Racial disparities in pregnancy-related acute kidney injury...factor for the future development of...

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Racial and Ethnic Disparities in Pregnancy-Related Acute Kidney Injury

Kelly Beers1,4*; Huei Hsun Wen2*; Aparna Saha2; Kinsuk Chauhan1; Mihir Dave3; Steven Coca1;

Girish Nadkarni1,2; Lili Chan1

1Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New

York, NY

2The Charles Bronfman Institute for Personalized Medicine, Department of Genetics and

Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY

3Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York,

NY

4Divsion of Nephrology and Hypertension, Albany Medical Center, Albany, NY

*KB and HHW contributed equally.

Correspondence:

Lili Chan, MD, MS

Icahn School of Medicine at Mount Sinai,

One Gustave L Levy Place, Box 1243,

New York, NY 10029

Email Address: [email protected]

Kidney360 Publish Ahead of Print, published on February 12, 2020 as doi:10.34067/KID.0000102019

Copyright 2020 by American Society of Nephrology.

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Abstract:

Introduction: Pregnancy-related acute kidney injury (PR-AKI) is increasing in the United

States. PR-AKI is associated with adverse maternal outcomes. Disparities in racial/ethnic

differences in PR-AKI by race have not been studied.

Methods: This was a retrospective cohort study utilizing the National Inpatient Sample (NIS)

from 2005-2015. We identified patients who were admitted for a pregnancy-related diagnosis

using the Neomat variable provided by the NIS database that indicates the presence of a

maternal or neonatal diagnosis code or procedure code. PR-AKI was identified utilizing ICD

codes. Survey logistic regression was used for multivariable analysis adjusting for age, medical

comorbidities, socioeconomic factors, and hospital/admission factors.

Results: From 48,316,430 maternal hospitalizations, 34,001 (0.07%) were complicated by PR-

AKI. Hospitalizations for PR-AKI increased from 3.5/10,000 hospitalizations in 2005 to

11.8/10,000 hospitalizations in 2015 with the largest increase seen in patients aged ≥ 35 and

black patients. PR-AKI was associated with higher odds of miscarriage (adjusted odds ratio

(aOR) 1.64, 95% CI 1.34-2.07) and mortality (aOR 1.53, 95% CI 1.25-1.88). After adjustment for

age, medical comorbidities, and socioeconomic factors, blacks were more likely than whites to

develop PR-AKI (aOR 1.17, 95% CI 1.04-1.33). On subgroup analyses in hospitalizations of

patients with PR-AKI, blacks and Hispanics were more likely to have preeclampsia/eclampsia

compared to whites (aOR 1.29, 95% CI 1.01 – 1.65 and aOR 1.69, 95% CI 1.23-2.31

respectively). Increased odds of mortality in PR-AKI compared to whites were only seen in black

patients (aOR 1.61, 95% CI 1.02-2.55).

Conclusion: The incidence of PR-AKI has increased and the largest increase was seen in older

patients and black patients. PR-AKI is associated with miscarriages, adverse discharge from

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hospital, and mortality. Black and Hispanic patients with PR-AKI were more likely to have

adverse outcomes than white patients. Further research is needed to identify factors

contributing to these discrepancies.

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Introduction

In the United States, there are approximately 6.5 million pregnancies resulting in

approximately 4 million live births every year.1 According to the Centers for Disease Control

(CDC), pregnancy-related morbidity, including severe maternal morbidity (defined by 21 severe

morbidity indicators and ICD-10 codes), and mortality has been on the rise.2,3 About 700 women

per year die of pregnancy-related complications, a rate of 26.4 per 100,000 births.4 The

significant increase in maternal morbidity and mortality in the US has caught the attention of the

lay media, with many recent exposes and commentaries published, including from National

Public Radio, the New York Times, and USA Today.5–7 This attention has led to a renewed

focus on maternal health in the United States.

Over the past 20 years, pregnancy-related acute kidney injury, or PR-AKI, increased

three-fold. While some of the increased incidence may be due to ascertainment bias, increased

recognition, and maternal risk factors, recent data suggests that adjustment for patient

comorbidities, maternal age, and method of delivery only partially explains this increased

incidence.8 PR-AKI is rare and still does not have a consensus definition, partially due to the fact

that the physiologic changes of pregnancy alter renal perfusion and glomerular filtration and

therefore small amounts of proteinuria are normal and “normal” serum creatinine levels are

much lower than in a non-pregnant state. However, PR-AKI is associated with a significant risk

of cesarean delivery, hemorrhage, HELLP syndrome, and maternal death in otherwise healthy

young women.9 Maternal hemorrhage is an important cause of PR-AKI due to decreased renal

perfusion, and the HELLP syndrome may precipitate PR-AKI as well. Additionally, there is a

higher incidence of stillbirth, perinatal death, lower mean gestational age at delivery, and lower

birth weight in babies born to women with PR-AKI.9 Most women with PR-AKI recover and very

few require renal replacement therapy (RRT). However, AKI is suspected to be an important risk

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factor for the future development of chronic kidney disease (CKD) and end stage renal disease

(ESRD) which places these young women at risk for significant long-term morbidity.10

Prior studies have found that non-Hispanic black women in the United States are at

three to four times higher risk of pregnancy-related mortality than white women, for reasons that

are poorly understood and undoubtedly complex.4 While racial/ethnic disparities in kidney

disease have been identified, to date, racial differences in PR-AKI by race have not been

studied.11–13 We hypothesized that black and Hispanic women will have a higher incidence of

PR-AKI when adjusted for common risk factors such as age, medical comorbidities, and

socioeconomic factors.

