|
|||||||||
|
|
|
|
|
|
|
|
|
|
|
CLINICAL OBSERVATIONS, INTERVENTIONS, AND THERAPEUTIC TRIALS
From the Department of Epidemiology and
Biostatistics, the Department of Anesthesiology and Critical Care, and
the Department of Medicine, Memorial Sloan-Kettering Cancer Center, the
Department of Medicine, The Mount Sinai Medical Center, New York, NY;
City of Hope National Medical Center, Duarte, CA; the Department of
Medicine, the University of Arkansas for Medical Sciences; and the John
L. McClellan Memorial Veterans Hospital, Little Rock, AK.
Patients who develop respiratory failure requiring mechanical
ventilation after hematopoietic stem cell transplantation (HSCT) have
very high mortality. Several investigators have identified prognostic
features that can be used to identify a subset of these patients who
are virtually certain to die, yet these have never been prospectively
assessed. The objectives of this study were to determine the accuracy
of published prognostic features for mortality and to determine the
survival of patients who recover from respiratory failure. A systematic
review of the literature was undertaken to identify reported poor
prognostic features and survival rates. The study validated the
reported poor prognostic features on a prospective, multicenter
inception cohort of 226 patients with respiratory failure requiring
mechanical ventilation after HSCT. The main outcome measures were
determination of a baseline probability of death, drawn from literature
review; likelihood ratio of mortality for each prognostic feature
determined from the validation cohort; conditional probability of death
in the presence of each feature; and 6-month survival of those
who recover. Patients requiring mechanical ventilation after HSCT have
a baseline probability of death of 82% to 96%. In the setting of
combined hepatic and renal dysfunction, the probability of death rises to 98% to 100%. Other previously reported prognostic features are
less strongly associated with mortality. For patients who recover from
respiratory failure, the proportion surviving 6 months or longer ranges
from 27% to 88%. It was concluded that in patients requiring
mechanical ventilation after HSCT, the presence of combined hepatic and
renal dysfunction is highly predictive of death. The presence of this
feature may justify the recommendation to withdraw life-sustaining measures.
(Blood. 2001;98:3234-3240) Autologous and allogeneic hematopoietic stem cell
transplantations (HSCTs) are frequently performed procedures with a
variety of indications. Unfortunately, 15% to 30% of HSCT patients
develop respiratory failure that requires mechanical ventilation (MV). Once this complication occurs, death ensues in 85% to 90% of the cases.1-5 Because HSCT is often performed on young
patients with curative intent, advance directives are frequently not
available when respiratory failure develops. In these circumstances,
patients and family members agonize over whether to withhold or
withdraw life-sustaining measures, and physicians are often asked to
guide these difficult decisions.
A number of investigators have supplemented this decision-making
process by identifying prognostic features that are associated with a
very high likelihood of death. The benefits of these investigations are
apparent for both providers and families: the knowledge that death is
inevitable, or even highly likely, makes the irrevocable decision to
withdraw support easier.6,7 A few researchers have also
extended this argument to suggest that the decision to withdraw care
can be made unilaterally in patients with certain of these prognostic
features.8-10
At present, none of the features that have been reported to be
associated with a poor prognosis have been prospectively validated. There are reasons why these prognostic features may not be as strongly
predictive of mortality when applied to a naïve cohort as they
are in the study cohorts from whence they were originally derived In this paper, we adopt a Bayesian approach to answering the 2 questions that form the cornerstones of this discussion.17 First, can we identify a subset of patients who are virtually certain
to die based on the presence of certain poor prognostic features?
Second, what is the duration of survival of patients who do recover
from respiratory failure? In other words, if we were to withdraw
support from someone who would have survived if given maximal
treatment, how egregious would be our error?
To answer the first question, we systematically reviewed all studies
that assess the outcomes of patients who require MV after HSCT, and
abstracted the reported survival statistics and poor prognostic
features. We then developed a measure of the predictive power of each
prognostic feature using Bayesian principles, which are based on the
observation that diagnostic tests are imperfect: not all patients who
die have poor prognostic features (there are false- negative
results), and some patients who live have poor prognostic features
(there are false-positive results). According to Bayes
theorem, the probability of death in the presence of a particular poor
prognostic feature depends both on the test characteristics of the
prognostic feature and the probability that any patient requiring MV
after HSCT will die. To estimate the chance of long-term survival, we
summarized results from published reports and assessed the survival of
patients in our "validation cohort" of 226 patients treated at 5 centers.
