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American Journal of Critical Care. 2008;17: 255-263
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CE Article

Predictors of Adverse Events in Patients After Discharge From the Intensive Care Unit

By Wendy Chaboyer, RN, PhD, Lukman Thalib, PhD, Michelle Foster, RN, MN, Carol Ball, RN, PhD and Brent Richards, MD. Wendy Chaboyer is a professor and director of the Research Centre for Clinical and Community Practice Innovation, Griffith University Gold Coast Campus, Queensland, Australia. Lukman Thalib is an associate professor in the Faculty of Medicine at the University of Kuwait, Safat, and is an adjunct professor with the Research Centre for Clinical and Community Practice Innovation, Griffith University Gold Coast Campus, Queensland, Australia. Michelle Foster is the nurse unit manager of the intensive care unit at Gold Coast Hospital in Southport, Queensland, Australia. Carol Ball is a consultant nurse in critical care at Royal Free Hospital in London, England. Brent Richards is the executive director of the Division of Surgery and Critical Care at Gold Coast Hospital in Southport, Queensland, Australia.

Corresponding author: Wendy Chaboyer, RN, PhD, Research Centre for Clinical and Community Practice Innovation, Griffith University Gold Coast Campus, Queensland, 4222 Australia (e-mail: W.Chaboyer{at}griffith.edu.au).


   Abstract
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Methods
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 Conclusion
 References
 
Background Patients discharged from the intensive care unit may be at risk of adverse events because of complex care needs.

Objective To identify the types, frequency, and predictors of adverse events that occur in the 72 hours after discharge from an intensive care unit when no evidence of adverse events was apparent before discharge.

Methods A predictive cohort study of 300 patients from an adult intensive care unit was undertaken. An internationally accepted protocol for chart audit was used. Frequency of adverse events was calculated, and logistic regression was used to determine independent predictors of adverse events.

Results A total of 147 adverse events, 17 (11.6%) of which were defined as major, were incurred by 92 patients (30.7%). The 3 most common adverse events, hospital-incurred infection or sepsis (n = 32, 21.8%), hospital-incurred accident or injury (n = 17, 11.6%), and other complication such as deep vein thrombosis, pulmonary edema, or myocardial infarction (n = 17, 11.6%) accounted for 44.9% (n = 66) of all adverse events. Two predictors, respiratory rate less than 10/min or greater than or equal to 25/min and pulse rate exceeding 110/min, were significant independent predictors; requiring a high level of nursing care at the time of discharge was a significant predictor in univariate analysis but not in multivariate analysis.

Conclusion Taking, recording, and reporting vital signs are important. Nursing care requirements of patients at discharge from the intensive care unit may be worthy of further investigation in studies of patients after discharge.

Notice to CE enrollees:
A closed-book, multiple-choice examination following this article tests your understanding of the following objectives:
  1. Identify adverse events related to discharge from the intensive care unit (ICU)
  2. Describe tools used to monitor adverse events in patients discharged from the ICU
  3. Establish protocols and follow-up chart audits for patients discharged from the ICU
To read this article and take the CE test online, visit www.ajcconline.org and click "CE Articles in This Issue." No CE test fee for AACN members.

The landmark Harvard Medical Practice Study heralded a focus on adverse events in hospitals and led to an increased focus on prevention in clinical practice. Adverse events occur in about 2.9% to 3.7% of hospitalizations in the United States,1 10.8% of hospitalizations in the United Kingdom,2 and 16.6% of hospitalizations in Australia.3 According to estimates, adverse events cost the United Kingdom £1 billion per year.2 Despite much speculation and analysis of cross-country variation,4 in an era of escalating health costs and attempts at cost containment, a focus on quality and safety in health care is a trend that will likely persist.

Adverse events generally have been defined as injuries or events that are due to health care management rather than to underlying disease and that result in prolonged hospitalization or some disability.1–3,5,6 Patients recently discharged from the intensive care unit (ICU) to an intermediate care unit may be particularly at risk for adverse events.

