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Journal of Clinical Oncology, Vol 26, No 15 (May 20), 2008: pp. 2538-2543
© 2008 American Society of Clinical Oncology.
DOI: 10.1200/JCO.2007.14.9518

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Assessing 2-Month Clinical Prognosis in Hospitalized Patients With Advanced Solid Tumors

Anne-Claire Barbot, Pascale Mussault, Pierre Ingrand, Jean-Marc Tourani

From the Palliative Care Support Team, Clinical Research Unit, Department of Oncology, Poitiers University Hospital, Poitiers, France

Corresponding author: Pierre Ingrand, MD, PhD, Clinical Research Center, Faculté de Médecine et de Pharmacie, 6 rue de la Milétrie, BP 199, 86005 Poitiers, Cedex, France; e-mail: pierre.ingrand{at}univ-poitiers.fr


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 Appendix: Patient Questionnaire
 REFERENCES
 
Purpose The aim of this study was to assess clinical, laboratory, and subjective (patient's preferences) prognostic factors in hospitalized patients with advanced solid tumors.

Patients and Methods This prospective study surveyed 177 patients from two French hospitals who had not reached the stage of active dying but had an estimated survival of less than 6 months (median survival, 58 days).

Results Univariate analysis showed that 10 of the 13 clinical and laboratory factors reported in the literature affected survival at 2 months. Poor prognostic factors were number of metastatic sites, cerebral metastasis, low Karnofsky index, dyspnea at rest, anorexia, edema, confusion, low serum albumin, extremely high leukocyte counts, and high lactate dehydrogenase (LDH) levels. The patient's desire to continue curative treatment was also associated with survival. The multivariate analysis selected four independent criteria: Karnofsky index (in three classes: ≤ 30%, 40% to 60%, or ≥ 70%), number of metastatic sites (≥ two or < two), low serum albumin (in three classes: ≤ 24, 24 to 33, and ≥ 33 g/L), and LDH concentration (≥ 600 IU or < 600 IU). The combination of these four criteria assessed prognosis better than the Karnofsky index alone, producing three prognostic profiles: one with short survival (< 2 months: no patient survived to 4 months); one with an expectation of intermediate survival (25% were alive at 4 months), and a final group surviving for several months (80% were alive at 4 months).

Conclusion The prognostic profiles defined by combinations of these four factors may be potentially useful but need further validation before their application in the daily practice.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 Appendix: Patient Questionnaire
 REFERENCES
 
Estimation of life expectancy of patients with serious diseases makes it possible to adapt their treatment plans and life projects to their individual disease phase. Accuracy in this difficult task requires that both the physician and the paramedical team have extensive experience. The literature shows that clinical survival is most often overestimated.1-6 This error sometimes results in treatment that is too aggressive. Although physicians are less often wrong in assessing short- (≤ 15 days) or long-term (> 6 months) survival, there is a substantial period of uncertainty during which better prognostic assessment could improve patient care.

Prognostic evaluation is based on different criteria in advanced-stage cancer than at earlier stages, when it depends mainly on the primary site and histologic grade.7,8 We studied a population of patients with advanced-stage cancer (all solid tumor types) who had not yet reached the stage of active dying that occurs in the last hours or days of life. Previous studies9-13 have shown the prognostic importance of functional, clinical, and laboratory criteria and combined them into scores. A weakness of the validated Palliative Prognostic score9,14 is its inclusion of a clinical prediction of survival. Although clinically useful, this clinician estimate yields a prediction that is inaccurate, consistently overestimated,5 subjective, and nonreproducible.15

The aim and originality of our work was to combine clinical and laboratory data with subjective data by taking into account the patient's desires and combativeness against the disease, as well as the physician's decision about whether the patient should be transferred to intensive care in case of acute aggravation. The interest of this study lies in its use of prognostic factors that can be applied by any physician, regardless of the initial cancer site, to guide the treatment decision, avoid unreasonable obstinacy,16 guide the psychosocial strategy, and set up supportive care.


    PATIENTS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 Appendix: Patient Questionnaire
 REFERENCES
 
Inclusion and Exclusion Criteria
Consecutive patients older than 18 years, hospitalized with an advanced-stage cancer (inoperable or metastasized solid tumor) in the departments of medicine or surgery at the Poitiers University Hospital (Poitiers, France) or the Niort Hospital (Niort, France) were considered for inclusion in the study when their physicians estimated their clinical survival at several days to 6 months, regardless of whether they were seen by a mobile palliative care team at this time. Patients with malignant blood diseases were not included in this study. Studies in which every test, procedure, and product is part of standard practice do not require formal institutional review board approval in France. The study was approved by the local medical departments, and participants provided informed verbal consent.

