Abstract
Data are limited on contemporary temporal trends in maternal characteristics and outcomes in hospitalized patients with peripartum cardiomyopathy (PC). We used the National Inpatient Sample database from January 1, 2004, to December 31, 2018, to identify PC hospitalizations in women aged 15 to 54 years. Weighted survey data were used to derive national estimates for the United States population and examine trends. Between 2004 and 2018, there was a total of 23,420 weighted hospitalizations for PC in women aged 15 to 54 years. The mean (standard error) age of this hospitalized PC population was 30.3 (0.1) years, with 44.6% White, 39.3% Black, 9.0% Hispanics, and 7.1% “Other” racial/ethnic groups. There was a nonsignificant increase in the PC hospitalization per 100,000 live births from 33.6 in 2004 to 42.4 in 2018 (p-trend = 0.06) over the study period, driven by a statistically significant increase in the younger women age group 15 to 35 years (p-trend = 0.04). The PC hospitalizations per 100,000 live births for women aged 36 to 54 years were more than double that observed in women aged 15 to 35 years (77.6 vs 33.5). PC hospitalizations were more than threefold greater in Black versus White women (103.5 vs 32.0 per 100,000 live births). Overall, inpatient mortality was 0.8%; the adjusted inpatient mortality showed a nonsignificant overall decrease from 1.1% in 2004 to 0.5% in 2018 (p-trend = 0.15). The overall mean length of stay was 4.6 days; the adjusted mean length of stay decreased from 5.8 days in 2004 to 4.6 days in 2018 (p-trend <0.01). In conclusion, there has been a nonsignificant increase in hospitalizations for PC, driven by an increasing rate of hospitalizations in younger women. The older maternal age group and Black patients had a higher proportional hospitalization as compared with the younger age group and White patients. There was a nonsignificant decrease in inpatient mortality.
Peripartum cardiomyopathy (PC) is characterized by systolic dysfunction affecting women of childbearing age during the end of pregnancy or early postpartum period, in women with no known cardiac disease before pregnancy.1 It is a diagnosis of exclusion and is characterized by reduced left ventricular ejection fraction (LVEF) of <45% with or without dilatation.2 Risk factors associated with PC include Black race, preeclampsia, hypertension, multi-gestational pregnancies, older maternal age, and genetic predisposition.1,3,4 It is associated with poor obstetric outcomes including stillbirth, reduced birth weight, and obstetric challenges regarding management and timing of delivery.4 Maternal complications include cardiac arrest, need for mechanical support (MCS), persistent cardiomyopathy, arrhythmia, thromboembolism, recurrence of PC on a subsequent pregnancy, and mortality.1,4 There is limited data regarding the contemporary trends within the United States sample patient population about associations of risk factors and clinical outcomes of PC which may inform clinical practice, policy, and future research. In this study, we used nationwide data from 2004 to 2018 to evaluate the associated risk factors, comorbidities, and clinical outcomes of PC.
Methods
We used the National Inpatient Sample (NIS), an all-payer database that constitutes approximately 20% of United States inpatient hospitalizations across different hospital types and geographic regions that participate in the Healthcare Cost Use Project.5 It is sponsored by the Agency for Healthcare Research and Quality. NIS contains clinical and resource use information on approximately 8 million annual hospital discharges from 47 United States states, covering over 97% of the United States population. Our study was exempt from Institutional Board Review approval because of the deidentified nature of the database, which is publicly available.
The observational analysis included data from January 1, 2004, to December 31, 2018, to identify all hospitalizations for PC in women of ages ≥15 and ≤54 using International Classification of Diseases (ICD), Ninth Revision, Clinical Modification code “674.5 × ” and ICD-10 Clinical Modification code “O90.3” in the primary discharge diagnosis. These codes have been used in the previous NIS-based studies.3,6 We analyzed patient profiles including demographics, comorbidities, insurance status, household income, and discharge disposition using ICD-9 Clinical Modification and IC-10 Clinical Modification, codes (listed in Supplementary Table 1). Race was self-reported and defined in the NIS dataset as White, Black, Hispanic, Asian or Pacific Islander, Native American, and “Other.” Given small numbers, we included Asian, Pacific Islander, and Native American with the “Other” category. The patient population was divided into quartiles based on the median household income of the ZIP code of patients. The insurance status of the admitted patient population with PC was also analyzed. Discharge disposition was classified into the following: (1) home (routine or with home health care); (2) transfer to short-term hospital (3) facility (skilled nursing, intermediate care, or another facility); and (4) against medical advice.