Literature review
Abstraction of cohort characteristics and outcomes
Abstraction and reporting of poor prognostic features Within each report, we sought poor prognostic features that had been assessed for their association with short-term mortality. We specifically sought analyses of categorical predictors (alone, or in combination) that had been reported in a manner enabling calculation of standard descriptive statistics, such as false-positive rate.18 We then identified prognostic features that warranted further evaluation based on 3 criteria. First, the feature had to be statistically associated with death in the original report (ie, 2-sided; P < .05). Second, the feature had to be relevant to at least 25% of patients. Third, the feature had to be objectively determined, and defined in a manner that would allow for the feature to be assessed in other settings.We report the following attributes determined from the original
studies: the proportion of patients who had the prognostic feature (the
prevalence), and the proportion of patients with the
prognostic feature who died (positive predictive value).
Some investigators presented only percentages, some presented only graphical data, and one presented data in the form of a nested case-control study. In these cases, we determined the relevant attributes using appropriate techniques. In addition, in some reports
individual patients appeared multiple times within a cohort Validation cohort The data collection procedure and study population have been previously described.19 In brief, data were obtained on all patients who required intensive care within one year of receiving an infusion of hematopoietic progenitor cells harvested either from bone marrow or from peripheral blood after receiving myeloablative treatment (chemotherapy and/or radiation). These patients received care at one of 5 tertiary care hospitals during the time period July 1, 1994, to June 23, 1997. The study was approved by the institutional review boards of all participating sites: Memorial Sloan-Kettering Cancer Center, New York, NY; City of Hope National Medical Center, Duarte, CA; The University of Texas M. D. Anderson Cancer Center, Houston, TX; Mount Sinai Medical Center, New York, NY; and The Johns Hopkins Oncology Center, Baltimore, MD.Demographic, clinical, laboratory, and physiologic variables were obtained within one hour, at 24 hours, and at 72 hours after ICU admission. Vital status at discharge from both the ICU and the hospital were recorded. Events after hospital discharge were ascertained from medical records at each institution. Estimating the probability of mortality in the presence of a poor prognostic feature The probability of death in the presence of each of the poor prognostic features (the posttest probability), is determined through combining the overall probability of death (the prior probability) and the incremental probability of death conferred by having the poor prognostic feature (the likelihood ratio). The likelihood ratio equals the true-positive rate divided by the false positive rate. In this study, we determined the posttest probability by combining the pretest mortality probabilities from the systematic review with the likelihood ratio for each prognostic feature as determined from the validation cohort. Although Bayes formula requires transforming the probabilities into odds (as shown below), we present all of our results as probabilities for clarity.20
In estimating likelihood ratios using the validation cohort,
only the initial admission requiring MV for each patient was analyzed. In most cases, the reported prognostic features could be
tested using the exact criteria suggested by the authors. We constructed definitions that closely matched those that could not be
tested directly: the features described by Rubenfeld et al3
and Denardo et al,21 and the Acute Physiology and Chronic Health Evaluation II score (APACHE II)22 (Table
1). We assessed all other plausible
definitions in these cases as well, and for none did we see
substantively different results. The poor prognostic features presented
by Price et al were derived on a subset of patients at one of the
centers also involved in our multicenter study (The University of Texas
M. D. Anderson Cancer Center, Houston, TX).23 These
features were assessed only on patients from the other 4 centers. If
patients had insufficient data to assess a predictor, they were
eliminated from the analysis. In estimating the pretest probability of
death, we used a range rather than a point estimate in order to obtain
a generalizable result. The range of prior probabilities used in this
article reflects the span of reported mortality rates from all
retrieved studies with 2 caveats: we excluded the most extreme result
at each end of the distribution, and we collapsed the results of Price
et al's study into the mortality estimate from the validation cohort
for the reasons cited above.23
Statistical analysis Date of last follow-up in the medical record was chosen as the censoring date for the validation cohort. Follow-up was complete on all patients for a minimum of 200 days. Therefore, the 6-month survival estimate is exact; the median survival estimate uses the Kaplan-Meier method.24 Prognostic features reported as associated with mortality in the original studies were assessed using the Fisher exact statistic.25 Likelihood ratios and prior probabilities are combined using the odds-likelihood form of Bayes formula as described above.20 All P values quoted are 2-sided, and a level less than .05 is considered significant. All analyses were performed using Stata 6.0 (Statacorp, College Station, TX).
