403 PREDICTORS OF SURVIVAL IN SYSTEMIC SCLEROSIS (SCLERODERMA) ROY D. ALTMAN, THOMAS A . MEDSGER, JR., DANIEL A. BLOCH, and BEAT A. MICHEL We conducted followup of 264 patients with definite systemic sclerosis (SSc) who were entered into the multicenter Scleroderma Criteria Cooperative Study (SCCS) during 1973-1977. At the end of the study (average 5.2 years of followup), 38% were known to be alive, 50% were dead (68% of these deaths definitely related to SSc), and 12% were lost to followup. Survival analyses of 484 demographic, clinical, and laboratory items recorded at entry into the SCCS (within 2 years of physician diagnosis of SSc) were performed. Survival declined linearly, and the cumulative survival rate was <SO% at 2 years, 50% at 8.5 years, and 30% at 12 years after entry. Analysis using combinations of entry variFrom the University of Miami School of Medicine and the Arthritis Division, Miami Veterans Administration Medical Center, Miami, Florida; the Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; and the Divisions of Rheumatology and Biostatistics, Stanford University School of Medicine, Stanford, California. Supported in part by NIH grants AR-21393 and ZROl-GM21215-15, the United Scleroderma Foundation, the RGK Foundation, Austin, Texas, the American College of Rheumatology, the Perlman Scleroderma Fund, Miami, Florida, the Arthritis Foundation, Western Pennsylvania Chapter (Shoemaker fund), and the Miami Veterans Administration. Roy D. Altman, MD: Professor of Medicine, University of Miami School of Medicine, and Chief, Arthritis Division, Miami Veterans Administration Medical Center; Thomas A. Medsger, Jr., MD: Professor of Medicine and Chief, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine; Daniel A. Bloch, PhD: Senior Research Associate, Divisions of Rheumatology and Biostatistics, Stanford University School of Medicine; Beat A. Michel, MD: Fellow in Rheumatology, Stanford University School of Medicine (current address: University Hospital of Zurich, Zurich, Switzerland). Address reprint requests to Roy D. Altman, MD, Department of Medicine, University of Miami School of Medicine, PO Box 01696u (VA 111), Miami, F L 33101. Submitted for publication January 23, 1990; accepted in revised form October 12, 1990. Arthritis and Rheumatism, Vol. 34, No. 4 (April 1991) ables identifying organ system involvement confirmed that renal, cardiac, pulmonary, and gastrointestinal involvement in SSc predicted reduced survival; however, data on organ system involvement at study entry could not be used to consistently predict which organ system would ultimately be involved as the primary cause of death. By survival tree analysis, the individual entry variables best predicting reduced survival included older age (>64 years), reduced renal function (blood urea nitrogen >16 mgldl), anemia (hemoglobin 5 1 1 gm/dl), reduced pulmonary diffusing capacity for carbon monoxide (550% of predicted), reduced total serum protein level ( 5 6 gm/dl), and reduced pulmonary reserve (forced vital capacity <SO% with hemoglobin >14 gmldl or forced vital capacity <65% with hemoglobin 1 1 4 gm/dl). Cox proportional hazards model analysis confirmed these results. Different combinations of variables led to markedly different survival rates. The poorest prospects for survival were in patients with SSc who were 1 6 4 years old with a hemoglobin level 511 gm/dl, and in those >64 years old with a blood urea nitrogen level >16 mg/dl. These results may be useful in predicting individual patients at risk for shortened survival. Systemic sclerosis (SSc; scleroderma) is classified as one of the connective tissue diseases. It is characterized by vascular abnormalities such as Raynaud’s phenomenon and telangiectasias; induration and thickening of the skin; and dysfunction of other organs, including the kidneys, lungs, heart, and gastrointestinal (GI) and musculoskeletal systems (13). Clinical severity and progression vary, from a subtype of SSc with limited cutaneous changes to SSc with diffuse cutaneous changes; the latter tends to be ALTMAN ET AL 404 associated with more frequent and more severe visceral involvement (1,2). M a n y retrospective studies on survival among patients with SSc have been published (4-15). All of the authors agree that the development of serious visceral involvement in SSc portends early mortality. T h e case series reported here is unique in that it represents a geographically diverse, multicenter effort in which patients were enrolled consecutively, mostly within 2 years of diagnosis; comprehensive demographic, clinical, and laboratory data w e r e collected in a standardized manner at entry; and many patients w e r e followed for more than 10 years. PATIENTS AND METHODS Patients. Patients were identified through the multicenter Scleroderma Criteria Cooperative Study (SCCS), performed by the Subcommittee for Scleroderma Criteria of the American College of Rheumatology (formerly the American Rheumatism Association) Diagnostic and Therapeutic Criteria Committee. The