Correspondenc / American Journal of Emergency Medicine 36 (2018) 875-906 901

References

1. Czekajlo MS, Zawada B. Comparison of Intubrite and Macintosh laryngoscope use in immobilized cervical spine by novice nurses. A mannequin trial. Am J Emerg Med 2017. https://doi.org/10.1016/j.ajem.2017.08.035.
2. Bielski K, Smereka J, Ladny JR, Szarpak L. A comparison of the Macintosh laryngoscope and blind intubation via I-gel in intubating an entrapped patient: a randomized cross- over manikin study. Am J Emerg Med Mar 2017;35(3):484-7. https://doi.org/10. 1016/j.ajem.2016.11.048.
3. Smereka J, Ladny JR, Szarpak L. Comparison of the Intubrite and Macintosh laryngo- scopes in a difficult airway scenario. Am J Emerg Med Jun 2017;35(6):925. https:// doi.org/10.1016/j.ajem.2017.03.067.
4. Szarpak L, Smereka J, Ladny JR. Comparison of Macintosh and INTUBRITE laryngoscopes for intubation performed by novice physicians in a difficult airway scenario. Am J Emerg Med May 2017;35(5):796-7. https://doi.org/10.1016/j.ajem.2017.01.005.
5. Szarpak L, Czyzewski L, Truszewski Z, Kurowski A, Gaszynski T. Comparison of Coopdech(R), CoPilot(R), Intubrite(R), and Macintosh laryngoscopes for tracheal intuba- tion during Pediatric cardiopulmonary resuscitation: a randomized, controlled cross- over simulation trial. Eur J Pediatr Nov 2015;174(11):1517-23. https://doi.org/10. 1007/s00431-015-2567-8.

   Comments on validation of the criteria for early critical care resource use in assessing the effectiveness of field triage

   Dear Editor-in-Chief,

   We were interested to read the article by Ahn et al. that was pub- lished in the The American Journal of Emergency Medicine [1]. The au- thors aimed to assess validity of early critical care resource (CCR) use in the prediction of in-hospital mortality and comparing the Predictive performance of early CCR use and Severity Score (ISS) N 15 criteria. The results have demonstrated that area under the receiver operating characteristic (AUROC) curves (95% confidence interval) of early CCR use and ISS criteria for discrimination between survivors and non- survivors were 0.89 (0.85-0.91) and 0.84 (0.79-0.86), respectively [1]. The results were very interesting, however, we think some method- ological issues should be noticed to avoid misinterpretations. The authors used the same study population to assess the validity of early CCR use. In other words, they conducted both of model development and prediction on the same dataset. A problem with doing this is that the interpretation about validity early CCR use is very optimistic. As a general rule in validation studies, the study sample is randomly divided into two or more independent samples so that in one sample the model will be created and in the remainder the predictive performance will be

   tested [2,3].

   It is crucial to emphasize that differences in estimated AUCs of early CCR use and ISS criteria is nothing and even clinically negligible. We suggest the difference between AUCs to be tested with efficient and simple statistical methods [4,5].

   Moreover, the prediction model using the cross-sectional study con- ducted by Ahn and colleagues may be misleading. In a Cross sectional study, predictor and outcome will measured at the same and temporal relationship between predictor and outcome is not possible [6].

   Conflict of interest

   None.

   Acknowledgment

   This work was not supported by any organization.

   Erfan Ayubi

   Department of Epidemiology, School of Public Health, Shahid Beheshti

   University of Medical Sciences, Tehran, Iran Department of Epidemiology & Biostatistics, School of Public Health, Tehran

   University of Medical Sciences, Tehran, Iran

   Saeid Safiri Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences,

   Maragheh, Iran

   Corresponding author.

   E-mail address: [email protected].

   4 August 2017

   https://doi.org/10.1016/j.ajem.2017.09.046

   References

   Ahn KO, Kim SC, Park JO, Shin SD, Song KJ, Hong KJ. Validation of the criteria for early critical care resource use in assessing the effectiveness of field triage. Am J Emerg Med 2018;36(2)257-261.

