A prognostic index for AIDS-associated Kaposi's sarcoma in the era of highly active antiretroviral therapy

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A prognostic index for AIDS-associated Kaposi’s sarcoma in the era of highly active antiretroviral therapy Justin Stebbing, Adam Sanitt, Mark Nelson, Tom Powles, Brian Gazzard, Mark Bower

Summary Background AIDS-associated Kaposi’s sarcoma remains common in individuals with HIV-1 infection in the era of highly active antiretroviral therapy (HAART). We developed a simple model for predicting mortality on the basis of clinical characteristics present at the time of diagnosis of Kaposi’s sarcoma. Methods Of 5873 individuals with HIV-1 infection, 326 (6%) developed Kaposi’s sarcoma; for 262 (80%) this was their first AIDS-defining illness. We did univariate and multivariate Cox regression analyses to identify covariates predictive of overall survival and validated our model with an independent data set of 446 patients with Kaposi’s sarcoma. Results In the primary model, we developed a prognostic score from 0 to 15 starting at 10. Having Kaposi’s sarcoma as the AIDS-defining illness (–3 points) and increasing CD4 count (–1 point for every complete 100 cells per mm³) improved prognosis; age of 50 years or older (2 points) and having another AIDS-associated illness at the same time (3 points) conveyed a poorer prognosis. In individuals with prognostic scores of 0, 5, 10, and 15, probability of survival at 1-year was 0·993, 0·967, 0·834, and 0·378, and at 5 years was 0·984, 0·918, 0·631, and 0·084, respectively. Increasing prognostic score by 1 increased 1-year death hazard ratio by 40% (95% CI 28–53%; bootstrapped hazard ratio 1·39, 1·25–1·51). The index had concordance of 76·8% (71·7–82·3).

Lancet 2006; 367: 1495–502 Department of Oncology and Department of HIV Medicine, Chelsea and Westminster Hospital, London SW10 9NH, UK (J Stebbing PhD, A Sanitt MA, M Nelson MD, T Powles MD, Prof B Gazzard MD, M Bower PhD) Correspondence to: Dr J Stebbing [email protected] or Dr M Bower [email protected]

Interpretation We identified four prognostic factors that can be used to obtain an accurate prognostic index at diagnosis of AIDS-associated Kaposi’s sarcoma. This index is widely applicable and can be used to guide therapeutic options.

Introduction In people with HIV/AIDS, the use of highly active antiretroviral therapy (HAART) has reduced the incidence of opportunistic infections1,2 and decreased the incidence of HIV-associated cancers.3–5 Kaposi’s sarcoma was found to be associated with HIV at the onset of the epidemic,6 and remains the commonest HIV-associated cancer.7 The causative agent of Kaposi’s sarcoma is human herpesvirus-8 (also known as Kaposi sarcoma-associated herpesvirus)8 and this virus fulfils most of Koch’s postulates.9 Infection with this γ-herpesvirus precedes development of Kaposi’s sarcoma, and it is found in every case of the disease.10 Human herpesvirus-8 seems able to transform human endothelial cells and its genome includes a number of potential oncogenes.11 In people with HIV/AIDS, HIV-1 transactivating (Tat) protein may contribute to the Kaposi’s sarcoma disease phenotype; transgenic mice overexpressing Tat develop Kaposi’s sarcoma-like lesions.12 The precise mechanism by which HAART leads to resolution of Kaposi’s sarcoma lesions remains controversial,13,14 but includes: immune reconstitution15 and a general improvement in immune function as a consequence of HAART,16 direct antiangiogenic effects of protease inhibitors on Kaposi’s sarcoma;17 and decrease in concentrations of HIV-1 Tat protein18 or Tat-induced angiogenic cytokine release.19 The debate surrounding the mechanisms involved is fuelled by the clinical observation of Kaposi’s sarcoma developing in people with HIV/AIDS on stable HAART www.thelancet.com Vol 367 May 6, 2006

