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Analisis Survival Menggunakan Regresi Weibull Pada Laju Kesembuhan Pasien Jantung Koroner

Introduction

Analisis survival menggunakan regresi weibull pada laju kesembuhan pasien jantung koroner. Pelajari analisis survival dengan regresi Weibull pada laju kesembuhan pasien jantung koroner. Temukan faktor seperti umur, komplikasi, merokok, hipertensi, dan obesitas yang memengaruhi pemulihan PJK.

Abstract

Penyakit Jantung Koroner (PJK) adalah penyumbatan yang menghambat aliran darah ke jantung sehingga menyebabkan jantung kekurangan oksigen dan nutrisi, terjadi ketika pembuluh darah yang memasok darah ke jantung. Tujuan dari penelitian ini akan mengkaji model persamaan regresi Weibull dari kondisi klinis penderita penyakit jantung koroner dan mengetahui faktor yang mempengaruhi kesembuhan pasien jantung koroner. Regresi Weibull merupakan metode analisis survival yang digunakan untuk mengetahui efek variabel independen dengan data survival sebagai variabel dependen. Kurva Kaplan Meier dilakukan untuk menghitung kurva survival berdasarkan data waktu survival untuk melihat perbedaan antar kelompok dalam satu kurva. Hasil uji parsial menunjukkan bahwa umur, komplikasi, merokok,hipertensi dan obesitas sangat memengaruhi model. Dalam studi kasus ini, model regresi Weibull diperoleh untuk data waktu sembuh pasien jantung koroner sebagai berikut. S(t|x)=exp(-exp(1.3654+0.0874x1+0.0251x2+0.0789x3+0.0404x4+0.0339x5)t)^a. Coronary Heart Disease CHD occurs when the blood vessels that supply blood to the heart become narrowed or become blocked, blocking blood flow to the heart, causing the heart to lack oxygen and nutrients. The aim of this research is to examine the Weibull regression equation model of the clinical condition of coronary heart disease sufferers and determine the factors that influence the recovery of coronary heart disease patients. Weibull regression is a survival analysis method used to determine the effect of independent variables with survival data as the dependent variable. The Kaplan Meier curve was performed to calculate a survival curve based on survival time data to see differences between groups in one curve. Partial test results show that age, complications, smoking, hypertension and obesity greatly influence the model. In this case study, the Weibull regression model was obtained for data on recovery time for coronary heart patients as follows. S(t|x)=exp(-exp(1.3654+0.0874x1+0.0251x2+0.0789x3+0.0404x4+0.0339x5)t)^a.

Review

This study, titled "Analisis Survival Menggunakan Regresi Weibull Pada Laju Kesembuhan Pasien Jantung Koroner," addresses a highly relevant clinical problem: understanding the factors influencing recovery rates in patients with Coronary Heart Disease (CHD). The authors aim to construct a Weibull regression model to identify key clinical and lifestyle variables affecting the recovery time of CHD patients. The selection of Weibull regression, complemented by Kaplan-Meier curves for group comparisons, is an appropriate methodological choice for analyzing time-to-event data, offering insights into the survival or recovery probabilities over time and the impact of covariates. A significant strength of this research lies in its application of a robust survival analysis technique to a critical health outcome. The abstract clearly identifies several factors – age, complications, smoking, hypertension, and obesity – as significantly influencing the recovery model. These findings reinforce known risk factors for cardiovascular disease progression and highlight their continued relevance in recovery trajectories. The provision of the actual Weibull regression equation, S(t|x)=exp(-exp(1.3654+0.0874x1+0.0251x2+0.0789x3+0.0404x4+0.0339x5)t)^a, offers a concrete and reproducible outcome, demonstrating a clear analytical effort to model the recovery process. While the abstract presents promising findings, a full manuscript would benefit from providing further details essential for a comprehensive understanding and evaluation. Specifically, information regarding the study's sample size, data source, specific definitions of variables (e.g., what constitutes "complications," how "recovery" is defined and measured), and the mapping of x1-x5 to the identified covariates would enhance clarity. Additionally, a discussion on the interpretation of the regression coefficients, the value of the shape parameter 'a', model diagnostics, and any limitations of the study would strengthen its scientific contribution. Despite these points, the research provides valuable preliminary insights into predictors of CHD recovery, laying a foundation for future, more in-depth investigations and potentially informing clinical prognostication and intervention strategies.

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