C-CHEWS – what does each bit mean?


Most inpatient pediatric arrests are preventable by early recognition/treatment of deterioration.

What is the basis of the assertion of “most”?
Here in PEWS scoring in general


Children with cardiac disease have the highest arrest rates; however, early warning scoring systems have not been validated in this population.


The objective of this study was to validate the Cardiac Children’s Hospital Early Warning Score (C-CHEWS) tool in inpatient pediatric cardiac patients.

What makes a validation process that results in a valid tool?


DESIGN Sensitivity and specificity were estimated based on retrospective review of patients that experienced unplanned CICU transfer/arrest (n = 64) and a comparison sample (n = 248) of admissions.

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DESIGN The previously validated Pediatric Early Warning Score (PEWS) tool was used for comparison.

Which version of PEWS was used for this study? Or what version is in use at Boston Children’s?


DESIGN Patients’ highest C-CHEWS scores were compared with calculated PEWS scores.

Content

DESIGN Area under the receiver operating characteristic (AUROC) curve was calculated for PEWS and C-CHEWS to measure discrimination.

Content for item 5

RESULTS The AUROC curve for C-CHEWS was 0.917 compared with PEWS 0.785 (P < .001). The algorithm AUROC curve was 0.902 vs. PEWS of 0.782.

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RESULTS C-CHEWS algorithm sensitivity was 96.9 (score ≥ 2), 79.7 (≥4), and 67.2 (≥5) vs. PEWS of 81.1(≥2), 37.5 (≥4), and 23.4 (≥5).

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RESULTS C-CHEWS specificity was 58.1 (≥2), 85.5 (≥4), and 93.6 (≥5) vs. PEWS of 81.1 (≥2), 94.8 (≥4) and 97.6 (≥5).

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RESULTS Lead time of elevated C-CHEWS scores (≥2) was a median of 9.25 hours prior to event vs. PEWS, which was 2.25 hours and lead time for critical C-CHEWS scores (≥5) was 2 hours vs. 0 hours for PEWS (P < .001).

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CONCLUSIONS C-CHEWS has excellent discrimination to identify deterioration in children with cardiac disease and performed significantly better than PEWS both as an ordinal variable and when choosing cut points to maximize AUROC.

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CONCLUSIONS C-CHEWS has a higher sensitivity than PEWS at all cut points.

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Culture of Safety

High-reliability organizations consistently minimize adverse events despite carrying out intrinsically hazardous work. Such organizations establish a culture of safety by maintaining a commitment to safety at all levels, from frontline providers to managers and executives.

Early Warning Biblio – GB


1.Carberry M. Implementing the modified early warning system: our experiences.

1.Carberry M. Implementing the modified early warning system: our experiences. Nursing in critical care. 2002 Sep-Oct;7(5):220-226. UI/MI:12448503 ISSN:1362-1017
Abstract:A modified early warning system (MEWS) has been developed and implemented in acute surgical care across Lanarkshire. This move comes in response to evidence of suboptimal care prior to admission to the intensive care unit (ICU) and the recommendations of the critical care review papers (Department of Health, 2000; Scottish Executive, 2000). The main aim of this paper is to outline the experiences of modifying the early warning system, the educational preparation of medical, nursing and paramedical staff and the results of the pilot study. Further studies require to be undertaken in order to investigate the efficacy of early warning systems on clinical outcomes and subsequent morbidity. 


2. Cooper N. Patient at risk!

2. Cooper N. Patient at risk!. [Review] [16 refs]. Clinical Medicine. 2001 Jul-Aug;1(4):309-311. UI/MI:11525580 ISSN:1470-2118
Abstract:Recent Department of Health-led reviews have recommended wide-ranging changes in the provision of critical care which will affect most physicians. Critically ill patients on general wards are too often missed, and intervention is often too late. Early warning scoring systems can improve outcome by detecting critical illness earlier and by acting as triage tools. The re-classification of care, levels 0-3, means that physicians and intensivists will increasingly work together to provide the level of care required for our sickest patients.