regimens with undetectable HIV viraemia and relatively high CD4 cell counts. People with HIV/AIDS who have T1 disease (visceral/ ulcerating/oral disease, according to the AIDS Clinical Trials Group staging classification20,21) are understandably thought to have distinctly reduced survival,22–24 leading to the suggestion that patients with T1 Kaposi’s sarcoma should be treated with both HAART and chemotherapy without awaiting the effects of HAART alone.25 Contrary to this proposal, a cohort of patients who presented with non-cutaneous Kaposi’s sarcoma did not have significantly reduced survival,26 showing uncertainty about the outcome for these individuals. A prognostic index is of most clinical use in a disease where survival varies a great deal between individuals, as with AIDS-associated Kaposi’s sarcoma. Under these circumstances, such an index can aid the planning and evolution of trials and treatments, and allow an exchange of information between treatment centres. Additionally, it provides information to individual doctors and patients for use in everyday clinical practice. The algorithm of care for patients with AIDS-associated Kaposi’s sarcoma includes HAART with or without anti-tumour treatments such as systemic chemotherapy. The identification of patients who have a poor survival from diagnosis of Kaposi’s sarcoma and who are likely to be candidates for systemic chemotherapy in addition to HAART, is essential to this treatment strategy. We aimed to develop a simple scoring system to predict survival in individuals with this condition. 1495

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Methods

10

Study population

Procedures and analyses CD4 cell counts (total lymphocyte subset analysis using whole blood stained with murine anti-human monoclonal antibodies to CD4 and assessed on Epics XL-MCL multiparametric four colour flow cytometer, Beckman Coulter, High Wycombe, UK) and HIV viral loads (measured with Quantiplex HIV RNA 3.0, Chiron, Halstead, UK; recorded since 1998 with a lower limit of detection of 50 copies RNA per mL) were measured at varying times after diagnosis for each patient and the data were assembled in two forms: one listing the covariates at the time of diagnosis of Kaposi’s sarcoma, and a second prepared in a counting process format,30,31 which incorporated the results of additional measurements of CD4 count and viral load, as well as HAART status, in time-dependent covariates. Data were prepared with a Perl script and analysed with the R computer language.32,33 Curves for overall duration of survival were plotted by the Kaplan-Meier approach.34 The log-rank method was used to test for the significance of differences in survival distributions and univariate Cox proportional hazards regression was used to identify the covariates of possible prognostic significance.35 Although neither age nor any transformation of age was identified as a prognostic factor in the univariate Cox analysis, inspection of the underlying data and clinical considerations suggested an increased hazard in older patients. We divided the patients into two groups: aged younger than 50 years or aged 50 years and older at the date of diagnosis. This categorical variable was highly significant (p=0·001) in a univariate Cox analysis. Representation of continuous variables by a categorical variable can lead to loss of information and, if chosen by reference to the data, can lead to overfitting.36,37 Accordingly, we took steps to ensure the validity of the categorical age variable. First, the cutpoint of 50 years old was chosen according to previous clinical considerations without specific reference to the data. Second, age was plotted as a non-linear covariate in a univariate Cox regression using natural splines.38 The resulting curve clearly showed two distinct levels of effect for age: no significant effect along most of the curve, rising to a significant effect above a certain age. Third, cutpoint analysis was used to determine 1496

5

0 Age coefficient (SE)

We identified all people with HIV/AIDS who had attended the Chelsea and Westminster Hospital, since routine prospective data collection began in 1983. We defined HAART as treatment consisting of at least three antiretroviral drugs in accord with American and European published guidelines (dual nucleoside analogues alone are not considered HAART)27–29 and we focused on cohort members who continued to be followed up after Jan 1, 1996, when HAART became routinely available at our institution, and many others.

−5

−10

−15

−20

20

30

40

50

60

70

Age (years)

Figure 1: Age versus coefficient for that age, used to confirm age cutoff limit Dotted lines=SE. Lower rug plot shows number of measurements at each age.

