American Journal of Critical Care. 2006;15: 47-53
Copyright © 2006 by the American Association of Critical-Care Nurses.
Prognostic Accuracy of Acute Physiology and Chronic Health Evaluation II Scores in Critically Ill Cancer Patients
By
Lilu Chang, RN, MSN,
Cheng-Fang Horng, MS,
Yuh-Chin T. Huang, MD, MHS and
Yen-Yau Hsieh, MD.
From
Departments of Nursing (LC), Research (C-FH), and Medicine (Y-YH), Koo Foundation Sun Yat-Sen Cancer Center, Taipei, Taiwan, and Department of Medicine, Duke University Medical Center, Durham, North Carolina (Y-CTH).
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Abstract
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• Background The predictive accuracy of scores on the Acute Physiology and Chronic Health Evaluation II (APACHE II) for in-hospital mortality among critically ill cancer patients varies.
• Objective To evaluate the predictive accuracy of APACHE II scores for severity of illness in critically ill cancer patients and to find clinical indicators to improve the accuracy.
• Methods Actual hospital mortality rates were compared with predicted rates. Data were collected prospectively from 1263 cancer patients admitted to the intensive care unit during a 5-year period in a cancer center in Taiwan. The APACHE II score for each patient was calculated at admission. Stepwise logistic regression was used to identify clinical predictors associated with increased mortality.
• Results The scores ranged from 2 to 54. The mortality rates were 19% overall, 45% for medical patients, and 1% for surgical patients. The fit of the scores was good for the medical patients (Hosmer-Lemeshow statistic 8.2, P = .41). The estimated odds ratios for mortality of presence of metastasis and respiratory failure were 4.18 (95% CI 2.65–6.59) and 2.03 (95% CI 1.22–3.38), respectively. When metastasis and respiratory failure were incorporated into the APACHE II model, the area under the receiver operating characteristic curve for medical patients increased from 0.82 to 0.86. The fit of the modified model was excellent (Hosmer and Lemeshow statistic 6.57, P=.58).
• Conclusions APACHE II scores are predictive of hospital mortality in critically ill cancer patients. The presence of metastasis and respiratory failure at admission are also associated with outcome.
Although the number of patients with newly diagnosed cancers has increased each year, from 1990 to 1997, the cancer mortality rate declined by 0.8%.1 The reduction of mortality can be attributed in part to better screening, which allows early detection of cancer, and to the development of innovative and effective therapeutic regimens, which lead to long-term remission and even cure.1 The advancements in comprehensive supportive care have also allowed patients to better tolerate more aggressive therapies. A large part of such comprehensive care is delivered in the intensive care unit (ICU).
The impact of ICU care on the outcome of cancer patients, however, is not yet well evaluated. The prognosis of cancer patients who become critically ill and need intensive care is poor, with mortality rates of 40% to 80%.2,3 The discrepant results of past studies may be due to differences in the types and stages of cancers included, patients’ comorbid conditions, and the effectiveness of therapies for underlying cancer and nonmalignant conditions. For example, cancer patients with neutropenia who are admitted to ICUs have the worst prognosis of all cancer patients.2,4
| The prognosis of cancer patients requiring intensive care is poor, with mortality rates of 40% to 80%.
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Various prognostic scoring systems have been developed to estimate in-hospital mortality for patients admitted to the ICU to help physicians determine which types of patients might benefit from ICU care.5–7 One of the most widely used scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE) II,8 provides accurate estimates of the probability of in-hospital mortality for ICU patients.9–15 APACHE II scores have also been advocated as a measurement of quality of ICU care by providing an adjustment for severity of disease and case mix.16,17
The applicability of the APACHE II to critically ill cancer patients, however, is not known. On the basis of APACHE II scores, mortality in a group of patients with hematologic malignant neoplasms who had bone marrow transplantation was underestimated.18 On the other hand, APACHE II scores were accurate predictors of prognosis in patients with hematologic malignant neoplasms who had granulo-cytopenia19 and in patients who had breast cancer.20 In a recent study,21 neither APACHE II scores nor values on the Simplified Acute Physiology Score II were accurate enough to be predictive of mortality in a group of cancer patients admitted to the ICU because of acute illnesses. The number of patients in these studies,18–21 however, was relatively small. In a study3 of a cohort of cancer patients, use of other scoring systems, such as the Mortality Prediction Model II, resulted in significant underestimates of mortality.
