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Zhang Z, Chen L, Zhang H, Xiao W, Yang J, Huang J, Hu Q, Jin K, Hong Y. Genetic correlations and causal relationships between cardio-metabolic traits and sepsis. Sci Rep 2024; 14:5718. [PMID: 38459230 PMCID: PMC10923865 DOI: 10.1038/s41598-024-56467-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 03/06/2024] [Indexed: 03/10/2024] Open
Abstract
Cardio-metabolic traits have been reported to be associated with the development of sepsis. It is, however, unclear whether these co-morbidities reflect causal associations, shared genetic heritability, or are confounded by environmental factors. We performed three analyses to explore the relationships between cardio-metabolic traits and sepsis. Mendelian randomization (MR) study to evaluate the causal effects of multiple cardio-metabolic traits on sepsis. Global genetic correlation analysis to explore the correlations between cardio-metabolic traits and sepsis. Local genetic correlation (GC) analysis to explore shared genetic heritability between cardio-metabolic traits and sepsis. Some loci were further examined for related genes responsible for the causal relationships. Genetic associations were obtained from the UK Biobank data or published large-scale genome-wide association studies with sample sizes between 200,000 to 750,000. In MR, we found causality between BMI and sepsis (OR: 1.53 [1.4-1.67]; p < 0.001). Body mass index (BMI), which is confirmed by sensitivity analyses and multivariable MR adjusting for confounding factors. Global GC analysis showed a significant correlation between BMI and sepsis (rg = 0.55, p < 0.001). More cardio-metabolic traits were identified to be correlated to the sepsis onset such as CRP (rg = 0.37, p = 0.035), type 2 diabetes (rg = 0.33, p < 0.001), HDL (rg = - 0.41, p < 0.001), and coronary artery disease (rg = 0.43, p < 0.001). Local GC revealed some shared genetic loci responsible for the causality. The top locus 1126 was located at chromosome 7 and comprised genes HIBADH, JAZF1, and CREB5. The present study provides evidence for an independent causal effect of BMI on sepsis. Further detailed analysis of the shared genetic heritability between cardio-metabolic traits and sepsis provides the opportunity to improve the preventive strategies for sepsis.
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Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
| | - Lin Chen
- Neurological Intensive Care Unit, Department of Neurosurgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Haoyang Zhang
- School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Wei Xiao
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Jie Yang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Jiajie Huang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Qichao Hu
- Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Dian Diagnostics Group Co., Ltd., Hangzhou, Zhejiang, China
| | - Ketao Jin
- Department of Gastrointestinal, Colorectal and Anal Surgery, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, Zhejiang, China
| | - Yucai Hong
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
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Bani Hamad D, Rababa M, Tanash MI, Abuali R. The Predictors of Perceived Barriers and Facilitators of Applying Sepsis Six Guidelines Among Critical Care Nurses. Cureus 2024; 16:e57355. [PMID: 38694411 PMCID: PMC11060988 DOI: 10.7759/cureus.57355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/31/2024] [Indexed: 05/04/2024] Open
Abstract
BACKGROUND Sepsis is a life-threatening condition that demands quick and cautious interventions from nurses, as they are the frontline caregivers, so they are essential in recognizing early signs of sepsis, initiating prompt healthcare interventions, and providing comprehensive care to improve patient outcomes. This study aimed to examine the predictors of perceived barriers and facilitators of applying evidence-based sepsis guidelines among critical care nurses. METHODS This cross-sectional descriptive study was conducted on a convenience sample of 180 nurses working in critical care settings (ICU, critical care unit, ED, burning unit, dialysis unit) at a university hospital. A valid and reliable questionnaire was used to examine the predictors of perceived barriers and facilitators of applying evidence-based sepsis guidelines among critical care nurses. RESULTS This study revealed that the main barriers faced by critical care nurses are lack of sepsis recognition during observational rounds and delay in sepsis diagnosis by medical staff. For the most common facilitators of applying Sepsis Six guidelines, the participating nurses reported the presence of a written tool/protocol for sepsis identification and management. CONCLUSIONS The study emphasized the importance of the presence of evidence-based protocols for sepsis assessment and management and nurses' compliance with guidelines. Ongoing education training for nurses and providing step-by-step written checklists are a cornerstone to improving nurses' knowledge and the practical skills of early identification and management of sepsis.