6. Safiri S, Khazaei S, Mansori K, Sani M, Ayubi E. Differing predictive relationships be- tween baseline LDL-C, systolic blood pressure, and cardiovascular outcomes: method- ological issues. Int J Cardiol 2017;229:141.
7. Safiri S, Ayubi E. Predictors of mortality in adults with sickle cell patients admitted to intensive care unit in SMC: Methodological issues. J Crit Care 2018;43:29.
8. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837-45.
9. Hanley JA, McNeil BJA. method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983;148:839-43.
10. Ashrafi-Asgarabad A, Ayubi E, Safiri S. Predictors of health-related quality of life in

    people with amyotrophic lateral sclerosis: methodological issues. J Neurol Sci 2017; 372:228.

    Increasing the quality of medication histories in the ED with pharmacy students

    Dear Reader,

    adverse drug events (ADEs) are associated with Patient harm, in- creased hospital length of stay and cost [1]. Although pharmacists are important in identifying medication related ADEs and discrepancies in documented medication histories throughout transitions of care, it is difficult to devote resources [2-5]. Pharmacy technicians require exten- sive training, however pharmacy students are taught early in their pro- fessional curriculum, to identify and address drug-related problems making them ideal to obtain accurate medication information.

    This pilot program was conducted in a 120-bed emergency depart- ment (ED) (~ 100,000 visits) at an 850-bed academic medical center. Pharmacy students, following completion of two (of four) years of pro- fessional education, worked in the ED for approximately 5 weeks (21 weekdays, daytime hours, one student at a time). Focus was on patients admitted to inpatient status, with the most complicated medication reg- imens, identified by a high number of outpatient medications in the electronic medical record (EMR).

    There was no formalized training program outside of the institution process and EMR navigation. Pharmacy student initial screening in- volved review of the EMR medication list (generated by insurance claims, outpatient pharmacy records, and previous healthcare system encounters). At the time of pharmacy student review this list was al- ready reviewed and completed by an emergency medicine (EM) or in- patient clinician for the current presentation/admission. The pharmacy student was responsible for developing the Best possible medication history (systematic review of medication history and attainment of in- formation through reliable sources) through interviewing patients, family members, outpatient pharmacies, or primary care/specialty phy- sicians. All medication list changes (including rationale) were docu- mented in the EMR and the covering clinician was paged to review and attest the updated EMR list.

    902 Correspondence / American Journal of Emergency Medicine 36 (2018) 875-906

    A total of 164 complex patients were interviewed during the study period (approximately 8 patients per day). Median number of medica- tions/patient was 10 (IQR 7-14.5) and 143 patients (87.2%) had >= 6 medications. Median time to complete a best possible medication history was 20 min (IQR 10-30); 9 required evaluation for >= 40 min. Overall 443 discrepancies were identified in 164 patients (1775 total medications reviewed); mean 2.7 +- 2.9/patient. Median number of dis- crepancies/patient was two and 42 patients (25.6%) had zero discrepan- cies identified. In those with at least one discrepancy (n = 122), the mean number of discrepancies/patient was 3.6 +- 2.9. These patients were also prescribed more outpatient medications compared to those without discrepancies, median 11 (IQR 7-15) vs. 8 (IQR 4.25-10) med- ications, p b 0.0001. There was a moderate positive correlation between number of outpatient medications prescribed and discrepancies found, spearman correlation coefficient 0.4, p b 0.0001 (SAS version 9.3, SAS In- stitute Inc., Cary, NC).

    Most common changes were medication additions (179 [40.4%]) or deletions (143 [32.3%]). Others included incorrect dose (14.7%), sched- ule (10.6%), drug (1.6%), or route (0.5%). Majority of discrepancies (n = 426) were identified via telephone call (96.2%) compared to face-to- face patient conversation (3.8%); 210 telephone calls (identifying 426 discrepancies in 148 patients) were made. Outpatient pharmacies were called most often (73.3%) compared to outpatient offices/long term care (15.2%) and family (11.4%).

    Almost 75% of patients reviewed had at least one medication history discrepancy, despite initial medication history having already been completed by a clinician illustrating the importance of intensive review and limitations of the EMR. It is concerning the EMR auto-populated list may be relied upon and could contribute to ADEs.

    A similar study, in the inpatient setting, found a comparable discrepancy rate (2.8 +- 3.1 per patient) to our study (2.7 +- 2.9 per pa- tient) even though our students completed less years of professional training and had a brief orientation compared to extensive training [6]. A positive correlation regarding number of outpatient medications and discrepancies emphasizes importance of targeting complex patients.

