Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19Plain-Language Summary
Rationale & Objective: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, but validated, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI in hospitalized patients with coronavirus disease 2019 and assess its performance ove...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Kidney Medicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590059522000760 |
| _version_ | 1818546907946942464 |
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| author | Meredith C. McAdams Pin Xu Sameh N. Saleh Michael Li Mauricio Ostrosky-Frid L. Parker Gregg Duwayne L. Willett Ferdinand Velasco Christoph U. Lehmann S. Susan Hedayati |
| author_facet | Meredith C. McAdams Pin Xu Sameh N. Saleh Michael Li Mauricio Ostrosky-Frid L. Parker Gregg Duwayne L. Willett Ferdinand Velasco Christoph U. Lehmann S. Susan Hedayati |
| author_sort | Meredith C. McAdams |
| collection | DOAJ |
| description | Rationale & Objective: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, but validated, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI in hospitalized patients with coronavirus disease 2019 and assess its performance over time with the emergence of vaccines and the Delta variant. Study Design: Longitudinal cohort study. Setting & Participants: Hospitalized patients with a positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction result between March 1, 2020, and August 20, 2021 at 19 hospitals in Texas. Exposures: Comorbid conditions, baseline laboratory data, inflammatory biomarkers. Outcomes: AKI defined by KDIGO (Kidney Disease: Improving Global Outcomes) creatinine criteria. Analytical Approach: Three nested models for AKI were built in a development cohort and validated in 2 out-of-time cohorts. Model discrimination and calibration measures were compared among cohorts to assess performance over time. Results: Of 10,034 patients, 5,676, 2,917, and 1,441 were in the development, validation 1, and validation 2 cohorts, respectively, of whom 776 (13.7%), 368 (12.6%), and 179 (12.4%) developed AKI, respectively (P = 0.26). Patients in the validation cohort 2 had fewer comorbid conditions and were younger than those in the development cohort or validation cohort 1 (mean age, 54 ± 16.8 years vs 61.4 ± 17.5 and 61.7 ± 17.3 years, respectively, P < 0.001). The validation cohort 2 had higher median high-sensitivity C-reactive protein level (81.7 mg/L) versus the development cohort (74.5 mg/L; P < 0.01) and higher median ferritin level (696 ng/mL) versus both the development cohort (444 ng/mL) and validation cohort 1 (496 ng/mL; P < 0.001). The final model, which added high-sensitivity C-reactive protein, ferritin, and D-dimer levels, had an area under the curve of 0.781 (95% CI, 0.763-0.799). Compared with the development cohort, discrimination by area under the curve (validation 1: 0.785 [0.760-0.810], P = 0.79, and validation 2: 0.754 [0.716-0.795], P = 0.53) and calibration by estimated calibration index (validation 1: 0.116 [0.041-0.281], P = 0.11, and validation 2: 0.081 [0.045-0.295], P = 0.11) showed stable performance over time. Limitations: Potential billing and coding bias. Conclusions: We developed and externally validated a model to accurately predict AKI in patients with coronavirus disease 2019. The performance of the model withstood changes in practice patterns and virus variants. |
| first_indexed | 2024-12-12T07:59:31Z |
| format | Article |
| id | doaj.art-862300bebf1d40008612cb72b04aa00c |
| institution | Directory Open Access Journal |
| issn | 2590-0595 |
| language | English |
| last_indexed | 2024-12-12T07:59:31Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kidney Medicine |
| spelling | doaj.art-862300bebf1d40008612cb72b04aa00c2022-12-22T00:32:11ZengElsevierKidney Medicine2590-05952022-06-0146100463Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19Plain-Language SummaryMeredith C. McAdams0Pin Xu1Sameh N. Saleh2Michael Li3Mauricio Ostrosky-Frid4L. Parker Gregg5Duwayne L. Willett6Ferdinand Velasco7Christoph U. Lehmann8S. Susan Hedayati9Division of Nephrology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TXDivision of Nephrology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TXClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TXUniversity of Texas Southwestern College of Medicine, Dallas, TXDepartment