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Received: 21 January 2003Accepted: 28 May 2003Published online: 5 August 2003

© Springer-Verlag 2003

Funding for initiation and coordination of this project was provided by MemorialSloan Kettering Cancer Center. Participat-ing institutions providing resources for site-specific data collection. This paper was pre-sented in part at the annual meeting of theAmerican Society of Clinical Oncology inMay, 2001 held in San Francisco, CA, USA

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ity in critically ill cancer patients who are admitted to anICU [19]. While severity-of-illness models apply to co-horts of patients and not individuals, having a numericalprobability of survival may help foster dialogue to for-mulate goals for duration and intensity of ICU care.

Once a patient is admitted to the ICU, regardless of whether they have an underlying malignancy, it is appro-

priate that clinical goals of care be reassessed on a regu-lar basis. Decisions about ongoing support of criticallyill patients with cancer are formed by the prognosis of the underlying malignancy, the patient’s clinical statusafter a period of aggressive ICU care, and the wishes of the patient. While many patients may improve or die inthe first 72 h of ICU management, a large percentagewill remain critically ill. With that in mind, we presentthe results of a multicenter project to develop a modelfor predicting survival of cancer patients remaining inthe ICU after 72 h of care. The value of this informationlies in its ability to objectify a situation for caregiversand families, as well as for patients, when decisions of 

the human cost of an intervention are being weighedagainst its potential benefits. While no model is capableof predicting the outcome of any individual patient, awell-validated model can provide a sometimes-neededperspective.

Patients and methods

Data were prospectively collected in four academic tertiary carehospitals beginning 1 July 1994 to develop and validate a multi-variable logistic regression model to estimate the probability of hospital mortality among cancer patients admitted to the ICU. Par-ticipating units were the Medical/Surgical ICU of Memorial Sloan

Kettering Cancer Center (MSKCC), New York, NY, USA; theCity of Hope National Medical Center, Duarte, CA, USA; theMedical ICU of The University of Texas, M.D. Anderson CancerCenter, Houston, TX, USA: and the Mount Sinai Medical Center,New York, NY, USA. The study was approved by the InstitutionalReview Board of all participating sites.

All cancer patients admitted to the ICU were included, with theexception of patients younger than 18 years, burn patients, andcoronary patients defined by a primary diagnosis of myocardial in-farction (MI) or rule-out MI with no secondary diagnosis. The out-come of interest was vital status at hospital discharge. For patientswith multiple admissions to the ICU during the study period, themost recent admission was used in the analysis.

Demographic, clinical, laboratory, and physiological variableswere obtained on consecutive cancer patients within 1 h of ICUadmission, at 24 and 72 h of ICU admission, and at ICU and hos-

pital discharge. No specific clinical interventions were performedon any patient for the purpose of developing the outcome model.All categorical variables were defined prior to patient accrual,made available to each data collector, and were previously report-ed [19].

Patients were classified as being in one of four tumor groups:solid tumor, solid tumor with metastasis, leukemia or hematopoi-etic bone or peripheral stem cell transplant, and lymphoma or my-eloma. Patients with leukemia, bone marrow transplant, lympho-ma, or myeloma were classified as having nonmetastatic disease.Patients were classified as either medical or surgical, with surgicalpatients being those who had a surgical procedure prior to ICU ad-

mission during the same hospitalization. Patients with leukemia,bone marrow transplant, lymphoma, or myeloma who had under-gone open lung biopsy for diagnosis of the etiology of respiratoryinsufficiency were classified as medical patients.

Patients were randomly divided into a model development set(2/3) and a model validation set (1/3). Analyses were conducted toexamine the distribution of each variable. For variables for whichno specific data were recorded for a patient, a response of “No” or“Never” or “Normal” was imputed if the variable was categorical-ly scaled, and a value within normal limits was imputed if thevariable was continuously scaled. Univariate analyses were per-formed to test the significance of each variable in relation to vitalstatus at hospital discharge, using chi-square tests for categoricalvariables and t tests for continuous variables. All data are present-ed as means plus or minus standard deviation unless otherwisenoted.