Previous researchers have tended to examine specific types of adverse events experienced by patients after the patients were discharged from the ICU. For example, in a systematic review7 of ICU readmissions, the variables most frequently associated with readmission included hypoxia, increased respirations (>24/min), and increased heart rate (>104/min). In a study of deaths after ICU discharge, Wallis et al8 found that about three-quarters of patients who died after the discharge were either at risk of dying or were expected to die; however, 20% were expected to survive but died. The predominant causes of death among patients expected to survive were pneumonia, sepsis, and myocardial infarction, consistent with the reasons for unexpected deaths after discharge from the ICU in another study.9

In summary, previous research on adverse events after leaving the ICU has focused on severe or major adverse events such as ICU readmissions and deaths; however, little is known about other adverse events such as complications, inappropriate discharge home, and adverse reactions to drugs. In this predictive study, we used a broader, internationally accepted definition of adverse events to identify the types and frequency of adverse events and factors associated with such events in patients recently discharged from the ICU. We hypothesized that, in addition to characteristics of patients, health care delivery factors would be associated with adverse events.


   Methods
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In this predictive cohort study, an established chart audit protocol and definitions3,10 were used to determine adverse events that occurred in the 72 hours after patients were transferred from the ICU to an intermediate care unit. The study was approved by the human ethics committees at the university and hospital.


   Sample
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The study was done in a 12-bed general ICU at Gold Coast Hospital, a 580-bed metropolitan hospital in Queensland, Australia. The hospital does not have a high-dependency unit; however, beds in the ICU can be designated as high dependency. The ICU admits both medical and surgical patients. Cardiac surgery is not performed at the site, and burn patients are transferred to another hospital after their condition is stable. The ICU admits about 1000 patients per year; the mean length of stay is 3.2 days. Approximately 60% of admissions are nonsurgical patients; 40% are surgical patients. The top 10 diagnostic groups admitted to the ICU are shown in Table 1Go.


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  		Table 1 Top 10 most common diagnostic categories (surgical and nonsurgical) among patients admitted to the intensive care unit at Gold Coast Hospital, Queensland, Australia

 
At the time of the study, the rate of unplanned readmission to the ICU within 72 hours of discharge was 1.8%, and the mortality ratio standardized to the Acute Physiology and Chronic Health Evaluation (APACHE) II was 95.6 (95% confidence interval, 80.4–112.6). Potential discharges are discussed at the morning round, during which registered nurses and doctors together plan the daily activities. Planned discharges are discussed with the hospital’s bed manager, who is responsible for allocation of beds in the intermediate care unit for patients transferred from the ICU.

All patients who stayed in the ICU 24 hours or longer and were transferred from the ICU to the intermediate care unit between July 2004 and February 2005 were eligible for the study. Patients were included only once, on their first admission to the ICU during the study period. Patients who were transferred to other hospitals or discharged directly home were excluded from the study.


   Definitions
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An adverse event was defined as an unintended injury or event that results in temporary or permanent disability, including increased length of stay, and is caused by health care management rather than the disease process.3 Seventeen criteria and their specific definitions were used as in previous research,3,10 except that unplanned hospital admission and unplanned hospital readmission during the preceding 12 months were combined into a single criterion for the current study. The criteria were combined because informal interviews with clinicians and during training of the research assistant indicated the difficulty of distinguishing between a readmission related to a problem that occurred 12 months previously and an admission that occurred within that same 12-month time frame.

We used the data dictionary from Woloshynowych et al10 with permission. Major adverse events were defined as cardiac or respiratory arrest, unplanned ICU readmission, and unexpected death. The criterion "other undesirable outcome" allowed the research assistants, who were registered nurses, to exercise judgment in reporting any complications or questionable outcomes not addressed by other criteria.10 Errors of omission were recognized under this criterion.10 Patients had to have experienced the adverse events during the 72 hours after leaving the ICU, with no evidence of the adverse event occurring in that patient before transfer to the intermediate care unit.