primary aim of this investigation was to develop preliminary criteria for the classification of SSc (16). The methods for data collection, patient entry, and diagnosis verification procedures, as well as entry demographic, clinical, and laboratory features of the patients have been reported (16,17). Briefly, between 1973 and 1977, 29 centers referred 264 patients with recently diagnosed definite SSc. Almost all were entered within 2 years of first being diagnosed by a physician as having SSc. A detailed protocol containing 484 items of information was completed on each patient, and each had a serum specimen stored. Followup data were requested 1 year after entry to verify diagnosis, and a 3-physician review panel examined all forms to confirm the entry diagnosis. In a second followup conducted at least 3 years after entry, the diagnosis was reconfirmed, patient outcome was determined, and information on 133 clinical and laboratory items was obtained. Sources of information were the patient, center coordinator, patient’s primary physician (if different from the center coordinator), and chart review. A final followup was conducted during 1983-1985. Patients who could not be readily located were traced through relatives, initial referring physicians, hospital records, telephone books, state motor vehicle registration systems, the Social Security Administration, and the National Death Index (National Center for Health Statistics, US Department of Health and Human Services, Hyattsville, MD). For patients known to have died, physician’s office, hospital, autopsy, and death certificate records were reviewed to establish the date and cause of death. Each death was categorized as either definitely SSc-related or possibly SSc-related. A patient was considered to have a definitely SSc-related death only if there was a clear medical association with SSc. For example, a death due to end-stage diabetes mellitus, malignancy, or suicide was not assigned to the definitely SSc-related category. If a patient died of septicemia in the setting of SSc renal crisis, the death was assigned to the definitely SSc-related category. Deaths attributed to SSc were further subdivided based on the organ system primarily responsible for the fatality, as above. During the past decade, several serum autoantibody systems that are relatively specific for SSc and are believed to have important clinical subclassification and prognostic significance have been identified (18,19). Sera saved frozen at -70°C from the original study were thawed, and anticentromere and anti-Scl-70 (anti-topoisomerase I) antibody tests were performed for inclusion in the present analysis. All data were entered into the data bank of the Arthritis, Rheumatism, and Aging Medical Information System. Computer printouts were reviewed for accuracy and internal consistency. Twenty percent of the printouts were compared with the original data collection forms; the percentage of entry errors was 0.05% in that sample. Definitions of organ system involvement. Organ system involvement at the initial visit was defined using the criteria published by Medsger et a1 (lo), with modifications. Only clinically detected antemortem evidence of involvement was used, and findings were accepted only when not attributable to some other disease process (e.g., congestive heart failure due to atherosclerotic heart disease). The findings listed below were required in order to classify a patient as having involvement of the particular organ system. Gastrointestinal involvement was defined as distal esophageal hypo- or aperistalsis, documented by either cineradiographic or manometric studies, typical small bowel malabsorption syndrome, or colonic “sacculations” characteristic of scleroderma. Pulmonary involvement was diagnosed by any one of the following: bilateral basilar interstitial fibrosis or infiltration on chest radiograph, active pleuritis (pain plus friction rub), cardiac catheterizationproven pulmonary hypertension, or diffusing capacity for carbon monoxide (DLco) reduced to <13 ml/minute/mm Hg. Cardiac involvement was characterized by any one of the following: active pericarditis (pain plus friction rub), congestive heart failure, or nodal or ventricular arrhythmias. We defined scleroderma renal involvement as rapid, progressive renal failure or proteinuria of 23.5 gmlday. Patients who did not meet any of the above criteria were classified as having no major organ system involvement. Statistical analysis. Demographic, clinical, and laboratory variables used in the analysis were derived from the initial SCCS entry visit data. The predictive value of each of the 484 variables with regard to association with death was initially examined by univariate statistical methods for differences in means (continuous data) or frequencies (categorical data), for all patients who died versus all who survived. Since this approach does not account for the factor of time, a second univariate analysis compared 2 patient groups with respect to time to death: those