Early Illness Features Associated With Mortality in the Juvenile Idiopathic Inflammatory Myopathies

Authors


  • Members of the Childhood Myositis Heterogeneity Collaborative Study Group are shown in Appendix A.

Abstract

Objective

Because juvenile idiopathic inflammatory myopathies (IIMs) are potentially life-threatening systemic autoimmune diseases, we examined risk factors for juvenile IIM mortality.

Methods

Mortality status was available for 405 patients (329 with juvenile dermatomyositis [DM], 30 with juvenile polymyositis [PM], and 46 with juvenile connective tissue disease–associated myositis [CTM]) enrolled in nationwide protocols. Standardized mortality ratios (SMRs) were calculated using US population statistics. Cox regression analysis was used to assess univariable associations with mortality, and random survival forest (RSF) classification and Cox regression analysis were used for multivariable associations.

Results

Of 17 deaths (4.2% overall mortality), 8 (2.4%) were in juvenile DM patients. Death was related to the pulmonary system (primarily interstitial lung disease [ILD]) in 7 patients, gastrointestinal system in 3, and multisystem in 3, and of unknown etiology in 4 patients. The SMR for juvenile IIMs overall was 14.4 (95% confidence interval [95% CI] 12.2–16.5) and was 8.3 (95% CI 6.4–10.3) for juvenile DM. The top mortality risk factors in the univariable analysis included clinical subgroup (juvenile CTM, juvenile PM), antisynthetase autoantibodies, older age at diagnosis, ILD, and Raynaud's phenomenon at diagnosis. In multivariable analyses, clinical subgroup, illness severity at onset, age at diagnosis, weight loss, and delay to diagnosis were the most important predictors from RSF; clinical subgroup and illness severity at onset were confirmed by multivariable Cox regression analysis.

Conclusion

Overall mortality was higher in juvenile IIM patients, and several early illness features were identified as risk factors. Clinical subgroup, antisynthetase autoantibodies, older age at diagnosis, and ILD are also recognized as mortality risk factors in adult myositis.

INTRODUCTION

The juvenile idiopathic inflammatory myopathies (IIMs) are rare, systemic autoimmune disorders characterized by proximal muscle weakness, skin rashes, and the potential for involvement of other systems, including pulmonary, cardiac, and gastrointestinal systems ([1]). Juvenile dermatomyositis (DM), juvenile polymyositis (PM), and juvenile connective tissue disease–associated myositis (CTM) are the most common clinical phenotypes of juvenile IIMs ([2]). Distinct myositis autoantibody phenotypes are recognized in juvenile IIMs, and they are similar to those present in adult IIMs ([3]). In general, although juvenile IIMs are serious illnesses that can result in death, it is uncommon. The factors associated with mortality in adults with IIMs have been well studied ([4-14]). However, risk factors for mortality have not been examined in juvenile IIMs.

Prior to routine use of corticosteroids and other immunosuppressive therapies as the standard of care treatment for juvenile IIMs, more than one-third of children with juvenile DM died ([15]). The mortality rate has decreased markedly since those medications were introduced to treat juvenile IIMs, with recent reviews describing mortality rates of less than 2% ([1, 16]). However, specific data regarding mortality rates for juvenile IIMs have been infrequently obtained. A large pediatric rheumatology registry that included 662 children with juvenile DM diagnosed between 1992 and 2001 in the US identified 5 deaths (0.8%) and a standardized mortality ratio (SMR) of 2.64 ([17]). In addition, 2 recent, large cohort studies reported mortality rates for juvenile DM between 0.7% and 3.1% ([18, 19]). Those reports documented that although mortality is no longer common in juvenile DM, it remains an important concern. Furthermore, there are no data concerning mortality in other juvenile IIM clinical or autoantibody phenotypes.

Little is known about the factors associated with mortality in juvenile IIMs. The goals of this study were to determine demographic, clinical, and laboratory features associated with death in patients with juvenile IIMs and to compare them with risk factors for mortality previously identified in adult IIM patients.

Box 1. Significance & Innovations

  • Mortality is increased in the juvenile idiopathic inflammatory myopathies (IIMs).
  • Risk factors for mortality in juvenile myositis have been identified and many of these are shared with adult IIMs, including clinical subgroup, anti-aminoacyl-transfer RNA synthetase autoantibodies, older age at diagnosis, and interstitial lung disease.

