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(Chest. 2004;126:1905-1909.)
© 2004 American College of Chest Physicians

Identifying Potentially Ineffective Care in the Sickest Critically Ill Patients on the Third ICU Day*

Bekele Afessa, MD, FCCP; Mark T. Keegan, MB, MRCPI; Zulfiqar Mohammad, MD; Javier D. Finkielman, MD and Steve G. Peters, MD, FCCP

* From the Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine (Drs. Afessa, Mohammad, Finkielman, and Peters), and Division of Critical Care, Department of Anesthesia (Dr. Keegan), Mayo Clinic College of Medicine, Rochester, MN.

Correspondence to: Bekele Afessa, MD, FCCP, Division of Pulmonary and Critical Care Medicine, Mayo Clinic College of Medicine, 200 First St SW, Rochester, MN 55905; e-mail: Afessa.Bekele{at}mayo.edu


   Abstract
TOP
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Objective: To determine if an increase in the third-ICU-day acute physiology score (APS) of the APACHE (acute physiology and chronic health evaluation) III prognostic system can identify potentially ineffective care.

Design: Retrospective cohort study.

Setting: Academic medical center.

Patients: Adult patients with first-ICU-day predicted mortality rate ≥ 80%.

Measurements: Demographics, ICU admission source, admission type, ICU admission diagnosis, first- and third-ICU-day APSs, APACHE III score, APACHE III-predicted hospital mortality, hospital discharge status, 100-day survival, and ICU and hospital length of stay.

Results: A total of 302 patients (age [mean ± SD], 64.7 ± 15.8 years; 54.3% male gender) were included in the study. Respiratory failure was the most common reason for ICU admission. Nonoperative admissions accounted for 94.7%. The first- and third-ICU-day APSs were 106.8 ± 19.8 and 70.5 ± 29.9, respectively. The first- and third-ICU-day predicted hospital mortality rates were 87.8 ± 5.3% and 86.5 ± 14.8%, respectively. The hospital mortality rate was 61.3%, and the 100-day survival rate 28.5%. The third-ICU-day APS was higher than the first-ICU-day APS in 34 patients (11.3%). Only 2 of these 34 patients (6%) survived to hospital discharge, compared to 115 of 268 patients (43%) without an increase in APS (p < 0.0001). Of the two hospital survivors with increased APS, only one patient survived 100 days after hospital discharge. In predicting 100-day mortality, the sensitivity of an increase in the third-ICU-day APS was 15.3% (95% confidence interval, 11.1 to 20.7%), specificity was 98.8% (95% confidence interval, 93.7 to 99.8%), positive predictive value was 97.1% (95% confidence interval, 85.1 to 99.5%), and negative predictive value was 31.7% (95% confidence interval, 26.4 to 37.5%).

Conclusions: A higher APS on the third ICU day, compared to the first ICU day, identifies potentially ineffective care in patients with the first-day predicted hospital mortality rate ≥ 80%.

Key Words: acute physiology and chronic health evaluation • hospital mortality • ICU • length of stay • medical futility • prognosis


   Introduction
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 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
There is no consensus regarding the definition of futility, and there are no reliable ways to identify patients for whom ICU care is futile.123456 A number of studies789 have reported poor performance of critical care providers in predicting futility of care. Various prognostic models are used to predict mortality of adult patients admitted to ICU based on the first-ICU-day findings.10111213 Some prognostic models have developed daily scoring systems for predicting mortality based on values from the subsequent ICU days.1415 Although ICU prognostic models are not as reliable at predicting risk of death for an individual as they are for predicting the risk of death for groups, knowledge of trends of daily APACHE (acute physiology and chronic health evaluation) II scores and the number and duration of organ failure can improve prediction of the likelihood of survival of critically ill patients.816171819

The purpose of this retrospective study was to identify trends in APACHE III severity measures that are associated with futile care in the sickest patients admitted to the ICU. We hypothesized that an increase in the acute physiology score (APS) from the first to the third ICU day would identify patients for whom prolonged care is futile. Our study differs from previous studies by focusing on the sickest ICU patients and on the third ICU day.


   Materials and Methods
TOP
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
In this retrospective, cohort study, we examined the prospectively collected APACHE III database of adult patients consecutively admitted to the ICUs of Mayo Medical Center, Rochester, MN, between October 1994 and December 2002. The Mayo Foundation Institutional Review Board approved the study, and a waiver of informed consent was granted. Patients who did not authorize their medical records to be reviewed for research were excluded.

Mayo Medical Center in Rochester includes two hospitals with a total of approximately 1,900 beds. Patients who were admitted to one medical, two surgical, and one multispecialty ICUs were identified from the APACHE III database. Patients admitted to the neurologic, cardiovascular surgery, and coronary ICUs were not included since they were not part of the APACHE III database.

