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"ABSTRACTBackground: sarcopenia is one of many geriatric problems that may lead to major clinical outcomes. Calf and thigh circumference have good correlation with muscle mass, whereas SARC-F questionnaire is very predictive of muscle function. There has not been a study that evaluates the diagnostic performance of calf and thigh circumference in combination with SARC-F questionnaire in detecting sarcopenia. The aim of this study was to investigate the diagnostic performance of calf and thigh circumference in combination with SARC-F questionnaire compared to standard diagnostic methods of sarcopenia according to the Asian Working Group for Sarcopenia (AWGS) to predict sarcopenia in patient aged 60 years or older. METHODS: this cross-sectional study was conducted in Geriatric Clinic Cipto Mangunkusumo Hospital, Jakarta, Indonesia during April-June 2018. Analysis was performed using receiver operating characteristic (ROC) curve to determine the cut-off point as well as sensitivity (Sn), specificity (Sp), positive and negative predictive value (PPV and NPV), positive and negative likelihood ratio (LR+ and LR-) of calf and thigh circumference as an indicator of low muscle mass, and SARC-F questionnaire score to detect decreased muscle function. RESULTS: from 120 participants, there were 46 men (38.3%) and 74 women (61.7%). The combination of calf circumference with cut-off point below 34 cm in men and below 29 cm in women, thigh circumference below 49 cm in men and below 44 cm in women with SARC-F questionnaire score of ≥4 have Sn, Sp, PPV, NPV, LR+, and LR- of 15.79%; 99.01%; 75.00%; 86.21%; 15.95; and 0.85 respectively. CONCLUSION: combination of calf and thigh circumference with SARC-F questionnaire showed good diagnostic accuracy in predicting sarcopenia in elderly outpatients."

"Latar Belakang: Sarkopenia merupakan salah satu sindrom geriatri yang dapat menyebabkan luaran yang buruk. Dibutuhkan pemeriksaan yang lebih sederhana dibandingkan Bioelectrical Impedance Analysis (BIA) atau Dual energy X- ray Absorptiometry (DXA) untuk mengukur massa otot sebagai komponen penting sarkopenia. Namun, belum ada studi di Indonesia yang meneliti perannya dalam memprediksi massa otot pada pasien usia 60 tahun atau lebih. Tujuan: Mengetahui performa diagnostik lingkar betis untuk estimasi massa otot sebagai komponen sarkopenia pada pasien usia 60 tahun atau lebih. Metode: Penelitian ini merupakan suatu uji diagnostik menggunakan desain uji potong lintang yang dilakukan di poliklinik geriatri Departemen Ilmu Penyakit Dalam FKUI-RSCM selama bulan April-Juni 2018. Pengukuran massa otot menggunakan DXA dan penentuan titik potong berdasarkan Asian Working Group of Sarcopenia (AWGS). Hasil: Dari 120 subjek didapatkan 46 lelaki (38,3%) dan 74 perempuan (61,7%). Didapatkan titik potong lingkar betis kelompok lelaki dibawah 34 cm (sensitivitas 64.7%, spesifitas 79.3%, NDP 64.7%, NDN 79.3%, AUC 73.1%) dan 29 cm untuk perempuan (sensitivitas 71.4%, spesifitas 95.5%, NDP 62.5%, NDN 97.0%, AUC 96.4%). Simpulan: Akurasi diagnostik lingkar betis cukup baik sebagai prediktor massa otot pada pasien perempuan usia 60 tahun atau lebih.

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Background: Sarcopenia is one of the geriatric syndromes that lead to poor outcomes. A simpler method than Bioelectrical Impedance Analysis (BIA) or Dual energy X- ray Absorptiometry (DXA) is needed to measure muscle mass as essential component of sarcopenia. Previous studies have shown calf circumference (CC) as surrogate marker of muscle mass. However there has been no study on the role of CC in predicting muscle mass in both gender of elderly outpatient. Objectives: To investigate the diagnostic performance of CC to estimate muscle mass in elderly outpatient. Methods: A cross sectional study was conducted at Geriatric Outpatient Clinic of Cipto Mangunkusumo Hospital Jakarta during April-June 2018, using DXA as a reference test for measuring muscle mass. Asian Working Group of Sarcopenia (AWGS) criteria was used to classify muscle mass as normal or low. Results: Of the 120 subjects, 46 subjects were male (38.3%) and 74 were female (61.7%).The optimal Cut-off for CC that indicate low muscle mass was 34 cm for (sensitivity 64.7%, specificity 79.3%, PPV 64.7%, NPV 79.3%, AUC 73.1%) and 29 cm for female (sensitivity 71.4%, specificity 95.5%, PPV 62.5%, NPV 97.0%, AUC 96.4%). Conclusion: CC can be used to estimate muscle mass in female elderly outpatient, with good diagnostic performance."

