| Literature DB >> 23557396 |
Francesco Bonella1, Shinichiro Ohshimo, Cai Miaotian, Matthias Griese, Josune Guzman, Ulrich Costabel.
Abstract
BACKGROUND: Pulmonary alveolar proteinosis (PAP) is a rare disorder characterised by abundant alveolar accumulation of surfactant lipoproteins. Serum levels of KL-6, high molecular weight human MUC1 mucin, are increased in the majority of patients with PAP. The prognostic significance of KL-6 in PAP is still unknown. Aim of the study was to evaluate whether serum KL-6 levels correlate with the outcome of the disease. PATIENTS AND METHODS: From 2006 to 2012, we prospectively studied 33 patients with primary autoimmune PAP. We measured serum KL-6 levels by ELISA (Eisai, Tokyo, Japan), and evaluated the correlation between initial KL-6 levels and clinical variables. Disease progression was defined as deterioration of symptoms, and/or lung function, and/or chest imaging. MAINEntities:
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Year: 2013 PMID: 23557396 PMCID: PMC3629718 DOI: 10.1186/1750-1172-8-53
Source DB: PubMed Journal: Orphanet J Rare Dis ISSN: 1750-1172 Impact factor: 4.123
Baseline demographics and patients’ characteristics of all patients and of subgroups according to disease outcome (all data collected at baseline)
| | ||||
|---|---|---|---|---|
| 18/15 (54/46) | 6/11 | 12/4 | 0.016† | |
| 49±12 (19–79) | 50±9 | 47±15 | n.s. | |
| 5/12/16 (15/36/49) | 5/5/7 | 0/7/9 | 0.055† | |
| 26±5 (19–36) | 28±4 | 25±5 | n.s. | |
| 72±15 (47–117) | 78±13 | 63±12 | 0.002 | |
| 37±14 (11–63) | 31±11 | 44±14 | 0.005 | |
| 80±16 (44–123) | 88±13 | 69±13 | 0.001 | |
| 75±16 (43–104) | 80±15 | 67±13 | 0.014 | |
| 79±17 (42–116) | 85±16 | 70±14 | 0.009 | |
| 57±19 (21–96) | 66±12 | 42±13 | 0.001 | |
| 48±22 | 60±17 | 44±13 | 0.55 | |
| 2049±1893 | 1084±585 | 3334±2267 | 0.001 | |
| 283±93 (140–522) | 243±69 | 338±103 | 0.005 | |
| 8.5±7 (1–27) | 6±4.5 | 12±5.5 | 0.048 | |
Unless otherwise indicated, values are expressed as mean ± SD (range).
n.s.= not significant.
* patients who improved or remained stable and did not receive WLL within 18 months prior to the last evaluation.
§ the cut-off of normality for each biomarker is reported in the methods.
† Fisher's exact test, for all other comparisons in the table Student's t-test was used.
Figure 1Initial KL-6 serum levels and disease outcome. The graph shows the initial KL-6 serum concentrations in patients having progression or remission of disease during the follow-up. Dots represent single patients. Grid line represents the upper limit of normal (<458 U/mL). Bold lines represent the mean value.
Figure 2Correlation between initial KL-6 serum levels and pulmonary diffusing capacity. The graph shows the correlation between initial KL-6 serum levels and (a) DLCO, (b) A-aDO2.
Figure 3Correlation between change in KL-6 serum levels and pulmonary diffusing capacity over time. The graph shows the correlation between initial KL-6 serum levels and change in (a) DLCO and (b) A-aDO2 over time. Shown are % values (=relative change from baseline).
Figure 4Receiver operating characteristic curve analysis. The curves show the power of initial serum KL-6, LDH and GM-SCF for predicting (a) disease progression, and (b) necessity of repeated WLL.
Prognostic value of serum KL-6 for disease progression and for necessity of treatment with repeated WLL
| Disease progression | 81 | 94 | 93 | 84 | 88 |
| Necessity of repeated WLL | 83 | 96 | 91 | 92 | 94 |
Se=sensitivity, Sp=specificity, PPV= positive predictive value, NPV= negative predictive value.