Systematic review of the literature The initial search of the literature yielded 61 studies, and a search of their reference lists yielded 2 additional articles. We evaluated these studies and eliminated letters,2 case reports and case series,10 studies that only addressed more general ICU populations,6 review articles or editorials,7 studies that only addressed specific clinical syndromes or treatments,14 and studies that focused on children,9 leaving 15 studies. The characteristics of patients included in these studies are listed in Table 2. Across these studies there is considerable heterogeneity in age and sex distribution, in the type of HSCT studied, and in the time interval between HSCT and the requirement for MV. The majority of the studies focus on patients undergoing HSCT for a hematologic malignancy or disorder, although many include small numbers of patients undergoing HSCT as a component of treatment for a solid tumor. Not shown in the table are that 26 (54%) of the patients in Price et al's study, and 8 (47%) of the patients in Shorr et al's study received stem cells harvested from peripheral blood, rather than from bone marrow.23,33
Validation cohort The 226 patients in the validation cohort drawn from 5 medical centers are described in Table 3. These patients have a somewhat higher mean age than the patients in the prior studies. The cohort contains both patients who underwent allogeneic (71%) and autologous (29%) HSCT. In the vast majority of the cases (88%), the indication for HSCT was for a hematologic malignancy or disorder, similar to that seen in the prior studies.
Survival of patients requiring MV after HSCT: determining the pretest probability of death Fifteen studies reported (and we calculated for the validation cohort) short-term survival of patients requiring MV after HSCT. Survival rates ranged from 40% to 2% (Table 4). Based on these results, we discarded one value at each end of the extreme, and incorporated the estimate from Price et al's23 subset of the validation cohort into the validation set, yielding an estimate of the pretest probability of death ranging from 82% to 96%. This range includes the point estimates from 12 of the 15 studies, the mortality rate of 86% that was observed in the validation cohort, and the mortality rate reported by Staudinger et al.26
Reported poor prognostic features Investigators reported a variety of features as predictive of a poor prognosis. The features that were reported with sufficient detail to calculate test characteristics are shown in Table 5. Investigators reported 3 clinical variables as strongly predictive of mortality: requiring mechanical ventilation for 4 days or longer,21 requiring mechanical ventilation more than 30 days after HSCT,27 and requiring mechanical ventilation less than 90 days after HSCT. This latter predictor was reported in 2 studies, one with calculable statistics8 and one without.28 Two studies reported that age group was predictive of mortality aged 21 or older
in one study,2 and aged 40 or older in another
study.8 Age was not predictive of mortality in the studies
of Denardo et al,21 Afessa et al,29 Paz et
al,30 or Huaringa et al.27 Examining a subset
of patients from the validation cohort, Price et al reported that
receipt of stem cells of bone marrow origin rather than peripheral origin was associated with a poor prognosis, as was having a high respiratory rate at the time of admission.23
Five studies focused on physiologic measures. Jackson et
al31 and Rubenfeld and Crawford3 both reported
that combined measures of hepatic, renal, and circulatory dysfunction
were predictive of death. Jackson et al31 presented only a
multivariable analysis, whereas Rubenfeld and Crawford3
presented data sufficient for calculation (Table 5). Paz et
al30 and Shorr et al33 using APACHE II, and
Epner et al32 using APACHE III, reported an association between these measures of physiologic derangement and mortality. Shorr
reported that patients with an APACHE II score Bayesian assessment of reported poor prognostic features Using the likelihood ratios as determined on the validation cohort (Table 6), we found that the presence of simultaneous hepatic dysfunction (bilirubin > 68 µmol/L or 4 mg/dL) and renal dysfunction (serum creatinine > 177 µmol/L or 2 mg/dL) as defined by Rubenfeld and Crawford3 was associated with the highest posttest probability of death. In our validation cohort, all 51 patients with this prognostic feature died, yielding an infinite likelihood ratio and an incalculable posttest probability. Given this limitation, we assessed this characteristic as if it were a little less predictivethat is, that one of the patients with this characteristic
had actually survived. This approach led to a likelihood ratio of 9.48, and a posttest probability of death ranging from 98% (when the
baseline mortality rate was 82%) to 100% (when the baseline mortality
rate was 96%).
