Predictors of Agitation in Critically Ill Adults Ruth S. Burk, Mary Jo Grap, Cindy L. Munro, Christine M. Schubert and Curtis N. Sessler Am J Crit Care 2014;23:414-423 doi: 10.4037/ajcc2014714 © 2014 American Association of Critical-Care Nurses Published online http://www.ajcconline.org Personal use only. For copyright permission information: http://ajcc.aacnjournals.org/cgi/external_ref?link_type=PERMISSIONDIRECT
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AJCC, the American Journal of Critical Care, is the official peer-reviewed research journal of the American Association of Critical-Care Nurses (AACN), published bimonthly by The InnoVision Group, 101 Columbia, Aliso Viejo, CA 92656. Telephone: (800) 899-1712, (949) 362-2050, ext. 532. Fax: (949) 362-2049. Copyright © 2014 by AACN. All rights reserved.
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Critical Care Evaluation
P
REDICTORS OF
AGITATION IN CRITICALLY ILL ADULTS By Ruth S. Burk, RN, PhD, ANP-BC, Mary Jo Grap, RN, PhD, Cindy L. Munro, RN, PhD, ANP-C, Christine M. Schubert, PhD, and Curtis N. Sessler, MD
CNE
1.0 Hour
Notice to CNE enrollees: A closed-book, multiple-choice examination following this article tests your understanding of the following objectives: 1. Identify predictors of agitation. 2. Enumerate demographic predictive factors related to agitation. 3. Explain clinical factors related to agitation. To read this article and take the CNE test online, visit www.ajcconline.org and click “CNE Articles in This Issue.” No CNE test fee for AACN members. ©2014 American Association of Critical-Care Nurses doi: http://dx.doi.org/10.4037/ajcc2014714
414
Background Agitation in critically ill adults is a frequent complication of hospitalization and results in multiple adverse outcomes. Potential causes of agitation are numerous; however, data on factors predictive of agitation are limited. Objectives To identify predictors of agitation by examining demographic and clinical characteristics of critically ill patients. Methods A medical record review was performed. Documentation of agitation was indicated by scores on the Richmond Agitation-Sedation Scale or the use of an agitation keyword. Records of 200 patients from 1 medical and 1 surgical intensive care unit were used for the study. Risk factors were determined for 2 points in time: admission to the intensive care unit and within 24 hours before the first episode of agitation. Data on baseline demographics, preadmission risk factors, and clinical data were collected and were evaluated by using logistic multivariable regression to determine predictors of agitation. Results Predictors of agitation on admission to intensive care were history of use of illicit substances, height, respiratory and central nervous system subscores on the Sequential Organ Failure Assessment, and use of restraints. Predictors of agitation within 24 hours before the onset of agitation were history of psychiatric diagnosis, height, score on the Sequential Organ Failure Assessment, ratio of PaO2 to fraction of inspired oxygen less than 200, serum pH, percentage of hours with restraints, percentage of hours of mechanical ventilation, pain, and presence of genitourinary catheters. Conclusions Predictors of agitation on admission and within 24 hours before the onset of agitation were primarily clinical variables. (American Journal of Critical Care. 2014;23:414-423)
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O
ne of the more frequent complications in the intensive care unit (ICU) is agitation. Agitation is associated with poorer outcomes, including longer ICU stay, longer duration of mechanical ventilation, higher rate of self-extubation, increased use of resources, and increased ICU costs.1-5 Studies2-6 indicate that 42% to 71% of critically ill patients experience agitation. Recognizing the impact of agitation, the Society of Critical Care Medicine recently updated its sedation and analgesia guidelines7 to include agitation, emphasizing the need for prompt identification of this complication.
Potential causes of agitation in critically ill patients are numerous; however, data on factors predictive of agitation are limited. Because agitation is often identified after overtly agitated behavior is observed, a critical barrier to progress has been the lack of identification of the precursors of agitation. Empirically based information would help care providers identify patients at risk for agitation and also predict agitation, providing an opportunity to implement preventive strategies. The purpose of this study was to examine the relationship between demographic and clinical characteristics of critically ill patients in the development of agitation.