Characteristics of the patients stratified according to the KL-6 predictive cut-off for disease progression (N=33)
| 9/10 | 9/5 | ns* | |
| 52±10 | 46±11 | ns** | |
| 15/4 | 12/2 | ns* | |
| | | | |
| -PAP-related death (yes/no) | 0/19 | 2/12 | ns* |
| -disease progression (yes/no) | 3/16 | 13/1 | <0.0001* |
| 6/13 | 12/2 | 0.03* | |
| -cumulative number of WLL | 3.5±3 | 7±6.5 | 0.06 |
| 78±14 | 66±12 | 0.012** | |
| 31±12 | 42±14 | 0.023** | |
| 88±15 | 72±14 | 0.002** | |
| 81±15 | 67±14 | 0.009** | |
| 86±17 | 72±16 | 0.017** | |
| 70±15 | 42±12 | <0.0001** | |
| 44±19 | 55±13 | ns** | |
| 930±352 | 3934±1756 | <0.0001** | |
| 244±78 | 327±109 | ns** | |
| 5.5±3.8 | 7.5±6 | ns** |
Unless otherwise indicated, values are expressed as mean ± SD (range).
ns= not significant.
* Fischer´ s exact test.
**Student´ s t-test.
Figure 5Kaplan-Meier analysis. The graph shows the predictive value of initial serum KL-6 levels for (a) disease progression and (b) the necessity of repeated WLL.
Univariate Cox proportional hazard model evaluating predictors for disease progression and the necessity of repeated WLL
| | | | | |
| 1.000 | 1.000 | 1.001 | 0.002 | |
| 6.284 | 1.767 | 22.352 | 0.005 | |
| 0.967 | 0.922 | 1.010 | 0.164 | |
| 5.448 | 1.509 | 19.671 | 0.010 | |
| 27.203 | 0.087 | 8539.00 | 0.260 | |
| 1.007 | 1.002 | 1.012 | 0.003 | |
| 0.993 | 0.968 | 1.020 | 0.617 | |
| 0.935 | 0.892 | 0.980 | 0.005 | |
| 1.067 | 1.020 | 1.115 | 0.010 | |
| 0.955 | 0.922 | 0.989 | 0.009 | |
| 0.948 | 0.909 | 0.989 | 0.012 | |
| | | | | |
| 1.000 | 1.000 | 1.000 | <0.001 | |
| 20.776 | 4.316 | 100.015 | <0.0001 | |
| 0.934 | 0.883 | 0.989 | 0.019 | |
| 2.910 | 0.786 | 10.769 | 0.110 | |
| 28.329 | 0.060 | 13470.548 | 0.288 | |
| 1.010 | 1.004 | 1.017 | 0.001 | |
| 0.996 | 0.967 | 1.025 | 0.767 | |
| 0.923 | 0.875 | 0.975 | 0.004 | |
| 1.095 | 1.032 | 1.162 | 0.003 | |
| 0.940 | 0.906 | 0.975 | 0.001 | |
| 0.935 | 0.892 | 0.980 | 0.005 | |
Definition of abbreviation: CI; confidence interval.
Multivariate Cox proportional hazard model evaluating predictors for disease progression and the necessity of repeated WLL
| | | | | |
| 0.999 | 0.998 | 1.000 | 0.090 | |
| 3.844 | 0.460 | 288.120 | 0.046 | |
| Gender (female=1) (binary) | 18.030 | 2.378 | 136.696 | 0.051 |
| | | | | |
| 1.000 | 1.000 | 1.001 | 0.196 | |
| PaO2, mmHg (cont.) | 0.897 | 0.579 | 0.969 | 0.028 |
| 9.408 | 0.794 | 111.511 | 0.008 | |
| PaO2, mmHg (cont.) | 0.97 | 0.93 | 1.00 | 0.038 |
| LDH, IU/L (cont) | 46.449 | 1.661 | 1299,262 | 0.024 |
Definition of abbreviation: CI; confidence interval.
* Calculated by stepwise conditional LR method. All significant predicting factors in the univariate analysis were included in the multivariate model. Only the factors that influence the model (adjusting factors) are reported in the Table.