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"Kelelahan merupakan bahaya yang dapat mempengaruhi produktivitas pekerja. Sebagai garda terdepan dalam memberikan pelayanan kesehatan, perawat di rumah sakit memiliki tanggung jawab besar terutama dalam menghadapi beban kerja yang signifikan. Penurunan kondisi perawat akibat kelelahan dapat mengganggu kinerja perawat dan membahayakan kondisi kesejahteraan pasien. Penelitian ini dilakukan untuk mengetahui tingkat kelelahan kerja dari perawat rumah sakit. Metode dalam penelitian ini menggunakan pendekatan kombinasi, yaitu penggunaan heart rate variability (HRV) untuk mengukur variabilitas detak jantung secara objektif dan Swedish Occupational Fatigue Inventory (HRV) untuk mengambil data subjektif tentang tingkat kelelahan kerja yang dirasakan oleh perawat rumah sakit. Sampel penelitian didapatkan dari salah satu rumah sakit di Depok. Hasil pengolahan data time domain dan frequency domain HRV menunjukkan bahwa heart rate variability dapat digunakan untuk menggambarkan tingkat kelelahan kerja perawat. Korelasi antara data HRV dengan SOFI menunjukkan indikasi Lack of Motivation, Sleepiness, Physical Exertion, dan Physical Discomfort berpengaruh secara signifikan terhadap tingkat kelelahan kerja perawat. Selain itu, faktor usia, BMI tubuh, dan lama tidur sebelum bekerja juga memiliki pengaruh yang signifikan terhadap kelelahan kerja perawat. Hasil penelitian ini diharapkan dapat memberikan pemahaman terhadap tingkat kelelahan kerja yang dialami oleh perawat rumah sakit. Temuan dari penelitian ini juga diharapkan dapat memberikan kontribusi dalam pengembangan rekomendasi yang dapat mengurangi tingkat kelelahan kerja pada perawat.

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Fatigue is a hazard that can affect worker productivity. As frontline providers of healthcare services, nurses in hospitals bear a significant responsibility, especially when faced with a substantial workload. A decline in nurses' condition due to fatigue can disrupt their performance and jeopardize patient well-being. This study aims to determine the level of work fatigue among hospital nurses. The research methodology employs a mixed approach, using Heart Rate Variability (HRV) to objectively measure heart rate variability and the Swedish Occupational Fatigue Inventory (SOFI) to gather subjective data on the perceived work fatigue levels of hospital nurses. The research sample was obtained from a hospital in Depok. The results of the time domain and frequency domain HRV data analysis indicate that heart rate variability can be used to describe the work fatigue levels of nurses. The correlation between HRV data and SOFI shows that Lack of Motivation, Sleepiness, Physical Exertion, and Physical Discomfort significantly influence nurses' work fatigue levels. Additionally, factors such as age, BMI, and sleep duration before work also significantly impact nurses' work fatigue. The results of this study are expected to provide insight into the level of work fatigue experienced by hospital nurses. The findings of this research are also anticipated to contribute to the development of recommendations that can reduce work fatigue levels among nurses."

"[ABSTRAKLatar Belakang: Major adverse cardiac events (MACE) merupakan komplikasi serius pada pasien pasca sindrom koroner akut (SKA) sehingga perlu suatu metode yang andal dalam memprediksi kejadiannya. Heart rate variability (HRV) yang menggambarkan ketidakseimbangan sistem otonom pasca SKA dan dapat dilakukan dengan cara yang lebih cepat, mudah, dan praktis berpotensi dapat digunakan sebagai alat stratifikasi risiko MACE.Tujuan: Mengetahui kemampuan HRV awal perawatan yang diukur melalui metode pulse photoplethysmograph (PPG) dalam memprediksi MACE pada pasien pasca SKA yang dirawat di intensive cardiac care unit (ICCU).Metode: Studi ini adalah studi kohort prospektif dengan subjek pasien SKA yang menjalani perawatan di ICCU. Pemeriksaan HRV dilakukan dengan metode PPG dalam 48 jam pasca diagnosis SKA dan adanya MACE dideteksi selama perawatan di ICCU. Komplikasi yang digolongkan sebagai MACE adalah kematian, aritmia fatal, gagal jantung, syok kardiogenik, re-infark, dan komplikasi mekanik. Kemampuan HRV dalam memprediksi MACE dinyatakan melalui AUC (+IK95%) dan untuk parameter yang memiliki kemampuan prediksi baik akan dihitung nilai prediksi positif (PPV) dan nilai prediksi negatif (NPV) beserta IK95% parameter tersebut.Hasil: Sebanyak 75 subjek SKA menjalani pengukuran HRV < 48 jam pasca diagnosis dan sebanyak 18,7% di antaranya mengalami MACE. Parameter LF dengan AUC 0,697 (0,543-0,850) dan rasio LF/HF dengan AUC 0,851 (0,741-0,962) memiliki kemampuan diskriminasi MACE yang paling baik. Parameter LF pada titik potong 89,673 memiliki PPV dan NPV sebesar 13% dan 71%, sedangkan rasio LF/HF pada titik potong 1,718 sebesar 6% dan 50%.Kesimpulan: Variabel LF dan rasio LF/HF merupakan parameter HRV yang dinilai memiliki kemampuan diskriminasi cukup baik terhadap MACE. Kedua variabel tersebut memiliki nilai prediksi negatif sehingga dapat digunakan untuk menyingkirkan kemungkinan terjadinya MACE pada mereka dengan nilai LF > 89,673 dan rasio LF/HR > 1,718.

