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"Cases of coronavirus disease 2019 (COVID-19) in Indonesia are still increasing and even higher in the last few weeks. Contact tracing and surveillance are important to locate cases in the community, including asymptomatic individuals. Diagnosis of COVID-19 depends on the detection of viral RNA, viral antigen, or indirectly, viral antibodies. Molecular diagnosis, using real time, reverse transcriptase polymerase chain reaction (RT-PCR), is the common standard method; however, it is not widely available in Indonesia and requires a high standard laboratory. Rapid, point-of-care antibody testing has been widely used as an alternative; however, interpretation of the results is not simple and now it is no longer used by the Indonesian government as a screening test for people travelling between locations. Thus, the rapid antigen detection test (Ag-RDT) is used by the Indonesian government as a screening test for travellers. As a result, many people buy the kit online and perform self-Ag-RDT at home. This raises the question of how safe and accurate it is to perform self-Ag-RDT at home. Before a test is applied, it is suggested to research its sensitivity and specificity, as compared to gold standard, and its limitations. In this article, laboratory diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is discussed, with an emphasis on Ag-RDT and the recommendation to use it properly in daily practice."

"Latar Belakang: Aktivasi komplemen dapat menyebabkan respon imun berlebihan dan merupakan salah satu faktor yang berpengaruh terhadap morbiditas serta mortalitas pasien COVID-19. Beberapa penghambat aktivasi komplemen saat ini sedang dipelajari untuk menghambat aktivasi sistem komplemen yang berlebihan pada pasien COVID-19. Resiko, keuntungan, waktu pemberian dan bagian dari sistem yang akan ditargetkan perlu dipertimbangkan pada saat akan menggunakan penghambat komplemen, oleh karena itu telaah sistematis ini dibuat untuk mengambil kesimpulan apakah pemberian terapi penghambat sistem komplemen dapat menurunkan mortalitas pasien COVID-19 yang dirawat di Rumah Sakit berdasarkan penelitian-penelitian yang tersedia.Tujuan: Mengetahui efek pemberian terapi penghambat sistem komplemen terhadap mortalitas pasien COVID-19 yang dirawat di Rumah Sakit.Metode: Dengan menggunakan kata kunci spesifik, dilakukan pencarian artikel potensial secara komprehensif pada PubMed, Embase, Cochrane, dan Scopus database dengan pembatasan waktu 2019 sampai dengan sampai 31 Desember 2022. Protokol studi ini telah diregistrasi di PROSPERO (CRD42022306632). Semua penelitian pemberian terapi penghambat komplemen pada pasien COVID-19 dimasukkan. Analisis statistik dilakukan dengan menggunakan perangkat lunak Review Manager 5.4.Hasil: 5 penelitian memenuhi kriteria dan dimasukkan dalam telaah sistematis serta meta-analisis dengan total 739 pasien COVID-19. Hasil analisis Forest plot menunjukan bahwa pemberian terapi penghambat sistem komplemen menurunkan mortalitas sebesar 28% pada pasien COVID-19 yang dirawat di Rumah Sakit (RR 0,72; 95% CI: 0,46 – 1,14, I2 = 61%, P-value = 0.16).Kesimpulan: Pemberian terapi penghambat sistem komplemen secara statistik tidak signifikan menurunkan mortalitas pada pasien COVID-19 yang dirawat di Rumah Sakit

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Background: Complement activation can cause an exaggerated immune response and is one of the factors that influence the morbidity and mortality of COVID-19 patients. Several complement activation inhibitors are currently being studied to inhibit excessive complement activation in COVID-19 patients. The risks, benefits, time of administration and the part of the system to be targeted need to be considered when using complement inhibition, therefore this systematic review was made to conclude whether the administration of complement system inhibition therapy can reduce the mortality of COVID-19 patients who are hospitalized based on available studies.

Objective: To determine the effect of complement system inhibitory therapy on the mortality of hospitalized COVID-19 patients

Methods: Using specific keywords, we comprehensively searched the PubMed, Embase, Cochrane, and Scopus databases for potential articles from 2019 to December 31, 2022. The research protocol was registered with PROSPERO (CRD42022306632). All studies administering complement inhibitory therapy to COVID-19 patients were processed. Statistical analysis was performed using Review Manager 5.4 software.

