Ambulance UK December 2019

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FEATUREDERIVATION AND INTERNAL VALIDATION OF THESCREENING TO ENHANCE PREHOSPITAL IDENTIFICATIONOF SEPSIS (SEPSIS) SCORE IN ADULTS ON ARRIVAL ATTHE EMERGENCY DEPARTMENTMichael A. Smyth 1,2,3* , Daniel Gallacher 1 , Peter K. Kimani 1 , Mark Ragoo 4 , Matthew Ward 2 and Gavin D. Perkins 1,5Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 2019 27:67 https://doi.org/10.1186/s13049-019-0642-2© The Author(s). 2019, Published online 16 July 2019Reproduced with permission from the Scandinavian Journal of Trauma, Resuscitation and Emergency MedicineAbstractBackgroundPrehospital recognition of sepsis may inform case managementby ambulance clinicians, as well as inform transport decisions. Theobjective of this study was to develop a prehospital sepsis screeningtool for use by ambulance clinicians.MethodsWe derived and validated a sepsis screening tool, utilisingunivariable logistic regression models to identify predictors forinclusion, and multivariable logistic regression to generate theSEPSIS score. We utilised a retrospective cohort of adult patientstransported by ambulance (n = 38483) to hospital between 01July 2013 and 30 June 2014. Records were linked using LinkPlus ®software. Successful linkage was achieved in 33289 cases (86%).Eligible patients included adult, non-trauma, non-mental health,non-cardiac arrest cases. Of 33289 linked cases, 22945 caseswere eligible. Eligible cases were divided into derivation (n = 16063,70%) and validation (n = 6882, 30%) cohorts. The primary outcomemeasure was high risk of severe illness or death from sepsis, asdefined by the National Institute for Health and Care ExcellenceSepsis guideline.Results‘High risk of severe illness or death from sepsis’ was present in 3.7% ofderivation (n = 593) and validation (n = 254) cohorts. The SEPSIS scorecomprises the following variables: age, respiratory rate, peripheral oxygensaturations, heart rate, systolic blood pressure, temperature and level ofconsciousness (p < 0.001 for all variables). Area under the curve was 0.87(95%CI 0.85–0.88) for the derivation cohort, and 0.86 (95%CI 0.84–0.88)for the validation cohort. In an undifferentiated adult medical population,for a SEPSIS score ≥ 5, sensitivity was 0.37 (0.31–0.44), specificity was0.96 (0.96–0.97), positive predictive value was 0.27 (0.23–0.32), negativepredictive value was 0.97 (0.96–0.97), positive likelihood value was 13.5(9.7–18.73) and the negative likelihood value was 0.83 (0.78–0.88).ConclusionThis is the first screening tool developed to identify NICE high risk ofsevere illness or death from sepsis. The SEPSIS score is significantlyassociated with high risk of severe illness or death from sepsis onarrival at the Emergency Department. It may assist ambulance cliniciansto identify those patients with sepsis in need of antibiotic therapy.However, it requires external validation, in clinical practice by ambulanceclinicians, in an independent population.KeywordsSepsis, Prehospital, Ambulance, Screening tool, Prediction modelIntroductionAMBULANCE UK - DECEMBERSepsis is a common and potentially life threatening response to aninfection [1]. Worldwide there are an estimated 31.5 million casesof uncomplicated sepsis and 19.4 million cases of severe sepsis orseptic shock resulting in 5.3 million deaths each year [2]. The majorityof these cases originate in the community and will present to hospitalvia the Emergency Department (ED) [3, 4]. More than half of EDsepsis cases will arrive via Emergency Medical Services (EMS) [5-10]. These patients are likely to be sicker than those arriving by othermeans [6, 8-11].International guidelines for sepsis advocate that treatment be initiatedat the earliest possible opportunity [1, 12]. Recent data suggest eachhour delay to antibiotic therapy results in an increase in mortalityamong patients with septic shock of 2.8% [13] whereas for each hourdelay in delivering a 3 h resuscitation bundle (intravenous antibiotics,vascular therapy and obtaining blood cultures) sees a 4% increasein mortality [14]. Early EMS intervention has helped to improveoutcomes for other time critical, life-threatening conditions such asacute myocardial infarction [15], stroke [16] and major trauma [17].It remains to be seen if early EMS intervention in sepsis improvesoutcomes.Small observational studies indicate prehospital care reduces timeto antibiotics for patients with sepsis, without improving clinicaloutcomes [5, 18, 19]. Thus far, trials of prehospital antibiotics havefailed to demonstrate improved clinical outcomes [20, 21]. Onepotential reason for this is inclusion of low acuity sepsis patients withinprehospital studies [21].Despite frequent exposure to patients with potentially life-threatening206For further recruitment vacancies visit: www.ambulanceukonline.com
FEATUREsepsis [22], prehospital recognition of sepsis is challenging [18,23-27]. Indeed, a recent analysis of 240 patients transported byAmbulance Victoria, who were subsequently enrolled in the ARISEstudy, showed that despite the presence of demonstrable physiologicabnormalities, only 165 patients had documentation of infection in theirprehospital record [28]. There are several reasons why this may beso, including, suboptimal teaching and understanding of the condition[6, 29-31], encountering sepsis cases earlier in the disease processwhen the clinical presentation is less obvious [32], lack of in-hospitaldiagnostic capability [18] and dependence upon SIRS criteria toformulate a diagnosis [33, 34]. Reliance upon paramedic gestalt maytherefore mean patients with significant pathology are not identifieduntil after arrival at hospital. It has been argued that a prehospitalsepsis screening tool to assist prehospital clinicians