"Otot merupakan fungsi dari aktivitas sehari-hari. Seiring bertambahnya usia, perubahan organ tubuh menyebabkan penurunan massa otot yang berakibat pada individu lanjut usia mengalami penurunan kekuatan tubuh sehingga mobilitasnya berkurang, kesulitan dalam melakukan aktivitas sehari-hari, kesulitan menjaga keseimbangan tubuh, meningkatkan resiko seseorang mengidap penyakit. orang lanjut usia mudah jatuh dan mengalami patah tulang. Namun demikian tidak semua metode pengukuran massa otot apendikuler praktis dan murah sehingga diperlukan metode lain yang dapat mengukur massa otot apendikuler dengan biaya yang sederhana, praktis, dan murah. Tujuan penelitian ini adalah untuk mendapatkan model prediksi massa otot apendikuler berdasarkan lingkar tengah paha, lingkar betis dan lingkar lengan atas sebagai alternatif pengukuran massa otot pada lansia. Penelitian ini menggunakan desain penelitian potong lintang dengan jumlah sampel 101 individu berusia ≥60 tahun (37 laki-laki dan 64 perempuan) di Desa Kadumanggu. Model prediksi yang dihasilkan adalah Massa Otot Apendikuler (kg) = (64.171 x Tinggi Badan (m)) + (1.710 x Indeks Massa Tubuh (kg / m2)) - (0.109 x Lingkar Lengan Atas (cm)) + 0.178 x Lingkar Betis (cm)) + (0,033 x Lingkar Paha Tengah (cm)) - (0,535 x Berat Badan (kg)) - (0,065 x Usia (tahun)) - 98,098 untuk pria lanjut usia (R2 = 0,710; LIHAT = 1, 43 kg ; p <0,05) dan Massa Otot Apendikular (kg) = (8,987 x Tinggi Badan (m)) - (0,170 x Indeks Massa Tubuh (kg / m2)) - (0,117 x Lingkar Lengan Atas (cm)) + (0,121 x Lingkar Betis (cm)) - (0,025 x Lingkar Paha Tengah (cm)) + (0,160 x Berat Badan (kg)) - (0,059 x Usia (tahun)) - 6,491 untuk wanita (R2 = 0,700; LIHAT = 1,23 kg; p <0,05). Model prediksi ini menunjukkan bahwa berat badan, tinggi badan, indeks massa tubuh, umur, lingkar tengah paha, lingkar betis, dan lingkar lengan atas memiliki hubungan yang signifikan dengan massa otot apendikuler.

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Muscle is a function of daily activities. With age, changes in body organs cause a decrease in muscle mass which results in elderly individuals experiencing a decrease in body strength so that their mobility is reduced, difficulty in carrying out daily activities, difficulty maintaining body balance, increasing a person's risk of suffering from disease. elderly people fall easily and have broken bones. However, not all methods of measuring appendicular muscle mass are practical and inexpensive so that another method is needed that can measure appendicular muscle mass at a cost that is simple, practical, and inexpensive. The purpose of this study was to obtain a predictive model for appendicular muscle mass based on mid-thigh circumference, calf circumference and upper arm circumference as an alternative to measuring muscle mass in the elderly. This study used a cross-sectional study design with a total sample of 101 individuals aged ≥60 years (37 males and 64 females) in Kadumanggu Village. The resulting prediction model is Appendicular Muscle Mass (kg) = (64,171 x Body Height (m)) + (1,710 x Body Mass Index (kg / m2)) - (0.109 x Upper Arm Circumference (cm)) + 0.178 x Calf Circumference (cm)) + (0.033 x Mid Thigh Circumference (cm)) - (0.535 x Body Weight (kg)) - (0.065 x Age (years)) - 98.098 for elderly men (R2 = 0.710; VIEW = 1.43 kg; p <0.05) and Appendicular Muscle Mass (kg) = (8.987 x Body Height (m)) - (0.170 x Body Mass Index (kg / m2)) - (0.117 x Upper Arm Circumference (cm)) + (0.121 x Calf Circumference (cm)) - (0.025 x Mid Thigh Circumference (cm)) + (0.160 x Body Weight (kg)) - (0.059 x Age (years)) - 6.491 for women (R2 = 0.700; VIEW = 1.23 kg; p <0.05). This predictive model shows that body weight, height, body mass index, age, mid-thigh circumference, calf circumference, and upper arm circumference have a significant relationship with appendicular muscle mass."

