Table 1.

	3	2	1	0	1	2	3
Systolic blood pressure (mmHg)	< 70	71-80	81-100	101-199		≥200
Heart rate (bpm)		< 40	41-50	51-100	101-110	111-129	≥130
Respiratory rate (bpm)		< 9		9-14	15-20	21-29	≥30
Temperature (ºC)		< 35		35-38.4		≥38.5
AVPU score				Alert	Reacting to voice	Reacting to pain	Unresponsive

The Modified Early Warning Score

Table 2.

Outcome	Frequency	Per cent	95% CI
Discharged	966	87.3	85.1-89.1
Deceased	102	9.2	7.6-11.1
Transferred	39	3.5	2.5-4.8
Total	1107	100.0

Main Outcomes of the Study

Table 3.

MEWS	Frequency	Per cent	Cumulative per cent	95% CI
0	628	56.7	56.7	53.7-59.7
1	189	17.1	73.8	14.9-19.5
2	120	10.8	84.6	9.1-12.9
3	71	6.4	91.1	5.1-8.1
4	44	4.0	95.0	2.9-5.3
5	27	2.4	97.5	1.6-3.6
6	16	1.4	98.9	0.9-2.4
7	7	0.6	99.5	0.3-1.4
8	2	0.2	99.7	0.0-0.7
9	2	0.2	99.9	0.0-0.7
10	1	0.1	100.0	0.0-0.6

Modified Early Warning Score at Admission

MEWS = Modified Early Warning Score.

Table 4.

MEWS	Deceased	Per cent	OR	95% CI
0	18	2.9
1	15	7.9	2.92	1.37-6.23
2	17	14.2	5.59	2.65-11.81
3	16	22.5	9.86	4.49-21.65
4	14	31.8	15.81	6.70-37.35
≥5	22	40	22.59	10.45-49.16

Odds Ratios for the Risk of Death Compared With Patients With a Score of Zero

MEWS = Modified Early Warning Score.

Table 5.

MEWS	Deceased or transferred	Per cent	OR	95% CI
0	40	6.4
1	21	11.1	1.84	1.02-3.31
2	21	17.5	3.12	1.70-5.71
3	19	26.8	5.37	2.77-10.36
4	16	36.4	8.40	3.97-17.7
≥5	24	43.6	11.38	5.84-22.19

Odds Ratios for the Combined Risk of Death or Transfer Compared With Patients With a Score of Zero

MEWS = Modified Early Warning Score.

Schein RMH, Hazday N, Pena M, Ruben BH, Sprung CL. Clinical antecedents to in-hospital cardiopulmonary arrest. Chest 1990; 98: 1388-92.
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Morgan RJM, Williams F, Wright MM. An Early Warning Scoring System for detecting developing critical illness. Clin Intens Care 1997; 8: 100.
Subbe CP, Kruger M, Rutherford P, Gemmel L. Validation of a modified Early Warning Score in medical admissions. Q J Med 2001; 94: 521-6.
World Medical Association. Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. http://www.wma.net/e/policy/b3.htm (accessed December 2008).
Goldhill DR, McNarry AF. Physiological abnormalities in early warning scores are related to mortality in adult inpatients. Br J Anaesth 2004; 92: 882-4.
Quarterman CPJ, Thomas AN, McKenna M, Mc Namee R. Use of a patient information system to audit the introduction of modified early warning scoring. J Eval Clin Pract 2005; 11: 133-8.
Goldhill DR, McNarry AF, Mandersloot G, McGinley A. Physiologically-based early warning score for ward patients: the association between score and outcome. Anaesthesia 2005; 60: 547-53.
Paterson R, MacLeod DC, Thetford D et al. Prediction of in-hospital mortality and length of stay using an early warning scoring system: clinical audit. Clin Med 2006; 6: 281-4.
Subbe CP, Davies RG, Williams E, Rutherford P, Gemmel L. Effect of introducing the Modified Early Warning Score on clinical outcomes, cardio-pulmonary arrests and intensive care utilization in acute medical admissions. Anaesthesia 2003; 58: 797-802.
McBride J, Knight D, Piper J, Smith GB. Long-term effect of introducing an early warning score on respiratory rate charting on general wards. Resuscitation 2005; 65: 41-4.
Subbe C, Falcus J, Rutherford P, Gemmel L. Capacity planning. Knowing the score. Health Serv J 2003; 113: 32-3.
Groarke JD, Gallagher J, Stack J et al. Use of an admission Early Warning Score to predict patient morbidity and mortality and treatment success - a prospective cohort study. Eur J Intern Med 2008; 19: e8.
Ridley S. The recognition and early management of critical illness. Ann R Coll Surg Engl 2005; 87: 315-22.

