Severe maternal morbidity

INTRODUCTION — Severe maternal morbidity (SMM) is variously defined but generally refers to health-impacting and life-threatening events that occur during hospitalization for childbirth. It may precede or be associated with maternal mortality and is more common: approximately 70 cases of SMM occur for each maternal death in the United States [1]. Clinicians, researchers, governmental organizations, and other stakeholders in obstetric health care delivery use SMM as an indicator of potential systems issues that can be addressed to improve patient outcomes. In one statewide SMM review, 42 percent of cases had opportunities to improve care: approximately half of these cases were considered potentially preventable and the other half warranted improvements in care despite being considered nonpreventable [2].

Detailed evaluation of SMM is limited by the absence of a reliable and reproducible means of collecting data and the rarity of many complications. In the United States, the National Inpatient Sample (NIS) is a useful resource as it includes up to 40 diagnostic and 25 procedural codes per patient from a sample of 20 percent of United States community hospitals and represents approximately seven million hospitalizations annually. The State Inpatient Databases (SID) is another useful resource as it contains inpatient discharge records from community hospitals from all payer sources (eg, private insurance, Medicare, Medicaid, uninsured) in a particular state. Data in the SID are abstracted from these discharge records and include clinical information, such as diagnoses and procedures, as well as nonclinical details, such as patient demographics, charges, and length of stay.

This topic will provide an overview of SMM, including initiatives to reduce its rate. Maternal mortality is reviewed separately. (See "Overview of maternal mortality" and "Approaches to reduction of maternal mortality in resource-limited settings".)

DEFINITIONS FROM INTERNATIONAL AND NATIONAL ORGANIZATIONS — While there is no consensus on a single definition of, or approach to, SMM, various stakeholders, such as the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC), have developed processes to help standardize the approach to learning more about life-threatening and health-impacting events surrounding childbirth.

World Health Organization — The WHO defines SMM as "potentially life-threatening conditions" and proposes a comprehensive approach to the review of maternal care to capture all relevant events [3].

In 2011, WHO published a proposal for a systematic approach for evaluating the quality of obstetric care and monitoring the impact of interventions in health care delivery. Many of the definitions used in this proposal were derived from the proceedings of a 2007 working group composed of obstetricians, midwives, epidemiologists, and public health professionals. This group borrowed the term "near miss" from the airline industry and defined it for SMM purposes by specific clinical conditions (eg, stroke), laboratory measures of organ dysfunction (eg, serum lactate >5 mmol/L), or management interventions (eg, blood transfusion) [3,4]. The proposed clinical, laboratory, and management criteria for a maternal near miss are shown in the following table (table 1).

Data in the following categories are captured to assess quality of care:

●Severe maternal complications.

●Critical interventions (ie, those required in the management of life-threatening or potentially life-threatening conditions or intensive care unit [ICU] use).

●Maternal near-miss cases (ie, patients who survived organ dysfunction that was life-threatening).

●Maternal vital status (ie, maternal mortality).

The qualifying criteria for each of these categories are listed in the table (table 2).

For quality of care assessment, the category of life-threatening conditions (near-miss criteria) captured evidence of deranged physiology without emphasis on the precipitating cause [5].

The implementation of this assessment requires an operational plan to ensure detailed record keeping, timely and accurate data entry, and the capacity to perform ongoing systematic review. The intention is to provide local health care delivery systems with a means to understand patterns in maternal morbidity and mortality, the use of interventions, and the impact of changes in care. However, it may not be feasible to implement in all settings. It also has limitations, such as the need for additional time and personnel and difficulty capturing data when patients receive care from different care systems during pregnancy versus the postpartum period. The latter can result in a gap in ascertainment of data and highlights the relative strength of proposed regional programs for assessment and the advantages of national data systems.

