26 research outputs found

    ๊ฐœ์ธ ๋งž์ถค ์•ฝ๋ฌผ ์ œ๊ณต์— ๊ธฐ์—ฌํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์•ฝ๋ฌผ ํŒจ์Šค์›จ์ด ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ •์˜๋ฃŒ์ •๋ณดํ•™์ „๊ณต, 2020. 8. ๊น€์ฃผํ•œ.Introduction: Genetic variations in human enzymes or transporters cause changes in the drug concentration inside the human body, which result in individual differences in drug response. Therefore, maintaining the optimal drug concentration by adjusting drug doses or selecting alternatives is necessary to maximize drug efficacy and safety. Recently, Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines based on pharmacogenetics have been published. However, these guidelines have some problems with clinical applications or regulations or standardization related to system development. To this end, I propose a pharmacogenomics pathway: PG-path, which can predict changes in the plasma drug concentration inside the body. Methods: The gene set that interacts with a specific drug is extracted from DrugBank, and the interaction types (enzyme, transporter, target) and action types (inhibition, induction, substrate) are obtained. The next step produces a frame and a background image to build the pharmacokinetic (PK) pathway and the pharmacodynamic (PD) pathway. Then, extracted elements are applied to the designed frame to visualize the interaction of drugs and genes. PathVisio is used to standardize components and formats. Results: The PG-path consists of a frame with organs in the human body and a background image with each organ figure. The interaction type and action type between drugs and genes were drawn with a standard symbol. The frame with the diagram was merged with the background image. We improved the understanding of the PG-path by hyperlinking the window containing information on genes and drugs to each node and popping it up. Genes were placed similar to inside the body to visualize the flow of the drug in the body. We applied the gene-wise variant burden (GVB) score which is the degree of accumulative damage of the gene to each gene, to make an individualized pathway. Conclusions: The PG-path is designed to visualize the general flow of drugs in the human body. Applying the GVB score to each gene makes it possible to predict the change in plasma drug concentration. Since focusing on each drug, the PG-path can predict the effect of drug-gene-drug interactions on the drug response when multiple drugs are administered. Adding PK properties and clinical factors to the PG-path could improve the ability to predict drug response.