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Materials and Methods:

Data Source

We extracted our study cohort from the National Inpatient Sample (NIS) database

provided by the Agency for Healthcare Research and Quality.14 The NIS is the largest publicly

available all-payer inpatient healthcare database in the United States. The NIS contains all-

payer discharge data from inpatient hospitalizations from 20% of all hospitals in 44

participating states. The NIS utilizes data from roughly 1000 hospitals each year to create a

sample representing > 95% of the US population. Each individual hospitalization in this

database is de-identified and maintained as a unique entry with one primary discharge

diagnosis, < 24 secondary diagnoses along with < 15 procedural codes during that

hospitalization. Weights provided by the NIS were used to generate national estimates. Since

we used publically available, de-identified data the study was considered to be institutional

review board exempt at the Icahn School of Medicine at Mount Sinai.

Study Population and Design

Pregnancy-related hospitalizations from the year 2005 to 2015 were identified using

the data element ‘‘Neomat.’’ This indicator was created in the NIS database to identify maternal

and/or neonatal diagnosis records on the basis of International Classification of Diseases, Ninth

and 10 Revision, Clinical Modifications (ICD-9/10-CM) diagnosis and procedure codes for

pregnancy and delivery.15 This method of identifying pregnancy-related hospitalization has been

used previously in other pregnancy-related articles.16,17 We excluded hospitalizations with age

<12 years and >50 years (Supplemental Figure 1). Diagnosis of AKI among these

hospitalizations was identified using ICD-9 and 10 CM diagnosis codes in any diagnosis

fields. A list of ICD codes used for cohort and outcome identification is included in

https://www.nature.com/articles/s41372-018-0129-4#Fig1

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Supplemental Table 1. Ultimately, our population under consideration was divided into 2

groups for comparison: pregnancy hospitalizations with AKI and pregnancy hospitalizations

without AKI.

Definition of Variables

We examined baseline characteristics of the study population for the potential of

confounding. Patient-level characteristics included age, race/ethnicity, median household

income according to ZIP Code by quartile, primary payer (Medicare/Medicaid, private

insurance, self-pay, or no charge), admission type; and hospital-level characteristics such as

hospital bed size (small, medium, and large), region (Northeast, Midwest, or North Central,

South, and West), and teaching status were identified. Race/ethnicity was grouped into

whites, blacks, Hispanics, and other/missing. Length of stay was calculated only for

survivors. We extracted information regarding various co-morbidities as listed in Table 1,

using the Elixhauser comorbidity index developed by the AHRQ which groups different

comorbidities using ICD-9/10-CM diagnoses codes.18 These comorbidities are not directly

related to the principal diagnosis or the main reason for admission and are likely to have

originated before the hospital stay. We determined the mortality risk with the use of the

validated All-Patient Refined Diagnosis Related Group (APR-DRG) mortality score.19,20

Definition of Outcomes

The outcomes were in-hospital mortality, adverse discharge, and pregnancy-related

complications of miscarriage, preterm labor, and preeclampsia/eclampsia. ICD-9/10 CM

diagnosis codes were used to identify pregnancy related complications. Adverse discharge

was defined as discharge to skilled nursing facility, intermediate care center, medical facility

or long-term care hospital.21–23

Statistical Analysis

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NIS represents a 20% stratified random sample of US hospitals. So, analyses were

performed using hospital-level discharge weights provided by the NIS, to obtain national

estimates. We compared the baseline characteristics of pregnancy-related hospitalizations

with and without AKI. To estimate differences, we used the chi-square test for categorical

variables, Student’s t-test for normally distributed continuous variables, and Wilcoxon rank-

sum test for non-normally distributed continuous variables. We calculated trends of

pregnancy hospitalizations with AKI from 2005-2015. P-value of <0.05 was considered

significant for all analyses. For trend analysis, the chi-square test of trend for proportions was

used with the Cochrane Armitage test via the ‘‘trend’’ command in SAS®. Survey logistic

regression was used to estimate the impact of AKI on outcomes of mortality, adverse

discharge, and pregnancy-related complications of miscarriage, preterm labor, and

preeclampsia/eclampsia. Adjusted odds ratios (aOR) for the above-mentioned outcomes

were calculated after adjusting with age, APDRG risk score, diabetes mellitus (DM) (both type

1 and type 2), hypertension (HTN), anemia, chronic pulmonary disease, congestive heart failure

(CHF), hypothyroidism, electrolyte imbalance, chronic liver disease, obesity, chronic kidney

disease (CKD), acquired immune deficiency syndrome (AIDS), metastatic cancer , rheumatoid

arthritis, psychoses, alcohol abuse, drug abuse, median household income, primary payer

(Medicare, Medicaid, private insurance, self-pay, or no charge), admission type, and hospital-

level characteristics of hospital bed size (small, medium, and large), region (Northeast,

Midwest, or North Central, South, and West), and teaching status. The variables we adjusted

for were based off of prior literature and to account for systemic factors and social

determinants that may potentially impact outcome.24–28 Given the complex interplay between

preeclampsia/eclampsia, PR-AKI, and race/ethnicity we performed sensitivity analysis

adjusting for preeclampsia/eclampsia for the outcomes of miscarriage, preterm labor,

adverse discharge, and maternal mortality. We performed subgroup analysis by

race/ethnicity to determine differences in adverse outcomes by race/ethnicity. Statistical

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analysis system (SAS®) 9.4 (SAS Institute Inc., Cary, North Carolina) was utilized for all

analyses.