Result: 5 studies met the criteria and were included in a systematic review and meta-analysis of a total of 739 COVID-19 patients. The results of the Forest plot analysis showed that administration of complement system inhibitor therapy reduced mortality by 28% in hospitalized COVID-19 patients (RR 0.72; 95% CI: 0.46 – 1.14, I2 = 61%, P -value = 0.16).

Conclusion: Providing complement system inhibitor therapy did not statistically significantly reduce mortality in hospitalized COVID-19 patients"

"Pandemi Coronavirus Disease 2019 (COVID-19) merupakan pandemi disebabkan oleh virus SARS-CoV-2. Indonesia diketahui sebagai salah satu negara dengan tingkat infeksi COVID-19 paling tinggi di dunia. Deteksi cepat secara Real Time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) merupakan salah satu langkah yang diperlukan untuk menekan laju penyebaran COVID-19. Kit deteksi BioCore 2019-nCoV Real Time PCR Kit adalah salah satu kit dignosis COVID-19 produksi BioCore. Ltd., Korea Selatan. Kit diagnosis BioCore telah beredar di Indonesia dan perlu diuji keakuratan diagnosis yang dihasilkan untuk menghindari hasil negatif palsu. Pengujian dilakukan menggunakan protokol Penjaminan Mutu Eksternal (PME) Kementerian Kesehatan Indonesia dengan melibatkan 30 sampel uji dan membandingkan hasil uji terhadap kit gold standard CDC dengan gen target N1, N2, dan HRP. Alur kerja penelitian dimulai dari proses pengambilan sampel, ekstraksi RNA, persiapan mastermix, adisi template RNA, dan amplifikasi template dengan metode rRT-PCR. Hasil penelitian menunjukkan adanya amplifikasi pada kontrol yang digunakan, sehingga proses diagnosis dapat dilakukan. Nilai Ct IC kit Biocore dan IC CDC menunjukkan perbedaan signifikan (P 0,05; CI=95%). Gen target SARS-CoV-2 tidak terdeteksi pada kit Biocore dengan nilai Ct>35, serta didapatkan nilai sensitivitas dan spesifisitas analitik kit Biocore berturut-turut sebesar 75% dan 100%. Hasil uji Kit Biocore terhadap pasien terinfeksi COVID-19 di Indonesia tidak memenuhi standar kit diagnosis yang ditetapkan oleh WHO, yaitu memiliki sensitivitas analitik sebesar 95%. Peninjauan ulang primer pada kit Biocore perlu dilakukan untuk memperbaiki mutu kit dalam deteksi awal virus SARS-CoV-2 di Indonesia.

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The Coronavirus Disease 2019 (COVID-19) pandemic is a pandemic caused by the SARS-CoV-2 virus. Indonesia is known as one of the countries with the highest COVID-19 infection rate in the world. Real Time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) detection is one of the steps needed to accelerate the spread of COVID-19. The BioCore 2019-nCoV Real Time PCR Kit is one of the COVID-19 diagnosis kits produced by BioCore. Ltd., South Korea. The BioCore diagnostic kit has been circulating in Indonesia and needs to be tested for the accuracy of the resulting diagnosis to avoid false negative results. The test was carried out using the External Quality Assurance (PME) protocol of the Indonesian Ministry of Health involving 30 test samples and test results against the CDC gold standard kit with target genes N1, N2, and HRP. The research workflow starts from the sampling process, RNA extraction, mastermix preparation, RNA template addition, and template amplification using the rRT-PCR method. The results showed that there was amplification of the controls used, so that the diagnosis process could be carried out. The Ct values ​​of the Biocore IC kit and the CDC IC showed a significant difference (P 0.05; CI=95%). The SARS-CoV-2 target gene was not detected in the Biocore kit with a Ct value>35, ​​and the sensitivity and analytical specificity of the Biocore kit were 75% and 100%, respectively. The results of the Biocore Kit test on patients infected with COVID-19 in Indonesia do not meet the diagnostic kit standard set by WHO, which has an analytical sensitivity of 95%. Primary review on the Biocore kit needs to be done to improve the quality of the kit in early detection of the SARS-CoV-2 virus in Indonesia."