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ABSTRACTIntroduction: Major adverse cardiac events (MACE) are serious complications needed to be predicted rapidly and accurately in acute coronary syndrome (ACS) patients. Heart rate variability (HRV), reflecting autonomic system imbalance post ACS, is currently available in quick, easy, and practical method. This parameter has potential to be used in MACE risk stratification.Aim: To find the ability of HRV measurement with pulse photoplethysmograph (PPG) method in predicting MACE in post ACS patients hospitalized in intensive cardiac care unit (ICCU).Method: This study is a prospective study using ACS patients in ICCU as its subjects. Measurement of HRV by means of PPG is conducted within 48 hours post diagnosis and the incidence of MACE is identified during ICCU stay. Events classified as MACE are including death, lethal arrhytmia, heart failure, cardiogenic shock, re-infarction, and other mechanical complications. The ability of HRV in predicting MACE was listed as AUC (+95%CI) and for specific HRV parameters which had adequate capability, positive predictive value (PPV) and negative predictive value (NPV) would be calculated.Result: HRV measurements were done in 75 ACS subjects < 48 h post-diagnosis. Among the subjects, 18,7% suffered from MACE. Measurement of LF with AUC 0,697 (0,543-0,850) and LF/HF ratio with AUC 0,851 (0,741-0,962) had the best discrimination values. The former variable had PPV and NPV of 13% and 71% in the cutoff point of 89,673, while the latter had the number of 6% and 50% in the cutoff point of 1,718, respectively.Conclusion: LF and LF/HF ratio are the only HRV variables having adequate MACE discrimination. Both variables have better NPV so that they can be applied in reducing MACE risk in patients with LF > 89,673 and LF/HF ratio > 1,718.;Introduction: Major adverse cardiac events (MACE) are serious complications needed to be predicted rapidly and accurately in acute coronary syndrome (ACS) patients. Heart rate variability (HRV), reflecting autonomic system imbalance post ACS, is currently available in quick, easy, and practical method. This parameter has potential to be used in MACE risk stratification.Aim: To find the ability of HRV measurement with pulse photoplethysmograph (PPG) method in predicting MACE in post ACS patients hospitalized in intensive cardiac care unit (ICCU).Method: This study is a prospective study using ACS patients in ICCU as its subjects. Measurement of HRV by means of PPG is conducted within 48 hours post diagnosis and the incidence of MACE is identified during ICCU stay. Events classified as MACE are including death, lethal arrhytmia, heart failure, cardiogenic shock, re-infarction, and other mechanical complications. The ability of HRV in predicting MACE was listed as AUC (+95%CI) and for specific HRV parameters which had adequate capability, positive predictive value (PPV) and negative predictive value (NPV) would be calculated.Result: HRV measurements were done in 75 ACS subjects < 48 h post-diagnosis. Among the subjects, 18,7% suffered from MACE. Measurement of LF with AUC 0,697 (0,543-0,850) and LF/HF ratio with AUC 0,851 (0,741-0,962) had the best discrimination values. The former variable had PPV and NPV of 13% and 71% in the cutoff point of 89,673, while the latter had the number of 6% and 50% in the cutoff point of 1,718, respectively.Conclusion: LF and LF/HF ratio are the only HRV variables having adequate MACE discrimination. Both variables have better NPV so that they can be applied in reducing MACE risk in patients with LF > 89,673 and LF/HF ratio > 1,718., Introduction: Major adverse cardiac events (MACE) are serious complications needed to be predicted rapidly and accurately in acute coronary syndrome (ACS) patients. Heart rate variability (HRV), reflecting autonomic system imbalance post ACS, is currently available in quick, easy, and practical method. This parameter has potential to be used in MACE risk stratification.Aim: To find the ability of HRV measurement with pulse photoplethysmograph (PPG) method in predicting MACE in post ACS patients hospitalized in intensive cardiac care unit (ICCU).Method: This study is a prospective study using ACS patients in ICCU as its subjects. Measurement of HRV by means of PPG is conducted within 48 hours post diagnosis and the incidence of MACE is identified during ICCU stay. Events classified as MACE are including death, lethal arrhytmia, heart failure, cardiogenic shock, re-infarction, and other mechanical complications. The ability of HRV in predicting MACE was listed as AUC (+95%CI) and for specific HRV parameters which had adequate capability, positive predictive value (PPV) and negative predictive value (NPV) would be calculated.Result: HRV measurements were done in 75 ACS subjects < 48 h post-diagnosis. Among the subjects, 18,7% suffered from MACE. Measurement of LF with AUC 0,697 (0,543-0,850) and LF/HF ratio with AUC 0,851 (0,741-0,962) had the best discrimination values. The former variable had PPV and NPV of 13% and 71% in the cutoff point of 89,673, while the latter had the number of 6% and 50% in the cutoff point of 1,718, respectively.Conclusion: LF and LF/HF ratio are the only HRV variables having adequate MACE discrimination. Both variables have better NPV so that they can be applied in reducing MACE risk in patients with LF > 89,673 and LF/HF ratio > 1,718.]"

