Ambulance UK December 2019

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FEATURETable 2 Patient characteristicsVariableLocationDerivationn = 16,063 (70%)Validationn = 6,882 (30%)Home, n (%) 11,408 (71) 4,964 (72)Nursing home, n (%) 1,028 (6) 414 (6)Other, n (%) 3,627 (23) 1,504 (22)Age (years), mean (SD) 63 (21) 62 (21)GenderMale, n (%) 7,884 (49) 3,346 (49)Respirations (breaths/min), mean (SD) 20 (6) 20 (6)Oxygen saturation (%), mean (SD) 96 (5) 96 (5)Heart rate (beats/min), mean (SD) 92 (24) 92 (24)Systolic blood pressure (mmHg), mean (SD) 133 (27) 133 (27)Diastolic blood pressure (mmHg), mean (SD) 78 (17) 78 (17)Temperature (°C), mean (SD) 36.8 (0.9) 36.8 (0.9)Blood sugar (mmol/L), mean (SD) 7.0 (3.4) 7.0 (3.3)Glasgow Coma Score, median (IQR) 15 (15–15) 15 (15–15)Capillary bed refill timeSkinNormal (< 2 s), n (%) 15,319 (95) 6,567 (95)Delayed (> 2 s), n (%) 744 (5) 315 (5)Normal, n (%) 10,366 (73) 5,320 (77)Pallor, n (%) 2,669 (19) 1,037 (15)Flushed, n (%) 856 (6) 359 (5)Cyanosed, n (%) 181 (1) 83 (1)Jaundice, n (%) 92 (0.6) 51 (0.7)Mottled, n (%) 53 (0.4) 20 (0.2)Rash, n (%) 21 (0.1) 12 (0.1)Pupil size (mm), median (IQR) 3 (3–4) 3 (3–4)Pupil reactionBrisk, n (%) 13,447 (93) 6,462 (94)Sluggish, n (%) 923 (6) 381 (6)Fixed, n (%) 128 (0.8) 39 (0.5)NICE riskNo risk (no infection), n (%) 13,083 (81.4) 5,607 (81.5)Infection, n(%) 2980 (18.6) 1275 (18.5)Low risk, n (%) 1,048 (6.5) 448 (6.5)Moderate risk, n (%) 1,339 (8.3) 573 (8.3)High risk, n (%) 593 (3.7) 254 (3.7)AMBULANCE UK - DECEMBERdata concerning the left pupil were excluded from further analysis.Strong correlations between GCS sum, GCS components (GCS eye,GCS verbal and GCS motor) and AVPU score (used to document levelof consciousness) were identified. It is unclear which measure of levelof consciousness would generate the most effective predictive modelof sepsis. Three candidate models, using GCS sum, GCS componentsand AVPU score as their respective measure of consciousness, weregenerated.Multivariable logistic regression analysis identified the following variablesto be significant: location, age, respirations, oxygen saturations, pulserate, systolic blood pressure, temperature, skin colour and level ofconsciousness. The number of instances each variable was selected, andthe related Wald test statistic, for each model is reported in Table 3.Categorisation of continuous variables is summarised in Table 4.Simple weighted scores to enable calculation of the SEPSIS score wereobtained by rounding regression coefficients to the nearest integer(Additional file 1: Tables S5, S6 & S7). Intervals for continuous variableswith the same weighted score were merged to simplify calculation ofthe SEPSIS score (Additional file 1: Tables S8, S9 & S10). The SEPSISscore is defined as the sum of the simplified weighted scores for eachvariable.210For further recruitment vacancies visit: www.ambulanceukonline.com
FEATURETable 3 Selection of variables for inclusion in multivariable modelsPredictorvariableIncidence of variable selectionWald test p-valueGCS (sum) model GCS (components) model AVPU model GCS (sum) model GCS (components) model AVPU modelLocation 50 50 50 0.002 0.004 0.003Age 50 50 50 < 0.001 < 0.001 < 0.001Gender 0 0 0 – – –Resps 50 50 50 < 0.001 < 0.001 < 0.001SpO2 50 50 50 < 0.001 < 0.001 < 0.001Pulse 50 50 50 < 0.001 < 0.001 < 0.001SBP 50 50 50 < 0.001 < 0.001 < 0.001DBP 0 0 0 – – –Temp 50 50 50 < 0.001 < 0.001 < 0.01BM 1 1 0 0.86 * 0.85 * 0.87 *Skin 50 50 50 0.007 0.007 0.014CBRT 0 0 0 – – –RPupilReact 11 12 8 0.42 * 0.41 * 0.46 *RPupilSize 0 0 0 – – –GCS_sum 50 NA NA < 0.001 NA NAGCS_eye NA 0 NA NA – NAGCS_verbal NA 50 NA NA < 0.001 