The Anaesthetics Unit, The Royal London Hospital, London E1 1BB, UK
*Corresponding author. Anaesthesia and Critical Care, The Royal National Orthopaedic Hospital Trust, Brockely Hill, Stanmore, Middlesex, HA7 4LP, UK. E-mail: david.goldhill@rnoh.nhs.uk This article is accompanied by Editorial II. Presented to the Anaesthetic Research Society in Glasgow, April 2003.
Accepted for publication: December 17, 2003
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Abstract |
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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 1639). 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.
Br J Anaesth 2004; 92: 8824
Keywords: complications, abnormal physiology; intensive care, audit, scoring systems; intensive care, outcome; intensive care outreach; mortality, inpatient
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Introduction |
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Methods and results |
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If recorded within 8 h of our visit, we used the most recent values on the patients observation chart. If not, we made new observations ourselves. Outcome at 30 days (discharged alive/died in hospital/inpatient) was retrieved from the hospital records system. We used the normal ranges defined by our intensive care outreach service (called the Patient At Risk Team in our hospital): respiratory rate 1019 bpm; HR 5099 beats min1; systolic pressure 100179 mm Hg; temperature 36.037.4°C; SpO2 95%; LOC alert; urine output (catheterized patients) 0.53 ml kg1 min1.
Statistical analysis was with SPSS v10. For the backward stepwise logistic regression (likelihood ratio) model, all variables except age were treated as normal or abnormal (binary variables); age was treated as a continuous variable. The odds ratios thus provided relate to the increased risk of mortality if the variable is abnormal (using the definitions listed) except for age where the odds ratio is the risk of mortality caused by being 1 yr older. At each step in the regression, for a variable to be removed from the model it had to have a P value greater than 0.1.
We surveyed 548 beds of which 98 were unoccupied on two visits. We excluded 13 intensive care unit (ICU) patients, three patients known to be not for resuscitation and one duplicate observation caused by a transfer, leaving 433 data sets for analysis. Values are given as mean (SD) or median (interquartile range).
The 26 patients who died were older than the survivors (P<0.001, unpaired t-test), mean age 73 (range 3891) yr vs 60 (1897) yr. Their median hospital stay was 26 (1639) days, with death at a median of 10.5 (421) days after the study. The only patient with no abnormalities who died did so 21 days after the study. Mortality increased significantly with the number of abnormalities (P<0.001, logistic regression; explanatory variable: total number of abnormalities) (Table 1). Respiratory rate and HR were the variables most frequently abnormal (abnormal in 54% and 13%, respectively, of cases with one abnormality and in 96% and 63%, respectively, of cases with at least three abnormalities).
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This model had a sensitivity of 7.7%, specificity of 99.8% and a positive predictive value of 66.7%. The sensitivity and specificity of the model was calculated using a classification cut-off of 0.5; that is, if the probability of death obtained from the model was greater than 0.5 the subject was classified as having died, conversely if the probability of death obtained from the model was less than 0.5 the subject was classified as a survivor.
Urine output was only recorded for patients with catheters (n=52, 12%) and was not included in the logistic regression model. Oxygen therapy was given to 39 patients (9%). In these two groups (catheterized or receiving oxygen) 30-day mortality was 19.2% and 30%, respectively.
Estimated levels of care were available for 384 (88.7%) of all patients, which included 23 (88.5%) of the 26 who died. The 34 patients thought by data collectors to be receiving a lower level care than desirable had a greater mortality (20.6%, P<0.01, Fishers exact test) compared with the 349 patients (mortality 5.4%) judged to be receiving appropriate care.
Forty-three (9.9%) of the patients in the study were seen at some point during their hospital stay by the intensive care outreach team. The team reviewed six of the patients who died. In three of these patients, increased treatment was considered inappropriate, one was admitted to ICU and two were discharged from outreach follow-up before the study day. Only two of the other 20 patients who died were transferred to the ICU.
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Comment |
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Experience suggests that respiratory rate is an important indicator of an at-risk patient.6 Although ventilatory frequency was the most common abnormality we found, it did not make an independent statistically significant contribution to the logistic regression model. There may be several explanations for this. These include the limitations of the study, with outcome sometimes days or weeks after data collection, or that an abnormal respiratory rate may co-exist with other abnormalities that make a greater contribution to the model. Large-scale prospective studies are necessary to determine the physiological variables and values that identify high-risk patients.
The data collectors were not confident enough to assess the appropriate level of care requirement for some of the patients. Considering the 89% of patients for whom this information is available, those cared for at a lower level than ideal had an increased mortality. We have found that the longer patients are in hospital before they are admitted to ICU, the greater their mortality.7 About 25% of admissions to ICU from the ward occur after the patient has deteriorated to the point of cardiorespiratory arrest.8 Patients at high risk are present on the wards and their condition may deteriorate during their hospital admission. Early intervention may be beneficial and this should include assessment for critical care.
We excluded three patients known, on the study day, to be not for further resuscitation. We may not have known of other patients for whom treatment was limited. Three patients who died were assessed by our outreach service at some time during their hospital stay and an increase in treatment was considered inappropriate. The majority (87%) of patients with three or more abnormalities were in level-0 beds. If only half of these patients could have benefited from critical care, we would need twice the number of critical care beds in the hospital to accommodate them.
We found an association between easily recordable physiological derangements and mortality. Most patients with physiological abnormalities who died were in hospital for many days. This suggests that an early warning score could identify some patients early enough to allow interventions to take place in an appropriate location. Therefore, the opportunity exists to intervene and improve outcome for high-risk ward patients.
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Acknowledgements |
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References |
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2 Royal College of Physicians of London.The Interface Between Acute General Medicine and Critical Care. Report of a working party of the Royal College of Physicians. London: Royal College of Physicians of London, 2002
3 Intensive Care Society. Guidelines for the Introduction of Outreach Services. Intensive Care Society Standards. London: Intensive Care Society, 2002
4 Department of Health. Comprehensive Critical Care: a Review of Adult Critical Care Services. London: DoH, 2000
5 Intensive Care Society. Levels of Critical Care for Adult Patients. Intensive Care Society Standards. London: The Intensive Care Society, 2002
6 Goldhill DR, Worthington L, Mulcahy A, Tarling M, Sumner A. The patient-at-risk team: identifying and managing seriously ill ward patients. Anaesthesia 1999; 54: 85360[CrossRef][ISI][Medline]
7 Goldhill DR, McNarry A. The longer the patient is in hospital before ICU admission the higher the mortality. Br J Anaesth 2002; 89: 3567P
8 Goldhill DR, Sumner A. Outcome of intensive care patients in a group of British intensive care units. Crit Care Med 1998; 26: 133745[ISI][Medline]