PATIENTS AND METHODS

Patients

Four hundred forty-one patients with probable or definite juvenile IIMs ([20]) were enrolled in National Institutes of Health or Food and Drug Administration Institutional Review Board–approved natural history protocols between March 1989 and April 2011; all patients or their parents provided informed consent. A physician questionnaire containing demographic, clinical, and laboratory data; outcome information; and a blood sample were obtained as previously described ([2]). Approximately 85 illness features were assessed (see Supplementary Table 1, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.22212/abstract). The referring physician recorded the month and year of each illness feature, as well as the presenting signs and symptoms of illness; only those features present prior to or at the time of diagnosis were included. The questionnaire was completed at the time of enrollment by the referring physician. Illness severity at onset and onset speed were assigned by the enrolling physician using a categorical scale without knowledge of mortality outcome ([2]). For 70% of patients, a pediatric rheumatologist (GM or LGR) reviewed the medical record to confirm the questionnaire data and complete missing information. Sera were tested for myositis-specific autoantibodies and myositis-associated autoantibodies by using validated immunoprecipitation and immunoblotting methods ([2]).

Mortality status was established using the Social Security Death Index (SSDI). The SSDI was last searched on April 11, 2011. Mortality status could not be confirmed in 22 patients because of insufficient identifying information; therefore, they were excluded. Fourteen non-American patients were also excluded. This left 405 patients (329 with juvenile DM, 30 with juvenile PM, and 46 with juvenile CTM) in this study. The cause of death was determined through death certificates and/or review of clinical charts.

Methods

Statistical analysis was conducted using Stata/IC 10.1 for Windows (StataCorp). Continuous variables were summarized using the median and interquartile range. Comparisons of proportions were made using Fisher's exact test, and continuous variables were compared using the Mann-Whitney U test. This analysis was considered exploratory, so no corrections for multiple comparisons were made.

The sex-adjusted SMR for this cohort was calculated using the life table for the US population for 1999–2001 ([21]). The yearly hazards were calculated using the method described by Therneau and Grambsch ([22]), and the cumulative hazard was determined as the sum of the yearly hazards. Expected deaths were then calculated and compared to observed deaths to generate the SMR. This was done for all juvenile IIMs and for each clinical subgroup (juvenile DM, juvenile PM, and juvenile CTM); SMRs for males and females in both groups were also calculated for all juvenile IIMs and juvenile DM, but not for juvenile PM and juvenile CTM due to small numbers of patients. Because of the small numbers of patients and short exposure time, the SMR calculation for juvenile PM and juvenile CTM was considered preliminary and confidence intervals (CIs) were not calculated.

Univariable analysis was conducted using Cox regression analysis. Followup duration was defined as the time from diagnosis to the date of death for patients who died and the time from diagnosis to the last followup visit for those who were still alive. Ever hospitalized was included in the analysis, since it was assumed that most of the hospital admissions occurred at the time of diagnosis. Results for univariable analyses with P values of 0.10 or less were reported, since this analysis was considered exploratory. Analyses were excluded when missing data resulted in 14 or fewer deaths being available. Consequently, fever, fatigue, and dyspnea on exertion were excluded. To facilitate analysis, after the univariable analysis, disease subtype (juvenile DM versus not juvenile DM) and onset severity (mild/moderate versus severe/very severe) were dichotomized.

The multivariable analysis was conducted using a 2-stage approach to further identify variables that were important predictors of mortality. First, we used random survival forests (RSFs) ([23, 24]) to identify and validate the predictors from a large group of candidate variables from the univariable Cox proportional hazards modeling. A conservative approach was taken by including all variables from the univariable Cox regression analysis with P values less than 0.10 in order to avoid excluding potentially important variables. Second, we used multivariable stepwise Cox regression modeling to confirm the top RSF variables. Demographic, clinical, laboratory, and outcome variables identified in the univariable analysis with P values of 0.10 or less were entered in the RSF models. The RSF analysis was performed using the R software package Random Survival Forests, version 2.13.1 (The R Foundation for Statistical Computing, 2011) ([23, 24]). Each model was run 10 times using 20,000 trees per run. The average importance for each variable was calculated across runs, and then the average relative importance was obtained by assigning the variable with the highest importance a relative importance of 1.0. Error rates for the RSF models averaged 23.4%. Variables with the highest relative importance from the RSF analysis were then entered into a multivariable backward-stepwise Cox regression model (a criterion of P less than 0.2 was used to drop variables from the model). A final multivariable Cox regression model was created for those variables with P values of 0.10 or less.

