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Koyner JL, Mackey RH, Rosenthal NA, Carabuena LA, Kampf JP, McPherson P, Rodriguez T, Sanghani A, Textoris J. Outcomes, Healthcare Resource Utilization, and Costs of Overall, Community-Acquired, and Hospital-Acquired Acute Kidney Injury in COVID-19 Patients. JOURNAL OF HEALTH ECONOMICS AND OUTCOMES RESEARCH 2023; 10:31-40. [PMID: 36852155 PMCID: PMC9961448 DOI: 10.36469/001c.57651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 06/18/2023]
Abstract
Background: In hospitalized patients with COVID-19, acute kidney injury (AKI) is associated with higher mortality, but data are lacking on healthcare resource utilization (HRU) and costs related to AKI, community-acquired AKI (CA-AKI), and hospital-acquired AKI (HA-AKI). Objectives: To quantify the burden of AKI, CA-AKI, and HA-AKI among inpatients with COVID-19. Methods: This retrospective cohort study included inpatients with COVID-19 discharged from US hospitals in the Premier PINC AI™ Healthcare Database April 1-October 31, 2020, categorized as AKI, CA-AKI, HA-AKI, or no AKI by ICD-10-CM diagnosis codes. Outcomes were assessed during index (initial) hospitalization and 30 days postdischarge. Results: Among 208 583 COVID-19 inpatients, 30%, 25%, and 5% had AKI, CA-AKI, and HA-AKI, of whom 10%, 7%, and 23% received dialysis, respectively. Excess mortality, HRU, and costs were greater for HA-AKI than CA-AKI. In adjusted models, for patients with AKI vs no AKI and HA-AKI vs CA-AKI, odds ratios (ORs) (95% CI) were 3.70 (3.61-3.79) and 4.11 (3.92-4.31) for intensive care unit use and 3.52 (3.41-3.63) and 2.64 (2.52-2.78) for in-hospital mortality; mean length of stay (LOS) differences and LOS ratios (95% CI) were 1.8 days and 1.24 (1.23-1.25) and 5.1 days and 1.57 (1.54-1.59); and mean cost differences and cost ratios were $7163 and 1.35 (1.34-1.36) and $19 127 and 1.78 (1.75-1.81) (all P < .001). During the 30 days postdischarge, readmission LOS was ≥6% longer for AKI vs no AKI and HA-AKI vs CA-AKI; outpatient costs were ≥41% higher for HA-AKI vs CA-AKI or no AKI. Only 30-day new dialysis (among patients without index hospitalization dialysis) had similar odds for HA-AKI vs CA-AKI (2.37-2.8 times higher for AKI, HA-AKI, or CA-AKI vs no AKI). Discussion: Among inpatients with COVID-19, HA-AKI had higher excess mortality, HRU, and costs than CA-AKI. Other studies suggest that interventions to prevent HA-AKI could decrease excess morbidity, HRU, and costs among inpatients with COVID-19. Conclusions: In adjusted models among COVID-19 inpatients, AKI, especially HA-AKI, was associated with significantly higher mortality, HRU, and costs during index admission, and higher dialysis and longer readmission LOS during the 30 days postdischarge. These findings support implementation of interventions to prevent HA-AKI in COVID-19 patients.
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Affiliation(s)
- Jay L Koyner
- Section of Nephrology University of Chicago, Chicago, Illinois
| | - Rachel H Mackey
- Premier, Inc., PINC AI Applied Sciences, Charlotte, North Carolina
- Department of Epidemiology University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania
| | - Ning A Rosenthal
- Premier, Inc., PINC AI Applied Sciences, Charlotte, North Carolina
| | | | - J Patrick Kampf
- Astute Medical Inc. (a bioMerieux company), San Diego, California
| | - Paul McPherson
- Astute Medical Inc. (a bioMerieux company), San Diego, California
| | - Toni Rodriguez
- Global Medical Affairs bioMerieux, Inc., Durham, North Carolina
| | - Aarti Sanghani
- bioMerieux, Inc., Global Medical Affairs, Durham, North Carolina
| | - Julien Textoris
- bioMerieux, SA, Global Medical Affairs, Lyons, France
- Service d´Anesthésie et de Réanimation, Lyons, France
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Figueiredo FDA, Ramos LEF, Silva RT, Ponce D, de Carvalho RLR, Schwarzbold AV, Maurílio ADO, Scotton ALBA, Garbini AF, Farace BL, Garcia BM, da Silva CTCA, Cimini CCR, de Carvalho CA, Dias CDS, Silveira DV, Manenti ERF, Cenci EPDA, Anschau F, Aranha FG, de Aguiar FC, Bartolazzi F, Vietta GG, Nascimento GF, Noal HC, Duani H, Vianna HR, Guimarães HC, de Alvarenga JC, Chatkin JM, de Morais JDP, Machado-Rugolo J, Ruschel KB, Martins KPMP, Menezes LSM, Couto LSF, de Castro LC, Nasi LA, Cabral MADS, Floriani MA, Souza MD, Souza-Silva MVR, Carneiro M, de Godoy MF, Bicalho MAC, Lima MCPB, Aliberti MJR, Nogueira MCA, Martins MFL, Guimarães-Júnior MH, Sampaio NDCS, de Oliveira NR, Ziegelmann PK, Andrade PGS, Assaf PL, Martelli PJDL, Delfino-Pereira P, Martins RC, Menezes RM, Francisco SC, Araújo SF, Oliveira TF, de Oliveira TC, Sales TLS, Avelino-Silva TJ, Ramires YC, Pires MC, Marcolino MS. Development and validation of the MMCD score to predict kidney replacement therapy in COVID-19 patients. BMC Med 2022; 20:324. [PMID: 36056335 PMCID: PMC9438299 DOI: 10.1186/s12916-022-02503-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 07/28/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement. METHODS This study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). RESULTS The median age of the model-derivation cohort was 59 (IQR 47-70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918-0.939) and validation (temporal AUROC 0.927, 95% CI 0.911-0.941; geographic AUROC 0.819, 95% CI 0.792-0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator ( https://www.mmcdscore.com/ ). CONCLUSIONS The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.