Centers for Disease Control and Prevention — The CDC defines SMM as unexpected outcomes of labor and delivery that result in significant short- or long-term consequences to the individual's health [6]. They have developed a list of 21 adverse maternal health events indicative of SMM and their corresponding International Classification of Diseases, 10^th revision (ICD-10) diagnostic and procedural codes (table 3), reflecting their decision to use administrative hospital discharge data for surveillance of SMM [7]. Hospital discharge data more accurately capture SMM than birth certificate data [8]

Using administrative databases to capture maternal morbidity statistics has advantages and disadvantages. By operationalizing the definition of SMM using diagnostic and procedural codes, the CDC facilitates analysis of trends over time for these relatively uncommon events and analysis of associations between SMM and other risk factors or outcomes. The use of a defined group of codes is convenient because these codes are universal among health care delivery systems in the United States.

On the other hand, coding may be incomplete or inaccurate. Diagnostic codes are intended primarily for billing purposes and do not necessarily reflect the clinical details of a delivery hospitalization [9]. This concern was addressed in a validation study comparing older ICD-9 criteria with information from patient chart review [10]. The ICD-9 had sensitivity of 77 percent and positive predictive value of 44 percent for identifying SMM in the study cohort of patients from California. Another concern with this approach is that outcomes related to labor and delivery may not be recognized during the delivery hospitalization. Complications may be diagnosed in the outpatient setting or during a hospital readmission and, therefore, not captured unless linked to a delivery episode. In a United States study, approximately 15 percent of SMM cases developed de novo in the six weeks (and usually in the two weeks) after delivery hospitalization [11].

American College of Obstetricians and Gynecologists and the Society for Maternal-Fetal Medicine — The American College of Obstetricians and Gynecologists (ACOG) and the Society for Maternal-Fetal Medicine (SMFM) published a consensus statement in 2016 outlining recommendations for screening and review of cases of SMM [12]. Their intention was to propose guidelines for delivery facilities to implement to systematically improve patient care. However, they acknowledged that neither organization has agreed upon a comprehensive definition of SMM and not all cases that meet criteria for review will be determined to be preventable.

Regardless of the definition used, ACOG and SMFM recommend that all facilities:

●Establish a screening process to detect cases of SMM. The two recommended screening criteria for SMM are transfusion ≥4 units of blood and peripartum ICU admission; individual institutions may choose additional criteria.

●Review all cases that meet screening criteria with the intent of determining whether the morbidity could have been avoided and whether an opportunity exists for system change and improvement.

In-depth review of screen-positive cases makes it possible to identify potential systems issues in particular circumstances that might have contributed to morbidity. For example, a patient with scheduled complicated potentially bloody cesarean birth can receive a preoperative transfusion to prevent severe anemia, whereas prevention is more difficult in a low-risk patient who has an unanticipated postpartum hemorrhage. A patient with a heart condition can be scheduled for ICU admission after giving birth for close monitoring and appropriate management, whereas amniotic fluid embolism is unpredictable and can result in cardiorespiratory collapse. While this approach facilitates local review of practices, it may limit understanding of the broader epidemiologic patterns of maternal morbidity. As an example, case ascertainment may vary according to local practices (eg, thresholds for transfusion, availability of intensivist services on labor and delivery units versus the ICU).

UK Obstetric Surveillance System — The UK Obstetric Surveillance System (UKOSS) is a database designed to support epidemiologic research of rare conditions of pregnancy, including morbidity events. Each month, UKOSS sends a survey containing a list of conditions under surveillance to a representative at each hospital with an obstetric facility. When cases are identified, more detailed information is requested about each case. UKOSS also collects data on a control group of pregnant people without the specified conditions.

The list of conditions changes as studies are completed and applications for new studies are accepted. For example, ongoing surveillance in 2019 included amniotic fluid embolism, peripartum hyponatremia, extremely preterm prelabor rupture of membranes, and any pregnancy complicated by diabetic ketoacidosis, cirrhosis, previous Fontan procedure, or antithrombin/protein C deficiency [13]. The list of completed studies is available online.

UKOSS collects a broad spectrum of information about antenatal, peripartum, and postpartum care. By establishing a system for data collection across multiple institutions, rare conditions and events can be studied. The scope of the data collection system at any given point in time facilitates chart level abstraction, which may improve data accuracy. However, because the process is not automated, data for the same conditions are not tracked over time and longitudinal changes may not be seen.

