Emergency department patients with sepsis: risk stratification and clinical prediction rules

Abstract

IntroductionAccurate risk stratification of ED patients with infection is vital for informing priorities of investigation and treatment, disposition location, and communication to patients, families and inpatient specialists. Risk stratification systems for ED patients with infection exist in the form of ‘sepsis syndromes’ classifications and clinical prediction rules derived through mathematical modelling. However most studies designed to validate these risk stratification tools have examined patient cohorts assembled using discharge coding, critical care admission or administrative databases, or re-analyse data from studies conducted for another purpose. These factors can contribute to multiple types of bias and questionable applicability.The aims of this thesis are to demonstrate that (1) with foreknowledge of contributors to bias, a quality Australian ED dataset comprising patients with a wide spectrum of disease severity can be compiled, and 2) validations of risk stratification and clinical prediction models with these data can provide valuable insights relevant to practising ED physicians and researchers.MethodsConsecutive ED patients with infection admitted to a tertiary metropolitan hospital were enrolled in a prospective observational database. Concurrence between ED and admitting inpatient clinicians that infection was the most likely cause for admission was the primary inclusion criterion. Detailed data were recorded regarding suspected source, physiology and treatment in the ED, investigations, co-morbidities, admission location and length of stay. Mortality outcomes were sourced from a national database. Data enabled classification according to sepsis syndromes and established clinical outcome prediction models. Chapter 2 details methods as published.ResultsData were collected over 162 weeks. The study cohort comprised 9719 admissions with overall 30-day mortality 3.7%. Four papers, comprising the basis of chapters 3-6 of this thesis were published, each examining an aspect of risk stratification of ED patients admitted with infection.Sepsis syndromes: Chapter 3 examined risk stratification using ‘sepsis syndromes’ (infection without SIRS, sepsis, severe sepsis and septic shock). A 2016 reclassification examining large administrative databases advocated abandoning SIRS, proposed sequential organ function assessment (SOFA) based criteria to determine organ dysfunction, and conceived ‘q’(quick) SOFA to screen for patients with sepsis outside ICU. The paper in this chapter reported SIRS was associated with increased risk of organ dysfunction (RR 3.5) and mortality in patients without organ dysfunction (OR 3.2). SIRS and qSOFA displayed equivalent discrimination for organ dysfunction (AUROCs 0.72 vs 0.73), but SIRS provided greater sensitivity at determined operating points (72.3% vs 29.2%). Substantial variation was revealed in mortality associated with SOFA-determined dysfunction in various organ systems. A hybrid system of classification was proposed, consisting infection without SIRS, sepsis (infection with SIRS), severe sepsis (infection with organ dysfunction) and septic shock (infection with cardiovascular dysfunction).Septic shock is the subject of chapter 4, with stark contrast demonstrated between consecutive, unselected patients with this condition in the study database and cohorts enrolled in recent RCTs recruiting ED patients with septic shock. Increasing severity of illness and mortality was demonstrated for patients satisfying lactate, hypotension, and both diagnostic criteria for septic shock (mortality 14.8%, 21.3% and 27.5% respectively). Most patients with septic shock (62.7%) were not admitted to ICU, and mortality for patents admitted to ICU was lower than for patients treated on wards (12.1% vs 20.8%).Severity scores: Established clinical prediction models [MEDS, SOFA, APACHE II, SAPS II and a new ‘Severe Sepsis Score’ were validated in chapter 5, most for the first time in Australian ED patients. Spectrum bias was explored through repeated analysis in varied patient groups. MEDS showed optimal performance (AUROC 0.92), however some MEDS variables were compromised by subjective interpretation and information bias. Older scores such as APACHE II and SAPS II discriminated well (AUROC for both 0.90), but displayed poor calibration, consistently overestimating mortality.Community-acquired pneumonia (CAP). Several CAP clinical prediction models have progressed to later developmental stages including impact assessment and incorporation into guidelines. Patient disposition location is informed through prediction model stratification in several national guidelines. The final paper (chapter 6) assessed performance of establishedCAP scores, some for the first time in Australian patients. Newer scores such as SMARTCOP, CURXO and IDSA/ATS 2007 minor criteria showed higher discrimination (AUROCs 0.84-0.87) than older scores (0.70 for both PSI and CURB65). Performance of low scores was assessed for prediction of brief admission (≤ 48 hours), potentially to an ED short stay unit. No score performed sufficiently to justify this indication (AUROCs 0.64-0.74).ConclusionsThrough analysis of detailed prospective data from consecutive ED patients admitted with infection of all severities, new perspectives have emerged to challenge established constructs derived from convenience or selective sources such as administrative or RCT data. Examples include prognostic import of SIRS and insensitivity of qSOFA in the ED, and interactions between hyperlactataemia and hypotension in septic shock. Performance of severity scores was shown to be influenced by cohort selection, and endpoints. Appraisal of research examining risk stratification tools should take account of representativeness of the study cohort.