who died within 2 years of entry versus those who were alive at the end of the study and had been followed more than 2 years from entry. No variables were lost, and a few additional variables were found to be significant, using this approach. Variables significant at the designated P < 0.05 level, as well as other variables considered important based on previous investigations (4-15), were chosen for multivariate study. Table 1 lists the 72 selected variables, subdivided into 9 subsets. SURVIVAL IN SSc Multivariate analysis included creation of a survival tree (20,21). With this method, a group of patients is split into two subgroups according to values for a selected variable. A split partitions the group into patients with values lower than a particular cutoff point and those with values greater than this cutoff point, e.g., age 564 years and age >64 years. At every group of the tree which is to be subdivided, all variables are examined, and the “best” one is selected, along with its splitting value. Best is assessed in terms of a goodness-of-splitindex that can be evaluated for any split of any group of the tree. In this study, the Mantel-Haenszel (log rank) statistic was used. Two subgroups result from every potential split, and these are treated as the two samples for which this statistic is computed. The split implemented at any group of the tree is that which results in the highest value of the Mantel-Haenszelstatistic. As defined, the (best) split selected most clearly separates the two derived subgroups’ Kaplan-Meier survival curves, which were used to estimate survival (22). Multivariate analysis with the survival tree method and the stepwise Cox proportional hazards regression model (22) was performed separately on each of the 9 subsets, containing 5-1 1 variables each, as listed in Table I. The Cox proportional hazards regression model was also applied to all 72 variables in these subsets. This model does not allow for missing values; hence, mean normal values were entered as replacements, on the premise that a missing value for a particular variable was most likely to be normal. Many of the 72 variables had some values missing, and a few had a high proportion of missing values (e.g., erythrocyte sedimentation rate [ESR] 35%, Po, 50%, and DLco 29% of predicted) (Table 1). A survival curve was constructed for a general population that was matched to the patient group for age at entry, year of entry, sex, race, and length of followup. This was based on survival probabilities as published by the National Center for Health Statistics. Organ system involvement was related to survival, using Kaplan-Meier survival curves and Mantel-Haenszel statistics. RESULTS The mean (kSEM) age at the time of study entry was 49.1 +- 0.8 years. Study entry took place an average of 5.7 _t 0.2 years after appearance of the first symptom attributable to SSc and 1.9 t 0.1 years from the time of physician diagnosis. Followup averaged 5.2 t 0.2 years from entry and 7.2 -I 0.2 years from diagnosis. The survival curve (Kaplan-Meier) for all 264 patients from the date of entry to the last followup is shown in Figure 1. The survival curve for the general population (matched to the SSc group) is also displayed in Figure 1 . At 2 years, survival in the general population was >98%; survival in the SSc group was <80%. At the end of 12 years, survival in the general 405 population was 89%, but the survival rate was only 30% for the SSc patients. Of the 264 patients, 101 (38%) were known to be alive at the last followup, 131 (50%) were known to have died, and 32 (12%) were lost to followup. In this group of 264 patients with SSc, all patients had generalized disease, with arm, face, and/or truncal involvement. Although 63 patients could be identified as having CREST syndrome (calcinosis, Raynaud’s phenomenon, esophageal dysmotility, sclerodactyly, and telangiectasias), REST, or CRST, they also appeared to have diffuse cutaneous disease. Eighty-nine (68%) of the 131 deaths were definitely related to SSc, and 42 (32%) were possibly related to SSc (Table 2). Of the deaths attributed to SSc, renal involvement predominated as the cause of death (39%). In 2 patients, it was not possible to separate cardiac from pulmonary SSc as the event leading to death. Of the deaths possibly related to SSc, cardiac disease was most frequent (36%), especially atherosclerotic cardiovascular disease with myocardial infarction. There were 18 cancers detected in 16 (6%) of the 264 patients, all within 4 years of the diagnosis of scleroderma, and 9 deaths were attributed to malignancy. The primary cancer sites included lung (4 patients), breast (3 patients), leukemia (3 patients), colon (2 patients), skin (2 patients), carcinoid (1 patient), cervix (1 patient), common bile duct (1 patient), and kidney (1 patient). Findings of univariate analysis. Univariate analysis was performed for all variables, comparing the 58 patients who died within 2 years with the 94 patients who were alive at the end of the study and were followed longer than 2 years from the time of entry into the SCCS. The statistical significance of the differences between the two groups are presented in Table 1. Among patients who were taking prednisone, the mean daily dosage was higber, but not statistically significantly higher, in those who died in less than 2 years (mean ? SD 29 If: 6 mg) than in those who were alive at last followup and were followed longer than 2 years (19 t 4 mg) (P< 0.08). Corticosteroids were the only therapeutic agents for which there were adequate data for analysis. Microscopic hematuria, when present, was greater in those who died in less than 2 years (mean SEM 10 ? 