Data Collection
The clinical and subjective criteria retained after a review of the literature were collected at inclusion from the medical file and by patient examination. The laboratory data were collected at inclusion from a recent work-up (ie, performed within the previous week) or from the patient's next planned work-up (within the 8 days after inclusion). No blood samples were taken specifically for this study.

The following data were collected: number of metastatic sites17,18; existence of cerebral metastasis; Karnofsky performance score (KPS)3,4,9,19-21; weight loss7,19,22-25 (calculated and expressed as a percentage); clinical signs (anorexia,19,20,24 edema, dyspnea at rest,19,24,26,27 and confusion,13,22,24,27 all determined as present or absent at the clinical examination at inclusion); hemoglobin26,27; leukocyte count14,27 and percentage of T lymphocytes14,27,28; serum albumin14,29,30; and lactate dehydrogenase (LDH).18,26,30 Laboratory data collection was limited to the measurements routinely collected in the two hospitals involved in the study; thus LDH values were collected, but not C-reactive protein.31 The subjective data we collected did not include the physician's clinical survival estimate, which is often overestimated, but we did analyze the patient's preferences.32 These were determined from a questionnaire at inclusion: the patient expressed a choice among four attitudes, ranging from very curative to very palliative. For the patients for whom an evaluation could not be performed (because of mental incapacity, coma, or refusal to respond to the questionnaire), "impossible" was noted (see Appendix, online only). Referring physicians were also asked for a determination as to whether the patient should or should not be transferred to intensive care in case of acute problems.

Statistical Methodology
Statistical analysis was performed with SAS version 8.2 for Windows (SAS Institute, Cary, NC). Survival data were treated according to the Kaplan-Meier method and summarized by the cumulative 2-month survival rate with its 95% CI and by the median duration of survival.

For each variable, a preliminary analysis considered the frequency of missing data and looked for any possible information bias associated with the different frequencies of missing data between patients who did and did not survive through 2 months (Fisher's exact test). If this analysis showed a significant difference, bias was highly suspected and the variable was excluded from further analysis.

In the absence of missing data or notable bias, the univariate analysis compared percentages (qualitative variables) with Pearson's {chi}2 test for independent series or with Fisher's exact test when appropriate. Means were compared with the Mann-Whitney U test. Continuous variables were categorized into two or three classes. Cutoff values were identified from an exploratory receiver operating characteristic analysis of 2-month survival, with SAS Proc Logistic (SAS Institute). Candidate cutoffs and resulting classes were tested in univariate Cox survival regression analyses, with SAS Proc Phreg (SAS Institute) and the likelihood-ratio test, to retain the minimum number of discriminating classes.

Multivariate analysis of survival with a Cox model retained the variables significant at a P value of .15 after the univariate analysis. Variables were selected to enter the model after a forward stepwise selection procedure at the .05 significance level in Wald's test. The proportional hazards assumptions were graphically checked and tested with martingale residuals. Finally, a multivariate logistic regression of survival at 2 months tested whether these results could be used to develop a prognostic score applicable in clinical settings. A clinical prediction rule was derived from a simple linear combination of the final logistic regression estimates; integer-rounded weights were associated with each categorized variable and summed to obtain a score ranging between 0 and 10. The distribution of survival as a function of these scores was analyzed to obtain groups with distinct survival probabilities.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 Appendix: Patient Questionnaire
 REFERENCES
 
From January 2004 through July 2004, we included 177 patients from two hospitals (Niort, n = 78; Poitiers, n = 99). Mean age was 62.6 years ± 13.5 years and ranged from 20 to 93 years. Men accounted for 56.5% of the sample. The most frequent primary sites were as follows: breast (13.6%), lung (10.7%), pancreas (10.2%), colon/rectum (9.6%), head and neck (7.9%), and prostate (7.3%). Nearly half the patients (49.2%) had more than two metastatic sites and 16.9% of the sample had a secondary cerebral site. More than half the population (53.5%) had received one or two lines of chemotherapy; 20% of patients had a KPS of 30% to 40%, and 70% of patients had a KPS ≥ 50%. Serum albumin levels were lower than 33 g/L in 75% of patients and lower than 24 g/L in 25% of patients. The mean leukocyte count was 10,064/mm3 ± 6,629/mm3. Dyspnea at rest was found in 17.5% of patients, edema was found in 15.8%, anorexia was found in 46.3%, and confusion was found in 13%. Median survival was 58 days, with a 95% CI of 47 to 80 days, and the 2-month survival rate was 49.2%. Missing data for lymphocyte counts and percentage of weight loss made it impossible to analyze these two criteria.