The APACHE II system has been used for evaluating severity of illness for ICU patients in most of the ICUs in Taiwan. However, this tool has not been evaluated for its predictive accuracy in ICU patients in Taiwan or other Asian countries. None of the general ICU prognostic scoring systems have been validated in a large group of cancer patients. Therefore, the purpose of this study was to evaluate the validity and predictive accuracy of APACHE II scores by comparing actual in-hospital mortality with predicted mortality for cancer patients admitted to the ICU at the Koo Foundation Sun Yat-Sen Cancer Center in Taiwan. We also sought to identify clinical predictors that might improve the predictive model.
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Methods
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Sample
Data were collected prospectively from consecutive patients admitted to the combined medical and surgical ICU in the cancer center between January 1, 1996, and December 31, 2000. Patients who did not have a malignant neoplasm or who were less than 12 years old were excluded. The patients were classified as either medical or surgical on the basis of their admission to the ICU.
APACHE II Score
The APACHE II score for each patient was determined at the time of admission by ICU nurses trained in using this instrument. The 12 variables used to calculate the APACHE II score are blood pressure, heart rate, respiratory rate, body temperature, serum level of sodium, serum level of potassium, serum level of creatinine, arterial pH, alveolar-arterial oxygen gradient, hematocrit, white blood cell count, and score on the Glasgow Coma Scale. The APACHE II scores were calculated from the sum of weighted points representing the extent of physiological derangements (acute physiology score) and points based on the patient’s age and chronic illness. The worst acute physiology scores recorded during the initial 24 hours of the ICU stay were used.
All APACHE II scores were reviewed by 2 additional trained nurses before the data were entered into the database for analysis. The interrater reliability was more than 99%. The APACHE II score, the diagnostic category weighing system, and the logistic regression formula developed by Knaus et al8 were used to calculate predicted mortality, and then the predicted mortality was compared with the actual in-hospital mortality.
Other clinical variables recorded included diagnosis at the time of ICU admission, ICU length of stay for each patient, do-not-resuscitate status, underlying malignant neoplasm, and the presence of metastasis.
Statistical Analysis
The Hosmer-Lemeshow goodness-of-fit test was used to compare mortality predicted on the basis of the APACHE II scores with actual mortality. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative power of the APACHE II scoring system on in-hospital mortality.22,23 The area under each ROC curve was used as a measure of overall predictive accuracy. An area under the ROC curve greater than 70% is generally considered evidence of good predictive value.24,25 Stepwise logistic regression was also used to detect other clinical predictors (eg, sex, metastasis, shock, respiratory failure) of mortality in medical patients. The significant clinical predictors were also added to the APACHE II predictive model to determine how these factors improved the predictability. A P value less than .05 was considered statistically significant. All statistical analyses were performed by using SAS software.26
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Results
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Characteristics of the Sample
A total of 1263 patients were included in the study. The cancer diagnoses of the patients are listed in Table 1
. A total of 465 patients (36.8%) had metastatic or recurrent diseases. A total of 410 patients were female (32.5%), and 853 were male (67.6%). The mean age was 56 years (range 13–92 years).
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Table 1 Cancer patients admitted to the intensive care unit, categorized by type of malignant neoplasm and nature of admission (medical vs surgical)
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The sample included 522 medical patients (41.3%) and 741 surgical patients (58.7%). Among the medical patients, 35.1% had cancer of the gastrointestinal tract, 15.5% had lung cancer, and 13.4% had cancer of the head and neck. Among the surgical patients, 37.9% had cancer of the gastrointestinal tract, 27.1% had head and neck cancer, and 11.6% had urological and male genital cancer. Other details of the distribution are found in Table 1
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ICU Utilization
The main indications for ICU admissions are given in Table 2. For medical patients, the main indications were sepsis with or without shock (34.5%), respiratory failure (20.3%), hypovolemic shock (9.6%), and hemorrhage of the gastrointestinal tract (9.4%). The mortality rates for each these were 43%, 67%, 38%, and 33%, respectively.
