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1. Predicting outcomes of the acute phase of COVID-19. High sensitive prognostic model, based on the results of the international registry 'analysis of chronic non-infectious diseases dynamics after COVID-19 infection in adult patients' (ACTIV)

Author

Gregory P. Arutyunov, Ekaterina I. Tarlovskaya, Dmitry S. Polyakov, Tatiana I. Batluk, and Alexander G. Arutyunov

Subjects
COVID-19, Predicting in-hospital mortality, ACTIV scale, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

The aim of this study is to investigate the course of the acute period of COVID-19 and devise a prognostic scale for patients hospitalized. Materials and methods: The ACTIV registry encompassed both male and female patients aged 18 years and above, who were diagnosed with COVID-19 and subsequently hospitalized. Between June 2020 and March 2021, a total of 9364 patients were enrolled across 26 medical centers in seven countries. Data collected during the patients' hospital stay were subjected to multivariate analysis within the R computational environment. A predictive mathematical model, utilizing the “Random Forest'' machine learning algorithm, was established to assess the risk of reaching the endpoint (defined as in-hospital death from any cause). This model was constructed using a training subsample (70% of patients), and subsequently tested using a control subsample (30% of patients). Results: Out of the 9364 hospitalized COVID-19 patients, 545 (5.8%) died. Multivariate analysis resulted in the selection of eleven variables for the final model: minimum oxygen saturation, glomerular filtration rate, age, hemoglobin level, lymphocyte percentage, white blood cell count, platelet count, aspartate aminotransferase, glucose, heart rate, and respiratory rate. Receiver operating characteristic analysis yielded an area under the curve of 89.2%, a sensitivity of 86.2%, and a specificity of 76.0%. Utilizing the final model, a predictive equation and nomogram (termed the ACTIV scale) were devised for estimating in-hospital mortality amongst COVID-19 patients. Conclusion: The ACTIV scale provides a valuable tool for practicing clinicians to predict the risk of in-hospital death in patients hospitalized with COVID-19.

Published
2024
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2. ACTIV SARS-CoV-2 registry (Analysis of Chronic Non-infectious Diseases Dynamics After COVID-19 Infection in Adult Patients). Assessment of impact of combined original comorbid diseases in patients with COVID-19 on the prognosis

