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Abstrak :
Latar belakang: Angka morbiditas dan mortalitas meningkat pada pasien fibrilasi atrium (FA) yang mengalami gagal jantung akut. Pada pasien irama sinus, left atrial volume index (LAVI) dan heart rate variability (HRV) merupakan prediktor kuat terjadinya komplikasi kardiovaskular. Penelitian LAVI dan HRV pada pasien FA hingga saat ini belum konklusif. Tujuan: Mengetahui hubungan LAVI dan HRV dengan kejadian gagal jantung akut pada pasien FA Metode: Studi kohort retrospektif dengan populasi terjangkau pasien dewasa FA di Rumah Sakit dr. Cipto Mangunkusumo (RSCM) 1 Januari 2020 hingga 31 Desember 2021 yang berasal dari registri Optimal INR measures for Indonesians (OPTIMA). Data sekunder LAVI diukur dengan ekokardiografi dan parameter HRV terdiri dari standar deviation of NN intervals (SDNN), root mean square of successive differences (RMSSD), rasio low frequency dan high frequency (LF/HF) diukur menggunakan alat HRV portabel. Pasien diikuti hingga 30 Januari 2023, luaran dinilai dengan melihat catatan medik atau melalui telepon. Hasil: Dilakukan analisis pada 144 sampel. Proporsi kejadian gagal jantung akut sebesar 15,3%. Tidak terdapat hubungan antara SDNN dengan kejadian gagal jantung akut (RR 1,75; IK95% 0,260 – 11,779, p=0,565). Tidak terdapat hubungan antara LF/HF dengan kejadian gagal jantung akut (RR 2,865; IK 95% 0,765 – 10,732, p=0,118). Terdapat hubungan antara LAVI dengan kejadian gagal jantung akut (adjusted RR 2,501; IK 95% 1,003 – 6,236, p=0,049). Diabetes melitus dan hipertensi merupakan faktor perancu pada penelitian ini. Kesimpulan: Peningkatan LAVI berhubungan dengan kejadian gagal jantung akut pada pasien FA. HRV tidak berhubungan dengan kejadian gagal jantung akut pada pasien FA. ......Background Morbidity and mortality rates increase in patients with atrial fibrillation (AF) who experience acute heart failure. In patients with sinus rhythm, left atrial volume index (LAVI) and heart rate variability (HRV) are strong predictors of cardiovascular complications. Research on LAVI and HRV in AF patients has so far not been conclusive. Objectives: To determine the relationship between LAVI and HRV and the incidence of acute heart failure in AF patients. Methods: A retrospective cohort study was conducted with an accessible population of adult AF patients at RSCM from January 1, 2020, to December 31, 2021, originating from the Optimal measures INR for Indonesians (OPTIMA) registry. LAVI was measured by echocardiography, and HRV parameters consist of the standard deviation of NN intervals (SDNN), the root mean square of successive differences (RMSSD), and the ratio of low frequency and high frequency (LF/HF) measured using a portable ECG device. Patients were followed until January 30, 2023, and outcomes were assessed by looking at medical records or by telephone. Result: A total of 144 subjects were analysed. The proportion of acute heart failure is 15.3%. There was no relationship between SDNN and the incidence of acute heart failure (RR 1.75; 95% CI 0.260–11.779, p=0.565). There was no relationship between LF/HF and the incidence of acute heart failure (RR 2.865; 95% CI 0.765–10.732, p=0.118). There is a relationship between LAVI and the incidence of acute heart failure (adjusted RR 2.501; 95% CI 1.003–6.236, p = 0.049). DM and hypertension were confounding factors in this study. Conclusion: The elevation of LAVI is associated with the incidence of acute heart failure in AF patients. HRV is not associated with the incidence of acute heart failure in AF patients.

Abstrak :
ABSTRAK
Ketika manusia mengalami stres, tubuhnya akan memproduksi hormon stres serta menimbulkan respon fisiologis yang berkaitan dengan sistem saraf otonom atau autonomous nervous system (ANS). Salah satu respon fisiologis dari timbulnya stres pada tubuh yaitu meningkatnya variabilitas detak jantung atau heart rate variability (HRV). Data HRV merupakan beberapa feature yang didapatkan dari interval R-R yang berasal dari sinyal Electrocardiograph (ECG). HRV didapatkan dengan menggunakan analisis domain waktu dan analisis domain frekuensi. Dalam penelitian ini, akan dijelaskan mengenai pengembangan sistem pendeteksi stres berbasis detak jantung dengan menghitung dan membandingkan feature HRV berdasarkan analisis domain waktu dan frekuensi serta mengklasifikasikan feature tersebut dengan algoritma k-Nearest Neighbors (kNN). Sistem diimplementasikan pada perangkat Android dan juga Laptop. Hasil yang diperoleh yaitu feature HRV gabungan dari hasil analisis domain waktu dan frekuensi yang paling merepresentasikan stres dari detak jantung serta menghasilkan akurasi sebesar 79,17% menggunakan algoritma kNN pada Laptop dan akurasi sebesar 79,166% dari klasifikasi kNN pada aplikasi Android yang dibuat.

