Ditemukan 10 dokumen yang sesuai dengan query :: Simpan CSV
Latar Belakang Gagal jantung adalah kondisi kronis dan progresif, dengan prevalensi di dunia 1-3% dan di Indonesia 5% (peringkat ke 4 di dunia) dengan kematian 50% dalam 5 tahun. Angka readmisi dalam 90 hari adalah 50%-75% dan dalam 30 hari 2-3%, sedangkan di Indonesia angka readmisi dalam 30 hari adalah 17%. Biaya rawat inap gagal jantung dapat mencapai empat ratus juta rupiah per pasien per tahun. Data BPJS Kesehatan 2018 terdapat 130.275 kejadian rawat inap tingkat lanjut pasien gagal jantung kongestif dan berdasarkan tarif JKN 2023 perkiraan biaya rawat inapnya akan berkisar antara 379 milyar sampai 4,2 triliun rupiah. Dengan memanfaatkan teknologi kekinian dari Artificial Intelligence dan kapabilitas serta kebiasaan masyarakat paska pandemi Covid-19, penelitian ini membuat model prediksi berbasis machine learning dengan menemukan faktor-faktor risiko yang dapat menjadi prediktor rawat inap berulang, yang kemudian diimplementasikan di dalam prototype yang digunakan dalam kolaborasi antara penyedia layanan kesehatan dengan pasien yang turut terlibat melakukan monitoring mandiri sehingga dapat mempertahankan kualitas hidupnya dan mengendalikan biaya perawatan baik yang dibayarkan oleh pasien sendiri, menggunakan asuransi ataupun dengan pendanaan pemerintah. Metode Penelitian ini terdiri atas beberapa tahap, dengan studi kuantitatif dan kualitatif menggunakan data rekam medis pasien gagal jantung di Rumah Sakit Jantung dan Pembuluh Darah Harapan Kita, Jakarta. Dimulai dengan Systematic Literature Review untuk menemukan faktor risiko rawat inap berulang di rumah sakit dan untuk menemukan novelty, pemodelan prediksi dengan studi kohort retrospektif, analisis kebutuhan sistem dengan studi kualitatif, pengembangan prototype, dan uji prototype dengan studi kohort prospektif. Hasil Systematic Literature Review tentang prediktor readmisi gagal jantung dengan machine learning dari PubMed, Science Direct, ProQuest, Scopus, Embase, google scholar menghasilkan 19 artikel terseleksi. 13 studi berasal dari USA, tidak ditemukan studi serupa di Indonesia, dengan algoritma terbaik adalah Neural Network. Pada tahap pemodelan prediksi diperoleh 2738 data pasien gagal jantung paska rawat inap di RS Jantung dan Pembuluh Darah Harapan Kita Jakarta, dengan ketersediaan 64 variabel. Dengan Orange Data Mining, terseleksi sebanyak 31 features. Model terbaik menggunakan Random Forest, dengan AUC 0,976, CA 0,912, F1 0,912, Precision 0,916 dan Recall 0,912, diimplementasikan dalam prototype aplikasi Fineheart dengan fitur aplikasi profil pasien, dashboard, catatan harian jantungku, penilaian kualitas hidup, rencana kontrol, instruksi medis dan obat, catatan asupan makanan dan cairan, edukasi, konsultasi. Uji efikasi prototype menunjukkan angka readmisi pada kelompok intervensi (20%), lebih rendah daripada kelompok kontrol (43,3%). Perubahan signifikan terjadi pada 2 parameter KCCQ yaitu Quality of Life (p=0,029) dan Overall Summary Score (p=0,001). Tingkat kepatuhan menggunakan prototype aplikasi juga berpengaruh signifikan terhadap kedua parameter tersebut dan mencegah readmisi. Kesimpulan Model prediksi readmisi pasien gagal jantung dengan machine learning yang diimplementasikan ke prototype aplikasi dapat digunakan untuk monitoring di rumah untuk mencegah readmisi dan mempertahankan kualitas hidup.
Background Heart failure is a chronic and progressive condition, with a prevalence in the world of 1-3% and in Indonesia 5% (ranked 4th in the world) with a mortality of 50% within 5 years. The readmission rate in 90 days is 50%-75% and in 30 days it is 2-3%, while in Indonesia the readmission rate in 30 days is 17%. The cost of hospitalization for heart failure can reach four hundred million rupiah per patient per year. The government health insurance of Indonesia (BPJS Kesehatan) data for 2018 shows 130,275 advanced hospitalizations for congestive heart failure patients and based on the 2023 tariff, the estimated cost of hospitalization will range from 379 billion to 4.2 trillion rupiah. By utilizing the latest technology from Artificial Intelligence and the capabilities and habits of society after the Covid-19 pandemic, this research creates a machine learning-based predictive model by finding risk factors that can lead to hospital readmission, which are then implemented in the prototype that is used in collaboration between health care providers with patients who are also involved in conducting self-monitoring so that they can maintain their quality of life and control the costs of care whether paid by the patient himself, using insurance or with government funding. Method This research consisted of several stages, with quantitative and qualitative studies using medical records of heart failure patients at the Harapan Kita Cardiovascular Center. Starting with a Systematic Literature Review to find risk factors of readmission and to find novelties, predictive modeling with retrospective cohort study, system requirements analysis with qualitative studies, prototype development, and prototype testing with prospective cohort study. Results A systematic literature review on predictors of heart failure readmission using machine learning from PubMed, Science Direct, ProQuest, Scopus, Embase, Google Scholar resulted in 19 selected articles. 13 studies came from the USA, no similar studies were found in Indonesia, with the best algorithm being Neural Network. At the prediction modeling stage, data was obtained on 2738 post-hospitalization heart failure patients at Harapan Kita Cardiovascular Hospital, Jakarta, with the availability of 64 variables. With Orange Data Mining, 31 features are selected. The best model uses Random Forest, with AUC 0,976, CA 0,912, F1 0,912, Precision 0,916 and Recall 0,912, implemented in the Fineheart application prototype with patient profile application features, dashboard, my heart diary, quality of life assessment, control plan, medical instructions and medication, food and fluid intake records, education, consultation. The prototype efficacy test showed that the readmission rate in the intervention group (20%), was lower than the control group (43.3%). Significant changes occurred in 2 KCCQ parameters, Quality of Life (p=0.029) and Overall Summary Score (p=0.001). The level of presence of application prototypes also has a significant effect on these two parameters and prevents readmissions. Conclusion The readmission prediction model for heart failure patients with machine learning implemented in the application prototype can be used for home monitoring to prevent readmissions and maintain quality of life.