Methods Patients and Setting The study was conducted in an 865-bed academic, level I trauma center in 2 adult ICUs (medical respiratory and surgical trauma). All adult patients 18 years and older who were consecutively admitted to the 2 units during a 2-month period were evaluated for inclusion in the study by reviewing medical records. Approval was obtained from the appropriate institutional review board. Exclusion criteria were ICU length of stay less than 24 hours, unavailable medical records, and previous admission during the
About the Authors Ruth S. Burk is an assistant professor, Department of Acute and Continuing Care, University of Texas Health Science Center in Houston. Mary Jo Grap is the Nursing Alumni Distinguished Professor, Adult Health and Nursing Systems Department, School of Nursing, and Curtis N. Sessler is the Orhan Muren Professor of Medicine, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Virginia Commonwealth University, Richmond, Virginia. Cindy L. Munro is an associate dean, Research and Innovation, and a professor, University of South Florida College of Nursing, Tampa, Florida. Christine M. Schubert is an associate professor, Department of Mathematics and Statistics, Air Force Institute of Technology, Wright-Patterson Air Force Base, Dayton, Ohio. Corresponding author: Ruth S. Burk, 34 High Bank Dr, Missouri City, TX 77459 (e-mail: [email protected]).
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study. Other exclusion criteria were conditions that interfere with determining scores on sedation scales: administration of paralytic agents, chronic neuromuscular disorders, and head trauma or stroke. Measures Agitation. Agitation was identified by using documentation of scores on the Richmond Agitation-Sedation Scale (RASS).8 The RASS has excellent interrater reliability and criterion, construct, and face validity in critical care settings.8-11 The RASS is used routinely every 4 hours in both ICUs. RASS scores of +1 (restless) through +4 (combative) were considered indicative of agitation. The +1 score was an indicator for agitation because positive scores on the RASS have been documented as indicating agitation.8 Agitation was also identified by the presence of some form of the keyword agitation (eg, agitated, agitation, agit) recorded in the medical record by using physicians’ and nurses’ notes, emergency department documentation, operating room notes, and circle-the-item reporting agitation in flow sheets. Both RASS scores and clinical observations were used to identify agitation because the RASS procedure, step 1 specifically, relies on nurses’ adjudication and identification of agitation in order to score severity; nurses documented agitation between scheduled RASS assessments (as infrequent as every 4 hours), thus providing a more complete record of agitation; and a high level of congruency existed between the written documentation of agitation and RASS scores. Studies12,13 have shown high agreement between RASS scores determined by bedside nurses and a reference-standard rater score. Predictors of Agitation. Risk factors previously associated with agitation in critically ill patients were identified from the literature. Data collected at the time of ICU admission included demographic
Agitation is associated with poorer outcomes in the intensive care unit.
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characteristics (age, sex, ethnicity, race), marital status, weight, height, body mass index, source of hospital admission (clinic, emergency department, home, long-term care, or other hospital), source of ICU admission (operating room, general hospital unit, emergency department, other hospital), category of admitting diagnosis, and severity of illness data from the Acute Physiology and Chronic Health Evaluation (APACHE)14 III, the Sequential Organ Failure Assessment (SOFA),15 and the Charlson Age-Comorbidity Index.16 SOFA scores were calculated daily; data for the APACHE III were obtained on the day of the first event of agitation. Additional data recorded were history of diabetes, alcohol abuse, use of illicit substances, use of tobacco, psychiatric diagnosis, and overuse or abuse and prescribed use of psychiatric medications. Clinical factors were also identified from the literature. Clinical data collected on ICU admission and within 24 hours before the first episode of agitation were values most extreme or farthest from the mean reference range for serum levels of creatinine and urea nitrogen, daily urine output, serum level of bilirubin, hematocrit, and blood glucose level; score on the Glasgow Coma Scale17; PaO2; heart rate; mean arterial pressure; respirations; fraction of inspired oxygen; and temperature. Data collected hourly included pain rating (numerical rating scale), RASS score, use and type of restraints, use and type of mechanical ventilation, total number of catheters and number in each category (peripherally inserted, centrally inserted, genitourinary, gastrointestinal), use of dialysis, presence of sepsis (according to criteria of Bone18), and hospital- and community-acquired pneumonia. Use of restraints and use of mechanical ventilation were calculated as hourly percentages of time used. For acute renal failure, the RIFLE19 (Risk of renal dysfunction; Injury of the kidney; Failure of kidney function, Loss of kidney function, and End-stage kidney disease) rating was computed. The following laboratory values were recorded each hour: pH; serum levels of sodium, potassium, albumin, and magnesium; white blood cell count; and hemoglobin level. Clinical Outcomes. Data were collected on ICU and hospital length of stay, discharge destination or outcome, and adverse events.