INCIDENCE

United States

Overall rates — The Healthcare Cost and Utilization Project provides regularly updated statistics on hospital care in the United States. The data are derived from the National Inpatient Sample (national statistics) and State Inpatient Databases (state statistics). For 2020, the national rate of SMM was 88.2 per 10,000 in-hospital deliveries. Although data were not available for every state, state-level data showed SMM rates varied from <63.1 to >84.3 per 10,000 in-hospital deliveries (figure 1) [14].

Analyses performed using data from individuals with Medicaid insurance indicate an overall rate of SMM without blood transfusion of 146.2 per 10,000 in-hospital deliveries, with significant rate variation among states (eg, Utah: 80.3 per 10,000 in-hospital deliveries, Washington, DC: 210.4 per 10,000 in-hospital deliveries) [15]. An analysis using Epic Systems Corporation's (Epic) Cosmos research platform, a US electronic health record database, reported SMM rates consistent with those derived from coding databases, SMM without blood transfusions: 87 per 10,000 in-hospital deliveries, SMM including blood transfusions: 192 per 10,000 in-hospital deliveries [16].

It should be noted that the risk for SMM extends beyond delivery hospitalization, with as many as 15 percent of new cases occurring in the six weeks following initial discharge [11].

Consistent with recent federal SMM surveillance, blood transfusion was not included in the overall definition because transfusion alone without other indicators of SMM lacks specificity and lowers positive predictive value for
"near miss" events [5,6].

Specific morbidities — Blood transfusion has been identified as a major contributor to historical increases in SMM (figure 2). Between 1993 and 2014, the overall rate of SMM increased from 49.5 to 144.0 per 10,000 delivery hospitalizations (190 percent increase); however, when blood transfusions are excluded, the rate only increased from 28.6 to 35.0 per 10,000 delivery hospitalizations (22 percent increase). Accordingly, over this same time period, blood transfusions increased from 24.5 to 122.3 per 10,000 deliveries (400 percent increase) [17]. The Healthcare Cost and Utilization Project Statistical Brief presented similar findings using data from the State Inpatient Databases during the period from 2006 to 2015: the rate of deliveries involving blood transfusion increased by 54 percent while the rate of deliveries involving all of the other 20 SMM indicators increased by 24 percent [18]. It is unclear the extent to which prevailing practices in management of hemorrhage (eg, published thresholds for transfusion, guidelines on the management of obstetric hemorrhage, etc) influence the reported rates of this common obstetric complication. Data on more recent trends have yet to be published.

Prior research has shown that most other Centers for Disease Control and Prevention SMM indicators (ie, acute myocardial infarction or aneurysm, acute kidney failure, adult respiratory distress syndrome, cardiac arrest, fibrillation, conversion of cardiac rhythm, shock, ventilation/temporary tracheostomy, sepsis, and hysterectomy ) have also increased over time but by much less. A study of the national rate of SMM from 2012 to 2019 reported a 15 percent increase overall, with the largest individual increases attributable to acute kidney injury (up 139 percent) and sepsis (up 155 percent) [19]. These increases were not substantially associated with transition to ICD-10, but several other indicators did have changes associated with transition. For example, DIC decreased by 33 percent, largely associated with ICD-10 changes. Smaller magnitude changes attributable to the ICD-10 transition included decreases for severe anesthesia complications and heart failure during surgery, and increases in air and thrombotic embolism and pulmonary edema/acute heart failure. More recent data have not been published.

International

Resource-abundant countries — In resource-abundant countries, SMM statistics can be collected from diagnostic and procedural codes generated from delivery and/or postpartum admissions. However, comparison of SMM incidence remains limited, primarily by the lack of a consistent definition, but also by differences in methodology [20]. Despite the differences in the definition of SMM, resource-abundant countries have reported increases in many of the less common SMM indicators analyzed, such as myocardial infarction, pulmonary edema, and mechanical ventilation, as in the United States [21].