3. Day BA. Early warning system scores and response times: an audit.

3. Day BA. Early warning system scores and response times: an audit. Nursing in critical care. 2003 Jul-Aug;8(4):156-164. UI/MI:12940691 ISSN:1362-1017
Abstract:In response to NHS reforms within critical care, the surgical directorate of the Southern Derbyshire Acute Hospitals NHS Trust developed and introduced a modified early warning system (DMEWS). Anecdotal evidence from nursing staff indicated that response times by doctors, when triggered by use of the DMEWS, were outside the established timescale. An audit was undertaken to determine the response times to calls for assistance triggered by use of the DMEWS and to identify any disparity between response times. The audit confirmed that whilst DMEWS triggered the nurses to initiate action for immediate treatment, response by members of the surgical teams was below the agreed standards. Further studies are planned to indicate whether longer response times have an adverse effect on patient welfare or outcome. 

4. Frost P. In response to 'Effect of introducing the Modified Early Warning score on clinical outcomes, cardio-pulmonary arrests and intensive care utilisation in acute medical admissions'

4. Frost P. In response to ‘Effect of introducing the Modified Early Warning score on clinical outcomes, cardio-pulmonary arrests and intensive care utilisation in acute medical admissions’, Subbe CP et al., Anaesthesia 2003; 58: 797-802.[comment]. Anaesthesia. 2003 Nov;58(11):1154. UI/MI:14616654 ISSN:0003-2409


5. Goldhill DR, McNarry AF. Physiological abnormalities in early warning scores are related to mortality in adult inpatients.

5. Goldhill DR, McNarry AF. Physiological abnormalities in early warning scores are related to mortality in adult inpatients.[see comment]. British journal of anaesthesia. 2004 Jun;92(6):882-884. UI/MI:15064245 ISSN:0007-0912
Abstract:BACKGROUND: Early warning scores using physiological measurements may help identify ward patients who are, or who may become, critically ill. We studied the value of abnormal physiology scores to identify high-risk hospital patients. METHODS: On a single day we recorded the following data from 433 adult non-obstetric inpatients: respiratory rate, heart rate, systolic pressure, temperature, oxygen saturation, level of consciousness, urine output for catheterized patients, age and inspired oxygen. We also noted the care required and given. RESULTS: Twenty-six patients (6%) died within 30 days. They were significantly older than survivors (P<0.001). Their median hospital stay was 26 days (interquartile range 16-39). Mortality increased with the number of physiological abnormalities (P<0.001), being 0.7% with no abnormalities, 4.4% with one, 9.2% with two and 21.3% with three or more. Patients receiving a lower level of care than desirable also had an increased mortality (P<0.01). Logistic regression modelling identified level of consciousness, heart rate, age, systolic pressure and respiratory rate as important variables in predicting outcome. CONCLUSIONS: Simple physiological observations identify high-risk hospital inpatients. Those who die are often inpatients for days or weeks before death, allowing time for clinicians to intervene and potentially change outcome. Access to critical care beds could decrease mortality.


6. Goldhill DR, McNarry AF, Mandersloot G and McGinley A. A physiologically-based early warning score for ward patients: the association between score and outcome.
6. Goldhill DR, McNarry AF, Mandersloot G and McGinley A. A physiologically-based early warning score for ward patients: the association between score and outcome. Anaesthesia. 2005 Jun;60(6):547-553. UI/MI:15918825 ISSN:0003-2409
Abstract:We analysed the physiological values and early warning score obtained from 1047 ward patients assessed by an intensive care outreach service. Patients were either referred directly from the wards (n = 245, 23.4%) or were routine critical care follow-ups. Decisions were made to admit 135 patients (12.9%) to a critical care area and limit treatment in another 78 (7.4%). An increasing number of physiological abnormalities was associated with higher hospital mortality (p < 0.0001) ranging from 4.0% with no abnormalities to 51.9% with five or more. An increasing early warning score was associated with more intervention (p < 0.0001) and higher hospital mortality (p < 0.0001). For patients with scores above one (n = 660), decisions to admit to a critical care area or limit treatment were taken in 200 (30.3%). Scores of all physiological variables except temperature contributed to the need for intervention and all variables except temperature and heart rate were associated with hospital mortality. FULL TEXT LINK or Request This From the Library:

