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Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics

Adhikari, Lasith ; Ozrazgat-Baslanti, Tezcan ; Ruppert, Matthew ; Madushani, R W M A ; Paliwal, Srajan ; Hashemighouchani, Haleh ; Zheng, Feng ; Tao, Ming ; Lopes, Juliano M ; Li, Xiaolin ; Rashidi, Parisa ; Bihorac, Azra Valenza, Gaetano

PloS one, 2019-04, Vol.14 (4), p.e0214904-e0214904 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics
Autor: Adhikari, Lasith ; Ozrazgat-Baslanti, Tezcan ; Ruppert, Matthew ; Madushani, R W M A ; Paliwal, Srajan ; Hashemighouchani, Haleh ; Zheng, Feng ; Tao, Ming ; Lopes, Juliano M ; Li, Xiaolin ; Rashidi, Parisa ; Bihorac, Azra
Valenza, Gaetano
Assuntos: Acute kidney failure ; Acute Kidney Injury - etiology ; Acute renal failure ; Adult ; Adults ; Aged ; Algorithms ; Analytics ; Anesthesiology ; Area Under Curve ; Biology and Life Sciences ; Blood pressure ; Cardiovascular diseases ; Cohort Studies ; Colleges & universities ; Complications and side effects ; Computer engineering ; Data processing ; Diagnosis ; Electronic health records ; Enzymes ; Female ; Health ; Health aspects ; Heart surgery ; Hospital costs ; Humans ; Hypertension ; Injury analysis ; Intraoperative Period ; Kidneys ; Learning algorithms ; Machine Learning ; Male ; Mathematical models ; Medicine ; Medicine and Health Sciences ; Middle Aged ; Models, Statistical ; Morbidity ; Mortality ; Nephrology ; Performance prediction ; Physiological aspects ; Physiology ; Postoperative Complications - etiology ; Prediction models ; Predictive Value of Tests ; Prognosis ; Reclassification ; Retrospective Studies ; Risk ; Risk Factors ; Sensitivity and Specificity ; Statistical analysis ; Surgery ; Time series ; Transplants & implants
É parte de: PloS one, 2019-04, Vol.14 (4), p.e0214904-e0214904
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
Descrição: Acute kidney injury (AKI) is a common complication after surgery that is associated with increased morbidity and mortality. The majority of existing perioperative AKI risk prediction models are limited in their generalizability and do not fully utilize intraoperative physiological time-series data. Thus, there is a need for intelligent, accurate, and robust systems to leverage new information as it becomes available to predict the risk of developing postoperative AKI. A retrospective single-center cohort of 2,911 adults who underwent surgery at the University of Florida Health between 2000 and 2010 was utilized for this study. Machine learning and statistical analysis techniques were used to develop perioperative models to predict the risk of developing AKI during the first three days after surgery, first seven days after surgery, and overall (after surgery during the index hospitalization). The improvement in risk prediction was examined by incorporating intraoperative physiological time-series variables. Our proposed model enriched a preoperative model that produced a probabilistic AKI risk score by integrating intraoperative statistical features through a machine learning stacking approach inside a random forest classifier. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and Net Reclassification Improvement (NRI). The predictive performance of the proposed model is better than the preoperative data only model. The proposed model had an AUC of 0.86 (accuracy of 0.78) for the seven-day AKI outcome, while the preoperative model had an AUC of 0.84 (accuracy of 0.76). Furthermore, by integrating intraoperative features, the algorithm was able to reclassify 40% of the false negative patients from the preoperative model. The NRI for each outcome was AKI at three days (8%), seven days (7%), and overall (4%). Postoperative AKI prediction was improved with high sensitivity and specificity through a machine learning approach that dynamically incorporated intraoperative data.
Editor: United States: Public Library of Science
Idioma: Inglês

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Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics

Adhikari, Lasith ; Ozrazgat-Baslanti, Tezcan ; Ruppert, Matthew ; Madushani, R W M A ; Paliwal, Srajan ; Hashemighouchani, Haleh ; Zheng, Feng ; Tao, Ming ; Lopes, Juliano M ; Li, Xiaolin ; Rashidi, Parisa ; Bihorac, Azra Valenza, Gaetano

United States: Public Library of Science

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