A range of predictor variables were examined (Table 2Go); the variables were based on a review of the literature and informal interviews with critical care experts. Some variables included demographic and clinical characteristics, extracted from the Australian and New Zealand Intensive Care Society’s Adult Patient Database (ANZICS APD), related to the ICU experience. The data dictionary for the ANZICS APD contains definitions for data collected related to the ICU episode of care. Data are collected locally and entered into the APD locally, before transfer to the central database maintained by ANZICS. Out-of-hour discharge was defined as discharge between 6:01 PM and 6:59 AM.11,12 Similar to Harrison et al,13 we identified additional variables from informal interviews with ICU medical and nursing experts. These variables were (1) discharged from ICU with a tracheostomy; (2) high level of nursing care, judged at the time of ICU discharge to be beyond the allocated 5 nursing hours per patient day used at the study site for medical and surgical patients (assessed by the chart reviewers, who were registered nurses, on the basis of the ICU nursing discharge summary); (3) discharged to an appropriate ward (medical patients to medical intermediate care units and surgical patients to surgical intermediate care units); and (4) documented evidence of at least one ICU liaison nurse visit.14 Other variables were related to care factors or patients’ characteristics in the intermediate care unit during the 3 days after discharge from the ICU (Table 2Go).


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  		Table 2 Predictor variables

 

Adverse events occur in as much as 3.7% of hospitalizations in the United States, 10.8% in the United Kingdom, and 16.6% in Australia.

 

More than 30% of patients discharged from the intensive care unit experienced an adverse event; 5.7% experienced a major adverse event.

 


   Data Collection
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Two experienced nurses with ICU qualifications and postgraduate research training audited all charts by using a paper-and-pen audit form.10 The protocol for the chart audit involved careful reviewing of each patient’s medical records, including various flow sheets, medication records, laboratory results, and medical and nursing notes. During this review, data were extracted and recorded on a paper form designed for the study. When adverse events were identified, additional supporting secondary information was sought.


Common adverse events were hospital-acquired infection/sepsis and hospital-incurred accident or injury.

 

The data collectors received 10 hours of training, which included a review of the audit process, the criteria for adverse events, and the data extraction form. They audited several charts for practice, first together (2 charts) and then individually (5 charts). An examination for consistency of the completed reviews was followed by additional focused training. In order to establish interrater reliability, throughout data collection, 30 randomly chosen charts (10%) were reviewed independently by the 2 data collectors. A total of 6 differences in 1200 data points (0.05%) were found; however, none were related to the occurrence of an adverse event.

Data from the ANZICS APD were transferred electronically to the SPSS database (SPSS Inc, Chicago, Illinois). These data are collected by an experienced and trained data manager. The database has built-in quality checks, and manual checks of the data quality are conducted on a monthly basis. Data from the audit forms were entered into the SPSS database with double entry of 30 forms (10%) to establish rates of errors in data entry. In 1200 data entries, 11 errors (0.9%) were found.

All data were analyzed by using SPSS version 11. Data analyses began with the description of the sample by computing frequency distribution of the baseline characteristics and then number and types of adverse events. The sample was then divided into patients who had experienced adverse events and patients who had not. Baseline characteristics of patients were compared between these 2 groups by using an appropriate statistical test, either the Mann-Whitney test for continuous but skewed variables or a Z test for proportion for categorical variables, which is conceptually similar to the {chi}2 test.

Univariate and multivariate logistic regression models were used to identify the predictors of any adverse event and of major adverse events. Univariate models indicated all clinical predictors associated with adverse events; multivariate models indicated only those predictors that were independently and significantly related. Odds ratios and 95% confidence intervals along with exact P values are reported for significant predictors.