We reanalyzed this result after incorporating the information that was
lost from the 19 patients (8% of validation cohort) who had missing
data, and who were therefore eliminated from the primary analysis. All
19 of these patients died, ruling out the possibility that these
patients may have increased the false positive rate. Considering the
most extreme alternative Some of the other reported poor prognostic features are also associated
with an increased probability of death, such as requiring MV for more
than 4 days Long-term survival Seven studies reported the 6-month survival rates for the minority of patients who recovered from respiratory failure, with results ranging from 27% to 88% (Table 4). Of the 32 patients in the validation cohort who survived to discharge from the hospital, 20 (63%) were alive at 6 months (9% of entire cohort). Median survival for those who recovered was reported by Rubenfeld and colleagues3 as 634 days (21 months), and median survival for recovering patients in the validation cohort was 451 days (15 months). One study we found reported that 6 of 7 patients who recovered and survived more than one year rated their quality of life as "good."34
The vast majority of patients who develop respiratory failure after hematopoietic stem cell transplantation do not survive. In this study, we asked 2 questions in order to determine when it might be appropriate for physicians to recommend withdrawing life-sustaining care from these patients. First, based on available clinical information, are there patients for whom we can know that death is virtually certain, thus allowing us to use clinical features to guide end-of-life decisions? Second, what is the survival rate of those patients who recover from respiratory failure? We found that the presence of simultaneous elevation of bilirubin (> 68 µmol/L or 4 mg/dL) and serum creatinine (> 177 µmol/L or 2 mg/dL) is highly predictive of death in patients requiring MV after HSCT. After assuming a false positive rate for this characteristic, we estimated that the probability of death in the presence of this characteristic is 98% to 100%. Supporting this finding is the observation that approximately 383 (98%) of the 391 patients with this characteristic in Rubenfeld and Crawford's original study died.3 We did not find that any of the other 10 reported prognostic features were as reliably associated with death. In order to make a recommendation based on this finding, we considered the possible consequences of withdrawing support from a patient who would have survived otherwise. If the consequences of premature withdrawal of MV are minimal because prognosis is poor regardless, clinicians and family members can agonize less about these decisions. On the other hand, if those patients who do recover from respiratory failure have reasonable long-term survival, the recommendation to withdraw MV should only be made at a very high threshold of certainty about death. We found that for those subjects who do recover, survival is considerable, and quality of life may be good. This finding underscores the importance of our first aim, identifying prognostic features that are highly predictive of death. There are several limitations to our assessment of the predictive power of these prognostic features. First, we did not collect data in precisely the same manner as the authors of some other studies, limiting our ability to evaluate their findings. Second, we cannot determine whether the limited predictive ability of some of the previously published prognostic features is due to differences between cohorts (ie, heterogeneity), inaccurate estimates of predictive power due to multiple analyses, or changes in critical care technologies. Third, we constrained ourselves to the cutpoints of continuous variables presented by other authors. In some cases, the same variables when dichotomized differently might have been more strongly predictive of mortality. Lastly, the predictive power of the features that we assessed may change with advances in conditioning regimens, stem cell preparation, prophylaxis against infection, and supportive care technologies. There are also some limitations to our assessment of long-term survival. Although we report that survival after recovery is substantial, due to sample size limitations we are unable to reliably estimate the long-term survival of patients with the poor prognostic features, leaving open the possibility that these patients experienced substantially shorter survival after recovery than did patients without these features. In addition, we cannot determine the quality of life experienced by those surviving, nor do we have data on what proportion of surviving patients were free of relapse. Several authors have advocated using certain poor prognostic features to identify those patients for whom the recommendation to withdraw or withhold support should be made. Others, after evaluating the outcomes of these patients, have suggested that the available evidence may support unilaterally limiting or withdrawing life-sustaining care in certain identifiable subsets of patients.3,6-9 The reasoning behind these recommendations is understandable: ICU beds are typically scarce, lengths of stay for this condition are protracted and costly, and prolonged critical illness is emotionally exhausting for patients and their family members. Due to the irrevocable nature of the decision to withdraw support, and
the considerable survival we demonstrated for those patients
who recovered, we conclude that most of the prognostic features that we
review here are not predictive enough to support their use in
end-of-life decision-making. The criterion of combined hepatic
and renal dysfunction proposed by Rubenfeld and
colleagues3 is the notable exception, and appears to
identify patients with a very high probability of death
We are indebted to Alvin I. Mushlin, MD, and Ennapadam S. Venkatraman, PhD, for their recommendations and assistance with the analyses, and to the research team led by Kristen J. Price, MD, and Peter F. Thall, PhD, at the University of Texas, M. D. Anderson Cancer Center, for their contribution of subjects and their review of earlier versions of this manuscript.
Submitted February 28, 2001; accepted July 26, 2001.