the optimal cutpoint for the age covariate.39 As the determination of a cutpoint might be unstable with respect to perturbations of the data, this analysis was confirmed using non-parametric bootstrapping; we took 2000 samples from the original distribution with replacement and repeated the cutpoint analysis (figure 1). Cox multivariate modelling was used to identify independent variables predictive of survival. Backward variable selection based on the Bayes Information Criterion and allowing up to two-way interactions between variables was used to produce the final multivariate model.40 The validity of the hazard ratio (HR) and CI obtained by the multivariate Cox analysis was confirmed by non-parametric bootstrapping with replacement. Robust estimates of the HR and SE, together with CI, were recalculated from these bootstrap statistics.41,42 To facilitate ease of use, the prognostic index was designed to range from 0 and increase in whole number increments to 15, with each integer representing a constant proportionate increase in risk. To accomplish this, the index was based on the HR for each prognostic factor in the final model. First, these were transformed into whole number increments, by dividing each by the smallest HR and rounding the resulting ratios to the nearest integer. This led to a logarithmic scale, where the whole numbers could be added or subtracted to reflect changes in proportionate risk. Secondly, for the index to have its lower range at 0, the additions and subtractions commenced at 10, rather than 0, which resulted in a continuous prognostic scale from 0 to 15. Calibration was used to measure the difference between the observed and predicted probability of survival. The c statistic, a discriminating measure of concordance between observed and expected survival analogous to the area under a receiver operating characteristic curve, was www.thelancet.com Vol 367 May 6, 2006

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Number (%) or median (IQR) Demographic information Median age, years (IQR) Male

37·9 (33·2 to 43·7) 311 (95%)

Ethnic origin European Caucasian

255 (78%)

Days exposed Protease inhibitors Boosted regimens

35 481

Non-boosted regimens

16 507

Non-nucleoside reverse transcriptase inhibitors Efavirenz based

27 808

Nevirapine based

10 856

African

36 (11%)

Nucleoside analogue backbone

Asian

14 (4%)

Thymidine analogues

91 955

Caribbean

9 (3%)

Cytidine analogues

69 255

Mixed

4 (1%)

Other

54 576

Other

8 (3%)

HIV-related information KS as first AIDS-defining illness

262 (80%)

I stage

Table 2: Days of exposure to individual antiretroviral agents (>90 days of treatment) in individuals on HAART at diagnosis of Kaposi’s sarcoma

0

137 (42%)

1

189 (58%)

study and had final responsibility for the decision to submit for publication.

0

241 (74%)

Results

1

85 (26%)

S stage

Immune reconstitution inflammatory syndrome-KS15 Median CD4 count cells per mm3 (IQR) Median HIV-1 viral load, copies per mL (IQR)

11 (3%) 132 (35 to 276) 85570 (<50 to 100 000)

Tumour characteristics at diagnosis T stage 0

214 (66%)

1

112 (34%)

Tumour-associated oedema

34 (10%)

Pulmonary KS

27 (8%)

Gastric KS

17 (5%)

Extensive oral KS

52 (16%)

KS=Kaposi’s sarcoma.

Table 1: Patients’ characteristics

calculated for the prognostic score. We validated the c statistic by a similar bootstrap procedure using resampled data.43 For external validation,44 we obtained survival data on 446 patients with AIDS-associated Kaposi’s sarcoma since 1996 from the USA using HIV/AIDS cancer registries from the following regions that participated in the HIV/AIDS Cancer Match Study: the states of Colorado, Connecticut, Florida, Georgia, Massachusetts, Michigan, and New Jersey; and the metropolitan areas of Los Angeles, San Diego, and San Francisco (CA), New York City (NY), and Seattle (WA). We resampled these data with replacement 2000 times, measured the concordance in each resampling and used the resulting distribution to obtain CI. The investigator (AS) was masked to data categories during analysis. The study received appropriate ethics approval from Chelsea and Westminster Hospital.

Role of the funding source There was no funding source for this study. The corresponding author had full access to all the data in the www.thelancet.com Vol 367 May 6, 2006