NAGCS_motor NA 0 NA NA – NAAVPU NA NA 50 NA NA < 0.001SpO2 peripheral oxygen saturation, SBP systolic blood pressure, DBP diastolic blood pressure, Temp temperature (C), BM blood sugar mmol/L, CBRT capillary bedrefill time, GCS Glasgow coma score, AVPU alert, verbal, pain or unresponsive* Did not reach statistical significanceRelative performance of the three parallel models is reported in Table5. The model utilising GCS sum as the measure of consciousnesswas calculated to have the best performance statistically. The finalparsimonious model, with merged intervals and weighted scores, isreported in Table 6.patients identified as not septic, 6528 (95.6%) did not have sepsis (truenegative), while 159 (62.6%) did have ‘high risk’ on arrival at the ED butwere incorrectly classified by the SEPSIS score as not having sepsis(false negatives).The calibration slope for the derivation and validation datasets was 1.0and 0.97 respectively, suggesting the SEPSIS score has adequate fit.The AUROC was 0.87 (95% CI 0.85–0.88) for the derivation datasetand 0.86 (95% CI 0.84–0.88) for the validation dataset. We reportperformance measures for each point score of the SEPSIS score inAdditional file 1: Table S11, and categorise patients as low likelihood(< 10%), moderate likelihood (10–20%) or high likelihood (> 20%) byapplying different thresholds for the SEPSIS score (see Fig. 2).Where the SEPSIS score indicates greater than 20% likelihood of ‘highrisk’ at ED (SEPSIS score ≥ 5), we observed satisfactory performancecharacteristics as reported in Table 7. If, as per the NICE sepsisguideline [NG51], the presence of infection is required before adiagnosis of sepsis can be made [12], then in the validation cohort of6882 adult patients, 195 (2.8%) patients were classified as being having‘high risk’, and 6687 (97.2%) were classified as not having ‘high risk’.Among those patients classified as ‘high risk’, 95 (48.7%) did have ‘highrisk’ (true positive), while 100 (51.3%) patients had their risk of sepsisoverestimated i.e. were incorrectly identified as having ‘high risk’ (falsepositive). Within the 100 false positive cases, 79 (40.5%) patients had‘moderate risk’ and 21 (10.8%) had ‘low risk’ on arrival at the ED. AmongDiscussionScreening tool performance is commonly described in terms ofsensitivity, specificity, positive predictive value (PPV) and positivelikelihood ratio (PLR). Sensitivity describes the ability of a test tocorrectly identify those with the disease (true positive rate), whereasspecificity describes the ability of a test to correctly identify thosewithout the disease (true negative rate). PPV represents the proportionof patients with positive test who actually have the disease, while thePLR shows how much more likely someone is to get a positive test if he/she has the disease, compared with a person without disease [55].The performance characteristics of several existing screening tools usedto support paramedic recognition of sepsis are reported in Table 8.Existing data may suggest the PreSep score is the best performingsepsis screening tool. However, when applied to the same validationdataset used to test the SEPSIS score, the PreSep score remains moresensitive (0.61 (95%CI 0.55–0.67)), but has poorer specificity 0.95(95%CI 0.95–0.96), PPV 0.33 (95%CI 0.29–0.37) and PLR 12.76 (95%CI11.03–14.76). These data suggest the PreSep score may not in fact beAMBULANCE UK - DECEMBERDo you have anything you would like to add or include in Features? Please contact us and let us know.211
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Ambulance UK December 2019

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