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Abstrak :
Latar Belakang: COVID-19 merupakan infeksi yang disebabkan oleh virus SARS-CoV 2. Baku emas diagnosis COVID-19 dengan pemeriksaan RT-PCR untuk mendeteksi asam nukleat virus, namun pemeriksaan ini memiliki kendala karena jumlah laboratorium yang terbatas, sehingga respon time hasil pemeriksaan memanjang. Keputusan diagnosis yang cepat dan tatalaksana segera merupakan pilar penting untuk menurunkan angka morbiditas dan mortalitas dan agar rantai penularan dapat diputus. Perlu diketahui model diagnosis lain dengan data yang praktis, sederhana dan tersedia luas untuk dijadikan suatu model determinan diagnostik yang dapat membantu klinisi mengambil keputusan lebih cepat. Tujuan: Membuat model determinan diagnosis infeksi COVID-19 berdasarkan kombinasi gejala klinis, radiologis dan laboratorium. Metode: Penelitian ini menggunakan desain potong lintang dengan metode konsekutif. Penelitian dilakukan di RSUPN Dr. Cipto Mangunkusumo, Jakarta yang dimulai bulan Maret s.d Juni 2020. Data diambil dari rekam medis berupa riwayat kontak, gejala klinis, pemeriksaan laboratorium dan foto toraks. Variabel yang diteliti akan dianalisis univariat, bivariat kemudian dilanjutkan analisis multivariat, kurva ROC dan kalibrasi Hosmer-Lemeshow. Hasil: Subjek pada penelitian sebanyak 187 pasien, dengan mayoritas subjek pada kelompok usia < 60 tahun sebesar 65,2% dan jenis kelamin laki-laki sebanyak 53,47%. Komorbid terbanyak adalah diabetes mellitus dan hipertensi. Variabel akhir yang masuk kedalam sistem skoring adalah riwayat kontak (3 poin), demam/riwayat demam (1 poin), sesak dengan frekuensi napas > 20 x/menit (2 poin), leukosit ≤ 10.000 sel /µL (2 poin) dan gambaran foto toraks tipikal (2 poin). Model skoring ini mempunyai AUC: 0,777, P: < 0,001, IK 95% (0,706-0,847) dengan nilai cut off skor ≥ 4 dari total 10 poin memiliki probabilitas 82 %, NDP: 74%, NDN: 77%. Simpulan: Model determinan diagnosis infeksi COVID-19 berdasarkan kombinasi dari riwayat kontak, demam/riwayat demam, sesak napas, kadar leukosit ≤ 10.000 sel /µL dan gambaran tipikal foto toraks cukup baik dalam membantu diagnosis COVID-19.

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Background: COVID-19 is an infection caused by SARS-CoV 2. RT-PCR test is gold standart to diagnose COVID-19 by detecting SARS-CoV 2 nucleic acid. However, this test still have a problem due to the limited laboratory centers. Respond time of the RT-PCR result will lengthen. The prompt diagnosis and immediate management are important to decrease mortality, morbidity rate and also prevent transmission. A simple and practice model diagnostic by using determinant factors is needed to guide the physician for taking a quick decision. Objective: Analyze of model determinant diagnosis of COVID-19 based on combination of clinical manifestation, chest radiography and laboratory test. Methods: This study is a cross sectional study with consecutive methods. The study was conducted at RSUPN Dr. Cipto Mangunkusumo, Jakarta from March to June 2020. Hystory of contact with COVID-19, clinical symptoms, laboratory examinations and chest radiographs data were taken from medical records. The steps of analysis were univariate, bivariate multivariate analyze, ROC curve and calibrate Hosmer-Lemeshow. Results: There were 187 patients with the majority of subjects in the age group < 60 years (65.2%) and male sex (53.47%). The most frequent comorbid were diabetes mellitus and hypertension. The selected variables in this scoring are contact history,fever/ history of fever, dyspnea with respiratory rate >20 x/minute, leucocyte ≤ 10.000 cell/µL and typical chest radiography. The area under the curve for this model is 0,777 (P : 0,000 IK 95% (0,706-0,847). The probability is 82% with cut off point ≥ 4 score. Conclusions: Determinant model for diagnosing COVID-19 based on combination of contact history, fever/history of fever, dyspnea, leucocyte ≤ 10.000 cell/µL and typical chest radiography are quite good for helping diagnosis of COVID-19.