The initial assessment was undertaken on a cohort of 9621 individuals. Of 5873 patients observed in the HAART era, 326 (5·5%) with AIDS-associated Kaposi’s sarcoma were identified. The majority were white and male, as were most members of the entire group, and most had Kaposi’s sarcoma as a first AIDS-defining illness (table 1). Most patients had immune-system impairment and poorly controlled HIV viraemia at diagnosis of Kaposi’s sarcoma. Of 326 individuals who developed Kaposi’s sarcoma, only 73 (22%) had been on HAART regimen based on either a protease inhibitor (35 patients) or nonnucleoside reverse transcriptase inhibitor (37); or both (two) for at least 3 months at the time of diagnosis. Duration of exposure to individual antiretroviral drugs is shown in table 2. Boosted protease-inhibitor-based regimens using a ritonavir dose of less than 400 mg twice a day included those containing lopinavir, saquinavir, tripanavir, fosampranavir, and atazanavir. Non-boosted (without ritonavir) regimens included those containing nelfinavir and indinavir. Thymidine analogues included were zidovudine and stavudine, cytidine analogues were lamivudine and emtricitabine, and others included tenofovir, didanosine, abacavir, and enfuvirtide. There were no significant differences in numbers of individuals or days exposed to HAART based on either protease inhibitor or non-nucleoside reverse transcriptase inhibitor. At diagnosis of Kaposi’s sarcoma, median CD4 cell count was 132 cells per mm³ (IQR 35–276) and median HIV viral load was 85 570 copies HIV-1 RNA per mL (<50 to 100 000; table 1). With regards to Kaposi’s sarcoma staging, 65·6% had T0 disease (T=tumour extent: 0=skin or lymph node only; 1=non-lymph node visceral disease, tumour-associated oedema or ulceration, or extensive oral involvement), 42% had I0 disease (I=significant immunosuppression: 0 >150 CD4 cells per mm³, 1 <150 CD4 cells per mm³), and 74% had S0 disease (S=other symptomatic illness; 0=none, 1=yes). 1497

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Non-time-dependent

Time-dependent

Log rank

p

Log rank

p

Coefficient

Exp (coefficient) (95% CI)

p

AIDS-defining illness

27·2

<0·0001

16

<0·0001

–1·23

0·292 (0·18–0·473)

<0·0001

I stage

23·4

<0·0001

26·9

<0·0001

1·57

4·8 (2·4–9·6)

<0·0001

T stage

14·4

0·0002

11·2

0·0008

0·91

2·49 (1·53–4·04)

0·0002

S stage

38

<0·0001

20·8

<0·0001

1·42

4·13 (2·53–6·74)

<0·0001

On HAART

Cox regression

0

0·931

0

0·969

0·0158

1·02 (0·463–2·23)

0·97

Pulmonary KS

12·1

0·0005

5·58

0·0182

1·09

2·99 (1·58–5·65)

0·0008

Gastric KS

16·4

<0·0001

5·58

0·0181

1·33

3·78 (1·81–7·9)

0·0004

Oedema

2·7

0·099

2·15

0·142

0·565

1·76 (0·946–3·27)

0·074

Extensive oral KS

0·5

0·461

0·48

0·49

0·239

1·27 (0·67–2·41)

0·46

IRIS

1·5

0·214

3·43

0·0639

–1·18

0·307 (0·04–2·16)

0·24

CD4 cell count

–0·00524

0·995 (0·991–0·998)

0·0032

HIV viral load

19·6 1·54

<0·0001 0·214

<0·001

1 (1–1)

0·38

Age

1·41

0·235

<0·001

1 (1–1)

0·2

KS=Kaposi’s sarcoma. IRIS=immune reconstitution inflammatory syndrome. HAART=on HAART for >3 months at the time of Kaposi’s sarcoma diagnosis. Coefficients show strength of effect of that variable on the univariate analysis with the HR eco–efficient representing the effect on the univariate analysis.

Table 3: Univariate Cox regression analysis and log rank tests

The survival of people with Kaposi’s sarcoma from the primary cohort is shown in figure 2; median survival has not been reached for the entire cohort. Table 3 shows the univariate Cox regression for each variable taken alone, hypothesising that two samples of patients with and without each variable are drawn from the same population. Results for both time-dependent and nontime-dependent formats were similar; the difference between them was caused primarily by the measurement of robust variance (using a jackknife for the former). In Cox regression, patients with a more advanced T, I, or S stage of Kaposi’s sarcoma had significantly worse overall survival for each of the variables measured: T1 versus T0, HR 2·49 (95% CI 1·53–4·04); I1 versus I0, 4·8 (2·4–9·6); and S1 versus S0, 4·13 (2·53–6·74); pulmonary Kaposi’s sarcoma, 2·99 (1·58–5·65); and gastric Kaposi’s sarcoma, 3·78 (1·81–7·9). Tumour-associated oedema,

Coefficient

Exp (coefficient)

SE

Z

p value

Multivariate analysis AIDS-defining illness Age ≥50 years CD4 (per 100 cells per mm3)

–0·8905

0·4104 (0·24–0·702)

0·274

–3·25

0·7121

2·038 (1·06–3·910)

0·332

2·14

0·0011 0·032

–0·3408

0·7112 (0·52–0·973)