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Affiliation(s)
- Dania Bani Hamad
- Department of Applied Sciences/Nursing, Al-Balqa Applied University, Al-Salt, JOR
| | - Mohammad Rababa
- Department of Adult Health Nursing, Jordan University of Science and Technology, Irbid, JOR
| | - Mu'ath I Tanash
- Department of Adult Health Nursing, The Hashemite University, Zarqa, JOR
| | - Raeda Abuali
- Department of Applied Sciences/Nursing, Al-Balqa Applied University, Al-Salt, JOR
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Alturki A, Al-Eyadhy A, Alfayez A, Bendahmash A, Aljofan F, Alanzi F, Alsubaie H, Alabdulsalam M, Alayed T, Alofisan T, Alnajem A. Impact of an electronic alert system for pediatric sepsis screening a tertiary hospital experience. Sci Rep 2022; 12:12436. [PMID: 35859000 PMCID: PMC9300636 DOI: 10.1038/s41598-022-16632-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 07/13/2022] [Indexed: 11/20/2022] Open
Abstract
This study aimed to assess the potential impact of implementing an electronic alert system (EAS) for systemic inflammatory syndrome (SIRS) and sepsis in pediatric patients mortality. This retrospective study had a pre and post design. We enrolled patients aged ≤ 14 years who were diagnosed with sepsis/severe sepsis upon admission to the pediatric intensive care unit (PICU) of our tertiary hospital from January 2014 to December 2018. We implemented an EAS for the patients with SIRS/sepsis. The patients who met the inclusion criteria pre-EAS implementation comprised the control group, and the group post-EAS implementation was the experimental group. Mortality was the primary outcome, while length of stay (LOS) and mechanical ventilation in the first hour were the secondary outcomes. Of the 308 enrolled patients, 147 were in the pre-EAS group and 161 in the post-EAS group. In terms of mortality, 44 patients in the pre-EAS group and 28 in the post-EAS group died (p 0.011). The average LOS in the PICU was 7.9 days for the pre-EAS group and 6.8 days for the post-EAS group (p 0.442). Considering the EAS initiation time as the “zero time”, early recognition of SIRS and sepsis via the EAS led to faster treatment interventions in post-EAS group, which included fluid boluses with median (25th, 75th percentile) time of 107 (37, 218) min vs. 30 (11,112) min, p < 0.001) and time to initiate antimicrobial therapy median (25th, 75th percentile) of 170.5 (66,320) min vs. 131 (53,279) min, p 0.042). The difference in mechanical ventilation in the first hour of admission was not significant between the groups (25.17% vs. 24.22%, p 0.895). The implementation of the EAS resulted in a statistically significant reduction in the mortality rate among the patients admitted to the PICU in our study. An EAS can play an important role in saving lives and subsequent reduction in healthcare costs. Further enhancement of systematic screening is therefore highly recommended to improve the prognosis of pediatric SIRS and sepsis. The implementation of the EAS, warrants further validation in multicenter or national studies.