   Results
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A total of 507 patients were admitted during the study period, and 300 charts (59.2%) were reviewed. Figure 1Go provides an overview of the recruitment and final sample. Characteristics of the sample are given in Table 3Go. Most patients were male, and nonsurgical patients accounted for about half of the sample. About one-quarter of patients were discharged on the weekend; however, less than one-fifth were discharged out of hours. About one-third of the patients had the liaison nurse service, and fewer than 1 in 10 were discharged to an inappropriate intermediate care unit. Four charts (1.3%) contained no data on respiratory rate or pulse during the 72 hours after ICU discharge.


Figure 1
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  		Figure 1 Recruitment of the sample.

 

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  		Table 3 Baseline characteristics of the study population (n = 300) with and without any adverse events and with major adverse eventsa

 
A total of 92 patients (30.7%) experienced an adverse event; 17 patients (5.7%) experienced major adverse events. Those patients who experienced any type of adverse event had significantly higher APACHE II scores at ICU admission than did patients who did not have an adverse event. Patients who had an adverse event also remained in the hospital longer than did patients who did not have such an event, as was expected on the basis of the definition of adverse event used in the study. In addition, patients who experienced an adverse event were more likely to have been assessed as requiring a high level of nursing care. Older patients and those who had higher APACHE II scores at admission were more likely to experience a major adverse event than were younger patients and patients with lower scores.

A total of 147 adverse events were experienced by the 92 patients. A total of 60 patients (20.0%) experienced 1 adverse event; however, 32 patients (10.7%) experienced 2 or more adverse events (Figure 2Go). Table 4Go contains a description of the types of adverse events that occurred. Two adverse events, hospital-acquired infection/sepsis and hospital-acquired accident or injury, accounted for one-third of all adverse events.


Figure 2
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  		Figure 2 Frequency of adverse events.

 

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  		Table 4 Types of adverse events

 
Table 5Go lists the significant predictors of any adverse event and major adverse events. Univariate logistic regression showed that patients with higher APACHE II scores were slightly more likely to experience an adverse event than were those with lower scores, and patients with abnormal serum levels of potassium were almost twice as likely to have an adverse event as those with normal levels. Patients judged to require a high level of nursing care at ICU discharge and patients who required 1:1 nursing care in the intermediate care unit were more than twice as likely to have an adverse event. High pulse, low oxygen saturation, and abnormal respiratory rates were even more predictive of an adverse event in the univariate logistic regression analysis. Abnormal serum levels of potassium, requiring 1:1 nursing care in the intermediate care unit, abnormal respiratory rate, a decrease in score on the Glasgow Coma Scale of 2 or more, and an oxygen saturation less than 90% were all predictive of a major adverse event; however, because of the small sample size for this variable (n=17), the results must be viewed with caution.


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  		Table 5 Univariate and multivariate predictors of adverse events

 

Patients with abnormal respiratory rates were 3 times more likely to experience an adverse event.

 

Multivariate logistic regression models yielded 2 significant predictors of adverse events: respiratory rate and pulse rate (Table 5Go). After all other potentially confounding variables were adjusted for, patients with abnormal respiratory rates (<10/min or =25/min) had about 3 times more chance of an adverse event developing. Likewise, those with higher pulse rate (>110/min) had 2 times more risk of an adverse event developing after leaving the ICU. The third strongest predictor of adverse events, required high levels of nursing care, was not significant in this multivariate analysis (P = .06). No other variables were significant in the multivariate models. Because of the small number of major adverse events (n=17), multivariate analysis was not undertaken; any predictive model would be expected to be highly unstable statistically.


   Discussion
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A total of 92 patients (30.7%) in this study experienced 147 adverse events in the 72 hours after discharge from the ICU. This rate is similar to that in an early French study of adverse events in the ICU,15 but higher than the rates reported for other groups of hospitalized patients.1–3 Patients recently discharged from the ICU likely are a high-risk group because of their complex care needs. Other investigators9,16,17 have reported that difficulties associated with ICU transfer are primarily related to patients’ acuity and staff skill mix, lack of care coordination, and poor communication between medical and nursing staff. Additionally, staff in general care areas may not have the knowledge or skills to provide appropriate care for the complex needs of patients discharged from the ICU.18–20 Inasmuch as patients recently discharged from the ICU appear to be at an increased risk of adverse events, health services such as intermediate care units21,22 and ICU outreach teams23–25 may fulfill an important need. For this reason, careful consideration of the way staff are allocated to care for these patients may be warranted.