0·160

–2·13

0·033

S stage

1·098

2·997 (1·75–5·132)

0·274

4·00

<0·0001

Prognostic index

0·337

(1·28–1·530)

0·0441

7·62

<0·0001

–2·901

0·0037

Bootstrap validation AIDS-defining illness Age ≥50 years CD4 (per 100 cells per mm3)

–0·8975 0·7128 –0·3778

S stage

1·117

Prognostic index

0·342

0·4076 (0·2280–0·7433) 0·3094 2·040 (1·059–4·329)

0·3594

1·983

0·0474

0·6854 (0·5516–1·134)

0·1796

–2·103

0·0355

0·3003

3·720

0·0002

0·0460

7·426

<0·0001

(1·642–5·308) 1·41 (1·254–1·509)

Missing data comprised less than 0·1% of all values and where necessary medians were used in the bootstrap analysis; for external validation we imputed missing data using regression analysis based on other factors present.

Table 4: Validation of coefficients in Cox analysis of KS data (HAART era)

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extensive oral Kaposi’s sarcoma, and receiving HAART for at least 90 days at the time of diagnosis were not associated with a significant change in survival. Kaposi’s sarcoma that presented as the first AIDS-defining illness was significantly associated with improved survival (HR 0·29, 95% CI 0·18–0·47), as were higher CD4 cell counts (as a continuous variable; 0·995, 0·991–0·998). Neither the HIV viral load in plasma nor its log transformed value were significantly associated with prognosis. There were no significant differences in survival comparing HAART regimens based on protease-inhibitor or nonnucleoside reverse transcriptase inhibitor (p=0·84). When the factors that were significant or approached significance were placed in a multivariate Cox regression model, four factors were statistically significant (ie, p<0·05; table 4). Scaling the smallest of these four HRs to –1 and expressing the others as multiples of this value gave scaled values of –2·6, 2·1, –1, and 3·2. Rounding these values to the nearest whole number produced a value of –3 for Kaposi’s sarcoma as first AIDS-defining event, 2 for age 50 years or older (figure 1), –1 for CD4 cell count (per 100 cells per mm³), and 3 for S1 stage. These integers were used as scores in the prognostic index, which was found to provide an even scale from 0 to 15, and to avoid negative integers when started at 10. The CI for the bootstrapped coefficients was similar to that in the original analysis, although wider, as expected. HR and CI were calculated from the original and bootstrapped coefficients and their CI. The bootstrapping procedure was repeated for the prognostic score, and the prognostic index was found to be well calibrated for outcomes at all years of the model (figure 3). The prognostic index for the primary cohort of patients is shown in table 4. Figure 4 shows Kaplan Meier curves for the primary cohort and the external www.thelancet.com Vol 367 May 6, 2006

1·0

1·0

0·8

0·8 Observed probability

Probability

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0·6

0·4

0·6

0·4

0·2

0·2

0

0 0

2

4

6

8

PI 5 PI 5–10 PI>10

0

10

2

Survival (years) Numbers at risk

Figure 2: Kaplan Meier curve showing overall survival Dotted line=95% CI.

PI<5 PI 5–10 PI>10

4

6

8

10

Survival (years) 25 17 90 61 24 18

8 25 7

0 0 0

4

5

8 86 58

0 48 30

58 181 87

38 126 41

0

1

2

15 181 250

14 149 152

14 136 125

1·0 1·0

Observed probability

0·8

Fraction surviving 3 years

0·8

0·6

0·6

0·4

0·2 0·4 0 0·2

3

Survival (years) Numbers at risk 0 0·4

0·5

0·6

0·7

0·8

0·9

Predicted 3-year survival

Figure 3: Calibration plot of observed survival against predicted survival at 3 years Dotted line shows where observed survival and predicted survival are equal (perfect calibration). Calibration was used to measure difference between probability of survival as predicted by the prognostic index, and probability of survival as observed in the corresponding Kaplan-Meier survival curve. Error bars=95% CI.

validation set. For the primary cohort, the likelihood of a patient with a higher prognostic score having a worse survival outcome than a patient with a score that was one point lower was 76·8% (95% CI 71·7–82·3%). The concordance of the index in the external group of 446 American patients with Kaposi’s sarcoma was 60% (95% CI 56–63%; bootstrapping results were almost identical). Figure 5 shows the predicted survival curves for patients with prognostic indices of 0, 10, and 15. For patients with a prognostic score of 15, our worst score, the median predicted survival was 242 days (95% CI 186–675; table 5). www.thelancet.com Vol 367 May 6, 2006

PI<5 PI 5–10 PI>10

10 108 93

Figure 4: Kaplan Meier curves showing survival versus time for patients with prognostic indices in three ranges in (A) primary cohort and (B) external validation set PI=prognostic index score. Note different x axis scales.