Table 1 lists the results of the univariate analysis. Age, sex, and hemoglobin were not significant factors in survival. Ten factors had a negative influence on survival: two or more metastatic sites, cerebral metastases, low KPS, low serum albumin, hyperleukocytosis (≥ 8,500/mm3), LDH ≥ 600 IU, dyspnea at rest, anorexia, confusion, and edema. The median duration of survival was 32 days in patients with two or more metastatic sites (v 119 days); 23 days in patients with cerebral metastasis (v 70 days); 14, 39, and 146 days for KPS ≤ 30%, 40% to 60%, and ≥ 70%, respectively; 30, 55, and 126 days when serum albumin was less than 24 g/L, between 24 and 33 g/L, and ≥ 33 g/L, respectively; and 28 days for LDH ≥ 600 IU (v 102 days).


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Table 1. Summary of the Univariate Prognostic Analysis

 
Patients' preferences were also closely related to survival. Patients preferring a very palliative attitude (C, D, or I, Appendix) survived for less time (Table 2), with a median survival of 22 days versus 54 days for patients with reasonably curative expectations, and 126 days for those with very curative expectations. Continuing active treatment was most frequent in patients with very curative expectations (71% v 56% for reasonably curative, and 28% for palliative preference). When the referring physician recommended against transfer to intensive care, median survival was 43 days (v 146 days).


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Table 2. Patients' Preferences* and Survival at 2 Months

 
In the multivariate Cox analysis, four variables had an independent prognostic value: KPS, number of metastases, serum albumin, and LDH. Of the classes of serum albumin retained after the univariate analysis, only two were significant compared with the 33 g/L cutoff. Clinical symptoms and patient's preferences did not show independent prognostic value. The proportion of variance in survival33 explained by the patient's preference alone was 11.5% compared with 40.7% for the final Cox model. Good accuracy of the prognostic rule derived from the multivariate logistic regression of survival at 2 months was assessed by a 0.86 area under the receiver operating characteristic curve.

An integer ranging from –3 to +4 was attributed to each class of four variables according to its coefficient in the regression model. Together they defined a score from 0 to 10 (Table 3), with a high score associated with an unfavorable prognosis. The score distribution made it possible to construct three groups of patients with distinct prognoses (Fig 1): group A (n = 63), having probability of death at 2 months more than 83%, score 8 to 10, survival rate at 2 months = 8.3% ± 4.6%; group B (n = 55), having probability of death at 2 months of 29% to 83%, score 4 to 7, survival rate at 2 months = 42.7% ± 5.2%, and group C (n = 59), having probability of death at 2 months less than 29%, score 0 to 3, survival rate at 2 months = 92.2% ± 3.8%.


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Table 3. Multivariate Cox Survival Analysis

 

Figure 1
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Fig 1. Survival curves of three groups defined from the multivariate analysis. Group A, short survival (n = 63); group B, intermediate survival (n = 55); group C, longer survival (n = 59).

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 Appendix: Patient Questionnaire
 REFERENCES
 
Both clinical intuition and the literature guided our selection of prognostic laboratory and clinical factors. We chose criteria that were routinely collected and used by clinicians. For this reason, we did not consider levels of C-reactive protein, pseudocholinesterase, vitamin B12, or bilirubin, despite their prognostic strength in the literature.29,33,37,38

The patients in our population were not as close to a terminal phase as were those in larger studies.31 Our patients had an estimated survival of several days to 6 months and a median survival of 2 months. In the study of Maltoni et al,9 69% of the population had a KPS of less than 50%, compared with 30% in ours.

Our study confirmed the prognostic value of KPS.3,4,19,20,22-24 A decreasing KPS is often correlated with deteriorating prognosis. We refined its prognostic precision by combining it with three other factors: number of metastatic sites, LDH level, and serum albumin.

Hemoglobin is no longer associated with short survival,26,27 probably because of growth factor support. Clinical symptoms were significant in the univariate analysis, but in contrast to many other studies,7,13,19,20,22-27,36 dropped out of the multivariate model. The clinical symptoms monitored must be relevant both as prognostic factors and as symptoms requiring evaluation and treatment to make the patient feel better. Depending on the presence of other prognostic factors indicating severity, the treatment may be simply palliative or associated with curative treatments.