์„œ ๋ก : ์ธ์ฒด์˜ ์•ฝ๋ฌผ ๋ถ„ํ•ด ํšจ์†Œ๋‚˜ ์•ฝ๋ฌผ ์ˆ˜์†ก์ฒด์—์„œ์˜ ์œ ์ „์  ๋ณ€์ด๋Š” ์•ฝ๋ฌผ์˜ ๋†๋„์— ๋ณ€ํ™”๋ฅผ ์ผ์œผ์ผœ, ์•ฝ๋ฌผ ๋ฐ˜์‘์˜ ๊ฐœ์ธ์ฐจ๋ฅผ ๋‚˜ํƒ€๋‚˜๊ฒŒ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์™ธ๋ถ€์—์„œ ์•ฝ๋ฌผ์˜ ๋†๋„๋ฅผ ์กฐ์ ˆํ•˜๊ฑฐ๋‚˜ ์•ฝ๋ฌผ์„ ๋Œ€์ฒดํ•˜์—ฌ, ์ธ์ฒด ๋‚ด์—์„œ ์ตœ์ ์˜ ์•ฝ๋ฌผ ๋†๋„๊ฐ€ ๋˜๋„๋ก ๋งž์ถ”์–ด ์ฃผ์–ด์•ผ ํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์•ฝ๋ฌผ์˜ ํšจ๋Šฅ๊ณผ ์•ˆ์ „์„ฑ์˜ ๊ทน๋Œ€ํ™”๋ฅผ ์ถ”๊ตฌํ•  ์ˆ˜ ์žˆ๋‹ค. ํ˜„์žฌ์˜ ํ•ด๊ฒฐ ๋ฐฉ์•ˆ์œผ๋กœ ์•ฝ๋ฌผ ์œ ์ „ํ•™์— ๊ทผ๊ฑฐํ•œCPIC๊ฐ€์ด๋“œ๋ผ์ธ์ด ์ถœํŒ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ๊ฐ€์ด๋“œ๋ผ์ธ๋“ค์€ ์ž„์ƒ ์ ์šฉ๊ณผ ๊ทœ์ œ, ๋˜๋Š” ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์—์„œ์˜ ํ‘œ์ค€ํ™” ๋“ฑ์— ๋ฌธ์ œ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ด์— ์•ฝ๋ฌผ๊ณผ ์œ ์ „์ž๋“ค ์‚ฌ์ด์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ์‹œ๊ฐํ™”ํ•˜๊ณ  ๋ณ€์ด ๋ฐ์ดํ„ฐ๋ฅผ ์ ์šฉํ•˜์—ฌ, ์ธ์ฒด ๋‚ด ์•ฝ๋ฌผ ๋†๋„ ๋ณ€ํ™”๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š”, ์•ฝ๋ฌผ ์œ ์ „์ฒดํ•™ ํŒจ์Šค์›จ์ด์ธ PG-path๋ฅผ ์ œ์ž‘ํ•ด ๋ณด๊ณ ์ž ํ•œ๋‹ค. ๋ฐฉ ๋ฒ•: DrugBank์—์„œ ํŠน์ • ์•ฝ๋ฌผ๊ณผ ์œ ์ „์ž ์…‹์„ ์ถ”์ถœํ•˜๊ณ , ๊ทธ๋“ค ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ ์œ ํ˜•(ํšจ์†Œ, ์ˆ˜์†ก์ฒด, ์ˆ˜์šฉ์ฒด)๊ณผ ์ž‘์šฉ ์œ ํ˜•(์–ต์ œ, ์œ ๋„, ๊ธฐ์งˆ)์„ ์ถ”์ถœํ•œ๋‹ค. ์ „์‹  ์ˆ˜์ค€์—์„œ ํก์ˆ˜, ๋ถ„ํฌ, ๋Œ€์‚ฌ, ๋ฐฐ์„ค ๊ณผ์ •์„ ๋‹ค๋ฃจ๋Š” ์•ฝ๋™ํ•™์  ํŒจ์Šค์›จ์ด์™€, ๋ถ„์ž-์„ธํฌ ์ˆ˜์ค€์—์„œ์˜ ์•ฝ๋ฌผ ์ž‘์šฉ์„ ๋‹ค๋ฃจ๋Š” ์•ฝ๋ ฅํ•™์  ํŒจ์Šค์›จ์ด๋กœ ๋‚˜๋ˆ„์–ด, ํ‹€๊ณผ ๋ฐฐ๊ฒฝ ๊ทธ๋ฆผ์„ ์ œ์ž‘ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ , DrugBank์—์„œ ์ถ”์ถœํ•œ ์š”์†Œ๋“ค์„ ์ œ์ž‘๋œ ํ‹€์— ์ ์šฉํ•˜์—ฌ, ์•ฝ๋ฌผ๊ณผ ์œ ์ „์ž์˜ ์ƒํ˜ธ ์ž‘์šฉ์„ ์‹œ๊ฐํ™” ํ•œ๋‹ค. ์„ฑ๋ถ„ ์š”์†Œ์™€ ํฌ๋งท์˜ ํ‘œ์ค€ํ™”๋ฅผ ์œ„ํ•ด ๊ณต์šฉ ์†Œํ”„ํŠธ์›จ์–ด์ธ PathVisio๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ฒฐ ๊ณผ: PG-path๋Š” ์ธ์ฒด์— ์กด์žฌํ•˜๋Š” ์žฅ๊ธฐ๋ฅผ ํ‘œํ˜„ํ•œ ํ•˜๋‚˜์˜ ํ‹€๊ณผ, ๊ฐ ์žฅ๊ธฐ๋“ค์˜ ๋ฐฐ๊ฒฝ ์ด๋ฏธ์ง€๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์•ฝ๋ฌผ๊ณผ ์œ ์ „์ž ์‚ฌ์ด์˜ ์ƒํ˜ธ์ž‘์šฉ ์œ ํ˜•๊ณผ ์ž‘์šฉ ์œ ํ˜•์„ ํ‘œ์ค€ ํฌ๋งท์— ๋งž๊ฒŒ ํ‹€ ์œ„์— ๊ทธ๋ ค ์ฃผ๊ณ , ๋‹ค์ด์–ด๊ทธ๋žจ์ด ๊ทธ๋ ค์ง„ ํ‹€๊ณผ ๋ฐฐ๊ฒฝ ๊ทธ๋ฆผ์„ ๋ณ‘ํ•ฉํ•˜์˜€๋‹ค. ์œ ์ „์ž์™€ ์•ฝ๋ฌผ ์ •๋ณด๋ฅผ ๋‹ด์€ ์œˆ๋„์šฐ๋ฅผ ํŒจ์Šค์›จ์ด์™€ ์—ฐ๊ฒฐํ•˜๊ณ  ํŒ์—… ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•จ์œผ๋กœ์จ, ํŒจ์Šค์›จ์ด์˜ ์ดํ•ด๋„๋ฅผ ๋†’์ด๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ฐ ์œ ์ „์ž๋ฅผ ์ธ์ฒด์™€ ๋น„์Šทํ•œ ์œ„์น˜์— ๋ฐฐ์น˜ํ•˜์—ฌ, ์•ฝ๋ฌผ์˜ ์ธ์ฒด ๋‚ด์—์„œ ํ๋ฆ„์„ ์‹œ๊ฐํ™” ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ , ๊ฐ ์œ ์ „์ž์— ์œ ์ „์ž์˜ ๋ˆ„์  ์†์ƒ ์ •๋„๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” GVB ์ ์ˆ˜๋ฅผ ์ ์šฉํ•˜์—ฌ ๊ฐœ์ธ๋ณ„ ์•ฝ๋ฌผ ์œ ์ „์ฒดํ•™ ๊ธฐ๋ฐ˜์˜ ํŒจ์Šค์›จ์ด๋ฅผ ๋งŒ๋“ค์–ด ๋ณด์•˜๋‹ค. ๊ฒฐ ๋ก : PG-path๋Š” ์ธ์ฒด ๋‚ด ์•ฝ๋ฌผ์˜ ์ผ๋ฐ˜์  ํ๋ฆ„์„ ์•Œ ์ˆ˜ ์žˆ๊ฒŒ ์ œ์ž‘๋˜์—ˆ๋‹ค. GVB ์ ์ˆ˜๋ฅผ ๊ฐ ์œ ์ „์ž์— ์ ์šฉํ•จ์œผ๋กœ์จ, ํ˜ˆ์žฅ ๋‚ด ์•ฝ๋ฌผ์˜ ๋†๋„ ๋ณ€ํ™”๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, PG-path๋Š” ์•ฝ๋ฌผ ๊ฐœ๋ณ„๋กœ ๊ทธ๋ ค์กŒ๊ธฐ ๋•Œ๋ฌธ์—, ์—ฌ๋Ÿฌ ์•ฝ๋ฌผ์„ ํˆฌ์•ฝํ•˜์˜€์„ ๋•Œ, ์•ฝ๋ฌผ-์œ ์ „์ž-์•ฝ๋ฌผ ๊ฐ„์˜ ์ƒํ˜ธ ์ž‘์šฉ์ด ์•ฝ๋ฌผ ๋ฐ˜์‘์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋‹ค. ์œ ์ „ ์š”์†Œ๋งŒ์„ ๋‹ค๋ฃฌ PG-path์— ์•ฝ๋™ํ•™์  ์š”์†Œ๋‚˜ ์ž„์ƒ์  ์š”์†Œ ๋“ฑ์ด ์ถ”๊ฐ€๋˜๋ฉด, PG-path์˜ ์•ฝ๋ฌผ ๋ฐ˜์‘ ์˜ˆ์ธก ๋Šฅ๋ ฅ์€ ๋” ๊ณ ๋„ํ™”๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.Introduction 1 Personalized medicine based on pharmacogenetics 3 Prescription guideline 3 Implementation of the PGx-based prescription guideline system 6 Personalized medicine based on pharmacogenomics 9 The current pharmacogenomics-based pathways 9 Objective of this study 13 Materials and Methods 14 Materials 14 DrugBank 5.0.1 14 Sorting Intolerant From Tolerant (SIFT) 14 The 1000 Genomes Project 15 Gene-wise Variant Burden (GVB) score 15 Pathway development methods 16 Pathway development software 21 Results 22 Nodes and edges 22 Background frame and image 26 Convert and merge 28 Information on drugs and genes 31 Drawing a pathway with PathVisio 36 Usage of PG-path: Pathway analysis and visualization with GVB score 38 Discussion 42 Prediction of changes in plasma drug concentration by gene placement in pathways 42 PG-path: Single drug-centered pathway 46 Pathway analysis: GVB scoring method using variations obtained from DNA sequencing 50 Comparison of variant set between GVB scoring method and CPIC guideline 51 Conclusion 52 Reference 53 ๊ตญ๋ฌธ ์ดˆ๋ก 61Docto

