External validation of a smartphone app model to predict the need for massive transfusion using five different definitions.

BACKGROUND: Previously, a model to predict massive transfusion protocol (MTP) (activation) was derived using a single-institution data set. The PRospective, Observational, Multicenter, Major Trauma Transfusion database was used to externally validate this model's ability to predict both MTP activation and massive transfusion (MT) administration using multiple MT definitions. METHODS: The app model was used to calculate the predicted probability of MTP activation or MT delivery. The five definitions of MT used were: (1) 10 units packed red blood cells (PRBCs) in 24 hours, (2) Resuscitation Intensity score ≥ 4, (3) critical administration threshold, (4) 4 units PRBCs in 4 hours; and (5) 6 units PRBCs in 6 hours. Receiver operating curves were plotted to compare the predicted probability of MT with observed outcomes. RESULTS: Of 1,245 patients in the data set, 297 (24%) met definition 1, 570 (47%) met definition 2, 364 (33%) met definition 3, 599 met definition 4 (49.1%), and 395 met definition 5 (32.4%). Regardless of the outcome (MTP activation or MT administration), the predictive ability of the app model was consistent: when predicting activation of the MTP, the area under the curve for the model was 0.694 and when predicting MT administration, the area under the curve ranged from 0.695 to 0.711. CONCLUSION: Regardless of the definition of MT used, the app model demonstrates moderate ability to predict the need for MT in an external, homogenous population. Importantly, the app allows the model to be iteratively recalibrated ("machine learning") and thus could improve its predictive capability as additional data are accrued. LEVEL OF EVIDENCE: Diagnostic test study/Prognostic study, level III.

Estimating the prevalence of coinfection with influenza virus and the atypical bacteria Bordetella pertussis, Chlamydophila pneumoniae, and Mycoplasma pneumoniae.

Coinfections with common bacterial respiratory pathogens and influenza viruses are well-known causes of disease, often via synergistic interactions between the influenza virus, the bacteria, and the human host. However, relatively little is known about interactions between atypical bacteria and influenza viruses. A recent report by Reinton et al. explored this issue by analyzing data from 3,661 patients seeking medical assistance for the presence of Mycoplasma pneumoniae, Chlamydophila pneumoniae, and Bordetella pertussis, as well as influenza A or B virus in nasal swab specimens. The report, however, did not accurately assess the epidemiologic interactions of these pathogens. We aimed to describe the interactions between these bacterial species and influenza infections. Strong and highly statistically significant antagonistic interspecies interactions were detected between C. pneumoniae and influenza virus [odds ratio (OR): 0.09; p < 0.0001) and M. pneumoniae and influenza virus infections (OR: 0.29; p = 0.003). No association was detected between B. pertussis and influenza infection (p = 0.34), contrary to the initial report, and coinfection was not detected at a higher-than-by-chance frequency within the population. Further support of these results is supplied by the analysis of two earlier investigations reporting data on influenza virus and these atypical bacteria. Our results supplement the large body of literature regarding interactions between influenza virus and typical respiratory pathogens, providing a fuller picture of the spectrum of interactions between influenza viruses and respiratory bacteria. Further, we demonstrate the importance of choosing the most appropriate reference populations for the analysis being performed and describe the pitfalls that may occur when care is not taken in this regard.