Heart failure is a clinical syndrome that occurs when the heart fails to meet the body’s demand for oxygen and nutrients. The prevalence and mortality rate of heart failure in Indonesia are relatively high compared to other Southeast Asian countries. The occurrence of heart failure in young adults increases the risk of premature death, recurrent rehospitalization, reduced quality of life, and a greater burden on the healthcare system. Several factors such as obesity, type 2 diabetes mellitus (T2DM), hypertension, smoking, dyslipidemia, family history of premature coronary artery disease (PCAD), and sex have been identified as being associated with heart failure. Developing a predictive model to identify the most influential risk factors for heart failure in young adults is crucial for preventive strategies and early interventions. This study employed a fixed retrospective cohort design involving patients aged 18–54 years who visited the cardiology outpatient clinic or were hospitalized at four tertiary hospitals in Indonesia (National Cardiovascular Center Harapan Kita, Jakarta; Hasan Sadikin Hospital, Bandung; Sebelas Maret University Hospital, Solo; and Adam Malik Hospital, Medan) in 2021. Patients without an initial diagnosis of heart failure were included, and their risk factors were recorded according to the study variables. The patients were followed monthly from 2021 until the end of observation in 2024 to determine whether they developed heart failure. Descriptive, bivariate, and multivariable analyses were conducted using the Poisson Generalized Linear Model (GLM) to estimate coefficients, incidence rate ratios (IRR) with 95% confidence intervals, and to construct the most accurate predictive model. Based on the model, a scoring system and probability value for the occurrence of heart failure were developed. A total of 321 participants met the inclusion and exclusion criteria, with a median age of 51 years (P25–P75: 46–52 years). After four years of observation, the cumulative probability of developing heart failure was 0.713 (95% CI: 0.661–0.760). The analysis identified three significant predictors for heart failure in young adults: obesity (IRR 1.87; 95% CI 1.31–2.68), dyslipidemia (IRR 2.58; 95% CI 1.87–3.56), and T2DM (IRR 2.79; 95% CI 2.01–3.87). The IDD Score (Body Mass Index–Dyslipidemia–Diabetes) was developed as a predictive scoring system for heart failure in young adults, with a total score of 13 corresponding to a 76.8% probability. Obesity, dyslipidemia, and T2DM were found to be significant risk factors for heart failure in young adults. The proposed IDD Score demonstrated good sensitivity and specificity in predicting the occurrence of heart failure within this population.
Kematian pasien Penyakit Ginjal Kronik (PGK) pada usia dewasa yang menjalankan hemodialisis setelah tiga bulan adalah jarang. Namun, masih mungkin terjadi. Padahal layanan hemodialisis dibutuhkan seumur hidup. Tujuan studi ini adalah engetahui faktor – faktor risiko yang berhubungan dengan kematian pasien PGK usia dewasa yang menjalankan hemodialisis reguler. Metode yang digunakan adalah kasus kontrol tanpa pencocokkan dengan perbandingan 1:2. Uji statistik yang digunakan adalah regresi logistik. Faktor risiko yang berhubungan terhadap kematian adalah riwayat gagal jantung (OR = 2,3; IK 95% = 1,2 – 4,4; nilai p = 0,009), riwayat obstruksi pasca ginjal (OR = 3,5; IK 95% = 1,6 – 7,6; nilai p = 0,002), glukosa sewaktu ≥140 mg/dl (OR = 2,1; IK 95% = 1,2 – 3,6; nilai p = 0,011), Gangguan Ginjal Akut (GGA) (OR = 6,5; IK 95% = 3,8 – 11,1; nilai p = 0,000), dan Indeks Massa Tubuh (IMT) <18,5 kg/mm2 (OR = 3,0; IK 95% = 1,2 – 7,6; nilai p = 0,019).
Faktor – faktor risiko yang berhubungan dengan kematian pasien PGK pada usia dewasa yang menjalankan hemodialisis reguler adalah riwayat gagal jantung, riwayat obstruksi pasca ginjal, glukosa sewaktu ≥140 mg/dl, GGA, dan IMT <18,5 kg/mm2
Mortality of Chronic Kidney Disease (CKD) patients in adults undergoing hemodialysis after three months is rare. However, it is still possible. Even though hemodialysis services are needed for life. The objective of this study is to determine the risk factors associated with the death of adult CKD patients undergoing regular hemodialysis. The study design was unmatched control case with a ratio of 1:2. The statistical test used was logistic regression. Risk factors were history of heart failure (OR = 2.3; CI 95% = 1.2 – 4.4; p value = 0.009), history of obstruction post renal (OR = 3.5; CI 95% = 1.6 – 7.6; p value = 0.002), random glucose ≥140 mg/dl (OR = 2.1; CI 95% = 1.2 – 3.6; p value= 0.011), Acute Kidney Injury (AKI) (OR = 6.5; CI 95% = 3.8 – 11.1; p value = 0.000), and Body Mass Index