    The effect of paramedicโ€™s emergency patient simulation training - course using standardized communication tools and simulation

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    Background : Since primary emergency treatment should be performed appropriately and promptly, efficient and accurate communication between paramedics and medical staff is paramount to a successful primary emergency treatment and patient handover. The problem of the training program in Korea is that it concentrates more on in-class lectures, often delivered by non-medical specialists, who may lack in practical experience and without proper communication training. To solve this problem, we have devised a simulation based training that focuses on event debriefings and two-way communication. Methods : 62 paramedics from 3 stations enrolled in the study. 4 different courses with different emergency situations were created and each course was taken twice resulting in a total of 8 classes. All courses were based on actual cases. The curriculum consisted of subject lectures with guidelines, skill practice courses, and simulation courses based on hands-on method. In simulation courses, paramedics use standardized check list to communicate with medical specialists. All curriculums except subject lectures include debriefing, which allows free talking with educators comprised of medical specialists. In order to measure the educational impact, all students performed self-assessment through a structured questionnaire before and after the training. Results : Regardless different situations and paramedicsโ€™ education level, their performance and communication skills have improved after simulation training course. Paramedics mentioned learning skills in simulation course through communication with medical staffs as the biggest advantage. Conclusion : Receiving the simulation training with standardized communication tools is effective at enhancing the communication between the paramedics and medical staff.ope

    Telomerase-dependent cell cycle regulation requires NOL1 and TINF2 mRNA degradation by HuR modulates telomere function

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    ์น˜๊ณผ๋Œ€ํ•™/๋ฐ•์‚ฌTelomerase is a ribonucleoprotein enzyme that plays a critical role in the maintenance of telomere repeats in most eukaryotic organisms. Although overexpression of telomerase in normal human somatic cells is sufficient to overcome replicative senescence and extend a lifespan, the ability of telomerase to promote tumorigenesis could require additional activities that are independent of its role in telomere extension. Here we identify NOL1 (proliferation-associated nuclear antigen 120) as a TERC-binding protein, which is found in association with catalytically active telomerase. We show that NOL1 binds to cyclin D1 promoter at the TCF binding element and activates its transcription. Moreover, telomerase further enhances expression of cyclin D1 gene by interacting with NOL1 and recruitment to the cyclin D1 promoter, demonstrating a role of telomerase as a modulator of NOL1-dependent transcription in human cancer cells. These data suggest that NOL1 could represent a novel mechanism by which telomerase promotes the prolonged expression of growth-promoting genes critical for the maintenance of tumor survival and cell proliferation. (Chi and Delgado-Olguin 2013) These data suggest that a functional interplay between NOL1 and telomerase plays a critical role in bypassing checkpoint signaling pathways and maintaining cell proliferation capacity, essential properties of telomerase required for cancer progression.ope

    Simulation study: the development of a respiratory barrier enclosure with negative pressure and the analysis of its protective effect during intubation

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    Objective: Within the last 2 years, coronavirus disease 2019 has spread rapidly across several continents, with 100 million confirmed infected patients. Physical barrier enclosure, also called โ€œaerosol-box,โ€ is a solution for the shortage of protective devices and spaces. In this study, we examined the safety of the novel barrier enclosure. Methods: We simulated droplets by nebulizing 1% glycerol+99% ethanol solution. Two experienced physicians performed intubation under two conditions, such as the isolator condition (applying isolator without negative condition) and the negative pressure condition (applying isolator with the negative condition). We compared two conditions with two control groups, including negative control (room air) and positive control (synthetizing droplet air). During the procedure, particles were counted for 30 seconds, and this was repeated 10 times. At each condition, depending on the result of the normality test (Shapiro-Wilk test), an independent t-test was used when normality was satisfied, and a Mann-Whitney Utest was used when normality was not satisfied. Results: The total number of particles in the positive control was 308,788 (175,936-461,124). The total number of particles for both conditions was significantly less than the positive control. Total number of particles in the isolator condition was 30,952 (27,592-33,244, P=0.001) and that in the negative pressure condition was 27,890 (27,165-29,786, P=0.001). Conclusion: The novel barrier significantly reduces synthetizing droplets exposure during intubation. Application of negative pressure through the isolator results in an additional decrease in particle exposure. Studies involving a larger population of operators and prolonged procedures are required.ope