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ABSTRACT
When humans deal with stress, they produce stress hormones which create physiological responses related to the autonomic nervous system (ANS). One of the physiological responses to stress in the body is a variation in heart rate or heart rate variability (HRV). HRV data are some features obtained from the R-R interval derived from Electrocardiograph (ECG) signals. HRV is obtained using time domain analysis and frequency domain analysis. In this study, we will discuss the development of a stress detection system based on heart rate by calculating and comparing HRV features from time and frequency domain analysis and classifying these features with the k-Nearest Neighbors (kNN) algorithm. The system is implemented on Android device and PC. The results obtained were combined HRV features from the results of time and frequency domain analysis are the best features to represent stress from heart rate with accuracy of 79,17% using the kNN algorithm on PC and accuracy of 79,166% from the kNN classification on Android application.

 

Abstrak :
Cardiovascular diseases (CVD) merupakan salah satu penyebab utama kematian di dunia. WHO memperkirakan angka 17,9 juta kematian pada tahun 2021 disebabkan oleh CVD. Di Indonesia sendiri, prevalensi penyakit jantung mencapai angka 1,5% atau sekitar 2,7 juta orang pada tahun 2018. CVD mencakup berbagai macam jenis penyakit jantung. Salah satu tipe penyakit jantung tersebut adalah congestive heart failure. Congestive heart failure (CHF) adalah kondisi dimana jantung tidak dapat memompa darah yang cukup ke seluruh bagian tubuh. CHF dapat terjadi dikarenakan melemahnya kemampuan otot jantung untuk memompa darah sehingga mempengaruhi heart rate atau detak jantung manusia. Heart rate dapat direpresentasikan menggunakan sinyal yang dapat diukur menggunakan alat rekaman electrocardiogram (ECG/EKG). EKG adalah rekaman aktivitas elektrik jantung yang ditangkap melalui bagian permukaan tubuh. Heart rate variability (HRV) diketahui berkorelasi dengan berbagai penyakit jantung dan salah satunya adalah CHF. Dengan berkembangnya teknologi, terdapat beberapa penelitian mengenai implementasi artificial intelligence (AI) untuk mendeteksi keberadaan CHF menggunakan model machine learning dan HRV sebagai fitur bagi model. Pada penelitian ini, akan dibangun dan dievaluasi kinerja model XGBoost untuk mendeteksi eksistensi penyakit CHF pada short-term HRV dari rekaman EKG 5 menit. Dataset yang digunakan berasal dari empat database yang berbeda yang diambil dari situs PhysioNet, yaitu NSRDB dan NSR2DB sebagai kelas sehat dan CHFDB dan CHF2DB sebagai kelas CHF. Masing-masing database memiliki rekaman long-term EKG. Seluruh rekaman tersebut dilakukan segmentasi selama 5 menit pada 2 jam pertama rekaman. Dari hasil segmentasi rekaman 5 menit tersebut akan dihitung nilai HRV yang akan menjadi fitur bagi model XGBoost. XGBoost dilatih menggunakan kombinasi teknik Grid Search dan K-Fold Cross Validation dengan nilai 𝐾 = 10. Terdapat 4 metrik yang dijadikan objektif optimisasi Grid Search, yaitu akurasi, sensitivitas, spesifisitas, dan skor AUC. XGBoost yang dilatih dengan mengoptimasi akurasi berhasil mencapai nilai akurasi sebesar 0,954, sensitivitas sebesar 0,935, spesifisitas sebesar 0,96, dan skor AUC sebesar 0,947. XGBoost yang dilatih dengan mengoptimasi sensitivitas berhasil mencapai nilai akurasi sebesar 0,966, sensitivitas sebesar 0,977, spesifisitas sebesar 0,963, dan skor AUC sebesar 0,97. XGBoost yang dilatih dengan mengoptimasi spesifisitas berhasil mencapai nilai akurasi sebesar 0,962, sensitivitas sebesar 0,931, spesifisitas sebesar 0,971, dan skor AUC sebesar 0,951. Kemudian XGBoost yang dilatih dengan mengoptimasi skor AUC berhasil mencapai nilai akurasi sebesar 0,955, sensitivitas sebesar 0,935, spesifisitas sebesar 0,962, dan skor AUC sebesar 0,948. ......Cardiovascular diseases (CVD) is one of the major causes of death in the world. WHO estimated that 17.9 million of deaths during 2021 are caused by CVD. In Indonesia alone, the prevalence of heart diseases reached 1.5% or around 2,7 million people in 2018. CVD consists of various types of heart disease. Congestive heart failure is one of them. Congestive heart failure (CHF) is a condition where the heart cannot