Agitation was also identified if any form of the word agitation was used in the medical record.
Procedure All data were collected by 1 investigator (R.S.B.). A pilot study was performed with patients
416
who were not part of the study cohort. In data audits, convenience sampling of approximately 10% of all patients yielded an error rate less than 0.03%. The goal was to obtain an equal number of patients in each of the 2 ICUs spanning most of a 2-month period. Data were collected during the first 5 days of ICU stay because onset of agitation is highest in the first 3 to 5 days.2,5 The hour was used as the documentation epoch for all collection of recurrent data. An agitation hour was identified if the RASS score was +1 or greater or an agitation keyword was noted during that hour. If any agitation or multiple episodes of agitation were documented within the hour, the hour was considered to be 1 agitation hour. Data Analysis Descriptive statistics were computed on patients’ baseline demographic and clinical variables. Categorical data were described as number and percentage, normally distributed continuous data were described as mean and standard deviation, and nonnormal continuous data were described as the median with interquartile range. Only the first reported occurrence of agitation during the study period was examined for every patient. Any report of agitation during the 5 study days was used to identify 2 study groups (agitated vs nonagitated patients). Two separate models were examined to identify predictors of agitation. The first model included factors on admission to the ICU; the second primarily considered factors within 24 hours before the first report of agitation. The period of 24 hours was chosen to include slower responding physiological changes (eg, renal or hematologic indexes). For both models, each potential risk factor was examined univariately in simple logistic models; the response variable was agitated or not agitated. For screening, the α level for significance was .10. Next, logistic regression models were constructed by using all significant univariate variables and subsets. Subsets reduced multicollinearity generated by models including more than a single measure of severity of illness (ie, only total SOFA scores or only SOFA subscores). For each subset, backward elimination was used to select the significant predictors of agitation. In these models, the α level for significance was changed to .05. Testing was conducted by using the likelihood ratio. Alternative models were compared in terms of statistical significance, goodness-of-fit statistics, and predictive power (eg, area under the curve of the receiver operating characteristic curve and percentage correct classification) to determine the best model. Statistical analysis was
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Table 1 Demographics and other descriptors for entire sample and by presence of agitation (at least 1 observation of agitation for the study period)a Variable
Entire sample (N = 200)
Nonagitated (n = 82)
Agitated (n = 118)
Sex Male Female
113 (56) 87 (44)
42 (51) 40 (49)
73 (62) 45 (38)
Ethnicity Hispanic or Latino Not Hispanic or Latino
6 (3) 194 (97)
5 (6) 77 (94)
1 (1) 117 (99)
Race Asian Black or African American White
3 (2) 94 (47) 103 (52)
2 (2) 39 (48) 41 (50)
1 (1) 55 (47) 62 (53)
Type of ICU Medical respiratory Surgical trauma
100 (50) 100 (50)
36 (44) 46 (56)
64 (54) 54 (46)
Admission source Long-term care Home Clinic Outside hospital Emergency department
3 (2) 16 (8) 20 (10) 60 (30) 101 (50)
2 (1) 4 (2) 7 (3.5) 23 (12) 46 (23)
1 (1) 12 (6) 13 (6) 37 (18) 55 (28)
36 (18) 35 (18) 27 (14) 27 (14) 22 (11) 28 (14) 13 (6) 8 (4) 4 (2)
18 (22) 17 (21) 6 (7) 8 (10) 9 (11) 15 (19) 4 (5) 4 (5) 1 (1)
18 18 21 20 12 13 9 4 3
Admitting diagnosis Trauma Sepsis Respiratory failure Hematologic/oncological problem Other Renal/gastrointestinal problem/DKA Hepatic problem Cardiovascular problem Drug overdose or poisoning Age, mean (SD), y Total ICU length of stay, median (25th-75th percentile), d Total hospital length of stay, median (25th-75th percentile), d
(15) (15) (18) (17) (10) (11) (8) (3) (3)
55.5 (16.4)
56 (16.4)
55.1 (16.5)
3.9 (2.5-8)
2.7 (2-8)
4.8 (3-9.4)
11.1 (6.3-21.6)
9.1 (6-19.4)
12.8 (6.9-21.8)
APACHE III score, mean (SD)
68 (31.9)
57.7 (34.3)
74.7 (28.2)
SOFA score, mean (SD)
6.625 (3.8)
5.39 (3.7)
7.48 (3.7)
Charlson Comorbidity Index, mean (SD)
4.69 (3.3)
4.8 (3.3)
4.6 (3.4)
Abbreviations: APACHE, Acute Physiology and Chronic Health Evaluation; DKA, diabetic ketoacidosis; ICU, intensive care unit; SOFA, Sequential Organ Failure Assessment. a Values are expressed as number (%) of patients unless otherwise indicated. Because of rounding, percentages may not total 100.