Author

Gregory P. Arutyunov, Ekaterina I. Tarlovskaya, Alexander G. Arutyunov, Yuri N. Belenkov, Alexandra O. Konradi, Yury M. Lopatin, Andrey P. Rebrov, Sergey N. Tereshchenko, Anna I. Chesnikova, Hamlet G. Hayrapetyan, Aleksandr P. Babin, Igor G. Bakulin, Natalia V. Bakulina, Larisa A. Balykova, Anna S. Blagonravova, Marina V. Boldina, Alexandra R. Vaisberg, Albert S. Galyavich, Veronika V. Gomonova, Natalia Yu. Grigorieva, Irina V. Gubareva, Irina V. Demko, Angelika V. Evzerikhina, Aleksandr V. Zharkov, Umida K. Kamilova, Zulfia F. Kim, Tatiana Yu. Kuznetsova, Natalia V. Lareva, Ekaterina V. Makarova, Svetlana V. Malchikova, Sergey V. Nedogoda, Marina M. Petrova, Ilya G. Pochinka, Konstantin V. Protasov, Denis N. Protsenko, Dmitry Yu. Ruzanau, Sergey A. Sayganov, Akpay S. Sarybaev, Natalia M. Selezneva, Akhmetzhan B. Sugraliev, Igor V. Fomin, Olga V. Khlynova, Olga Yu. Chizhova, Igor I. Shaposhnik, Dmitry A. Sh'ukarev, Айгуль K. Abdrahmanova, Susanna A. Avetisian, Hovhannes G. Avoyan, Karine K. Azarian, Galiya T. Aimakhanova, Dinara A. Ayipova, Almazbek Ch. Akunov, Marianna K. Alieva, Aliona V. Aparkina, Olga R. Aruslanova, Ekaterina Yu. Ashina, Olga Yu. Badina, Olga Yu. Barisheva, Alina S. Batchayeva, Anna M. Bitieva, Ismail U. Bikhteyev, Nataliya A. Borodulina, Maksim V. Bragin, Angela M. Budu, Galina M. Bykova, Kristina R. Vagapova, Darina D. Varlamova, Natalia N. Vezikova, Elena A. Verbitskaya, Olga E. Vilkova, Elena A. Vinnikova, Vera V. Vustina, Elena A. Galova, Vadim V. Genkel, Elena I. Gorshenina, Roman V. Gostishev, Elena V. Grigorieva, Ekaterina Yu. Gubareva, Gaukhar M. Dabylova, Anastasiya I. Demchenko, Oleg Yu. Dolgikh, Melis Y. Duyshobayev, Dmitrii S. Evdokimov, Ksenia E. Egorova, Anastasiia N. Ermilova, Aliya E. Zheldybayeva, Nataliya V. Zarechnova, Yuliia D. Zimina, Svetlana Yu. Ivanova, Elena Yu. Ivanchenko, Mariya V. Ilina, Maria V. Kazakovtseva, Elena V. Kazymova, Yuliya S. Kalinina, Nadezhda A. Kamardina, Anastasiya M. Karachenova, Igor A. Karetnikov, Nina A. Karoli, Magomed Kh. Karsiev, Daria S. Кaskaeva, Karina F. Kasymova, Zhainagul B. Kerimbekova, Ailar Sh. Kerimova, Eugeni S. Kim, Nina V. Kiseleva, Darya A. Klimenko, Anastasiia V. Klimova, Olga V. Kovalishena, Elena V. Kolmakova, Tatyana P. Kolchinskaya, Maria I. Kolyadich, Olga V. Kondriakova, Marina P. Konoval, Dmitryi Yu. Konstantinov, Elena A. Konstantinova, Vera A. Kordukova, Ekaterina V. Koroleva, Angelina Yu. Kraposhina, Tamara V. Kriukova, Alla S. Kuznetsova, Tatyana Yu. Kuzmina, Kirill V. Kuzmichev, Cholpon K. Kulchoroevna, Tatiana V. Kuprina, Irina M. Kouranova, Liliya V. Kurenkova, Natalia Yu. Kurchugina, Nadira A. Kushubakova, Valeriia I. Levankova, Natalia A. Lyubavina, Nadezhda A. Magdeyeva, Konstantin V. Mazalov, Viktoryia I. Majseenko, Aleksandra S. Makarova, Abdirashit M. Maripov, Anastasiia A. Marusina, Evgenii S. Melnikov, Nikita B. Moiseenko, Fazliniso N. Muradova, Rimma G. Muradyan, Shahane N. Musaelian, Anton O. Myshak, Ekaterina S. Nekaeva, Nataliia M. Nikitina, Bela B. Ogurlieva, Alla A. Odegova, Yuliia V. Omarova, Nazgul A. Omurzakova, Shynar O. Ospanova, Ekaterina V. Pahomova, Liviu D. Petrov, Svetlana S. Plastinina, Vera A. Pogrebetskaya, Dmitry S. Polyakov, Ekaterina V. Ponomarenko, Larisa L. Popova, Natalya A. Prokofeva, Inna A. Pudova, Nikita A. Rakov, Abdurakhmon N. Rahimov, Nadezhda A. Rozanova, Saltanat Serikbolkyzy, Andrei A Simonov, Valeriya V. Skachkova, Daria V. Soloveva, Irina A. Soloveva, Alexander K. Subbotin, Irina M. Sukhomlinova, Anastasiia G. Sushilova, Dilnoza R. Tagayeva, Yuliia V. Titojkina, Elena P. Tikhonova, Danil S. Tokmin, Anastasiia A. Tolmacheva, Mariya S. Torgunakova, Ksenia V. Trenogina, Natalia A. Trostianetckaia, Dmitriy A. Trofimov, Alexander A. Tulichev, Assiya T. Tursunova, Nina D. Ulanova, Oleg V. Fatenkov, Olga V. Fedorishina, Tatiana S. Fil, Irina Yu. Fomina, Irina S. Fominova, Irina A. Frolova, Svetlana M. Tsvinger, Vera V. Tsoma, Meerim B. Cholponbaeva, Tatiana I. Chudinovskikh, Olga A. Shevchenko, Tatyana V. Sheshina, Ekaterina A. Shishkina, Konstantin Yu. Shishkov, Stanislav Yu. Sherbakov, and Ekaterina A. Yausheva

Subjects
covid-19, activ registry, cardiovascular diseases, diabetes mellitus, polymorbidity, disease combinations, Medicine
Abstract

Aim. Study the impact of various combinations of comorbid original diseases in patients infected with COVID-19 later on the disease progression and outcomes of the new coronavirus infection. Materials and methods. The ACTIV registry was created on the Eurasian Association of Therapists initiative. 5,808 patients have been included in the registry: men and women with COVID-19 treated at hospital or at home. ClinicalTrials.gov ID NCT04492384. Results. Most patients with COVID-19 have original comorbid diseases (oCDs). Polymorbidity assessed by way of simple counting of oCDs is an independent factor in negative outcomes of COVID-19. Search for most frequent combinations of 2, 3 and 4 oCDs has revealed absolute domination of cardiovascular diseases (all possible variants). The most unfavorable combination of 2 oCDs includes atrial hypertension (AH) and chronic heart failure (CHF). The most unfavorable combination of 3 oCDs includes AH, coronary heart disease (CHD) and CHF; the worst combination of 4 oCDs includes AH, CHD, CHF and diabetes mellitus. Such combinations increased the risk of lethal outcomes 3.963, 4.082 and 4.215 times respectively. Conclusion. Polymorbidity determined by way of simple counting of diseases may be estimated as a factor in the lethal outcome risk in the acute phase of COVID-19 in real practice. Most frequent combinations of 2, 3 and 4 diseases in patients with COVID-19 primarily include cardiovascular diseases (AH, CHD and CHF), diabetes mellitus and obesity. Combinations of such diseases increase the COVID-19 lethal outcome risk.

