Search

Introduction During high-fidelity simulations in the Critical Care Air Transport (CCAT) Advanced course, we identified a high frequency of insulin medication errors and sought strategies to reduce them using a human factors approach. Materials and Methods Of 169 eligible CCAT simulations, 22 were randomly selected for retrospective audio–video review to establish a baseline frequency of insulin medication errors. Using the Human Factors Analysis Classification System, dosing errors, defined as a physician ordering an inappropriate dose, were categorized as decision-based; administration errors, defined as a clinician preparing and administering a dose different than ordered, were categorized as skill-based. Next, 3 a priori interventions were developed to decrease the frequency of insulin medication errors, and these were grouped into 2 study arms. Arm 1 included a didactic session reviewing a sliding-scale insulin (SSI) dosing protocol and a hands-on exercise requiring all CCAT teams to practice preparing 10 units of insulin including a 2-person check. Arm 2 contained arm 1 interventions and added an SSI cognitive aid available to students during simulation. Frequency and type of insulin medication errors were collected for both arms with 93 simulations for arm 1 (January–August 2021) and 139 for arm 2 (August 2021–July 2022). The frequency of decision-based and skill-based errors was compared across control and intervention arms. Results Baseline insulin medication error rates were as follows: decision-based error occurred in 6/22 (27.3%) simulations and skill-based error occurred in 6/22 (27.3%). Five of the 6 skill-based errors resulted in administration of a 10-fold higher dose than ordered. The post-intervention decision-based error rates were 9/93 (9.7%) and 23/139 (2.2%), respectively, for arms 1 and 2. Compared to baseline error rates, both arm 1 (P  = .04) and arm 2 (P  < .001) had a significantly lower rate of decision-based errors. Additionally, arm 2 had a significantly lower decision-based error rate compared to arm 1 (P  = .015). For skill-based preparation errors, 1/93 (1.1%) occurred in arm 1 and 4/139 (2.9%) occurred in arm 2. Compared to baseline, this represents a significant decrease in skill-based error in both arm 1 (P  < .001) and arm 2 (P  < .001). There were no significant differences in skill-based error between arms 1 and 2. Conclusions This study demonstrates the value of descriptive error analysis during high-fidelity simulation using audio–video review and effective risk mitigation using training and cognitive aids to reduce medication errors in CCAT. As demonstrated by post-intervention observations, a human factors approach successfully reduced decision-based error by using didactic training and cognitive aids and reduced skill-based error using hands-on training. We recommend the development of a Clinical Practice Guideline including an SSI protocol, guidelines for a 2-person check, and a cognitive aid for implementation with deployed CCAT teams. Furthermore, hands-on training for insulin preparation and administration should be incorporated into home station sustainment training to reduced medication errors in the operational environment. [ABSTRACT FROM AUTHOR]

Background The Critical Care Air Transport (CCAT) Advanced Course utilizes fully immersive high-fidelity simulations to assess personnel readiness for deployment. This study aims to determine whether simple well-defined demographic identifiers can be used to predict CCAT students' performance at CCAT Advanced. Materials and Methods CCAT Advanced student survey data and course status (pass/fail) between March 2006 and April 2020 were analyzed. The data included students' Air Force Specialty Code (AFSC), military status (active duty and reserve/guard), CCAT deployment experience (yes/no), prior CCAT Advanced training (yes/no), medical specialty, rank, and unit sustainment training frequency (never, frequency less often than monthly, and frequency at least monthly). Following descriptive analysis and comparative tests, multivariable regression was used to identify the predictors of passing the CCAT Advanced course for each provider type. Results A total of 2,576 student surveys were analyzed: 694 (27%) physicians (MDs), 1,051 (40%) registered nurses (RNs), and 842 (33%) respiratory therapists (RTs). The overall passing rates were 92.2%, 90.3%, and 85.4% for the MDs, RNs, and RTs, respectively. The students were composed of 579 (22.5%) reserve/guard personnel, 636 (24.7%) with CCAT deployment experience, and 616 (23.9%) with prior CCAT Advanced training. Regression analysis identified groups with lower odds of passing; these included (1) RNs who promoted from Captain to Major (post-hoc analysis, P  = .03), (2) RTs with rank Senior Airman, as compared to Master Sergeants (post-hoc analysis, P  = .04), and (3) MDs with a nontraditional AFSC (P  = .0004). Predictors of passing included MDs and RNs with CCAT deployment experience, odds ratio 2.97 (P  = .02) and 2.65 (P  = .002), respectively; and RTs who engaged in unit CCAT sustainment at least monthly (P  = .02). The identifiers prior CCAT Advanced training or reserve/guard military status did not confer a passing advantage. Conclusion Our main result is that simple readily available metrics available to unit commanders can identify those members at risk for poor performance at CCAT Advanced readiness training; these include RNs with rank Major or above, RTs with rank Senior Airman, and RTs who engage in unit sustainment training less often than monthly. Finally, MD specialties which are nontraditional for CCAT have significantly lower CCAT Advanced passing rates, reserve/guard students did not outperform active duty students, there was no difference in the performance between different RN specialties, and for MD and RN students' previous deployment experience was a strong predictor of passing. [ABSTRACT FROM AUTHOR]