performed by using SAS 9.3 and SAS JMP Pro, version 10.0, software (SAS Institute Inc).
Results Patients Data collection for up to 5 days of ICU stay for the 200 patients resulted in 791 patient-days (17 938 hours of data). A total of 383 patients sequentially admitted into the ICUs were screened, 179 from the medical-respiratory unit and 204 from the surgical-trauma unit. A total of 79 patients in the medical-respiratory unit and 104 in the surgical-trauma unit did not meet inclusion crite-
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ria. In all, 200 patients, 100 from each ICU, were included in the final analyses. A full report of the sample has been published.4 Overall the patients in the units had a mean age of 55.5 years and were primarily men, of nonHispanic ethnicity, and white or African-American race (Table 1). More than 75% of the agitated patients in the study were identified by using the RASS score; the balance were identified by using an agitation keyword. Of the 200 patients, 118 (59%) were agitated at some point during the 5-day study period and made up the agitated group for this analysis; 102 patients (86%) had agitation on day 1.
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Table 2 Demographic, preadmission, and clinical risk factors with univariate significance At admission Type
Nonagitated (n = 82)
24 hours before onset of agitation
Agitateda (n = 118)
Nonagitated
Agitateda
— —
— —
Demographic and preadmission risk factors Sex, No. (%) of patients Male Female
73 (62)b 45 (38)b
42 (51) 40 (49)
Height, mean (SD), cm Weight, mean (SD), kg Medical history, No. (%) of patients Illicit substance use Psychiatric diagnosis
167.3 (10.3)
172.1 (10.7)c
—
—
77.1 (22.5)
(26.1)b
—
—
— —
— —
83.9
55 (47)c 28 (24)c
26 (32) 10 (12) Severity of illness scores 6.6 (3.8) 0.8 (0.9) 1.2 (1.3)
5.4 (3.7)c 1.3 (1.1)c 2.5 (1.2)c
5.9 (4.1) 0.9 (1) 1.3 (1.3)
7.4 (3.7)c 1.2 (1.1)b 2.5 (1.1)b
Glasgow Coma Scale score, mean (SD)
12.3 (3.8)
8.6 (3.6)c
12.3 (3.8)
8.6 (3.6)c
APACHE III score, mean (SD)
57.7 (34.3)
74.7 (28.2)c
57.7 (34.3)
73.7 (29.3)c
Total SOFA score, mean (SD) Respiratory subscore, mean (SD) CNS subscore, mean (SD)
Clinical risk factors 8 (4)
23 (11.5)b
0.5 (0.4-0.7)c
0.36 (0.21-0.53)
0.5 (0.4-0.7)c
7.39 (0.07)
7.36 (0.095)c
7.39 (0.09)
7.36 (0.09)c
Serum level of magnesium, mean (SD), mEq/L
1.89 (0.32)
1.99
(0.47)b
1.85 (0.39)
1.98 (0.45)b
Serum level of hemoglobin, mean (SD), g/dL
10.3 (2.5)
10.3 (2.2)
Serum level of glucose, mean (SD), mg/dL
146 (66)
168 (84)b
146 (66)
165 (84)b
12 (15)
35 (30)c
12 (15)
37 (31)c
19 (23)
(40)c
19 (23)
49 (42)c
6 (7)
40 (34)c
12 (15)
63 (53)c
15 (8)
70 (35)c
18 (9)
80 (40)c
PaO2/FIO2 < 200, No. (%) of patients
1 (1)
FIO2, median (25th-75th percentile)
0.36 (0.21-0.53)
Serum pH, mean (SD)
Body temperature ≥38ºC, No. (%) of patients Body temperature
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AJCC, the American Journal of Critical Care, is the official peer-reviewed research journal of the American Association of Critical-Care Nurses (AACN), published bimonthly by The InnoVision Group, 101 Columbia, Aliso Viejo, CA 92656. Telephone: (800) 899-1712, (949) 362-2050, ext. 532. Fax: (949) 362-2049. Copyright © 2014 by AACN. All rights reserved.
