Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jul 3;6(7):e2324176.
doi: 10.1001/jamanetworkopen.2023.24176.

Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults

Affiliations

Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults

Thomas F Byrd 4th et al. JAMA Netw Open. .

Abstract

Importance: The Deterioration Index (DTI), used by hospitals for predicting patient deterioration, has not been extensively validated externally, raising concerns about performance and equitable predictions.

Objective: To locally validate DTI performance and assess its potential for bias in predicting patient clinical deterioration.

Design, setting, and participants: This retrospective prognostic study included 13 737 patients admitted to 8 heterogenous Midwestern US hospitals varying in size and type, including academic, community, urban, and rural hospitals. Patients were 18 years or older and admitted between January 1 and May 31, 2021.

Exposure: DTI predictions made every 15 minutes.

Main outcomes and measures: Deterioration, defined as the occurrence of any of the following while hospitalized: mechanical ventilation, intensive care unit transfer, or death. Performance of the DTI was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Bias measures were calculated across demographic subgroups.

Results: A total of 5 143 513 DTI predictions were made for 13 737 patients across 14 834 hospitalizations. Among 13 918 encounters, the mean (SD) age of patients was 60.3 (19.2) years; 7636 (54.9%) were female, 11 345 (81.5%) were White, and 12 392 (89.0%) were of other ethnicity than Hispanic or Latino. The prevalence of deterioration was 10.3% (n = 1436). The DTI produced AUROCs of 0.759 (95% CI, 0.756-0.762) at the observation level and 0.685 (95% CI, 0.671-0.700) at the encounter level. Corresponding AUPRCs were 0.039 (95% CI, 0.037-0.040) at the observation level and 0.248 (95% CI, 0.227-0.273) at the encounter level. Bias measures varied across demographic subgroups and were 14.0% worse for patients identifying as American Indian or Alaska Native and 19.0% worse for those who chose not to disclose their ethnicity.

Conclusions and relevance: In this prognostic study, the DTI had modest ability to predict patient deterioration, with varying degrees of performance at the observation and encounter levels and across different demographic groups. Disparate performance across subgroups suggests the need for more transparency in model training data and reinforces the need to locally validate externally developed prediction models.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest Disclosures: Dr Melton-Meaux reported receiving grants from the Agency for Healthcare Research and Quality, the National Institutes of Health, and the US Department of Defense; personal fees from the Washington University External Advisory Board and the Clinical Decision Support Innovation Collaborative Steering Committee; and serving on the American Medical Informatics Association Board of Directors outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Flowchart of Patient Selection and Inclusion and Exclusion Criteria
DTI indicates Deterioration Index; and ICU, intensive care unit.
Figure 2.
Figure 2.. Variation in Model Performance
Variation was measured by the sum of 3 bias measures across patient subgroups. Data were measured at the encounter level. Reference group parity values were equal to 1 for each individual bias measure within each subgroup. Reference groups were White patients, patients who identified as other ethnicity, male patients, and patients 60 years or older. The dashed line represents the reference group parity value for the sum of all 3 bias measures. AUROC indicates area under the receiver operating characteristic curve; and PPV, positive predictive value.

References

    1. Thomson R, Luettel D, Healey F, Scobie S; National Patient Safety Agency . Safer care for the acutely ill patient: learning from serious incidents. Patient Safety Network, Agency for Healthcare Research and Quality. October 24, 2007. Accessed May 22, 2022. https://psnet.ahrq.gov/issue/safer-care-acutely-ill-patient-learning-ser...
    1. Burke JR, Downey C, Almoudaris AM. Failure to rescue deteriorating patients: a systematic review of root causes and improvement strategies. J Patient Saf. 2022;18(1):e140-e155. doi:10.1097/PTS.0000000000000720 - DOI - PubMed
    1. McGaughey J, O’Halloran P, Porter S, Blackwood B. Early warning systems and rapid response to the deteriorating patient in hospital: a systematic realist review. J Adv Nurs. 2017;73(12):2877-2891. doi:10.1111/jan.13398 - DOI - PubMed
    1. Verma AA, Pou-Prom C, McCoy LG, et al. . Developing and validating a prediction model for death or critical illness in hospitalized adults, an opportunity for human-computer collaboration. Crit Care Explor. 2023;5(5):e0897. doi:10.1097/CCE.0000000000000897 - DOI - PMC - PubMed
    1. Bedoya AD, Clement ME, Phelan M, Steorts RC, O’Brien C, Goldstein BA. Minimal impact of implemented early warning score and best practice alert for patient deterioration. Crit Care Med. 2019;47(1):49-55. doi:10.1097/CCM.0000000000003439 - DOI - PMC - PubMed

Publication types