of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TXSelzman Institute for Kidney Health, Section of Nephrology, Department of Medicine, Baylor College of Medicine, Houston, TX; Section of Nephrology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX; Veterans Affairs Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety, Houston, TXDivision of Cardiology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TXTexas Health Resources, Dallas, TXClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TXDivision of Nephrology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX; Address for Correspondence: S. Susan Hedayati, MD, MHSc, 5939 Harry Hines Blvd, MC 8516, Dallas, TX 75390.Rationale & Objective: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, but validated, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI in hospitalized patients with coronavirus disease 2019 and assess its performance over time with the emergence of vaccines and the Delta variant. Study Design: Longitudinal cohort study. Setting & Participants: Hospitalized patients with a positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction result between March 1, 2020, and August 20, 2021 at 19 hospitals in Texas. Exposures: Comorbid conditions, baseline laboratory data, inflammatory biomarkers. Outcomes: AKI defined by KDIGO (Kidney Disease: Improving Global Outcomes) creatinine criteria. Analytical Approach: Three nested models for AKI were built in a development cohort and validated in 2 out-of-time cohorts. Model discrimination and calibration measures were compared among cohorts to assess performance over time. Results: Of 10,034 patients, 5,676, 2,917, and 1,441 were in the development, validation 1, and validation 2 cohorts, respectively, of whom 776 (13.7%), 368 (12.6%), and 179 (12.4%) developed AKI, respectively (P = 0.26). Patients in the validation cohort 2 had fewer comorbid conditions and were younger than those in the development cohort or validation cohort 1 (mean age, 54 ± 16.8 years vs 61.4 ± 17.5 and 61.7 ± 17.3 years, respectively, P < 0.001). The validation cohort 2 had higher median high-sensitivity C-reactive protein level (81.7 mg/L) versus the development cohort (74.5 mg/L; P < 0.01) and higher median ferritin level (696 ng/mL) versus both the development cohort (444 ng/mL) and validation cohort 1 (496 ng/mL; P < 0.001). The final model, which added high-sensitivity C-reactive protein, ferritin, and D-dimer levels, had an area under the curve of 0.781 (95% CI, 0.763-0.799). Compared with the development cohort, discrimination by area under the curve (validation 1: 0.785 [0.760-0.810], P = 0.79, and validation 2: 0.754 [0.716-0.795], P = 0.53) and calibration by estimated calibration index (validation 1: 0.116 [0.041-0.281], P = 0.11, and validation 2: 0.081 [0.045-0.295], P = 0.11) showed stable performance over time. Limitations: Potential billing and coding bias. Conclusions: We developed and externally validated a model to accurately predict AKI in patients with coronavirus disease 2019. The performance of the model withstood changes in practice patterns and virus variants.http://www.sciencedirect.com/science/article/pii/S2590059522000760Acute kidney injuryCOVID-19Delta variantmodel validationpredictive model |
| spellingShingle | Meredith C. McAdams Pin Xu Sameh N. Saleh Michael Li Mauricio Ostrosky-Frid L. Parker Gregg Duwayne L. Willett Ferdinand Velasco Christoph U. Lehmann S. Susan Hedayati Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19Plain-Language Summary Kidney Medicine Acute kidney injury COVID-19 Delta variant model validation predictive model |
| title | Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19Plain-Language Summary |
| title_full | Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19Plain-Language Summary |
| title_fullStr | Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19Plain-Language Summary |
| title_full_unstemmed | Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19Plain-Language Summary |
| title_short | Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19Plain-Language Summary |
| title_sort | risk prediction for acute kidney injury in patients hospitalized with covid 19plain language summary |
| topic | Acute kidney injury COVID-19 Delta variant model validation predictive model |
| url | http://www.sciencedirect.com/science/article/pii/S2590059522000760 |
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