The outcomes of interest were trends in clinical characteristics, inpatient mortality, maternal major adverse events (MAEs) (composite of inpatient mortality, in-hospital cardiac arrest and use of MCS devices), comorbid characteristics, clinical complications, length of stay (LOS), and inflation-adjusted care costs in hospitalizations for PC.
National estimates of the entire United States hospitalized population were calculated using the Agency for Healthcare Research and Quality sampling and weighted method.7 The study population was divided into three 5-year periods (2004 to 2008, 2009 to 2013, and 2014 to 2018). Continuous variables were described as mean and standard error and categorical variables as frequencies and percentages. We used the Rao-Scott chi-square test for between-group comparisons for categorical variables and weighted linear regression for continuous variables.
Hospital total charges were converted to cost estimates using hospital-specific cost to charge ratios provided by Healthcare Cost Use Project. We inflated total costs to 2019 United States dollars using the Consumer Price Index inflation calculator published by the United States Bureau of Labor Statistics.8 We estimated PC hospitalizations per 100,000 live births, the denominator for which was extracted from the United States Census Bureau estimates of live births for each study year.9 The prevalence of PC hospitalizations per 100,000 live births was estimated separately for each racial group, the younger maternal age group (15 to 35 years) and the older group (36 to 54 years). As the missing data for the race categories were substantial (14.1%), we applied multivariate imputation by chained equation to impute the missing data for race to accurately estimate the prevalence stratified by race groups. Inpatient mortality was estimated as a proportion of deaths in PC hospitalizations over the total number of PC hospitalizations.10
The trends in PC hospitalizations were examined using linear regression, LOS, and inflation-adjusted cost of care using a weighted linear regression model, and inpatient mortality using logistic regression analysis. We adjusted trends of inpatient mortality, LOS, and inflation-adjusted cost for the following variables: age, comorbidities using Charlson Comorbidity Index,11 insurance status, hospital bed size, hospital location/teaching status, and hospital region. We used Stata Statistical Software Version 16.1 (StataCorp, College Station, Texas) for all analyses, which were survey-specific, using Stata’s “svy” functions.12 A value of p <0.05 was considered statistically significant for all analyses.
Results
Between 2004 and 2018, a total of 23,420 weighted hospitalizations for PC in women aged ≥15 and ≤54 years were identified (Supplementary Table 2). The mean (standard error) age of the population was 30.3 (0.1) years, with racial/ethnic distribution consisting of 44.6% White, 39.3% Black, 9.0% Hispanics, and 7.1% “Other” groups (Table 1). Most PC hospitalizations were identified in urban teaching hospitals (65.2%), large bed-sized hospitals (66.3%), and the population living in ZIP codes with the lowest quartile of national household income 0% to 25% (37.6%).
Table 1.
Baseline characteristics of primary peripartum cardiomyopathy hospitalizations, age ≥ 15 and ≤ 54, in the United States, 2004 to 2018
| Variables | 2004–2008 (weighted n=7663) | 2009–2013 (weighted n=7987) | 2014–2018 (weighted n=7770) | Total (weighted n=23420) | P-value | ||||
|---|---|---|---|---|---|---|---|---|---|
| Age (mean [S.E]) years | 29.8 | (0.18) | 30.3 | (0.17) | 30.8 | (0.16) | 30.3 | (0.1) | <0.01 |
| Race | 0.015 | ||||||||
