.7% of full cohort), all predictors except postpartum visit were significantly associated with primary care visit completion.

CONCLUSIONS

Postpartum patients at federally qualified health centers transitioned to primary care at low rates; insurance loss was one significant barrier to care. Strategies to increase continuity, including improving insurance access, should be studied. Future research is needed to study structural inequity, the impact of primary care on maternal outcomes, and patient experience.

Assessment of a Crisis Standards of Care Scoring System for Resource Prioritization and Estimated Excess Mortality by Race, Ethnicity, and Socially Vulnerable Area During a Regional Surge in COVID-19

IMPORTANCE

Crisis standards of care (CSOC) scores designed to allocate scarce resources during the COVID-19 pandemic could exacerbate racial disparities in health care.

OBJECTIVE

To analyze the association of a CSOC scoring system with resource prioritization and estimated excess mortality by race, ethnicity, and residence in a socially vulnerable area.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort analysis included adult patients in the intensive care unit during a regional COVID-19 surge from April 13 to May 22, 2020, at 6 hospitals in a health care network in greater Boston, Massachusetts. Participants were scored by acute severity of illness using the Sequential Organ Failure Assessment score and chronic severity of illness using comorbidity and life expectancy scores, and only participants with complete scores were included. The score was ordinal, with cutoff points suggested by the Massachusetts guidelines.

EXPOSURES

Race, ethnicity, Social Vulnerability Index.

MAIN OUTCOMES AND MEASURES

The primary outcome was proportion of patients in the lowest priority score category stratified by self-reported race. Secondary outcomes were discrimination and calibration of the score overall and by race, ethnicity, and neighborhood Social Vulnerability Index. Projected excess deaths were modeled by race, using the priority scoring system and a random lottery.

RESULTS

Of 608 patients in the intensive care unit during the study period, 498 had complete data and were included in the analysis; this population had a median (IQR) age of 67 (56-75) years, 191 (38.4%) female participants, 79 (15.9%) Black participants, and 225 patients (45.7%) with COVID-19. The area under the receiver operating characteristic curve for the priority score was 0.79 and was similar across racial groups. Black patients were more likely than others to be in the lowest priority group (12 [15.2%] vs 34 [8.1%]; P = .046). In an exploratory simulation model using the score for ventilator allocation, with only those in the highest priority group receiving ventilators, there were 43.9% excess deaths among Black patients (18 of 41 patients) and 28.6% (58 of 203 patients among all others (P = .05); when the highest and intermediate priority groups received ventilators, there were 4.9% (2 of 41 patients) excess deaths among Black patients and 3.0% (6 of 203) among all others (P = .53). A random lottery resulted in more excess deaths than the score.

CONCLUSIONS AND RELEVANCE

In this study, a CSOC priority score resulted in lower prioritization of Black patients to receive scarce resources. A model using a random lottery resulted in more estimated excess deaths overall without improving equity by race. CSOC policies must be evaluated for their potential association with racial disparities in health care.

Development and Validation of an Obstetric Comorbidity Risk Score for Clinical Use

Background: A comorbidity summary score may support early and systematic identification of women at high risk for adverse obstetric outcomes. The objective of this study was to conduct the initial development and validation of an obstetrics comorbidity risk score for automated implementation in the electronic health record (EHR) for clinical use. Methods: The score was developed and validated using EHR data for a retrospective cohort of pregnancies with delivery between 2010 and 2018 at Kaiser Permanente Northern California, an integrated health care system. The outcome used for model development consisted of adverse obstetric events from delivery hospitalization (e.g., eclampsia, hemorrhage, death). Candidate predictors included maternal age, parity, multiple gestation, and any maternal diagnoses assigned in health care encounters in the 12 months before admission for delivery. We used penalized regression for variable selection, logistic regression to fit the model, and internal validation for model evaluation. We also evaluated prenatal model performance at 18 weeks of pregnancy. Results: The development cohort (n = 227,405 pregnancies) had an outcome rate of 3.8% and the validation cohort (n = 41,683) had an outcome rate of 2.9%. Of 276 candidate predictors, 37 were included in the final model. The final model had a validation c-statistic of 0.72 (95% confidence interval [CI] 0.70-0.73). When evaluated at 18 weeks of pregnancy, discrimination was modestly diminished (c-statistic 0.68 [95% CI 0.67-0.70]). Conclusions: The obstetric comorbidity score demonstrated good discrimination for adverse obstetric outcomes. After additional appropriate validation, the score can be automated in the EHR to support early identification of high-risk women and assist efforts to ensure risk-appropriate maternal care.