RESULTS

Key demographic and illness features of the study population are shown in Table 1. The median disease duration at study enrollment was 1.6 years (25th, 75th percentiles 0.5, 3.7 years) from diagnosis, median age at enrollment was 10.6 years (25th, 75th percentiles 7.4, 15.3 years), and median total followup since time of diagnosis was 4.3 years (25th, 75th percentiles 2.2, 7.5 years) for all patients. There were 17 deaths, for a 4.2% overall mortality rate. Of these, 8 deaths were in patients with juvenile DM, yielding a juvenile DM mortality rate of 2.4%. Two deaths were in patients with juvenile PM and 7 deaths were in patients with juvenile CTM, for mortality rates of 6.7% and 15.2%, respectively. The median age at death was 18.4 years (25th, 75th percentiles 16.6, 27.4 years) and disease duration at the time of death ranged from 0.1–38.5 years after diagnosis, with a median disease duration of 5.6 years at the time of death. Death occurred more frequently in patients who were older at diagnosis (median 14.7 years in those deceased versus 7.4 years in those alive). Patients with severe illness at onset had a higher mortality rate than those with mild or moderate illness at onset. Patients with certain myositis autoantibodies, including any anti–aminoacyl–transfer RNA (tRNA) synthetase autoantibodies, as well as anti–alanyl–tRNA synthetase, anti-Ku, or anti-La autoantibodies, were more likely to have died than patients without these autoantibodies (Table 1).

Table 1. Baseline demographic features and myositis autoantibodies of 405 juvenile idiopathic inflammatory myopathy patients*
 Alive at last followup (n = 388)Deceased (n = 17)P
  1. Values are the median (25th, 75th percentiles) unless indicated otherwise. Totals may not add up due to data missing for some variables for some patients. DM = dermatomyositis; PM = polymyositis; CTM = myositis associated with an underlying connective tissue disease; anti-HisRS = anti–histidyl–transfer RNA (tRNA) synthetase; anti-AlaRS = anti–alanyl–tRNA synthetase; anti-SRP = anti–signal recognition particle.
  2. aBy Fisher's exact test.
  3. bBy the Mann-Whitney U test.
Clinical subgroup, no. (%)   
Juvenile DM321 (83)8 (47) 
Juvenile PM28 (7)2 (12)0.001a
Juvenile CTM39 (10)7 (41) 
Sex, no. (%)   
Female281 (72)14 (82)0.6a
Male107 (28)3 (18) 
Race, no. (%)   
White265 (68)13 (76) 
African American58 (15)3 (18) 
Hispanic24 (6)00.9a
Asian7 (2)0 
Other34 (9)1 (6) 
Age at diagnosis, years7.4 (5.1, 11.6)14.7 (9.0, 16.4)0.001b
Diagnosis delay, months4.0 (2.0, 9.0)6.0 (2.0, 12.7)0.5b
Age at study enrollment, years10.5 (7.3, 15.1)16.0 (10.5, 27.6)0.005b
Disease duration at study enrollment, years1.6 (0.5, 3.6)0.6 (0.2, 10.5)0.4b
Disease duration at last followup/death, years4.3 (2.2, 7.4)5.6 (1.6, 9.8)0.6b
Onset speed, no. (%)   
Very slow (>6 months)136 (35)7 (47) 
Slow (3–6 months)112 (29)4 (27) 
Moderate (<3 months)98 (25)2 (13)0.6a
Rapid (<1 month)36 (9)2 (13) 
Very rapid (<1 week)3 (1)0 
Illness severity at onset, no. (%)   
Mild40 (10)0 
Moderate231 (60)7 (41)0.06a
Severe105 (27)10 (59) 
Very severe9 (2)0 
Myositis autoantibodies, no. (%)   
Any anti–aminoacyl–tRNA synthetase autoantibodies14 (4)3 (18)0.03a
Anti-HisRS autoantibodies (Jo-1)9 (2)1 (6)0.4a
Anti-AlaRS autoantibodies (anti–PL-12)3 (0.8)2 (12)0.02a
Anti–Mi-2 autoantibodies10 (3)0 (0)1.0a
Anti-p155/140 autoantibodies124 (32)3 (18)0.3a
Anti-MJ autoantibodies81 (21)1 (6)0.2a
Anti-SRP autoantibodies6 (2)0 (0)1.0a
Anti–PM-Scl autoantibodies11 (3)1 (6)0.4a
Anti-Ku autoantibodies1 (0.3)1 (6)0.01a
Anti-Ro autoantibodies26 (7)2 (12)0.3a
Anti-La autoantibodies2 (0.5)1 (6)0.1a
Anti-Sm autoantibodies5 (1)1 (6)0.2a

The SMR for juvenile IIMs overall was 14.4 (95% CI 12.2–16.5). The SMR for females with juvenile IIMs was 18.3 (95% CI 16.6–20.1) and for males with juvenile IIMs was 7.1 (95% CI 5.9–8.4). The overall SMR for juvenile DM was 8.3 (95% CI 6.4–10.3), while the SMRs for females and males with juvenile DM were 10.7 (95% CI 9.1–12.3) and 3.3 (95% CI 2.2–4.4), respectively. The overall SMR for juvenile PM was 30.7 and the overall SMR for juvenile CTM was 66.9. Females had a larger SMR for juvenile IIMs overall and for juvenile DM, but no difference was seen in the univariable analysis for sex (P = 0.6).