pump enough blood for the entire body. CHF can occur due to a weakening of the heart muscle's ability to pump blood, thereby affecting the human heart rate. Heart rate can be represented using signal that can be measured using electrocardiogram (ECG/EKG) recording. EKG is a recording of the heart's electrical activity captured through the surface of the body. Heart rate variability (HRV) have been known to be correlated with various heart diseases with CHF is one of it. With the advance of technology, there have been various research regarding the implementation of artificial intelligence (AI) to detect the presence of CHF using machine learning model and HRV as features for the model. In this research, we built and evaluated the performance of XGBoost model to detect the existence of CHF on short-term HRV from 5 minutes EKG recording. The dataset came from four different databases that can be accessed from PhysioNet website. Those are NSRDB and NSR2DB datasets to represent healthy class and CHFDB and CHF2DB to represent CHF class. Each database contains long-term EKG. All records are segmented by 5 minutes on the first 2 hours of the recording. HRV metrics are calculated from those 5 minutes segments to become features for the XGBoost model. XGBoost was trained using a combination of Grid Search and K-Fold Cross Validation techniques with 𝐾 = 10. There are 4 metrics that become the objective scoring function for the Grid Search. Those are accuracy, sensitivity, specificity, and AUC score. XGBoost trained to optimize accuracy managed to achieve 0.954 accuracy, 0.935 sensitivity, 0.96 specificity, and 0.947 AUC score. XGBoost trained to optimize sensitivity managed to achieve 0.966 accuracy, 0.977 sensitivity, 0.963 specificity, and 0.97 AUC score. XGBoost trained to optimize specificity managed to achieve 0.962 accuracy, 0.931 sensitivity, 0.971 specificity, and 0.951 AUC score. Lastly, XGBoost trained to optimize AUC score managed to achieve 0.955 accuracy, 0.935 sensitivity, 0.962 specificity, and 0.948 AUC score.

Abstrak :
Latar belakang : Perawat memiliki tingkat stres cukup tinggi terpajan risiko psikososial, termasuk bekerja dengan jadwal kerja shift. Selama ini deteksi kasus stres berdasarkan kuesioner yang validitas dan relibialitasnya cukup baik, seperti antara lain kuesioner PSS. Penelitian bertujuan menguji markah biologi sebagai penanda stres pada perawat shift. Metode : Penelitian dilakukan Desember 2019 sampai Juni 2020, pada perawat shift dan non-shift, masing-masing 40 orang, dari RSCM, dengan mengambil data secara consecutive sampling. Pemeriksaan kortisol, melatonin dan CRF masing-masing dua kali, yaitu pada kelompok shift sebelum bekerja (=pre) pada pukul 24.00 pada hari jaga terakhir (malam kedua), kemudian pasca bekerja (=post) pukul 08.00 keesokannya. Pada non-shift pada hari kerja pukul 08.00 (=pre) dan 16.00 (=post). Pengukuran HRV dilakukan dua kali dan Q-EEG satu kali pada saat lepas jaga (untuk shift) dan saat bekerja (untuk non-shift). Hasil : Perawat usia reproduktif yang bekerja shift dan memiliki tingkat stres sedang-berat lebih banyak jumlahnya daripada jumlah perawat yang bekerja non-shift dan memiliki tingkat stres-berat sedang (30% vs 25%). Terdapat perbedaan bermakna rerata kadar kortisol shift=87,9±79,1 ng/ml dan non-shift=128,8±51,4 ng/ml pra kerja (p<0,001), rerata kadar kortisol shift=139,8±77,7 ng/ml dan non-shift=86,4±51,8 ng/ml pasca kerja (p= 0,001); rerata kadar melatonin shift=51,5±41,2 ng/ml dan non-shift=17,1±20,5 ng/ml pra kerja (p<0,001), serta rerata kadar melatonin shift=24,3±21,2 ng/ml dan non-shift=10,8±7,8 ng/ml pasca kerja (p<0,001). Terdapat rerata kadar melatonin=10,8±7,8 pg/ml (2,15-38,30) pukul 16.00 dan rerata kadar melatonin=51,5±41,2 pg/ml (0,8-135) pukul 24.00. Rerata kadar CRF=19,8±4,9 pg/ml (10,20-36,06) pukul 08.00, rerata kadar CRF=17,8±5,3 pg/ml (8,08-32,20) pukul 16.00 dan rerata kadar CRF=18,0±6,8 pg/ml (7,69-30,59) pukul 24.00. Komponen HRV SDNN cenderung shift=38,1±11,6 ms > non-shift=34,2± 10,7 ms; RMSSD cenderung shift=31,4±11,9 ms > non-shift=28,7±12,6 ms, dan rasio LF/HF cenderung shift=1,2±1,6 < non-shift=1,8±1,3. Q-EEG non-shift kecenderungan peningkatan menonjol di sekitar 10Hz area gelombang Alpha (8-13Hz), yang menunjukkan kondisi dewasa normal terjaga dan tenang; non-shift