Pregnancy outcomes after living kidney donation from a nationwide population-based cohort study from Korea

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Abstract

While most living kidney donors experience good outcomes and high rates of satisfaction, kidney donation can increase the risk of gestational hypertension or preeclampsia. However, pregnancy outcomes in non-white donors are limited. We conducted a nationwide cohort study of 112 living kidney donors and 672 matched healthy non-donors using the Korean National Health Insurance Claims Database. Donors and healthy non-donors were matched according to age, year of cohort entry, residency, income, number of pregnancies, and the time to the first pregnancy after cohort entry. We assessed pregnancy outcomes of live kidney donors compared with matched healthy non-donors using the nationwide database. Gestational hypertension or preeclampsia was more common in kidney donors than in non-donors (8.9% vs. 1.8%; adjusted odds ratio, 2.68; 95% confidence interval, 1.11–6.50). However, the incidence of severe gestational hypertension or preeclampsia that required antihypertensive medication was comparable (2.7% vs. 0.9%; P = 0.121). The time from donation to delivery within 5 years and primiparity were risk factors for preeclampsia in donors. Low birth weight, stillbirth, and ectopic pregnancy were not significantly different between the two groups. Maternal death occurred in two non-donor cases, but none occurred in donors compared to non-donors. Our findings indicate that kidney donors are associated with an increased risk of gestational hypertension or preeclampsia than matched healthy non-donors. However, the probabilities of serious maternal and fetal outcomes remained low and are not increased significantly after kidney donation.

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Most cited references 29

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Hypertensive Disorders of Pregnancy

Mark Brown, Laura Magee, Louise C. Kenny … (2018)

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Is Open Access

Data Resource Profile: The National Health Information Database of the National Health Insurance Service in South Korea

Sang Cheol Seong, Yeon-Yong Kim, Young-Ho Khang … (2016)

Data resource basics The National Health Information Database (NHID) is a public database on health care utilization, health screening, socio-demographic variables, and mortality for the whole population of South Korea, formed by the National Health Insurance Service. The population included in the data is over 50 million, and the participation rate in the health screening programs was 74.8% in 2014. The NHID covers data between 2002 and 2014. Those insured by NHI pay insurance contributions and receive medical services from their health care providers. The NHIS, as the single insurer, pays costs based on the billing records of health care providers (Figure 1). To govern and carry out these processes in the NHI, the NHIS built a data warehouse to collect the required information on insurance eligibility, insurance contributions, medical history, and medical institutions. In 2012, the NHIS formed the NHID using information from medical treatment and health screening records and eligibility data from an existing database system. Figure 1. The governance of the National Health Insurance of South Korea. Data collected The eligibility database includes information about income-based insurance contributions, demographic variables, and date of death. The national health screening database includes information on health behaviors and bio-clinical variables. The health care utilization database includes information on records on inpatient and outpatient usage (diagnosis, length of stay, treatment costs, services received) and prescription records (drug code, days prescribed, daily dosage). The long-term care insurance database includes information about activities of daily living and service grades. The health care provider database includes data about the types of institutions, human resources, and equipment. In the NHID, de-identified join keys replacing the personal identifiers are used to interlink these databases. Data resource use Papers published covered various diseases or health conditions like infectious diseases, cancer, cardiovascular diseases, hypertension, diabetes mellitus, and injuries and risk factors such as smoking, alcohol consumption, and obesity. The impacts of health care and public health policies on health care utilization have been also explored since the data include all the necessary information reflecting patterns of health care utilization. Reasons to be cautious First, information on diagnosis and disease may not be optimal for identifying disease occurrence and prevalence since the data have been collected for medical service claims and reimbursement. However, the NHID also collects prescription data with secondary diagnosis, so the accuracy of the disease information can be improved. Second, the data linkage with other secondary national data is not widely available due to privacy issues in Korea. Governmental discussions on the statutory reform of data linkage using the NHID are under way. Collaboration and data access Access to the NHID can be obtained through the Health Insurance Data Service home page (http://nhiss.nhis.or.kr). An ethics approval from the researchers’ institutional review board is required with submission of a study proposal, which is reviewed by the NHIS review committee before providing data. Further inquiries on data use can be obtained by contacting the corresponding author. Funding and competing interests This work was supported by the NHIS in South Korea. The authors declare no competing interests.

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New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality.

Vijaya Sundararajan, Toni Henderson, Catherine Perry … (2004)

The ICD-9-CM adaptation of the Charlson comorbidity score has been a valuable resource for health services researchers. With the transition into ICD-10 coding worldwide, an ICD-10 version of the Deyo adaptation was developed and validated using population-based hospital data from Victoria, Australia. The algorithm was translated from ICD-9-CM into ICD-10-AM (Australian modification) in a multistep process. After a mapping algorithm was used to develop an initial translation, these codes were manually examined by the coding experts and a general physician for face validity. Because the ICD-10 system is country specific, our goal was to keep many of the translated code at the three-digit level for generalizability of the new index. There appears to be little difference in the distribution of the Charlson Index score between the two versions. A strong association between increasing index scores and mortality exists: the area under the ROC curve is 0.865 for the last year using the ICD-9-CM version and remains high, at 0.855, for the ICD-10 version. This work represents the first rigorous adaptation of the Charlson comorbidity index for use with ICD-10 data. In comparison with a well-established ICD-9-CM coding algorithm, it yields closely similar prevalence and prognosis information by comorbidity category.

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Author and article information

Contributors

Hyung Soon Lee: soon0925@nhimc.or.kr

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 27 December 2022

Publication date PMC-release: 27 December 2022

Publication date Collection: 2022

Volume: 12

Electronic Location Identifier: 22412

Affiliations

[1 ]GRID grid.15444.30, ISNI 0000 0004 0470 5454, Department of Surgery, , Yonsei University College of Medicine, ; Seoul, Republic of Korea

[2 ]GRID grid.416665.6, ISNI 0000 0004 0647 2391, Research Institute, National Health Insurance Service Ilsan Hospital, ; Goyang, Republic of Korea

[3 ]GRID grid.416665.6, ISNI 0000 0004 0647 2391, Department of Surgery, , National Health Insurance Service Ilsan Hospital, ; 100 Ilsan-Ro, Ilsandong-Gu, Goyang, 10444 Republic of Korea

Article

Publisher ID: 27094

DOI: 10.1038/s41598-022-27094-x

PMC ID: 9794799

PubMed ID: 36575198

SO-VID: 10689c1b-be36-4f1b-b31d-6af870b53d0e

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 29 June 2022

Date accepted : 26 December 2022

Custom metadata

ScienceOpen disciplines: Uncategorized

Keywords: kidney,kidney diseases

Data availability:

ScienceOpen disciplines: Uncategorized

Keywords: kidney, kidney diseases

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