5 red blood cells/high power field) than in those who were followed longer than 2 years and alive at last followup (2 ? 0.5 red blood celldhigh power field) (P < 0.001). Organ system involvement. Kaplan-Meier survival curves according to organ system SSc involve- * ALTMAN ET A L 406 Table 1. Univariate analysis of 72 variables: differences between patients who died within 2 years of followup and those who were alive at the end of the study and were followedup more than 2 years* Variable General Age at entry, years Sex (% female) Race (% nonwhite) Married (%) Physical laborer (%) Tobacco use, pack years Alcohol use, index Pulse, beats per minute Microangiopathy by blood smear (%) Taking prednisone (%) Dermatologic Dry eyes (%) Alopecia (%) Generalized scleroderma (%) Interlip distance, m m Digital tip pitting scars (%) Musculoskeletal Promixal muscle weakness (%) Symmetry of joint involvement (%) Tenosynovitis (%) Joint swelling (%) Joint deformity (%) CPKt Serum aldo1ase.t Gastrointestinal Dysphagia (%) Fecal fat excretion, 72 hours, gmiday D-xylose test, gm Abnormal esophageal manometry (%) Colonic sacculations (%)§ P u 1m on a r y Dyspnea (%) Dry basilar rales (%) Increased cardiac pulmonic second sound (P2) (%) Bibasilar interstitial fibrosis (%)§ Pleural effusion (%)§ Arterial Po,, m m Hg Vital capacity, ml Forced vital capacity, % predicted Diffusion capacity, mm Hgiminute Diffusion capacity, % predicted Cardiac Orthopnea (%) Neck vein distention (%) Ventricular gallop (%) Pedal edema (%) EKG Right ventricular hyperthrophy (%) Nonspecific ST-T changes (%) Arrhythmia (%) Left ventricular enlargement (%)$ Renal Blood pressure, m m Hg Systolic Diastolic BUN, mg/dl Serum creatinine, mg/dl Proteinuria, I + to 4+ (%) Dead within 2 years (n = 58) Alive and followedup >2 years (n = 94) Pt * 46 <0.01 * 22 f 3 28 f 8 81 t 1 0 13 12 7 97 53 2 28 24 9 5 47 t 7 60 t 22 88 I 6 31 f 1 19 14 8 12 Values entered (%) NS <0.001 NS 100 I00 100 99 97 92 89 100 60 <0.01 100 <0.05 <0.05 100 <0.001 NS NS NS NS 2 17 100 42 f 1 53 45 t I 43 <0.001 100 43 19 100 28 67 1.7 f 0.3 1.0 f 0.1 15 9 I00 14 45 1.0 ? 0.3 0.7 t 0.1 <0.001 NS NS <0.05 <0.05 NS CO.05 100 100 22 99 86 84 39 12 6.1 t 1 3.1 ? 0.5 76 17 5 5.1 t 1 5.4 f 0.3 62 14 NS NS 80 11 29 100 47 NS NS <0.001 <0.01 NS 100 100 82 67 31 21 36 13 9 <0.001 <0.05 I00 CO.01 100 37 14 74.6 f 2.0 2,523 f 133 69 t 3.2 14 t 1 64 t 5 14 1 <0.01 98 99 50 84 82 73 71 87.2 f 1.5 3,100 f 88 84 f 1.7 18 t 1 83 +- 3 <0.01 <0.001 <0.01 <0.001 <0.01 <0.01 19 19 22 26 3 1 3 4 <0.001 <0.01 14 34 31 26 2 23 13 4 <0.001 <0.01 132 f 3 81 f 2 25 f 5 1.2 f 0.1 35 124 ? 2 78 2 1 14 2 1 0.9 f 0.03 10 <0.01 <0.001 <0.01 <0.001 <0.05 NS <0.05 <0.05 <0.001 100 99 78 81 99 97 95 92 96 100 100 96 98 95 SURVIVAL IN SSc 407 Table 1. (Cont’d) Variable Renal Urine >O RBC/HPF (%) >O granular casts/HPF (%) Creatinine, mg/24 hours Creatinine clearance, m h i n u t e Immunologic Positive skin test for mumps (%) Serum globulin, gm/dl Rheumatoid factor, latex agglutination titer Log latex fixation, titer Fluorescent antinuclear antibody titer Anticentromere antibody (%) Anti-topoisomerase I (%) Laboratory Hemoglobin, g d d l WBC, x I ,000/mm3 % neutrophils % lymphocytes % monocytes % eosinophils Westergren ESR, mm/hour Total protein, gm/dl Albumin, gmidl SGOT$ LDHt Dead within 2 years (n = 58) 35 21 946 2 65 84 t 6 Alive and followedup <2 years (n = 94) Pt (%I 20 4 1,073 t 42 88 t 3 <0.01 <0.01 NS NS 96 96 17 73 Values entered 46 3.2 t 0.1 2,429 2 1,785 73 3.2 2 0.1 302 t 72 <0.05 NS NS 51 92 86 8.2 t 2.3 4,274 f 3,844 5 51 13.4 t 0.7 162 t 35 19 21 <0.001 0.05 <0.05 <0.01 86 88 78 76 12.3 t 0.3 9.0 2 0.5 71 f 2 21 t 1 5 2 1 2 t 1 42 t 5 6.8 t 0.1 3.5 f 0.1 1.0 t 0.2 1.3 t 0.1 13.2 -t 0.2 1.3 t 0.2 67 f 1 24 t 1 6-Cl 3 t l 23 t 3 7.0 t 0.1 3.8 t 0.1 0.7 t 0.1 1.0 t 0.1 <0.05 97 99 97 97 97 89 65 93 93 97 17 <0.01 <0.05 <0.05 <0.05 <0.05 <0.01 NS <0.001 <0.05 <0.01 * Unless otherwise indicated, values are the mean f SEM. CPK = creatine phosphokinase; EKG = electrocardiography; BUN = blood urea nitrogen; RBC = red blood cells; HPF = high power field; WBC = white blood cells; ESR = erythrocyte sedimentation rate; SGOT = serum glutamic oxaloacetic transaminase; LDH = lactate dehydrogenase. t The significance of the difference in means was assessed with the 2-sample t-test. The significance of the difference in percents was assessed with