The mean ICU length of stay for all admissions was 2.97 days (median 1, range 1–76). For medical patients, the mean ICU length of stay was 4.14 days (median 2, range 1–76) for those who survived to discharge from the hospital and 3.94 days (median 2, range 1–64) for those who died in the hospital. For surgical patients, the mean ICU length of stay was 2.12 days (median 1, range 1–25) for those who survived to discharge from the hospital and 9.75 days (median 2.5, range: 1–42) for those who died in the hospital. Patients who had metastatic or recurrent diseases did not have longer ICU stays (mean 3.3 days, SD 5.75, median 1) than those who did not have such diseases (mean 2.8 days, SD 4.49, median 2; P = .09).
APACHE II Scores
The APACHE II scores ranged from 2 to 54. Figure 1 shows the distribution of scores for both groups of patients. The majority of surgical patients (68%) had APACHE II scores between 5 and 14; the scores for the medical patients were distributed more evenly across all scoring categories. Mean APACHE II scores were 21.9 (SD 9.1) for medical patients and 11.6 (SD 5.6) for surgical patients. Patients who had metastatic or recurrent diseases had higher mean APACHE II scores at the time of ICU admission than did patients who did not have such diseases (18.7 vs 14.0; P < .001).
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Figure 1 Distribution of scores on the Acute Physiology and Chronic Health Evaluation II for medical and surgical cancer patients admitted to the intensive care unit.
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APACHE II Score and Mortality
The overall hospital mortality was 19%. The mortality rates were 45% for medical patients and 1% for surgical patients. Figure 2 shows the relationship between APACHE II scores and the observed hospital mortality rate for both groups of patients. Mortality rates when the APACHE II score was greater than 35 were 90% for medical patients and 50% for surgical patients.
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Figure 2 Relationship between scores on the Acute Physiology and Chronic Health Evaluation II and the observed in-hospital mortality for medical and surgical cancer patients admitted to the intensive care unit.
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| When the APACHE II score was greater than 35, medical patients had a 90% mortality rate, and surgical patients had a 50% mortality rate.
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The fit of the APACHE II system was good in cancer patients: Hosmer-Lemeshow statistic 9.8 and P=.28. The area under the ROC curve (Figure 3) was 0.82 for all patients, which indicates good discriminative power. The evaluations of the goodness-of-fit of APACHE II scores in the medical patients and surgical patients are given in Table 3 and Table 4, respectively, which show the correspondence between observed and expected mortality on the basis of the model probability. The fit of the system was good in the medical patients, with a Hosmer-Lemeshow statistic of 8.2 and P = .41, and in the surgical patients, with a Hosmer-Lemeshow statistic of 9.25 and P = .32.
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Figure 3 The area under the receiver operating characteristic curve is 0.82, which indicates good discriminative power.
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Table 3 Goodness-of-fit table for the Acute Physiology and Chronic Health Evaluation II system in medical patients
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Table 4 Goodness-of-fit table for the Acute Physiology and Chronic Health Evaluation II system in surgical patients
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Clinical Predictors of Mortality
To determine if additional clinical predictors would improve the APACHE II model in medical patients, we performed stepwise logistic regression with sex, the APACHE II score (cutoff at every 5 points), and presence of metastasis, shock, and respiratory failure as independent variables. The analysis is summarized in Table 5. Besides the APACHE II score, presence of metastasis and presence of respiratory failure were significantly correlated with mortality in the medical patients. Patients who had metastasis were more likely to die (odds ratio 4.18, 95% CI 2.65–6.59) as were those who had respiratory failure (odds ratio 2.03, 95% CI 1.22–3.38). When these 2 factors were incorporated into the APACHE II model, the area under the ROC curve for medical patients increased from 0.82 to 0.86; we used bootstrap samples to test the difference and the result was significant (P < .001). The evaluation of the goodness-of-fit of the modified APACHE II system in the medical patients is given in Table 6. The fit of the modified model was excellent, with a Hosmer and Lemeshow statistic of 6.57 and P = .58. The likelihood ratio test for the difference between the original system and the modified system (a test for the significance of metastasis and respiratory failure) yielded a value of 55.86.