Discussion The objective of our prospective cohort study was to develop a simple prognostic index for AIDS-associated Kaposi’s sarcoma in the HAART era. We identified four widely available prognostic factors that can be combined in a simple calculation to produce a prognostic score based on their clinical characteristics at the time of Kaposi’s sarcoma presentation. These factors were age, the occurrence of Kaposi’s sarcoma at or after AIDS onset, the presence of co-morbid conditions, and immune status as measured by CD4 cell count. Surprisingly, Kaposi’s sarcoma tumour sites including pulmonary or gastric involvement were not found to be relevant to the index despite significantly reduced survival in univariate analyses. However, other tumour-related factors were of prognostic importance, suggesting that factors related to tumours and HIV are 1499

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1·0 PI=0 PI=10 PI=15

Predicted probability

0·8

0·6

0·4

0·2

0 0

2

4

6

8

10

Survival (years)

Figure 5: Predicted survival curves for patients with prognostic indices of 0, 10, and 15 Dotted lines=95% CI.

both relevant in the outcome of these patients. This prognostic index extended from 0 to 15 in easily quantifiable steps and should be useful in almost all resource settings. Increasing the prognostic score by 1 increased the 1-year death HR by 40%, which suggests that the index should be useful for clinicians and patients alike. AIDS-associated Kaposi’s sarcoma is a disease of immunosuppression,4 so our finding that higher CD4 cell counts are associated with better prognosis is unsurprising. The significance of age is recognised in several clinically useful prognostic indexes for cancer,45–47 including a recent one for systemic AIDS-related nonHodgkin lymphoma.48 The observation that all four of these indexes involve cancers with close relations to γ-herpesviridae suggests that the decreased immune response to viral infection that occurs with age49 might have a role in the progression of these cancers. In fewer patients, however, others have suggested that CD4 count and age are not associated with survival.23,50 Some investigators have suggested that viral load of human herpesvirus-8 is associated with disease progression,51,52 although other results have not supported these findings.53,54 Unfortunately, information on human herpesvirus-8 was not available in this study. Our index differs substantially from the AIDS Clinical Trials Group staging system,20,21 which collected information on individuals diagnosed and treated in the pre-HAART era, used I stage as a dichotomised rather than continuous variable, and suggested that T stage was of prognostic relevance. The importance of S stage was recognised by its original incorporation into the AIDS 6 months

1 year

2 years

0·998 (0·995–1·000)

0·993 (0·988–0·999)

0·990 (0·981–0·999) 0·984 (0·971–0·998)

5

0·987 (0·979–0·996)

0·967 (0·949–0·985)

0·946 (0·920–0·972) 0·918 (0·882–0·956)

10

0·933 (0·900–0·969)

0·834 (0·782–0·891)

0·741 (0·676–0·811)

15

0·692 (0·536–0·892)

0·378 (0·216–0·660)

0·199 (0·082–0·486) 0·084 (0·022–0·322)

0

5 years

0·631 (0·557–0·715)

Table 5: Probability of survival (95% CI) by prognostic score and from diagnosis of Kaposi’s sarcoma