Development and internal validation of a model predicting severe maternal morbidity using pre-conception and early pregnancy variables: a population-based study in Ontario, Canada

BACKGROUND

Improvement in the prediction and prevention of severe maternal morbidity (SMM) - a range of life-threatening conditions during pregnancy, at delivery or within 42 days postpartum - is a public health priority. Reduction of SMM at a population level would be facilitated by early identification and prediction. We sought to develop and internally validate a model to predict maternal end-organ injury or death using variables routinely collected during pre-pregnancy and the early pregnancy period.

METHODS

We performed a population-based cohort study using linked administrative health data in Ontario, Canada, from April 1, 2006 to March 31, 2014. We included women aged 18-60 years with a livebirth or stillbirth, of which one birth was randomly selected per woman. We constructed a clinical prediction model for the primary composite outcome of any maternal end-organ injury or death, arising between 20 weeks' gestation and 42 days after the birth hospital discharge date. Our model included variables collected from 12 months before estimated conception until 19 weeks' gestation. We developed a separate model for parous women to allow for the inclusion of factors from previous pregnancy(ies).

RESULTS

Of 634,290 women, 1969 experienced the primary composite outcome (3.1 per 1000). Predictive factors in the main model included maternal world region of origin, chronic medical conditions, parity, and obstetrical/perinatal issues - with moderate model discrimination (C-statistic 0.68, 95% CI 0.66-0.69). Among 333,435 parous women, the C-statistic was 0.71 (0.69-0.73) in the model using variables from the current (index) pregnancy as well as pre-pregnancy predictors and variables from any previous pregnancy.

CONCLUSIONS

A combination of factors ascertained early in pregnancy through a basic medical history help to identify women at risk for severe morbidity, who may benefit from targeted preventive and surveillance strategies including appropriate specialty-based antenatal care pathways. Further refinement and external validation of this model are warranted and can support evidence-based improvements in clinical practice.

Maternal Risk Modeling in Critical Care-Development of a Multivariable Risk Prediction Model for Death and Prolonged Intensive Care

OBJECTIVES

We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care.

DESIGN

We used data from a high-quality prospectively collected national database, supported with literature review and expert opinion. We tested univariable associations between each risk factor and outcome. We then developed two separate multivariable logistic regression models for the outcomes of acute hospital mortality and death or prolonged ICU length of stay. We validated two parsimonious risk prediction models specific for a maternal population.

SETTING

The Intensive Care National Audit and Research Centre Case Mix Programme is the national clinical audit for adult critical care in England, Wales, and Northern Ireland.

PATIENTS

All female admissions to adult general critical care units, for the period January 1, 2007-December 31, 2016, 16-50 years old, and admitted either while pregnant or within 42 days of delivery-a cohort of 15,480 women.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care. For the primary outcome of acute hospital mortality, our parsimonious risk model consisting of eight variables had an area under the receiver operating characteristic of 0.96 (95% CI, 0.91-1.00); these variables are commonly available for all maternal admissions. For the secondary composite outcome of death or ICU length of stay greater than 48 hours, the risk model consisting of 17 variables had an area under the receiver operating characteristic of 0.80 (95% CI, 0.78-0.83).