"ABSTRAKNama : Utih ArupahNPM : 1506787121Program : Magister Ilmu Kesehatan MasyarakatJudul : Model Prediksi Berat Badan Menggunakan Prediktor LingkarLengan Atas, Lingkar Pinggang, Lingkar Paha, Lingkar Betis,dan Panjang BadanPengukuran berat badan di rumah sakit merupakan parameter yang objektif,akan tetapi tidak semua pasien yang dirawat dapat dilakukan penimbanganberat badan dengan timbangan biasa, karena pasien tidak bisa berdiri tegak,ketidakmampuan pasien untuk berdiri,lemah tubuh, kesadaran menurun, karenapenyakit tertentu sehingga data yang dihasilkan memiliki reliabilitas yangkurang baik. Lingkar lengan, lingkar pinggang, lingkar paha, lingkar betis danpanjang badan merupakan salah satu ukuran antropometri yang kuat dapatdigunakan untuk memprediksi berat badan. Penelitian ini bertujuan untukmengembangkan model prediksi berat badan berdasarkan lingkar lengan atas,lingkar pinggang, lingkar paha, lingkar betis dan panjang badan. Penelitiandilakukan pada bulan nopember 2017. Disain yang digunakan adalah crosssectional jumlah sampel 160 orang pegawai yang diambil secara simplerandom sampling di RSCM. Variabel yang dikumpuli meliputi berat badan,lingkar lengan atas, lingkar pinggang, lingkar paha, lingkar betis, dan panjangbadan. Berat badan diukur dengan penimbangan dan lingkar lengan atas,lingkar pinggang, lingkar paha, lingkar betis dengan melingkari pita, panjangbadan dengan ukuran meteran. Hasil akhir dari penelitian menghasilkan modelprediksi berat badan untuk mendapatkan berat badan prediksi. Menghasilkan18 model prediksi berat badan memiliki nilai R square tinggi yaitu: 2 modelprediksi berat berat untuk laki-laki R2= 0,898, dan R2= 0,930, 9 model prediksiberat badan untuk perempuan R2=0,960, R2=0,952, R2=0,953, R2=0,956,R2=0,968, R2=0,949, R2=0,945, R2=0,963, R2= 0,944 dan 7 model prediksiuntuk gabungan laki-laki dan perempuan R2=0,949, R2=0,934, R2=0,893,R2=0,935, R2=0,914, R2=0,913, R2=0,929. Peneliti menyimpulkan bahwamodel prediksi berat badan yang dihasilkan akurat untuk memprediksi beratbadan dewasa. Namun perlu dilakukan penelitian kembali pada populasi yanglebih luas.Kata Kunci : Model Prediksi, Berat Badan, Lingkar Lengan Atas