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Critical Care Evaluation
P
REDICTORS OF
AGITATION IN CRITICALLY ILL ADULTS By Ruth S. Burk, RN, PhD, ANP-BC, Mary Jo Grap, RN, PhD, Cindy L. Munro, RN, PhD, ANP-C, Christine M. Schubert, PhD, and Curtis N. Sessler, MD
CNE
1.0 Hour
Notice to CNE enrollees: A closed-book, multiple-choice examination following this article tests your understanding of the following objectives: 1. Identify predictors of agitation. 2. Enumerate demographic predictive factors related to agitation. 3. Explain clinical factors related to agitation. To read this article and take the CNE test online, visit www.ajcconline.org and click “CNE Articles in This Issue.” No CNE test fee for AACN members. ©2014 American Association of Critical-Care Nurses doi: http://dx.doi.org/10.4037/ajcc2014714
414
Background Agitation in critically ill adults is a frequent complication of hospitalization and results in multiple adverse outcomes. Potential causes of agitation are numerous; however, data on factors predictive of agitation are limited. Objectives To identify predictors of agitation by examining demographic and clinical characteristics of critically ill patients. Methods A medical record review was performed. Documentation of agitation was indicated by scores on the Richmond Agitation-Sedation Scale or the use of an agitation keyword. Records of 200 patients from 1 medical and 1 surgical intensive care unit were used for the study. Risk factors were determined for 2 points in time: admission to the intensive care unit and within 24 hours before the first episode of agitation. Data on baseline demographics, preadmission risk factors, and clinical data were collected and were evaluated by using logistic multivariable regression to determine predictors of agitation. Results Predictors of agitation on admission to intensive care were history of use of illicit substances, height, respiratory and central nervous system subscores on the Sequential Organ Failure Assessment, and use of restraints. Predictors of agitation within 24 hours before the onset of agitation were history of psychiatric diagnosis, height, score on the Sequential Organ Failure Assessment, ratio of PaO2 to fraction of inspired oxygen less than 200, serum pH, percentage of hours with restraints, percentage of hours of mechanical ventilation, pain, and presence of genitourinary catheters. Conclusions Predictors of agitation on admission and within 24 hours before the onset of agitation were primarily clinical variables. (American Journal of Critical Care. 2014;23:414-423)
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O
ne of the more frequent complications in the intensive care unit (ICU) is agitation. Agitation is associated with poorer outcomes, including longer ICU stay, longer duration of mechanical ventilation, higher rate of self-extubation, increased use of resources, and increased ICU costs.1-5 Studies2-6 indicate that 42% to 71% of critically ill patients experience agitation. Recognizing the impact of agitation, the Society of Critical Care Medicine recently updated its sedation and analgesia guidelines7 to include agitation, emphasizing the need for prompt identification of this complication.
Potential causes of agitation in critically ill patients are numerous; however, data on factors predictive of agitation are limited. Because agitation is often identified after overtly agitated behavior is observed, a critical barrier to progress has been the lack of identification of the precursors of agitation. Empirically based information would help care providers identify patients at risk for agitation and also predict agitation, providing an opportunity to implement preventive strategies. The purpose of this study was to examine the relationship between demographic and clinical characteristics of critically ill patients in the development of agitation.