In this study, patients who had an adverse event also had higher APACHE II scores at ICU admission. However, APACHE II scores were not predictive of adverse events in the multivariate analysis. Admission APACHE II scores are unlikely to reflect a patient’s condition at ICU discharge. Unfortunately, at Gold Coast Hospital, scores such as the Sequential Organ Failure Assessment26 are not recorded sequentially or at ICU discharge, so patients’ severity of illness at the time of discharge was unknown.

The most frequent adverse events were hospital-incurred infection or sepsis and hospital-incurred accident or injury. The urgency for treatment of hospital-acquired infection is evidenced by the institution of the Surviving Sepsis Campaign and the production of guidelines for management of sepsis.27 Together with the Saving 100 000 Lives campaign in the United States and the Saving Lives program in the United Kingdom, the concept of evidence-based bundles of care to reduce the incidence of hospital-acquired infection is now being advocated. Our results add further evidence of the need to focus on preventing hospital-incurred infection in the whole hospital environment in order to increase the safety of the environment into which patients recovering from critical illness are discharged. The occurrence of hospital-incurred injury also indicates the need to focus educational programs on the safe administration of medications and on competence in skills acquisition.

Multivariate analysis showed that abnormal pulse and respirations were significant predictors of adverse events. The importance of abnormal vital signs before cardiac arrest has been recognized.13,28,29 In a recent study,30 a complete recording of vital signs (heart rate, blood pressure, respiratory rate, and body temperature) was not available in 35% of 189 patient charts reviewed. Similarly, in a trial of a medical emergency team system in 23 Australian hospitals, Hillman et al31 found that monitoring and documentation of vital signs were inadequate. We did not collect data on the number of times that vital signs were missing from the record, but a small proportion of patients had no record of respiratory and heart rates in the 72 hours after ICU discharge.

Collectively, the evidence suggests that taking, recording, and reporting of vital signs is an area that must be addressed. Because nurses are primarily responsible for these activities, it becomes important to consider how these skills are taught in nursing education and how they are practiced in the reality of a busy clinical environment. Also, abnormal results may have been reported but not acted upon by medical staff. Because of the increasing evidence related to both a lack of charting and responses to abnormalities in vital signs,13,28–31 including our findings, the importance of abnormal vital signs should be stressed in continuing education at the workplace.

Other researchers13,28,32 also focused on antecedents of major adverse events, generally defined as unexpected deaths, cardiac arrest, severe respiratory distress, and ICU readmission. These outcomes often have been associated with the failure of staff in the intermediate care units to appreciate the clinical importance of abnormal vital signs and the urgency with which these should be treated.33,34 Abnormal respiratory rate and a high pulse rate were predictive of adverse events in our study. These findings support the use of these parameters in objective risk assessment forms, designed to detect which patients are at risk after ICU discharge. For example, these 2 vital signs are used in both the patient at risk score35 and the Modified Early Warning Score.36,37 Our results indicate that both abnormal respiratory rate and a high pulse rate are independent predictors of adverse events and suggest that these variables should be retained in future modifications of these tools.


APACHE II scores were not predictive of adverse events in multivariate analysis.

 

The finding that patients requiring a high level of nursing care at the time of ICU discharge was significant in univariate logistic regression analysis but not in multivariate analysis (P = .06) may be related to the relatively small sample. We considered high levels of nursing care a clinically important variable that should be included because of the limited ability of staff in intermediate care units to provide complex care.18–20 Providing such higher level of nursing care will most likely require closer clinical assessment and the use of critical thinking and decision-making skills related to patients’ changing conditions. Plausibly, patients who require a higher level of nursing care at ICU discharge are at higher risk for an adverse event than are patients who did not require such care.