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Results

Of 48,316,430 maternal hospitalizations, 34,001 (0.07%) had an ICD code associated

with AKI. The baseline characteristics of patients are seen in Table 1. Patients with

hospitalizations complicated by PR-AKI were more likely to be black (29% vs. 13%), older

(mean age 29±8 years vs 28±6 years), and had significantly more comorbidities than patients

without AKI. Comorbidities included CKD (12% vs 0.04%), DM (8.0% vs 1.1%), HTN (24.7%vs

2.3%), anemia (24.3% vs 7.6%), electrolyte imbalances (48.6% vs 1.2%), and obesity (10.9% vs

4.2%) (Table 1). Hospitalizations complicated by PR-AKI had a higher proportion of patients in

the lowest quartile of income than hospitalizations without PR-AKI (37.2% vs. 27.6%) and more

patients that had Medicare/Medicaid insurance (55% vs. 44%). 79.3% of admissions were

emergency or urgent in patients with PR-AKI compared to 52.8% in hospitalized without PR-

AKI. Patients with hospitalizations with PR-AKI were more likely to be in a large hospital (69.7%

vs. 61.0%) and more likely to be in an urban teaching hospital (70.4% vs. 49.9%). Patients with

hospitalizations with PR-AKI had significantly higher rates of adverse discharge (18.1% vs. 2.6

%). The length of stay for patients with PR-AKI was longer, mean length of stay (LOS) of

9.5±11.9 days compared to 2.7±2.6days. Mortality was significantly higher in patients with

hospitalizations with PR-AKI, 3.9% compared to 0.01% of patients without.

Hospitalizations for PR-AKI increased by more than 3 times from 3.5/10,000

hospitalizations in 2005 to 11.8/10,000 hospitalizations in 2015 (Figure 1A). Patients aged ≥ 35

and black patients had the largest increase and the highest incidence of pregnancy

hospitalizations complicated by PR-AKI (Figure 1B and 1C). Black patients had the highest

proportion of PR-AKI and the largest increase across all age groups (Figure 2).

Even after adjustment for patient and hospital factors, blacks were more likely than

whites to develop PR-AKI (adjusted OR (aOR) 1.17, 95% CI 1.04-1.33). The additional

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adjustment of preeclampsia/eclampsia decreased the odds ratio (aOR 1.16, 95% CI 1.0-1.3) but

there remained a significant association. Hospitalizations of patients with CKD (aOR 9.97, 95%

CI 7.99-12.44), electrolyte imbalances (aOR 3.2, 95% CI 2.87-3.56), and HTN (aOR 1.77, 95%

CI 1.55-2.02) had the highest aOR for PR-AKI (Table 2). While other studies found an

association between AKI and hemorrhage we did not find an association in our adjusted model,

aOR 1.0, 95% CI 0.9-1.1.

After adjustment for socioeconomic factors, age, comorbidities, and hospital

characteristics as detailed in the methods section, PR-AKI remained significantly associated

with higher odds of miscarriage (aOR 1.64, 95% CI 1.3-2.07) and mortality (aOR 1.53, 95% CI

1.25-1.88) but was no longer significant for preterm labor, preeclampsia/eclampsia, or adverse

discharge (Table 3). Additional adjustment for preeclampsia/eclampsia for the other maternal

outcomes did not substantially change the odd ratios (Supplemental Table 2).

On subgroup analyses of outcomes by racial/ethnic groups between hospitalizations with

and without PR-AKI, in whites after adjustment, PR-AKI was associated with increased odds of

mortality (aOR 1.62, 95% CI 1.09-2.41) and miscarriage (aOR 1.61, 95% CI 1.06-2.44). For

blacks after adjustment, PRI-AKI was associated with miscarriage (aOR 2.3, 95% CI 1.54-3.5).

In Hispanic women after adjustment, PR-AKI was only associated with mortality (aOR 1.94, CI

1.01 - 3.5). Finally, in women of other or not reported race, after adjustment, PR-AKI was no

longer associated with any outcome (Table 4).

On subgroup analyses in hospitalizations of only patients with PR-AKI, using white

patients as a reference, there were no between-group differences in odds of miscarriage with

PR-AKI. There was a higher risk of preeclampsia/eclampsia in blacks and Hispanics when

compared to whites, (aOR 1.29, 95% CI 1.01 – 1.65 for Blacks and aOR 1.69, 95% CI 1.23-2.31

for Hispanics (Figure 3). Those with other or not reported race (aOR 1.56, 95% CI 1.06-2.3) had

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increased odds of pre-term labor in the setting of PR-AKI. Increased odds of mortality in PR-AKI

with white patients as the reference was only seen in black patients (aOR 1.61, 95% CI 1.02-

2.55) when adjusting for all factors (Figure 3).

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Discussion

In a large nationally representative database, we have found that PR-AKI is increasing

and the largest increase was seen in older patients and black patients. We also show that

several patient characteristics were significantly different between PR-AKI and non-PR-AKI

hospitalizations including socioeconomic factors and medical comorbidities. PR-AKI was

associated with several adverse maternal outcomes and this persisted in several race/ethnic

groups. Finally, even after adjustment for age, medical comorbidities, and socioeconomic and

hospital factors, in patients with PR-AKI there remained higher odds of adverse events in

minority patients compared to white patients.

Contrary to the decreasing trend of PR-AKI in developing countries, we and others have

found an increase in the incidence of PR-AKI.8,29,30 In particular, older patients and black

patients had the highest incidence and largest increase in incidence. While part of this increase

may be due to increased coding and recognition, this would not be expected to affect

racial/ethnic groups differently. This is supported by the increases in PR-AKI and maternal

mortality found in this study and others.8 How the increase in PR-AKI incidence contributes to

the increasing maternal mortality rates in the US needs to be further explored.