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Affiliation(s)
- Flávio de Azevedo Figueiredo
- Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, Belo Horizonte, 190, Brazil. .,Department of Medicine, Universidade Federal de Lavras, R. Tomas Antonio Gonzaga, 277, Lavras, Brazil.
| | - Lucas Emanuel Ferreira Ramos
- Department of Statistics, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, Belo Horizonte, 6627, Brazil
| | - Rafael Tavares Silva
- Department of Statistics, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, Belo Horizonte, 6627, Brazil
| | - Daniela Ponce
- Botucatu Medical School, Universidade Estadual Paulista "Júlio de Mesquita Filho", Av. Prof. Mário Rubens Guimarães Montenegro, s/n, Botucatu, Brazil
| | | | | | | | | | - Andresa Fontoura Garbini
- Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Av. Francisco Trein, 326, Porto Alegre, Brazil
| | | | | | | | - Christiane Corrêa Rodrigues Cimini
- Hospital Santa Rosália, R. do Cruzeiro, 01, Teófilo Otoni, Brazil.,Mucuri Medical School, Universidade Federal dos Vales do Jequitinhonha e Mucuri, R. Cruzeiro, 01, Teófilo Otoni, Brazil
| | | | - Cristiane Dos Santos Dias
- Department of Pediatrics, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, Belo Horizonte, Brazil
| | | | | | | | - Fernando Anschau
- Hospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Av. Francisco Trein, 326, Porto Alegre, Brazil
| | | | - Filipe Carrilho de Aguiar
- Hospital das Clínicas da Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, 1235, Recife, Brazil
| | - Frederico Bartolazzi
- Hospital Santo Antônio, Praça Dr. Márcio Carvalho Lopes Filho, 501, Curvelo, Brazil
| | | | | | - Helena Carolina Noal
- Hospital Universitário da Universidade Federal de Santa Maria, Av. Roraima, 1000, Santa Maria, Brazil
| | - Helena Duani
- Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, Belo Horizonte, 190, Brazil
| | | | | | | | | | | | - Juliana Machado-Rugolo
- Botucatu Medical School, Universidade Estadual Paulista "Júlio de Mesquita Filho", Av. Prof. Mário Rubens Guimarães Montenegro, s/n, Botucatu, Brazil
| | - Karen Brasil Ruschel
- Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil.,Hospital Mãe de Deus, R. José de Alencar, 286, Porto Alegre, Brazil
| | - Karina Paula Medeiros Prado Martins
- Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil.,Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, Belo Horizonte, 190, Brazil
| | - Luanna Silva Monteiro Menezes
- Hospital Luxemburgo, R. Gentios, 1350, Belo Horizonte, Brazil.,Hospital Metropolitano Odilon Behrens, R. Formiga, 50, Belo Horizonte, Brazil
| | | | | | - Luiz Antônio Nasi
- Hospital Moinhos de Vento, R. Ramiro Barcelos, 910, Porto Alegre, Brazil
| | - Máderson Alvares de Souza Cabral
- Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, Belo Horizonte, 190, Brazil
| | | | - Maíra Dias Souza
- Hospital Metropolitano Odilon Behrens, R. Formiga, 50, Belo Horizonte, Brazil
| | - Maira Viana Rego Souza-Silva
- Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, Belo Horizonte, 190, Brazil
| | - Marcelo Carneiro
- Hospital Santa Cruz, R. Fernando Abott, 174, Santa Cruz do Sul, Brazil
| | | | - Maria Aparecida Camargos Bicalho
- Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, Belo Horizonte, 190, Brazil.,Hospital Júlia Kubitschek, R. Dr. Cristiano Rezende, 2745, Belo Horizonte, Brazil
| | | | - Márlon Juliano Romero Aliberti
- Laboratorio de Investigacao Medica em Envelhecimento (LIM-66), Serviço de Geriatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil.,Research Institute, Hospital Sirio-Libanes, Sao Paulo, Brazil
| | | | | | | | | | | | - Patricia Klarmann Ziegelmann
- Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil.,Hospital Tacchini, R. Dr. José Mário Mônaco, 358, Bento Gonçalves, Brazil
| | | | - Pedro Ledic Assaf
- Hospital Metropolitano Doutor Célio de Castro, R. Dona Luiza, 311, Belo Horizonte, Brazil
| | | | - Polianna Delfino-Pereira
- Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, Belo Horizonte, 190, Brazil.,Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil
| | | | | | | | | | | | | | - Thaís Lorenna Souza Sales
- Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil.,Universidade Federal de São João del-Rei, R. Sebastião Gonçalves Coelho, 400, Divinópolis, Brazil
| | - Thiago Junqueira Avelino-Silva
- Laboratorio de Investigacao Medica em Envelhecimento (LIM-66), Serviço de Geriatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil.,Faculdade Israelita de Ciencias da Saúde Albert Einstein, Hospital Israelita Albert Einstein, Sao Paulo, Brazil
| | | | - Magda Carvalho Pires
- Department of Statistics, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, Belo Horizonte, 6627, Brazil
| | - Milena Soriano Marcolino
- Department of Internal Medicine, Medical School, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, Belo Horizonte, 190, Brazil.,Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, Porto Alegre, 2359, Brazil.,Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, Belo Horizonte, 190, Brazil.,Telehealth Center, University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 110, Belo Horizonte, Brazil
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