| White | 3612 | (47.1%) | 3593 | (45.0%) | 3250 | (41.8%) | 10455 | (44.6%) | |
| Black | 2844 | (37.1%) | 3202 | (40.1%) | 3146 | (40.5%) | 9192 | (39.3%) | |
| Hispanic | 739 | (9.6%) | 655 | (8.2%) | 716 | (9.2%) | 2110 | (9.0%) | |
| Others | 468 | (6.1%) | 537 | (6.7%) | 658 | (8.5%) | 1663 | (7.1%) | |
| Comorbidities | |||||||||
| Chronic pulmonary disease | 749 | (9.8%) | 909 | (11.4%) | 1115 | (14.4%) | 2774 | (11.8%) | <0.001 |
| Diabetes mellitus | 362 | (4.7%) | 456 | (5.7%) | 645 | (8.3%) | 1463 | (6.3%) | <0.001 |
| Hypertension | 2358 | (30.8%) | 2873 | (36.0%) | 3250 | (41.8%) | 8481 | (36.2%) | <0.001 |
| Obesity | 835 | (10.9%) | 1295 | (16.2%) | 1995 | (25.7%) | 4124 | (17.6%) | <0.001 |
| Peripheral vascular disease | 19 | (0.3%) | 45 | (0.6%) | 60 | (0.8%) | 124 | (0.5%) | 0.13 |
| Kidney failure | 119 | (1.6%) | 186 | (2.3%) | 180 | (2.3%) | 485 | (2.1%) | 0.22 |
| Liver disease | < 11 | - | 39 | (0.5%) | 95 | (1.2%) | 144 | (0.6%) | <0.001 |
| Neurological disorders | 116 | (1.5%) | 141 | (1.8%) | 280 | (3.6%) | 537 | (2.3%) | <0.001 |
| Deficiency anemias | 1557 | (20.3%) | 2104 | (26.4%) | 1865 | (24.0%) | 5527 | (23.6%) | <0.001 |
| Hypothyroidism | 193 | (2.5%) | 336 | (4.2%) | 345 | (4.4%) | 874 | (3.7%) | 0.01 |
| Smoker (current or past) | 1043 | (13.6%) | 1036 | (13.0%) | 1960 | (25.2%) | 4038 | (17.2%) | 0.03 |
| Alcohol abuse | 55 | (0.7%) | 55 | (0.7%) | 30 | (0.4%) | 140 | (0.6%) | 0.42 |
| Drug abuse | 176 | (2.3%) | 330 | (4.1%) | 410 | (5.3%) | 916 | (3.9%) | <0.001 |
| Prior CVD | < 11 | - | 85 | (1.1%) | 80 | (1.0%) | 174 | (0.7%) | <0.01 |
| Hospital location | <0.001 | ||||||||
| Rural | 652 | (8.5%) | 458 | (5.8%) | 455 | (5.9%) | 1565 | (6.7%) | |
| Urban non-teaching | 2642 | (34.5%) | 2466 | (31.3%) | 1450 | (18.7%) | 6557 | (28.1%) | |
| Urban teaching | 4365 | (57.0%) | 4954 | (62.9%) | 5865 | (75.5%) | 15183 | (65.2%) | |
| Bed size of the hospital | <0.001 | ||||||||
| Small | 600 | (7.8%) | 586 | (7.4%) | 985 | (12.7%) | 2171 | (9.3%) | |
| Medium | 1963 | (25.6%) | 1576 | (20.0%) | 2135 | (27.5%) | 5674 | (24.4%) | |
| Large | 5095 | (66.5%) | 5716 | (72.6%) | 4650 | (59.9%) | 15461 | (66.3%) | |
| Region | 0.33 | ||||||||
| Northeast | 1169 | (15.3%) | 1297 | (16.2%) | 990 | (12.7%) | 3456 | (14.8%) | |
| Midwest | 1574 | (20.5%) | 1718 | (21.5%) | 1695 | (21.8%) | 4987 | (21.3%) | |
| South | 3615 | (47.2%) | 3653 | (45.7%) | 3840 | (49.4%) | 11108 | (47.4%) | |
| West | 1305 | (17.0%) | 1319 | (16.5%) | 1245 | (16.0%) | 3868 | (16.5%) | |
| Median household income for patient’s ZIP code | 0.05 | ||||||||
| 0–25th | 2745 | (36.6%) | 2902 | (36.8%) | 3040 | (39.5%) | 8687 | (37.6%) | |
| 26–50th | 1806 | (24.1%) | 1815 | (23.0%) | 1985 | (25.8%) | 5607 | (24.3%) | |
| 50–75th | 1679 | (22.4%) | 1744 | (22.1%) | 1620 | (21.0%) | 5043 | (21.8%) | |
| 75–100th | 1272 | (17.0%) | 1419 | (18.0%) | 1060 | (13.8%) | 3751 | (16.3%) | |
| Primary expected payer | <0.001 | ||||||||
| Medicare or Medicaid | 3702 | (48.3%) | 4201 | (52.7%) | 4365 | (56.3%) | 12268 | (52.4%) | |
| Private insurance | 3331 | (43.5%) | 3235 | (40.6%) | 2975 | (38.4%) | 9541 | (40.8%) | |
| Self-pay, no charge, or other | 625 | (8.2%) | 532 | (6.7%) | 415 | (5.4%) | 1571 | (6.7%) | |
| Charlson Comorbidity Index | <0.001 | ||||||||
| 0 | 2500 | (32.6%) | 2318 | (29.0%) | 1840 | (23.7%) | 6658 | (28.4%) | |
| 1 | 4248 | (55.4%) | 4434 | (55.5%) | 4245 | (54.6%) | 12927 | (55.2%) | |
| 2 | 727 | (9.5%) | 882 | (11.0%) | 1130 | (14.5%) | 2739 | (11.7%) | |
| >=3 | 188 | (2.5%) | 353 | (4.4%) | 555 | (7.1%) | 1096 | (4.7%) | |
CVD = cerebrovascular disease; n = frequency; % = percentage.