The increase in adverse events in the AKI group is likely related to a higher prevalence

of comorbidities including CKD, HTN, and DM. This is most evident for the outcome of mortality

given the marked reduction in OR between the unadjusted and adjusted models. It has been

previously demonstrated that CKD is associated with adverse maternal and fetal outcomes and

this risk increases with the stage of CKD.25,31 Additionally, chronic HTN not only increases the

risk of PR-AKI but is associated with increased maternal and perinatal outcomes. Lastly,

patients with any form of DM have an increased risk for both fetal and maternal outcomes

including AKI, HTN, and mortality.24 Of great concern is the increasing proportion of patients

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with preexisting DM during pregnancy in the US.32 Unfortunately, NIS does not have vital signs

or laboratory information and we are unable to determine differences in CKD stage and HTN

and DM control between racial/ethnic groups. However, in patients with PR-AKI even after

adjustment for comorbidities, social demographic factors, and hospital characteristics, there

remained a higher risk of pre-eclampsia/eclampsia in blacks and hispanics compared to white

patients. It has been previously documented that pre-eclampsia is more common in non-white

patients without PR-AKI and we demonstrate that this also holds true in PR-AKI patients.22

There is a complex interplay between race/ethnicity, AKI, and preeclampsia/eclampsia and this

needs to be studied further.

Despite the overall low rates of PR-AKI, hospitalizations complicated by PR-AKI had

over a 50% increase in the odds of maternal mortality in women who are historically considered

to be healthy. The increase in mortality held true for whites and Hispanics but was most

pronounced in Hispanic patients. Hospitalizations with PR-AKI also had a higher risk of

miscarriage; however, after adjustment, this was only significant in whites and blacks.

Prenatal care prior to delivery is an important predictor of maternal and fetal adverse

outcomes. According to the CDC, black and Hispanic women, compared to white women, have

approximately double the proportion of women who receive late or no prenatal cares.33

Unfortunately, as NIS is an inpatient database, outpatient prenatal care information is not

available.

Our study should be interpreted in light of the following limitations. The NIS is an

administrative database, therefore information such as medications and laboratory values are

unavailable and we cannot determine the degree and duration of AKI. Unfortunately, we do not

have any data regarding prior pregnancies which may potentially impact outcomes of future

pregnancies.24 Only limited social determinants of health are captured in NIS, therefore we are

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unable to determine if additional social determinants of health contribute to the discrepancies in

PR-AKI outcomes we have identified. We are unable to capture non-medically related reasons

(e.g. preexisting homelessness) for adverse discharges which may be confounding our results.

Despite the limitations, this is the first paper to look at racial/ethnic disparities in pregnancy

outcomes in patients who have PR-AKI utilizing a nationally representative database.

In conclusion, while overall rates of PR-AKI are low they have increased over the past

decade. While PR-AKI has increased in all races/ethnicities, it is most pronounced in black

patients. PR-AKI is associated with miscarriages, adverse discharge, and mortality. Even after

adjustment for patient age, medical comorbid conditions, socioeconomic and hospital factors,

black and Hispanic patients with PR-AKI were more likely to have adverse maternal and fetal

outcomes than white patients with PR-AKI. Further research is needed to identify patient and

system-level features contributing to these discrepancies.

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Author contributions:

Kelly Beers: Conceptualization; Writing - original draft; Writing - review and editing

Huei Hsun Wen: Formal analysis; Methodology

Aparna Saha: Formal analysis; Methodology

Kinsuk Chauhan: Data curation; Formal analysis

Mihir Dave: Formal analysis

Steven Coca: Conceptualization; Writing - review and editing

Girish Nadkarni: Conceptualization; Writing - review and editing

Lili Chan: Conceptualization; Formal analysis; Supervision

Disclosures:

S Coca reports grants, personal fees, and other from RenalytixAI, personal fees from CHF

Solutions, personal fees from Takeda, personal fees from Janssen, personal fees from Relypsa,

personal fees from Goldfinch, and personal fees and other from pulseData outside the

submitted work. G Nadkarni reports grants, personal fees, and non-financial support from

Renalytix AI, non-financial support from Pensieve Health , personal fees from AstraZeneca,

grants from Goldfinch Bio, personal fees from Reata Pharma, personal fees from BioVie, and

personal fees from GLG consulting outside the submitted work. The remaining authors have

nothing to disclose.

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References

1. Ventura SJ, Curtin SC, Abma JC: Estimated Pregnancy Rates and Rates of Pregnancy

Outcomes for the United States, 1990-2008 [Internet]. Available from:

http://www.cdc.gov/nchs/nvss/bridged_race.htm. [cited 2019 Sep 26]

2. Severe Maternal Morbidity in the United States | Pregnancy | Reproductive Health |CDC

[Internet]. Available from:

https://www.cdc.gov/reproductivehealth/maternalinfanthealth/severematernalmorbidity.ht

ml [cited 2019 Sep 26]

3. How Does CDC Identify Severe Maternal Morbidity? | CDC [Internet]. Available from:

https://www.cdc.gov/reproductivehealth/maternalinfanthealth/smm/severe-morbidity-

ICD.htm [cited 2019 Dec 4]

4. Pregnancy-Related Deaths | CDC [Internet]. Available from:

https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pregnancy-

relatedmortality.htm [cited 2019 Sep 26]

5. Why Racial Gaps in Maternal Mortality Persist [Internet]. Available from:

https://www.npr.org/sections/health-shots/2019/05/10/722143121/why-racial-gaps-in-

maternal-mortality-persist [cited 2019 Sep 26]

