Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Oct 16, 2020
Date Accepted: Apr 3, 2021
Date Submitted to PubMed: Apr 22, 2021

The final, peer-reviewed published version of this preprint can be found here:

Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods

Cummings BC, Ansari S, Motyka JR, Wang G, Medlin RP Jr, Kronick SL, Singh K, Park PK, Napolitano LM, Dickson RP, Mathis MR, Sjoding MW, Admon AJ, Blank R, McSparron JI, Ward KR, Gillies CE

Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods

JMIR Med Inform 2021;9(4):e25066

DOI: 10.2196/25066

PMID: 33818393

PMCID: 8061893

Predicting Unexpected Deterioration in COVID-19 Patients using PICTURE Analytic: Validation and Comparison to Existing Methods

  • Brandon C Cummings; 
  • Sardar Ansari; 
  • Jonathan R Motyka; 
  • Guan Wang; 
  • Richard P Medlin Jr; 
  • Steven L Kronick; 
  • Karandeep Singh; 
  • Pauline K Park; 
  • Lena M Napolitano; 
  • Robert P Dickson; 
  • Michael R Mathis; 
  • Michael W Sjoding; 
  • Andrew J Admon; 
  • Ross Blank; 
  • Jakob I McSparron; 
  • Kevin R Ward; 
  • Christopher E Gillies

ABSTRACT

Background:

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.

Objective:

To validate the PICTURE model’s ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model which has recently been assessed for use in 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 EDI and NEWS scores 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). Shapley values were used to provide explanations for PICTURE predictions.

Results:

In non-COVID-19 general ward patients, PICTURE achieved an AUROC (95% CI) of 0.819 (0.805 - 0.834) per observation, compared to the EDI’s 0.763 (0.746 - 0.781) (n = 21,740, P < 0.001). In patients testing positive for COVID-19, PICTURE again outperformed the EDI with an AUROC (95% CI) of 0.849 (0.820 – 0.878) compared to the EDI’s 0.803 (0.772 – 0.838) (n = 607, P < 0.001). 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).

Conclusions:

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 potential incipient waves of COVID-19 infections. The generalizability of the model will require testing in other health care systems for validation.


 Citation

Please cite as:

Cummings BC, Ansari S, Motyka JR, Wang G, Medlin RP Jr, Kronick SL, Singh K, Park PK, Napolitano LM, Dickson RP, Mathis MR, Sjoding MW, Admon AJ, Blank R, McSparron JI, Ward KR, Gillies CE

Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods

JMIR Med Inform 2021;9(4):e25066

DOI: 10.2196/25066

PMID: 33818393

PMCID: 8061893

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.