Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective study
BackgroundPostoperative acute kidney injury (PO-AKI) is a prevalent complication among patients with acute type A aortic dissection (aTAAD) for which unrecognized trajectories of renal function recovery, and their heterogeneity, may underpin poor success in identifying effective therapies.MethodsThi...
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2024.1364332/full |
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| author | Weiwei Zhao Ya-peng Wang Xinlong Tang Yi Jiang Yunxing Xue Yali Wang Qiuju Ding Huimei Chen Dongjin Wang YongQing Cheng Min Ge Qing Zhou |
| author_facet | Weiwei Zhao Ya-peng Wang Xinlong Tang Yi Jiang Yunxing Xue Yali Wang Qiuju Ding Huimei Chen Dongjin Wang YongQing Cheng Min Ge Qing Zhou |
| author_sort | Weiwei Zhao |
| collection | DOAJ |
| description | BackgroundPostoperative acute kidney injury (PO-AKI) is a prevalent complication among patients with acute type A aortic dissection (aTAAD) for which unrecognized trajectories of renal function recovery, and their heterogeneity, may underpin poor success in identifying effective therapies.MethodsThis was a retrospective, single-center cohort study in a regional Great Vessel Center including patients undergoing aortic dissection surgery. Estimated glomerular filtration rate (eGFR) recovery trajectories of PO-AKI were defined through the unsupervised latent class mixture modeling (LCMM), with an assessment of patient and procedural characteristics, complications, and early-term survival. Internal validation was performed by resampling.ResultsA total of 1,295 aTAAD patients underwent surgery and 645 (49.8%) developed PO-AKI. Among the PO-AKI cohort, the LCMM identified two distinct eGFR trajectories: early recovery (ER-AKI, 51.8% of patients) and late or no recovery (LNR-AKI, 48.2% of patients). Binary logistic regression identified five critical determinants regarding poor renal recovery, including chronic kidney disease (CKD) history, renal hypoperfusion, circulation arrest time, intraoperative urine, and myoglobin. LNR-AKI was associated with increased mortality, continuous renal replacement therapies, mechanical ventilation, ICU stay, and hospital stay. The assessment of the predictive model was good, with an area under the curve (AUC) of 0.73 (95% CI: 0.69–0.76), sensitivity of 61.74%, and specificity of 75.15%. The internal validation derived a consistent average AUC of 0.73. The nomogram was constructed for clinicians' convenience.ConclusionOur study explored the PO-AKI recovery patterns among surgical aTAAD patients and identified critical determinants that help to predict individuals at risk of poor recovery of renal function. |
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| language | English |
| last_indexed | 2024-04-24T07:43:30Z |
| publishDate | 2024-04-01 |
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| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-66dcbbfb2c6f4961a38ceb272e1dfc8a2024-04-19T05:09:13ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2024-04-011110.3389/fcvm.2024.13643321364332Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective studyWeiwei Zhao0Ya-peng Wang1Xinlong Tang2Yi Jiang3Yunxing Xue4Yali Wang5Qiuju Ding6Huimei Chen7Dongjin Wang8YongQing Cheng9Min Ge10Qing Zhou11Department of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, ChinaDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, ChinaDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaProgramme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, SingaporeDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaBackgroundPostoperative acute kidney injury (PO-AKI) is a prevalent complication among patients with acute type A aortic dissection (aTAAD) for which unrecognized trajectories of renal function recovery, and their heterogeneity, may underpin poor success in identifying effective therapies.MethodsThis was a retrospective, single-center cohort study in a regional Great Vessel Center including patients undergoing aortic dissection surgery. Estimated glomerular filtration rate (eGFR) recovery trajectories of PO-AKI were defined through the unsupervised latent class mixture modeling (LCMM), with an assessment of patient and procedural characteristics, complications, and early-term survival. Internal validation was performed by resampling.ResultsA total of 1,295 aTAAD patients underwent surgery and 645 (49.8%) developed PO-AKI. Among the PO-AKI cohort, the LCMM identified two distinct eGFR trajectories: early recovery (ER-AKI, 51.8% of patients) and late or no recovery (LNR-AKI, 48.2% of patients). Binary logistic regression identified five critical determinants regarding poor renal recovery, including chronic kidney disease (CKD) history, renal hypoperfusion, circulation arrest time, intraoperative urine, and myoglobin. LNR-AKI was associated with increased mortality, continuous renal replacement therapies, mechanical ventilation, ICU stay, and hospital stay. The assessment of the predictive model was good, with an area under the curve (AUC) of 0.73 (95% CI: 0.69–0.76), sensitivity of 61.74%, and specificity of 75.15%. The internal validation derived a consistent average AUC of 0.73. The nomogram was constructed for clinicians' convenience.ConclusionOur study explored the PO-AKI recovery patterns among surgical aTAAD patients and identified critical determinants that help to predict individuals at risk of poor recovery of renal function.https://www.frontiersin.org/articles/10.3389/fcvm.2024.1364332/fulltype A aortic dissectionacute kidney injuryrecoverytrajectoryglomerular filtration rate |
| spellingShingle | Weiwei Zhao Ya-peng Wang Xinlong Tang Yi Jiang Yunxing Xue Yali Wang Qiuju Ding Huimei Chen Dongjin Wang YongQing Cheng Min Ge Qing Zhou Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective study Frontiers in Cardiovascular Medicine type A aortic dissection acute kidney injury recovery trajectory glomerular filtration rate |
| title | Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective study |
| title_full | Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective study |
| title_fullStr | Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective study |
| title_full_unstemmed | Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective study |
| title_short | Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective study |
| title_sort | development and validation of lcmm prediction algorithms to estimate recovery pattern of postoperative aki in type a aortic dissection a retrospective study |
| topic | type A aortic dissection acute kidney injury recovery trajectory glomerular filtration rate |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2024.1364332/full |
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