the chi-square test for 2 x 2 tables. NS = not significant. $ Multiple of normal, derived by dividing the observed value by the upper limit of normal value for the laboratory. J By radiography or barium contrast radiography. ment at entry into the study are displayed in Figure 2. Renal disease had the poorest median (_tSE) survival, at 3 t 25 months (i.e., 50% of the group had died at 3 months), and 7 of 10 patients (70%) did not survive until the end of the study. Overall survival was poorer for patients with reral disease than that for patients with GI involvement (with no renal, cardiac, or pulmonary disease) (P = 0.03, by Mantel-Haenszel test) or with no major organ system involvement ( P = 0.02). Cardiac involvement (without renal involvement) predicted a median survival time of 32 5 40 months, and 9 of 15 patients did not survive until the end of the study. Patients with pulmonary SSc (with no renal or cardiac involvement) survived a median of 78 f 17 months, but 60 of 104 (58%) died before the last followup. These patients with pulmonary SSc had significantly poorer survival prospects than did patients with GI involvement with no renal, cardiac, or pulmonary disease ( P = 0.04, by Mantel-Haenszel test). Patients with GI involvement (with no renal, cardiac, or pulmonary disease) survived a median of 99 17 months, and 35 of 84 (42%)died before the last followup. SSc patients with no renal, cardiac, pulmonary, or GI involvement survived for a median of 108 2 9 months, and 20 of 51 (39%) did not survive until the end of the study. Other group differences with respect to survival were not statistically significant (Mantel-Haenszel test). Of the 10 patients in whom renal involvement had been identified at the time of study entry, 6 died of renal disease and 1 died of cancer. However, in 29 of the 35 patients who died of SSc renal disease (83%), * ALTMAN ET AL 408 Table 2. Diseased organ system or event leading to death in patients with systemic sclerosis (SSc) 0 0 24 48 72 96 120 144 168 MONTHS OF FOLLOW-UP Cause of death Definitely related to ssc (n = 89) Renal Cardiac Pulmonary Gastrointestinal Cancer Cardiopulmonary Infection Suicide Vasculitis Cerebrovascular accident Musculoskeletal Other* 35 18 18 13 0 2 0 0 2 0 1 0 Possibly related to ssc (n = 42) Total (n = 131) 35 33 18 14 9 2 3 3 2 2 0 15 0 1 9 0 3 3 0 2 0 9 1 9 Figure 1. Observed wrvival curve for 264 patients with systemic sclerosis (SSc) (0)and expected survival for a general population matched to the SSc group for date of entry into the study, duration of followup, age, sex, and race (E). * One patient died of complications of second- and third-degree burns over 80% of the body; 8 died of unknown causes. there was no evidence of renal involvement at the time of the initial evaluation. In general, there was little direct correlation between organ system involvement at the time of the initial examination and recorded cause of death, except in the case of renal involve- ment. Both loose and strict definitions of organ system involvement yielded similar low correlations. Findings of multivariate analysis. Variables found to be significant by univariate analysis and those reported in the literature as predictive of survival were "t, 0 24 I 48 I 72 I 96 I MONTHS OF FOLLOW-UP 120 I -- 1 L.. ..." - 144 : "I 168 1 Figure 2. Kaplan-Meier survival curves for 264 patients with systemic sclerosis (SSc), by organ system involvement at entry into the study. Curves are shown for those with renal involvement (R; n = lo), heart involvement without renal involvement (H; n = 15), pulmonary involvement without renal or heart involvement (P; n = 104), gastrointestinal involvement without renal, heart, or pulmonary involvement (G; n = 84), and SSc without renal, heart, pulmonary, or gastrointestinal involvement (N; n = 51). 409 SURVIVAL IN SSc subjected to multivariate analysis, using a survival tree and a stepwise Cox proportional hazards model. Figure 3 depicts the survival tree that resulted from an analysis in which the 72 variables from Table 1 were included as being potentially predictive. The strongest predictor of survival was age at study entry. The median survival for the 32 patients who were >64 years old at entry was 25 months (2.1 years), whereas that for the 232 patients who were 5 6 4 years old at entry was 107 months (8.9 years). The combinations of Table 3. Terminal subgroups from survival tree Median survival in months* Patient characteristicst 8 (3) Age 564 years Hb 5 1 1 gmidl 9 (6) Age >64 years BUN >16 mgidl 30 (7) Age 564 years Hb > 1 1 gm/dl DLco 550% predicted 35 (9) Age 564 years Hb > 1 1 gm/dl DLco >50% predicted Total serum protein 5 6 gmidl 45 ( 5 ) Age >64 years BUN 5 1 6 mg/dl 46 (15) Age 564 years Hb >14 gmidl DLco >50% predicted Total serum protein >6 gm/dl FVC <80% predicted 89 (13) Age 564 years Hb > 1 1 gm/dl and 5 14 gm/dl DLco >50% predicted Total serum protein >6 gmidl FVC 5 6 5 % predicted 104 (16) Age 564 years Hb >14 gm/dl DLco >50% predicted Total serum protein >6 gmidl FVC 