We included the sickest patients who were treated in the ICU for ≥ 3 days. We defined the sickest patients as those with the first-ICU-day APACHE III-predicted mortality rate ≥ 80%. Among 43,605 ICU admissions entered in the APACHE III database during the study period, 15,512 patients (35.6%) stayed in the ICU for ≥ 3 days, and 748 patients (1.7%) had first-day predicted mortality rate ≥ 80%. Of the 748 patients with a predicted mortality rate ≥ 80%, 308 patients (41%) survived 3 days in the ICU. Excluding six patients who did not authorize their medical records to be reviewed for research, 302 patients were included in the study.

Data were obtained from the APACHE III database using the software provided by Cerner Corporation (Kansas City, MO) and computerized patient records. Data collected included age; ethnicity; gender; ICU admission source; admission type (postoperative or nonoperative); ICU admission diagnosis; first- and third-ICU-day APS; APACHE III score; APACHE III-predicted hospital mortality; ICU and hospital discharge status and location; and ICU and hospital length of stay. The APS, APACHE III score, and predicted hospital mortality rate for each patient were calculated as described by Knaus and colleagues.11 The changes in APS and predicted hospital mortality rates were defined as the difference between the first-day and third-day APS and predicted hospital mortality rates, respectively. For patients discharged alive from the hospital, follow-up information about their survival status was obtained from computerized medical records. For patients with higher third-ICU-day APS compared to the first day, we obtained additional data including use of mechanical ventilation, renal replacement therapy, continuous IV vasopressors, artificial liver support, and pulmonary artery catheterization. We also noted the occurrence and timing of withholding and withdrawal of life support. Futile ICU care was defined by the patient’s death during the hospitalization, or within 100 days of hospital discharge.

Descriptive data are summarized as mean ± SD, median (interquartile range [IQR]), or percentages. We used a {chi}2 test to compare categorical variables and Student t test and rank-sum test to compare continuous variables. Patients with missing data were excluded from analysis involving the missing elements. Sensitivity, specificity, negative predictive value, and positive predicted value were calculated with their 95% confidence intervals; p < 0.05 was considered statistically significant.


   Results
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 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
The patients were predominantly white (Table 1 ). The most common reasons for ICU admission were of respiratory origin (Table 2 ). The first- and third-ICU-day severity measures are listed in Table 1. The median change in APS was – 37 (IQR, – 57 to – 17), and the median change in predicted hospital mortality rate was + 2% (IQR, – 5.9 to + 8.5%). The observed ICU and hospital mortality rates were 42.4% and 61.3%, respectively. Of the 117 patients who survived to hospital discharge, 72 patients (61.5%) were documented to be dead at the completion of this study, January 31, 2004. The 100-day survival rate was 28.5% (86 of 302 patients). There were no statistically significant differences in age, gender, ethnicity, source of ICU admission, ICU type, and admission type between 100-day survivors and nonsurvivors. The third-day APS, APACHE III score, and predicted mortality rates as well as the first-day APACHE III score and predicted mortality rate were higher in 100-day nonsurvivors than survivors (Table 3 ).


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  		Table 1. Baseline Characteristics of the Sickest 302 Patients Admitted to the ICU*

 

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  		Table 2. Reasons for ICU Admission of the Sickest 302 Patients

 

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  		Table 3. Differences Between 100-Day Survivors and Nonsurvivors of the Sickest 302 Patients Admitted to the ICU*

 
The APS was higher on the third ICU day than on the first ICU day in 34 patients (11.3%). Only 2 of these 34 patients (6%) with higher APS on the third ICU day survived to hospital discharge, compared to 115 of the other 268 patients (43%) [p < 0.0001]. Of the two patients with higher APS on the third ICU day who survived to hospital discharge, only one patient, who was admitted to the ICU for multiple trauma, survived for > 100 days after hospital discharge; the second patient was transferred to a hospice and died 2 days later. In contrast, the 100-day survival rate of the patients with nonrising APS was 32% (p < 0.0001). The specificities of increase in APS and APACHE III-predicted mortality rate on the third ICU day in predicting 100-day mortality were high, with low sensitivities (Table 4 ).


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  		Table 4. Accuracy of Increase in APS and APACHE III-Predicted Mortality in Predicting 100-Day Mortality Rate*

 
The median length of ICU stay of the 34 patients with an increase in APS was 3 days, and the majority received aggressive hemodynamic monitoring and organ support (Table 5 ). Among these 34 patients, death followed withdrawal of life support in 22 patients, withholding of life support in 10 patients, and ineffective resuscitation in 1 patient. The decision to withdraw and withhold life support was made after discussion with family members and based on the patients’ wishes. The withdrawal of life support occurred at a median of 3.0 days (IQR, 3.0 to 5.8) after ICU admission and 0 days (IQR, 0 to 0) days before discharge. The withholding of life support occurred at a median of 3.0 days (IQR, 1.5 to 8.5) after ICU admission and 0.5 days (IQR, 0 to 1.5) before discharge.