"Although typically patients with coronavirus disease-19 (COVID-19) have pulmonary symptoms atypical cases can occasionally present with extra-pulmonary symptoms. We report an interesting case of COVID-19 female patient presenting with combination of central nervous system disorder and acute myocardial infarct as initial manifestation. Multiorgan involvement in COVID-19 might lead to multiple atypical presentation which could be overlooked by the physician."

"Skripsi ini merupakan pengembangan dari teknologi video surveillance system atau sistem pengawasan video. Video surveillance system yang selama ini diterapkan untuk tujuan deteksi objek menggunakan suatu metode umum yaitu dual background model. Model tersebut bekerja dengan memisahkan latar depan dan latar belakang dari suatu video frame dan memposisikan target deteksi di latar depan sebagai output. Salah satu tujuan dari skripsi ini adalah melakukan pengembangan dari sistem tersebut agar dapat mengklasifikasikan objek yang terdeteksi menjadi abandoned, stolen, dan ghost region. Untuk mencapai tujuan tersebut, digunakan metode pemelajaran mesin Mask R – CNN yang dapat melakukan segmentasi objek berbasis pemaskeran. Selain dari Mask R – CNN, terdapat model pemelajaran mesin lain yang cukup umum digunakan untuk deteksi objek dan segmentasi objek yaitu model YOLACT. Penelitian ini menggunakan video situasi natural di tempat umum seperti stasiun atau jalanan yang akan diproses menggunakan dual background model dan kemudian disegmentasi menggunakan Mask R – CNN atau YOLACT. Hasil penelitian ini diharapkan bisa membuka wawasan tentang penggunaan model pemelajaran mesin dalam aplikasi object detection, sekaligus menganalisis model mana yang paling efektif dan efisien berupa hasil kuantitatif yaitu Frame Rate per Seccond ( FPS ), waktu segmentasi, serta Intersection Over Union ( IOU ).

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This thesis is an advancement in video surveillance technology. The existing video surveillance system commonly employs a dual background model for object detection. This model functions by separating the foreground and background within a video frame and positions the detected target in the foreground as the output. One of the goals of this thesis is to enhance this system to classify detected objects into abandoned, stolen, and ghost regions. To achieve this, the Mask R-CNN machine learning method is used, which can perform object segmentation based on masking. Apart from Mask R-CNN, another commonly used machine learning model for object detection and segmentation is the YOLACT model. This research utilizes natural situation videos in public places like stations or streets, processed using the dual background model and then segmented using Mask R-CNN or YOLACT. The anticipated outcome of this study is to broaden insights into the use of machine learning models in object detection applications while analyzing which model is most effective and efficient for similar applications."

"Coronavirus Disease (COVID-19) adalah penyakit baru yang melanda dunia tahun 2020.Penyakit ini diperkirakan berasal dari Wuhan, China (Rothan HA, 2020). WHOmenetapkan COVID-19 sebagai pandemi karena penyakit ini telah berhasil menginfeksilebih dari 190 negara di dunia. DKI Jakarta adalah Ibu Kota di Indonesia yang turutmenjadi salah satu Provinsi dengan kasus konfirmasi positif COVID-19 tertinggi sampaiakhir Juli 2020. Penelitian ini bertujuan untuk memodelkan rate kasus COVID-19 pada15 kecamatan Jakarta dengan intensitas tertinggi. Alasan dipilihnya 15 kecamatan denganintensitas tertinggi untuk menjadi area penelitian karena lebih dari 63,43% kasuskonfirmasi COVID-19 dilaporkan dari 15 kecamatan, yakni Kecamatan Gambir,Menteng, Sawah Besar, Kemayoran, Taman Sari, Senen, Tanah Abang, Johar Baru,Tambora, Grogol Petamburan, Cempaka Putih, Pademangan, Setia Budi, Matraman, danPalmerah. Rate kasus COVID-19 pada area ini kemudian dibuat model GSTAR, modelini merupakan salah satu pemodelan dalam time seriesstokastik yang mempertimbangkanindeks spasial atau lokasi dan waktu (Budi, 2019). Matriks bobot biner, matriks bobotseragam, dan matriks bobot jarak pada penelitian ini dibentuk sebagai matriks dependensispasial antar lokasi atau disebut matriks bobot W. Hasil identifikasi STACF dan STPACFuntuk semua matriks pembobot spasial didapatkan model yang sama, yaitu GSTAR(3,1).Pendugaan parameter model GSTAR(3,1) dilakukan untuk setiap matriks pembobottersebut. Model GSTAR(3,1) yang terbaik diperoleh berdasarkan matriks pembobotjarak, dengan RMSE terkecil yaitu 0.1271.

