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Laboratory tests to identify patients at risk of early major adverse events: a prospective pilot study M. Kaufman,1 B. Bebee,1 J. Bailey,2 R. Robbins,3 G. K. Hart1 and R. Bellomo1,4 1
Department of Intensive Care, 3Department of Administrative Informatics, Austin Hospital, 2Department of Computing and Information Systems, The University of Melbourne and 4Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
Key words biochemistry, mortality, hematology, intensive care. Correspondence Rinaldo Bellomo, Department of Intensive Care, Austin Hospital, Heidelberg, Melbourne, Vic. 3084, Australia. Email: [email protected] Received 15 May 2014; accepted 10 June 2014. doi:10.1111/imj.12509
Abstract Background/Aims: To test whether commonly measured laboratory variables can identify surgical patients at risk of major adverse events (death, unplanned intensive care unit (ICU) admission or rapid response team (RRT) activation). Methods: We conducted a prospective observational study in a surgical ward of a university-affiliated hospital in a cohort of 834 surgical patients admitted for >24 h. We applied a previously validated multivariable model-derived risk assessment to each combined set of common laboratory tests to identify patients at risk. We compared the clinical course of such patients with that of control patients from the same ward who had blood tests but were identified as low risk. Results: We studied 7955 batches and 73 428 individual tests in 834 patients (males 55%; average age 65.8 ± 17.6 years). Among these patients, 66 (7.9%) were identified as ‘high risk’. High-risk patients were older (75.9 vs 61.8 years of age; P < 0.0001), had much greater early (48 h) mortality (6/66 (9%) vs 4/768 (0.5%); P < 0.0001) and greater overall hospital mortality (11/66 (16.7%) vs 9/768 (1.2%); P < 0.0001). They also had more early (8/66 (12.1%) vs 14/768 (1.8%); P = 0.0001) and overall in-hospital unplanned ICU admissions (12/66 (18.2%) vs 18/768 (2.3%); P < 0.0001) and more early (26/66 (39.3%) vs 50/768 (6.5%); P < 0.0001) and overall in-hospital RRT calls (26/66 (39.4%) vs 55/768 (7.2%); P < 0.0001). Conclusions: Commonly performed laboratory tests identify surgical ward patients at risk of early major adverse events. Further studies are needed to assess whether such identification system can be used to trigger interventions that help improve patient outcomes.
Introduction Among hospital patients, serious adverse events (SAE), including death, are relatively common.1–3 Many such events and deaths appear potentially preventable1–6 and are preceded by physiological and clinical deterioration over hours or days. Multiple attempts have been made to prevent such SAE,5–10 including the introduction of rapid response team (RRT) systems.9 Such systems, however, are imperfect because the identification of patients at risk is subject to the accuracy of observation,9 judgment about
Funding: This project was partially supported by the Cooperative Research Centres Program for Smart Services funded by the Australian Government. Conflict of interest: J. Bailey, G. K. Hart and R. Bellomo have a pending patent application for a method to identify deteriorating ward patients using laboratory data. © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
the patient’s condition,8 diligence in the measurements of vital signs,8–11 vigilance during the entire 24-h period,11 and, finally, willingness to call for help in a timely fashion.12–15 These shortcomings contribute to incorrect non-activation or delayed activation of an appropriate response.14–16 Non-activation and delayed activation are, in turn, associated with increased mortality.14,15,17,18 There is a need for a better approach so that an appropriate response can occur, or where necessary, earlier end of life discussions can take place and unnecessary and unwanted chest compression avoided in the event of cardiac arrest. Objective electronic data already exist in essentially all hospitals of developed countries in the form of common laboratory tests (e.g. biochemistry, haematology, arterial blood gases) and may help identify at risk patients. In association with clinical information, they have already been found helpful in estimating risk of the death after 1005
Kaufman et al.
intensive care unit (ICU) admission.19,20 It seems, therefore, physiologically plausible and, by analogy, clinically logical, that laboratory data might similarly help identify other hospital patients at high risk. In support of this notion, in a recent large retrospective observational study, we found that a predictive equation using commonly performed laboratory tests was able to identify accurately ward patients at risk of imminent serious adverse events.21 While this seems clinical common sense, there are, in fact, few published systematic approaches to risk assignment and structured preemptive intervention using laboratory data. In this study, we prospectively assessed the ability of such a predictive equation-based system to identify patients at risk of adverse events in a surgical ward, which admits patients receiving major gastrointestinal surgery at our institution. We hypothesised that a laboratory-based model would identify patients at high risk of early (within 48 h) death, early unplanned ICU admission or early RRT activation.