CONCLUSIONS

We developed risk prediction models specific to the maternal critical care population. The models compare favorably against general adult ICU risk prediction models in current use within this population.

Society for Maternal-Fetal Medicine Consult Series #54: Assessing the risk of maternal morbidity and mortality

The rates of maternal morbidity and mortality in the United States demand a comprehensive approach to assessing pregnancy-related risks. Numerous medical and nonmedical factors contribute to maternal morbidity and mortality. Reducing the number of women who experience pregnancy morbidity requires identifying which women are at greatest risk and initiating appropriate interventions early in the reproductive life course. The purpose of this Consult is to educate all healthcare practitioners about factors contributing to a high-risk pregnancy, strategies to assess maternal health risks due to pregnancy, and the importance of risk assessment across the reproductive spectrum in reducing maternal morbidity and mortality.

Decentralization and Regionalization: Redesigning Health Systems for High Quality Maternity Care Comment on "Decentralization and Regionalization of Surgical Care: A Review of Evidence for the Optimal Distribution of Surgical Services in Low- and Middle-Income Countries"

The question of how to optimally design health systems in low- and middle-income countries (LMICs) for high quality care and survival requires context-specific evidence on which level of the health system is best positioned to deliver services. Given documented poor quality of care for surgical conditions in LMICs, evidence to support intentional health system design is urgently needed. Iverson and colleagues address this very important question. This commentary explores their findings with particular attention to how they apply to maternity care. Though surgical maternity care is a common healthcare need, maternal complications are often unpredictable and require immediate surgical attention in order to avert serious morbidity or mortality. A discussion of decentralization for maternity services must grapple with this tension and differentiate between facilities that can provide emergency surgical care and those that can not.

Predicting mortality in adults with suspected infection in a Rwandan hospital: an evaluation of the adapted MEWS, qSOFA and UVA scores

RATIONALE

Mortality prediction scores are increasingly being evaluated in low and middle income countries (LMICs) for research comparisons, quality improvement and clinical decision-making. The modified early warning score (MEWS), quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA), and Universal Vital Assessment (UVA) score use variables that are feasible to obtain, and have demonstrated potential to predict mortality in LMIC cohorts.

OBJECTIVE

To determine the predictive capacity of adapted MEWS, qSOFA and UVA in a Rwandan hospital.

DESIGN, SETTING, PARTICIPANTS AND OUTCOME MEASURES

We prospectively collected data on all adult patients admitted to a tertiary hospital in Rwanda with suspected infection over 7 months. We calculated an adapted MEWS, qSOFA and UVA score for each participant. The predictive capacity of each score was assessed including sensitivity, specificity, positive and negative predictive value, OR, area under the receiver operating curve (AUROC) and performance by underlying risk quartile.

RESULTS

We screened 19 178 patient days, and enrolled 647 unique patients. Median age was 35 years, and in-hospital mortality was 18.1%. The proportion of data missing for each variable ranged from 0% to 11.7%. The sensitivities and specificities of the scores were: adapted MEWS >4, 50.4% and 74.9%, respectively; qSOFA >2, 24.8% and 90.4%, respectively; and UVA >4, 28.2% and 91.1%, respectively. The scores as continuous variables demonstrated the following AUROCs: adapted MEWS 0.69 (95% CI 0.64 to 0.74), qSOFA 0.65 (95% CI 0.60 to 0.70), and UVA 0.71 (95% CI 0.66 to 0.76); there was no statistically significant difference between the discriminative capacities of the scores.

CONCLUSION

Three scores demonstrated a modest ability to predict mortality in a prospective study of inpatients with suspected infection at a Rwandan tertiary hospital. Careful consideration must be given to their adequacy before using them in research comparisons, quality improvement or clinical decision-making.