7. Goldhill DR, Worthington L, Mulcahy A, Tarling M and Sumner A. The patient-at-risk team: identifying and managing seriously ill ward patients.
7. Goldhill DR, Worthington L, Mulcahy A, Tarling M and Sumner A. The patient-at-risk team: identifying and managing seriously ill ward patients.[see comment]. Anaesthesia. 1999 Sep;54(9):853-860. UI/MI:10460556 ISSN:0003-2409
Abstract:A ‘patient-at-risk team’, established to allow the early identification of seriously ill patients on hospital wards, made 69 assessments on 63 patients over 6 months. Predefined physiological criteria were not able to reliably predict which patients would be admitted to the intensive care unit. The incidence of cardiopulmonary resuscitation before intensive care admission was 3.6% for patients seen by the team and 30.4% for those not seen (p < 0.005). Of admissions seen by the team, 25% died on the intensive care unit compared with 45% of those not seen (not significant, p = 0.07). Among those not seen by the team, mortality was 40% for those who did not require resuscitation and 57% for those who did (not significant). Many critically ill ward patients had abnormal physiological values before intensive care unit admission. Identification of critically ill patients on the ward and early advice and active management are likely to prevent the need for cardiopulmonary resuscitation and to improve outcome. FULL TEXT LINK or Request This From the Library:

8. Wright MM, Stenhouse CW and Morgan RJ. Early detection of patients at risk (PART)
8. Wright MM, Stenhouse CW and Morgan RJ. Early detection of patients at risk (PART)[see comment]. Anaesthesia. 2000 Apr;55(4):391-392. UI/MI:10781131 ISSN:0003-2409

9. McBride J, Knight D, Piper J and Smith GB. Long-term effect of introducing an early warning score on respiratory rate charting on general wards.
9. McBride J, Knight D, Piper J and Smith GB. Long-term effect of introducing an early warning score on respiratory rate charting on general wards. Resuscitation. 2005 Apr;65(1):41-44. UI/MI:15797273 ISSN:0300-9572
Abstract:The respiratory rate is an early indicator of disease, yet many clinicians underestimate its importance and hospitals report a poor level of respiratory rate recording. We studied the short- and long-term effects of introducing a new patient vital signs chart and the modified early warning score (MEWS), which incorporates respiratory rate on the prevalence of respiratory rate recording in six general wards of our hospital. Prior to the commencement of the study, the average percentage of occupied beds where at least one respiratory rate recording had been made in a single 24-h period was 29.5+/-13.5%. After the introduction of the new vital signs chart to all six wards, and the introduction of MEWS to three wards, this rose to 68.9+/-20.9%. When all six wards had been using both the new chart and the MEWS system for almost 1 year, the figure had reached 91.2+/-5.6%. During the pre-introduction period, there was no difference in the prevalence of respiratory rate recording between the specialties (orthopaedic, 26.9%; surgery, 32.9%; medicine, 29.8%; p=0.118). During the second two audit periods, the prevalence of respiratory rate monitoring was consistently higher on medical wards than on surgical and orthopaedic wards (p<0.001). The study confirms the long-term beneficial effect of introducing the MEWS system on respiratory rate recording into the general wards of our hospital. As respiratory rate abnormalities are early markers of disease, it is hoped that improved monitoring will have an impact on the nature and timeliness of the response to critical illness. This may have an impact on the future incidence of potentially avoidable cardiac arrest, deaths and unanticipated intensive care unit admission.