For example:

●A survey performed in Scotland defined SMM by disease (eg, eclampsia, cardiac arrest) and organ system criteria (eg, acute kidney dysfunction, acute respiratory dysfunction) and reported 3.8 cases per 1000 births [22]. A population-based study in the Netherlands defined SMM by disease (eg, uterine rupture, eclampsia) and management criteria (eg, intensive care unit admission) and reported 7.1 cases per 1000 births [23]. Even though both studies included major obstetric hemorrhage in the definition, this was considered a transfusion of ≥5 units in the Scottish study and ≥4 units in the Dutch study, a difference in terms that largely accounted for the differences in incidence [20,22,23].

●Studies conducted in Canada, Finland, and Australia have used similar methodologies, relying on administrative data to describe the incidence of SMM. However, the specific diagnostic and procedural codes chosen differ, making direct comparison challenging (table 4). Furthermore, the inclusion criteria for diagnostic and procedural codes in each of these studies differ. For example, the criteria in the Canadian study were near-miss conditions with the potential to cause maternal death that were reliably recorded (ie, not obviously subject to coding error), whereas in the Finnish study, the inclusion criteria were conditions "indicating a severe maternal complication" that were not necessarily subject to validation. SMM rates of 4.38 and 7.60 per 1000 births were reported in Canada and Finland, respectively [21,24].

In Australia, data are collected and analyzed on a state level as a result of the structure of public health care in that country [25]. The Victorian Perinatal Data Collection database is a population-based surveillance system that records maternal and neonatal information to track morbidities statewide. The Australian Maternal Morbidity Outcome Indicator is a composite of diagnostic and procedural ICD codes that has been validated as a definition of SMM. In a 2015 analysis, the prevalence was 5.3 per 1000 births [26].

Resource-limited countries — In comparison, in resource-limited countries, near-miss criteria examined at a local level may provide the best insight into SMM [27-32]. Reported rates of SMM range from 5.06 in Baghdad, Iraq, to 25.4 per 1000 deliveries in the Madang Province of Papua New Guinea; the latter has one of the highest maternal mortality rates in the world [27-32].

In resource-limited countries, hemorrhage is the leading cause of maternal near-miss cases [27-32]. While trends in specific indicators over time are not as readily available for resource-limited countries, other leading causes of maternal near-miss cases in recent analyses include hypertensive disorders of pregnancy and infection [27-33].

RISK FACTORS — Internationally, risk factors for SMM seem to vary by availability of resources and the structure of the health care system. Data are available on the following risk factors.

Maternal age — In the United States, SMM is increased in patients under age 20 years, then falls to a nadir in the 20-to-29 year age group, and then increases with age 30 to 39 years, with the highest rates among patients ages 40 years and older (196.0, 131.2, 134.5, and 246.0 per 10,000 delivery hospitalizations, respectively [34]) [18,34-36]. This increased risk extends into the postpartum period [37]. A similar association between maternal age and increasing risk for SMM has been reported in other resource-abundant countries [26,38]. The increase in SMM with age appears to be due to increases in age-specific rates rather than a shift to older maternal age among individuals giving birth, except in non-Hispanic Black people [39].

In resource-limited countries, age >30 years has been associated with maternal near-miss cases [27,29].

Racial and ethnic minorities — Racial and ethnic minority status appears to be an important risk factor consistently associated with SMM. Lifelong exposure to systemic racism and discrimination may account for the increased risk of SMM in minority individuals [40]. Failure to rescue from SMM is a major factor for the excess maternal mortality of racial and ethnic minority individuals in the US [41].