6. Mortality among Infants of Black as Compared with White College-Educated Parents

[Internet]. Available from: https://www.nytimes.com/2018/04/11/magazine/black-mothers-

babies-death-maternal-mortality.html

7. Maternal death is rising. This data could help moms but it’s secret. [Internet]. Available

from: https://www.usatoday.com/in-depth/news/investigations/deadly-

deliveries/2019/03/07/maternal-death-rates-secret-hospital-safety-records-childbirth-

deaths/2953224002/ [cited 2019 Sep 26]

8. Mehrabadi A, Dahhou M, Joseph KS, Kramer MS: Investigation of a Rise in Obstetric

18

Acute Renal Failure in the United States, 1999–2011. Obstet. Gynecol. [Internet] 127:

899–906, 2016 Available from: http://www.ncbi.nlm.nih.gov/pubmed/27054929 [cited

2019 Sep 26]

9. Liu Y, Ma X, Zheng J, Liu X, Yan T: Pregnancy outcomes in patients with acute kidney

injury during pregnancy: a systematic review and meta-analysis. BMC Pregnancy

Childbirth [Internet] 17: 235, 2017 Available from:

http://www.ncbi.nlm.nih.gov/pubmed/28720086 [cited 2019 Sep 26]

10. Rifkin DE, Coca SG, Kalantar-Zadeh K: Does AKI truly lead to CKD? J. Am. Soc.

Nephrol. [Internet] 23: 979–84, 2012 Available from:


11. Wen Y, Jiang C, Koncicki H, Horowitz C, Cooper R, Coca S, Nadkarni G, Chan L: Trends

and Racial Disparities of Palliative Care Use among Hospitalized Patients with End Stage

Kidney Disease on Dialysis. J. Am. Soc. Nephrol.

12. Gill J, Dong J, Rose C, Johnston O, Landsberg D, Gill J: The effect of race and income

on living kidney donation in the United States. J. Am. Soc. Nephrol. [Internet] 24: 1872–9,

2013 Available from: http://www.ncbi.nlm.nih.gov/pubmed/23990679 [cited 2018 Mar 12]

13. Grams ME, Matsushita K, Sang Y, Estrella MM, Foster MC, Tin A, Kao WHL, Coresh J:

Explaining the racial difference in AKI incidence. J. Am. Soc. Nephrol. [Internet] 25:

1834–41, 2014 Available from: http://www.jasn.org/cgi/doi/10.1681/ASN.2013080867

[cited 2016 Dec 5]

14. NIS Database Documentation [Internet]. Available from: https://www.hcup-

us.ahrq.gov/db/nation/nis/nisdbdocumentation.jsp [cited 2019 Sep 26]

15. Merrill C, Owens PL: Reasons for Being Admitted to the Hospital through the Emergency

Department for Children and Adolescents, 2004: Statistical Brief #33 [Internet]. Agency

for Healthcare Research and Quality (US); Available from:


19

16. Whiteman VE, Salemi JL, Mogos MF, Cain MA, Aliyu MH, Salihu HM: Maternal Opioid

Drug Use during Pregnancy and Its Impact on Perinatal Morbidity, Mortality, and the

Costs of Medical Care in the United States. J. Pregnancy [Internet] 2014: 2014 Available

from: https://www.hindawi.com/journals/jp/2014/906723/ [cited 2019 Sep 26]

17. Dennis EM, Hao Y, Tamambang M, Roshan TN, Gatlin KJ, Bghigh H, Ogunyemi OT,

Diallo F, Spooner KK, Salemi JL, Olaleye OA, Khan KZ, Aliyu MH, Salihu HM:

Tuberculosis during pregnancy in the United States: Racial/ethnic disparities in

pregnancy complications and in-hospital death. PLoS One [Internet] 13: e0194836, 2018

Available from: http://www.ncbi.nlm.nih.gov/pubmed/29579086 [cited 2019 Sep 26]

18. AHRQ: Elixhauser Comorbidity Software, Version 3.7 [Internet]. Available from:

https://www.hcup-us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp

19. Baram D, Daroowalla F, Garcia R, Zhang G, Chen JJ, Healy E, Riaz SA, Richman P: Use

of the All Patient Refined-Diagnosis Related Group (APR-DRG) Risk of Mortality Score

as a Severity Adjustor in the Medical ICU. Clin. Med. Circ. Respirat. Pulm. Med. [Internet]

2: 19–25, 2008 Available from:

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2990229&tool=pmcentrez&ren

dertype=abstract

20. Roberts M, Mapel D, Von Worley A, Beene J: Clinical factors, including All Patient

Refined Diagnosis Related Group severity, as predictors of early rehospitalization after

COPD exacerbation. Drugs Context [Internet] 4: 1–15, 2015 Available from:


21. Passias PG, Poorman GW, Bortz CA, Qureshi R, Diebo BG, Paul JC, Horn SR, Segreto

FA, Pyne A, Jalai CM, Lafage V, Bess S, Schwab FJ, Hassanzadeh H: Predictors of

adverse discharge disposition in adult spinal deformity and associated costs. Spine J. 18:

1845–1852, 2018

22. Murphy ME, Maloney PR, McCutcheon BA, Rinaldo L, Shepherd D, Kerezoudis P, Gilder

20

H, Ubl DS, Crowson CS, Freedman BA, Habermann EB, Mohamad Bydon: Predictors of

discharge to a nonhome facility in patients undergoing lumbar decompression without

fusion for degenerative spine disease. Clin. Neurosurg. 81: 638–649, 2017

23. Ehsani H, Mohler MJ, Golden T, Toosizadeh N: Upper-extremity function prospectively

predicts adverse discharge and all-cause COPD readmissions: A pilot study. Int. J.

