Development and validation of a preliminary multivariable diagnostic model for identifying unusual infections in hospitalized patients

Authors

  • Aysun Tekin Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA https://orcid.org/0000-0002-1891-2118
  • Mohammad Joghataee Department of Business Analytics and Information Systems, Auburn University, Auburn, AL, USA. https://orcid.org/0000-0002-8849-4695
  • Lucrezia Rovati Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA; School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
  • Hong Truong Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
  • Claudia Castillo-Zambrano Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA https://orcid.org/0000-0002-9469-6656
  • Kushagra Kushagra Department of Business Analytics and Information Systems, Auburn University, Auburn, AL, USA.
  • Nasrin Nikravangolsefid Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA https://orcid.org/0000-0003-1362-6769
  • Mahmut Ozkan Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA. https://orcid.org/0000-0001-5905-3498
  • Ashish Gupta Department of Business Analytics and Information Systems, Auburn University, Auburn, AL, USA.
  • Vitaly Herasevich Department of Anesthesiology and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA https://orcid.org/0000-0002-0214-0651
  • Juan Domecq Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
  • John O’Horo Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Infectious Diseases, Mayo Clinic, Rochester, MN, USA https://orcid.org/0000-0002-0880-4498
  • Ognjen Gajic Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA. https://orcid.org/0000-0003-4218-0890

DOI:

https://doi.org/10.17305/bb.2024.10447

Keywords:

Atypical infections, diagnostic delay, diagnostic model, multivariable model, rare infections

Abstract

Diagnostic delay leads to poor outcomes in infections, and it occurs more often when the causative agent is unusual. Delays are attributable to failing to consider such diagnoses in a timely fashion. Using routinely collected electronic health record (EHR) data, we built a preliminary multivariable diagnostic model for early identification of unusual fungal infections and tuberculosis in hospitalized patients. We conducted a two-gate case-control study. Cases encompassed adult patients admitted to 19 Mayo Clinic enterprise hospitals between January 2010 and March 2023 diagnosed with blastomycosis, cryptococcosis, histoplasmosis, mucormycosis, pneumocystosis, or tuberculosis. Control groups were drawn from all admitted patients (random controls) and those with community-acquired infections (ID-controls). Development and validation datasets were created using randomization for dividing cases and controls (7:3), with a secondary validation using ID-controls. A logistic regression model was constructed using baseline and laboratory variables, with the unusual infections of interest outcome. The derivation dataset comprised 1043 cases and 7000 random controls, while the 451 cases were compared to 3000 random controls and 1990 ID-controls for validation. Within the derivation dataset, the model achieved an area under the curve (AUC) of 0.88 (95% confidence interval [CI]: 0.87-0.89) with a good calibration accuracy (Hosmer-Lemeshow P = 0.623). Comparable performance was observed in the primary (AUC = 0.88; 95% CI: 0.86-0.9) and secondary validation datasets (AUC = 0.84; 95% CI: 0.82-0.86). In this multicenter study, an EHR-based preliminary diagnostic model accurately identified five unusual fungal infections and tuberculosis in hospitalized patients. With further validation, this model could help decrease time to diagnosis.

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Development and validation of a preliminary multivariable diagnostic model for identifying unusual infections in hospitalized patients

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Published

20-04-2024

Data Availability Statement

The individual participant data that underlie the results reported in this article, after de-identification, and the study protocol are available to researchers who provide a methodologically sound proposal from the corresponding author at any time.

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Section

Translational and Clinical Research

How to Cite

1.
Development and validation of a preliminary multivariable diagnostic model for identifying unusual infections in hospitalized patients. Biomol Biomed [Internet]. 2024 Apr. 20 [cited 2024 Jun. 26];. Available from: https://www.bjbms.org/ojs/index.php/bjbms/article/view/10447