1500

Clinical Trials Group clinical classification, and our data provide further evidence of the importance of factors such as performance status in determining survival, a feature also prevalent in the other routinely used prognostic indexes related to γ-herpesviridae.45–48 The effect of HAART on survival in AIDS-associated Kaposi’s sarcoma is of great clinical importance, and 22% of our patients were on HAART at the time of Kaposi’s sarcoma diagnosis. Three approaches could have been used to address this issue in the analysis. First, HAART could have been left outside the model, especially as it was not significant in a univariate model. Although this was a surprising finding, because HAART is well known to have a major effect on overall mortality in HIV seropositive individuals,1,2 a recent large study of 160 individuals with AIDS-associated Kaposi’s sarcoma found that outcome is not affected by the initiation of HAART before development of Kaposi’s sarcoma, lending support to our findings.55 Second, the non-HAART patients could have been excluded from the analysis. However, this approach has methodological difficulties, and would greatly reduce the data pool and hence the power of the study. Although the amount of non-HAART data is not very large (253 individuals had had some time without HAART, but the median time without HAART was only 160 days, so that the total patient-days at risk without HAART was 55 664 days, only 12% of the total days at risk for the cohort), it did contain 13 deaths, so it would be a loss to exclude these data. Thirdly, the data could have been stratified by HAART, permitting the inclusion of all the data. However, this approach requires accounting for possible interaction effects between HAART and the prognostic factors, which would reduce the power of the model. When the HAART-stratified analysis was undertaken there were in fact no significant interaction effects and the model produced was almost identical to the unstratified model. Accordingly, the model without HAART strata was preferred. When the model was applied to an independently assembled set of patients, it yielded similar results, although as predicted, the model effect was not as strong. Several factors limit the accuracy of the prognostic index in the validation dataset. Factors considered important in our prognostic index were not routinely collected in other institutions, or were obtained using different methods. Specifically, S stage was not recorded in the external validation set. In the initial model the definition of comorbidity (ie, S stage) included any patients with a poor performance status (Karnofsky score <70), B symptoms as well as opportunistic infections or thrush, or the presence of other less severe HIV-associated illness.21 In the absence of these data for the patients used for external validation, S stage was judged to have a value of 1 if there was co-morbidity with another AIDS-associated illness (which was recorded). Thus, comorbidity in the initial study was less severe. In fact, the concordance of the two datasets was almost identical if this measure of S stage www.thelancet.com Vol 367 May 6, 2006

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was excluded when calculating the prognostic index for the validation set. Additionally, we used precise dates of diagnosis, follow up, and death in our cohort, whereas in the external validation set, for reasons of confidentiality, all dates were recorded to within a month of diagnosis. Given the limitations of the external dataset, the concordance was within entirely acceptable limits. As with all prognostic indexes, the end point is death from any cause, and the death of patients with AIDSassociated Kaposi’s sarcoma is often multifactorial rather than simply related to progressive Kaposi’s sarcoma. Nevertheless in this cohort, this condition and the treatments given to treat it (particularly systemic chemotherapy) contributed to the cause of death in 70% of patients, even when the attributed cause was an opportunistic infection (Pneumocystis carinii pneumonia or Mycobacterium avium intracellulare). Interestingly, 6% died due to Castleman’s disease, a lymphoproliferation driven by human herpesvirus-8,56 and in a further 20% the cause of death was unknown or not recorded. Notably, this model included some variables that might indicate HIV progression rather than factors related to Kaposi’s sarcoma, although again the absence of well established prognostic factors in HIV such as HAART and HIV viral load from the model confirm that this model applies specifically to AIDS-associated Kaposi’s sarcoma rather than to HIV in general. People with HIV/AIDS are living longer, and Kaposi’s sarcoma remains a substantial cause of morbidity and mortality, and an important marker of advanced immunosuppression as shown by the preponderance of HIV-related factors in our index. We suggest that patients with a poor risk as judged by our prognostic index (score >12) should be initially treated with HAART and systemic chemotherapy together, because their outcomes are poor. Alternatively these patients should be considered for entry into clinical studies with novel agents. Patients with a low risk (score <5) should be treated initially with HAART alone, even if they have T1 disease. The regression of Kaposi’s sarcoma with HAART alone could take many months, but if they are responding to HAART alone, chemotherapy should be reserved for progressive disease. Further work may delineate the best treatment strategies for individuals with an intermediate prognostic score. Contributors J Stebbing and M Bower were the principal investigators and designed the study, supervised implementation, interpreted results, and wrote the final paper. A Sanitt did statistical analyses. T Powles, M Nelson, and B Gazzard supervised care of patients, organised data collection, and contributed to study implementation and the final manuscript. All authors approved the final version. Conflict of interest statement We declare that we have no conflict of interest. Acknowledgments We are enormously grateful to Robert J Biggar (Viral Epidemiology Branch, National Cancer Institute) and Timothy S McNeel (Information Management Services), for their help, advice, and data, without which this work, and the external validation in particular, would not have been possible.

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