280% predicted 153+ (14) Age 564 years Hb > 1 1 gm/dl and 5 14 gmidl DLco >50% predicted Total serum protein >6 gmidl FVC >65% predicted * Numbers in parentheses are the terminal subgroup numbers shown in Figure 3. I' Hb = hemoglobin; BUN = blood urea nitrogen; DLco = diffusing capacity for carbon monoxide; FVC = forced vital capacity. Figure 3. Survival tree for 264 patients with systemic sclerosis, using all of the variables shown in Table 1. The tree is derived from recursive partitioning (see Patients and Methods). Large circles show the variable on which the tree divides. The final subgroups formed are represented by squares. The upper number within each large circle and each square is the number of patients; the lower number (in parentheses) is the median survival, in months. Small circles identify the number of the terminal subgroup in the tree. Values at the arms of the tree represent the value upon which the variable divides. Hb = hemoglobin; BUN = blood urea nitrogen; DLCO = diffusing capacity for carbon monoxide; FVC = forced vital capacity. variables from the terminal subgroups from the survival tree are also displayed in Table 3 . Entry characteristics of survival tree subgroups did not consistently predict which initially involved organ system or event would lead to death. Azotemia was present in patients who died of renal disease, but did not consistently predict renal disease; it was often present in patients who died of cardiac involvement or cancer. Kaplan-Meier survival curves (Figure 4) depict the differences in survival between the various terminal groups, e.g., the curves for subgroups 4 and 3 (Figure 4A) illustrate the association of reduced survival with hemoglobin levels I1 1 gm/dl in SSc patients ALTMAN ET AL 410 A 0 I 0 24 I io ! 48 72 96 120 MONTHS OF FOLLOW-UP I I 144 168 B 040 I 24 48 72 96 120 MONTHS OF FOLLOW-UP 144 168 C 80 > Ob 0 I 24 48 72 96 120 MONTHS OF FOLLOW-UP 144 168 Figure 4. Survival curves for the 264 patients with systemic sclerosis, by subgroups (circled numbers at the end of each curve) from Figure 3 and Table 3. Curves are shown for the group with the poorest survival (A), the group with the median survival (B),and the group with the best survival (C). age 564 years. In contrast, SSc patients classified in subgroup 14 (Figure 4C) had the most favorable prospects for survival. Subgroup 14 was further split with regard to the variables of white blood cell count (number of cells/ml) and urine creatinine level (mg/24 hours). These splits are not shown because they were relatively weak and increased the complexity of the tree structure. A stepwise Cox proportional hazards analysis was performed on the 72 variables from Table 1 (results not shown). Most of the variables selected by the survival tree method were high in the stepwise COX model. There were 6 variables that did not appear in the survival tree analysis but appeared to be significant ( P < 0.05) in the stepwise Cox model. These were proximal muscle weakness, absolute lymphocytosis, proteinuria, microscopic hematuria, reduced arterial Po,, and radiographic evidence of left ventricular enlargement. Although individually these variables were predictive of reduced survival, cross correlations indicated interdependence with the selected tree variables. For example, proximal muscle weakness was significantly correlated with abnormal lung function (low DLco [% of predicted] and low forced vital capacity [FVC; % of predicted]), azotemia, and/or anemia. The effects of individual variables can be quantified using the relative risks estimated from a stepwise Cox model (Table 4).As assessed by the Cox model, all variables shown in the tree had significant associations with death. Thus, the negative coefficient for FVC (Table 4) implies that patients with lower FVC have poorer survival prospects. The relative risk of 0.98 indicates that, with all else being equal, a patient with a 1% higher FVC has a 2% reduced hazard of death. In contrast, with the tree method, 4 subgroups were defined (in terms of characteristics at entry) that have distinct survival prospects that depend on the FVC as well as other variables (subgroups 13-16, Table 3). The two methods pursue different goals: the Cox model ascribes an effect to a variable, while the tree defines subgroups that have distinct survival prospects. Hence, it was not surprising when variables found to be predictive in the tree structure, such as blood urea nitrogen (BUN) level, hemoglobin level, white blood cell count, and 24-hour urine creatinine level, were not significant in the Cox proportional hazards model when all variables were included. A tree structure as well as a Cox proportional hazards model was developed for each of the 9 subsets listed in Table 1 (not shown). The findings from these 41 1 SURVIVAL IN SSc Table 4. Stepwise Cox proportional hazards model applied to survival tree variables (including sex) ~~ Step no. Variable* 1 2 3 4 5 6 7 8 FVC (% predicted) BUN (mg/dl) Hemoglobin (gm/dl) WBC (x1,000/mm3) DLco (% predicted) Total serum protein (gmldl) Age (years) Sex (M/F) Coefficient rf- SE -0.018 rf- 0.006 0.016 rf- 0.004 -0.162 f 0.052 0.139 f 0.038 -0.013 2 0.004 -0.325 2 0.128 0.018 rf- 0.008 0.387 ? 