Development of a clinical prediction rule for sepsis in primary care: protocol for the TeSD-IT study

Diagnostic and Prognostic Research - Tập 4 - Trang 1-9 - 2020
Feike J. Loots1,2, Rogier Hopstaken3,4, Kevin Jenniskens1, Geert W. J. Frederix1, Alma C. van de Pol1, Ann Van den Bruel5, Jan Jelrik Oosterheert6, Arthur R. H. van Zanten7, Marleen Smits8, Theo J. M. Verheij1
1Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
2Emergency Department, Gelderse Vallei Hospital, Ede, The Netherlands
3Star-shl Diagnostic Centres, Etten-Leur, The Netherlands
4Department of General Practice, School of Public Health and Primary Care (CAPHRI), Maastricht University Medical Centre (MUMC+), Maastricht, The Netherlands
5Department of Internal Medicine, Division Infectious Disease, University Medical Centre Utrecht, Utrecht, The Netherlands
6Academic Centre for Primary Care, KU Leuven, Leuven, Belgium
7Department of Intensive Care Medicine, Gelderse Vallei Hospital, Ede, The Netherlands
8Radboud university medical center, Radboud Institute for Health Sciences, Scientific Center for Quality of Healthcare (IQ healthcare), Nijmegen, the Netherlands

Tóm tắt

Early recognition and treatment of sepsis is crucial to prevent detrimental outcomes. General practitioners (GPs) are often the first healthcare providers to encounter seriously ill patients. The aim of this study is to assess the value of clinical information and additional tests to develop a clinical prediction rule to support early diagnosis and management of sepsis by GPs. We will perform a diagnostic study in the setting of out-of-hours home visits in four GP cooperatives in the Netherlands. Acutely ill adult patients suspected of a serious infection will be screened for eligibility by the GP. The following candidate predictors will be prospectively recorded: (1) age, (2) body temperature, (3) systolic blood pressure, (4) heart rate, (5) respiratory rate, (6) peripheral oxygen saturation, (7) mental status, (8) history of rigors, and (9) rate of progression. After clinical assessment by the GP, blood samples will be collected in all patients to measure C-reactive protein, lactate, and procalcitonin. All patients will receive care as usual. The primary outcome is the presence or absence of sepsis within 72 h after inclusion, according to an expert panel. The need for hospital treatment for any indication will be assessed by the expert panel as a secondary outcome. Multivariable logistic regression will be used to design an optimal prediction model first and subsequently derive a simplified clinical prediction rule that enhances feasibility of using the model in daily clinical practice. Bootstrapping will be performed for internal validation of both the optimal model and simplified prediction rule. Performance of both models will be compared to existing clinical prediction rules for sepsis. This study will enable us to develop a clinical prediction rule for the recognition of sepsis in a high-risk primary care setting to aid in the decision which patients have to be immediately referred to a hospital and who can be safely treated at home. As clinical signs and blood samples will be obtained prospectively, near-complete data will be available for analyses. External validation will be needed before implementation in routine care and to determine in which pre-hospital settings care can be improved using the prediction rule. The study is registered in the Netherlands Trial Registry (registration number NTR7026).

Tài liệu tham khảo

Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–10.

Rhodes A, Evans LE, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med. 2017;43(3):304–77.

Sherwin R, Winters ME, Vilke GM, Wardi G. Does early and appropriate antibiotic administration improve mortality in emergency department patients with severe sepsis or septic shock? J Emerg Med. 2017;53(4):588–95. https://doi.org/10.1016/j.jemermed.2016.12.009.

Angus DC, van der Poll T. Severe sepsis and septic shock. N Engl J Med. 2013;369(9):840–51.

Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–11. https://doi.org/10.1016/S0140-6736(19)32989-7.

Reinhart K, Daniels R, Kissoon N, et al. Recognizing sepsis as a global health priority - a WHO resolution. N Engl J Med. 2017;377(5):414–7. https://doi.org/10.1056/NEJMp1707170.

Smits M, Rutten M, Keizer E, et al. The development and performance of after-hours primary care in the Netherlands: a narrative review. Ann Intern Med. 2017;166(10):737–42.

Søvsø MB, Christensen MB, Bech BH, et al. Contacting out-of-hours primary care or emergency medical services for time-critical conditions - impact on patient outcomes. BMC Health Serv Res. 2019;19(1):813. https://doi.org/10.1186/s12913-019-4674-0.

