About: Validation and comparison of PICTURE analytic and Epic Deterioration Index for COVID-19

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Validation and comparison of PICTURE analytic and Epic Deterioration Index for COVID-19
Creator	Wang, Guan Dickson, Robert Park, Pauline Sjoding, Michael Gillies, Christopher Singh, Karandeep Admon, Andrew Ansari, Sardar Cummings, Brandon Kronick, Steven Mathis, Michael Medlin, Richard Motyka, Jonathan Napolitano, Lena Ward, Kevin
Source	MedRxiv
abstract	Introduction The 2019 coronavirus (COVID-19) has led to unprecedented strain on healthcare facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here we present the results of an analytical model, PICTURE (Predicting Intensive Care Transfers and other UnfoReseen Events), to identify patients at a high risk for imminent intensive care unit (ICU) transfer, respiratory failure, or death with the intention to improve prediction of deterioration due to COVID-19. We compare PICTURE to the Epic Deterioration Index (EDI), a widespread system which has recently been assessed for use to triage COVID-19 patients. Methods The PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014-2018. It was then applied to two hold-out test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to the EDI for head-to-head comparison via Area Under the Receiver Operator Curve (AUROC) and Area Under the Precision Recall Curve (AUPRC). We compared the models' ability to predict an adverse event (defined as ICU transfer, mechanical ventilation use, or death) at two levels of granularity: (1) maximum score across an encounter with a minimum lead time before the first adverse event and (2) predictions at every observation with instances in the last 24 hours before the adverse event labeled as positive. PICTURE and the EDI were also compared on the encounter level using different lead times extending out to 24 hours. Shapley values were used to provide explanations for PICTURE predictions. Results PICTURE successfully delineated between high- and low-risk patients and consistently outperformed the EDI in both of our cohorts. In non-COVID-19 patients, PICTURE achieved an AUROC (95% CI) of 0.819 (0.805 - 0.834) and AUPRC of 0.109 (0.089 - 0.125) on the observation level, compared to the EDI AUROC of 0.762 (0.746 - 0.780) and AUPRC of 0.077 (0.062 - 0.090). On COVID-19 positive patients, PICTURE achieved an AUROC of 0.828 (0.794 - 0.869) and AUPRC of 0.160 (0.089 - 0.199), while the EDI scored an AUROC of 0.792 (0.754 - 0.835) and AUPRC of 0.131 (0.092 - 0.159). The most important variables influencing PICTURE predictions in the COVID-19 cohort were a rapid respiratory rate, a high level of oxygen support, low oxygen saturation, and impaired mental status (Glasgow coma score). Conclusion The PICTURE model is more accurate in predicting adverse patient outcomes for both general ward patients and COVID-19 positive patients in our cohorts compared to the EDI. The ability to consistently anticipate these events may be especially valuable when considering a potential incipient second wave of COVID-19 infections. PICTURE also has the ability to explain individual predictions to clinicians by ranking the most important features for a prediction. The generalizability of the model will require testing in other health care systems for validation.
has issue date	2020-07-10(xsd:dateTime)
bibo:doi	10.1101/2020.07.08.20145078
has license	medrxiv
sha1sum (hex)	afc6b92c3e4cd9bb86d96e19f91c50c58e0a6ab7
schema:url	https://doi.org/10.1101/2020.07.08.20145078
resource representing a document's title	Validation and comparison of PICTURE analytic and Epic Deterioration Index for COVID-19
resource representing a document's body	covid:afc6b92c3e4cd9bb86d96e19f91c50c58e0a6ab7#body_text
is schema:about of	named entity 'improve' named entity 'Epic' named entity 'intensive care unit (ICU)' named entity 'Index' covid:arg/afc6b92c3e4cd9bb86d96e19f91c50c58e0a6ab7 named entity 'improving' named entity 'identifying' named entity 'COVID-19' named entity 'United States' named entity 'triage' named entity 'intensive care unit' named entity 'healthcare facilities' named entity 'COVID-19' named entity 'Glasgow Coma Score' named entity 'prothrombin time' named entity 'Python' named entity 'academic medical center' named entity 'mechanical ventilation' named entity 'PPV' named entity 'PPV' named entity 'electronic medical record' named entity 'EDI' named entity 'oxygen supplementation' named entity 'adverse events' named entity 'EDI' named entity 'PPV' named entity 'COVID-19' named entity 'making predictions' named entity 'metabolic' named entity 'Pulse pressure' named entity 'adverse event' named entity 'ICU' named entity 'electronic health record' named entity 'Standard deviation' named entity 'EDI' named entity 'EDI' named entity 'glucose' named entity 't-test' named entity 'COVID-19' named entity 'COVID-19' named entity 'mechanical ventilation' named entity 'Positive values' named entity 'EDI' named entity 'Michigan Medicine' named entity 'COVID-19' named entity 'ICU' named entity 'oxygen' named entity 'non-representative' named entity 'Michigan Medicine' named entity 'ICU' named entity 'EDI' named entity 'platelet' named entity 'interventional radiology' named entity 'red blood cell' named entity 'ICU' named entity 'COVID-19' named entity 'EDI' named entity 'making predictions' named entity 'medRxiv' named entity 'log-odds' named entity 'intensive care unit' named entity 'systolic' named entity 'mechanical ventilation' named entity 'red cell distribution width' named entity 'blood pressure' named entity 'EDI' named entity 'vital sign' named entity 'EDI' named entity 'COVID-19' named entity 'COVID-19' named entity 'COVID-19'

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