Development of a Comprehensive Antenatal Risk Assessment Tool to Predict Adverse Maternal and Perinatal Outcomes in Rural Areas: An Exploratory Study

Objective: To develop a comprehensive antenatal risk assessment tool to predict adverse maternal and early perinatal outcomes in a rural setting.

Materials and methods: Cross-sectional study among women admitted for delivery in a rural maternity hospital, south India. Risk factors from Rotterdam Reproductive Risk Reduction (R4U) scorecard and social factors relevant to Indian rural context were included in questionnaire. Maternal and perinatal outcomes were obtained from in-patient records. Logistic regression of risk factors associated with adverse outcomes and weighted scores assigned using beta-coefficients. Cut-off score to predict adverse outcome was derived using Receiver Operator Characteristic Curve (ROC Curve) and Likelihood ratios.

Results: Adjusted odds for adverse outcome highest for small for gestational age by ultrasound scan [OR=7.4 (1.4-36.5)], tobacco chewing [OR=5.6 (1.8–28.5)] and hypertensive disorders of pregnancy [OR=3.5 (1.9-9.6)]. After assigning weighted scores, the 74-item antenatal risk assessment tool had a maximum possible score of 86. Risk score was calculated for all subjects. Cut-off score to predict adverse outcome was 4, using ROC curve, with a sensitivity of 98%, a specificity of 21% and positive likelihood ratio of 1.23 (1.10-1.37).

Conclusion: This comprehensive antenatal risk assessment tool is easy to administer, specific to rural areas and can help community-level workers to screen, monitor, and refer high risk pregnancies for further management to prevent adverse maternal and perinatal outcomes. This may be considered a prototype towards developing more robust antenatal risk screening and outcome prediction in rural settings.

Risk prediction models for emergence delirium in paediatric general anaesthesia: a systematic review

OBJECTIVES

Emergence delirium (ED) occurs in approximately 25% of paediatric general anaesthetics and has significant adverse effects. The goal of the current systematic review was to identify the existing literature investigating performance of predictive models for the development of paediatric ED following general anaesthesia and to determine their usability.

DESIGN

Systematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.

DATA SOURCES

Medline (Ovid), PubMed, Embase (Ovid), Cochrane Database of Systematic Reviews (Ovid), Cochrane CENTRAL (Ovid), PsycINFO (Ovid), Scopus (Elsevier) and Web of Science (Clarivate Analytics), ClinicalTrials.gov, International Clinical Trials Registry Platform and ProQuest Digital Dissertations and Theses International through 17 November 2020.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

All randomised controlled trials and cohort studies investigating predictive models for the development of ED in children undergoing general anaesthesia.

DATA EXTRACTION AND SYNTHESIS

Following title, abstract and full-text screening by two reviewers, data were extracted from all eligible studies, including demographic parameters, details of anaesthetics and performance characteristics of the predictive scores for ED. Evidence quality and predictive score usability were assessed according to the PROBAST framework.

RESULTS

The current systematic review yielded 9242 abstracts, of which only one study detailing the development and validation of the Emergence Agitation Risk Scale (EARS) met the inclusion criteria. EARS had good discrimination with c-index of 0.81 (95% CI 0.72 to 0.89). Calibration showed a non-significant Homer-Lemeshow goodness-of-fit test (p=0.97). Although the EARS demonstrated low concern of applicability, the high risk of bias compromised the overall usability of this model.

CONCLUSIONS

The current systematic review concluded that EARS has good discrimination performance but low usability to predict ED in a paediatric population. Further research is warranted to develop novel models for the prediction of ED in paediatric anaesthesia.

PROSPERO REGISTRATION NUMBER

CRD42019141950.

An Expanded Obstetric Comorbidity Scoring System for Predicting Severe Maternal Morbidity

OBJECTIVE

To develop and validate an expanded obstetric comorbidity score for predicting severe maternal morbidity that can be applied consistently across contemporary U.S. patient discharge data sets.