Cells with cell count <11 are not reported due to restrictions on cell size reporting by the National Inpatient Sample Database.
The average prevalence of PC hospitalizations per 100,000 live births was 38.7, with a nonsignificant overall initial an increase from 33.6 in 2004 to 42.4 in 2018 (p-trend = 0.06; Figure 1). The prevalence of PC per 100,000 live births was higher in the older maternal group than in the younger maternal group (77.6 vs 33.5). The younger maternal group showed a statistically significant steady increase in the prevalence during the study period from 28.4 in 2004 to 38.5 in 2018 (p-trend = 0.04; Figure 1). The racial/ethnic prevalence of PC hospitalizations per 100,000 live births was highest in Black women (103.5) followed by White (32.0) and Hispanic women (14.8) (Figure 2).
Figure 1.
Trends in prevalence of peripartum cardiomyopathy hospitalizations, age ≥15 and ≤54, per 100,000 live births in the United States, 2004 to 2018.
Figure 2.
Trends in prevalence of peripartum cardiomyopathy hospitalizations by race, age ≥15 and ≤54, per 100,000 live births in the United States, 2004 to 2018.
Approximately 55.2% of hospitalizations had a Charlson Comorbidity Index (CCI) of 1, 11.7% had a CCI of 2% and 4.7% had CCI of ≥3. We identified an upward trend in CCI of 2 and ≥3 from 2004 to 2008 up to 2014 to 2018 (p <0.001; Table 1). The most prevalent comorbidities in PC hospitalizations included hypertension (36.2%), deficiency anemias (23.6%), obesity (17.6%), smoking (17.2%), chronic pulmonary disease (11.8%), diabetes mellitus (6.3%) and drug abuse (3.91%) (Table 1). The prevalence of maternal MAE was 3.1% of inpatient hospitalizations (Figure 3). Overall, inpatient mortality in PC hospitalizations was 0.8%. The adjusted inpatient mortality showed a nonsignificant overall decrease from 1.1% in 2004 to 0.5% in 2018 (adjusted p-trend = 0.15) (Figure 4). We identified an increase in acute kidney injury (AKI) (2.4% to 5.2%, p <0.001), cardiogenic shock (2.2% to 5.2%, p <0.001), requirement of MCS (1.4% to 2.9%, p <0.01), and vasopressor use (0.6% to 1.5%; p = 0.03) (Table 2) from 2004 to 2008 up to 2014 to 2018. Overall mean LOS was 4.6 (0.12) days, and the mean inflation-adjusted cost was $ 15,231 (682) (Figure 6). Adjusted mean LOS showed significant overall decreasing trend from 5.8 days in 2004 to 4.6 days in 2018 (p-trend = 0.002; Figure 5). Discharge disposition to home constituted the major disposition showing nonsignificant increase from 2004 to 2008 up to 2014 to 2018 (91.2% to 93.0%, p = 0.38) (Supplementary Table 4).
Figure 3.
Trends of maternal major adverse events in peripartum cardiomyopathy hospitalizations, age ≥15 and ≤54, in the United States, 2004 to 2018.
Figure 4.
Trends of inpatient mortality in peripartum cardiomyopathy hospitalizations, age ≥15 and ≤54, in the United States, 2004 to 2018.
Table 2.
Clinical outcomes/resource use in primary peripartum cardiomyopathy hospitalizations, age ≥ 15 and ≤ 54, in the United States, 2004 to 2018
| Variables | 2004–2008 | 2009–2013 | 2014–2018 | Total | P-value |
|---|---|---|---|---|---|
| Vasopressor use | 45 (0.59%) | 71 (0.89%) | 120 (1.54%) | 237 (1.01%) | 0.03 |
| Invasive mechanical ventilation | 554 (7.24%) | 643 (8.05%) | 495 (6.37%) | 1692 (7.22%) | 0.2 |
| Cardiogenic shock | 171 (2.23%) | 337 (4.22%) | 400 (5.15%) | 908 (3.88%) | <0.001 |
| Acute kidney injury | 182 (2.38%) | 452 (5.66%) | 400 (5.15%) | 1035 (4.42%) | <0.001 |
| Mechanical circulatory support | 105 (1.37%) | 211 (2.65%) | 225 (2.9%) | 541 (2.31%) | 0.01 |
| Gastrostomy | 36 (0.47%) | 29 (0.37%) | 20 (0.26%) | 86 (0.37%) | 0.6 |
| Tracheostomy | 55 (0.71%) | 54 (0.68%) | 30 (0.39%) | 139 (0.59%) | 0.42 |
| Cardiac tamponade | <11 | 20 (0.25%) | 20 (0.26%) | 49 (0.21%) | 0.63 |
| Cardiopulmonary resuscitation | 49 (0.63%) | 52 (0.65%) | 30 (0.39%) | 131 (0.56%) | 0.53 |
n = frequency; % = percentage.