10. Odell M, Forster A, Rudman K and Bass F. The critical care outreach service and the early warning system on surgical wards.
10. Odell M, Forster A, Rudman K and Bass F. The critical care outreach service and the early warning system on surgical wards. Nursing in critical care. 2002 May-Jun;7(3):132-135. UI/MI:12226948 ISSN:1362-1017
Abstract:The implementation and evaluation of a modified early warning system (MEWS) on surgical wards are described. The MEWS was found to be a useful adjunct to the outreach service. Early data have shown that MEWS can help direct critical intervention. Ward staff have benefit from both the MEWS and the outreach service. FULL TEXT LINK or Request This From the Library: request UI:12226948

11. Parissopoulos S, Kotzabassaki S. Critical care outreach and the use of early warning scoring systems; a literature review.
11. Parissopoulos S, Kotzabassaki S. Critical care outreach and the use of early warning scoring systems; a literature review. Icus Nurs Web J. 2005 Jan-Mar;((21)):13p. UI/MI:2005063444 ISSN:1108-7366
Abstract:The aim of this paper is to look at patient surveillance of “at risk” patients and how this is provided by critical care outreach services in the UK. Patient surveillance is a relatively recent development in the assessment of the seriously ill patient, within the framework of the document C ritical Care Without Walls. Early recognition of potential and actual deterioration in the patient?s condition is essential, and should be accompanied by an appropriate response for early intervention. Timely access to high dependency and critical care facilities is crucial in effectively managing sick ward patients. Since the publication of Comprehensive Critical Care (2000), Early Warning Scoring systems (EWS) have been introduced onto the wards to improve the identification of patients deteriorating into critical illness. EWS tools are based upon the allocation of “points” to physiological observations, the calculation of a total “score” and the designation of an agreed calling “trigger” level. Many Trusts report evidence of the benefit of track and trigger warning systems, in improving single process steps in care of the critically ill. Physiological tracking and triggering systems can lead to measurable direct and indirect improvements in the quality of patient care. Whilst supporting the development of outreach services and EWS tools, it is imperative that the future of any outreach service must be responsive to post-implementation audit and research.

12. Quarterman CP, Thomas AN, McKenna M and McNamee R. Use of a patient information system to audit the introduction of modified early warning scoring
12. Quarterman CP, Thomas AN, McKenna M and McNamee R. Use of a patient information system to audit the introduction of modified early warning scoring. Journal of evaluation in clinical practice. 2005 Apr;11(2):133-138. UI/MI:15813711 ISSN:1356-1294
Abstract:Modified early warning scoring (MEWS) uses abnormalities in routine observations to identify patients at risk of critical illness. Nurses recorded scores at or above the medical response score of 3 on a hospital clinical information system during the first year of introducing MEWS to 10 wards in a university hospital. A total of 619 triggers were recorded in 365 patients. Fifty-nine required intensive care unit (ICU)/high dependency unit (HDU) care; 71 died. Survival was significantly worse for initial scores >4 (35/104 patients died) than for scores 3-4 (P<0.004). Multivariant analysis showed age (P<0.001) and trigger score (P<0.001) but not ward specialty (P=0.1) predicted death. Mean ages of survivors and non-survivors were 64 years (SD 18) and 74 years (SD 17), respectively. Addition of a score for age did not significantly increase the area under a receiver operator characteristic curve for the predictive value of MEWS scores. The study shows that increasing MEWS score is associated with worse outcome across a range of specialties and that nursing staff will use a patient information system to audit MEWS scores.

13. Sharpley JT, Holden JC. Introducing an early warning scoring system in a district general hospital.
13. Sharpley JT, Holden JC. Introducing an early warning scoring system in a district general hospital. Nursing in critical care. 2004 May-Jun;9(3):98-103. UI/MI:15152751 ISSN:1362-1017
Abstract:One of the critical care outreach service’s aims in this local hospital was to develop an assessment tool to help identify patients in danger of deterioration. This paper describes the introduction of an early warning scoring system between April 2001 and March 2002 to the surgical unit of a district general hospital. The informal and gradual approach used to optimize the effectiveness of introducing the early warning scoring system is highlighted. Explanations are given of the training processes undertaken, the pilot evaluation and lessons learned from the process. Using the experiences of the outreach service in introducing the early warning scoring system, this paper aims to provide thought for others considering a similar initiative in their area.