Supported by grants K23 CA 86968 (P.B.B.) and PO1 CA 23766 (J.W.Y.) from The National Cancer Institute, Bethesda, MD.
The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked "advertisement" in accordance with 18 U.S.C. section 1734.
Reprints: Peter B. Bach, Health Outcomes Research Group, Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Box 221, New York, NY 10021; e-mail: bachp{at}mskcc.org.
1. Crawford SW, Peterson FB. Long-term survival from respiratory failure after marrow transplantation for malignancy. Am Rev Respir Dis. 1992;145:510-514[Medline] [Order article via Infotrieve]. 2. Crawford SW, Schwartz DA, Peterson FB, Clark JG. Mechanical ventilation after marrow transplantation: risk factors and clinical outcome. Am Rev Respir Dis. 1988;137:682-687[Medline] [Order article via Infotrieve].
3.
Rubenfeld GD, Crawford SW.
Withdrawing life support from mechanically ventilated recipients of bone marrow tranplants: a case for evidence-based guidelines.
Ann Intern Med.
1996;125:625-633 4. Torrecilla C, Cortés JL, Chamorro C, Rubio JJ, Galdos P, Dominguez de Villota E. Prognostic assessment of the acute complications of bone marrow transplantation requiring intensive therapy. Intensive Care Med. 1988;14:393-398[CrossRef][Medline] [Order article via Infotrieve]. 5. Hollmig KA, Soehngen D, Leschke M, et al. Long-term survival of recipients of allogeneic bone-marrow transplantation after mechanical ventilation. Eur J Med Res. 1997;2:62-66[Medline] [Order article via Infotrieve]. 6. Crawford SW. Using outcomes research to improve the management of blood and marrow transplant recipients in the intensive care unit. New Horiz. 1998;6:69-74[Medline] [Order article via Infotrieve]. 7. Paz HL, Garland A, Weinar M, Crilley P, Brodsky I. Effect of clinical outcomes data on intensive care unit utilization by bone marrow transplant patients. Crit Care Med. 1998;26:66-70[CrossRef][Medline] [Order article via Infotrieve]. 8. Faber-Langendoen K, Caplan AL, McGlave PB. Survival of adult bone marrow transplant patients receiving mechanical ventilation: a case for restricted use. Bone Marrow Transplant. 1993;12:501-507[Medline] [Order article via Infotrieve].
9.
Caplan A.
Odds and ends: trust and debate over medical futility.
Ann Intern Med.
1996;125:688-689
10.
Nichols DJ, Maziarz RT, Haupt MT.
Mechanical ventilation in hematopoeitic stem cell transplant patients: is there need for reevaluation?
Chest.
1999;116:857-859 11. Wasson JH, Sox HC, Neff RK, Goldman L. Clinical prediction rules: applications and methodologic standards. New Engl J Med. 1985;313:793-799[Abstract]. 12. Laupacis A, Sekar N, Stiell IG. Clinical prediction rules: a review and suggested modifications of methodologic standards. JAMA. 1997;277:488-494[Abstract]. 13. Charlson ME, Ales KL, Simon R, MacKenzie R. Why predictive indexes perform less well in validation studies. Is it magic or methods? Arch Intern Med. 1987;147:2155-2161[Abstract].
14.
Justice AC, Covinsky KE, Berlin JA.
Assessing the generalizability of prognostic information.
Ann Intern Med.
1999;130:515-524 15. Heckerling PS, Conant RC, Tape TG, Wigton RS. Reproducibility of predictor variables from a validated clinical rule. Med Decis Making. 1992;12:280-285. 16. Godfrey K. Comparing the means of several groups. In: Bailar JCI,Mosteller F, eds. Medical Uses of Statistics. Boston, MA: NEJM Books; 1992:233-259. 17. Pauker SG, Kassirer JP. Decision analysis. In: Bailar JC,Mosteller F, eds. Medical Uses of Statistics. Boston, MA: NEJM books; 1992:159-179. 18. Selvin S. Statistical analysis of epidemiologic data. New York: Oxford University Press; 1996. 19. 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]. 20. Sox HC, Blatt MA, Higgins MC, Marton KI. Medical decision making. Stoneham, MA: Butterworth-Heinemann; 1988. 21. Denardo SJ, Oye RK, Bellamy PE. Efficacy of intensive care for bone marrow transplant patients in respiratory failure. Crit Care Med. 1989;17:4-6[Medline] [Order article via Infotrieve]. 22. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818-829[Medline] [Order article via Infotrieve].