Upon completion of univariate analyses, variables collected atadmission, 24 hours and 72 h were eligible for entry into a logisticregression model if their significance was less than or equal to0.10, as well as entered or omitted by clinical evaluation (e.g., if  p≈0.10 but the variable was clinically important/easy to use or p

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Results

Prospective data were collected on 3,005 consecutive pa-tients with a malignancy diagnosis or hematopoieticstem cell transplantation (HSCT) admitted to the ICUs of participating institutions. At 72 h, 1,354 patients re-mained in the ICU. After eliminating all but the most re-

cent admission of patients with multiple admissions(n=20) and those admitted with a diagnosis of rule-outMI, 1,242 patients were eligible for entry into model de-velopment and validation. Data were collected from 1July 1994 through 30 June 1998.

Patients were randomly divided into a developmentset (two thirds:  n=827) and a validation set (one third:n=415). Hospital patient mortality in the developmentset was 54% (447 deaths, 380 survivors) with an averageage of 55.6±16.5 (median 56.8) years. These patientswere hospitalized an average of 10.0±23.0 (median 5.0)days prior to ICU admission.

Variables selected for entry into preliminary stepwise

logistic regression models following univariable analysisand clinical judgment are listed in Table 1. All prelimi-nary models—whether using “normal” for all missingcategorical variables, substituting either “normal” or“means” for missing continuous variables, eliminatingall continuous variables with more than one third of val-ues missing, or by including all admission disease-relat-ed categorical variables and then only data collected at72 h—revealed acceptable calibration and discriminationwith the lowest p value for goodness of fit equal to 0.256as well as an AUC equal to 0.818.

Based on bootstrap results and our preliminary step-wise logistic regression analysis, a model was generated

where normal values were substituted for missing con-tinuous data elements and normal or never for missingcategorical variables. Based on clinical judgment, onlyvariables measured at 72 h were entered into the model,along with initial admission categorical data. Results of selected analyses of categorical and continuous data arepresented in Tables 2 and 3.

After clinical review, cut points were used to catego-rize heart rate (>100 beats/min), Glasgow coma score(≤5), blood urea nitrogen (BUN) (>40 mg/dl), arterialoxygen pressure/fractional inspiratory oxygen (PaO2 / FiO2) (

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Table 2 Results of selected univariate analyses on random two thirds development data set of cancer patients in intensive care unit(ICU) for 72 h. NED no evidence of disease, TBI total body irradiation, Y yes, N no,

Died Lived   P value

Number Percent Number Percent

Tumor

Leukemia 169 66 87 34 0.001Lymphoma/myeloma 77 57 59 43Solid metastatic 112 52 103 48Solid 82 41 114 58

Hematopoietic stem cell transplantation

Allogenic 88 73 32 27 0.001Autologous 24 47 27 53Never 332 51 315 49Chemotherapy>4 weeks ago 142 61 91 39 0.0032–4 weeks 50 57 38 43

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mer-Lemeshow statistic of 11.03 and p=0.354. The AUCwas 0.820 in the validation sample.

Using the model coefficients from Table 4, the datafor a cancer patient still in the ICU at 72 h can be used tocalculate the logit as

where βD is the constant and βixi is the estimated coeffi-cient for the ith variable times the value of the ith vari-able, with i taking on values from 1 to k, and k being thenumber of terms in the model.

Once the logit has been obtained by multiplying eachappropriately coded variable value by its correspondingcoefficient and summing, a simple calculation using thelogit produces the probability of hospital mortality forthe patient as follows:

To illustrate, Table 7 is provided to calculate the probabili-ty of hospital mortality for a hypothetical cancer patient at72 h of ICU care. In this example, the patient is admitted

to the ICU with evidence of disease progression and re-quiring some assistance for daily functioning. The patientwas intubated and mechanically ventilated. The heart ratewas 109, Glasgow coma score 12,   PaO2 /FiO2 ratio178 mmHg, platelet count 85,000, BUN 19 mg/dl, and se-rum bicarbonate (HCO3) 19 mEq/dl. Urine output was be-tween 10 and 50 ml/h over the past 24 h; however, at notime was the urine output