"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

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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."

"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.

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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."

"Latar Belakang Diabetes melitus dapat menyebabkan gangguan sistem saraf otonom (SSO) yang disebut sebagai neuropati otonom diabetik. SSO mengendalikan banyak sistem organ dan salah satu gangguannya dapat bermanifestasi sebagai disfungsi ereksi (DE). Prevalensi DE dan neuropati otonom diabetik di dunia masih beragam dan hubungan keduanya masih memiliki hasil yang bervariasi. Dengan deteksi dini neuropati otonom diabetik diharapakan dapat turut mendeteksi DE dan mencegah progresifitas DE menjadi lebih berat. Terdapat pilihan skrining untuk mendeteksi neuropati otonom salah satunya dengan Survey of Autonomic Symptom (SAS) dan pemeriksaan variabilitas detak jantung (HRV)Tujuan Mengetahui proporsi dan hubungan antara neuropati otonom dengan disfungsi ereksi pada DMT2 yang dinilai dengan kuesioner SAS dan pemeriksaan HRVMetode Penelitian ini menggunakan studi potong lintang dari 86 pasien DMT2 di Poliklinik Metabolik Endokrin RSUPN dr. Cipto Mangunkusumo sejak Agustus 2021 hingga November 2021. Pasien dilakukan wawancara dengan kuesioner SAS, IIEF-5, dan Pemeriksaan HRV. Dilakukan analisis multivariat untuk menilai hubungan variabel bebas dan terikat setelah dikontrol dengan variabel-variabel perancu yang berhubungan.Hasil Pada penelitian ini didapatkan proporsi pasien DE pada DMT2 sebanyak 59,3%. Proporsi pasien neuropati otonom yang dinilai dengan HRV sebanyak 94,3% dan neuropati otonom yang dinilai dengan kuesioner SAS sebanyak 41,9%. Terdapat hubungan secara statistik bermakna setelah dilakukan analisis multivariat antara neuropati otonom diabetik yang dinilai dengan kuesioner SAS dengan DE (adjusted OR 18,1 [IK95% 3,90-84.33]). Pemeriksaan HRV dalam penelitian ini tidak menunjukan hubungan yang signifikan secara statistik dengan DE.Kesimpulan Proporsi pasien dengan neuropati otonom diabetik yang dinilai dengan kuesioner SAS didapatkan sebesar lebih dari 40% dan yang dinilai dengan HRV lebih dari 90%. Terdapat hubungan yang secara statistik bermakna antara neuropati otonom diabetik yang dinilai dengan kuesioner SAS dengan DE.