23.
Price KJ, Thall PF, Kish SK, Shannon VR, Andersson BS.
Prognostic indicators for blood and marrow tranplant patients admitted to an intensive care unit.
Am J Respir Crit Care Med.
1998;158:876-884 24. Collett D. Modelling survival data in medical research. London, England: Chapman and Hall; 1994. 25. Agresti A. Categorical Data Analysis. New York: John Wiley and Sons; 1990. 26. 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[CrossRef][Medline] [Order article via Infotrieve]. 27. Huaringa AJ, Leyva FJ, Girald SA, et al. Outcome of bone marrow transplantation patients requiring mechanical ventilation. Crit Care Med. 2000;28:1014-1017[CrossRef][Medline] [Order article via Infotrieve]. 28. Ewig S, Torres A, Riquelme R, et al. Pulmonary complications in patients with hematological malignancies treated at a respiratory ICU. Eur Respir J. 1998;12:116-122[Abstract]. 29. 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] [Order article via Infotrieve].
30.
Paz HL, Crilley P, Weinar M, Brodsky I.
Outcome of patients requiring medical ICU admission following bone marrow transplantation.
Chest.
1993;104:527-531 31. Jackson SR, Tweeddale MG, Barnett MJ, et al. Admission of bone marrow transplant recipients to the intensive care unit: outcome, survival, and prognostic factors. Bone Marrow Transplant. 1998;21:697-704[CrossRef][Medline] [Order article via Infotrieve]. 32. Epner DE, White P, Krasnoff M, Khanduja S, Kimball KT, Knaus WA. Outcome of mechanical ventilation for adults with hematologic malignancy. J Investig Med. 1996;44:254-260[Medline] [Order article via Infotrieve].
33.
Shorr AF, Moores LK, Edenfield WJ, Christie RJ, Fitzpatrick TM.
Mechanical ventilation in hematopoietic stem cell transplantation: can we effectively predict outcomes?
Chest.
1999;116:1012-1018 34. Yau E, Rohatiner AZS, Lister TA, Hinds CJ. Long term prognosis and quality of life following intensive care for life-threatening complications of hematologic malignancy. Br J Cancer. 1991;64:938-942[Medline] [Order article via Infotrieve].
© 2001 by The American Society of Hematology.
| ||||||||||
 |
|
 |
|
  F. Pene, C. Aubron, E. Azoulay, F. Blot, G. Thiery, B. Raynard, B. Schlemmer, G. Nitenberg, A. Buzyn, P. Arnaud, et al. Outcome of Critically Ill Allogeneic Hematopoietic Stem-Cell Transplantation Recipients: A Reappraisal of Indications for Organ Failure Supports J. Clin. Oncol., February 1, 2006; 24(4): 643 - 649. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Parimon, D. K. Madtes, D. H. Au, J. G. Clark, and J. W. Chien Pretransplant Lung Function, Respiratory Failure, and Mortality after Stem Cell Transplantation Am. J. Respir. Crit. Care Med., August 1, 2005; 172(3): 384 - 390. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. B. Bach, E. B. Elkin, U. Pastorino, M. W. Kattan, A. I. Mushlin, C. B. Begg, and D. M. Parkin Benchmarking Lung Cancer Mortality Rates in Current and Former Smokers Chest, December 1, 2004; 126(6): 1742 - 1749. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Kluge, A. Meyer, P. Kuhnelt, H. J. Baumann, and G. Kreymann Percutaneous Tracheostomy Is Safe in Patients With Severe Thrombocytopenia Chest, August 1, 2004; 126(2): 547 - 551. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Fukuda, R. C. Hackman, K. A. Guthrie, B. M. Sandmaier, M. Boeckh, M. B. Maris, D. G. Maloney, H. J. Deeg, P. J. Martin, R. F. Storb, et al. Risks and outcomes of idiopathic pneumonia syndrome after nonmyeloablative and conventional conditioning regimens for allogeneic hematopoietic stem cell transplantation Blood, October 15, 2003; 102(8): 2777 - 2785. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. B. Bach, M. W. Kattan, M. D. Thornquist, M. G. Kris, R. C. Tate, M. J. Barnett, L. J. Hsieh, and C. B. Begg Variations in Lung Cancer Risk Among Smokers J Natl Cancer Inst, March 19, 2003; 95(6): 470 - 478. |
Top
Abstract
Introduction
29 had a 100%
mortality. The Apache II score was not predictive of mortality in the
study by Huaringa et al.