0.216 Relative risk? Improvement 0.98 1.02 0.85 1.15 0.99 0.72 I .02 1.47 <0.001 <0.001 0.003 <0.001 0.012 P 0.005 0.016 0.122 * FVC = forced vital capacity; BUN = blood urea nitrogen; WBC = white blood cells; DLco = diffusing capacity for carbon monoxide. -F Relative risk = relative multiplicative effect of the variable on the hazard function corresponding to a I-unit increase in that variable only. subset analyses added little insight to the more inclusive multivariate analyses already described, which considered all 72 variables as potentially predictive of shortened (or lengthened) survival. To illustrate the results, both multivariate methods found FVC and DLco to be important discriminators in the pulmonary subset, BUN in the renal subset, and neck vein and left ventricular enlargement by radiography in the cardiac subset. Further details are available from the authors upon request. Prior investigations (12,23,24) have found male sex to be a significant predictor of poor survival. In the present study, however, sex was not a predictor of outcome by either univariate or multivariate analysis (Table 4). DISCUSSION Systemic sclerosis has been reported to have an annual incidence of 1-20 new cases per million population (1,25), a prevalence of 1.3-10.5 per 100,000 population (14,25), and an annual mortality rate of 1.8 per million population (14). Overall mortality has been estimated at 39-65% (6,9,10,12,13,15). It has been suggested that these prior reports of SSc mortality are biased by including a larger proportion of the more seriously ill patients. Other reports have indicated a lower annual incidence of disease (13,26) but concur with the mortality rate of near 50%. In this study, we evaluated 264 patients with definite SSc, who were referred over a decade ago from 29 centers in North America, for development of criteria for the classification of the disease (16). The severity of SSc is apparent in that 50% of the patients are known to have died during a followup period that averaged 7.2 years from diagnosis and 5.2 years from entry into this study. The failure of the survival rate to “level off’ and parallel the expected survival for the general population suggests that the microvascular and fibrotic changes of SSc importantly reduce the reserve function of various organ systems. Thus, SSc patients would be at greater risk for mortality from other superimposed illnesses, such as degenerative vascular diseases and pneumonia. The dramatic reduction in survival of the entire SCCS group is emphasized when the group is compared with a matched population (Figure l), and is reminiscent of the curves expected with many forms of cancer. Most earlier studies have utilized univariate analysis to determine predictors of outcome. There are similarities, but also differences, between our findings and those in the prior reports. Older age is a consistent predictor of poor survival. Poorer survival has been predicted for patients age >40 (5,9), age >45 (lo), and age >50 (5,8,12,15). In the present study, age >64 years was selected as the main characteristic associated with poor prognosis. Poor prognosis had also been reported for nonwhites (12), women (12,23,24), physical laborers (13), and married persons (12-14), but these factors were not significantly associated with death in our study, by either univariate or multivariate methods. Similarly, poor prognosis previously reported in association with cigarette smoking (12,14), presence of a ventricular gallop (15), use of corticosteroids (15), elevated ESR, cardiac arrhythmia (27), and abnormal changes on electrocardiography (9) were shown to be present in this study by univariate techniques, but not after accounting for covariates. Univariate and multivariate analyses support the reported poorer prognosis related to reduced hemoglobin (5,12), elevated BUN level (9), proteinuria (12), reduced DLco (23), and reduced FVC (23). Although predictive by univariate analysis, multivariate analysis did not demonstrate any predictive power 412 for anticentromere or anti-Scl-70 antibody. The latter has been found to be both predictive (28) and not predictive (19) of survival, in separate studies. In contrast to two previous reports (5,12), average hemoglobin levels among the patients we studied were very similar for those who survived (mean 2 SEM 13.2 ? 0.2 gm/dl) and those who died during the study (12.3 ? 