    ์‘๊ธ‰ ํ˜‘์˜ ์ง„๋ฃŒ ์ „์‚ฐํ™” ์‹œ์Šคํ…œ์ด ์‘๊ธ‰ ์ง„๋ฃŒ๋ฅผ ์œ„ํ•œ ์˜๋ฃŒ์ง„ ์˜์‚ฌ ์†Œํ†ต์— ๋ฏธ์นœ ์˜ํ–ฅ

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    Purpose : In emergency department(ED), emergency consultation is often omitted or delayed, resulting in an increase of the length of stay for patients. The present study investigated the emergency consultation computerized system designed for prompt and accurate communication can shorten the time for consultation care. In addition, we tried to confirm how usersโ€™ satisfaction with communication for emergency consultation changed before and after using the system. Methods : We divided the period from arrival to exit of the emergency department into 4 stages, and the time taken for each stage was measured. In addition, the present study conducted a satisfaction survey onthe convenience and accuracy of communication among users. Results : After using the computerized system, the median value of time for emergency consultation treatment decreased significantly from 78 minutes to 39 minutes (p<0.001). In terms of communicationconvenience, more than two-thirds of the users responded positively. Conclusion : The system that computerized the initial communication shortened the time required for emergency consultation and increased satisfaction in terms of convenience of communication betweenmedical staff.ope

    The experience of remote videoconferencing to enhance emergency resident education using Google Hangouts

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    Objective: It is difficult for emergency residents to attend all the lectures that are required because of the limited labor time. The Google Hangouts program for has been used as a remote videoconference to overcome the limit to provide equal opportunities and reduce the time and costs since 2015. This article reports the authorsโ€™ experiences of running a residency education program using Google Hangouts. Methods: From 2015, topics on the emergency radiology were lectured to emergency residents in three different hospitals connected by Google Hangouts. From 2017, electrocardiography analysis, emergency radiology, ventilator application, and journal review were selected for the remote videoconference. The residents' self-assessment score, and a posteducation satisfaction questionnaire were surveyed. Results: Twenty-nine emergency residents responded to the questionnaire after using the Google Hangouts. The number of participants before and after Hangout increased significantly in other two hospitals. All the residents answered that the score on achieving the learning goal increased before and after the videoconference lectures. All the residents answered that the training program is more satisfactory after using the Google Hangouts than before. Conclusion: All emergency residents were satisfied and were more confident after the remote videoconference education using the Google Hangouts than before.ope

    The elderly age criterion for increased in-hospital mortality in trauma patients: a retrospective cohort study

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    Background: With an aging population, the number of elderly individuals exposed to traumatic injuries is increasing. The elderly age criterion for traumatic injuries has been inconsistent in the literature. This study aimed at specifying the elderly age criterion when the traumatic mortality rate increases. Methods: This is a multicenter retrospective cohort study that was conducted utilizing the data from the Emergency Department-based Injury In-depth Surveillance Registry of the Korea Disease Control and Prevention Agency, collected between January 2014 and December 2018 from 23 emergency departments. The outcome variable was in-hospital mortality. Multivariable logistic regression analysis was used to calculate the adjusted mortality rate for each age group. By using the shape-restricted regression splines method, the relationship between age and adjusted traumatic mortality was plotted and the point where the gradient of the graph had the greatest variation was calculated. Results: A total of 637,491 adult trauma patients were included. The number of in-hospital deaths was 6504 (1.0%). The age at which mortality increased the most was 65.06 years old. The adjusted odds ratio for the in-hospital mortality rate with age in the โ‰ค 64-year-old subgroup was 1.038 (95% confidence interval (CI) 1.032-1.044) and in the โ‰ฅ 65-year-old subgroup was 1.059 (95% CI 1.050-1.068). The adjusted odds ratio for in-hospital mortality in the โ‰ฅ 65-year-old compared to the โ‰ค 64-year-old subgroup was 4.585 (95% CI 4.158-5.055, p < 0.001). Conclusions: This study found that the in-hospital mortality rate rose with increasing age and that the increase was the most rapid from the age of 65 years. We propose to define the elderly age criterion for traumatic injuries as โ‰ฅ 65 years of age.ope