The primary causes of death were pulmonary in 7 patients, gastrointestinal in 3 patients, and multisystemic in 3 patients. Pulmonary causes were a contributing factor in 2 of the multisystem deaths (Table 2). Most of the pulmonary deaths resulted from complications of interstitial lung disease (ILD). Gastrointestinal deaths were related to complications of gastrointestinal ulceration and/or vasculopathy. In 4 patients, the cause of death was not available.

Table 2. Causes of death in 17 patients with juvenile idiopathic inflammatory myopathies*
SystemDiagnosisMyositis autoantibodiesSex/raceAge at diagnosis, yearsDisease duration at death, yearsCause of death
  1. Patients were diagnosed between 1981 and 2002 and were a median age of 18.4 years (25th, 75th percentiles 16.6, 27.4 years) at the time of death. CTM = myositis associated with an underlying connective tissue disease; ILD = interstitial lung disease; DM = dermatomyositis; PL-12 = anti–alanyl–transfer RNA (tRNA) synthetase autoantibodies; Jo-1 = anti–histidyl–tRNA synthetase autoantibodies; PM = polymyositis.
  2. aOverlap of juvenile DM and systemic lupus erythematosus (SLE) and overlap of juvenile DM and systemic sclerosis (SSc).
  3. bInformation from physician or medical record.
  4. cInformation from death certificate.
  5. dInformation from physician or medical record and death certificate.
  6. eOverlap of juvenile DM and SSc.
  7. fOverlap of juvenile PM and Sjögren's syndrome.
  8. gOverlap of juvenile PM and SLE.
PulmonaryJuvenile CTMaNoneFemale/white150.3ILD, progressive respiratory failure, acute cardiac failureb
 Juvenile DMPL-12Female/white151.4Respiratory failure, pulmonary fibrosisc
 Juvenile DMPL-12Female/white105.6ILD, chronic interstitial pneumonitisd
 Juvenile CTMeKuFemale/white86.6ILD (diffuse alveolar damage with marked interstitial fibrosis)
 Juvenile DMp155/140Female/white127.5ILD, secondary pulmonary hypertensionb
 Juvenile CTMfJo-1, Ro, LaFemale/African American179.5ILDb
 Juvenile CTMeNoneFemale/other512.6Pulmonary hypertension, multiple spontaneous pneumothoraces, secondary scleroderma, severe malnutrition, superior mesenteric artery syndromeb
GastrointestinalJuvenile PMRoMale/white10.5Intestinal perforationb
 Juvenile CTMaSmMale/African American154.0Colon perforation resulting in Escherichia coli sepsisb
 Juvenile DMp155/140Female/white710.3Gastrointestinal hemorrhageb
MultisystemJuvenile PMNoneFemale/white160.1Myocarditis, tachyarrhythmias, respiratory failure, pneumoniab
 Juvenile DMNoneFemale/African American162.0Acute respiratory distress syndrome, sepsis, congestive heart failure, intracranial hemorrhageb
 Juvenile CTMgNoneFemale/white163.5Liver failure, renal failurec
UnknownJuvenile CTMePM-SclFemale/white1616.7Unknown
 Juvenile DMMJFemale/white1431.7Unknown
 Juvenile DMNoneMale/white1035.1Unknown
 Juvenile DMp155/140Female/white738.5Unknown

In exploratory univariable analysis, 21 illness features were associated with a higher risk of mortality (P < 0.10) (Table 3). Clinical subgroup was strongly associated with a higher risk of mortality; the risk of mortality was highest for juvenile CTM, moderate for juvenile PM, and lowest for juvenile DM. One of the features most strongly associated with mortality was having an aminoacyl–tRNA synthetase autoantibody (particularly anti–alanyl–tRNA synthetase autoantibodies). All of these patients died of complications of ILD. Other autoantibodies associated with a higher risk of mortality included anti-Ku, anti-La, and anti-Sm autoantibodies, although these were each present in only 1 patient who died. Older age at illness onset and at diagnosis, dysphagia, and abdominal perforation as the first presenting features of illness, as well as ILD, Raynaud's phenomenon, and dysphonia preceding or at diagnosis, were features more strongly associated with a higher risk of mortality. Gottron's papules were associated with a reduced risk of mortality (hazard ratio <1.0). Some of these illness features were observed rarely (for example, dysphagia and abdominal perforation at illness onset, which were each present in only 1 patient who died).