Abstrak :
ABSTRAK
Latar Belakang: Obesitas merupakan faktor risiko utama osteoartritis (OA). Penelitian terdahulu mendapatkan bahwa faktor mekanik saja tidak cukup untuk menjelaskan hubungan OA dengan obesitas. Saat ini faktor metabolik yang berkaitan dengan massa lemak tubuh dianggap memiliki peranan penting, tetapi lemak mana yang paling berperan masih kontroversial apakah lemak viseral atau lemak subkutan. Tujuan penelitian ini untuk mendapatkan korelasi antara distribusi lemak tubuh dengan lebar celah sendi tibiofemoral medial Metode: Penelitian ini merupakan studi potong lintang pada penderita OA lutut dengan obesitas yang berobat di poliklinik Reumatologi, Geriatri dan Penyakit Dalam RSCM periode Januari-Maret 2016. Diagnosis OA lutut berdasarkan kriteria American College of Rheumatology (ACR) 1986. Pemeriksaan distribusi lemak tubuh menggunakan bioelectrical impedance analysis (BIA). Pemeriksaan radiologi lutut menggunakan radiologi konvensional (foto polos) untuk menilai lebar celah sendi tibiofemoral medial. Analisis statistik bivariat digunakan untuk mendapatkan korelasi antara distribusi lemak tubuh dengan lebar celah sendi tibiofemoral medial. Hasil: Sebanyak 56 orang pasien yang memenuhi kriteria inklusi dan bersedia ikut dalam penelitian, mayoritas subjek berjenis kelamin perempuan (73,2%). Median kadar lemak viseral adalah 12% (7.5-16,5) median lemak subkutan adalah 30,2% (16,5-37,9) dan median rasio lemak viseral/subkutan adalah 0,40 (0,26-0,80). Rerata lebar celah sendi tibiofemoral medial adalah 2,34 mm (SB 0,78). Korelasi antara lemak viseral dengan lebar celah sendi tibiofemoral medial (r: -0,474 p: < 0,001). Tidak didapatkan korelasi antara lemak subkutan dengan lebar celah sendi tibiofemoral medial (r: -0,187 p: 0,169) serta tidak didapatkan korelasi antara rasio lemak viseral/subkutan dengan lebar celah sendi tibiofemoral medial (r: -0,225 p: 0,09) Simpulan: Lemak viseral berkorelasi negatif sedang dengan lebar celah sendi tibiofemoral medial (r: -0,474 p: < 0,001). Tidak didapatkan korelasi antara lemak subkutan dan rasio lemak viseral/subkutan dengan lebar celah sendi tibiofemoral

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ABSTRACT
Background: Obesity is a major risk factor for knee osteoarthritis. The relationship between obesity and OA may not simply due to mechanical factor. Evidence suggests that metabolic factors related to body fat play important roles, but the specific type of fat that contributes to OA is unclear. The objective of this study was to examine the possible correlation between body fat distributions with knee OA Method: This study was a cross sectional study in OA patients with obesity visiting Rheumatology, Geriatric, Internal Medicine clinics in Cipto Mangunkusumo Hospital between January-March 2016. Samples were collected using consecutive sampling method. Knee OA was diagnosed from clinical and radiologic evaluation based on American College of Rheumatology 1986 criteria. Body fat distribution was measured by bioelectrical impedance analysis (BIA). Radiographs of the knee was measured by conventional radiography to evaluate joint space narrowing (JSN). The correlation between body fat distributions with joint space width was analyzed by bivariate analysis Result: A total of 56 subjects were recruited, with majority of subjects were women (73,2%). Median of visceral fat was 12% (7.5-16,5), median of subcutaneous fat was 30,2% (16,5-37,9) and median of visceral to subcutaneous fat ratio was 0,40 (0,26-0,80). Mean of medial tibiofemoral joint space width was 2,34 mm (SB 0,78). In bivariate analysis we found correlation between visceral fat and medial tibiofemoral joint space width (r: -0,474 p: < 0,001). There is no correlation between subcutaneous fat and medial tibiofemoral joint space width (r: -0,187 p: 0,169) and also visceral to subcutaneous fat ratio and medial tibiofemoral joint space width (r: -0,225 p: 0,09). Conclusion: Visceral fat is correlated with medial tibiofemoral joint space width (r: -0,474 p: < 0,001). There is no correlation between neither subcutaneous fat nor visceral to subcutaneous fat ratio and medial tibiofemoral joint space width.