METHODS

Discharge data from birth hospitalizations in California during 2016-2017 were used to develop the score. The outcomes were severe maternal morbidity, defined using the Centers for Disease Control and Prevention index, and nontransfusion severe maternal morbidity (excluding cases where transfusion was the only indicator of severe maternal morbidity). We selected 27 potential patient-level risk factors for severe maternal morbidity, identified using International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes. We used a targeted causal inference approach integrated with machine learning to rank the risk factors based on adjusted risk ratios (aRRs). We used these results to assign scores to each comorbidity, which sum to a single numeric score. We validated the score in California and national data sets and compared the performance to that of a previously developed obstetric comorbidity index.

RESULTS

Among 919,546 births, the rates of severe maternal morbidity and nontransfusion severe maternal morbidity were 168 and 74 per 10,000 births, respectively. The highest risk comorbidity was placenta accreta spectrum (aRR of 30.5 for severe maternal morbidity and 54.7 for nontransfusion severe maternal morbidity) and the lowest was gestational diabetes mellitus (aRR of 1.06 for severe maternal morbidity and 1.12 for nontransfusion severe maternal morbidity). Normalized scores based on the aRR were developed for each comorbidity, which ranged from 1 to 59 points for severe maternal morbidity and from 1 to 36 points for nontransfusion severe maternal morbidity. The overall performance of the expanded comorbidity scores was good (C-statistics were 0.78 for severe maternal morbidity and 0.84 for nontransfusion severe maternal morbidity in California data and 0.82 and 0.87, respectively, in national data) and improved on prior comorbidity indices developed for obstetric populations. Calibration plots showed good concordance between predicted and actual risks of the outcomes.

CONCLUSION

We developed and validated an expanded obstetric comorbidity score to improve comparisons of severe maternal morbidity rates across patient populations with different comorbidity case mixes.

A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)

BACKGROUND

Premature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable health policies and effective delivery of public health services. This study protocol proposes the development and validation of the Premature Mortality Risk Prediction Tool (PreMPoRT) that will predict the incidence of premature mortality using large population-based community health surveys and multivariable modeling approaches.

METHODS

PreMPoRT will be developed and validated using various training, validation, and test data sets generated from the six cycles of the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Database from 2000 to 2017. Population-level risk factor information on demographic characteristics, health behaviors, area level measures, and other health-related factors will be used to develop PreMPoRT and to predict the incidence of premature mortality, defined as death prior to age 75, over a 5-year period. Sex-specific Weibull accelerated failure time models will be developed using a Canadian provincial derivation cohort consisting of approximately 500,000 individuals, with approximately equal proportion of males and females, and about 12,000 events of premature mortality. External validation will be performed using separate linked files (CCHS cycles 2007-2008, 2009-2010, and 2011-2012) from the development cohort (CCHS cycles 2000-2001, 2003-2004, and 2005-2006) to check the robustness of the prediction model. Measures of overall predictive performance (e.g., Nagelkerke's R2), calibration (e.g., calibration plots), and discrimination (e.g., Harrell's concordance statistic) will be assessed, including calibration within defined subgroups of importance to knowledge users and policymakers.

DISCUSSION

Using routinely collected risk factor information, we anticipate that PreMPoRT will produce population-based estimates of premature mortality and will be used to inform population strategies for prevention.

Health system redesign for maternal and newborn survival: rethinking care models to close the global equity gap

Large disparities in maternal and neonatal mortality exist between low- and high-income countries. Mothers and babies continue to die at high rates in many countries despite substantial increases in facility birth. One reason for this may be the current design of health systems in most low-income countries where, unlike in high-income countries, a substantial proportion of births occur in primary care facilities that cannot offer definitive care for complications. We argue that the current inequity in care for childbirth is a global double standard that limits progress on maternal and newborn survival. We propose that health systems need to be redesigned to shift all deliveries to hospitals or other advanced care facilities to bring care in line with global best practice. Health system redesign will require investing in high-quality hospitals with excellent midwifery and obstetric care, boosting quality of primary care clinics for antenatal, postnatal, and newborn care, decreasing access and financial barriers, and mobilizing populations to demand high-quality care. Redesign is a structural reform that is contingent on political leadership that envisions a health system designed to deliver high-quality, respectful care to all women giving birth. Getting redesign right will require focused investments, local design and adaptation, and robust evaluation.