CME

In-hospital Mortality and Morbidity of Elderly Medical Patients Can Be Predicted at Admission by the Modified Early Warning Score: A Prospective Study

Authors: Marcelo Cei, MD; Carlo Bartolomei, MD; Nicola Mumoli, MD
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Target Audience and Goal Statement

This activity is intended for primary care clinicians, hospitalists, emergency physicians, nurse managers, and other clinicians who care for patients admitted to the hospital.

The goal of this activity is to describe the components of the Modified Early Warning Score (MEWS) and its utility in predicting mortality and transfer to higher-level facilities when administered at admission to the hospital.

Upon completion of this activity, participants will be able to:

Identify individual components of the Modified Early Warning Score (MEWS)
Describe the scoring system of the MEWS
List the types of outcomes that can be predicted by the MEWS
Describe the association between MEWS and mortality, transfer to higher-level facilities, and hospital length of stay
Describe the relative strength of the predictive value of different components of the MEWS

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Author(s)

Marcelo Cei, MD

USC II Medicina Interna, Viale Alfieri, Livorno, Italy

Disclosures

Disclosure: Marcelo Cei, MD, has disclosed no relevant financial relationships.

Carlo Bartolomei, MD

USC II Medicina Interna, Viale Alfieri, Livorno, Italy

Disclosures

Disclosure: Carlo Bartolomei, MD, has disclosed no relevant financial relationships.

Nicola Mumoli, MD

USC II Medicina Interna, Viale Alfieri, Livorno, Italy

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Disclosure: Nicola Mumoli, MD, has disclosed no relevant financial relationships.

CME Author

Désirée Lie, MD, MSEd

Clinical Professor, Family Medicine, University of California, Orange; Director, Division of Faculty Development, UCI Medical Center, Orange, California

Disclosures

Disclosure: Désirée Lie, MD, MSEd, has disclosed no relevant financial relationships.

Editor(s)

Graham Jackson, MD, FESC, FRCP, FACC

Consultant Cardiologist, Cardiothoracic Centre, Guy's and St. Thomas' Hospital, London, United Kingdom

Disclosures

Disclosure: Graham Jackson, MD, FESC, FRCP, FACC, has disclosed that he has served as an academic advisor to Pfizer Inc., Eli Lilly and Company, Plethora Solutions, Shire Pharmaceuticals Group, and SERVIER. Dr. Jackson has also disclosed that he has served as chairman of the Sexual Dysfunction Association.

Leslie L. Citrome, MD, MPH

Director, Clinical Research and Evaluation Facility, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York; Professor of Psychiatry, New York University School of Medicine, New York, NY

Disclosures

Disclosure: Leslie Citrome, MD, MPH, has disclosed that he has served as a consultant for, has received honoraria from, or has conducted clinical research supported by Abbott Laboratories, AstraZeneca Pharmaceuticals LP, AVANIR Pharmaceuticals, Azur Pharma Ltd, Barr Laboratories, Inc., Bristol-Myers Squibb Company, Eli Lilly and Company, Forest Research Institute, GlaxoSmithKline, Janssen, Jazz Pharmaceuticals, Inc., Pfizer Inc., and Vanda Pharmaceuticals Inc.