COPD 14: 39–49, 2019

24. Negrato CA, Mattar R, Gomes MB: Adverse pregnancy outcomes in women with

diabetes. Diabetol. Metab. Syndr. 4: 2012

25. Nevis IF, Reitsma A, Dominic A, McDonald S, Thabane L, Akl EA, Hladunewich M, Akbari

A, Joseph G, Sia W, Iansavichus A V., Garg AX: Pregnancy outcomes in women with

chronic kidney disease: A systematic review. Clin. J. Am. Soc. Nephrol. 6: 2587–2598,

2011

26. Conti-Ramsden FI, Nathan HL, De Greeff A, Hall DR, Seed PT, Chappell LC, Shennan

AH, Bramham K: Pregnancy-Related Acute Kidney Injury in Preeclampsia: Risk Factors

and Renal Outcomes. Hypertens. (Dallas, Tex. 1979) 74: 1144–1151, 2019

27. Piccoli G, Zakharova E, Attini R, Ibarra Hernandez M, Covella B, Alrukhaimi M, Liu Z-H,

Ashuntantang G, Orozco Guillen A, Cabiddu G, Li P, Garcia-Garcia G, Levin A: Acute

Kidney Injury in Pregnancy: The Need for Higher Awareness. A Pragmatic Review

Focused on What Could Be Improved in the Prevention and Care of Pregnancy-Related

AKI, in the Year Dedicated to Women and Kidney Diseases. J. Clin. Med. 7: 318, 2018

28. Liu D, He W, Li Y, Xiong M, Wang L, Huang J, Jia L, Yuan S, Nie S: Epidemiology of

acute kidney injury in hospitalized pregnant women in China. BMC Nephrol. 20: 2019

29. Mehrabadi A, Liu S, Bartholomew S, Hutcheon JA, Magee LA, Kramer MS, Liston RM,

Joseph KS, Canadian Perinatal Surveillance System Public Health Agency of Canada:

Hypertensive disorders of pregnancy and the recent increase in obstetric acute renal

failure in Canada: population based retrospective cohort study. BMJ [Internet] 349:

21

g4731–g4731, 2014 Available from: http://www.ncbi.nlm.nih.gov/pubmed/25077825 [cited

2019 Sep 26]

30. Prakash J, Ganiger VC: Acute Kidney Injury in Pregnancy-specific Disorders. Indian J.



31. Piccoli GB, Cabiddu G, Attini R, Vigotti FN, Maxia S, Lepori N, Tuveri M, Massidda M,

Marchi C, Mura S, Coscia A, Biolcati M, Gaglioti P, Nichelatti M, Pibiri L, Chessa G, Pani

A, Todros T: Risk of Adverse Pregnancy Outcomes in Women with CKD. J. Am. Soc.


http://www.ncbi.nlm.nih.gov/pubmed/25766536%5Cnhttp://www.pubmedcentral.nih.gov/a

rticlerender.fcgi?artid=PMC4520166

32. Lawrence JM, Contreras R, Chen W, Sacks DA: Trends in the prevalence of preexisting

diabetes and gestational diabetes mellitus among a racially/ethnically diverse population

of pregnant women, 1999-2005. Diabetes Care 31: 899–904, 2008

33. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P: National Vital Statistics

Reports Volume 67, Number 8, November 7, 2018 [Internet]. Available from:

https://www.cdc.gov/nchs/data_access/Vitalstatsonline.htm [cited 2019 Sep 26]

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Table 1: Baseline characteristics of women with and without pregnancy associated AKI

no PR-AKI N=48316430 (99.93)

PRAKI N=34001 (0.07)

P

Patient Demographics Racial/Ethnic groups, N (%) <.0001

White 21215744 (43.91) 11209 (32.97) Black 6020227 (12.46) 9852 (28.98)

Hispanic 9271923 (19.19) 5294 (15.57) Other and/or Missing 11803704 (24.43) 7645 (22.48)

Mean Age 27.74 (±6.11) 28.87 (± 6.89) Age <.0001

12-19 years 4410105 (9.13) 2866 (8.43) 20-34 years 36700000 (76.03) 23569 (69.32) 35-50 years 7172850 (14.85) 7565 (22.25)

All Patient Refined Diagnosis Related Group (APR-DRG) Risk Mortality Score

<.0001

1 47600000 (98.51) 2820 (8.29) 2 554993 (1.15) 9460 (27.82) 3 110085 (0.23) 10579 (31.12) 4 42608 (0.09) 11127 (32.72)

Co-morbidities Diabetes Mellitus 67825 (0.14) 1758 (5.17) <.0001

Hypertension 1088332 (2.25) 8400 (24.7) <.0001 Anemia 3654218 (7.56) 8257 (24.29) <.0001

Chronic Pulmonary disease 1761327 (3.65) 2221 (6.53) <.0001 Congestive Heart Failure 51072 (0.11) 2770 (8.15) <.0001

Hypothyroidism 1038187 (2.15) 1209 (3.56) <.0001 Electrolyte imbalance 584265 (1.21) 16534 (48.63) <.0001

Chronic Liver Disease 75199 (0.16) 703 (2.07) <.0001 Obesity 2025522 (4.19) 3712 (10.92) <.0001

Chronic kidney disease 19980 (0.04) 4287 (12.61) <.0001 Acquired Immune Deficiency

Syndrome 13443 (0.03) 107 (0.32) <.0001

Metastatic cancer 3606 (0.01) 106 (0.31) <.0001 Rheumatoid arthritis 125684 (0.26) 1192 (3.51) <.0001