14. Subbe CP, Davies RG, Williams E, Rutherford P and Gemmell L. Effect of introducing the Modified Early Warning score on clinical outcomes, cardio-pulmonary arrests and intensive care utilisation in acute medical admissions.
14. Subbe CP, Davies RG, Williams E, Rutherford P and Gemmell L. Effect of introducing the Modified Early Warning score on clinical outcomes, cardio-pulmonary arrests and intensive care utilisation in acute medical admissions.[see comment]. Anaesthesia. 2003 Aug;58(8):797-802. UI/MI:12859475 ISSN:0003-2409
Abstract:The effects of introducing Modified Early Warning scores to identify medical patients at risk of catastrophic deterioration have not been examined. We prospectively studied 1695 acute medical admissions. All patients were scored in the admissions unit. Patients with a Modified Early Warning score > 4 were referred for urgent medical and critical care outreach team review. Data was compared with an observational study performed in the same unit during the proceeding year. There was no change in mortality of patients with low, intermediate or high Modified Early Warning scores. Rates of cardio-pulmonary arrest, intensive care unit or high dependency unit admission were similar. Data analysis confirmed respiratory rate as the best discriminator in identifying high-risk patient groups. The therapeutic interventions performed in response to abnormal scores were not assessed. We are convinced that the Modified Early Warning score is a suitable scoring tool to identify patients at risk. However, outcomes in medical emergency admissions are influenced by a multitude of factors and so it may be difficult to demonstrate the score’s benefit without further standardizing the response to abnormal values.

15. Subbe CP, Kruger M, Rutherford P and Gemmel L. Validation of a modified Early Warning Score in medical admissions.
 15. Subbe CP, Kruger M, Rutherford P and Gemmel L. Validation of a modified Early Warning Score in medical admissions.[see comment]. Qjm. 2001 Oct;94(10):521-526. UI/MI:11588210 ISSN:1460-2725
Abstract:The Early Warning Score (EWS) is a simple physiological scoring system suitable for bedside application. The ability of a modified Early Warning Score (MEWS) to identify medical patients at risk of catastrophic deterioration in a busy clinical area was investigated. In a prospective cohort study, we applied MEWS to patients admitted to the 56-bed acute Medical Admissions Unit (MAU) of a District General Hospital (DGH). Data on 709 medical emergency admissions were collected during March 2000. Main outcome measures were death, intensive care unit (ICU) admission, high dependency unit (HDU) admission, cardiac arrest, survival and hospital discharge at 60 days. Scores of 5 or more were associated with increased risk of death (OR 5.4, 95%CI 2.8-10.7), ICU admission (OR 10.9, 95%CI 2.2-55.6) and HDU admission (OR 3.3, 95%CI 1.2-9.2). MEWS can be applied easily in a DGH medical admission unit, and identifies patients at risk of deterioration who require increased levels of care in the HDU or ICU. A clinical pathway could be created, using nurse practitioners and/or critical care physicians, to respond to high scores and intervene with appropriate changes in clinical management

 

16. Duncan H, Hutchison J, Parshuram CS.
16. Duncan H, Hutchison J, Parshuram CS.The Pediatric Early Warning System score: a severity of illness score to predict urgent medical need in hospitalized children. J Crit Care. 2006 Sep;21(3):271-8. MID: 16990097 [PubMed – indexed for MEDLINE]


17. VandenBerg SD, Hutchison JS, Parshuram CS; Paediatric Early Warning System Investigators. A cross-sectional survey of levels of care and response mechanisms for evolving critical illness in hospitalized children.
17. VandenBerg SD, Hutchison JS, Parshuram CS; Paediatric Early Warning System Investigators. A cross-sectional survey of levels of care and response mechanisms for evolving critical illness in hospitalized children. Pediatrics. 2007 Apr;119(4):e940-6. Epub 2007 Mar 26. PMID: 17387170 [PubMed – indexed for MEDLINE]

18. Fraser DD, Singh RN, Frewen T. The PEWS score: potential calling criteria for critical care response teams in children's hospitals.
18. Fraser DD, Singh RN, Frewen T. The PEWS score: potential calling criteria for critical care response teams in children’s hospitals. J Crit Care. 2006 Sep;21(3):278-9. No abstract available. PMID: 16990098 [PubMed – indexed for MEDLINE] 