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Table 5 Statistical summary of the logistic regression results for significant association between the clinical variables and in-hospital mortality in the medical group
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Discussion
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Cancer patients may require admissions to the ICU sometime during the course of treatment for critical illnesses related to the underlying malignant neoplasm or treatment. These patients tend to have high in-hospital mortality rates, from 40% to 80% depending on the cohort of patients studied.2–4,20,24,27–29 The outcome for these diverse ICU patients remains difficult to predict. Because admitting a cancer patient to an ICU is often a difficult decision, a simple system that has clinical significance would be useful to provide objective data or evidence to support the decision.
Several versions of the APACHE system, including APACHE II and APACHE III, have been used with variable success to predict outcomes in cancer patients. In a study30 of 414 cancer patients, APACHE III scores were higher in nonsurvivors than in survivors, but the scores were not predictive of individual outcomes. In a study27 of 112 recipients of hematopoietic stem cells, APACHE III scores had moderate discriminative power (area under the ROC curve 0.704) and good calibration for predicting hospital mortality. In an investigation31 of 120 patients with nonhematologic malignant neoplasms, an APACHE II score greater than 36 was predictive of high 30-day mortality. In a study20 of 52 breast cancer patients, APACHE II scores were independently associated with survival outcomes in patients admitted to the ICU. In another study32 of 84 patients with hematologic malignant neoplasms, however, APACHE II scores had no prognostic value. Other researchers4,14,18,19 also found that APACHE II scores in cancer patients admitted to the ICU resulted in underestimates of the probability of in-hospital mortality.
Our results indicated that APACHE II scores determined at the time of admission to the ICU can be used to estimate the probability of in-hospital mortality in cancer patients. In our study, the fit and power to discriminate prognosis were good. Surgical patients were admitted to the ICU mainly for postoperative care; at the time of ICU admission, they had temporary physiological derangements due to the effects of anesthesia. Therefore, it was not surprising that the use of APACHE II scores led to slight overestimates of mortality in this subgroup of patients. However, the fit of the model was still good, as indicated by the Hosmer-Lemeshow goodness-of-fit statistic.
Our results further indicated that the presence of metastasis and the presence of respiratory failure were associated with mortality independent of APACHE II scores in the medical patients. Presence of metastasis and presence of respiratory failure were associated with increases in mortality of approximately 4- and 2-fold, respectively. When these 2 factors were added to the APACHE II prediction model, the area under the ROC curve increased from 0.82 to 0.86. In addition, the likelihood ratio test for the difference between the original APACHE II and the modified instrument indicated that these 2 variables (metastasis and respiratory failure) added significance to the original model for the medical patients. We suggest incorporating these 2 variables to improve the predictive value of APACHE II scores for critically ill cancer patients with medical diagnoses. The modified predictive model also performed well when applied to several subgroups of medical patients, including patients with head and neck cancer, cancer of the gastrointestinal tract, lung cancer, breast cancer, and lymphoma.
| The presence of metastasis and respiratory failure were associated with increases in mortality of about 4-fold and 2-fold, respectively.
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Groeger et al3 developed a disease-specific, multivariable logistic regression model to estimate the probability of in-hospital mortality in critically ill cancer patients admitted to the ICU. Although that model and the APACHE II model share some common variables (respiratory rate, systolic blood pressure, score on the Glasgow Coma Scale, and alveolar-arterial gradient), some of the variables included in the model of Groeger et al (eg, intracranial mass effect and prothrombin time) may not be readily available at the time of ICU admission. Calculating an APACHE II score requires data on 12 readily measurable variables, and the score can be determined at the time of ICU admission. Compared with scores determined by using the model of Groeger et al, an APACHE II score provides an accurate and equivalent estimate of a cancer patient’s probability of in-hospital mortality (the area under ROC curve was 0.82 vs 0.80). The predictive accuracy of APACHE II scores was even better when 2 clinical variables, metastasis and respiratory failure, were added to the original APACHE II system.
| When metastasis and respiratory failure are included, the APACHE II scoring system provides an accurate estimate of a cancer patient’s in-hospital mortality.