Psychoses 461282 (0.95) 924 (2.72) <.0001 Alcohol abuse 80478 (0.17) 309 (0.91) <.0001

23

Drug abuse 852324 (1.76) 1974 (5.81) <.0001 Zip code Median Income <.0001

76-100th percentile 10500000 (21.73) 5227 (15.37) 51-75th percentile 11600000 (24.01) 7149 (21.03) 26-50th percentile 1200000 (24.76) 8415 (24.75) 0-25th percentile 13300000 (27.59) 12635 (37.16)

Payment Type <.0001 Medicare 362963 (1) 1555 (5) Medicaid 20997454 (44) 17255 (51)

Private insurance 23800000 (49.25) 12508 (36.79) Self-pay/no charge/others 3081177 (6.38) 2653 (7.8)

Admission type <.0001 Non elective (emergency/urgent) 25500000 (52.77) 26971 (79.33)

Elective 22600000 (46.8) 6890 (20.27) Hospital characteristics Hospital bed size <.0001

Large 29500000 (61.04) 23689 (69.67) Medium 12800000 (26.54) 7463 (21.95)

Small 5749200 (11.9) 2534 (7.45) Hospital Region <.0001

Northeast 7942042 (16.44) 5053 (14.86) Midwest or North Central 10300000 (21.37) 8007 (23.55)

South 18400000 (38.08) 14096 (41.46) West 11700000 (24.11) 6845 (20.13)

Hospital Teaching status <.0001 Urban Teaching 24100000 (49.88) 23923 (70.36)

Urban nonteaching 18700000 (38.78) 8524 (25.07) Rural 5226358 (10.82) 1239 (3.64)

Discharge <.0001 Home 46900000 (97.13) 25837 (75.99)

Against Medical Advice 133524 (0.28) 665 (1.96) Long/short term facility/Home health

care 1235959 (2.56) 6161 (18.12)

Length of Stay (days) 2.66 ± 2.61 9.54 ± 11.92 <.0001 Total Charges 13471 ± 14715 91465 ±

170215 <.0001

Maternal Outcomes Miscarriage 166299 (0.34) 906 (2.66) <.0001

Pre term labor 4016887 (8.31) 4599 (13.53) <.0001 Preeclampsia/Eclampsia 2061083 (4.27) 9639 (28.35) <.0001

Maternal death during hospitalization 4457 (0.01) 1323 (3.89) <.0001

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Table 2: Adjusted odds ratios for the association between patient demographics, admission characteristics, and hospital characteristics and pregnancy related acute kidney injury

Unadjusted OR Adjusted OR

PATIENT DEMOGRAPHICS Racial/Ethnic groups

White (Reference) - - Black 3.098 (CI 2.87-3.34) 1.17 (CI 1.04-1.33)

Hispanic 1.08 (CI 0,99-1.18) 0.92 (CI 0.79 – 1.07) Other and/or Missing 1.215 (CI 1.09-1.35) 1.03 (CI 0.88 – 1.2)

Age 12-19 years (Reference) - -

20-34 years 0.99 (CI 0.9 – 1.08) 0.85 (CI 0.72 – 1) 35-50 years 1.62 (CI 1.46 – 1.8) 0.86 (CI 0.7 – 1.04)

Comorbidities Acquired Immune Deficiency Syndrome 11.43 (CI 7.4 – 17.66) 0.52 (CI 0.29 - 0.92)

Alcohol Abuse 5.5 (CI 4.25 – 7.1) 0.88 (CI 0.52 - 1.48) Congestive Heart Failure 83.85 (CI 76.18 – 92.29) 0.69 (CI 0.57 - 0.83)

Chronic Liver Disease 13.54 (CI 11.34 – 16.15) 1.68 (CI 1.21 - 2.35) Chronic Pulmonary Disease 1.85 (CI 1.67 – 2.04) 0.65 (CI 0.53 - 0.79)

Deficiency Anemias 3.92 (CI 3.67 – 4.19) 1.25 (CI 1.11 - 1.4) Diabetes Mellitus 38.8 (CI 34.35 – 43.81) 1.22 (CI 0.9 - 1.65)

Drug Abuse 3.43 (CI 3.07 – 3.84) 1.17 (CI 0.93 - 1.48) Electrolyte Imbalance 77.34 (CI 73.22 – 81.69) 3.2 (CI 2.87 – 3.56)

Hypothyroidism 1.68 (CI 1.47-1.91) 1.09 (CI 0.84 – 1.41) Hypertension 14.24 (CI 13.45 – 15.07) 1.77 (CI 1.55 – 2.02)

Metastatic Cancer 42 (CI 27.36 – 64.51) 0.46 (CI 0.18 – 1.17) Obesity 2.8 (CI 2.57 – 3.05) 0.98 (CI 0.81 – 1.18)

Psychoses 2.9 (CI 2.48 – 3.39) 1.19 (CI 0.9- 1.58) Chronic kidney disease 348.76 (CI 319.89 – 380.23) 9.97 (CI 7.99 – 12.44)

Rheumatoid arthritis 13.93 (CI 12.07 – 16.08) 1.25 (CI 0.94 – 1.65) Median household income category for patient's zip codea

76-100th percentile (Reference) - - 51-75th percentile 1.24 (1.14 – 1.35) 0.88 (CI 0.78- 0.99) 26-50th percentile 1.41 (CI 1.3 – 1.54) 0.85 (CI 0.74 – 0.97) 0-25th percentile 1.9 (CI 1.75 – 2.1) 0.79 (CI 0.67 – 0.93)