0.3 gm/dl). However, as we found when using the survival tree analysis, the association of hemoglobin is non-monotonic: Both low (anemia) and high (polycythemia, perhaps chronic lung disease) hemoglobin levels are poor prognostic features. Such a distinction was not detected using the Cox proportional hazards model analysis, representing an advantage to the survival tree method. Prior studies have emphasized involvement of certain organ systems as predicting poor prognosis, including diffuse skin disease (5,29), truncal skin involvement (6,9,28), pulmonary disease (9,10,12), cardiac disease (4,5,10,12), and renal disease (4,5,7,8, 10,12). Among the patients in this study, the frequency of truncal skin thickening at entry was nearly the same in survivors (44%) and nonsurvivors (48%). Considering the findings in other studies, this result was unexpected, but it may reflect the fact that the majority of patients in this study developed diffuse cutaneous involvement, even though at the time of entry, truncal skin thickening was not recorded. The most common SSc-related cause of death was renal involvement, emphasizing the severity of this complication. The majority of the SCCS patients died prior to the availability of effective antihypertensive therapy, such as angiotensin-converting enzyme inhibiting agents. Since effective therapy is dependent upon early recognition of “renal crisis,” it is most important to identify the patients at greatest risk. Survival curves for the subsets derived from the survival tree reveal combinations of features that predict increased rates of early and late mortality. For example, in patients with SSc who were 5 6 4 years old, anemia (hemoglobin I1 1 gm/dl) predicted a median survival of only 8 months (subset 3, Figure 3) while a higher hemoglobin level (> 11 gm/dl) predicted a median survival of 126 months (subset 4). Also, in patients >64 years old, a BUN level >16 mg/dl (subset 6) predicted a median survival of 9 months, versus 45 months in those >64 years old with a BUN level 5 1 6 mg/dl (subset 5). Similar conclusions emerge with regard to total serum protein (subsets 9 and 10). Despite the inconsistencies found when attempting to relate subgroups to organ system or event ALTMAN ET AL leading to death, there were some trends within the subgroups. For example, patients in subgroup 3, with the worst median survival, had anemia and severe involvement of the kidney. Subgroup 6, with the second shortest median survival time, comprised older individuals with involvement of the lungs and severe involvement of the heart and kidneys. Patients in subgroup 7, with the next worst outcome (median survival 30 months), had significant lung involvement. These relative priorities (kidney, heart, lung) are consistent with the findings of other studies (10). Features in other subgroups suggest additional risk factors, i.e., malabsorption (subgroup 9), older age (subgroup 5 ) , and lung disease (subgroups 13 and 15). In general, these findings are supported by the proportion of patients who died of the associated causes. Although not as appealing clinically, the use of combinations of individual variables, some of which represent disease in more than one organ system, has greater predictive value than overall use of organ system involvement. It is suggested that the information obtained from the Cox proportional hazards model and the survival tree is more useful than organ system involvement data for describing patient outcome. This is emphasized by the nonspecific indicators of poor health detected using the survival tree, e.g., older age, low total serum protein levels, abnormal pulmonary function, both high and low hemoglobin levels, and high and low BUN values. Proximal muscle weakness was increased in all 4 of the worst prognosis subgroups (Figure 3), and increased urinary protein was present in 3 of the 6 survival tree subgroups with the worst prognosis. Although not as sensitive as the tree variables presented, reduced peripheral white blood cell count and reduced urinary creatinine also predicted reduced survival. In summary, we have studied a large number of patients with diffuse cutaneous systemic sclerosis over a prolonged period of time and identified several entry features associated with reduced survival. We are not aware of other studies that have followed as large a group of patients for a similar length of time or have utilized such a variety of analytic techniques. The findings reported herein may be of value in identifying specific SSc patients who are at risk for shortened survival. ACKNOWLEDGMENTS We acknowledge the late Dr. Gerald P. Rodnan, whose guiding force created the data bank used in this study. SURVIVAL IN SSc We also acknowledge Dr. Alfonse T. Masi, who contributed to the design of the data collection protocol, and the many contributors to the SCCS. It is impossible to individually recognize the multiple contributors, including statistics personnel, outcome assessors, and stenographers. REFERENCES 1 . Medsger TA Jr: Systemic sclerosis (scleroderma), eosinophilic fasciitis, and calcinosis, Arthritis and Allied Conditions. Eleventh edition. Edited by DJ McCarty. Philadelphia, Lea & Febiger, 1989 2. Seibold JR: Scleroderma, Textbook of Rheumatology. Third edition. Edited by WN Kelley, ED Harris Jr, S Ruddy, CM Sledge. Philadelphia, WB Saunders, 1989 3. Rocco VK, Hurd ER: Scleroderma and scleroderma-like disorders. 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