Methods This study of laboratory data and their link with deaths is part of an ongoing audit of emergency activity and mortality approved by the Austin Hospital Human Research Ethics Committees (H2001/04243), which waived the need for informed consent for this specific observational study. The study was conducted from 1 June 2012 until 31 January 2013.
3. Liver function test components (LFT): albumin and bilirubin 4. Arterial blood gases (ABG): pH, HCO3 or combinations 5. U&E + FBE 6. U&E + LFT 7. U&E + FBE + LFT 8. FBE + LFT Combinations involving blood gases were not considered, due to the rarity of historical data available for such combinations. Every batch taken from patients who were staying in the target ward was analysed. If two or more batches of type 1 to 3 became available simultaneously, then they were analysed as a combination (types 5 to 8). When a batch became available for screening, its risk was analysed, to test if the risk crossed a preset threshold. This threshold was based on an algorithm derived retrospectively from laboratory data from thousands of patients in our hospital. The details of this algorithm have been previously published.21 Once the preset criteria were fulfilled, an ‘alert’ was issued to a clinical team composed of an intensive care fellow and resident to prompt follow-up of such patients for physiological characteristics, nursing interventions, medical interventions and key outcomes at 48 h and at hospital discharge but without any communication or interference with care or specific contact or alert to the primary caring team. The goal was to obtain data in alert patients without interfering with patient treatment of creating awareness of a problem.
Study design
Statistical analysis
The study was a prospective observational investigation. In a single high-risk surgical ward, we used all laboratory data generated during standard patient care to perform data analysis and generate computerised laboratory alerts (see below). There was no involvement by the team in test ordering. The tests were chosen by the treating team and selected according to preset process (see below)
For each of the eight types of batches, we trained a logistic regression model using 6.5 years of historical laboratory data from the hospital, according to previous methodology.21 The independent variables used for each model were age plus the laboratory tests for the corresponding batch type and the dependent variable was ‘Death or ICU admission or RRT Call’. For each model, a cut-off threshold was chosen that corresponded to the risk score at the 95th percentile. An alert would thus be signalled whenever the risk score for a batch exceeded this cut-off threshold. Each of the 834 patients received one or more blood tests during their stay in the target ward. The 834 patients were divided into two groups, those who received at least one alert (‘Alert Population’) and those who did not receive any alert (‘Controls’). Patient data, such as vital signs, were summarised for the alert population using medians and interquartile ranges, with exclusion of missing values. Patient characteristics for the two populations were compared using the Wilcoxon rank-sum test
Data analysis We obtained data on 14 common laboratory measurements (see item 1, Appendix) taken from patients staying in the particular target ward of the hospital. Laboratory measurements came in batches, where each batch represented some subset of the 14 laboratory variables for a patient. Eight types of batches were analysed: 1. Urea and electrolytes (U&E): Na, K, Cl, urea, creatinine, total bicarbonate 2. Full blood examination (FBE): haemoglobin, haematocrit, white cell count, platelet count 1006
© 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
Lab identification of at risk patients
Table 1 Patient characteristics
Age Male gender Emergency admission Vascular disorder of intestine Intestinal adhesions with obstruction Acute kidney failureGastric malignancy Other acute pancreatitis Malignant neoplasm of ascending colon Post-procedural genitourinary disorders Neoplasms Diseases of the digestive system Genitourinary diseases Diseases of the circulatory system Respiratory disease Injury, poisonings
All patients (n = 834)
Controls (n = 768)
Alerts (n = 66)
P-value
63.0 ± 18.2 483 (58%) 67% 3 16 9 3 3 5 6 181 290 84 18 18 67
61.8 ± 18.3 453 (59%) 67% 0 13 7 1 1 3 4 164 265 76 15 18 65
75.9 ± 12.1 34 (51.5%) 62% 3 3 2 2 2 2 2 17 25 8 3 0 2
Laboratory tests to identify patients at risk of early major adverse events: a prospective pilot study M. Kaufman,1 B. Bebee,1 J. Bailey,2 R. Robbins,3 G. K. Hart1 and R. Bellomo1,4 1
Department of Intensive Care, 3Department of Administrative Informatics, Austin Hospital, 2Department of Computing and Information Systems, The University of Melbourne and 4Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