    Prognostic Factor Analysis Including Electrocardiogram Change in Patients with Subarachnoid Hemorrhage

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    Purpose: The prognostic factors of subarachnoid hemorrhage (SAH) are still not completely known. Several studies suggested that electrocardiogram (ECG) changes can act as a predictor of outcome in SAH patients. The purpose of this study was to describe the prognostic factors, including ECG changes, which are predictive of unfavorable outcome in non-traumatic SAH patients. Methods: We retrospectively selected patients from our prospectively collected database of 202 SAH patients who visited the emergency medical center. The outcome was assessed using the Glasgow Coma Scale at six months after the occurrence of SAH. Results: In the univariate analysis, a high score in one of the conventional systems (Hunt and Hess system, World Federation of Neurosurgical Societies [WFNS] scale, and Fisher grade), advanced age, accompanying intracranial hemorrhage or intraventricular hemorrhage, ECG changes (ST depression or Tall T), and a history of hypertension were associated with unfavorable outcome. The multivariate analysis showed three prognostic factors (ECG changes, age and high score in the conventional system) for unfavorable outcome. Using this result, three novel models corresponding to the three conventional systems were constructed to predict an unfavorable outcome in such patients. The area under the curve for model 1 (containing the WFNS scale) was 0.912, that of model 2 (containing the HH system) was 0.913, and that of model 3 (containing the Fisher system) was 0.885. Compared with the WFNS, HH or Fisher grade alone, each model exhibited superior accuracy. Conclusion: ECG can be described as an independent predictor of poor outcome, and the novel models which contain the ECG changes were found to be more accurate in predicting an unfavorable outcome in SAH patients compared with the conventional scoring system.ope

    Development and external validation of new nomograms by adding ECG changes (ST depression or tall T wave) and age to conventional scoring systems to improve the predictive capacity in patients with subarachnoid haemorrhage: a retrospective, observational study in Korea

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    OBJECTIVES: To develop new nomograms by adding ECG changes (ST depression or tall T wave) and age to three conventional scoring systems, namely, World Federation of Neurosurgical Societies (WFNS) scale, Hunt and Hess (HH) system and Fisher scale, that can predict prognosis in patients with subarachnoid haemorrhage (SAH) using our preliminary research results and to perform external validation of the three new nomograms. DESIGN: Retrospective, observational study SETTING: Emergency departments (ED) of two university-affiliated tertiary hospital between January 2009 and March 2015. PARTICIPANTS: Adult patients with SAH were enrolled. Exclusion criteria were age <19 years, no baseline ECG, cardiac arrest on arrival, traumatic SAH, referral from other hospital and referral to other hospitals from the ED. PRIMARY OUTCOME MEASURES: The 6โ€‰month prognosis was assessed using the Glasgow Outcome Scale (GOS). We defined a poor outcome as a GOS score of 1, 2 or 3. RESULTS: A total of 202 patients were included for analysis. From the preliminary study, age, ECG changes (ST depression or tall T wave), and three conventional scoring systems were selected to predict prognosis in patients with SAH using multi-variable logistic regression. We developed simplified nomograms using these variables. Discrimination of the developed nomograms including WFNS scale, HH system and Fisher scale was superior to those of WFNS scale, HH system and Fisher scale (0.912 vs 0.813; p<0.001, 0.913 vs 0.826; p<0.001, and 0.885 vs 0.746; p<0.001, respectively). The calibration plots showed excellent agreement. In the external validation, the discrimination of the newly developed nomograms incorporating the three scoring systems was also good, with an area under the receiver-operating characteristic curve value of 0.809, 0.812 and 0.772, respectively. CONCLUSIONS: We developed and externally validated new nomograms using only three independent variables. Our new nomograms were superior to the WFNS scale, HH systems, and Fisher scale in predicting prognosis and are readily available.ope
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