19. Chamberlain JM, Patel KM, Pollack MM. The Pediatric Risk of Hospital Admission score: a second-generation severity-of-illness score for pediatric emergency patients.
19. Chamberlain JM, Patel KM, Pollack MM. The Pediatric Risk of Hospital Admission score: a second-generation severity-of-illness score for pediatric emergency patients. Pediatrics. 2005 Feb;115(2):388-95. PMID: 15687449 [PubMed – indexed for MEDLINE]

20. Graciano AL, Balko JA, Rahn DS, Ahmad N, Giroir BP. The Pediatric Multiple Organ Dysfunction Score (P-MODS): development and validation of an objective scale to measure the severity of multiple organ dysfunction in critically ill children.
20. Graciano AL, Balko JA, Rahn DS, Ahmad N, Giroir BP. The Pediatric Multiple Organ Dysfunction Score (P-MODS): development and validation of an objective scale to measure the severity of multiple organ dysfunction in critically ill children. Crit Care Med. 2005 Jul;33(7):1484-91. PMID: 16003052 [PubMed – indexed for MEDLINE]

21. Sharek PJ, Parast LM, Leong K, Coombs J, Earnest K, Sullivan J, Frankel LR, Roth SJ. Effect of a rapid response team on hospital-wide mortality and code rates outside the ICU in a Children's Hospital.
21. Sharek PJ, Parast LM, Leong K, Coombs J, Earnest K, Sullivan J, Frankel LR, Roth SJ. Effect of a rapid response team on hospital-wide mortality and code rates outside the ICU in a Children’s Hospital. JAMA. 2007 Nov 21;298(19):2267-74. PMID: 18029830 [PubMed – indexed for MEDLINE]

22. Nishisaki A, Sullivan J 3rd, Steger B, Bayer CR, Dlugos D, Lin R, Ichord R, Helfaer MA, Nadkarni V. Retrospective analysis of the prognostic value of electroencephalography patterns obtained in pediatric in-hospital cardiac arrest survivors during three years.
22. Nishisaki A, Sullivan J 3rd, Steger B, Bayer CR, Dlugos D, Lin R, Ichord R, Helfaer MA, Nadkarni V. Retrospective analysis of the prognostic value of electroencephalography patterns obtained in pediatric in-hospital cardiac arrest survivors during three years. Pediatr Crit Care Med. 2007 Jan;8(1):10-7. PMID: 17251876 [PubMed – indexed for MEDLINE]

23. Donoghue AJ, Nadkarni VM, Elliott M, Durbin D;Effect of hospital characteristics on outcomes from pediatric cardiopulmonary resuscitation: a report from the national registry of cardiopulmonary resuscitation.

23. Donoghue AJ, Nadkarni VM, Elliott M, Durbin D; American Heart Assocation National Registry of Cardiopulmonary Resuscitation Investigators. Effect of hospital characteristics on outcomes from pediatric cardiopulmonary resuscitation: a report from the national registry of cardiopulmonary resuscitation. Pediatrics. 2006 Sep;118(3):995-1001. PMID: 16950990 [PubMed – indexed for MEDLINE]


24. Freishtat RJ, Klein BL, Teach SJ, Johns CM, Arapian LS, Perraut ME, Chamberlain JM. Admission predictor modeling in pediatric interhospital transport.

24.  Freishtat RJ, Klein BL, Teach SJ, Johns CM, Arapian LS, Perraut ME, Chamberlain JM. Admission predictor modeling in pediatric interhospital transport. Pediatr Emerg Care. 2004 Jul;20(7):443-7. PMID: 15232244 [PubMed – indexed for MEDLINE]


25. Thukral A, Lodha R, Irshad M, Arora NK. Performance of Pediatric Risk of Mortality (PRISM), Pediatric Index of Mortality (PIM), and PIM2 in a pediatric intensive care unit in a developing country.
 25. Thukral A, Lodha R, Irshad M, Arora NK. Performance of Pediatric Risk of Mortality (PRISM), Pediatric Index of Mortality (PIM), and PIM2 in a pediatric intensive care unit in a developing country. Pediatr Crit Care Med. 2006 Jul;7(4):356-61. PMID: 16738502 [PubMed – indexed for MEDLINE]