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Table 3 Mean, and standarddeviation (SD) of select contin-uous variables for patients wholived and patients who died.All collected at 72 h.PaO2 / FiO2 arterial oxygenpressure/ fractional inspiratoryoxygen, PT prothrombin time, INR international normalisedratio, PTT partial thromboplas-tin time, LDH lactic dehydro-genase, BUN blood urea nitro-gen, HCO3 serum bicarbonate

Variable Died Lived   P value

Mean SD Mean SD

Age (years) 54.9 16.4 56.4 16.4 0.21Hospital days prior to ICU 13.2 27.8 8.3 15.4

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observed value for the hypothetical patient is entered inthe table under the column labeled X. The code or value

to be used in the calculations is shown under the columnlabeled x, and the result of multiplying the code or valueby the coefficient is shown under the column labeled ..The values of are summed to obtain the logit, so thatg(x)=2.453. The patient’s probability of hospital mortali-ty is then calculated as:

Of 100 patients having this profile, 92 would be expect-ed by the model to die prior to hospital discharge andeight would be expected to be discharged from the hos-pital alive.

Discussion

We have previously presented a model to assess proba-bility of hospital mortality in cancer patients admitted tothe ICU on the basis of variables readily obtained on ad-mission [19]. We now apply the same principles to de-velopment and validation of a 72-h model distinguishing

those variables now of greater or lesser importance, asdefined by our development cohort and adjusting the co-

efficients of our model to reflect these new weights. Wewere motivated to develop this model as it reflects a pe-riod of time for clinicians to attempt to reverse a life-threatening complication, for patients and families to ad- just to and reflect on the global impact of a potentiallylethal change in clinical status, as well as to offer timefor the critical care staff to develop a rapport in order tocomfort and counsel family regarding goals of care.

The question of who will live and who will die is thefundamental query being addressed by these models. Ithas been shown that experienced clinicians fare quitewell against calculations previously designed to answerthis question [32]. Cancer patients admitted to the ICU

for critical illness continue to experience mortality rateshigher than their non-cancer-bearing, severity-of-illness-scored matched cohorts [53]. Published mortality ratesrange from 50 to 80% in this population [3, 7, 12, 13, 14,15, 16, 17, 20, 25, 40, 42, 46, 47, 48, 54, 55]. We ob-served a 54% mortality rate in our development group,falling in line with the above ranges; however, our co-hort is different, as they have already survived 72 h of ICU care. Table 2 demonstrates mortality-rate differ-ences among tumor categories, with hematologic malig-

Table 6 Hosmer-Lemeshow(H-L) goodness-of-fit table forthe model in the validationsample (n=415)

Group Prob* Died Lived Total

Observed Expected Observed Expected

1 0.11 4 4.6 37 36.4 412 0.20 3 8.1 38 32.9 413 0.27 16 11.0 25 30.0 414 0.35 12 14.4 29 26.6 41

5 0.43 15 17.5 26 23.5 416 0.52 23 21.1 18 19.9 417 0.60 24 24.7 17 16.3 418 0.69 31 28.3 10 12.7 419 0.79 35 32.2 6 8.8 41

10 0.89 40 41.0 6 5.0 46

* Predicted probability of mor-tality degrees of freedom=10Hosmer-Lemeshow (H-L),chi-squared statistic=11.03, p=0.354

Table 7 Calculations for eachvariable used to generate theprobability of hospital mortali-ty for a hypothetical cancer pa-tient at 72 h X observed vari-able value for the patient, x coded model value of the

variable for the patient.PaO2 / FiO2 arterial oxygenpressure/fractional inspiratoryoxygen, BUN blood ureanitrogen

Variable X x

Evidence of disease progression 0.4535 Yes 1 0.4535Performance statusAssistance required(Zubrod 2 or 3) 0.4775 Yes 1 0.4775or Bedridden (Zubrod 4) 1.4949 No 0 0.00000

Glasgow coma score ≤5 1.3178 12 0 0.00000Mechanically ventilated 1.1764 Yes 1 1.1764PaO2 /FiO2 ratio 100 beats/min 0.4012 yes 1 0.4012Platelets

