Search

Background: Dignitary medicine is an emerging field of training that involves the specialized care of diplomats, heads of state, and other high-ranking officials. In an effort to provide guidance on training in this nascent field, we convened a panel of experts in dignitary medicine and using the Delphi methodology, created a consensus curriculum for training in dignitary medicine., Methods: A three-round Delphi consensus process was performed with 42 experts in the field of dignitary medicine. Predetermined scores were required for an aspect of the curriculum to advance to the next round. The scores on the final round were used to determine the components of the curriculum. Scores below the threshold to advance were dropped in the subsequent round., Results: Our panel had a high degree of agreement on the required skills needed to practice dignitary medicine, with active practice in a provider's baseline specialty, current board certification, and skills in emergency care and resuscitation being the highest rated skills dignitary medicine physicians need. Skills related to vascular and emergency ultrasound and quality improvement were rated the lowest in the Delphi analysis. No skills were dropped from consideration., Conclusions: The results of our work can form the basis of formal fellowship training, continuing medical education, and publications in the field of dignitary medicine. It is clear that active medical practice and knowledge of resuscitation and emergency care are critical skills in this field, making emergency medicine physicians well suited to practicing dignitary medicine.

Background: Community-acquired pneumonia (CAP) is one of the leading causes of morbidity and mortality in the USA. Our objective was to assess the predictive value on critical illness and disposition of a sequential Bayesian Model that integrates Lactate and procalcitonin (PCT) for pneumonia., Methods: Sensitivity and specificity of lactate and PCT attained from pooled meta-analysis data. Likelihood ratios calculated and inserted in Bayesian/ Fagan nomogram to calculate posttest probabilities. Bayesian Diagnostic Gains (BDG) were analyzed comparing pre and post-test probability. To assess the value of integrating both PCT and Lactate in Severity of Illness Prediction we built a model that combined CURB65 with PCT as the Pre-Test markers and later integrated the Lactate Likelihood Ratio Values to generate a combined CURB 65 + Procalcitonin + Lactate Sequential value., Results: The BDG model integrated a CUBR65 Scores combined with Procalcitonin (LR+ and LR-) for Pre-Test Probability Intermediate and High with Lactate Positive Likelihood Ratios. This generated for the PCT LR+ Post-test Probability (POSITIVE TEST) Posterior probability: 93% (95% CI [91,96%]) and Post Test Probability (NEGATIVE TEST) of: 17% (95% CI [15-20%]) for the Intermediate subgroup and 97% for the high risk sub-group POSITIVE TEST: Post-Test probability:97% (95% CI [95,98%]) NEGATIVE TEST: Post-test probability: 33% (95% CI [31,36%]) . ANOVA analysis for CURB 65 (alone) vs CURB 65 and PCT (LR+) vs CURB 65 and PCT (LR+) and Lactate showed a statistically significant difference (P value = 0.013)., Conclusions: The sequential combination of CURB 65 plus PCT with Lactate yielded statistically significant results, demonstrating a greater predictive value for severity of illness thus ICU level care.