26. Kumar N, Thomas N, Singhal D, Puliyel JM, Sreenivas V. Triage score for severity of illness. Indian Pediatr.
 26. Kumar N, Thomas N, Singhal D, Puliyel JM, Sreenivas V. Triage score for severity of illness. Indian Pediatr. 2003 Mar;40(3):204-10. PMID: 12657751 [PubMed – indexed for MEDLINE]

27. Gorelick MH, Stevens MW, Schultz TR, Scribano PV. Performance of a novel clinical score, the Pediatric Asthma Severity Score (PASS), in the evaluation of acute asthma.
27. Gorelick MH, Stevens MW, Schultz TR, Scribano PV. Performance of a novel clinical score, the Pediatric Asthma Severity Score (PASS), in the evaluation of acute asthma. Acad Emerg Med. 2004 Jan;11(1):10-8. PMID: 14709423 [PubMed – indexed for MEDLINE]

28. Brilli RJ, Gibson R, Luria JW, Wheeler TA, Shaw J, Linam M, Kheir J, McLain P, Lingsch T, Hall-Haering A, McBride M. Implementation of a medical emergency team in a large pediatric teaching hospital prevents respiratory and cardiopulmonary arrests outside the intensive care unit.
28. Brilli RJ, Gibson R, Luria JW, Wheeler TA, Shaw J, Linam M, Kheir J, McLain P, Lingsch T, Hall-Haering A, McBride M. Implementation of a medical emergency team in a large pediatric teaching hospital prevents respiratory and cardiopulmonary arrests outside the intensive care unit. Pediatr Crit Care Med. 2007 May;8(3):236-46; quiz 247. PMID: 17417113 [PubMed – indexed for MEDLINE]

29. Nagele P, Kroesen G. Pediatric emergencies. An epidemiologic study of mobile care units in Innsbruck
 Gold DL1, Mihalov LK, Cohen DM.

Abstract

OBJECTIVES: 

The Pediatric Early Warning Score (PEWS) systems were developed to provide a reproducible assessment of a child’s clinical status while hospitalized. Most studies investigating the PEWS evaluate its usefulness in the inpatient setting. Limited studies evaluate the effectiveness and integration of PEWS in the pediatric emergency department (ED). The goal of this study was to explore the test characteristics of an ED-assigned PEWS score for intensive care unit (ICU) admission or clinical deterioration in admitted patients.

METHODS: 

This was a prospective 12-month observational study of patients, aged 0 to 21 years, admitted from the ED of an urban, tertiary care children’s hospital. ED nurses were instructed in PEWS assignment and electronic medical record (EMR) documentation. Interrater reliability between nurses was evaluated. PEWS scores were measured at initial assessment (P0 ) and time of admission (P1 ). Patients were stratified into outcome groups: those admitted to the ICU either from the ED or as transfers from the floor and those admitted to the floor only. Clinical deterioration was defined as transfer to the ICU within 6 hours or within 6 to 24 hours of admission. PEWS scores and receiver operating characteristic (ROC) curves were compared for patients admitted to the floor, ICU, and with clinical deterioration.

RESULTS: 