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Coronavirus Disease (COVID-19) is a new disease that hit the world in 2020. This disease

is thought to have originated in Wuhan, China (Rothan HA, 2020). WHO has designated

COVID-19 as a pandemic because this disease has successfully infected more than 190

countries in world. DKI Jakarta is the capital city in Indonesia which is also one of the

provinces with the highest positive confirmed cases of COVID-19 until the end of July

2020. This study aims to model the level of COVID-19 cases in 15 sub-districts of DKI

Jakarta with the highest intensity. The reason for choosing 15 sub-districts with the

highest intensity to be the research area was because more than 63.43% of confirmed

COVID-19 cases were reported from 15 sub-districts, that is Gambir, Menteng, Sawah

Besar, Kemayoran, Taman Sari, Senen, Tanah Abang, Johar Baru Districts , Tambora,

Grogol Petamburan, Cempaka Putih, Pademangan, Setia Budi, Matraman, and Palmerah.

Rate of COVID-19 cases in this area is then made a GSTAR model, this model is one of

the models in a stochastic time series that considers spatial index or location and time

(Budi, 2019). The binary weight matrix, uniform weight matrix, and distance weight

matrix in this study were formed as a spatial dependency matrix between locations or

called the W weight matrix. The results of STACF and STPACF services for all spatial

weighting matrices obtained the same model, that is GSTAR (3,1). Estimation of

parameters of the GSTAR model (3,1) is carried out for each weighting matrix. The best

GSTAR (3,1) model is based on a distance weighted matrix, with an RMSE of 0.1271"

"Coronavirus (CoV) adalah keluarga virus penyebab penyakit sistem pernapasan ringan hingga berat pada berbagai spesies hewan termasuk manusia. Salah satu spesies Coronavirus yang muncul pada akhir tahun 2019 yaitu SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) dan menimbulkan penyakit baru bernama Covid-19 (Coronavirus disease-2019) kemudian berstatus pandemi. Penyebaran Covid-19 yang cepat dan dengan tingkat kematian yang tinggi terus terjadi di berbagai negara. Oleh karena itu, deteksi dini patogen perlu dilakukan secara cepat dengan menggunakan data sekuens protein Coronavirus. Sekuens protein merupakan data struktur primer dari suatu protein yang memiliki 27 fitur berdasarkan discere. Dalam penerapannya, tidak semua fitur relevan dengan data yang digunakan sehingga perlu seleksi fitur untuk menghindari dimensi data yang tinggi dan tidak optimal. Seleksi fitur algoritma genetika memberikan fitur-fitur optimal pada data dan metode K-Nearest Neighbor (KNN) melakukan klasifikasi data sekuens protein Coronavirus dengan fitur hasil seleksi fitur algoritma genetika. Seleksi fitur algoritma genetika menghasilkan 11 fitur optimal yang meningkatkan performa running time metode klasifikasi KNN menjadi 0,0541 detik. Fitur optimal diperoleh dari karakteristik AA-count , secondary structure fraction , isoelectric point dan instability index. Hasil terbaik performa akurasi, spesifisitas beserta sensitifitas secara berurutan yaitu 96,68%, 98,7% dan 94,4% yang diperoleh pada nilai parameter K=3.