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ABSTRACTNama Utih ArupahNPM 1506787121Program Master of Public HealthJudul Weight Prediction Models Using Upper Arm CircumferencePredictor, Waist Circumference, Thigh Circumference, CalfCircumference and body LengthThe Weight measurement at Hospital is an objective parameter, however thereare only a few treated patients whose body weights can be measured withordinary scales. The reasons are mostly because of their inability to stand up bythemselves or because of certain disease so that the data results have lessreliability. Arm circumference, waist circumference, thigh circumference, calfcircumference and body length are one of the strongest anthropometry can beused to predict body weight. This research aims to develop a weight predictionmodel based on the upper arm circumference, waist circumference, thighcircumference, calf circumference and body length. This research wasconducted in November 2017. The design which used are cross sectional with160 samples of staffs which were taken by simple random in RSCM. Thecollected variables which consist of body weight, upper arm circumference,waist circumference, thigh circumference, calf circumference, and body length.Measurement of body weights can be done by weighing them. Measurement ofupper arm circumference, waist circumference, thigh circumference, calfcircumference can be done by using metering ribbon, and body length withstick meter. The final result of the research creates the formula of body weightprediction to get body weight rsquo s prediction. Producing 18 weight predictionmodels that have high lsquo R rsquo square value, that is 2 weight prediction models forman which are R2 0,898, and R2 0,930, 9 weight prediction models forwomen which are R2 0,960, R2 0,952, R2 0,953, R2 0,956, R2 0,968,R2 0,949, R2 0,945, R2 0,963, R2 0,944 and 7 weight prediction models ofmixed gender R2 0,949, R2 0,934, R2 0,893, R2 0,935, R2 0,914, R2 0,913,R2 0,929 . Scientists concluded that weight prediction models which wasdeveloped is accurate for predicting adult body weight. However, it needs to bere examined in the wider population.Keywords Prediction model, weight, upper arm circumference"

"ABSTRACTBackground: the use of bioelectrical impedance analysis (BIA) is affected by the population setting, the type of BIA, and the cut-off point being used. The aim of this study was to determine the diagnostic performance of BIA to measure muscle mass in Indonesian elderly outpatients aged 60 years or more. Methods: a cross-sectional study was conducted at the Geriatric Clinic of Cipto Mangunkusumo Hospital from April to June 2018. The muscle mass was measured using BIA Tanita MC-780MA (Tokyo, Japan) with dual-energy x-ray absorptiometry (DXA) as the reference test. Analysis on the cut-off point was performed based on the Asian Working Group of Sarcopenia (AWGS) criteria and the new cut-off point. Results: from 120 subjects, 74 were female (61.7%). The diagnostic performance of BIA based on AWGS criteria only showed sensitivity and specificity of 79.2% and 66.7%. The diagnostic performance of BIA based on the new cut-off point showed sensitivity and specificity of 75% and 92.7%. The new cut-off point using BIA was found to be <6.9 kg/m2 in males (sensitivity 70.6%; specificity 82.8%) and <5 kg/m2 in females (sensitivity 85.7%; specificity 97%). Conclusion: the diagnostic performance of BIA Tanita MC-780MA (Tokyo, Japan) was good to measure muscle mass in Indonesian elderly outpatients using a new cut-off point of <6.9 kg/m2 for males and <5 kg/m2 for females."

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Penelitian ini bertujuan untuk menentukan titik potong lingkar lengan atas pada posisi berbaring. Penelitian ini menggunakan desain cross-sectional. Data diambil dari rekam medis pasien poliklinik radioterapi RSUPN Dr. Cipto Mangunkusumo (n=207) dan dilakukan pengukuran antropometri pada pasien. Titik potong lingkar lengan atas diperoleh dari kurva ROC dan indeks Youden tertinggi. Dari penelitian ini didapatkan perbedaan rata-rata antara lingkar lengan atas pada posisi berdiri dan terlentang adalah 0,13 ± 0,33 cm (p<0,001). Lingkar lengan atas dari keseluruhan subjek memiliki korelasi yang kuat dan signifikan dengan indeks massa tubuh (r=0,932; p<0,001). Nilai AUC lingkar lengan atas untuk mendeteksi malnutrisi adalah 0,97 (95% CI 0,947-0,992; p<0,001). Lingkar lengan atas <23,4 cm menunjukkan sensitivitas 94,7% dan spesifisitas 95,6% untuk pria, dan sensitivitas 95% dan spesifisitas 89% untuk wanita. Sebagai kesimpulan, lingkar lengan atas <23,4 cm dapat digunakan sebagai salah satu alternatif pengukuran untuk mendeteksi malnutrisi, terutama bila indeks massa tubuh tidak dapat diukur.