    There is controversy regarding the role of the EM clinical pharmacist in obtaining medication histories [7]. Recently, a national survey of EM pharmacy services found that 80% of respondents (pharmacists practic- ing in the ED) were involved in medication history activities [8]. Our re- port shows that pharmacy students are cost-effective pharmacist- extenders. This responsibility provides beneficial learning experiences to students, reinforces professional curriculum, potentially decreases ADEs due to inaccurate medication histories, and allows EM clinical pharmacists to focUS time in expanded roles including response for time-dependent emergencies, pharmacotherapy consultations, and ad- ministrative/scholarly collaborations EM clinicians.

    Although true accuracy of our best possible medication history can- not be determined since there was no gold standard we do want to highlight that pharmacy students identified discrepancies in already completed clinician lists in 75% of cases and can infer that identifying discrepancies likely had positive impact on transitions of care and ADE prevention.

    Pharmacy students can take ownership and lead a medication histo- ry program in a busy ED, with minimal training and identify Medication discrepancies. This allows for EM clinicians and clinical pharmacists to prioritize more specialized bedside Patient Care Activities.

    Source of support

    None.

    Prior presentations

    Presented at the American Society of Health-System Pharmacists Annual Meeting, New Orleans, LA, December 2015.

    Maria Janda, PharmD Lauren Z. Gashlin, PharmD

    University of Rochester Medical Center, 601 Elmwood Ave., Box 638,

    Rochester, NY 14642, United States

    Nicole M. Acquisto, PharmD

    University of Rochester Medical Center, 601 Elmwood Ave., Box 638,

    Rochester, NY 14642, United States Department of Emergency Medicine, University of Rochester Medical Center, 601 Elmwood Ave., Box 638, Rochester, NY 14642, USA Corresponding author at: University of Rochester Medical Center, 601 Elmwood Ave., Box 638, Rochester, NY 14642, United States.

    E-mail address: [email protected].

    Elizabeth Dodds Ashley, PharmD, MS, BCPS (AQ-ID) Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, DUMC Box 103259, Room 170 Hanes House, Durham,

    NC 27710, United States

    20 June 2016

    https://doi.org/10.1016/j.ajem.2017.09.047

    References

    Weiss AJ, Elixhauser A. Characteristics of adverse drug events originating during the hospital stay, 2011: statistical brief #164. Healthcare Cost and Utilization Project (HCUP) statistical briefs. Rockville (MD): Agency for Health Care Policy and Research (US); 2006.

11. Hepler CD, Strand LM. Opportunities and responsibilities in pharmaceutical care. Am J

    Hosp Pharm 1990;47(3):533-43.

    Bruce Bayley K, Savitz LA, Maddalone T, Stoner SE, Hunt JS, Wells R. Evaluation of pa- tient care interventions and recommendations by a transitional care pharmacist. Ther Clin Risk Manag 2007;3(4):695-703.

12. Schnipper JL, Kirwin JL, Cotugno MC, et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med 2006;166(5): 565-71.
13. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med 2009;150(3):178-87.
14. Lubowski TJ, Cronin LM, Pavelka RW, Briscoe-Dwyer LA, Briceland LL, Hamilton RA. Effectiveness of a medication reconciliation project conducted by PharmD students. Am J Pharm Educ 2007;71(5):94.
15. Patanwala A. Emergency pharmacy practice and medication reconciliation. Am J Health Syst Pharm 2014;71:2167-8.
16. Thomas M, Acquisto NM, Shirk MB, Patanwala AE. A national survey of emergency pharmacy practice in the United States. Am J Health Syst Pharm 2016;73:386-94.

    Comparison of six scoring systems for predicting the mortality of severe sepsis patients in the emergency department

    To the Editor,

    The article by Samir Haydar et al. is definitely meaningful [1]. In this article, the authors discussed the value of the only two scoring methods (qSOFA and SIRS) on predicting the mortality of severe sepsis. Further- more, we also used some other early rapid predictive methods in our clinic practice, such as MEWS (modified early warning score) [2], MEDS(Mortality in ED Sepsis) [3]. In addition, SOFA (Sequential Organ Failure Assessment) [3,4] and APACHE_II(acute physiology and chronic health evaluation),which need more parameters, are also widely used for the Mortality prediction of in severe sepsis patients [5]. Some studies [2-6] suggested that these methods are valuable for predicting the risk of death in critical sepsis. The original article did not analyze the value of the other scores, so we conducted this retrospective pilot study to com- pare the six scoring system(qSOFA, SIRS, MEWS, MEDS, SOFA and