In the United States, SMM incidence appears to be highest among non-Hispanic Black individuals and lowest among non-Hispanic White individuals (225.7 versus 104.7 per 10,000 delivery hospitalizations [34]) [18,35,42-47]. It has been suggested that disparity among races and ethnicities in the United States is related to increased use of blood transfusions among racial and ethnic minorities compared with use in non-Hispanic White individuals, but even when patients with blood transfusions as the only criterion for SMM are excluded, non-Hispanic Black individuals have a higher rate of SMM than non-Hispanic White individuals (74.2 versus 33.4 per 10,000 delivery hospitalizations) [34]. However, for some categories of SMM (eg, hypertension), racial disparities between non-Hispanic Black and non-Hispanic White individuals may not persist after controlling for variables such as disease severity and other confounders [48]. The risk of SMM also differs among subgroups of the Asian and Pacific Islander community, ranging from 94 cases per 10,000 births among patients of Korean descent to 165 cases per 10,000 births among patients of Filipina descent [49]. The largest racial and ethnic disparities in SMM in the United States have been noted among those with multiple chronic conditions.

Similarly, in the United Kingdom, the odds of SMM are 83 percent higher for Black African individuals than for White European individuals [50]. Higher rates of SMM have also been noted for Black Caribbean, Bangladeshi, Pakistani, and other non-White, non-Asian individuals.

In an Australian study from Victoria, SMM was higher in aboriginal and African individuals [26,51].

Discontinuity of hospital care — Fragmentation of care, defined as being admitted to a different hospital for postpartum care than where birth occurred, has been associated with an increased risk for SMM [52].

Lower socioeconomic status — Analyses of social-structural determinants of risk factors for SMM have been limited by the depth and quality of available research and risk-factor interdependence remains poorly understood [53]. Lower socioeconomic status may be a risk factor for SMM [26,34,45]. One study looking at the extent to which socioeconomic status modified the effect of race and ethnicity on SMM among pregnant people in New York City determined that racial differences persisted across all socioeconomic groupings, and living in the poorest neighborhoods further increased the risk for SMM among Black non-Latin American and Latin American individuals [45]. In another study, living in an area with higher rental housing costs was associated with higher odds of SMM, except when publicly supported affordable housing was available [54].

However, other studies have not found an association between socioeconomic status and SMM in the United States [55].

Medical comorbidities — Studies in Canada, Australia, and the United States have reported an association between preexisting conditions and risk for SMM [21,26,34]. The risk for SMM increases with an increasing number of comorbidities (in multivariable regressions, adjusted ORs for 1, 2, or ≥3 comorbidities were 4.4, 6.6, and 9.1, respectively) [34,56]. In one analysis that described a dose-response relationship between the number of comorbid conditions and the risk for SMM, 45 percent of Black patients in the study had at least one comorbid condition compared with 33 percent of White patients, 32 percent of Hispanic patients, and 28 percent of Asian patients [34]. Efforts to improve comparison of SMM events have focused on expanding comorbidity scores used in these analyses [57].

Various studies have explored the association between specific preexisting medical conditions and SMM [58-71]. Further research is needed to explore the differences between potentially modifiable conditions (eg, anemia) and non-modifiable conditions (eg, congenital heart disease).

An association between high gestational weight gain and SMM has been reported [72,73]. In one, patients with gestational weight gain ≥10 pounds (4.5 kg) above the recommended body mass index-specific upper limit for gestational weight gain were 32 percent more likely to experience SMM than patients who exceeded the recommended upper limit by <10 pounds (4.5 kg) [73]. A study from the United States noted that pregnant people who had gestational weight gain in excess of National Academy of Medicine (formerly the Institute of Medicine [IOM]) guidelines were at increased risk of SMM compared with those who met weight gain target ranges, although the absolute increase in SMM was small (for 1 to 19 pounds above and ≥20 pounds above target ranges, the increase in SMM was 2.1 and 6.0 cases per 1000 deliveries, respectively) [74].

The COVID-19 pandemic prompted examination of the association between COVID-19 infection and maternal complications [75,76]. Of patients requiring hospitalization for COVID-19 during pregnancy, the literature suggests that approximately 10 percent required intensive care unit level of care, with use of mechanical ventilation in approximately 8 percent [77]. COVID-19 in the setting of pregnancy and at least one medical comorbidity has been associated with increased risk for maternal morbidity and mortality [78-81]. However, it is unclear whether COVID-19 infection during pregnancy increases the risk for SMM [82,83].