Methods Patients and Setting The study was conducted in an 865-bed academic, level I trauma center in 2 adult ICUs (medical respiratory and surgical trauma). All adult patients 18 years and older who were consecutively admitted to the 2 units during a 2-month period were evaluated for inclusion in the study by reviewing medical records. Approval was obtained from the appropriate institutional review board. Exclusion criteria were ICU length of stay less than 24 hours, unavailable medical records, and previous admission during the
About the Authors Ruth S. Burk is an assistant professor, Department of Acute and Continuing Care, University of Texas Health Science Center in Houston. Mary Jo Grap is the Nursing Alumni Distinguished Professor, Adult Health and Nursing Systems Department, School of Nursing, and Curtis N. Sessler is the Orhan Muren Professor of Medicine, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Virginia Commonwealth University, Richmond, Virginia. Cindy L. Munro is an associate dean, Research and Innovation, and a professor, University of South Florida College of Nursing, Tampa, Florida. Christine M. Schubert is an associate professor, Department of Mathematics and Statistics, Air Force Institute of Technology, Wright-Patterson Air Force Base, Dayton, Ohio. Corresponding author: Ruth S. Burk, 34 High Bank Dr, Missouri City, TX 77459 (e-mail: [email protected]).
www.ajcconline.org
study. Other exclusion criteria were conditions that interfere with determining scores on sedation scales: administration of paralytic agents, chronic neuromuscular disorders, and head trauma or stroke. Measures Agitation. Agitation was identified by using documentation of scores on the Richmond Agitation-Sedation Scale (RASS).8 The RASS has excellent interrater reliability and criterion, construct, and face validity in critical care settings.8-11 The RASS is used routinely every 4 hours in both ICUs. RASS scores of +1 (restless) through +4 (combative) were considered indicative of agitation. The +1 score was an indicator for agitation because positive scores on the RASS have been documented as indicating agitation.8 Agitation was also identified by the presence of some form of the keyword agitation (eg, agitated, agitation, agit) recorded in the medical record by using physicians’ and nurses’ notes, emergency department documentation, operating room notes, and circle-the-item reporting agitation in flow sheets. Both RASS scores and clinical observations were used to identify agitation because the RASS procedure, step 1 specifically, relies on nurses’ adjudication and identification of agitation in order to score severity; nurses documented agitation between scheduled RASS assessments (as infrequent as every 4 hours), thus providing a more complete record of agitation; and a high level of congruency existed between the written documentation of agitation and RASS scores. Studies12,13 have shown high agreement between RASS scores determined by bedside nurses and a reference-standard rater score. Predictors of Agitation. Risk factors previously associated with agitation in critically ill patients were identified from the literature. Data collected at the time of ICU admission included demographic
Agitation is associated with poorer outcomes in the intensive care unit.
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characteristics (age, sex, ethnicity, race), marital status, weight, height, body mass index, source of hospital admission (clinic, emergency department, home, long-term care, or other hospital), source of ICU admission (operating room, general hospital unit, emergency department, other hospital), category of admitting diagnosis, and severity of illness data from the Acute Physiology and Chronic Health Evaluation (APACHE)14 III, the Sequential Organ Failure Assessment (SOFA),15 and the Charlson Age-Comorbidity Index.16 SOFA scores were calculated daily; data for the APACHE III were obtained on the day of the first event of agitation. Additional data recorded were history of diabetes, alcohol abuse, use of illicit substances, use of tobacco, psychiatric diagnosis, and overuse or abuse and prescribed use of psychiatric medications. Clinical factors were also identified from the literature. Clinical data collected on ICU admission and within 24 hours before the first episode of agitation were values most extreme or farthest from the mean reference range for serum levels of creatinine and urea nitrogen, daily urine output, serum level of bilirubin, hematocrit, and blood glucose level; score on the Glasgow Coma Scale17; PaO2; heart rate; mean arterial pressure; respirations; fraction of inspired oxygen; and temperature. Data collected hourly included pain rating (numerical rating scale), RASS score, use and type of restraints, use and type of mechanical ventilation, total number of catheters and number in each category (peripherally inserted, centrally inserted, genitourinary, gastrointestinal), use of dialysis, presence of sepsis (according to criteria of Bone18), and hospital- and community-acquired pneumonia. Use of restraints and use of mechanical ventilation were calculated as hourly percentages of time used. For acute renal failure, the RIFLE19 (Risk of renal dysfunction; Injury of the kidney; Failure of kidney function, Loss of kidney function, and End-stage kidney disease) rating was computed. The following laboratory values were recorded each hour: pH; serum levels of sodium, potassium, albumin, and magnesium; white blood cell count; and hemoglobin level. Clinical Outcomes. Data were collected on ICU and hospital length of stay, discharge destination or outcome, and adverse events.
Agitation was also identified if any form of the word agitation was used in the medical record.