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Affiliation(s)
- Abdullah Alturki
- Department of Pediatrics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
| | - Ayman Al-Eyadhy
- Department of Pediatrics, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Ali Alfayez
- Maternity and Children's Hospital, Alhasa, Saudi Arabia
| | - Abdulrahman Bendahmash
- Department of Pediatrics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Fahad Aljofan
- Department of Pediatrics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Fawaz Alanzi
- Department of Pediatrics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Hadeel Alsubaie
- Department of Pediatrics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Moath Alabdulsalam
- Department of Pediatrics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Tareq Alayed
- Department of Pediatrics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Tariq Alofisan
- Department of Pediatrics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Afnan Alnajem
- Research Center, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
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Zhang Z, Chen L, Xu P, Wang Q, Zhang J, Chen K, Clements CM, Celi LA, Herasevich V, Hong Y. Effectiveness of automated alerting system compared to usual care for the management of sepsis. NPJ Digit Med 2022; 5:101. [PMID: 35854120 PMCID: PMC9296632 DOI: 10.1038/s41746-022-00650-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/04/2022] [Indexed: 01/18/2023] Open
Abstract
There is a large body of evidence showing that delayed initiation of sepsis bundle is associated with adverse clinical outcomes in patients with sepsis. However, it is controversial whether electronic automated alerts can help improve clinical outcomes of sepsis. Electronic databases are searched from inception to December 2021 for comparative effectiveness studies comparing automated alerts versus usual care for the management of sepsis. A total of 36 studies are eligible for analysis, including 6 randomized controlled trials and 30 non-randomized studies. There is significant heterogeneity in these studies concerning the study setting, design, and alerting methods. The Bayesian meta-analysis by using pooled effects of non-randomized studies as priors shows a beneficial effect of the alerting system (relative risk [RR]: 0.71; 95% credible interval: 0.62 to 0.81) in reducing mortality. The automated alerting system shows less beneficial effects in the intensive care unit (RR: 0.90; 95% CI: 0.73–1.11) than that in the emergency department (RR: 0.68; 95% CI: 0.51–0.90) and ward (RR: 0.71; 95% CI: 0.61–0.82). Furthermore, machine learning-based prediction methods can reduce mortality by a larger magnitude (RR: 0.56; 95% CI: 0.39–0.80) than rule-based methods (RR: 0.73; 95% CI: 0.63–0.85). The study shows a statistically significant beneficial effect of using the automated alerting system in the management of sepsis. Interestingly, machine learning monitoring systems coupled with better early interventions show promise, especially for patients outside of the intensive care unit.
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Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Lin Chen
- Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People's Republic of China
| | - Ping Xu
- Emergency Department, Zigong Fourth People's Hospital, Zigong, Sichuan, China.,Institute of Medical Big Data, Zigong Academy of Artificial Intelligence and Big Data for Medical Science Artificial Intelligence, Zigong, Sichuan, China.,Key Laboratory of Sichuan Province, Zigong, China
| | - Qing Wang
- Department of Surgery, University of Virginia, Charlottesville, VA, USA
| | - Jianjun Zhang
- Emergency Department, Zigong Fourth People's Hospital, Zigong, Sichuan, China
| | - Kun Chen
- Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People's Republic of China
| | - Casey M Clements
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA
| | - Leo Anthony Celi
- Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, USA.,Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, USA.,Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, USA
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
| | - Yucai Hong
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Arabi YM, Al Ghamdi AA, Al-Moamary M, Al Mutrafy A, AlHazme RH, Al Knawy BA. Electronic medical record implementation in a large healthcare system from a leadership perspective. BMC Med Inform Decis Mak 2022; 22:66. [PMID: 35292008 PMCID: PMC8922058 DOI: 10.1186/s12911-022-01801-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 03/03/2022] [Indexed: 11/17/2022] Open
Abstract
Background Information on the use of change management models to guide electronic medical records (EMR) implementation is limited. This case study describes the leadership aspects of a large-scale EMR implementation using Kotter’s change management model.
Methods This case study presents the experience in implementing a new EMR system from the leadership perspective at King Abdulaziz Medical City, a large tertiary care hospital in Riyadh, Kingdom of Saudi Arabia. We described the process of implementation and outlined the challenges and opportunities, throughout the journey from the pre-implementation to the post-implementation phases.
Results We described the corresponding actions to the eight domains of Kotter’s change management model: creating a sense of urgency, building the guiding team, developing a change vision and strategy, understanding and buy-in, removing obstacles, creating short-term wins, building on the change and anchoring the changes in corporate culture. Conclusions The case study highlights that EMR implementation is not a pure information technology project but rather is a technical-based complex social adaptive project that requires a specific set of leadership competencies that are central to its success. It demonstrates that change management models might be useful for large-scale EMR implementation.