Interpregnancy interval — In a study including over 14,000 patients with SMM, both short (<6 months) and long (≥60 months) interpregnancy intervals (IPI) were significantly, but modestly, associated with SMM [84]. After excluding transfusion from SMM, long IPI was still associated with SMM but short IPI was not a factor. Interpretation of findings was limited by a lack of within-mother analysis. (See "Interpregnancy interval: Optimizing time between pregnancies", section on 'Limitations of available data'.)

Preterm birth — Studies note a high proportion of SMM occurring in patients who have a preterm birth. One study found that the probability of SMM was 16 percent at 24 weeks compared with 2.2 percent at 38 weeks, with sepsis, acute respiratory distress syndrome, mechanical ventilation, and shock the most common specific SMMs for births prior to 28 weeks of gestation [85].

Stillbirth — Delivery of a stillborn is associated with an increased risk for SMM, particularly among pregnant people with preexisting medical conditions [86,87]. In resource-limited countries, anemia and a history of stillbirth have been associated with increased risk for SMM [27,32].

Cesarean birth — Cesarean birth, either in the index pregnancy or in a prior pregnancy, is associated with higher rates of SMM [24,26,35]. Patients who undergo cesarean birth in the very early preterm period may be at further increased risk for SMM [88].

In a study that evaluated SMM by mode of birth and risk factors (maternal age, medical comorbidity [obesity, preeclampsia, diabetes]), attempted vaginal birth was associated with lower risk for SMM for patients with each risk factor except for preeclampsia; patients with preeclampsia had a similar risk for SMM with planned cesarean and planned vaginal birth [89].

Successful assisted vaginal birth has been associated with a lower risk for SMM when compared with cesarean birth, however, failed assisted vaginal birth has been associated with an increased risk for SMM [90].

Restricted access to abortion — Restricted access to abortion appears to increase SMM. In a study that evaluated the association between state-level changes in reproductive health services and SMM, a 20 percent reduction in Planned Parenthood centers (which provide abortion services in addition to other services) was associated with a 3.79 percent (95% CI 1.89-5.69) increase in SMM, but other state-level indicators were not associated with differences in SMM [91]. Legislation on abortion may influence management options available for various pregnancy complications. A cross-sectional study of abortion among pregnant people with modified World Health Organization (mWHO) class IV cardiovascular conditions indicated that an association between restrictive state abortion policy and a lower number of abortions in this cohort of patients [92]. In a study of maternal morbidity in patients with obstetric complications at ≤22 weeks of gestation, expectant management was associated with a higher rate of SMM compared with immediate pregnancy interruption (57 versus 33 percent) [93].

Geographic location — State-based variation in SMM has been described among Medicaid patients [15]. Some analyses have also reported rural-urban disparities in SMM [94]. This effect may be related to specific risk factors for SMM (eg, placenta accreta spectrum) [95]. Hospital obstetric volume may also be an important variable in rural settings [96].

Previous SMM — In a population-based study, the risk of SMM at the second birth was threefold higher in patients with SMM in their first birth than in those without a prior SMM (65.2 versus 20.3 per 1000 in patients; adjusted relative risk 3.11, 95% CI 2.96-3.27) [97]. Similarly, in a retrospective cohort study of patients with prior SMM, the risk of SMM in a subsequent pregnancy was higher both for transfusion- and nontransfusion-related SMM [98].

NEONATAL OUTCOMES — In a population-based cohort study from Canada, maternal morbidity was associated with a higher risk of infant death [99]. Infant mortality occurred in 8.9 per 1000 live births with SMM versus 2.8 per 1000 live births without SMM. One observation from this study was the concurrent presence of maternal and neonatal sepsis.

LONG-TERM HEALTH OUTCOMES — There are few data regarding the long-term health outcomes of individuals who experience SMM. SMM has been associated with an increased risk for hospital readmission throughout the year following delivery [100]. An increased risk for postpartum psychiatric morbidity and substance use disorder has been described [101,102]. There is also evidence that patients who experience SMM may have an accelerated risk for mortality [103,104].