Comparison of Sequential Organ Failure Assessment (SOFA) and Sepsis in Obstetrics Score (SOS) in Women with Pregnancy-Associated Sepsis with Respect to Critical Care Admission and Mortality: A Prospective Observational Study

Objective

We aimed to determine performance of sequential organ failure assessment (SOFA) and Sepsis in Obstetrics Score (SOS), in women with pregnancy-associated sepsis (PAS) with respect to critical care admission and mortality.

Methods

Obstetric patients with PAS fulfilling any 2 of the quick SOFA (qSOFA) criteria were enrolled as cases. The various parameters of SOFA and SOS were recorded at admission and compared for outcomes.

Results

Critical care was required in 32 (50.7%) patients and associated mortality was high (31.7%). For our study population, a threshold of SOFA ≥ 6 had the best combination of sensitivity (84.4%) and specificity (61.3%) for critical care admission. For SOS, a cut-off value of ≥ 6 gave best sensitivity (64%) and specificity (40%) for the same.

Conclusions

SOFA was far more predictive of patient's critical condition as well as mortality compared to SOS. SOFA was superior to SOS in determining critical care admission and mortality for PAS.

Variability in intensive care unit admission among pregnant and postpartum women in Canada: a nationwide population-based observational study

Background

Pregnancy-related critical illness results in approximately 300,000 deaths globally each year. The objective was to describe the variation in ICU admission and the contribution of patient- and hospital-based factors in ICU admission among acute care hospitals for pregnant and postpartum women in Canada.

Methods

A nationwide cohort study between 2004 and 2015, comprising all pregnant or postpartum women admitted to Canadian hospitals. The primary outcome was ICU admission. Secondary outcomes were severe maternal morbidity (a potentially life-threatening condition) and maternal death (during and within 6 weeks after pregnancy). The proportion of total variability in ICU admission rates due to the differences among hospitals was described using the median odds ratio from multi-level logistic regression models, adjusting for individual hospital clusters.

Results

There were 3,157,248 identifiable pregnancies among women admitted to 342 Canadian hospitals. The overall ICU admission rate was 3.2 per 1000 pregnancies. The rate of severe maternal morbidity was 15.8 per 1000 pregnancies, of which 10% of women were admitted to an ICU. The most common severe maternal morbidity events included postpartum hemorrhage (n = 16,364, 0.52%) and sepsis (n = 11,557, 0.37%). Of the 195 maternal deaths (6.2 per 100,000 pregnancies), only 130 (67%) were admitted to ICUs. Patients dying in hospital, without admission to ICU, included those with cardiovascular compromise, hemorrhage, and sepsis. For 2 pregnant women with similar characteristics at different hospitals, the average (median) odds of being admitted to ICU was 1.92 in 1 hospital compared to another. Hospitals admitting the fewest number of pregnant patients had the highest incidence of severe maternal morbidity and mortality. Patient-level factors associated with ICU admission were maternal comorbidity index (OR 1.88 per 1 unit increase, 95%CI 1.86–1.99), urban residence (OR 1.09, 95%CI 1.02–1.16), and residing at the lowest income quintile (OR 1.44, 95%CI 1.34–1.55).

Conclusions

Most women who experience severe maternal morbidity are not admitted to an ICU. There exists a wide hospital-level variability in ICU admission, with patients living in urban locations and patients of lowest income levels most likely to be admitted to ICU. Cardiovascular compromise, hemorrhage, and sepsis represent an opportunity for improved patient care and outcomes.