Eylem Sercan ÖZGÜR, Arzu KANIK, Sibel ATIŞ, Münir TÜMKAYA, Suzan EKER, Cengiz ÖZGE

KOAH hastalarının progresyonlarını ne ölçüde tahmin edebiliriz?

Tıpta karar verme sürecinde tahmin modellerinin kullanılmasının, kronik hastalıklarda gelecekteki klinik sonuçların yüksek olasılıkla doğru tahmin edilmesinde önemli rolleri vardır. Bu amaçla, bu çalışmada kronik obstrüktif akciğer hastalığı (KOAH) olan hastaların gelecekteki progresyon riskini hesaplamak için bir prognostik indeks modeli geliştirilmiştir. Üç yıllık prospektif bir çalışmada KOAH’lı 75 hasta çalışmaya alındı. Monte Carlo Markov zinciri yöntemiyle Bayesian lojistik regresyon analizi kullanılarak KOAH progresyon riskini belirlemek için bir model geliştirildi. Bu modelde hastalığın progresyonunu değerlendirmede üç yıl boyunca birer yıllık periyotlar kullanıldı. Progresyon değerlendirmesinde esas değişkenler olarak üç yıl içinde bazal dispne indeksi (BDİ) skorunda kötüleşme, FEV1’de düşme ve atak sıklığı alındı. Yaş, sigara, beden kitle indeksi (BKİ), GOLD evresi, PaO2, PaCO2, IC, RV/TLC, DLCO zamansal olarak değişen kovaryatlar idi. Hastaların yaşı 57.1 ± 8.1 idi. BDİ atak sıklığı ile güçlü bir korelasyon gösterirken (p= 0.001), FEV1 kaybı ile korelasyonu bulunmadı. BKİ, dispne skorunda kötüleşmede belirleyici bir risk faktörüydü (p= 0.03). Atak sıklığını anlamlı olarak belirleyen bağımsız risk faktörleri: GOLD evresi (GOLD evre I ile karşılaştırıldığında Odds oranı GOLD evre II ve III için= 2.3 ve 4.0), hipoksemi (hafif olgularla karşılaştırıldığında orta ve ağır hipoksemi için= 2.1 ve 5.1) ve hiperinflasyon (OR= 1.6) idi. FEV1 düşüşünü belirleyen bağımsız risk faktörleri PaO2 (p= 0.026), IC (p= 0.02) ve RV/TLC (p= 0.03) idi. Geliştirilen model üç yıl önceki değerlere bakarak en son değerlendirilen BDİ, FEV1 ve atak sıklığı %95 güvenilirlik ve gerçeklikle tahmin etti (p< 0.001). Bu sonuçlara göre, bu model KOAH’lı hastaların şimdiki verilerine bakarak üç yıl sonraki durumlarını değerlendirmede %95 güvenli olarak değerlendirildi. Bir Bayesian tahmin modelini kullanarak, KOAH’lı hastaların şimdiki durumlarına bakarak gelecekteki prognozlarını yüksek olasılıkla tahmin etmek mümkün gibi görünmektedir.

How exactly can we predict the prognosis of COPD?

Predictive models play a pivotal role in the provision of accurate and useful probabilistic assessments of clinical outcomes in chronic diseases. This study was aimed to develop a dedicated prognostic index for quantifying progression risk in chronic obstructive pulmonary disease (COPD). Data were collected prospectively from 75 COPD patients during a three years period. A predictive model of progression risk of COPD was developed using Bayesian logistic regression analysis by Markov chain Monte Carlo method. One-year cycles were used for the disease progression in this model. Primary end points for progression were impairment in basal dyspne index (BDI) score, FEV1 decline, and exacerbation frequency in last three years. Time-varying covariates age, smoking, body mass index (BMI), severity of disease according to GOLD, PaO2, PaCO2, IC, RV/TLC, DLCO were used under the study. The mean age was 57.1 ± 8.1. BDI were strongly correlated with exacerbation frequency (p= 0.001) but not with FEV1 decline. BMI was found to be a predictor factor for impairment in BDI (p= 0.03). The following independent risk factors were significant to predict exacerbation frequency: GOLD staging (OR for GOLD I vs. II and III = 2.3 and 4.0), hypoxemia (OR for mild vs moderate and severe = 2.1 and 5.1) and hyperinflation (OR= 1.6). PaO2 (p= 0.026), IC (p= 0.02) and RV/TLC (p= 0.03) were found to be predictive factors for FEV1 decline. The model estimated BDI, lung function and exacerbation frequency at the last time point by testing initial data of three years with 95% reliability (p< 0.001). Accordingly, this model was evaluated as confident of 95% for assessing the future status of COPD patients. Using Bayesian predictive models, it was possible to develop a risk-stratification index that accurately predicted progression of COPD. This model can provide decision-making about future in COPD patients with high reliability looking clinical data of beginning.

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