PREVENTION

Scoring systems — Efforts to reduce SMM have largely focused on systematic evaluation of inpatient data that represent an abnormality in normal physiology. These data were used to create tools for early identification of patients at risk for SMM. For example:

●The Maternal Early Warning System, Maternal Early Obstetric Warning Scores, and Maternal Early Warning Trigger are sets of thresholds for abnormal vital signs (termed maternal early warning criteria [MEWC]) that should trigger a bedside patient evaluation [105-107]. The thresholds and possible causes for the abnormalities are shown in the tables (table 5 and table 6).

●The obstetric comorbidity index (OB-CMI) is a comorbidity-based screening tool for identifying patients who are at risk of SMM while on the labor and delivery unit [108]. In a prospective cohort, the frequency of SMM increased from 0.4 percent for patients with a score of 0/15 to 19 percent for those with a score ≥9/15 [108].

●Another SMM prediction tool uses a risk scoring tool based on antepartum, intrapartum, or combined risk factors to predict SMM [109]. In a prospective cohort, an antepartum score >5 points had sensitivity and specificity of 60 and 65 percent, respectively; an intrapartum score ≥2 had sensitivity and specificity of 68 and 78 percent, respectively; and a combined score ≥5 had sensitivity and specificity of 62 and 98 percent.

Use of such tools has been associated with decreased rates of SMM [110-113]. The California Maternal Quality Care Collaborative (CMQCC) uses statewide data to develop and continuously improve quality improvement toolkits focused on life-threatening obstetric complications such as hemorrhage and preeclampsia.

There is some evidence that a modified version of a scoring system may be appropriate in resource-limited settings [114]. At least one study examined a device that measures heart rate and blood pressure, calculates a shock index (heart rate divided by systolic blood pressure), and alerts the provider using a traffic light system (red, amber, green) reflecting previously validated shock index thresholds [115]. This helps to quickly identify patients at risk of compromise and who may need transfer to a facility with a higher level of care.

Standardized review of SMM — Review of SMM can occur at various levels. Some institutions have implemented processes for reviewing SMM events [116,117]. Some states have implemented a review of cases of SMM to identify opportunities for improvement. This review facilitates examination of social, systems, and clinical issues related to SMM [2]. A comparison of institution- and state-level SMM reviews found that conclusions differed based on whether the review occurred at the level of a health care facility or state [118]. State-level review identified more SMM cases that were potentially preventable and more opportunities to improve care than facility-level review.

Bundles — A "bundle" is a collection of checklists, protocols, and educational materials derived from evidence-based interventions with the goal of reducing a specific morbidity [119]. The Safe Motherhood Initiative from the American College of Obstetricians and Gynecologists District II has developed bundles for hypertension, hemorrhage, and venous thromboembolism [120]. There is evidence that such bundles have been effective in reducing SMM [121-123]. As an example, a study using a decision-analysis model showed that California's statewide perinatal quality collaborative (CMQCC) initiative to reduce SMM from hemorrhage was a cost-effective strategy that decreased morbidity and mortality [123].

Patient care — At the patient-provider level, providers should evaluate patients for factors that may impact the outcome of pregnancy and counsel them appropriately (table 7) [124]. This risk assessment should be started before pregnancy, repeated at the first prenatal visit and during the course of pregnancy, and again postpartum and between pregnancies [40].

Prevention and treatment of chronic conditions among females of childbearing age may also reduce SMM [34]. Individuals with medical or obstetric comorbid conditions may benefit from early consultation with a maternal-fetal medicine specialist to create a management plan, including the most appropriate location for delivery [56]. Mediating the pathways of certain etiologies of SMM (eg, hypertensive disorders of pregnancy, antepartum anemia) may also have an impact on racial disparities in SMM [67,125,126]. Enhancing care in the postpartum period may decrease SMM, possibly with a differential effect among racial groups [127].

Simulation — Simulation training has been studied as an intervention to reduce SMM. For example, training for postpartum hemorrhage interventions decreases SMM [128,129].