Table 3. Summary of univariable Cox regression analyses relating risk of death and illness features*
Illness featureaDeathsHR (95% CI)P
Illness feature absentIllness feature present
  1. Values are the number/total (percentage) unless indicated otherwise. HR = hazard ratio; 95% CI = 95% confidence interval; DM = dermatomyositis; CTM = myositis associated with an underlying connective tissue disease; PM = polymyositis; anti-AlaRS = anti–alanyl–transfer RNA (tRNA) synthetase.
  2. aAll illness features with P < 0.10 included.
  3. bMedian (25th, 75th percentiles) reported for those who did not die in the “illness feature absent” column and for those who did die in the “illness feature present” column.
  4. cFirst myositis symptom reported.
Clinical subgroup, juvenile DM vs. juvenile CTMDM: 8/329 (2.4)CTM: 7/46 (15.2)9.8 (3.1–31.4)< 0.001
Clinical subgroup, juvenile DM vs. juvenile PM PM: 2/30 (6.7)7.0 (1.3–37.7)0.03
Illness severity at onset, mild/moderate vs. severe/very severe7/278 (2.5)10/127 (7.9)2.9 (1.1–7.6)0.03
Age at diagnosis, yearsb7.4 (5.1, 11.6)14.7 (9.0, 16.4)1.2 (1.1–1.3)< 0.001
Age at onset, yearsb6.9 (4.5, 11.0)12.3 (8.5, 15.0)1.2 (1.1–1.3)0.002
Delay to diagnosis, monthsb4.0 (2.0, 9.0)6.0 (2.0, 12.7)1.02 (1.0–1.0)0.03
Clinical features present prior to or at diagnosis    
Dysphagiac14/389 (3.6)1/1 (100)375.5 (23.5–6003.2)< 0.001
Abdominal perforationc16/403 (4.0)1/1 (100)39.4 (4.8–325.2)0.001
Ever hospitalized1/183 (0.6)15/208 (7.2)10.5 (1.4–80.1)0.02
Interstitial lung disease12/385 (3.1)3/16 (18.8)9.8 (2.6–37.1)0.001
Raynaud's phenomenon9/366 (2.5)7/31 (22.6)8.8 (3.2–24.5)< 0.001
Gastroesophageal regurgitation12/363 (3.3)3/31 (9.7)4.3 (1.2–15.9)0.03
Dysphonia9/324 (2.8)6/64 (9.4)4.1 (1.4–11.9)0.009
Shawl sign rash10/351 (2.9)5/36 (13.9)3.8 (1.3–11.5)0.02
Weight loss7/270 (2.6)8/118 (6.8)2.9 (1.03–8.0)0.04
Abdominal pain11/313 (3.5)5/75 (6.7)2.9 (0.9–8.9)0.07
Gottron's papules7/85 (8.2)8/306 (2.6)0.3 (0.09–0.8)0.01
Myositis autoantibodies    
Anti-AlaRS autoantibodies (anti–PL-12)15/394 (3.8)2/5 (40)23.6 (4.7–117.3)< 0.001
Anti-Ku autoantibodies16/397 (4.0)1/2 (50)18.3 (2.2–147.9)0.006
Any aminoacyl–tRNA synthetase autoantibodies14/382 (3.7)3/17 (17.7)13.1 (3.4–50.4)< 0.001
Anti-La autoantibodies16/396 (4.0)1/3 (33.3)12.2 (1.5–96.5)0.02
Anti-Sm autoantibodies16/393 (4.1)1/6 (16.7)7.2 (0.9–56.2)0.06

Results of the RSF modeling are shown in Table 4. Relatively few features had mean relative importance values >0. The variables with the highest importance were clinical subgroup, severity at onset, older age at diagnosis, weight loss, and delay to diagnosis. In the multivariable regression modeling (Table 5), the features that were independently associated with risk of death were clinical subgroup and illness severity at onset.

Table 4. Summary of random survival forest analysis relating risk of death and clinical features*
Illness featureMean relative importance
  1. Mean importance values for 10 runs are compared to the highest mean importance value. All features with P < 0.10 in Cox regression analysis are included. DM = dermatomyositis; PM = polymyositis; CTM = myositis associated with an underlying connective tissue disease; anti-AlaRS = anti–alanyl–transfer RNA (tRNA) synthetase.
Clinical subgroup (juvenile DM vs. juvenile PM/juvenile CTM)1.00
Illness severity at onset0.33
Age at diagnosis0.32
Weight loss0.30
Delay to diagnosis0.23
Shawl sign rash0.08
Abdominal pain0.07
Age at onset0.06
Gottron's papules0.05
Anti-Ku autoantibodies0.00
Anti-Sm autoantibodies0.00
Anti-La autoantibodies0.00
Any aminoacyl–tRNA synthetase autoantibodies0.00
Anti-AlaRS (anti–PL-12) autoantibodies0.00
Dysphagia0.00
Interstitial lung disease0.00
Abdominal perforation (first symptom)0.00
Ever hospitalized0.00
Gastroesophageal regurgitation−0.04
Raynaud's phenomenon−0.04
Dysphonia−0.19
Table 5. Summary of final multivariable Cox regression analyses relating risk of death and illness features*
 HR (95% CI)P
  1. HR = hazard ratio; 95% CI = 95% confidence interval; DM = dermatomyositis; PM = polymyositis; CTM = myositis associated with an underlying connective tissue disease.
  2. aThe multivariable Cox regression model began with clinical subgroup (juvenile DM vs. juvenile PM/juvenile CTM), illness severity at onset, age at diagnosis, weight loss, and delay to diagnosis before backward-stepwise selection; 308 patients were included in the model.
Initial Cox regression model (with backward-stepwise selection)a  
Clinical subgroup (juvenile DM vs. juvenile PM/juvenile CTM)4.8 (1.2–19.6)0.03
Illness severity at onset5.1 (1.4–17.9)0.01
Age at diagnosis1.1 (0.98–1.3)0.09
Final Cox regression model  
Clinical subgroup (juvenile DM vs. juvenile PM/juvenile CTM)8.6 (2.5–30.2)0.001
Illness severity at onset5.7 (1.6–19.5)0.006