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nancies experiencing a 63% mortality rate as comparedto that of solid tumors, which shows a rate of 46%. Thisdifference is, in part, accounted for by the well-docu-mented, discordantly high though improving, mortalityrate associated with allogeneic bone marrow transplantpatients requiring ICU admission and, especially, me-chanical ventilation [1, 4, 13, 14, 15, 16, 20, 25, 41, 42,

43, 44, 54, 55].Much work has surfaced in recent years looking at

prognosis in cancer patient and the possibility of objecti-fying the mystique that surrounds this diagnosis. Scoringsystems have been developed to aid in this task but gener-ally lack the sensitivity and specificity required to answersuch complex questions [8, 11, 27, 34, 49, 53]. Some in-vestigators have looked at the applicability of standardscoring systems to this venue. It has been shown in recentwork that the probability of ICU mortality is more closelyassociated with the acute physiologic processes necessi-tating ICU admission than the underlying cancer diagno-sis or its stage [19, 21, 49]. In support of this contention,

one study concluded that APACHE II and SAPS II pre-dicted hospital and ICU mortality well, but the writersqualified this assertion by stating that it did not predictthese endpoints well enough to be useful in management[49]. Many other authors have repeatedly demonstratedthe inability to interchange scoring systems between pop-ulations in whom the system was not originally devel-oped [2, 5, 31, 36, 37, 51, 56]. It is on the basis of this as-sertion that we present this cancer-specific, 72-h modelso that those patients who remain in the ICU may be re-stratified at 3 days according to their new health status.

The 72-h time interval was chosen as it is a commonlyaccepted time frame for “trials of critical care” for those

patients whose indications for ICU admission may not beas clear. This is often related to questions of apatient’s severity of illness or prognosis. The authors en-dorse the idea that survival should always be given thebenefit of the doubt in these patients, and it is this princi-ple that is at the root of allowing patients to “declarethemselves” independent of any model that identifies aprobability of mortality upon ICU admission. Thethought that different variables would emerge as impor-tant after a trial of critical care is supported by our results.

Tumor burden, as manifested by recurrent or refractorydisease or with a poor performance status, purports a poor

prognosis for the cancer patient. These findings are sup-ported by previously documented prognostic factors forsurvival in advanced non-small-cell lung cancer, with ex-tent of disease and Karnofsky score listed as the primaryand secondary determinants of survival [38]. A separatestudy in small-cell lung cancer also revealed extent of dis-ease as the most important prognostic factor and addition-

ally found that Karnofsky score was the only predictive in-dependent variable as time passed [39]. This mirrors quitewell our findings at 72 h as well as at ICU admission.

Presence and degree of respiratory failure continuesto be an important variable at 72 h, as manifested by anincreased risk of dying if the patient is intubated for anyreason as well as having a low  PaO2 /FiO2. Renal dys-function is strongly supported by the literature as a valu-able prognosticator of outcome and remains so in our 72-h model, with the addition of a new parameter to not on-ly reflect azotemia but also urinary flow as defined bythe presence of outputs of less than 150 cc for any 8-hperiod [6, 10, 18, 26, 50, 52]. Tachycardia and metabolic

acidosis were not found to be discriminators on admis-sion, but those patients who remained tachycardic or hadan HCO3  ≤20 mEq/dl at 72 h were more likely to die.Naturally the continued presence of these findings at72 h describes a patient with persistently altered hemo-dynamics and cellular metabolism, portending a worseoutcome than the patient not so altered.

This new model used alone or in concert with the pre-viously validated admission model may serve to bettercharacterize the probable course for the majority of pa-tients stratified. This has great utility in discussions withpatients and their families to try and quantitate for themwhat their “chances” might be. Further, these models

may prove to be very useful in processes related to ICUbed utilization and review. These models have no validi-ty as a criterion to deny admission for any individual pa-tient. The authors hope that this information might beused to better clinical care delivered to the ICU patientwith cancer by more accurately and more rapidly identi-fying the patient at high risk of death so that their caremight be handled most appropriately, as dictated by theindividual clinical scenario.

Acknowledgment We are indebted to Peter B. Bach for his re-view and assistance with this manuscript.

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