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Background Diabetes mellitus (DM) affecting the autonomic nervous system known as diabetic autonomic neuropathy (DAN), which controls many organ systems and can manifest as erectile dysfunction (ED). The range of ED and DAN prevalence has been found to vary widely depending on the baseline comorbidities in the population of the subject studied. Autonomic neuropathy is still rarely studied and its relationship with erectile dysfunction needs to be explored whether the two variables are related. By early detection of autonomic neuropathy, it is hoped that can help detect ED and prevent the progression more severe. There are screening options to see autonomic neuropathy: survey of Autonomic Symptoms (SAS) questionnaire and Heart rate variability (HRV) test.

Objective To determine the proportion and relationship between diabetic autonomic neuropathy and erectile dysfunction in Type 2 DM using SAS questionnaire and HRV examination

Methods Cross-sectional study of 86 type 2 DM patients at the Metabolic Endocrine Polyclinic, dr. Cipto Mangunkusumo from August 2021 to November 2021. Patients were interviewed with the IIEF-5 questionnaire, SAS and HRV examination. Multivariate analysis with logistic regression analysis was performed to assess the relationship between diabetic autonomic neuropathy with ED in the type 2 DM population.

Results In this study, the proportion diabetic autonomic neuropathy in Type 2 DM was 41.9% with SAS questionnaire and 94,3% with HRV, and Proportion of ED was 59.3%. The proportion of autonomic neuropathy who had ED was 91.7% with SAS and 69,7% with HRV. There was a statistically significant relationship between diabetic autonomic neuropathy use SAS and ED (adjusted OR 18.1 [95% CI 3.90-84.33]). HRV examination did not show an association with ED in this study.

Conclusion More than half of the subjects had erectile dysfunction and almost all of the patients with diabetic autonomic neuropathy had erectile dysfunction. There is a statistically significant relationship between diabetic autonomic neuropathy using SAS questionnaire and ED."

"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 FAMetode: 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.

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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."

"Latar belakang: DM masih menjadi masalah besar bidang kesehatan global, dengan beban yang sangat besar akibat dari penyakitnya secara langsung dan komplikasinya, terutama komplikasi kardiovaskular. Komplikasi tersebut dipengaruhi gangguan neuropati autonom, yang dapat dinilai dengan HRV. Gangguan autonom dipikirkan akan menurunkan fungsi jantung, yang secara dini mungkin dapat diprediksi dengan melihat global longitudinal strain. Hubungan antara HRV dan GLS belum banyak diteliti Tujuan: Mengetahui korelasi antara fungsi autonom kardiak dengan fungsi ventrikel kiri pada pasien DM Tipe 2 Metode: Studi potong lintang dengan populasi terjangkau Pasien DM tipe 2 berusia dewasa yang tinggal di DKI Jakarta pada bulan Desember 2020. Parameter HRV terdiri dari interval RR, standard deviation of NN intervals (SDNN), root mean square of successive difference (RMSSD), low frequency (LF), high frequency (HF) dan rasio LF/HF dan Global longitudinal strain dianalisis menggunakan ekokardiografi Hasil: Dilakukan analisis pada 167 sampel. rerata GLS didapatkan -20,30 (±1,57). Tidak terdapat korelasi antara interval RR dengan GLS (r = -0,07, p = 0,377), tidak terdapat korelasi antara SDNN dengan GLS (r = -0,10, p = 0,189), tidak terdapat korelasi antara RMSSD dengan GLS (r = -0,12, p = 0,098), tidak terdapat korelasi antara LF dengan GLS (r = -0,003, p = 0,968), tidak terdapat korelasi antara HF dengan GLS (r = -0,09, p = 0,21), tidak terdapat korelasi antara LF/HF dengan GLS (r = -0,10, p = 0,189). Tidak terdapat faktor perancu yang berhubungan pada penelitian ini Kesimpulan: Tidak Terdapat korelasi antara heart rate variability dengan global longitudinal strain pada pasien DM Tipe 2.