The authors evaluated 12,306 consecutively admitted patients, with 99% having a PEWS documented in the EMR. Interrater reliability was excellent (intraclass coefficient = 0.91). A total of 1,300 (10.6%) patients were admitted to the ICU and 11,066 (89.4%) were admitted to the floor. PEWS scores were higher for patients in the ICU group (P0  = 2.8, SD ± 2.4; P1  = 3.2, SD ± 2.4; p < 0.0001) versus floor patients (P0  = 0.7, SD ± 1.2; P1  = 0.5, SD ± 0.9; p < 0.0001). To predict the need for ICU admission, the optimal cutoff points on the ROC are P0  = 1 and P1  = 2, with areas under the ROC curve (AUCs) of 0.79 and 0.86, respectively. The likelihood ratios (LRs) for these optimal cutoff points were as follows: P0 +LR = 2.5 (95% confidence interval [CI] = 2.4 to 2.6, p < 0.05), -LR = 0.32 (95% CI = 0.28 to 0.36, p < 0.05); and P1 +LR = 6.2 (95% CI = 5.8 to 6.6, p < 0.05), -LR = 0.32 (95% CI = 0.29 to 0.35, p < 0.05). For every unit increase in P0 and P1 , the odds of admission to the ICU were 1.9 times greater (95% CI = 1.8 to 1.9, p < 0.0001) and 2.9 times greater (95% CI = 2.7 to 3.1, p < 0.0001) than to the floor. There were 89 patients in the clinical deterioration group, with 36 (0.3%) patients transferred to the ICU within 6 hours of admission and 53 (0.4%) patients transferred within 6 to 24 hours. In this group, an elevated P0 and P1 were statistically associated with an increased risk of transfer with optimal cutoff points similar to above; however, there were poorer AUCs and test characteristics.

CONCLUSIONS: 

A PEWS system was implemented in this pediatric ED with excellent data capture and nurse interrater reliability. The study found that an elevated PEWS is associated with need for ICU admission directly from the ED and as a transfer, but lacks the necessary test characteristics to be used independently in the ED environment.

Validation of the Cardiac Children's Hospital Early Warning Score: an early warning scoring tool to prevent cardiopulmonary arrests in children with heart disease.

Validation of the Cardiac Children’s Hospital Early Warning Score: an early warning scoring tool to prevent cardiopulmonary arrests in children with heart disease. Congenit Heart Dis. 2014 May-Jun;9(3):194-202. doi: 10.1111/chd.12132. Epub 2013 Aug 20.McLellan MC1, Gauvreau K, Connor JA.

Abstract

OBJECTIVE: 

Most inpatient pediatric arrests are preventable by early recognition/treatment of deterioration. Children with cardiac disease have the highest arrest rates; however, early warning scoring systems have not been validated in this population. The objective of this study was to validate the Cardiac Children’s Hospital Early Warning Score (C-CHEWS) tool in inpatient pediatric cardiac patients. The associated escalation of care algorithm directs: routine care (score 0-2), increased assessment/intervention (3-4), or cardiac intensive care unit (CICU) consult/transfer (≥5).

DESIGN: 

Sensitivity and specificity were estimated based on retrospective review of patients that experienced unplanned CICU transfer/arrest (n = 64) and a comparison sample (n = 248) of admissions. The previously validated Pediatric Early Warning Score (PEWS) tool was used for comparison. Patients’ highest C-CHEWS scores were compared with calculated PEWS scores. Area under the receiver operating characteristic (AUROC) curve was calculated for PEWS and C-CHEWS to measure discrimination.

RESULTS: 

The AUROC curve for C-CHEWS was 0.917 compared with PEWS 0.785 (P < .001). The algorithm AUROC curve was 0.902 vs. PEWS of 0.782. C-CHEWS algorithm sensitivity was 96.9 (score ≥ 2), 79.7 (≥4), and 67.2 (≥5) vs. PEWS of 81.1(≥2), 37.5 (≥4), and 23.4 (≥5). C-CHEWS specificity was 58.1 (≥2), 85.5 (≥4), and 93.6 (≥5) vs. PEWS of 81.1 (≥2), 94.8 (≥4) and 97.6 (≥5). Lead time of elevated C-CHEWS scores (≥2) was a median of 9.25 hours prior to event vs. PEWS, which was 2.25 hours and lead time for critical C-CHEWS scores (≥5) was 2 hours vs. 0 hours for PEWS (P < .001).

CONCLUSIONS: 

C-CHEWS has excellent discrimination to identify deterioration in children with cardiac disease and performed significantly better than PEWS both as an ordinal variable and when choosing cut points to maximize AUROC. C-CHEWS has a higher sensitivity than PEWS at all cut points.