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Reasons exist for managing some cancer patients in the ICU, such as postoperative recovery, intensive anticancer treatment, and monitoring and managing critical complications of the disease or its treatment. The decision to admit a cancer patient in these situations is often difficult for clinicians, because they have no objective data to support their decisions. The APACHE II can be a simple and useful tool to help determine which groups of cancer patients may benefit from ICU care, but the APACHE II score cannot be used to make decisions for individual patients. The APACHE II can also be a useful tool to define populations of patients for purposes of research or auditing.
Of note, the mean ICU stay of medical patients in our study who survived was short (4.14 days), suggesting that many of these patients had acutely reversible conditions. Physicians often hesitate to admit cancer patients to the ICU, especially when patients have signed do-not-resuscitate orders or are in a terminal stage. Further research comparing patients’ outcomes between patients who are treated in the ICU and those who are not, with the APACHE II scores for both groups, would be useful. Such data could support decisions on whether cancer patients are likely to benefit from maximal support in the ICU.
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Conclusions
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APACHE II scores determined at the time of admission to an ICU were predictive of in-hospital mortality in critically ill cancer patients. The presence of metastasis and respiratory failure at the time of admission were associated with patients’ outcome, and these 2 variables added significance to the original APACHE II model for prediction of hospital mortality in critically ill cancer patients admitted to the ICU because of medical indications. Either model, APACHE II scores alone or scores obtained when the variables of metastasis and respiratory failure were added, can be used at the time of ICU admission to estimate the probability of in-hospital mortality in cancer patients.
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ACKNOWLEDGMENTS
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The research was performed at Koo Foundation Sun Yat-Sen Cancer Center, Taipei, Taiwan. We thank all intensive care unit nurses at the center for their help with data collection, and we specially thank Ms Li-Chen Sheu for her great help with data management.
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.
Commentary by Mary Jo Grap (see shaded boxes).
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REFERENCES
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|---|
- American Cancer Society. Cancer Facts and Figures. American Cancer Society 2003.
- Guiguet M, Blot F, Escudier B, Antoun S, Leclercq B, Nitenberg G. Severity-of-illness scores for neutropenic cancer patients in an intensive care unit: which is the best predictor? Do multiple assessment times improve the predictive value? Crit Care Med. 1998;26:488–493.[Medline]
- Groeger JS, Lemeshow S, Price K, et al. Multicenter outcome study of cancer patients admitted to the intensive care unit: a probability of mortality model. J Clin Oncol. 1998;16:761–770.[Abstract]
- Lloyd-Thomas AR, Wright I, Lister TA, Hinds CJ. Prognosis of patients receiving intensive care for lifethreatening medical complications of haematological malignancy. Br Med J (Clin Res Ed). 1988;296:1025–1029.
- Knaus WA, Harrell FE Jr, Lynn J, et al. The SUPPORT prognostic model: objective estimates of survival for seriously ill hospitalized adults. Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments. Ann Intern Med. 1995;122:191–203.[Abstract/Free Full Text]
- Markgraf R, Deutschinoff G, Pientka L, Scholten T. Comparison of Acute Physiology and Chronic Health Evaluation II and III and Simplified Acute Physiology Score II: a prospective cohort study evaluating these methods to predict outcome in a German interdisciplinary intensive care unit. Crit Care Med. 2000;28:26–33.[Medline]
- Lemeshow S, Teres D, Pastides H, Avrunin JS, Steingrub JS. A method for predicting survival and mortality of ICU patients using objectively derived weights. Crit Care Med. 1985;13:519–525.[Medline]
- Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–829.[Medline]
- Knaus WA, Draper EA, Wagner DP, Zimmerman JE. An evaluation of outcome from intensive care in major medical centers. Ann Intern Med. 1986;104:410–418.[Abstract/Free Full Text]
- Bohnen JM, Mustard R, Oxholm SE, Schouten BD. APACHE II score and abdominal sepsis: a prospective study. Arch Surg. 1988;123:225–229.[Abstract/Free Full Text]
- Kruse JA, Thill-Baharozian MC, Carlson RW. Comparison of clinical assessment with APACHE II for predicting mortality risk in patients admitted to a medical intensive care unit. JAMA. 1988;260:1739–1742.[Abstract/Free Full Text]
- Larvin M, McMahon MJ. APACHE-II score for assessment and monitoring of acute pancreatitis. Lancet. 1989;2:201–205.[Medline]
- Raffin TA. Intensive care unit survival of patients with systemic illness. Am Rev Respir Dis. 1989;140(2 Pt 2):S28–S35.[Medline]
- Marsh HM, Krishan I, Naessens JM, et al. Assessment of prediction of mortality by using the APACHE II scoring system in intensive-care units. Mayo Clin Proc. 1990;65:1549–1557.[Medline]
- Glance LG, Osler TM, Papadakos P. Effect of mortality rate on the performance of the Acute Physiology and Chronic Health Evaluation II: a simulation study. Crit Med Care. 2000;28:3424–3428.