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Coronaviruses (CoV) are a family of viruses that cause mild to severe respiratory system diseases in various animal species including humans. One of the Coronavirus species that emerged at the end of 2019 was SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) and caused a new disease called Covid-19 (Coronavirus disease-2019) then had a pandemic status. The rapid spread of Covid-19 and with a high death rate continues to occur in most of countries. Therefore, early detection of pathogens needs to be done quickly using Coronavirus protein sequence data. Protein sequences are primary structural data of a protein that has 27 features but not all of the existing features are relevant to the data used, so feature selection is necessary to avoid high and suboptimal data dimensions. The genetic algorithm feature selection provides optimal features to the data and the K-Nearest Neighbor (KNN) method performs the classification of Coronavirus protein sequences data with features resulting from the genetic algorithm feature selection. The genetic algorithm feature selection produces 11 optimal features that improve the running time performance of the KNN classification method. The average result of running time is 0.0541 second. The best results were accuracy performance, specificity and sensitivity are 96.68%, 98.7% and 94.4% respectively which were obtained at the parameter value K=3."

"Latar Belakang: Angka mortalitas pasien Coronavirus Disease 2019 (COVID-19) di Indonesia cukup tinggi, salah satu organ yang terlibat dalam memengaruhi tingginya mortalitas ini adalah jantung. Evaluasi ekokardiografi dapat membantu mengidentifikasi secara dini gangguan jantung yang berhubungan dengan mortalitas pasien. Saat ini data kelainan jantung dengan pemeriksaan ekokardiografi pada pasien COVID-19 derajat berat-kritis di Indonesia.Tujuan: Penelitian ini bertujuan untuk mencari hubungan antara kelainan ekokardiografi dengan mortalitas pada pasien COVID-19 derajat berat dan kritis.Metode: Data pasien COVID-19 derajat berat dan kritis di RSCM diambil berdasarkan rekam medis lalu dilakukan analisis data dengan menampilkan tabel deskriptif, analisis bivariat dengan chi-square dan multivariat dengan regresi logistik.Hasil: Dari total 83 pasien, sebanyak 48 pasien (57,8%) meninggal. Kelainan fungsional yang paling banyak ditemukan pada ekokardiografi adalah mPAP yaitu sebanyak 51 pasien (68%) dan gangguan struktural terbanyak adalah remodelling konsentrik ventrikel kiri yaitu 48 pasien (58%). Hasil analisis chi-square terhadap variabel ekokardiografi memperlihatkan bahwa Tricuspid Annular Plane Systolic Excursion (TAPSE) dengan RR 7,29 (IK95%; 0,98 - 54,36), Right Ventricular Systolic Pressure (RVSP) dengan RR 10,21 (IK95%; 1,41 - 74,04), mean Pulmonary Artery Pressure (mPAP) dengan RR 1,44 (IK95%; 1,02 - 2,04), Pulmonary Acceleration Time (PAT) dengan RR 2,36 (IK95%; 1,14 - 4,86), dan Right Atrial Pressure (RAP) dengan RR 3,40 (IK95%; 1,06 - 10,95), memiliki hubungan signifikan dengan mortalitas. Sedangkan Left Ventricular Ejection Fraction (LVEF), disfungsi diastolik ventrikel kiri (E/e’) dan cardiac output (CO) tidak terdapat hubungan yang signifikan. Analisis multivariat dengan regresi logistik menunjukan bahwa variabel ekokardiografi yang secara independen berhubungan dengan mortalitas adalah RVSP dan RAP.Simpulan: Kelainan ekokardiografi yang berhubungan dengan mortalitas pasien COVID 19 derajat berat dan kritis adalah TAPSE, RVSP, mPAP, PAT dan estimasi RAP.