Procedure All data were collected by 1 investigator (R.S.B.). A pilot study was performed with patients
416
who were not part of the study cohort. In data audits, convenience sampling of approximately 10% of all patients yielded an error rate less than 0.03%. The goal was to obtain an equal number of patients in each of the 2 ICUs spanning most of a 2-month period. Data were collected during the first 5 days of ICU stay because onset of agitation is highest in the first 3 to 5 days.2,5 The hour was used as the documentation epoch for all collection of recurrent data. An agitation hour was identified if the RASS score was +1 or greater or an agitation keyword was noted during that hour. If any agitation or multiple episodes of agitation were documented within the hour, the hour was considered to be 1 agitation hour. Data Analysis Descriptive statistics were computed on patients’ baseline demographic and clinical variables. Categorical data were described as number and percentage, normally distributed continuous data were described as mean and standard deviation, and nonnormal continuous data were described as the median with interquartile range. Only the first reported occurrence of agitation during the study period was examined for every patient. Any report of agitation during the 5 study days was used to identify 2 study groups (agitated vs nonagitated patients). Two separate models were examined to identify predictors of agitation. The first model included factors on admission to the ICU; the second primarily considered factors within 24 hours before the first report of agitation. The period of 24 hours was chosen to include slower responding physiological changes (eg, renal or hematologic indexes). For both models, each potential risk factor was examined univariately in simple logistic models; the response variable was agitated or not agitated. For screening, the α level for significance was .10. Next, logistic regression models were constructed by using all significant univariate variables and subsets. Subsets reduced multicollinearity generated by models including more than a single measure of severity of illness (ie, only total SOFA scores or only SOFA subscores). For each subset, backward elimination was used to select the significant predictors of agitation. In these models, the α level for significance was changed to .05. Testing was conducted by using the likelihood ratio. Alternative models were compared in terms of statistical significance, goodness-of-fit statistics, and predictive power (eg, area under the curve of the receiver operating characteristic curve and percentage correct classification) to determine the best model. Statistical analysis was
AJCC AMERICAN JOURNAL OF CRITICAL CARE, September 2014, Volume 23, No. 5 Downloaded from ajcc.aacnjournals.org by guest on October 26, 2014
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Table 1 Demographics and other descriptors for entire sample and by presence of agitation (at least 1 observation of agitation for the study period)a Variable
Entire sample (N = 200)
Nonagitated (n = 82)
Agitated (n = 118)
Sex Male Female
113 (56) 87 (44)
42 (51) 40 (49)
73 (62) 45 (38)
Ethnicity Hispanic or Latino Not Hispanic or Latino
6 (3) 194 (97)
5 (6) 77 (94)
1 (1) 117 (99)
Race Asian Black or African American White
3 (2) 94 (47) 103 (52)
2 (2) 39 (48) 41 (50)
1 (1) 55 (47) 62 (53)
Type of ICU Medical respiratory Surgical trauma
100 (50) 100 (50)
36 (44) 46 (56)
64 (54) 54 (46)
Admission source Long-term care Home Clinic Outside hospital Emergency department
3 (2) 16 (8) 20 (10) 60 (30) 101 (50)
2 (1) 4 (2) 7 (3.5) 23 (12) 46 (23)
1 (1) 12 (6) 13 (6) 37 (18) 55 (28)
36 (18) 35 (18) 27 (14) 27 (14) 22 (11) 28 (14) 13 (6) 8 (4) 4 (2)
18 (22) 17 (21) 6 (7) 8 (10) 9 (11) 15 (19) 4 (5) 4 (5) 1 (1)
18 18 21 20 12 13 9 4 3
Admitting diagnosis Trauma Sepsis Respiratory failure Hematologic/oncological problem Other Renal/gastrointestinal problem/DKA Hepatic problem Cardiovascular problem Drug overdose or poisoning Age, mean (SD), y Total ICU length of stay, median (25th-75th percentile), d Total hospital length of stay, median (25th-75th percentile), d
(15) (15) (18) (17) (10) (11) (8) (3) (3)
55.5 (16.4)
56 (16.4)
55.1 (16.5)
3.9 (2.5-8)
2.7 (2-8)
4.8 (3-9.4)
11.1 (6.3-21.6)
9.1 (6-19.4)
12.8 (6.9-21.8)
APACHE III score, mean (SD)
68 (31.9)
57.7 (34.3)
74.7 (28.2)
SOFA score, mean (SD)
6.625 (3.8)
5.39 (3.7)
7.48 (3.7)
Charlson Comorbidity Index, mean (SD)
4.69 (3.3)
4.8 (3.3)
4.6 (3.4)
Abbreviations: APACHE, Acute Physiology and Chronic Health Evaluation; DKA, diabetic ketoacidosis; ICU, intensive care unit; SOFA, Sequential Organ Failure Assessment. a Values are expressed as number (%) of patients unless otherwise indicated. Because of rounding, percentages may not total 100.
