ABSTRAK
Latar Belakang. Terdapat gangguan sistem imun pada sepsis. Fase awal ditandaidengan hiperinflamasi, sedangkan fase lanjut ditandai dengan imunosupresi.Kematian kumulatif lebih banyak pada fase lanjut. Saat ini belum terdapatpenelitian yang secara khusus meneliti faktor prognostik mortalitas sepsis faselanjut dan mengembangkan model prediksi mortalitasnya.Tujuan. Mengetahui faktor prognostik mortalitas sepsis berat fase lanjut di ICUdan mengembangkan sistem skor untuk memprediksi mortalitas.Metode. Penelitian kohort retrospektif dilakukan pada pasien dewasa yangmengalami sepsis berat di ICU RSCM pada periode Oktober 2011 – November2012 dan masih bertahan setelah > 72 jam diagnosis sepsis ditegakkan di ICU.Tujuh faktor prognostik diidentifikasi saat diagnosis sepsis berat ditegakkan diICU. Prediktor independen diidentifikasi dengan analisis Cox’s proportionalhazard. Prediktor yang bermakna secara statistik dikuantifikasi dalam modelprediksi. Kalibrasi model dinilai dengan uji Hosmer-Lemeshow dan kemampuandiskriminasi dinilai dari area under curve (AUC) dari receiver operating curve.Hasil. Subjek penelitian terdiri atas 220 pasien. Mortalitas 28 hari sepsis beratfase lanjut adalah 40%. Faktor prognostik yang bermakna adalah alasan masukICU (medis (HR 2,75; IK95%:1,56-4,84), pembedahan emergensi (HR 1,96;IK95%:0,99 – 3,90), indeks komorbiditas Charlson > 2 (HR 2,07; IK95%:1,32-3,23), dan skor MSOFA > 4 (HR 2,84; IK95%:1,54-5,24). Model prediksimemiliki kemampuan diskriminasi yang baik (AUC 0,844) dan kalibrasi yangbaik (uji Hosmer-Lemeshow p 0,674). Berdasarkan model tersebut risikomortalitas dapat dibagi menjadi rendah (skor 0, mortalitas 5,4%), sedang (skor 1 –2,5, mortalitas 20,6%), dan tinggi (skor > 2,5, mortalitas 73,6%).Simpulan. Alasan masuk medis dan pembedahan emergensi, indeks komorbiditasCharlson > 2, dan skor MSOFA > 4 merupakan faktor prognostik mortalitassepsis berat fase lanjut di ICU RSCM. Sebuah model telah dikembangkan untukmemprediksi dan mengklasifikasikan risiko mortalitas.
ABSTRACT
Background. Immune system derrangement occurs during the course of sepsis,characterized by hyperinflamation in early phase and hypoinflamation andimmunosupression in late phase. The number of patient die during late phase islarger than early phase. Until now, there is no study specifically addressingprognostic factors of mortality from late sepsis and developing a mortalityprediction model.Aim. To determine prognostic factors of mortality from late phase of severesepsis in ICU and to develop scoring system to predict mortality.Method. A retrospective cohort study was conducted to identify prognosticfactors associated with mortality. Adult patients admitted to ICU duringNovember 2011 until October 2012 who developed severe sepsis and still alivefor minimum 72 hours were included in this study. Seven predefined prognosticfactors were indentified at the onset of severe sepsis in ICU. Cox’s proportionalhazard ratio was used to identify independent prognostic factors. Eachindependent factors was quantified to develop a prediction model. Calibration ofthe model was tested by Hosmer-Lemeshow, and its discrimination ability wascalculated from area under receiver operating curve.Result. Subjects consist of 220 patients. Twenty eight-day mortality was 40%.Significant prognostic factors indentified were admission source (medical (HR2.75; CI95%: 1.56 – 4.84), emergency surgery (HR 1.96; CI95%:0.99 – 3.90),Charlson comorbidity index > 2(HR 2.07; CI95%:1.32 – 3.23), and MSOFA score> 4 (HR 2.84; CI95% : 1.54 – 5.24). Prediction model developed has gooddiscrimination ability (AUC 0.844) and good calibration (Hosmer-Lemeshow testp 0.674). Based on the model mortality risk can be classified as low (score 0,mortality 5.4%), moderate (score 1 – 2.5, mortality 20.6%), and high (score > 2.5,mortality 73.6%).Conclusion. Medical and emergency surgery admission, Charlson comorbidityindex > 2, and MSOFA score > 4 were prognostic factors of mortality from latephase of severe sepsis in ICU at Dr.Cipto Mangunkusumo general hospital. Amodel has been developed to predict and classify mortality risk. |
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