Loots FJ, Smits M, van Steensel C, et al. Management of sepsis in out-of-hours primary care: a retrospective study of patients admitted to the intensive care unit. BMJ Open. 2018;8(9):e022832. https://doi.org/10.1136/bmjopen-2018-022832.

Bone RC, Balk RA, Cerra FB, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992;101(6):1644–55.

Williams JM, Greenslade JH, McKenzie JV, et al. Systemic inflammatory response syndrome, quick sequential organ function assessment, and organ dysfunction: insights from a prospective database of ED patients with infection. Chest. 2017;151(3):586–96. https://doi.org/10.1016/j.chest.2016.10.057.

Fernando SM, Tran A, Taljaard M, et al. Prognostic accuracy of the quick sequential organ failure assessment for mortality in patients with suspected infection: a systematic review and meta-analysis. Ann Intern Med. 2018;168(4):266–75. https://doi.org/10.7326/M17-2820.

Dimitrov E, Minkov G, Enchev E, et al. A combination of C-reactive protein and quick sequential organ failure assessment (qSOFA) score has better prognostic accuracy than qSOFA alone in patients with complicated intra-abdominal infections. Acta Chir Belg. 2019;16:1–5. https://doi.org/10.1080/00015458.2019.1642579.

Yu H, Nie L, Liu A, et al. Combining procalcitonin with the qSOFA and sepsis mortality prediction. Medicine (Baltimore). 2019;98(23):e15981. https://doi.org/10.1097/MD.00000000000159813-14.

Baumann BM, Greenwood JC, Lewis K, et al. Combining qSOFA criteria with initial lactate levels: improved screening of septic patients for critical illness. Am J Emerg Med. 2019. https://doi.org/10.1016/j.ajem.2019.07.003.

Nederlands Triage Systeem [Dutch Triage System]. 2017. www.de-nts.nl.

National Institute for Health and Care Excellence. Sepsis: recognition, diagnosis and early management. 2017. https://www.nice.org.uk/guidance/ng51. Accessed 16 Jan 2020.

Physicians RCo. National Early Warning Score (NEWS) 2: standardising the assessment of acute-illness severity in the NHS. London, UK: RCP; 2017.

Tan M, Lu Y, Jiang H, Zhang L. The diagnostic accuracy of procalcitonin and C-reactive protein for sepsis: a systematic review and meta-analysis. J Cell Biochem. 2019;120(4):5852–9. https://doi.org/10.1002/jcb.27870.

Vincent JL, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22(7):707–10.

Jenniskens K, Naaktgeboren CA, Reitsma JB, et al. Forcing dichotomous disease classification from reference standards leads to bias in diagnostic accuracy estimates: a simulation study. J Clin Epidemiol. 2019;111:1–10. https://doi.org/10.1016/j.jclinepi.2019.03.002.

EQ-5D instruments. https://euroqol.org/. Accessed 16 Jan 2020.

Peduzzi P, Concato J, Kemper E, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9.

Sakamoto Y, Ishiguro M, Kitagawa G. Akaike information criterion statistics. Dordrecht, The Netherlands: D. Reidel. 1986;81.

Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology (Cambridge, Mass.). 2010;21:128–1.

Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio) marker. Heart. 2012;98(9):683–90.

van Smeden M, Moons KG, de Groot JA, et al. Sample size for binary logistic prediction models: beyond events per variable criteria. Stat Methods Med Res. 2019;28(8):2455–74. https://doi.org/10.1177/0962280218784726.

https://mvansmeden.shinyapps.io/BeyondEPV/. Accessed 31 Mar 2020.

Vincent JL, Beumier M. Diagnostic and prognostic markers in sepsis. Anti Infect Ther. 2013;11(3):265–75. https://doi.org/10.1586/eri.13.9.

van Engelen TSR, Wiersinga WJ, Scicluna BP, van der Poll T. Biomarkers in sepsis. Crit Care Clin. 2018;34(1):139–52. https://doi.org/10.1016/j.ccc.2017.08.010.

Smyth MA, Brace-McDonnell SJ, Perkins GD. Identification of adults with sepsis in the prehospital environment: a systematic review. BMJ Open. 2016;6:e011218. https://doi.org/10.1136/bmjopen-2016-011218.

Thông tin
Thông tin xuất bản

Diagnostic and Prognostic Research

Tập 4

1-9

Thông tin tác giả