DISCUSSION

This study identified several illness features that were associated with a higher risk of death in this large cohort of patients with juvenile IIMs. This is the first study to investigate mortality risk factors in juvenile IIMs and is an important step in understanding risk factors for death in these rare illnesses. In this study, 17 (4.2%) of 405 patients with juvenile IIMs died, with an overall SMR of 14.4 (95% CI 12.2–16.5), suggesting greater risk of mortality in patients with juvenile IIMs compared to the healthy population. It is difficult to be certain how the SMR relates to the actual mortality rate in these illnesses. For example, although this study enrolled patients without consideration of disease severity, it is not known whether disease severity influenced the likelihood of study enrollment. For these reasons, our results might over- or underestimate the actual mortality rate for juvenile IIMs. Also, in calculating the SMR, we used life-table data for 1999–2001, which may provide only a crude estimate of the SMR, since our juvenile IIM patients died between 1995 and 2007. Finally, with the small number of deaths, our estimates of the SMR are likely quite unstable. Females in our study had higher SMRs for juvenile IIMs overall and for juvenile DM than males, but no difference was seen between sexes in the univariable analysis. There are some potential explanations for this discrepancy. Our cohort may have been too small to show this difference in the univariable analysis. However, it is more likely that the difference in SMR is related to differences in mortality rates in the general population, since males consistently have higher mortality rates over the age range of relevance in this study.

It is difficult to directly compare our results to previous data, since few studies exist. The only study to provide SMRs in children with juvenile DM utilized a large registry in the US that enrolled patients with a variety of pediatric rheumatic diseases diagnosed between 1992 and 2001 ([17]). Patients in that study had a mean of 7.9 years of followup; the juvenile DM–specific followup duration was not provided. Using the SSDI data, those authors identified 5 deaths (0.8%) in 662 juvenile DM patients and calculated the SMR to be 2.64 (95% CI 0.86–6.17). The juvenile DM–specific mortality rate in our study was 2.4% (8 of 329 juvenile DM patients with a mean followup duration of 1.6 years) and the overall SMR for juvenile DM was 8.3 (95% CI 6.4–10.3). It appears that the mortality rate in our study was higher over a shorter period of time. However, given differences in methodology and limited details in the study by Hashkes et al ([17]), it is difficult to further evaluate the difference in mortality rates between the 2 studies.

We can also compare our results to recent cohort-based studies. In an international cohort of patients from Europe and Latin America with a mean followup duration of 7.7 years, Guseinova et al ([18]) described 15 deaths (3.1%) among 490 juvenile DM patients. In contrast, in the UK, McCann et al ([19]) reported only 1 death (0.7%) in 151 children with juvenile IIMs (including 120 patients with juvenile DM) with a mean followup duration of 3.1 years. Differences in these results may be attributable to differences in juvenile IIM populations (geographic, ethnic, and juvenile IIM phenotype distribution), as well as differences in care and followup duration.

Comparisons between SMRs in children and adults are of limited value, given the higher baseline mortality rates in adults. Nevertheless, Limaye et al ([8]) calculated SMRs of 1.75 (95% CI 1.41–2.15) for Australian adults with IIMs, 1.56 (95% CI 1.12–2.10) for PM, and 2.4 (95% CI 1.1–4.55) for DM. That study included patients with inclusion body myositis, which has been shown to have a relatively lower mortality rate ([25]). Airio et al ([4]) reported an SMR of 2.92 (95% CI 2.48–3.44) for Finnish patients with PM and DM combined. Kuo et al ([7]) found an SMR of 7.68 (95% CI 6.41–9.01) in Taiwanese patients with DM and 5.29 (95% CI 4.28–6.48) in patients with PM. Although the SMR values for our study are higher than those reported in these adult studies, it is not possible to conclude that juvenile IIM is more serious or has a higher mortality rate than adult IIM.

