Peter I. Featherstone

Patterns in the recording of vital signs and early warning scores: compliance with a clinical escalation protocol

Background The recognition of patient deterioration depends largely on identifying abnormal vital signs, yet little is known about the daily pattern of vital signs measurement and charting. Methods We compared the pattern of vital signs and VitalPAC Early Warning Score (ViEWS) data collected from admissions to all adult inpatient areas (except high care areas, such as critical care units) of a NHS district general hospital from 1 May 2010 to 30 April 2011, to the hospitals clinical escalation protocol. Main outcome measures were hourly and daily patterns of vital signs and ViEWS value documentation; numbers of vital signs in the periods 08:00–11:59 and 20:00–23:59 with subsequent vital signs recorded in the following 6 h; and time to next observation (TTNO) for vital signs recorded in the periods 08:00–11:59 and 20:00–23:59. Results 950 043 vital sign datasets were recorded. The daily pattern of observation documentation was not uniform; there were large morning and evening peaks, and lower night-time documentation. The pattern was identical on all days. 23.84% of vital sign datasets with ViEWS ≥ 9 were measured at night compared with 10.12–19.97% for other ViEWS values. 47.42% of patients with ViEWS=7–8 and 31.22% of those with ViEWS ≥ 9 in the period 20:00–23:59 did not have vital signs recorded in the following 6 h. TTNO decreased with increasing ViEWS value, but less than expected by the monitoring protocol. Conclusions There was only partial adherence to the vital signs monitoring protocol. Sicker patients appear more likely to have vital signs measured overnight, but even their observations were often not followed by timely repeat assessments. The observed pattern of monitoring may reflect the impact of competing clinical priorities.

Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS).

AIM OF STUDY To compare the performance of a human-generated, trial and error-optimised early warning score (EWS), i.e., National Early Warning Score (NEWS), with one generated entirely algorithmically using Decision Tree (DT) analysis. MATERIALS AND METHODS We used DT analysis to construct a decision-tree EWS (DTEWS) from a database of 198,755 vital signs observation sets collected from 35,585 consecutive, completed acute medical admissions. We evaluated the ability of DTEWS to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission or death, each within 24h of a given vital signs observation. We compared the performance of DTEWS and NEWS using the area under the receiver-operating characteristic (AUROC) curve. RESULTS The structures of DTEWS and NEWS were very similar. The AUROC (95% CI) for DTEWS for cardiac arrest, unanticipated ICU admission, death, and any of the outcomes, all within 24h, were 0.708 (0.669-0.747), 0.862 (0.852-0.872), 0.899 (0.892-0.907), and 0.877 (0.870-0.883), respectively. Values for NEWS were 0.722 (0.685-0.759) [cardiac arrest], 0.857 (0.847-0.868) [unanticipated ICU admission}, 0.894 (0.887-0.902) [death], and 0.873 (0.866-0.879) [any outcome]. CONCLUSIONS The decision-tree technique independently validates the composition and weightings of NEWS. The DT approach quickly provided an almost identical EWS to NEWS, although one that admittedly would benefit from fine-tuning using clinical knowledge. We believe that DT analysis could be used to quickly develop candidate models for disease-specific EWSs, which may be required in future.

Development and validation of a decision tree early warning score based on routine laboratory test results for the discrimination of hospital mortality in emergency medical admissions

AIM OF STUDY To build an early warning score (EWS) based exclusively on routinely undertaken laboratory tests that might provide early discrimination of in-hospital death and could be easily implemented on paper. MATERIALS AND METHODS Using a database of combined haematology and biochemistry results for 86,472 discharged adult patients for whom the admission specialty was Medicine, we used decision tree (DT) analysis to generate a laboratory decision tree early warning score (LDT-EWS) for each gender. LDT-EWS was developed for a single set (n=3496) (Q1) and validated in 22 other discrete sets each of three months long (Q2, Q3…Q23) (total n=82,976; range of n=3428 to 4093) by testing its ability to discriminate in-hospital death using the area under the receiver-operating characteristic (AUROC) curve. RESULTS The data generated slightly different models for male and female patients. The ranges of AUROC values (95% CI) for LDT-EWS with in-hospital death as the outcome for the validation sets Q2-Q23 were: 0.755 (0.727-0.783) (Q16) to 0.801 (0.776-0.826) [all patients combined, n=82,976]; 0.744 (0.704-0.784, Q16) to 0.824 (0.792-0.856, Q2) [39,591 males]; and 0.742 (0.707-0.777, Q10) to 0.826 (0.796-0.856, Q12) [43,385 females]. CONCLUSIONS This study provides evidence that the results of commonly measured laboratory tests collected soon after hospital admission can be represented in a simple, paper-based EWS (LDT-EWS) to discriminate in-hospital mortality. We hypothesise that, with appropriate modification, it might be possible to extend the use of LDT-EWS throughout the patients hospital stay.

A reply to: O’Driscoll, Bakerly, Murphy and Turkington “… SpO2 values in acute medical admissions…”

We are surprised that O’Driscoll et al. do not believe that our ata justify our suggestion that the British Thoracic Society (BTS) . . .should consider changing its target saturation for. . .patients ot at risk of hypercapnic respiratory failure (from 94-98%) to 968%. . .”1 We made this suggestion based upon a variety of data. irst, the finding that the mean (SD) recorded SpO2 in a study popuation of 37,593 adult patients from four hospitals breathing air was 6.4% (2.6%), with a median (IQR) of 97% (95–98%).1 Our findings ere if anything likely to be skewed by patient age and concurent illness, both of which would lower SpO2 values rather than nflate the values erroneously. Second, the similarity of our results o those of Witting and Scharf, who found that 858/871 (98.5%) of he “healthy”, awake, asymptomatic adults in their study had SpO2 alues ≥96%.2 Third, the similarity of our findings to those of the npublished audit of 320 stable hospital patients with no history f lung disease (referred to in the BTS guidelines1) which found a ean (SD) SaO2 of 96.7% (1.77%) in patients aged >71 years.3 It is our view that, taken together, these three indepenent observations support a call to review the normal and arget SpO2 ranges, which were based upon the findings of rapo et al.4 (n = 96 healthy volunteers). Although other studies ere used by the BTS to identify the normal and target SpO2 anges for elderly patients, they too are small (range of subects = 46–194) and used measurements of PaO2 or SaO2, not SpO2. ur data included SpO2 measurements from 14,558 patients aged ≥ 5 years. Most of the rest of O’Driscoll et al.’s criticisms of our work focus n our finding of an association between SpO2 and final hospital ortality. However, we attributed no greater significance to this bservation, other than that it provided “. . . some support. . .”, o the choice of a new lower SpO2 limit of 96%.1 Such an assoiation does not, of course, demonstrate a cause and effect and e agree with O’Driscoll et al. that further information, such as atient age, severity of illness and diagnostic category, is required o understand this relationship fully. However, whilst informaion regarding the patients’ diagnostic categories was not available o us, we feel that it is unlikely that the presence of a large ercentage of patients with chronic obstructive pulmonary disase (COPD) contributed significantly to the observed relationship etween mortality and oxygen saturation levels. If this were to e the case, it would also mean that we have further underesimated the normal and target SpO2 ranges for patients not at isk of hypercapnic respiratory failure, as large numbers of COPD atients would inevitably lower the observed mean and median pO2.