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Affiliation(s)
- Yaseen M Arabi
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. .,King Abdullah International Medical Research Center, Riyadh, Saudi Arabia. .,Intensive Care Department, King Abdulaziz Medical City, Riyadh, Saudi Arabia.
| | - Abdullah Ali Al Ghamdi
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,Clinical Affairs, Family Medicine and Primary Healthcare, King Abdulaziz Medical City, Riyadh, Saudi Arabia
| | - Mohamed Al-Moamary
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,Development and Quality Management, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdulaziz Medical City, Riyadh, Saudi Arabia
| | - Abdullah Al Mutrafy
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,King Abdullah Specialized Children's Hospital, Riyadh, Saudi Arabia.,Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Raed H AlHazme
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.,College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,Information Technology Department, King Abdulaziz Medical City, Riyadh, Saudi Arabia.,College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Bandar Abdulmohsen Al Knawy
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.,King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
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Arabi YM, Vishwakarma RK, Al-Dorzi HM, Al Qasim E, Abdukahil SA, Al-Rabeah FK, Al Ghamdi H, Al Ghamdi E. Statistical analysis plan for the Steppedwedge Cluster Randomized trial of Electronic Early Notification of sepsis in hospitalized ward patients (SCREEN). Trials 2021; 22:828. [PMID: 34809672 PMCID: PMC8607063 DOI: 10.1186/s13063-021-05788-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/29/2021] [Indexed: 12/14/2022] Open
Abstract
Background It is unclear whether screening for sepsis using an electronic alert in hospitalized ward patients improves outcomes. The objective of the Stepped-wedge Cluster Randomized Trial of Electronic Early Notification of Sepsis in Hospitalized Ward Patients (SCREEN) trial is to evaluate whether an electronic screening for sepsis compared to no screening among hospitalized ward patients reduces all-cause 90-day in-hospital mortality. Methods and design This study is designed as a stepped-wedge cluster randomized trial in which the unit of randomization or cluster is the hospital ward. An electronic alert for sepsis was developed in the electronic medical record (EMR), with the feature of being active (visible to treating team) or masked (inactive in EMR frontend for the treating team but active in the backend of the EMR). Forty-five clusters in 5 hospitals are randomized into 9 sequences of 5 clusters each to receive the intervention (active alert) over 10 periods, 2 months each, the first being the baseline period. Data are extracted from EMR and are compared between the intervention (active alert) and control group (masked alert). During the study period, some of the hospital wards were allocated to manage patients with COVID-19. The primary outcome of all-cause hospital mortality by day 90 will be compared using a generalized linear mixed model with a binary distribution and a log-link function to estimate the relative risk as a measure of effect. We will include two levels of random effects to account for nested clustering within wards and periods and two levels of fixed effects: hospitals and COVID-19 ward status in addition to the intervention. Results will be expressed as relative risk with a 95% confidence interval. Conclusion The SCREEN trial provides an opportunity for a novel trial design and analysis of routinely collected and entered data to evaluate the effectiveness of an intervention (alert) for a common medical problem (sepsis in ward patients). In this statistical analysis plan, we outline details of the planned analyses in advance of trial completion. Prior specification of the statistical methods and outcome analysis will facilitate unbiased analyses of these important clinical data. Trial registration ClinicalTrials.gov NCT04078594. Registered on September 6, 2019 Supplementary Information The online version contains supplementary material available at 10.1186/s13063-021-05788-3.
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Affiliation(s)
- Yaseen M Arabi
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Intensive Care Department, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.
| | - Ramesh Kumar Vishwakarma
- Biostatistics and Bioinformatics Department, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.,Statistics Department, European Organisation for Research and Treatment of Cancer (EORTC) Headquarters, Brussels, Belgium
| | - Hasan M Al-Dorzi
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Intensive Care Department, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Eman Al Qasim
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Intensive Care Department, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Sheryl Ann Abdukahil
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Intensive Care Department, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Fawaz K Al-Rabeah
- College of Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Information Systems and Informatics Division, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Huda Al Ghamdi
- College of Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Information Systems and Informatics Division, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Ebtisam Al Ghamdi
- College of Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Information Systems and Informatics Division, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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