Introduction Inappropriate fluid management during patient transport may lead to casualty morbidity. Percent systolic pressure variation (%SPV) is one of several technologies that perform a dynamic assessment of fluid responsiveness (FT-DYN). Trained anesthesia providers can visually estimate and use %SPV to limit the incidence of erroneous volume management decisions to 1-4%. However, the accuracy of visually estimated %SPV by other specialties is unknown. The aim of this article is to determine the accuracy of estimated %SPV and the incidence of erroneous volume management decisions for Critical Care Air Transport (CCAT) team members before and after training to visually estimate and utilize %SPV. Material and Methods In one sitting, CCAT team providers received didactics defining %SPV and indicators of fluid responsiveness and treatment with %SPV ≤7 and ≥14.5 defining a fluid nonresponsive and responsive patient, respectively; they were then shown ten 45-second training arterial waveforms on a simulated Propaq M portable monitor's screen. Study subjects were asked to visually estimate %SPV for each arterial waveform and queried whether they would treat with a fluid bolus. After each training simulation, they were told the true %SPV. Seven days post-training, the subjects were shown a different set of ten 45-second testing simulations and asked to estimate %SPV and choose to treat, or not. Nonparametric limits of agreement for differences between true and estimated %SPV were analyzed using Bland–Altman graphs. In addition, three errors were defined: (1) %SPV visual estimate errors that would label a volume responsive patient as nonresponsive, or vice versa; (2) incorrect treatment decisions based on estimated %SPV (algorithm application errors); and (3) incorrect treatment decisions based on true %SPV (clinically significant treatment errors). For the training and testing simulations, these error rates were compared between, and within, provider groups. Results Sixty-one physicians (MDs), 64 registered nurses (RNs), and 53 respiratory technicians (RTs) participated in the study. For testing simulations, the incidence and 95% CI for %SPV estimate errors with sufficient magnitude to result in a treatment error were 1.4% (0.5%, 3.2%), 1.6% (0.6%, 3.4%), and 4.1% (2.2%, 6.9%) for MDs, RNs, and RTs, respectively. However, clinically significant treatment errors were statistically more common for all provider types, occurring at a rate of 7%, 10%, and 23% (all P  < .05). Finally, students did not show clinically relevant reductions in their errors between training and testing simulations. Conclusions Although most practitioners correctly visually estimated %SPV and all students completed the training in interpreting and applying %SPV, all groups persisted in making clinically significant treatment errors with moderate to high frequency. This suggests that the treatment errors were more often driven by misapplying FT-DYN algorithms rather than by inaccurate visual estimation of %SPV. Furthermore, these errors were not responsive to training, suggesting that a decision-making cognitive aid may improve CCAT teams' ability to apply FT-DYN technologies. [ABSTRACT FROM AUTHOR]

Objective: To provide a brief summary on food additives and to outline a practical approach for evaluating subjects suspected of having reactions to food additives. Data Sources: Information was derived from selected reviews and original articles published in peer-reviewed journals, supplemented by the clinical experience of the authors. Study Selection: Priority was given to studies that used blinded, placebo controlled, oral challenges to confirm adverse reactions to food additives. In addition, selected, appropriately evaluated case reports were included. Results: A large number of food additives are widely used in the food industry. Allergic reactions to additives seem to be rare but are very likely underdiagnosed, primarily due to a low index of suspicion. A wide variety of symptoms to food additives have been reported, but a cause-and-effect relationship has not been well documented in the majority of cases. Conclusion: Reactions to food additives should be suspected in patients who report symptoms related to multiple foods or to a certain food when commercially prepared but not when home made. It is also prudent to investigate food additives in subjects considered to have "idiopathic" reactions. Except for a limited number of natural additives, there is a small role for skin tests or in vitro testing. Oral challenge, in stages, with commonly used additives is the definitive procedure for detecting the offending agent. Once the specific additive is identified, management is strict avoidance, which can be difficult. [ABSTRACT FROM AUTHOR]

We hypothesize that one brief training episode on the visual estimation of percent systolic pressure variation (%SPV) and % SPV-based fluid administration decision making will result in less than 10% fluid administration errors. B Conclusions: b CCAT MDs and RNs performed the best, their incorrect fluid treatments occurred 6.6-9.6% of the time; the largest component of their errors resulted from choosing an incorrect fluid therapy after an estimate of %SPV was made. [Extracted from the article]

The Muller F element (4.2 Mb, ~80 protein-coding genes) is an unusual autosome of Drosophila melanogaster; it is mostly heterochromatic with a low recombination rate. To investigate how these properties impact the evolution of repeats and genes, we manually improved the sequence and annotated the genes on the D. erecta, D. mojavensis, and D. grimshawi F elements and euchromatic domains from the Muller D element. We find that F elements have greater transposon density (25-50%) than euchromatic reference regions (3-11%). Among the F elements, D. grimshawi has the lowest transposon density (particularly DINE-1: 2% vs. 11-27%). F element genes have larger coding spans, more coding exons, larger introns, and lower codon bias. Comparison of the Effective Number of Codons with the Codon Adaptation Index shows that, in contrast to the other species, codon bias in D. grimshawi F element genes can be attributed primarily to selection instead of mutational biases, suggesting that density and types of transposons affect the degree of local heterochromatin formation. F element genes have lower estimated DNA melting temperatures than D element genes, potentially facilitating transcription through heterochromatin. Most F element genes (~90%) have remained on that element, but the F element has smaller syntenic blocks than genome averages (3.4-3.6 vs. 8.4-8.8 genes per block), indicating greater rates of inversion despite lower rates of recombination. Overall, the F element has maintained characteristics that are distinct from other autosomes in the Drosophila lineage, illuminating the constraints imposed by a heterochromatic milieu., (Copyright © 2015 Leung et al.)