Key words biochemistry, mortality, hematology, intensive care. Correspondence Rinaldo Bellomo, Department of Intensive Care, Austin Hospital, Heidelberg, Melbourne, Vic. 3084, Australia. Email: [email protected] Received 15 May 2014; accepted 10 June 2014. doi:10.1111/imj.12509
Abstract Background/Aims: To test whether commonly measured laboratory variables can identify surgical patients at risk of major adverse events (death, unplanned intensive care unit (ICU) admission or rapid response team (RRT) activation). Methods: We conducted a prospective observational study in a surgical ward of a university-affiliated hospital in a cohort of 834 surgical patients admitted for >24 h. We applied a previously validated multivariable model-derived risk assessment to each combined set of common laboratory tests to identify patients at risk. We compared the clinical course of such patients with that of control patients from the same ward who had blood tests but were identified as low risk. Results: We studied 7955 batches and 73 428 individual tests in 834 patients (males 55%; average age 65.8 ± 17.6 years). Among these patients, 66 (7.9%) were identified as ‘high risk’. High-risk patients were older (75.9 vs 61.8 years of age; P < 0.0001), had much greater early (48 h) mortality (6/66 (9%) vs 4/768 (0.5%); P < 0.0001) and greater overall hospital mortality (11/66 (16.7%) vs 9/768 (1.2%); P < 0.0001). They also had more early (8/66 (12.1%) vs 14/768 (1.8%); P = 0.0001) and overall in-hospital unplanned ICU admissions (12/66 (18.2%) vs 18/768 (2.3%); P < 0.0001) and more early (26/66 (39.3%) vs 50/768 (6.5%); P < 0.0001) and overall in-hospital RRT calls (26/66 (39.4%) vs 55/768 (7.2%); P < 0.0001). Conclusions: Commonly performed laboratory tests identify surgical ward patients at risk of early major adverse events. Further studies are needed to assess whether such identification system can be used to trigger interventions that help improve patient outcomes.
Introduction Among hospital patients, serious adverse events (SAE), including death, are relatively common.1–3 Many such events and deaths appear potentially preventable1–6 and are preceded by physiological and clinical deterioration over hours or days. Multiple attempts have been made to prevent such SAE,5–10 including the introduction of rapid response team (RRT) systems.9 Such systems, however, are imperfect because the identification of patients at risk is subject to the accuracy of observation,9 judgment about
Funding: This project was partially supported by the Cooperative Research Centres Program for Smart Services funded by the Australian Government. Conflict of interest: J. Bailey, G. K. Hart and R. Bellomo have a pending patent application for a method to identify deteriorating ward patients using laboratory data. © 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
the patient’s condition,8 diligence in the measurements of vital signs,8–11 vigilance during the entire 24-h period,11 and, finally, willingness to call for help in a timely fashion.12–15 These shortcomings contribute to incorrect non-activation or delayed activation of an appropriate response.14–16 Non-activation and delayed activation are, in turn, associated with increased mortality.14,15,17,18 There is a need for a better approach so that an appropriate response can occur, or where necessary, earlier end of life discussions can take place and unnecessary and unwanted chest compression avoided in the event of cardiac arrest. Objective electronic data already exist in essentially all hospitals of developed countries in the form of common laboratory tests (e.g. biochemistry, haematology, arterial blood gases) and may help identify at risk patients. In association with clinical information, they have already been found helpful in estimating risk of the death after 1005
Kaufman et al.
intensive care unit (ICU) admission.19,20 It seems, therefore, physiologically plausible and, by analogy, clinically logical, that laboratory data might similarly help identify other hospital patients at high risk. In support of this notion, in a recent large retrospective observational study, we found that a predictive equation using commonly performed laboratory tests was able to identify accurately ward patients at risk of imminent serious adverse events.21 While this seems clinical common sense, there are, in fact, few published systematic approaches to risk assignment and structured preemptive intervention using laboratory data. In this study, we prospectively assessed the ability of such a predictive equation-based system to identify patients at risk of adverse events in a surgical ward, which admits patients receiving major gastrointestinal surgery at our institution. We hypothesised that a laboratory-based model would identify patients at high risk of early (within 48 h) death, early unplanned ICU admission or early RRT activation.