Cells with cell count <11 are not reported because of restrictions on cell size reporting by the National Inpatient Sample Database.
Figure 6.
Trends of inflation-adjusted cost (in United States dollars) in peripartum cardiomyopathy hospitalizations, age ≥15 and ≤54, in the United States, 2004 to 2018.
Figure 5.
Trends of mean length of stay in days in peripartum cardiomyopathy hospitalizations, age ≥15 and ≤54, in the United States, 2004 to 2018.
Discussion
In this nationwide contemporary analysis of hospitalized patients with PC between 2004 and 2018, we found that there was a nonsignificant trend toward increase in prevalence of PC hospitalizations. The prevalence of PC hospitalizations in the older maternal age group (36 to 54 years) was more than double the younger maternal age group (15 to 35 years), although PC hospitalizations increased significantly in younger women. The prevalence of PC hospitalization in Black patients was >3 times that in White patients. Maternal MAE did not change significantly during the study period. The adjusted inpatient mortality showed a nonsignificant decrease from 2004 to 2018. There was an increase in the number of co-morbidities and resource use; however, there was a decrease in the adjusted mean LOS.
The overall increase in hospitalizations for PC throughout the study period is consistent with the trends reported in earlier studies.1,6,13,14 Mielniczuk et al13 in the National Hospital Discharge Survey study from 1990 to 2002 reported PC incidence of 1 in 3,189 live births. Kolte et al6 in a previous NIS-based study from 2004 to 2011 reported a mean PC prevalence rate of 1 in 968 live births with an increasing trend during the study period. Our estimated prevalence of PC hospitalization is 1 per live 2,583 births. The lower prevalence as compared with other studies can be because of the inclusion of admissions with only the primary discharge diagnosis of PC to increase the specificity of the results whereas Kolte et al6 included the patients with PC in all the diagnosis fields. Notably, there was a modest but statistically significant increase in PC hospitalizations in younger women as compared with a stable trend of hospitalization in older maternal age. The increasing prevalence of PC hospitalization in younger women can be driven by the increasing prevalence of risk factors such as hypertension, preeclampsia/eclampsia, multiparity, obesity, and diabetes mellitus in pregnancy also evidenced by increasing comorbidities in our study results.1,6,15 Increasing maternal age is a risk factor associated with PC with more than half the cases of PC reported in age >30 years which was corroborated by our results as well.6,13,16
Incidence of PC is higher in women of African American ancestry/Black race than other races/ethnicity.17−19 Previous population-based studies have reported nearly 3 to 4 times higher prevalence of PC in Black women when compared with White women.16,18 One retrospective study reported that the Black patient population with PC were younger, had delayed diagnosis with comparatively lower LVEF at diagnosis, higher prevalence of risk factors, and worse outcomes as compared with non-Black patients.19 The increased prevalence and poor prognosis in the Black patient population with PC may suggest a different genetic and pathophysiologic mechanism,20 and increased prevalence of cardiovascular risk factors.17,19 Moreover, social determinants of health and structural racism are likely key drivers of the excess burden of cardiovascular risk experienced by Black women.21 The higher prevalence of PC in the patients living in the ZIP codes with the lowest quartile of household income may be indicative of healthcare disparities because of socioeconomic status and increased prevalence of risk factors such as hypertension, preeclampsia, and diabetes mellitus in this stratum of the population.22 Since the Black patient population and minorities tend to be in the lowest income quartile of the patient population, increased incidence of PC in this patient population is understandable. Interestingly the Southern states accounted for almost half of the PC hospitalization cases. This is consistent with the overall increased prevalence of cardiovascular diseases in general within these geographical regions.23
The maternal MAE was lower in our study as compared with earlier studies.6,24,25 The difference is because of the use of different definitions of MAE. In previous studies, the overall PC associated mortality has been reported to be 2.5% to 7% over 4 to 8 years follow-up period.16,25 Our study used NIS, which has inpatient data only, so understandably our reported mortality was low. Kolte et al6 reported a mortality rate of 1.3% from the NIS database 2004 to 2011 with a slight increase during the study period. Similarly, Mielniczuk et al13 reported a mortality rate ranging from 1.4% to 2.1% from the National Hospital Discharge Survey study period 1990 to 2002. The lower mortality in our study period could be because of a decrease in mortality potentially secondary to more widely available use of bridging MCS therapies as evidenced by our analysis. Cardiogenic shock in PC is associated with high mortality.26 Mechanical circulatory support devices improve survival in refractory heart failure and cardiogenic shock in patients with PC.27
The most prevalent co-morbidity significantly associated with PC was hypertension (36.2%) which is similar to the prevalence reported in a metanalysis based on 22 studies.28 Deficiency anemias (23.6%) was the second most prevalent co-morbidity related to PC which has also been reported in earlier studies.3,6 However, Cherubin et al29 in a metanalysis of 31 case-control studies showed the only marginal difference between hemoglobin in patients with PC and controls. Therefore, an association of anemia, iron profile, and PC needs to be evaluated in future studies. Development of AKI in PC is a known poor prognostic marker in patients with heart failure with reduced and preserved ejection fraction.30 Zhu et al31 found that AKI in PC was an independent risk factor in nonrecovery of LVEF in patients with PC leading to poor prognosis.