performed by using SAS 9.3 and SAS JMP Pro, version 10.0, software (SAS Institute Inc).
Results Patients Data collection for up to 5 days of ICU stay for the 200 patients resulted in 791 patient-days (17 938 hours of data). A total of 383 patients sequentially admitted into the ICUs were screened, 179 from the medical-respiratory unit and 204 from the surgical-trauma unit. A total of 79 patients in the medical-respiratory unit and 104 in the surgical-trauma unit did not meet inclusion crite-
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ria. In all, 200 patients, 100 from each ICU, were included in the final analyses. A full report of the sample has been published.4 Overall the patients in the units had a mean age of 55.5 years and were primarily men, of nonHispanic ethnicity, and white or African-American race (Table 1). More than 75% of the agitated patients in the study were identified by using the RASS score; the balance were identified by using an agitation keyword. Of the 200 patients, 118 (59%) were agitated at some point during the 5-day study period and made up the agitated group for this analysis; 102 patients (86%) had agitation on day 1.
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Table 2 Demographic, preadmission, and clinical risk factors with univariate significance At admission Type
Nonagitated (n = 82)
24 hours before onset of agitation
Agitateda (n = 118)
Nonagitated
Agitateda
— —
— —
Demographic and preadmission risk factors Sex, No. (%) of patients Male Female
73 (62)b 45 (38)b
42 (51) 40 (49)
Height, mean (SD), cm Weight, mean (SD), kg Medical history, No. (%) of patients Illicit substance use Psychiatric diagnosis
167.3 (10.3)
172.1 (10.7)c
—
—
77.1 (22.5)
(26.1)b
—
—
— —
— —
83.9
55 (47)c 28 (24)c
26 (32) 10 (12) Severity of illness scores 6.6 (3.8) 0.8 (0.9) 1.2 (1.3)
5.4 (3.7)c 1.3 (1.1)c 2.5 (1.2)c
5.9 (4.1) 0.9 (1) 1.3 (1.3)
7.4 (3.7)c 1.2 (1.1)b 2.5 (1.1)b
Glasgow Coma Scale score, mean (SD)
12.3 (3.8)
8.6 (3.6)c
12.3 (3.8)
8.6 (3.6)c
APACHE III score, mean (SD)
57.7 (34.3)
74.7 (28.2)c
57.7 (34.3)
73.7 (29.3)c
Total SOFA score, mean (SD) Respiratory subscore, mean (SD) CNS subscore, mean (SD)
Clinical risk factors 8 (4)
23 (11.5)b
0.5 (0.4-0.7)c
0.36 (0.21-0.53)
0.5 (0.4-0.7)c
7.39 (0.07)
7.36 (0.095)c
7.39 (0.09)
7.36 (0.09)c
Serum level of magnesium, mean (SD), mEq/L
1.89 (0.32)
1.99
(0.47)b
1.85 (0.39)
1.98 (0.45)b
Serum level of hemoglobin, mean (SD), g/dL
10.3 (2.5)
10.3 (2.2)
Serum level of glucose, mean (SD), mg/dL
146 (66)
168 (84)b
146 (66)
165 (84)b
12 (15)
35 (30)c
12 (15)
37 (31)c
19 (23)
(40)c
19 (23)
49 (42)c
6 (7)
40 (34)c
12 (15)
63 (53)c
15 (8)
70 (35)c
18 (9)
80 (40)c
PaO2/FIO2 < 200, No. (%) of patients
1 (1)
FIO2, median (25th-75th percentile)
0.36 (0.21-0.53)
Serum pH, mean (SD)
Body temperature ≥38ºC, No. (%) of patients Body temperature