UpToDate topics address identification and management of common causes of SMM. For example:

●(See "Overview of postpartum hemorrhage" and "Postpartum hemorrhage: Medical and minimally invasive management" and "Postpartum hemorrhage: Management approaches requiring laparotomy".)

●(See "Preeclampsia: Clinical features and diagnosis" and "Preeclampsia: Antepartum management and timing of delivery" and "HELLP syndrome (hemolysis, elevated liver enzymes, and low platelets)" and "Eclampsia".)

●(See "Sepsis syndromes in adults: Epidemiology, definitions, clinical presentation, diagnosis, and prognosis" and "Evaluation and management of suspected sepsis and septic shock in adults".)

●(See "Uterine rupture: After previous cesarean birth" and "Uterine rupture: Unscarred uterus".)

●(See "Placenta accreta spectrum: Clinical features, diagnosis, and potential consequences" and "Placenta accreta spectrum: Management".)

●(See "Disseminated intravascular coagulation (DIC) during pregnancy: Clinical findings, etiology, and diagnosis".)

●(See "Deep vein thrombosis in pregnancy: Clinical presentation and diagnosis" and "Venous thromboembolism in pregnancy and postpartum: Treatment" and "Venous thromboembolism in pregnancy: Prevention".)

●(See "Acute respiratory failure during pregnancy and the peripartum period".)

●(See "Acquired heart disease and pregnancy" and "Pregnancy in women with congenital heart disease: General principles" and "Pregnancy and valve disease".)

●(See "Pregnancy and contraception in patients with nondialysis chronic kidney disease" and "Pregnancy in patients on dialysis".)

SUMMARY AND RECOMMENDATIONS

●Definition – There is no single definition of severe maternal morbidity (SMM). Approaches to collecting data about SMM include analysis of existing data from diagnostic and billing databases as well as chart-level review based on screening criteria. There are advantages and disadvantages to each approach. (See 'Definitions from international and national organizations' above.)

•The World Health Organization has proposed a system for assessing the quality of maternal care (table 2), taking into consideration severe maternal complications, critical interventions, maternal near-miss cases, and maternal mortality. (See 'World Health Organization' above.)

•The Centers for Disease Control and Prevention has compiled a list of indicators of SMM that includes various diagnoses that reflect organ dysfunction (table 3). (See 'Centers for Disease Control and Prevention' above.)

•The American College of Obstetricians and Gynecologists and the Society for Maternal-Fetal Medicine recommend that health care systems establish a screening process to detect cases of SMM, starting with cases including transfusion of ≥4 units of blood or admission to the intensive care unit, and review all cases that meet screening criteria with attention to opportunities for system improvement. (See 'American College of Obstetricians and Gynecologists and the Society for Maternal-Fetal Medicine' above.)

●Prevalence – SMM has increased over the last few decades in resource-rich countries. Obstetric hemorrhage remains the leading cause in both resource-rich and resource-limited countries. (See 'Incidence' above.)

●Risk factors – Risk factors for SMM include younger or older age, racial and ethnic minority status, lower socioeconomic status, medical comorbidities, and cesarean birth. (See 'Risk factors' above.)

●Prevention

•Efforts to reduce SMM have focused on early identification and systematic evaluation of inpatient data that represent an abnormality in normal physiology. The Maternal Early Warning System, Maternal Early Obstetric Warning Scores, and Maternal Early Warning Trigger are sets of thresholds for abnormal vital signs that trigger a bedside evaluation of the patient (table 5 and table 6). (See 'Scoring systems' above.)

•A "bundle" is a collection of checklists, protocols, and educational materials derived from evidence-based interventions with the goal of reducing a specific morbidity. The Safe Motherhood Initiative has developed bundles for hypertension, hemorrhage, and venous thromboembolism. (See 'Bundles' above.)

•At the patient-provider level, providers should evaluate patients for factors that may impact the outcome of pregnancy and counsel them appropriately (table 7). This risk assessment should be started before pregnancy, repeated at the first prenatal visit and during the course of pregnancy, and again postpartum and between pregnancies. (See 'Patient care' above.)