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This study aims to establish a cut-off point for mid-upper arm circumference in the supine position. This is a cross-sectional study. Data were taken from patients at the radiotherapy clinic of Dr. Cipto Mangunkusumo General Hospital (n=207) by medical records, and anthropometric measurements were performed. The cut-off point of the mid-upper arm circumference was obtained from the ROC curve and the highest Youden’s index. This study found that the mean difference between mid-upper arm circumference in the standing and supine positions is 0.13±0.33 cm (p<0.001). The mid-upper arm circumference from all subjects strongly and significantly correlates to body mass index (r=0.932; p<0.001). The area under the curve of the mid-upper arm circumference for detecting malnutrition was 0.97 (95% CI 0.947–0.992; p<0.001). The mid-upper arm circumference of <23.4 cm presents a sensitivity of 94.7% and a specificity of 95.6% for men, and a sensitivity of 95% and a specificity of 89% for women. In conclusion, the mid-upper arm circumference of <23.4 cm can be used as an alternative measurement to detect malnutrition, particularly when body mass index cannot be measured.
 

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"Massa otot memiliki banyak manfaat, termasuk untuk aktivitas kehidupan sehari-hari dan memengaruhi dalam kinerja olahraga. Selain itu, otot juga berperan sebagai pencegahan dari berbagai kondisi patologis dan penyakit kronis yang umum terjadi. Kemajuan teknologi telah membuat massa otot semakin mudah diukur dengan akurat, namun tidak semua kegiatan dapat mengakses alat ukur massa otot dengan mudah terkait alat ukur yang terbatas dan terbilang mahal. Tujuan penelitian ini adalah untuk menciptakan metode alternatif menghitung massa otot berdasarkan ukuran lingkar betis, lingkar otot lengan atas, dan lingkar lengan atas pada karyawan Fakultas Kesehatan Masyarakat Universitas Indonesia. Penelitian ini menggunakan desain cross sectional dengan total sampel 96 responden. Hasil penelitian menunjukkan adanya korelasi kuat pada jenis kelamin yang tidak dibedakan antara lingkar otot lengan atas dengan massa otot r = 0,545, korelasi kuat pada laki-laki antara lingkar lengan atas dengan massa otot r = 0,650, serta korelasi kuat pada perempuan antara lingkar betis dengan massa otot r = 0,716. Model prediksi yang paling ideal digunakan adalah Massa Otot kg = 11,964 JK 1,108 LiLA cm 0,07 LOLA cm 5,757 dengan nilai akurasi 0,829 dan pertimbangan akurasi yang tinggi serta kemudahan pengaplikasian di lapangan.

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Muscle mass has many benefits, including for daily activities and sports performance. In addition, muscle also serves as a prevention of various pathological conditions and chronic diseases are common. Advanced technology makes easier to measure muscle mass accurately, but not all activities can easily access muscle mass measurements with limited and costly measuring instruments.

The purpose of this study is to create an alternative method of calculating muscle mass based on calf circumference, mid upper arm muscle circumference, and mid upper arm circumference on employees of Public Health Faculty, Universitas Indonesia. This study used cross sectional design and samples total in this study are 96 respondents.

The results showed a strong correlation of all samples between mid upper arm muscle circumference and muscle mass r 0,545, strong correlation in males sample between mid upper arm circumference and muscle mass r 0,650, and strong correlation in women samples between calf circumference and muscle mass r 0,716. The most ideal prediction model used is Muscle Mass kg 11,964 JK 1,108 LiLA cm-0,07 LOLA cm 5,757 with correlation value 0,829, high accuracy and applicable in the field. "