Methods This study of laboratory data and their link with deaths is part of an ongoing audit of emergency activity and mortality approved by the Austin Hospital Human Research Ethics Committees (H2001/04243), which waived the need for informed consent for this specific observational study. The study was conducted from 1 June 2012 until 31 January 2013.
3. Liver function test components (LFT): albumin and bilirubin 4. Arterial blood gases (ABG): pH, HCO3 or combinations 5. U&E + FBE 6. U&E + LFT 7. U&E + FBE + LFT 8. FBE + LFT Combinations involving blood gases were not considered, due to the rarity of historical data available for such combinations. Every batch taken from patients who were staying in the target ward was analysed. If two or more batches of type 1 to 3 became available simultaneously, then they were analysed as a combination (types 5 to 8). When a batch became available for screening, its risk was analysed, to test if the risk crossed a preset threshold. This threshold was based on an algorithm derived retrospectively from laboratory data from thousands of patients in our hospital. The details of this algorithm have been previously published.21 Once the preset criteria were fulfilled, an ‘alert’ was issued to a clinical team composed of an intensive care fellow and resident to prompt follow-up of such patients for physiological characteristics, nursing interventions, medical interventions and key outcomes at 48 h and at hospital discharge but without any communication or interference with care or specific contact or alert to the primary caring team. The goal was to obtain data in alert patients without interfering with patient treatment of creating awareness of a problem.
Study design
Statistical analysis
The study was a prospective observational investigation. In a single high-risk surgical ward, we used all laboratory data generated during standard patient care to perform data analysis and generate computerised laboratory alerts (see below). There was no involvement by the team in test ordering. The tests were chosen by the treating team and selected according to preset process (see below)
For each of the eight types of batches, we trained a logistic regression model using 6.5 years of historical laboratory data from the hospital, according to previous methodology.21 The independent variables used for each model were age plus the laboratory tests for the corresponding batch type and the dependent variable was ‘Death or ICU admission or RRT Call’. For each model, a cut-off threshold was chosen that corresponded to the risk score at the 95th percentile. An alert would thus be signalled whenever the risk score for a batch exceeded this cut-off threshold. Each of the 834 patients received one or more blood tests during their stay in the target ward. The 834 patients were divided into two groups, those who received at least one alert (‘Alert Population’) and those who did not receive any alert (‘Controls’). Patient data, such as vital signs, were summarised for the alert population using medians and interquartile ranges, with exclusion of missing values. Patient characteristics for the two populations were compared using the Wilcoxon rank-sum test
Data analysis We obtained data on 14 common laboratory measurements (see item 1, Appendix) taken from patients staying in the particular target ward of the hospital. Laboratory measurements came in batches, where each batch represented some subset of the 14 laboratory variables for a patient. Eight types of batches were analysed: 1. Urea and electrolytes (U&E): Na, K, Cl, urea, creatinine, total bicarbonate 2. Full blood examination (FBE): haemoglobin, haematocrit, white cell count, platelet count 1006
© 2014 The Authors Internal Medicine Journal © 2014 Royal Australasian College of Physicians
Lab identification of at risk patients
Table 1 Patient characteristics
Age Male gender Emergency admission Vascular disorder of intestine Intestinal adhesions with obstruction Acute kidney failureGastric malignancy Other acute pancreatitis Malignant neoplasm of ascending colon Post-procedural genitourinary disorders Neoplasms Diseases of the digestive system Genitourinary diseases Diseases of the circulatory system Respiratory disease Injury, poisonings
All patients (n = 834)
Controls (n = 768)
Alerts (n = 66)
P-value
63.0 ± 18.2 483 (58%) 67% 3 16 9 3 3 5 6 181 290 84 18 18 67
61.8 ± 18.3 453 (59%) 67% 0 13 7 1 1 3 4 164 265 76 15 18 65
75.9 ± 12.1 34 (51.5%) 62% 3 3 2 2 2 2 2 17 25 8 3 0 2