When we consider the causes of death in our cohort of juvenile IIM patients, pulmonary disease was the most common association. Of the 13 patients with a known cause of death, it was the primary cause for 7 patients and an associated cause in 2 patients. This is consistent with findings from adult IIM studies ([6, 12, 13]). Only 3 of these patients had an anti–aminoacyl–tRNA synthetase autoantibody, suggesting that in juvenile IIMs, there are additional factors contributing to the development of pulmonary disease. In contrast, cardiac disease was a contributing factor to death in only 3 juvenile IIM patients, although it is an important factor in adult studies ([6, 11]). The cardiac disease in our study is similar to that described in adult myositis studies, which includes rhythm disturbances, conduction abnormalities, pericardial effusion, left ventricular dysfunction, myocarditis, cardiomyopathy, and congestive failure ([6, 11]). Finally, gastrointestinal bleeding or perforation was the cause of death in 3 patients. Although this cause of death is not described in adult studies, it is consistent with clinical experience in juvenile IIMs ([16]).

We identified, by univariable analysis, several illness features associated with a higher risk of death in juvenile IIM patients. To avoid the problem of differential followup, we examined only those illness features present prior to or at the time of diagnosis. Factors that were associated with an increased risk of mortality included clinical subgroup, anti–aminoacyl–tRNA synthetase autoantibodies, older age at illness onset and at diagnosis, and some early illness features present prior to or at diagnosis, including ILD, Raynaud's phenomenon, and dysphonia. Given the few deaths and many analyses conducted, these illness features should be confirmed in other juvenile IIM cohorts.

The relative rarity and infrequent mortality of patients with juvenile IIMs are a major challenge in this kind of research. However, we used a variety of methods to determine significant illness features by univariable analyses and then conducted exploratory multivariable analyses. We used RSF, a novel statistical method that extends the machine-learning tool random forest to survival analysis, to examine the relationship of a number of univariable factors and to identify their relative importance in predicting the risk of mortality ([23, 24]) (Table 4). Random forests and RSF have been similarly used successfully by other investigators to determine mortality risk factors for other rheumatic illnesses, cardiovascular disease, and malignancies ([23, 26-28]). Some of the risk factors that we identified, including clinical subgroup and ILD, are similar to those found in studies of adult IIMs ([6, 12-14]).

The multivariable regression model confirmed and quantitated the strength of association of key illness features that were most associated with risk of mortality in juvenile IIMs, including clinical subgroup and illness severity at onset. These findings suggest that early disease severity is an important risk factor for death.

We compared risk factors for mortality in our study to those documented for adult IIMs and found similarities and differences. Of the mortality features from our univariable analyses, those also described in adult IIM studies included older age at illness onset ([4-6, 10-13, 29]), delay in diagnosis or treatment ([4, 5, 10]), ILD ([6, 12-14]), antisynthetase autoantibodies ([8, 9, 30]), and dysphagia ([6]). Our study of juvenile IIMs did not identify other important mortality risk factors that have been seen in adult IIMs, such as malignancy ([4, 5, 10-13]), cardiac involvement and ischemic cardiovascular disease ([6, 8, 11]), skin ulcers ([10, 13]), and male sex ([6, 11, 29]). The presence of anti–signal recognition particle (anti-SRP) autoantibodies has been found to be a risk factor for death by some investigators ([30]), but not by others ([31]); anti-SRP autoantibodies did not appear to increase the risk of death in juvenile IIMs in our study. The rarity of cardiac disease in children, with a lack of adequate followup into the adult years, and the rarity of malignancy in juvenile IIMs ([2]) may explain their absence as mortality risk factors for juvenile IIMs.

Our study also identified risk factors that have not been described in studies of adult IIMs. For example, in the multivariable analysis, illness severity at onset was associated with a higher risk of death. Given the number of risk factors for death that are common between children and adults with IIMs, this novel risk factor warrants further study in adult IIMs and confirmation in other juvenile IIM populations.

Despite the careful conduct of this study, there are some important limitations. First, we had relatively few deaths to analyze, since death in juvenile IIMs has become uncommon. This limited our ability to conduct robust, multivariable modeling. Our results must therefore be considered preliminary and will require confirmation in additional large cohorts. Our univariable analysis results are simply presented using a very conservative cutoff of P < 0.1, but limited conclusions are drawn. Inclusion of variables in the multivariable analysis (both RSF and Cox regression analysis) is also based on a very conservative P value cutoff, and it would not be appropriate to lower this, since potentially important variables could be discarded prematurely. Also, because RSF is a predictive model rather than an associative model, it is possible that by filtering the features for RSF based on the results of univariable analyses, we have missed predictive features in the RSF. In contrast, the multiple testing used in the multivariable stepwise logistic regression modeling increases the risk of Type I statistical error and the possibility of false-positive results. However, given that the study was exploratory, we did not correct for multiple comparisons.