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Background: The mortality rate of Coronavirus Disease 2019 (COVID-19) patient in Indonesia is high and one of the organ that may contribute mortality is heart problem. Echocardiography can help clinician to identify early functional abnormalities and structural defect of heart that may correlates to mortality and patient’s prognosis. Currently, there are limited data in Indonesia about cardiac abnormalities in severe-critically ill COVID-19 patients that evaluated with echocardiography.Objective: This study aims to determine the relationship between echocardiography abnormalities and mortality in severe and critically ill COVID-19 patients.Methods: Severe and critically ill COVID-19 patients data taken from medical records and then analyzed by presenting descriptive table, bivariate analysis using chi square and multivariate analysis using logistic regression.Results: Out of total 83 patients, 48 ​​patients (57.8%) died. The most common functional abnormality found on echocardiography was mPAP found in 51 patients (68%) and the most common structural defect was left ventricular concentric remodeling in 48 patients (58%). The results of chi-square analysis of echocardiographic variables are Tricuspid Annular Plane Systolic Excursion (TAPSE) RR 7,29 (95%CI; 0,98 - 54,36), Right Ventricular Systolic Pressure (RVSP) RR 10,21 (95% CI; 1,41 - 74,04), mean Pulmonary Artery Pressure (mPAP) RR 1,44 (95%CI; 1,02 - 2,04), Pulmonary Acceleration Time (PAT) RR 2,36 (95%CI; 1,14 - 4,86), dan Right Atrial Pressure (RAP) RR 3,40 (95% CI; 1,06 - 10,95)had a significant relationship with mortality. Meanwhile, left ventricular ejection fraction (LVEF), left ventricular diastolic dysfunction (E/e') and cardiac output (CO) had no significant relationship. Multivariate analysis using logistic regression showed that RVSP and RAP were independent variables that correlates to mortality.Conclusions: Echocardiography abnormalities that associated with mortality in severe-critically ill patients are TAPSE, RVSP, mPAP, PAT and estimated RAP"

"Kasus pasien yang terkonfirmasi COVID 19 di Indonesia terus mengalami peningkatan hingga saat ini. Coronavirus Disease (COVID-19) adalah Penyakit menular yang disebabkan  oleh Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV,  jenis virus baru yang belum perna terdeteksi sebelumnya pada manusia. Ada dua jenis Coronavirus yang dapat menimbulkan gejala berat seperti Middle East Respiratory Syndrome (MERS) dan Severe Acute Respiratory Syndrome (SARS). Penularan dari manusi ke manusia saat ini menjadi agresif sehingga tingkat kasus terkonfirmasi menjadi tinggi, penularan dari orang yang terkonfimasi dan memiliki gejala terjadi saat batuk atau bersin. Penelitian ini memiliki tujuan untuk mengidentifikasi gambaran pengetahuan dan sikap keluarga pasien yang terkonfrimasi COVID 19 yang sedang dirawat disalah satu rumah sakit pemerintah.  Penelitian ini adalah penelitian dengan metode deskriptif, mendistribusikan pertanyaan tertutup dalam kuesioner.Responden dalam penelitian ini berjumlah 90. hasil penelitian menunjukan bahwa pengetahuan responden tentang pencegahan dan penularan COVID 19 hampir seluruhnya memiliki tingkat pengetahuan yang tinggi yaitu sebanyak 78 responden  (86,7%). Sikap atau perilaku responden dalam pencegahan dan penularan virus COVID 19 menunjukan sebagian besar menunjukan sikap yang baik yakni 50 responden (55,6%). Dari hasil tersebut dapat disimpulkan bahwa peningkatan pengetahuan keluarga pasien sangat diperlukan untuk meningkatkan sikap dan perilaku dalam mencegah terjadinya penularan dalam rumah tangga.

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The number of confirmed cases of COVID-19 in Indonesia continues to increase until today. Coronavirus Disease (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV is a new type of virus that has never been detected in humans before. There are two types of coronaviruses that can cause severe symptoms, such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Human-to-human transmission is now becoming aggressive so that confirmed cases are high. Transmission from a confirmed person who has symptoms occurs when coughing or sneezing. This study aims to describe the knowledge and attitudes of confirmed COVID-19 families who are being treated at a government hospital. This study uses a descriptive method and distributes closed questions in a questionnaire.  Respondents in this study were 90 people.  The results showed that almost all the respondents had a high level of knowledge of the prevention and transmission of COVID-19, namely 78 respondents (86.7%). The attitude or behavior of respondents in preventing and transmitting the COVID-19 virus showed that most of them have a good attitude, namely 50 respondents (55.6%). From these results, it can be concluded that increasing family knowledge is needed to improve attitudes and behavior in preventing transmission in the household. "