Our study used the administrative NIS database using the ICD coding system thus it is subject to inherent coding errors and misclassifications. However, the NIS database has been shown to be reasonably accurate in estimating primary diagnosis, outcome trends, and cost of the stay.3,6,32 It is unclear as to what extent the improvement in the coding in the recent year affected data analysis and its interpretation. We did not have granular details regarding the individual clinical profiles of the patient population and socioeconomic status because of the limitation of the database and unavailability of race/ethnicity data in all patients. Therefore, limited conclusions can be drawn regarding subgroup analysis including racial and gender subgroup outcomes. The NIS database provides no details regarding longitudinal follow-up after discharge, so long-term outcomes cannot be studied. Moreover, the NIS database does not account for readmissions so we were unable to solely identify unique admissions. Despite these limitations, NIS provides a large sample population and ensures the absence of reporting bias hence providing valuable and comprehensive insight into trends in in-hospital outcomes in the United States patient population.
The prevalence of PC hospitalizations has shown a nonsignificant increasing trend with a statistically significant increase in hospitalizations in younger women. The older maternal age group and Black patients have a higher prevalence of PC hospitalizations as compared with the younger maternal age group and White patients, respectively. The inpatient mortality has shown a nonsignificant decreasing trend. Understanding these PC trends by sociodemographic groups can hopefully inform screening and prevention strategies, and inform future research and policy work.
Supplementary Material
Footnotes
Disclosures
Outside of this work, Dr. Michos reports Advisory Boards with Novartis, Esperion, Amarin, and AstraZeneca. The remaining authors have no conflicts of interest to declare.
Supplementary materials
Supplementary material associated with this article can be found in the online version at https://doi.org/10.1016/j.amjcard.2021.12.034.
References
- 1.Davis MB, Arany Z, McNamara DM, Goland S, Elkayam U. Peripartum cardiomyopathy: JACC state-of-the-art review. J Am Coll Cardiol 2020;75:207–221. [DOI] [PubMed] [Google Scholar]
- 2.Sliwa K, Hilfiker-Kleiner D, Petrie MC, Mebazaa A, Pieske B, Buchmann E, Regitz-Zagrosek V, Schaufelberger M, Tavazzi L, van Veldhuisen DJ, Watkins H, Shah AJ, Seferovic PM, Elkayam U, Pankuweit S, Papp Z, Mouquet F, McMurray JJV. Heart Failure Association of the European Society of Cardiology Working Group on Peripartum Cardiomyopathy. Current state of knowledge on aetiology, diagnosis, management, and therapy of peripartum cardiomyopathy: a position statement from the Heart Failure Association of the European Society of Cardiology Working Group on peripartum cardiomyopathy. Eur J Heart Fail 2010;12:767–778. [DOI] [PubMed] [Google Scholar]
- 3.Afana M, Brinjikji W, Kao D, Jackson E, Maddox TM, Childers D, Eagle KA, Davis MB. Characteristics and in-hospital outcomes of peripartum cardiomyopathy diagnosed during delivery in the United States From the Nationwide Inpatient Sample (NIS) Database. J Card Fail 2016;22:512–519. [DOI] [PubMed] [Google Scholar]
- 4.Arany Z, cardiomyopathy Elkayam UPeripartum. Circulation 2016;133:1397–1409. [DOI] [PubMed] [Google Scholar]
- 5.Agency for Healthcare Research and Quality, HCUP. NIS. overview Available at: https://www.hcup-us.ahrq.gov/nisoverview.jsp. Accessed on February 6, 2021.
- 6.Kolte D, Khera S, Aronow WS, Palaniswamy C, Mujib M, Ahn C, Jain D, Gass A, Ahmed A, Panza JA, Fonarow GC. Temporal trends in incidence and outcomes of peripartum cardiomyopathy in the United States: a nationwide population-based study. J Am Heart Assoc 2014;3:e001056. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Agency for Healthcare Research and Quality, HCUP. Trend weights for HCUP NIS data. Available at: https://www.hcup-us.ahrq.gov/db/nation/nis/trendwghts.jsp. Accessed on June 2, 2021.