identify ‘sick’sepsis patients would be helpful [6, 35].The NICE guideline “Recognition and management of sepsis (NG51)”,stratifies the risk of “severe illness or death from sepsis” (see Table 1)[12]. It recommends that patients categorised as “high risk of severeillness or death from sepsis” should receive antibiotics within 1 h [12].The aim of this study was to develop a simple scoring system thatwould help identify those adult patients who might benefit most fromearly intervention. For brevity we refer to the NICE categorisation of“high risk of severe illness or death from sepsis” as ‘high risk’.MethodsStudy design and populationThis study was conducted and reported consistent with TRIPODreporting guidelines [36]. We utilised a retrospective sample ofconsecutive adult patients (age ≥ 18 years) transported by WestMidlands Ambulance Service NHS Foundation Trust (WMAS) to RoyalStoke University Hospital NHS Trust (previously University HospitalNorth Staffordshire NHS Foundation Trust) between 01 July 2013 and 30June 2014. Exclusion criteria were age under 18 years, cardiac arrest,trauma or mental health aetiology (determined from hospital dischargediagnosis). No interventions were undertaken as part of this study.Patient involvementA study committee was convened to oversee the Clinical DoctoralResearch Fellowship awarded to MAS. This committee included apatient representative who contributed to the initial research plan,and commented on chapters of the doctoral thesis. The patientrepresentative did not contribute to writing or reviewing this manuscript.Primary outcome measureThe primary outcome measure was categorisation as ‘high risk ofsevere illness or death from sepsis’, as per the National Institutefor Health and Care Excellence (NICE) Guideline “Recognition andmanagement of sepsis (NG51)” [12], on arrival at the ED. For eachincluded patient, category of ‘risk of severe illness or death from sepsis’was assigned as ‘no risk’ i.e. no infection present, ‘low risk’, ‘moderaterisk’ or ‘high risk’, dependent upon presence of infection and presentingvital signs. Presence of infection was determined using the EDdischarge diagnosis. Classification of the risk of severe illness or deathfrom sepsis was determined utilising clinical data recorded in the ED inaccordance with Table 1.Record linkageLinkPlus ® software (version 3.0 beta, Centres for Disease Control andPrevention Cancer Division, Atlanta, Georgia), a probabilistic linkageprogram was used to link ambulance and ED records. First name,surname, gender, date of birth, home address post code and incidentdate were used to link records. All candidate record pairs were manuallyreviewed. Following linkage patient identifiable data were deleted.Missing dataStatistical analyses were performed using R (version 3.3.1) in R Studio(version 0.99.903). Missing data were processed by multiple imputationusing the R package Multiple Imputation by Chained Equations(MICE) (version 2.25) [37] with a Fully Conditional Specification. Toensure robust imputation of missing values, the number of imputeddatasets required was slightly higher than the percentage of cases withincomplete data. For example if 18% of cases had incomplete data 20imputations would be required. Variables that were functions of anothervariable were not imputed, rather their component variables wereimputed and the function determined after imputation. For example,Glasgow Coma Score (GCS) sum is a function of three variables GCSeye, GCS verbal and GCS motor. GCS sum was not imputed, ratherGCS eye, GCS verbal and GCS motor were imputed and GCS sum wascalculated from the component values.Satisfactory imputation of missing data was confirmed by inspectionof both convergence plots and density plots of imputed valuesand observed data (R package MICE (version 2.25) [37] as well ascalculation of R-hat convergence statistics using the R packageMICEadds (version 1.9–0) [38].Model developmentFollowing the imputation of missing data we developed a multivariablelogistic model for high risk of severe illness or death from sepsis (‘highrisk’) on arrival at the ED using several steps. First, the data weredivided into derivation and validation cohorts using the R package Caret(version 6.0–71) [39]. The number of cases assigned to the derivationand validation cohorts were based upon recommendations by Harrell,Royston, Steyerberg and Vergouwe [40-47]. It has been argued that,when developing a predictive model, at least ten instances of theoutcome of interest are required, per candidate predictor included in themodel, to ensure statistically valid results [40-46]. Similarly, Vergouwe etal [47] argue that at least 100 events and 100 non-events are requiredto assess model performance in the validation dataset. However,Steyerberg [45] suggests that, to detect small differences in modelperformance, the validation dataset should contain at least 250 cases ofthe outcome of interest.Derivation of the SEPSIS score was undertaken using the derivationdataset. We assessed the quality of candidate predictor variablesusing univariable logistic regression. Then, we constructed candidateparsimonious multivariable logistic models. Next, we assigned weightedpoint scores to included predictor variables. Thereafter we comparedthe performance of candidate models. Finally, we undertook internalvalidation of the SEPSIS score using the validation dataset.Simple logistic regression was undertaken in an attempt to quantify therelationship between individual candidate predictor variables and theprimary outcome measure (‘high risk’). It is common at this stage toAMBULANCE UK - DECEMBERDo you have anything you would like to add or include in Features? Please contact us and let us know.207
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