- Glance LG, Osler TM, Dick A. Rating the quality of intensive care units: is it a function of the intensive care unit scoring system? Crit Care Med. 2002;30:1976–1982.[Medline]
- Livingston BM, Mackirdy FG, Howie JC, Jones R, Norrie JD. Assessment of the performance of five intensive care scoring models within a large Scottish database. Crit Care Med. 2000;28:1820–1827.[Medline]
- Afessa B, Tefferi A, Hoagland HC, Letendre L, Peters SG. Outcome of recipients of bone marrow transplants who require intensive-care unit support. Mayo Clin Proc. 1992;67:117–122.[Medline]
- Johnson MH, Gordon PW, Fitzgerald FT. Stratification of prognosis in granulocytopenic patients with hematologic malignancies using the APACHE-II severity of illness score. Crit Care Med. 1986;14:693–697.[Medline]
- Headley J, Theriault R, Smith TL. Independent validation of APACHE II severity of illness score for predicting mortality in patients with breast cancer admitted to the intensive care unit. Cancer. 1992;70:497–503.[Medline]
- Sculier JP, Paesmans M, Markiewicz E, Berghmans T. Scoring systems in cancer patients admitted for an acute complication in a medical intensive care unit. Crit Care Med. 2000;28:2786–2792.[Medline]
- Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.[Abstract/Free Full Text]
- Pepe MS. Receiver operating characteristic methodology. J Am Stat Assoc. 2000;95:308–311.
- Sculier JP, Markiewicz E. Cardiopulmonary resuscitation in medical cancer patients: the experience of a medical intensive-care unit of a cancer center. Support Care Cancer. 1993;1:135–138.[Medline]
- Wagner DP, Knaus WA, Draper EA. Statistical validation of a severity of illness measure. Am J Public Health. 1983;73:878–884.[Abstract/Free Full Text]
- SAS Institute Inc. SAS/STAT, version 8. Cary, NC: SAS Institute Inc; 1999:chap 39.
- Afessa B, Tefferi A, Dunn WF, Litzow MR, Peters SG. Intensive care unit support and Acute Physiology and Chronic Health Evaluation III performance in hematopoietic stem cell transplant recipients. Crit Care Med. 2003;31:715–721.
- Hauser MJ, Tabak J, Baier H. Survival of patients with cancer in a medical critical care unit. Arch Intern Med. 1982;142:527–529.[Abstract/Free Full Text]
- Schapira DV, Studnicki J, Bradham DD, Wolff P, Jarrett A. Intensive care, survival, and expense of treating critically ill cancer patients. JAMA. 1993; 269:783–786.[Abstract/Free Full Text]
- Staudinger T, Stoiser B, Mullner M, et al. Outcome and prognostic factors in critically ill cancer patients admitted to the intensive care unit. Crit Care Med. 2000;28:1322–1328.[Medline]
- Azoulay E, Moreau D, Alberti C, et al. Predictors of short-term mortality in critically ill patients with solid malignancies. Intensive Care Med. 2000; 26:1817–1823.[Medline]
- Massion PB, Dive AM, Doyen C, et al. Prognosis of hematologic malignancies does not predict intensive care unit mortality. Crit Care Med. 2002;30:2260–2270.[Medline]
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Abstract
Methods