- 8.U.S. Bureau of Labor Statistics. CPI inflation calculator. Available at: https://www.bls.gov/data/inflation_calculator.htm. Accessed on June 2, 2021.
- 9.Centers for Disease Control and Prevention. About natality, 2007–2020. Available at: https://wonder.cdc.gov/natality-current.html. Accessed on August 3, 2021.
- 10.Centers for Disease Control and Prevention. Underlying cause of death1999–2019. Available at: https://wonder.cdc.gov/wonder/help/ucd.html. Accessed on June 2, 2021.
- 11.Boston College Department of Economics. Stata module to calculate Charlson index of comorbidity. Available at: https://ideas.repec.org/c/boc/bocode/s456719.html Accessed on August 22, 2021.
- 12.STATA. Stata: software for statistics and data science. Available at: https://www.stata.com/. Accessed on June 2, 2021.
- 13.Mielniczuk LM, Williams K, Davis DR, Tang ASL, Lemery R, Green MS, Gollob MH, Haddad H, Birnie DH. Frequency of peripartum cardiomyopathy. Am J Cardiol 2006;97:1765–1768. [DOI] [PubMed] [Google Scholar]
- 14.Honigberg MC, Givertz MM. Peripartum cardiomyopathy. BMJ 2019;364:k5287. [DOI] [PubMed] [Google Scholar]
- 15.Minhas AS, Ogunwole SM, Vaught AJ, Wu P, Mamas MA, Gulati M, Zhao D, Hays AG, Michos ED. Racial disparities in cardiovascular complications with pregnancy-induced hypertension in the United States. Hypertension 2021;78:480–488. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Brar SS, Khan SS, Sandhu GK, Jorgensen MB, Parikh N, Hsu JW, Shen AY-J. Incidence, mortality, and racial differences in peripartum cardiomyopathy. Am J Cardiol 2007;100:302–304. [DOI] [PubMed] [Google Scholar]
- 17.Goland S, Modi K, Hatamizadeh P, Elkayam U. Differences in clinical profile of African-American women With peripartum cardiomyopathy in the United States. J Card Fail 2013;19:214–218. [DOI] [PubMed] [Google Scholar]
- 18.Harper MA, Meyer RE, Berg CJ. Peripartum cardiomyopathy: population-based birth prevalence and 7-year mortality. Obstet Gynecol 2012;120:1013–1019. [DOI] [PubMed] [Google Scholar]
- 19.Irizarry OC, Levine LD, Lewey J, Boyer T, Riis V, Elovitz MA, Arany Z. Comparison of clinical characteristics and outcomes of peripartum cardiomyopathy Between African American and non−African American women. JAMA Cardiol 2017;2:1256–1260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ware JS, Li J, Mazaika E, Yasso CM, DeSouza T, Cappola TP, Tsai EJ, Hilfiker-Kleiner D, Kamiya CA, Mazzarotto F, Cook SA, Halder I, Prasad SK, Pisarcik J, Hanley-Yanez K, Alharethi R, Damp J, Hsich E, Elkayam U, Sheppard R, Kealey A, Alexis J, Ramani G, Safirstein J, Boehmer J, Pauly DF, Wittstein IS, Thohan V, Zucker MJ, Liu P, Gorcsan J, McNamara DM, Seidman CE, Seidman JG, Arany Z, IMAC-2 and IPAC Investigators. Shared genetic predisposition in peripartum and dilated cardiomyopathies. N Engl J Med 2016;374:233–241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Churchwell K, Elkind MSV, Benjamin RM, Carson AP, Chang EK, Lawrence W, Mills A, Odom TM, Rodriguez CJ, Rodriguez F, Sanchez E, Sharrief AZ, Sims M, Williams O, American Heart Association. Call to Action: structural Racism as a fundamental Driver of health Disparities: A Presidential Advisory From the American Heart Association. Circulation 2020;142:e454–e468. [DOI] [PubMed] [Google Scholar]
