Combat-Related Invasive Fungal Infections: Development of a Clinically Applicable Clinical Decision Support System for Early Risk Stratification
| Autor: | Jonathan A. Forsberg, Elizabeth Silvius, Arnaud Belard, Benjamin K. Potter, Amy C. Weintrob, Eric A. Elster, Matthew B. Wagner, Seth Schobel, Vivek Khatri, David R. Tribble |
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| Rok vydání: | 2018 |
| Předmět: |
021110 strategic
defence & security studies medicine.medical_specialty Receiver operating characteristic business.industry 0211 other engineering and technologies Public Health Environmental and Occupational Health MEDLINE 02 engineering and technology General Medicine Tertiary care Clinical decision support system Feature Article and Original Research 03 medical and health sciences Bayes' theorem High morbidity 0302 clinical medicine Threshold probability Risk stratification Emergency medicine medicine 030212 general & internal medicine business |
| Zdroj: | Military Medicine. 184:e235-e242 |
| ISSN: | 1930-613X 0026-4075 |
| DOI: | 10.1093/milmed/usy182 |
| Popis: | Introduction Invasive fungal infections (IFI) are associated with high morbidity and mortality. A better method of risk stratifying trauma patients for combat-related IFI is needed to improve clinical outcomes while minimizing morbidity related to overtreatment. We sought to develop combat-related IFI clinical decision support (CDS) tools to assist providers to make treatment decisions both near the point of injury and subsequently at definitive treatment centers. Materials and Methods We utilized a training dataset containing information from 227 combat-injured military personnel to build a Bayesian belief network (BBN) to predict the likelihood of developing IFI using information available at the point of initial resuscitation (THEATER model) and in the tertiary care setting (MEDCEN model). After selecting BBN models, external validation used a separate test dataset of 350 wounded warriors. Furthermore, the performance of the BBN models was compared with a “two-rule model” alone (based on physician experience) and combinations of the BBN models plus the two-rule model. The two-rule model contains plausible IFI criteria, but it has not been formally evaluated, and they are not currently actual clinical guidelines. Results We found receiver operating characteristic areas under the curve (AUC) of 0.70 (95% CI: [0.62, 0.77]) and 0.68 (95% CI: [0.59, 0.76]) for the THEATER and MEDCEN BBN models, respectively, on cross-validation. External validation with the highest AUC BBN models produced THEATER AUC of 0.68 (95% CI: [0.58, 0.78]) and MEDCEN AUC of 0.67 (95% CI: [0.57, 0.78]). With the incorporation of two-rule model in low IFI-prevalence populations, external validation AUC increased to 0.77 (95% CI: [0.69, 0.84]) for the THEATER model and 0.76 (95% CI:[0.68, 0.85]) for the LRMC model. The two-rule model alone has an AUC of 0.72 (95% CI: [0.63, 0.81]). Conclusions Overall, the IFI tools produced clinically useful, robust models. However, the clinical utility of these models is highly dependent upon the clinician’s individual risk tolerance. The threshold probability for optimal clinical use of this CDS tool is currently being evaluated in an ongoing clinical utilization study. CDS tools, such as these, may facilitate early diagnosis of patients with or at risk for IFI, permitting early or prophylactic treatment with the aim of improving outcomes. |
| Databáze: | OpenAIRE |
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