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  		Table 5. Organ Support and ICU Length of Stay in the 34 Patients With Increased APS Organ Support and ICU Stay*

 
The median ICU length of stay of the 100-day survivors was 7.6 days (IQR, 4.8 to 14.8), compared to 6.0 days (IQR, 3.5 to 10.5) of nonsurvivors (p = 0.0048). The median hospital length of stay of the 100-day survivors was 37.0 days (IQR, 17.6 to 58.5), compared to 20.5 days (IQR, 8.8 to 37.3) of nonsurvivors (p < 0.0001).


   Discussion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
In this retrospective study based on prospectively collected data, we described the outcome of the sickest critically ill patients with first-ICU-day predicted mortality rate ≥ 80%. Approximately 2% of the 43,605 ICU admissions had a predicted hospital mortality rate ≥ 80%. Fifty-nine percent of the patients with a first-ICU-day predicted mortality rate ≥ 80% died within 3 days of ICU admission. Among the 41% of the sickest patients who survived for at least 3 days in the ICU, the hospital mortality rate was 61.3% and the 1-year survival rate was 28.5%. There was an increase in APS on the third ICU day, relative to the first ICU day, in 34 patients (11.3%). Only 2 of these 34 patients (6%) survived to hospital discharge, and 1 patient survived 100 days after hospital discharge.

Approximately 4.8% of ICU patients receive potentially ineffective care.20 A significant number of the deaths of ICU patients occur following withdrawal or withholding of life support based on medical futility.212223 In some institutions, patients are refused admission to the ICU based on perceived futility.9 Most patients do not want to receive futile care to prolong their suffering. In an excellent article by Schneiderman et al,24 quantitative futility was defined as a situation that arises when physicians find a medical treatment to be useless in the last 100 cases, based on personal experience, experiences shared with colleagues, or consideration of reported data. However, accurately documented experiences or reported data may not exist for many conditions. Patients and family members often ask critical care physicians about the probability of short-term and long-term survival. Although the overall performances of the ICU prognostic models are good in discriminating hospital survivors from nonsurvivors, they are not used in decision making for individual patients. However, the severity assessment scores and predictions obtained from such models have the potential to improve the accuracy of the decision making process by patients and health-care providers. In the current study of patients with predicted mortality rate ≥ 80% based on the first-ICU-day data, we found that the trend in the APS on the third ICU day can identify potentially ineffective care in a small number of patients.

Most of the ICU mortality prediction models calculate the probability of hospital death based on the worst values for the physiologic variable during the first ICU day.10111213 Since these values do not reflect the response to treatment, they are unlikely to help identify patients receiving potentially ineffective care. To improve the accuracy of predicting potentially ineffective care, previous studies8141617 have applied mortality prediction models based on multiple ICU days. More recent studies,1819 based on the APACHE III prognostic system, have reported that the product of day-1 and day-5 risk assessment has specificity > 90% and sensitivity < 50% in identifying potentially ineffective care. Our study differs from the previously reported APACHE III-based studies1819 by our limiting the study entry criteria to the sickest patients with predicted mortality rate of ≥ 80% and using first-day and third-day APS instead of the product of day-1 and day-5 APACHE III mortality predictions. However, our results were similar to previous studies, with high specificity and positive predictive value but low sensitivity and negative predictive value. Due to the clinical implication of labeling nonfutile cases as futile, a model that maximizes specificity, rather than sensitivity, is preferred.

Our study has several limitations. Because of the retrospective design limited to a single medical center with a unique referral pattern, the findings may not apply to other populations. We have not addressed quality-of-life and resource-use issues. Because of our focus on the sickest of the critically ill patients, our sample size was small.

Despite these limitations, we found that an increase in the APS on the third ICU day of the sickest patients identifies a group of patients whose short-term and long-term prognoses are dismal. Such information can be useful for physicians as well as for patients and family members during the decision-making process regarding potentially ineffective care. Future, prospective multicenter studies should validate our findings and evaluate the impact of incorporating such data on avoiding futile ICU interventions.


   Footnotes
 
Abbreviations: APACHE = acute physiology and chronic health evaluation; APS = acute physiology score; IQR = interquartile range

Supported by the Anesthesia Clinical Research Unit and Pulmonary and Critical Care Division Research Fund, Mayo Clinic and Foundation.

Received for publication February 17, 2004. Accepted for publication May 28, 2004.


   References
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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