FACTS In May 2014, Jacqueline Beadle and Karyl Cox were roommates who shared a one-story, three-bedroom house in the 3000 block of Bragg Street in Bossier City. We affirm the defendant’s convictions and sentences. He received consecutive sentences of life imprisonment without benefit of parole, probation, or suspension of sentence. Butler, was convicted as charged of two counts of first degree murder. Following a bench trial, the defendant, Brandon S. SCHUYLER MARVIN District Attorney Counsel for Appellee ANDREW JACOBS JOHN MICHAEL LAWRENCE DOUG STINSON Assistant District Attorneys ***** Before MOORE, GARRETT, and THOMPSON, JJ. 205,973 Honorable Michael Nerren, Judge ***** LOUISIANA APPELLATE PROJECT By: Douglas Lee Harville Counsel for Appellant J. BUTLER Appellant ***** Appealed from the Twenty-Sixth Judicial District Court for the Parish of Bossier, Louisiana Trial Court No. 53,360-KA COURT OF APPEAL SECOND CIRCUIT STATE OF LOUISIANA ***** STATE OF LOUISIANA Appellee versus BRANDON S. Application for rehearing may be filed within the delay allowed by Art. A data-driven tool that estimates the probability of 90-day mortality could be leveraged as a powerful supplementary aid to clinicians managing end-of-life care at oncology practices.Judgment rendered April 22, 2020. Conclusions: This study builds upon previous work and further establishes the utility of machine learning to predict risk of imminent mortality for advanced cancer patients using available EHR data. Further, external validation conducted using 3 independent holdout datasets demonstrated impressive generalizability marked by stable performance scores across multiple time periods (AUC between 0.84 and 0.85). The performance on the training cohort was given by a cross-validated AUC score of 0.85 (95% CI, 0.84 to 0.86). A logistic regression algorithm using L1 (lasso) regularization yielded the best performance compared to other model candidates. Results: A multivariable model to predict 90-day mortality was developed using a retrospective dataset derived from EHR data and Medicare claims data. To avoid bias, all holdout datasets used for validation were excluded from the model. As external validation, the final model was independently tested on 3 separate holdout datasets including OCM patients between Jand March 31, 2020. The training dataset was also used for internal validation and hyperparameter tuning until the final model was produced. The patients satisfying the selection criterion were used to train and optimize the model. Patients were excluded from the study cohort if they were not enrolled in the OCM program or did not have a diagnosis for metastatic cancer. Patients were required to have at least one record for lab values and vital signs in the EHR database. Methods: A retrospective study cohort was formed using patients with metastatic cancer from US Oncology Network (USON) practices participating in the Oncology Care Model (OCM) between Januand June 30, 2019. An automated algorithmic tool that can incorporate the wealth of available EHR data and rapidly identify patients with a high risk of imminent mortality could be a valuable asset to supplement important clinical decisions and improve timely hospice care. In particular, timely hospice enrollment is a leading quality metric in the Oncology Care Model that has substantial room for improvement. Background: End-of-life management is a well-known challenging aspect of cancer care.
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