arXiv subjects

Amir Motaei

Publications and source records attributed to Amir Motaei.

2 recordsLinked to original sources

arXiv2019

Development and validation of computable Phenotype to Identify and Characterize Kidney Health in Adult Hospitalized Patients

Background: Acute kidney injury (AKI) is a common complication in hospitalized patients and a common cause for chronic kidney disease (CKD) and increased hospital cost and mortality. By timely detection of AKI and AKI progression, effective preventive or therapeutic measures could be offered. This study aims to develop and validate an electronic phenotype to identify patients with CKD and AKI. Methods: A database with electronic health records data from a retrospective study cohort of 84,352 hospitalized adults was created. This repository includes demographics, comorbidities, vital signs, laboratory values, medications, diagnoses and procedure codes for all index admission, 12 months prior and 12 months follow-up encounters. We developed algorithms to identify CKD and AKI based on the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. To measure diagnostic performance of the algorithms, clinician experts performed clinical adjudication of AKI and CKD on 300 selected cases. Results: Among 149,136 encounters, identified CKD by medical history was 12% which increased to 16% using creatinine criteria. Among 130,081 encounters with sufficient data for AKI phenotyping 21% had AKI. The comparison of CKD phenotyping algorithm to manual chart review yielded PPV of 0.87, NPV of 0.99, sensitivity of 0.99, and specificity of 0.89. The comparison of AKI phenotyping algorithm to manual chart review yielded PPV of 0.99, NPV of 0.95 , sensitivity 0.98, and specificity 0.98. Conclusions: We developed phenotyping algorithms that yielded very good performance in identification of patients with CKD and AKI in validation cohort. This tool may be useful in identifying patients with kidney disease in a large population, in assessing the quality and value of care in such patients.

stat.AP↗

arXiv2018

Improved Predictive Models for Acute Kidney Injury with IDEAs: Intraoperative Data Embedded Analytics

Acute kidney injury (AKI) is a common and serious complication after a surgery which is associated with morbidity and mortality. The majority of existing perioperative AKI risk score prediction models are limited in their generalizability and do not fully utilize the physiological intraoperative time-series data. Thus, there is a need for intelligent, accurate, and robust systems, able to leverage information from large-scale data to predict patient's risk of developing postoperative AKI. A retrospective single-center cohort of 2,911 adult patients who underwent surgery at the University of Florida Health has been used for this study. We used machine learning and statistical analysis techniques to develop perioperative models to predict the risk of AKI (risk during the first 3 days, 7 days, and until the discharge day) before and after the surgery. In particular, we examined the improvement in risk prediction by incorporating three intraoperative physiologic time series data, i.e., mean arterial blood pressure, minimum alveolar concentration, and heart rate. For an individual patient, the preoperative model produces a probabilistic AKI risk score, which will be enriched by integrating intraoperative statistical features through a machine learning stacking approach inside a random forest classifier. We compared the performance of our model based on the area under the receiver operating characteristics curve (AUROC), accuracy and net reclassification improvement (NRI). The predictive performance of the proposed model is better than the preoperative data only model. For AKI-7day outcome: The AUC was 0.86 (accuracy was 0.78) in the proposed model, while the preoperative AUC was 0.84 (accuracy 0.76). Furthermore, with the integration of intraoperative features, we were able to classify patients who were misclassified in the preoperative model.

cs.CY↗