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Background: Diabetes Mellitus (DM) remains a major global health issue due to its direct consequences and complications, particularly cardiovascular complications. These complications are influenced by autonomic neuropathy, which can be assessed by Heart Rate Variability (HRV). Autonomic dysfunction is thought to impair heart function, which can potentially be predicted early by observing global longitudinal strain (GLS). The relationship between HRV and GLS has not been extensively studied. Objective: To determine the correlation between cardiac autonomic function and left ventricular function in Type 2 DM patients. Methods: A cross-sectional study with a population of adult Type 2 DM patients residing in Jakarta in December 2020. HRV parameters included RR interval, standard deviation of NN intervals (SDNN), root mean square of successive difference (RMSSD), low frequency (LF), high frequency (HF), and LF/HF ratio. Global longitudinal strain was analyzed using echocardiography. Results: Analysis was conducted on 167 samples. The average GLS was -20.30 (±1.57). There was no correlation between RR interval and GLS (r = -0.07, p = 0.377), no correlation between SDNN and GLS (r = -0.10, p = 0.189), no correlation between RMSSD and GLS (r = -0.12, p = 0.098), no correlation between LF and GLS (r = -0.003, p = 0.968), no correlation between HF and GLS (r = -0.09, p = 0.21), and no correlation between LF/HF ratio and GLS (r = -0.10, p = 0.189). There were no confounding factors associated with this study. Conclusion: There is no correlation between heart rate variability and global longitudinal strain in Type 2 DM patients."

"Penyakit kardiovaskular merupakan penyebab utama kematian global, termasuk di Indonesia. Evaluasi kesehatan dini, menggunakan heart rate variability (HRV) melalui pengukuran root mean square of successive RR interval differences (RMSSD) dan percentage of successive RR intervals that differ by more than 50 𝑚𝑠 (pNN50), menjadi penting untuk merefleksikan respons relaksasi, stres, kualitas tidur, dan aktivitas fisik. Evaluasi ini sebaiknya dilakukan saat seseorang masih dalam kondisi sehat. Sejalan dengan itu, penelitian ini bertujuan mengevaluasi kesehatan pasien dengan irama jantung normal melalui metode clustering pada variabel RMSSD, pNN50, dan usia, yang diambil dari rekaman elektrokardiogram milik online database Physionet. Setiap cluster yang terbentuk dapat memberikan informasi unik, memungkinkan penentuan risiko penyakit kardiovaskular serta penanganan yang tepat. Namun, karena pola data yang digunakan tidak jelas, mengandung outlier, dan berdimensi rendah, maka dilakukan perbandingan antara metode Hierarchical clustering dan Gaussian Mixture Models (GMM) clustering yang mampu mengatasi hal tersebut. Mengingat GMM clustering yang sangat sensitif terhadap inisialisasi awal, penelitian ini menggunakan dua pendekatan inisialisasi, yaitu acak dan K-Means. Penentuan metode terbaik dilakukan dengan mempertimbangkan metrik evaluasi (efektivitas) dan waktu komputasi metode (efisiensi). Hasil penelitian menunjukkan bahwa GMM clustering dengan inisialisasi K-Means adalah metode terbaik dengan membentuk tiga cluster. Meskipun alat EKG menilai pasien dalam kondisi sehat, namun analisis clustering dapat mengungkapkan informasi penting, terutama bagi pasien yang teridentifikasi memiliki tingkat HRV yang relatif rendah.

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Cardiovascular diseases are a leading cause of global mortality, including in Indonesia. Early health evaluation, utilizing heart rate variability (HRV) through root mean square of successive RR interval differences (RMSSD) and percentage of successive RR intervals that differ by more than 50 𝑚𝑠 (pNN50) measurements, is crucial to reflect responses to relaxation, stress, sleep quality, and physical activity. This evaluation is ideally conducted while an individual is still in a healthy condition. In line with that, this research aims to evaluate the health of patients with a normal sinus rhythm through clustering methods on variables like RMSSD, pNN50, and age, extracted from electrocardiogram recordings from the online Physionet database. Each cluster can provide unique information, enabling the identification of cardiovascular disease risks and appropriate interventions. However, due to unclear data patterns, the presence of outliers, and is low-dimensiona, a comparison is made between Hierarchical clustering and GMM methods, capable of addressing these issues. Given GMM clustering's sensitivity to initializations, this study employs two approaches, random and K-Means. The determination of the best method is based on considerations of evaluation metrics (effectiveness) and computational time (efficiency). Research results indicate that GMM clustering with K-Means initialization is the most effective and efficient method, forming three clusters. Despite ECG assessments indicating healthy conditions, clustering analysis can reveal crucial information, especially for patients identified with relatively low HRV levels."