Recent research38 in the United States indicated that nurses can interrupt a large proportion of serious errors in the ICU and that nurses can be viewed as a "safety mechanism," playing a role in error recognition and recovery.39 More research is required to describe and capitalize on the potential benefit of formal input by both ICU and intermediate care nurses in the discharge process.

Our results also have other possible explanations. First, although patients did not have abnormal vital signs at ICU discharge, the discharge may have been premature for some other reason. Additionally, the staffing levels and skill mix of the staff in the intermediate care unit may have influenced the findings. However, because the study was retrospective, we could not collect data on the actual "adequacy" of the staffing and skill mix in the intermediate care unit when the adverse event occurred.

Other researchers11,12 have found an association between day and time of discharge and mortality. We found no association between either factor or the occurrence of adverse events. These differences have several possible explanations. First, investigators in the previous 2 studies11,12 focused on a single adverse event, death, and not the more broad definition of adverse events. Second, Goldfrad and Rowan12 included all patients admitted who survived to ICU discharge, and Duke et al11 included patients who stayed in the ICU for 8 hours or longer, whereas we included only patients who remained in the ICU for 24 hours or longer. Thus, the groups of patients were different. Finally, the sample in our study was relatively small compared with the very large database analyses in the other studies.11,12 Perhaps a larger study would have yielded different results.

Our study has several limitations that lead to recommendations for future research. First, adverse events were identified by using chart audits. A number of methods for reporting adverse events, such as direct observation,40,41 self-reporting or facilitated reporting,40,42,43 and chart audit,6,10,44 have been described, and each has its strengths and limitations. Trained observers report more unintended events, but this method is expensive, labor intensive, and vulnerable to the Hawthorne effect.40 In one other study,43 similar rates of adverse events were identified when physician reporting was compared with chart audit; however, the events identified were not the same. Even when chart audit, incident reporting, general practitioner reporting, and external sources such as coroner’s review are used together, some adverse events will be missed.45 With these limitations in mind, future researchers should carefully consider the method they use to identify adverse events.

Second, although international guidelines were followed for the chart audit and the ICU discharge time was known, the actual times at which the predictors and adverse events occurred were not collected, although the events were not present at the time of ICU discharge. Future researchers may want to collect this information to determine if more adverse events occur during the night when staffing levels in intermediate care units are generally lower. Data on patients’ acuity and quantitative measures of estimated patient workload at the time of ICU discharge also may be useful. Finally, the ANZICS APD was accessed for some data. The database may have some errors; however, built-in and manual quality checks are used to prevent errors. Additionally, interrater reliability of the chart audit data was established.


   Conclusion
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 Results
 Discussion
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References
 
We used an internationally recognized definition for adverse events and a well-established protocol for chart audit to determine that 30.7% of patients discharged from an Australian ICU experienced an adverse event, with 5.7% experiencing a major adverse event. The 3 most common adverse events were hospital-incurred infection/sepsis, hospital-incurred accident or injury, and other complications such as deep vein thrombosis, pulmonary edema, and myocardial infarction. Independent predictors of adverse events were abnormal respiratory rate and high pulse rate. The need for high level of nursing care might have been a significant predictor had a larger sample been obtained. Although the first 2 factors are commonly used in risk assessment after ICU discharge, the third is less well established but may be worthy of future study. Consideration of nursing care requirements in intermediate care units may be an important part of the decision to discharge patients from the ICU to an intermediate care unit.

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FINANCIAL DISCLOSURES
This study was funded by a Griffith University internal research grant.

To purchase electronic or print reprints, contact The InnoVision Group, 101 Columbia, Aliso Viejo, CA 92656. Phone, (800) 809-2273 or (949) 362-2050 (ext 532); fax, (949) 362-2049; e-mail, reprints{at}aacn.org.


   REFERENCES
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