kecenderungan peningkatan pada area gelombang Beta (14-30Hz) dan Gama (> 30Hz). Uji multivariat Mantel-Haenszel peran bermakna markah biologi (kortisol, CRF, melatonin) terhadap skor PSS secara parsial maupun secara simultan; didapat dari kategori perubahan ketiga markah biologi terhadap stres berdasarkan kategori skor PSS. Simpulan: Perawat shift berpeluang mengalami stres sedang-berat dibandingkan perawat non-shift. Rerata kadar kortisol dan melatonin lebih tinggi pasca dibandingkan pre kerja.Gelombang Beta dan Gama cenderung lebih tinggi pada shift dibandingkan non-shift dan berpotensi sebagai predictor stres akibat kerja shift. Kortisol, CRF dan melatonin secara bersama-sama dapat digunakan sebagai markah biologi stres berdasarkan perubahan dari waktu ke waktu ......Background: A nurse has a high enough stress level because it is directly related to psychosocial hazards on shift work schedules. The Indonesian National Nurses Association stated that the prevalence of stress for nurses reached 50.9%. So far, the detection of stress cases is based on a questionnaire whose validity and relativity are quite good, such as the Perceived Stress Score (PSS) questionnaire. This study aimed to examine biological markers of stress among shift nurses. Method: The study was conducted at the FKUI Integrated Laboratory, “Laboratorium Kesehatan Daerah DKI”, RSCM Intermediate Polyclinic, RSCM Neurology Clinic and Medical Technology IMERI, from December 2019 to March 2020. Respondents came from the shift and non-shift nurses from RSCM, chosen by consecutive sampling. The study involved 40 people individuals in each group. Cortisol, melatonin and CRF were measured twice each, in the shift workgroup (=pre) at 12.00 am on the last watch day (second night), then during post-work, (= post,) at 08.00 am the following day. In the non-shift group blood samples were taken on weekdays at 08.00 am (= pre) and 04.00 pm (=post). HRV measurements were taken twice and Q-EEG once during off-duty (for shift workers) and at work (for non-shift workers). Results: The percentage of nurses who showed moderate stress levels in the shift group (30%) is higher compared to the non-shift group (25%). There were significant differences between the mean of shift group cortisol=87,9±79,1 ng/ml and non-shift group cortisol=128,8±51,4 ng/ml in pre-work (p< 0,001), the mean of shift group cortisol=139,8±77,7 ng/ml and non-shift group cortisol=86,4±51,8 ng/ml in post-work (p=0.001), the mean of shift group melatonin=51,5±41,2 ng/ml and non shift group melatonin=17,1±20,5 ng/ml (p<0.001) in the pre-work, and the mean of shift group melatonin=24,3±21,2 ng/ml and non-shift group melatonin=10,8±7,8 ng/ml in post-work (p<0.001). Melatonin levels mean=10.8±7,8 pg / ml (2.15-38.30) at 04.00 pm and 51.5±41,2 pg / ml (0.8-135) at 12.00 pm. CRF levels mean =19,8±4,9 pg / ml (10,2-36,1) at 08.00 am, 17.8±5,3 pg/ml (8,08-32.20) at 04.00 pm and 18.0±6,8 pg /ml (7.69-30.59) at 12.00 pm. In the HRV component, SDNN mean were higher in the shift group=38,1±11,6 ms than non-shift group=34,2±10,7 ms, higher RMSSD mean on shift group=31,4± 11,9 ms than non-shift group=28,7±12,6 ms, and LF/HF ratio mean on shift group=1,2± 1,6 compared to non-shift group=1,8±1,3. The brain wave image found a tendency of quite prominent increase around 10 Hz in the non-shift group, namely the frequency area Alpha waves (8-13 Hz), which indicate a normal adult state of wakefulness and calm. Brain waves in the shift group tended to increase in the Beta (14-30 Hz) and Gamma (> 30 Hz) wave areas. With the Mantel-Haenszel multivariate test, there is a significant role of biological markers (cortisol, CRF, melatonin) on the PSS score partially or simultaneously. This role is obtained from the category of changes in the three biological markers to stress based on the PSS score category. Conclusion: Nurses working shift are more likely to experience moderate-severe stress than non-shift nurses. The mean levels of cortisol are higher and melatonin is also higher after work than before work. Beta and Gama waves tend to be higher in the shift group than in non-shift groups, potentially as predictors of stress due to shift work. Cortisol, CRF and melatonin can be used together as biological markers of stress based on changes over time.