"Nilai titik potong lingkar lengan atas (LiLA) untuk diagnosis gizi buruk berdasarkan WHO adalah 11,5 cm. Nilai titik potong ini dinilai kurang sensitif dalam menjaring kasus gizi buruk pada balita. Berbagai nilai titik potong LiLA baru diusulkan dengan nilai diagnostik yang lebih baik namun memiliki interval yang lebar, 12 cm-14,1 cm. Saat penelitian ini dilakukan belum ada data mengenai evaluasi nilai titik potong LiLA 11,5 cm dalam diagnosis gizi buruk pada balita di Indonesia. Diperlukan penelitian untuk mengevaluasi nilai diagnostik LiLA dalam diagnosis gizi buruk dan mencari titik potong yang paling optimal pada balita Indonesia. Penelitian ini bertujuan untuk mengetahui nilai diagnostik LiLA dibandingkan dengan indeks BB/TB dalam diagnosis gizi buruk pada balita, mengetahui sensitivitas, spesifisitas, nilai duga positif, nilai duga negatif nilai titik potong LiLA < 11,5 cm dalam diagnosis gizi buruk dan mencari rekomendasi nilai titik potong LiLA yang memiliki nilai diagnostik yang lebih baik untuk skrining balita dengan gizi buruk. Pengambilan subyek penelitian pada studi diagnostik ini dilakukan secara konsekutif pada bulan Januari-Februari 2020 di RSCM dan Puskesmas Cengkareng Jakarta Barat. Penelitian ini melibatkan 421 subyek. Data dasar, jenis kelamin, usia didapatkan melalui wawancara singkat. Pengukuran antropometri berupa berat badan, tinggi badan/panjang badan dan lingkar lengan atas dilakukan oleh peneliti/asisten peneliti yang memiliki realibilitas pengukuran yang baik. LiLA memiliki nilai diagnostik yang tinggi ditandai dengan AUC 0,939 (CI95% 0,903-0,974). Nilai diagnostik LiLA dengan titik potong 11,5 cm memiliki sensitivitas yang rendah. Nilai diagnostik LiLA dengan nilai titik potong 11,5 cm: Se 21% Sp 99,7% NDP 80%, NDN 96%, IY 0,2. Nilai titik potong LiLA 13,3 cm memberikan hasil terbaik dalam identifikasi gizi buruk dengan Se 89%, Sp 87%, NDP 25%, NDN 99% dan IY 0,76. Nilai titik potong LiLA 11,5 cm untuk kasus gizi buruk memiliki sensitivitas yang rendah dan sebaiknya tidak digunakan dalam upaya skrining kasus gizi buruk di masyarakat. Nilai titik potong LiLA 13,3 cm memberikan nilai diagnostik yang lebih baik dalam upaya skrining gizi buruk pada balita usia 6-59 bulan.

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World Health Organization recommends 11,5 cm as cut off value of mid-upper arm circumference (MUAC) to diagnose severe acute malnutrition (SAM) in under-five. Many studies indicate that the recommended cut off value is not sensitive to screen severe acute malnutrition cases. Various new cut off values have been proposed with very wide interval, 12-14.1 cm. When this study started there was no available data regarding diagnostic value of MUAC in diagnosing severe acute malnutrition in under-five in Indonesia. Aims of this study are to evaluate diagnostic value of MUAC in diagnosing SAM compare to WHZ index, to evaluate sensitivity, specificity, positive prediction value, negative prediction value of MUAC with 11,5 cm as standard cut off in diagnosing SAM and to find alternative cut off value that may offer better diagnostic performance. This diagnostic study recruits subjects consecutively in January-February 2020 in Cipto Mangunkusumo hospital and Puskesmas Cengkareng. We collected 421 subjects. Demographic data was obtained by using brief conversation. Physical examination and anthropometric measurement were performed by researcher and research assistant that had been trained, evaluated and proven to have excellence reliability. In general, MUAC has excellent diagnostic value to assess SAM in under-five with AUC 0,939 (CI95% 0,903-0,974). The recommended cut off value has low sensitivity. Proportion SAM using WHZ index and MUAC < 11,5 cm are 4,5% and 1,2%. Diagnostic values MUAC using cut off 11,5 cm are Se 21%, Sp 99,7%, PPV 80%, NPV 96% and YI 0,2. By using 13.3 cm as new cut off value, MUAC will have Se 89%, Sp 87%, PPV 25%, NPV 99% and YI 0,76. We conclude that MUAC using 11,5 cm has low sensitivity to detect SAM cases in population, therefore should not be implemented in the community for screening SAM cases. The new cut of value 13,3 cm has better diagnostic value to screen SAM cases in under-fives."