Although children were enrolled from many sites in North America, it is not known how well this cohort represents the entire population of juvenile IIMs. There are also limitations to the use of the SSDI. Although the SSDI allows most deaths to be identified, it is possible that some deaths or some study participants could have been missed. Finally, our study is limited by the fact that participants were enrolled over a period of 22 years. This allowed a large cohort to be assembled and facilitated the identification of 17 deaths, but the treatment of children with juvenile IIMs has changed considerably during that time. Our study did not examine the impact of therapy on mortality. A study by Schiopu et al examined the role of therapy as a risk factor for mortality in adults with IIMs and found that patients receiving intravenous corticosteroids had a higher mortality rate, which was attributed to greater disease severity ([29]). While changes in treatment have certainly reduced mortality in juvenile IIMs, it is less clear if changes in treatment have affected predictors of mortality. Therefore, the lack of treatment data in our study does not negate the relevance of our findings.

In conclusion, we conducted the first study of risk factors for mortality in patients with juvenile IIMs. Through the use of RSF as a novel statistical approach, we identified a number of illness features associated with an increased risk of death. Several of these features previously have been identified as risk factors for mortality in adults with IIMs and therefore are likely to be true risk factors for mortality in juvenile IIMs as well, whereas others are novel. These new factors warrant additional study to confirm their association with mortality and contribution to juvenile IIM outcomes.

AUTHOR CONTRIBUTIONS

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Rider had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Lachenbruch, Miller, Rider.

Acquisition of data. Mamyrova, Lee, Katz, Targoff, Rider.

Analysis and interpretation of data. Huber, Mamyrova, Lachenbruch, Miller, Rider.

Acknowledgments

The authors would like to thank Dr. Abhijit Dasgupta for helpful discussions and guidance on the RSF analysis. We also thank Drs. Mark Gourley, Michael Ward, and Abhijit Dasgupta for critical reading of the manuscript and Dr. Paul Plotz for patient referrals and support of this project.

Appendix: A: MEMBERS OF THE CHILDHOOD MYOSITIS HETEROGENEITY COLLABORATIVE STUDY GROUP

Members of the Childhood Myositis Heterogeneity Collaborative Study Group who contributed to this study are as follows: Leslie S. Abramson, Daniel A. Albert, Bita Arabshahi, Alan N. Baer, Imelda M. Balboni, William P. Blocker, John F. Bohnsack, Michael S. Borzy, Gary R. Botstein, Suzanne Bowyer (deceased), Jon M. Burnham, Ruy Carrasco, Victoria W. Cartwright, Chun Peng T. Chao, Randy Q. Cron, Marietta M. DeGuzman, B. Anne Eberhard, Barbara S. Edelheit, John F. Eggert, Andrew H. Eichenfield, Melissa E. Elder, Terri H. Finkel, Irene Flatau, Robert C. Fuhlbrigge, Christos A. Gabriel, Vernon F. Garwood, Harry L. Gewanter, Ellen A. Goldmuntz, Donald P. Goldsmith, Gary V. Gordon, Alexia C. Gospodinoff, Beth S. Gottlieb, Thomas A. Griffin, Brandt P. Groh, Hillary M. Haftel, Michael Henrickson, Gloria C. Higgins, Mark F. Hoeltzel, J. Roger Hollister, Russell J. Hopp, Norman T. Ilowite, Laura James-Newton, Anna Jansen, James Jarvis, Rita S. Jerath, Courtney R. Johnson, Thomas V. Kantor, Ildy M. Katona, Yukiko Kimura, Daniel J. Kingsbury, Steven J. Klein, C. Michael Knee, W. Patrick Knibbe, Andrew Lasky, Johanan Levine, Carol B. Lindsley, Katherine L. Madson, Paul L. McCarthy, Stephen R. Mitchell, Hamid Jack Moallem, Frederick T. Murphy, Terrance O'Hanlon, Elif A. Oral, Barbara E. Ostrov, Lauren M. Pachman, Ramesh Pappu, Murray H. Passo, Maria D. Perez, Donald A. Person, Karin S. Peterson, Marilynn G. Punaro, C. Egla Rabinovich, Charles D. Radis, Robert M. Rennebohm, Peter D. Reuman, Rafael F. Rivas-Chacon, Deborah Rothman, Kenneth N. Schikler, Bracha Shaham, Robert M. Sheets, David D. Sherry, Edward M. Sills, Sara H. Sinal, Sangeeta D. Sule, Robert P. Sundel, Ilona Szer, Simeon I. Taylor, Richard K. Vehe, Scott A. Vogelgesang, Larry B. Vogler, Carol A. Wallace, Jennifer C. Wargula, Patience H. White, M. Jack Wilkenfeld, Lan Wu, and Lawrence S. Zemel.

Ancillary