- 22.Khariton Y, Nassif ME, Thomas L, Fonarow GC, Mi X, DeVore AD, Duffy C, Sharma PP, Albert NM, Patterson JH, Butler J, Hernandez AF, Williams FB, McCague K, Spertus JA. Health status disparities by sex, race/ethnicity, and socioeconomic status in outpatients with heart failure. JACC Heart Fail 2018;6:465–473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Global Burden of Cardiovascular Diseases Collaboration, Roth GA, Johnson CO, Abate KH, Abd-Allah F, Ahmed M, Alam K, Alam T, Alvis-Guzman N, Ansari H, Ärnlöv J, Atey TM, Awasthi A, Awoke T, Barac A, Bärnighausen T, Bedi N, Bennett D, Bensenor I, Biadgilign S, Castañeda-Orjuela C, Catalá-López F, Davletov K, Dharmaratne S, Ding EL, Dubey M, Faraon EJA, Farid T, Farvid MS, Feigin V, Fernandes J, Frostad J, Gebru A, Geleijnse JM, Gona PN, Griswold M, Hailu GB, Hankey GJ, Hassen HY, Havmoeller R, Hay S, Heckbert SR, Irvine CMS, James SL, Jara D, Kasaeian A, Khan AR, Khera S, Khoja AT, Khubchandani J, Kim D, Kolte D, Lal D, Larsson A, Linn S, Lotufo PA, Magdy El Razek H, Mazidi M, Meier T, Mendoza W, Mensah GA, Meretoja A, Mezgebe HB, Mirrakhimov E, Mohammed S, Moran AE, Nguyen G, Nguyen M, Ong KL, Owolabi M, Pletcher M, Pourmalek F, Purcell CA, Qorbani M, Rahman M, Rai RK, Ram U, Reitsma MB, Renzaho AMN, Rios-Blancas MJ, Safiri S, Salomon JA, Sartorius B, Sepanlou SG, Shaikh MA, Silva D, Stranges S, Tabarés-Seisdedos R, Tadele Atnafu N, Thakur JS, Topor-Madry R, Truelsen T, Tuzcu EM, Tyrovolas S, Ukwaja KN, Vasankari T, Vlassov V, Vollset SE, Wakayo T, Weintraub R, Wolfe C, Workicho A, Xu G, Yadgir S, Yano Y, Yip P, Yonemoto N, Younis M, Yu C, Zaidi Z, Zaki MES, Zipkin B, Afshin A, Gakidou E, Lim SS, Mokdad AH, Naghavi M, Vos T, Murray CJL. The burden of cardiovascular diseases Among US states, 1990−2016. JAMA Cardiol 2018;3:375–389. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Kao DP, Hsich E, Lindenfeld J. Characteristics, adverse events, and racial differences among delivering mothers with peripartum cardiomyopathy. JACC Heart Fail 2013;1:409–416. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Goland S, Modi K, Bitar F, Janmohamed M, Mirocha JM, Czer LSC, Illum S, Hatamizadeh P, Elkayam U. Clinical profile and predictors of complications in peripartum cardiomyopathy. J Card Fail 2009;15:645–650. [DOI] [PubMed] [Google Scholar]
- 26.Banayan J, Rana S, Mueller A, Tung A, Ramadan H, Arany Z, Nizamuddin J, Novack V, Scavone B, Brown SM, Shahul S. Cardiogenic shock in pregnancy: analysis from the National Inpatient Sample. Hypertens Pregnancy 2017;36:117–123. [DOI] [PubMed] [Google Scholar]
- 27.Loyaga-Rendon RY, Pamboukian SV, Tallaj JA, Acharya D, Cantor R, Starling RC, Naftel D, Kirklin J. Outcomes of patients with peripartum cardiomyopathy who received mechanical circulatory support. Data from the interagency Registry for mechanically Assisted Circulatory Support. Circ Heart Fail 2014;7:300–309. [DOI] [PubMed] [Google Scholar]
- 28.Bello N, Rendon ISH, Arany Z. The relationship Between pre-eclampsia and peripartum cardiomyopathy: A systematic review and meta-analysis. J Am Coll Cardiol 2013;62:1715–1723. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Cherubin S, Peoples T, Gillard J, Lakhal-Littleton S, Kurinczuk JJ, Nair M. Systematic review and meta-analysis of prolactin and iron deficiency in peripartum cardiomyopathy. Open Heart 2020;7: e001430. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Lala RI, Lungeanu D, Puschita M, Pop-Moldovan A, Darabantiu D. Acute kidney injury: a clinical issue in hospitalized patients with heart failure with mid-range ejection fraction. Pol Arch Intern Med 2018;128:746–754. [DOI] [PubMed] [Google Scholar]
- 31.Zhu J, Liu W. Acute kidney injury predicts poor left ventricular function for patients with peripartum cardiomyopathy. BMC Cardiovasc Disord 2021;21:205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Khan SU, Khan MZ, Alkhouli M. Trends of clinical outcomes and health care resource use in heart failure in the United States. J Am Heart Assoc 2020;9:e016782. [DOI] [PMC free article] [PubMed] [Google Scholar]
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