ADMISSION CHARACTERISTICS Admission Type

Elective (Reference) - - Non-Elective 3.47 (CI 3.23 – 3.73) 1.06 (CI 0.95 – 1.19)

All Patient Refined Diagnosis Related Group (APR-DRG) Risk Mortality Score

1 (Reference) - -

25

2 >999.99 (CI >999.99 - >999.99)

>999.99 (CI >999.99 - >999.99)

3 >999.99 (CI >999.99 - >999.99)

>999.99 (CI >999.99 - >999.99)

4 >999.99 (CI >999.99 - >999.99)

>999.99 (CI >999.99 - >999.99)

Primary Payer type Medicare (Reference) - -

Medicaid 0.19 (CI 0.16- 0.22) 0.84 (CI 0.64 – 1.12) Commercial 0.12 (CI 0.1- 0.14) 0.82 (CI 0.62 – 1.07)

Self-pay 0.24 (CI 0.2 – 0.29) 0.85 (CI 0.61 – 1.18) No Charge 0.26 (CI 0.15 – 0.46) 0.93 ( CI 0.39 – 2.22 )

Others 0.15 (CI 0.12 – 0.19) 0.76 (CI 0.52 – 1.1) HOSPITAL CHARACTERISTICS

Hospital bed sized* Large (Reference) - -

Medium 0.72 (CI 0.65 – 0.8) 0.82 (CI 0.66 – 1) Small 0.55 (CI 0.49 – 0.61) 0.89 (CI 0.74 – 1.07)

Hospital Region Northeast (Reference) - -

Midwest or North Central 1.22 (CI 1.08 – 1.37) 0.85 (CI 0.71 – 1.02) South 1.2 (CI 1.08 – 1.35) 0.87 ( CI 0.75 – 1) West 0.92 (CI 0.81 -1.05) 0.9 (CI 0.75 – 1.08)

Hospital Teaching Status Urban Teaching (Reference) - -

Urban nonteaching 0.46 (CI 0.42 – 0.5) 0.45 (CI 0.34 – 0.58) Rural 0.24 (CI 0.21-0.28) 0.78 (CI 0.68 -0.88)

* Adjusted for age, all comorbidities (diabetes mellitus, hypertension, anemia, chronic pulmonary disease, congestive heart failure, hypothyroidism, electrolyte imbalance, chronic liver disease, obesity, renal failure, acquired immune deficiency syndrome, metastatic cancer, rheumatoid arthritis, psychosis, alcohol abuse, drug abuse), median household income, primary payer (Medicare/Medicaid, private insurance, self-pay, or no charge), admission type and hospital-level characteristics such as hospital bed size (small, medium, and large), region (Northeast, Midwest, or North Central, South, and West), and teaching status.

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Table 3: Adjusted odds ratio for maternal outcomes by PR-AKI status

Outcomes Unadjusted OR Adjusted OR* Miscarriage 7.93 (CI 6.8 - 9.25) 1.64 (CI 1.3 – 2.07)

Preterm labor 1.72 (CI 1.61 - 1.85) 0.88 (CI 0.8 - 0.98) Pre / Eclampsia 8.88 (CI 8.39 - 9.41) 1.07 (CI 0.98- 1.16)

Adverse Discharge

7.96 (CI 6.61 – 9.45) 1.12 (CI 0.98 -1.27)

Maternal Mortality

439.01 (CI 381.50- 505.18)

1.53 (CI 1.25-1.88)


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Table 4: Adjusted odds ratio for pregnancy outcomes by PR-AKI status stratified by race/ethnicity


Miscarriage Preterm Pre/Eclampsia Adverse Discharge Mortality White 1.61 (CI 1.06 – 2.44) 0.85 (CI 0.7 – 1.0) 0.9 (CI 0.76 - 1.12) 1.14 (CI 0.93 – 1.39) 1.62 (CI 1.09 - 2.41) Black 2.3 ( CI 1.54 – 3.5) 0.95 ( CI 0.79 - 1.15) 0.92 ( CI 0.76 - 1.11) 0.80 (CI 0.61 – 1.06) 1.42 (CI 0.96 - 2.09)

Hispanic 1.34 ( CI 0.67 – 2.68 ) 0.8( CI 0.59 - 1.1 ) 1.2 ( CI 0.89- 1.73 ) 1.04 (CI 0.71 – 1.52) 1.9 (CI 1.01 - 3.5) Other 0.75 ( CI 0.22 – 2.59) 1.02( CI 0.7 - 1.48 ) 1.16 ( CI 0.76 - 1.78 ) 0.5 (CI 0.2 – 1.25) 1.8 (CI 0.86 - 3.78)

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Figure 1: Trends of AKI A) Overall, B) by age group, C) by race/ethnicityA

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Figure 2: Trends of AKI by race stratified by age

Figure 3: Adjusted odds ratios for maternal outcomes by race/ethnicity with white as reference in women hospitalized with pregnancy related acute kidney injury

Miscarriage

Preterm Labor

Preeclampsia/eclampsia

Adverse Discharge

Mortality

Ref

Ref

Ref

Ref

Ref

Adjusted for age, all comorbidities (diabetes mellitus, hypertension, anemia, chronic pulmonary disease, congestive heart failure, hypothyroidism, electrolyte imbalance, chronic liver disease, obesity, renal failure, acquired immune deficiency syndrome, metastatic cancer, rheumatoid arthritis, psychosis, alcohol abuse, drug abuse), median household income, primary payer (Medicare/Medicaid, private insurance, self-pay, or no charge), admission type and hospital-level characteristics such as hospital bed size (small, medium, and large), region (Northeast, Midwest, or North Central, South, and West), and teaching status.

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