Our study sheds light on poorly known and underestimated prognostic factors, which were significant in the multivariate analysis. In particular, survival decreased when patients had more than two metastatic sites and when LDH was ≥ 600 IU/L.

This pilot study, in considering patients' preferences, shows their ambivalence.35 Only two patients chose item D (preference for palliative care exclusively). Patients chose curative care (A and B) most often, even though they all had advanced-stage cancer. The reason is probably that patients always expect a proposal for active treatment by their oncologists, even the several who were clearly dying. Patients choosing attitude C (14%) were asking for both comfort (palliative) care and continuation of specific anticancer treatment. It was reassuring to observe that nearly all the patients who seem to prefer mainly palliative care died within 2 months. At the same time, those asking for a more curative attitude—those still fighting—lived longest. Patient desire for longevity may be a prognostic factor for survival, but it may also be a surrogate for their disease course (ie, patients whose tumors have shown better response to treatment may indicate desire for more aggressive care toward the end of life). Patients' preferences may also be influenced by the views of their expert providers who have indicated to them what might be reasonable to expect (perhaps even based on existing prediction rules). Overall, the subjective views of patients may be a mediating rather than moderating variable.

In this study we defined three prognostic groups of patients with short, intermediate, and long survival. It was especially interesting because it is valid for all types of solid cancers. According to the European Palliative Care Working Group,15 which reviewed all the studies on the subject according to their pertinence by the level (A to D) of their evidence, our study considers only factors with an evidence level of B (clinical and laboratory signs). The scores published in the literature, on the other hand, are assigned level A. Our score, composed of four factors in a multivariate analysis, merits comparison with existing scores. It has the advantages of being simple and usable before the terminal phase (survival of more or less 2 months). These factors can be used by any oncologist, palliative care physician, general practitioner, or other specialist, in a hospital or private practice. In our daily activity, these factors have been useful in making ethical decisions: continuation of invasive treatment, hospitalization or maintenance at home, and response to the patient and family's request for support. The use of the subjective factor we examined (the patient's preferences) has not been studied much, but it is important to palliative care teams and it bolsters the score's prognostic orientation, if used together with the clinical and laboratory factors described.

The clinical survival estimate described in several scores can of course be taken into account in the medical decisions, as can the patient's personal history. Using the factors in our study can provide real benefits by providing objectivity in multidisciplinary staff meetings and in the doctor-patient relationship. Limitations from the present data need to be taken into account before generalizing the application of our results in the daily practice until a prospective validation has been conducted. These preliminary results in French hospitalized patients may not generalize well, for example, to the outpatient setting in North America for similar patients with advanced solid tumors.


    AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
 TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 Appendix: Patient Questionnaire
 REFERENCES
 
The author(s) indicated no potential conflicts of interest.


    AUTHOR CONTRIBUTIONS
 TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 Appendix: Patient Questionnaire
 REFERENCES
 
Conception and design: Anne-Claire Barbot, Pascale Mussault, Pierre Ingrand, Jean-Marc Tourani

Provision of study materials or patients: Pascale Mussault

Collection and assembly of data: Anne-Claire Barbot

Data analysis and interpretation: Anne-Claire Barbot, Pascale Mussault, Pierre Ingrand

Manuscript writing: Anne-Claire Barbot, Pascale Mussault, Pierre Ingrand, Jean-Marc Tourani

Final approval of manuscript: Pascale Mussault, Pierre Ingrand, Jean-Marc Tourani


    Appendix: Patient Questionnaire
 TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 Appendix: Patient Questionnaire
 REFERENCES
 
Here are four possible attitudes that a person who had a tumor and is in treatment (chemotherapy, radiation therapy, surgery) might have.

Which seems closest to your attitude?

Mr (or Ms) A wants to be informed of all the new treatments that might fight his/her disease. He (or she) takes prescribed medicine every day and accepts all medical procedures despite their side effects and their constraints. He (or she) might agree to participate in treatment trials.

Mr (or Ms) B accepts these new treatments and discusses with the physician the benefits expected from them in relation to their adverse effects. None of the treatment, examinations or work-ups seem too constraining.

Mr (or Ms) C has asked the physician to stop some treatments that have side effects that are too painful or difficult to accept and has asked for effective pain treatment. He (or she) refuses some examinations or treatments that are too constraining (computed tomography, magnetic resonance imaging, transfusion, and so on).