Karen Costenbader, MD, MPH

Rheumatology and Immunology, Brigham and Women's Hospital, Boston, Massachusetts

Disclosures

Disclosure: Karen Costenbader, MD, MPH, has disclosed no relevant financial relationships.

Serge Jabbour, MD

Associate Professor of Clinical Medicine, Division of Endocrinology, Diabetes, and Metabolic Diseases, Department of Medicine, Jefferson Medical College/Thomas Jefferson University, Philadelphia, Pennsylvania

Disclosures

Disclosure: Serge Jabbour, MD, has disclosed that he has served on the speaker's bureau for Amylin Pharmaceuticals, Inc. and Eli Lilly and Company.

Rubin Minhas, MB, ChB

General Practitioner; Honorary Senior Lecturer, Faculty of Science, Technology, and Medical Studies, University of Kent, Kent, United Kingdom

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Disclosure: Rubin Minhas, MB, ChB, has disclosed no relevant financial relationships.

Matt Rosenberg, MD, BA

Medical Director, Mid-Michigan Health Centers, Jackson, Michigan

Disclosures

Disclosure: Matt Rosenberg, MD, BA, has disclosed that he has served as a consultant for or has received fees for non-CME services from Abbott Laboratories, Allergan, Inc., Astellas Pharma, Inc., Esprit Pharma, GlaxoSmithKline, Novartis Pharmaceuticals Corporation, Ortho-McNeil, Inc., Pfizer Inc., Roche, sanofi-aventis, and Verathon Inc. Dr. Rosenberg has also disclosed that he has conducted research funded by sanofi-aventis.

Jagdish Sharma, FRCP

Sherwood Forest NHS Trust, Nottinghamshire County Teaching PCT, Nottinghamshire, United Kingdom

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Disclosure: Jagdish Sharma, FRCP, has disclosed no relevant financial relationships.

Anthony Wierzbicki, FACA, FACB

Senior Lecturer in Chemical Pathology, St. Thomas' Hospital, London, United Kingdom

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Disclosure: Anthony Wierzbicki, FACA, FACB, has disclosed that he has received support for travel or lecture honoraria from Abbott Laboratories, AstraZeneca Pharmaceuticals LP, Bristol-Myers Squibb Company, Genzyme Corporation, Gilead Sciences, Inc., Merck KGaA, Merck Sharp & Dohme, Pfizer Inc., Roche, Schering-Plough, and Solvay Fournier. Dr. Wierzbicki has also disclosed that he has served as an advisory board member for Abbott, AstraZeneca, Bristol-Myers Squibb, Genzyme, Gilead, Kowa, LifeCyclePharma, Merck KGaA, Merck, Sharp & Dohme, Pfizer, Roche, Schering-Plough Corporation, Solvay Fournier, Surface Logix, and Takeda Pharmaceuticals North America, Inc.

Carol Peckham

Site Editorial Director, Medscape, LLC

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Disclosure: Carol Peckham has disclosed no relevant financial relationships.