"ABSTRAKKetika 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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ABSTRACTWhen 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.

 

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"Latar belakang: Pasien sindom koroner akut (SKA) dengan gejala ansietas berisiko mengalami luaran negatif yang dimediasi oleh disfungsi otonom yang dapat dinilai dengan variabilitas denyut jantung (VDJ). Penurunan VDJ ditemukan baik pada pasien SKA maupun ansietas. Penelitian ini bertujuan untuk mengetahui perbedaan nilai VDJ pada pasien SKA dengan gejala ansietas dibandingkan dengan tanpa gejala ansietas dan menentukan korelasi antara nilai VDJ dengan gejala ansietas. Metode: Penelitian ini merupakan studi potong lintang. Subjek penelitian diambil dari data penelitian utama pada pasien SKA yang dirawat di ruang intensif rawat jantung RSCM periode April-September 2021 secara total sampling. Gejala ansietas dinilai dengan kuesioner. Hospital Anxiety and Depression Scale (HADS). Data VDJ yang diambil adalah domain waktu (SDNN, RSSMD) dan frekuensi (LF, HF, rasio LF/HF). Uji Mann-Whitney dilakukan untuk perbedaan nilai VDJ antara subjek dengan gejala ansietas dibanding tanpa gejala ansietas, uji Spearman untuk korelasi antara nilai VDJ dengan gejala ansietas, dan analisis multivariat untuk faktor perancu. Hasil: Tujuh puluh subjek SKA yang dilibatkan terdiri dari 23 subjek dengan gejala ansietas dan 47 subjek tanpa gejala ansietas. Tidak didapatkan perbedaan nilai VDJ (SDNN, RMSSD, LF, HF, rasio LF/HF) antara subjek dengan gejala ansietas dibanding tanpa gejala ansietas secara statistik. Setelah mengontrol variabel perancu, gejala ansietas memiliki korelasi dengan SDNN (r = -0,563; p<0,001) yang dipengaruhi oleh usia (p<0,004); sementara nilai LF (r = -0,63; p< 0,001) dipengaruhi oleh usia (p = 0,007) dan penyekat beta (p = 0,030). Kesimpulan: Tidak didapatkan perbedaan nilai VDJ antara pasien SKA dengan gejala ansietas dibanding tanpa gejala ansietas yang bermakna secara statistik, namun terdapat penurunan nilai SDNN, HF, dan rasio LF/HF pada kelompok dengan gejala ansietas yang lebih besar. Terdapat korelasi antara nilai VDJ (SDNN dan LF) dengan gejala ansietas pada pasien SKA.

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Background: Acute coronary syndrome (ACS) patients with anxiety symptoms are at high risk of developing poor outcomes mediated by autonomic dysfunction that can be assessed with heart rate variability (HRV). Reductions in HRV are reported not only in ACS but also in anxiety. This study aims to compare HRV of ACS subjects with and without anxiety and to determine the correlation between HRV and anxiety symptoms.

Methods: This research is a cross-sectional study. The study subjects were taken from the primary research data of ACS patients treated at the ICCU of RSCM from April to September 2021 by total sampling. Anxiety symptoms are assessed with Hospital Anxiety and Depression Scale (HADS) questionnaire. HRV analysis consist of time (SDNN, RSSMD) and frequency (LF, HF, LF/HF ratio) domain. Data were analyzed using Mann- Whitney test for differences in HRV between ACS subjects with anxiety symptoms compared to those without anxiety symptoms, Spearman's test for the correlation between HRV and anxiety symptoms, and multivariate analysis for confounding factors.

Results: Seventy ACS subjects involved consisted of 23 subject with anxiety symptoms and 47 without anxiety symptoms. There was no statistical difference in comparison of HRV (SDNN, RMSSD, LF, HF, LF/HF ratio) between anxiety symptoms compare to those without anxiety symptoms. After controlling for confounding variables, SDNN has a correlation with anxiety symptoms (r = -0,563; p<0,001) which was influenced by age (p<0,004); while the LF has a correlation (r = -0,63; p< 0,001) which are influenced by age (p = 0,007) and beta blockers (p = 0,030).

Conclusion: There was no significant difference in HRV values (SDNN, RMSSD, LF, HF, ratio LF/HF) between ACS patients with anxiety symptoms compared to those without anxiety symptoms. There was a correlation between HRV (SDNN and LF) and anxiety symptoms. "