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      The ALERT scale: an observational study of early prediction of adverse hospital outcome for medical patients

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          Abstract

          Objectives

          Some medical patients are at greater risk of adverse outcomes than others and may benefit from higher observation hospital units. We constructed and validated a model predicting adverse hospital outcome for patients. Study results may be used to admit patients into planned tiered care units. Adverse outcome comprised death or cardiac arrest during the first 30 days of hospitalisation, or transfer to intensive care within the first 48 h of admission.

          Setting

          The study took place at two tertiary teaching hospitals and two community hospitals in Winnipeg, Manitoba, Canada.

          Participants

          We analysed data from 4883 consecutive admissions at a tertiary teaching hospital to construct the Early Prediction of Adverse Hospital Outcome for Medical Patients (ALERT) model using logistic regression. Robustness of the model was assessed through validation performed across four hospitals over two time periods, including 65 640 consecutive admissions.

          Outcome

          Receiver-operating characteristic curves (ROC) and sensitivity and specificity analyses were used to assess the usefulness of the model.

          Results

          9.3% of admitted patients experienced adverse outcomes. The final model included gender, age, Charlson Comorbidity Index, Activities of Daily Living Score, Glasgow Coma Score, systolic blood pressure, respiratory rate, heart rate and white cell count. The model was discriminative (ROC=0.83) in predicting adverse outcome. ALERT accurately predicted 75% of the adverse outcomes (sensitivity) and 75% of the non-adverse outcomes (specificity). Applying the same model to each validation hospital and time period produced similar accuracy and discrimination to that in the development hospital.

          Conclusions

          Used during initial assessment of patients admitted to general medical wards, the ALERT scale may complement other assessment measures to better screen patients. Those considered as higher risk by the ALERT scale may then be provided more effective care from action such as planned tiered care units.

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          Most cited references14

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          A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study.

          To develop and validate a new Simplified Acute Physiology Score, the SAPS II, from a large sample of surgical and medical patients, and to provide a method to convert the score to a probability of hospital mortality. The SAPS II and the probability of hospital mortality were developed and validated using data from consecutive admissions to 137 adult medical and/or surgical intensive care units in 12 countries. The 13,152 patients were randomly divided into developmental (65%) and validation (35%) samples. Patients younger than 18 years, burn patients, coronary care patients, and cardiac surgery patients were excluded. Vital status at hospital discharge. The SAPS II includes only 17 variables: 12 physiology variables, age, type of admission (scheduled surgical, unscheduled surgical, or medical), and three underlying disease variables (acquired immunodeficiency syndrome, metastatic cancer, and hematologic malignancy). Goodness-of-fit tests indicated that the model performed well in the developmental sample and validated well in an independent sample of patients (P = .883 and P = .104 in the developmental and validation samples, respectively). The area under the receiver operating characteristic curve was 0.88 in the developmental sample and 0.86 in the validation sample. The SAPS II, based on a large international sample of patients, provides an estimate of the risk of death without having to specify a primary diagnosis. This is a starting point for future evaluation of the efficiency of intensive care units.
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            Use and misuse of the receiver operating characteristic curve in risk prediction.

            The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
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              Progress in development of the index of ADL.

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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2015
                13 April 2015
                : 5
                : 4
                : e005501
                Affiliations
                [1 ]Department of Internal Medicine, University of Manitoba , Winnipeg, Manitoba, Canada
                [2 ]Division of Critical Care, Medicine Queen Elizabeth II HSC, Dalhousie University , Halifax, Nova Scotia, Canada
                [3 ]Internal Medicine Program, Winnipeg Regional Health Authority , Winnipeg, Manitoba, Canada
                [4 ]Capital District Health Authority, Performance Excellence , Halifax, Nova Scotia, Canada
                [5 ]Department of Geriatric Medicine, Dalhousie University Department of Medicine, Halifax, Nova Scotia, Canada
                [6 ]General Internal Medicine and Community Health Sciences, Department of Internal Medicine, University of Manitoba , Winnipeg, Manitoba, Canada
                Author notes
                [Correspondence to ] Dr Daniel Roberts; droberts@ 123456hsc.mb.ca
                Article
                bmjopen-2014-005501
                10.1136/bmjopen-2014-005501
                4401845
                25869679
                d1d018d2-b475-4b52-a1d0-38bb56831af6
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                History
                : 20 June 2014
                : 5 December 2014
                : 12 January 2015
                Categories
                General practice / Family practice
                Research
                1506
                1696
                1707
                1692

                Medicine
                adverse outcome,hospital ward,resource allocation
                Medicine
                adverse outcome, hospital ward, resource allocation

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