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Abstract and Introduction

Abstract

Objective: Although early warning scores were originally derived as bedside tools for alerting the medical staff, they may serve as decision rules for the admission of medical patients. We conducted this study to investigate the ability of the Modified Early Warning Score (MEWS) to identify a subset of patients at risk of deterioration, who might benefit from an increased level of attention.
Design: Prospective, single centre, cohort study.
Setting: A 64-bedded medical ward in a public, non-teaching Hospital in Italy.
Patients: All patients consecutively admitted from 15th November 2005 to 9th June 2006.
Interventions: On admission, the attending physician measured five physiological parameters (systolic blood pressure, pulse rate, respiratory rate, body temperature and level of consciousness) and calculated the MEWS. The main outcome measures were in-hospital mortality and a composite of mortality and transfer to a higher level of care. A secondary end-point was the length of stay for discharged patients.
Measurements and Results: In all, 1107 patients were admitted; 621 (56.1%) were women and 486 were men. Patients of female gender were also older (mean age 80.6 years) than men (mean age 77.1; p < 0.05). Of 1107, 995 patients (89.9%) were older than 64 years. A total of 966 patients were discharged, 102 deceased and 39 were transferred. In comparison with the lowest score, the risk of death was incremental among all the MEWS categories, as well as the risk of the combined outcome of death and transfer, and highly significant (risk of death, χ² for trend 136.307; risk of death or transfer, χ² for trend 105.762; p < 0.00001 for both). Patients with MEWS = 4 were discharged after a mean stay of 8.3 days, and alive patients with MEWS of five or more were discharged after a mean stay of 9.4 days (p = ns). A patient with a MEWS of zero at admission has a very low probability to die or to be transferred because of clinical instability (OR 0.14, 95% CI: 0.08-0.24).
Conclusions: We have confirmed that the MEWS, even when calculated once on admission, is a simple but highly useful tool to predict a worse in-hospital outcome.

Introduction

In modern health systems, the shortage of in-hospital beds is increasingly recognised, and physicians must face with the need to allocate patients on the basis of their clinical instability, to benefit from different care intensities. This is true not only for emergency physicians, who have to assign patients to intensive care units (ICUs) or to ordinary wards, but also for internists, who subsequently must decide to allocate patients in high dependence units (HDUs) or in standard beds. Furthermore, in internal medicine wards, professionals should be able to identify acute changes of the clinical status that put patients at risk of catastrophic deterioration and death, a phenomenon that has been already well recognised.^[1,2] However, because access to intensive or HDUs is restricted by definition, the triage strategies based on validated, simple and objective prognostic models are of paramount importance. Although early warning scores were originally derived as bedside tools for alerting the medical staff,^[3] they may serve as decision rules for medical patients' admission;^[4] nevertheless, their capacity to influence outcomes is still a matter of debate.^[5] As we decided to create a 'special' area with four monitored beds within the ward, where patients at risk of clinical deterioration should be first allocated, we conducted this study to investigate the ability of the Modified Early Warning Score (MEWS) to identify a subset of patients at risk of deterioration, who might benefit from an increased level of attention.

Page 1 of 5

Materials and Methods

Abstract and Introduction
Materials and Methods
Results
Discussion
Conclusions

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Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

References

Faculty and Disclosures

Author(s)

Marcelo Cei, MD

Carlo Bartolomei, MD

Nicola Mumoli, MD

CME Author

Désirée Lie, MD, MSEd

Editor(s)

Graham Jackson, MD, FESC, FRCP, FACC

Leslie L. Citrome, MD, MPH

Karen Costenbader, MD, MPH

Serge Jabbour, MD

Rubin Minhas, MB, ChB

Matt Rosenberg, MD, BA

Jagdish Sharma, FRCP

Anthony Wierzbicki, FACA, FACB

Carol Peckham

CME

In-hospital Mortality and Morbidity of Elderly Medical Patients Can Be Predicted at Admission by the Modified Early Warning Score: A Prospective Study

Target Audience and Goal Statement

Disclosures

Author(s)

Marcelo Cei, MD

Carlo Bartolomei, MD

Nicola Mumoli, MD

CME Author

Désirée Lie, MD, MSEd

Editor(s)

Graham Jackson, MD, FESC, FRCP, FACC

Leslie L. Citrome, MD, MPH

Karen Costenbader, MD, MPH

Serge Jabbour, MD

Rubin Minhas, MB, ChB

Matt Rosenberg, MD, BA

Jagdish Sharma, FRCP

Anthony Wierzbicki, FACA, FACB

Carol Peckham

Accreditation Statements

For Physicians

Instructions for Participation and Credit

In-hospital Mortality and Morbidity of Elderly Medical Patients Can Be Predicted at Admission by the Modified Early Warning Score: A Prospective Study

Abstract and Introduction

Abstract

Introduction

Table of Contents