Mr (or Ms) D refuses all medical procedures (perfusion, blood samples, radiologic examinations) and has stopped some treatment. He (or she) insists on treatment to relieve pain and anxiety and asks for support.

Mr (or Madame) I: response impossible or patient does not wish to respond.


    ACKNOWLEDGMENTS
 
We thank Laurence Bahuet, MD, and Samuel Gourjault, MD, of the oncology departments of Poitiers and Niort, France, and both mobile palliative care teams.


    NOTES
 
Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 PATIENTS AND METHODS
 RESULTS
 DISCUSSION
 AUTHORS' DISCLOSURES OF...
 AUTHOR CONTRIBUTIONS
 Appendix: Patient Questionnaire
 REFERENCES
 
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5. Glare P, Virik K, Jones M, et al: A systematic review of physicians' survival predictions in terminally ill cancer patients. BMJ 327:195-198, 2003[Abstract/Free Full Text]

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7. Viganò A, Dorgan M, Buckingham J, et al: Survival prediction in terminal cancer patients: A systematic review of the medical literature. Palliat Med 14:363-374, 2000[Abstract/Free Full Text]

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13. Caraceni A, Nanni O, Maltoni M, et al: Impact of delirium on the short term prognosis of advanced cancer patients: Italian Multicenter Study Group on Palliative Care. Cancer 89:1145-1149, 2000[CrossRef][Medline]

14. Pirovano M, Maltoni M, Nanni O, et al: A new palliative prognostic score: A first step for the staging of terminally ill cancer patients. J Pain Symptom Manage 17:231-239, 1999[CrossRef][Medline]

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16. Earle CC, Neville BA, Landrum MB, et al: Trends in the aggressiveness of cancer care near the end of life. J Clin Oncol 22:315-321, 2004[Abstract/Free Full Text]

17. Palmer P, Vinke J, Philip T, et al: Prognostic factors for survival in patients with advanced renal cell carcinoma treated with recombinant interleukin-2. Ann Oncol 3:475-480, 1992[Abstract/Free Full Text]

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27. Teunissen SC, de Graeff A, de Haes HC, et al: Prognostic significance of symptoms of hospitalised advanced cancer patients. Eur J Cancer 42:2510-2516, 2006[CrossRef][Medline]

28. Cohen MH, Makuch R, Johnston-Early A, et al: Laboratory parameters as an alternative to performance status in prognostic stratification of patients with small cell lung cancer. Cancer Treat Rep 65:187-195, 1981[Medline]

29. Maltoni M, Pirovano M, Nanni O, et al: Biological indices predictive of survival in 519 Italian terminally ill cancer patients. J Pain Symptom Manage 13:1-9, 1997[Medline]

30. Ventafridda V, De Conno F, Saita L, et al: Leucocyte-lymphocyte ratio as prognostic indicator of survival in cachectic cancer patients. Ann Oncol 2:196, 1991 (letter)[Free Full Text]

31. Goldwasser P, Feldman J: Association of serum albumin and mortality risk. J Clin Epidemiol 50:693-703, 1997[CrossRef][Medline]

32. Herrmann FR, Safran C, Levkoff SE, et al: Serum albumin level on admission as predictor of death, length of stay, and readmission. Arch Intern Med 152:125-130, 1992[Abstract/Free Full Text]

33. McMillan DC, Elahi MM, Setter N, et al: Measurement of the systemic inflammatory response predicts cancer-specific and non-cancer survival in patients with cancer. Nutr Cancer 41:64-69, 2001[CrossRef][Medline]

34. Elahi MM, McMillan DC, McArdle CS, et al: Score based on hypoalbuminemia and elevated C-reactive protein predicts survival in patients with advanced gastrointestinal cancer. Nutr Cancer 48:171-173, 2004[CrossRef][Medline]

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36. Chow E, Fung KW, Panzarella T, et al: A predictive model for survival in metastatic cancer patients attending an outpatient palliative radiotherapy clinic. Int J Radiat Oncol Biol Phys 53:1291-1302, 2002[CrossRef][Medline]

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38. Geissbühler P, Mermillod B, Rapin CH: Elevated serum vitamin B12 associated with CRP as a predictive factor of mortality in palliative care patients: A prospective study over five years. J Pain Symptom Manage 20:93-103, 2000[CrossRef][Medline]

Submitted October 23, 2007; accepted February 5, 2008.


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