"Infeksi SARS CoV-2 sebagai penyebab terjadinya pandemi Coronavirus Disease 2019 (COVID-19) kini menjadi perhatian kesehatan masyarakat. Pada kasus COVID-19 yang berat dapat menyebabkan pneumonia, sindrom pernapasan akut, gagal ginjal, dan bahkan kematian, sehingga tidak jarang membutuhkan perawatan intensif. Diduga komorbiditas akan memperberat kondisi tersebut. Penelitian ini bertujuan untuk mengetahui dampak komorbiditas yakni hipertensi, diabetes melitus, dan penyakit paru obstrktif kronis terhadap kejadian perawatan intensif pada pasien COVID-19 di DKI Jakarta. Penelitian ini menggunakan desain potong lintang dengan menggunakan data registri pasien COVID-19 milik Dinas Kesehatan Provinsi DKI Jakarta pada Maret-Juni 2020 yand diperoleh dari formulir pencatatan dan pelaporan COVID-19. Kriteria inklusi adalah usia lebih dari 18 tahun, terdiagnosis COVID-19 dari hasil pemeriksaan swab PCR positif, dan pasien dirawat di Rumah Sakit di DKI Jakarta. Kriteria eksklusi adalah memiliki kondisi imunodefisiensi (HIV, keganasan, sedang menjalani kemoterapi atau radiasi). Data dianalisis secara bivariat dan multivariat menggunakan regresi logistik multipel dengan mempertimbangkan kovariat berupa usia, jenis kelamin, jenis pekerjaan, jumlah gejala dan durasi gejala yang dialami. Berdasarkan 12 699 pasien terkonfirmasi COVID-19 pada periode penelitian, terdapat 6 359 pasien yang memenuhi kriteria penelitian ini. Diketahui 623 (9,8%) mengalami hipertensi, 421 (6,62%) mengalami diabetes melitus, dan 133 (2,09%) mengalami PPOK. Sebanyak 166 (2,61%) diantaranya mendapat perawatan di ICU. Setelah dikontrol kovariat, ketiga komorbiditas tersebut secara independen meningkatkan risiko kebutuhan perawatan di ICU, tertinggi pada penderita hipertensi tanpa diabetes yang memiliki lebih dari 2 gejala OR 23,98 (IK95% 12,83-44,83) diikuti penderita hipertensi yang disertai diabetes dan lebih dari 2 gejala OR 16,53 (IK95% 8,76-31,17). Penderita PPOK memiliki risiko OR 1,80 (IK95% 0,95-3,40) untuk dirawat di ICU. Disimpulkan bahwa hipertensi, diabetes melitus, dan PPOK meningkatkan risiko perawatan di ICU pada pasien COVID-19 di DKI Jakarta.

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COVID-19 cases can lead to pneumonia, acute respiratory distress syndrome, acute kidney failure, and death. The presence of comorbidities are tought to worsen that condition. This study aimed to investigate impact of hypertension, diabetes mellitus, and chronic obstructive pulmonary disease to admission to intensive care unit (ICU) among COVID-19 patients in DKI Jakarta. This cross sectional study utilize COVID-19 patients registry data owned by DKI Jakarta Provincial Health Office from March to June 2020. Inclusion criteria are aged 18 years old or older, confirmed by positive PCR swab test result, and hospitalized in DKI Jakarta. Exclusion criteria are patients with immunodeficiency condition (HIV, malignancy, in chemotherapy or radiation therapy). Data were analyzed in bivariate and multivariate analysis using multiple logistic regression by considering covariates (age, sex, working status, number of symptoms, and duration of symptoms). Among 12 699 patients, 6 359 were included. Approximately 623 (9,8%) had hypetension, 421 (6,62%) had diabetes mellitus, and 133 (2,09%) had COPD. Among them, 166 (2,61%) were admitted to ICU. After controlling for covariates, those comorbidities are independently increase risk of ICU admission. The highest risk are found among hypertension patients without diabetes melitus and had more than two symptoms OR 23,98 (95%CI 12,83-44,83) followed by hypertension patients with diabetes melitus and had more than two symptoms OR 16,53 (95%CI 8,76-31,17). COPD patients had risk OR 1,80 (95%CI 0,95-3,40) for ICU admission. In conclusion, hypertension, diabetes mellitus, and COPD increase risk of ICU admission among COVID-19 patients in DKI Jakarta."