Published
2022
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3. [ACTIV SARS-CoV-2 registry (Analysis of Chronic Non-infectious Diseases Dynamics After COVID-19 Infection in Adult Patients). Assessment of impact of combined original comorbid diseases in patients with COVID-19 on the prognosis]

Author

Gregory P. Arutyunov, Ekaterina I. Tarlovskaya, Alexander G. Arutyunov, Yuri N. Belenkov, Alexandra O. Konradi, Yury M. Lopatin, Andrey P. Rebrov, Sergey N. Tereshchenko, Anna I. Chesnikova, Hamlet G. Hayrapetyan, Aleksandr P. Babin, Igor G. Bakulin, Natalia V. Bakulina, Larisa A. Balykova, Anna S. Blagonravova, Marina V. Boldina, Alexandra R. Vaisberg, Albert S. Galyavich, Veronika V. Gomonova, Natalia Yu. Grigorieva, Irina V. Gubareva, Irina V. Demko, Angelika V. Evzerikhina, Aleksandr V. Zharkov, Umida K. Kamilova, Zulfia F. Kim, Tatiana Yu. Kuznetsova, Natalia V. Lareva, Ekaterina V. Makarova, Svetlana V. Malchikova, Sergey V. Nedogoda, Marina M. Petrova, Ilya G. Pochinka, Konstantin V. Protasov, Denis N. Protsenko, Dmitry Yu. Ruzanau, Sergey A. Sayganov, Akpay S. Sarybaev, Natalia M. Selezneva, Akhmetzhan B. Sugraliev, Igor V. Fomin, Olga V. Khlynova, Olga Yu. Chizhova, Igor I. Shaposhnik, Dmitry A. Sh'ukarev, Айгуль K. Abdrahmanova, Susanna A. Avetisian, Hovhannes G. Avoyan, Karine K. Azarian, Galiya T. Aimakhanova, Dinara A. Ayipova, Almazbek Ch. Akunov, Marianna K. Alieva, Aliona V. Aparkina, Olga R. Aruslanova, Ekaterina Yu. Ashina, Olga Yu. Badina, Olga Yu. Barisheva, Alina S. Batchayeva, Anna M. Bitieva, Ismail U. Bikhteyev, Nataliya A. Borodulina, Maksim V. Bragin, Angela M. Budu, Galina M. Bykova, Kristina R. Vagapova, Darina D. Varlamova, Natalia N. Vezikova, Elena A. Verbitskaya, Olga E. Vilkova, Elena A. Vinnikova, Vera V. Vustina, Elena A. Galova, Vadim V. Genkel, Elena I. Gorshenina, Roman V. Gostishev, Elena V. Grigorieva, Ekaterina Yu. Gubareva, Gaukhar M. Dabylova, Anastasiya I. Demchenko, Oleg Yu. Dolgikh, Melis Y. Duyshobayev, Dmitrii S. Evdokimov, Ksenia E. Egorova, Anastasiia N. Ermilova, Aliya E. Zheldybayeva, Nataliya V. Zarechnova, Yuliia D. Zimina, Svetlana Yu. Ivanova, Elena Yu. Ivanchenko, Mariya V. Ilina, Maria V. Kazakovtseva, Elena V. Kazymova, Yuliya S. Kalinina, Nadezhda A. Kamardina, Anastasiya M. Karachenova, Igor A. Karetnikov, Nina A. Karoli, Magomed Kh. Karsiev, Daria S. Кaskaeva, Karina F. Kasymova, Zhainagul B. Kerimbekova, Ailar Sh. Kerimova, Eugeni S. Kim, Nina V. Kiseleva, Darya A. Klimenko, Anastasiia V. Klimova, Olga V. Kovalishena, Elena V. Kolmakova, Tatyana P. Kolchinskaya, Maria I. Kolyadich, Olga V. Kondriakova, Marina P. Konoval, Dmitryi Yu. Konstantinov, Elena A. Konstantinova, Vera A. Kordukova, Ekaterina V. Koroleva, Angelina Yu. Kraposhina, Tamara V. Kriukova, Alla S. Kuznetsova, Tatyana Yu. Kuzmina, Kirill V. Kuzmichev, Cholpon K. Kulchoroevna, Tatiana V. Kuprina, Irina M. Kouranova, Liliya V. Kurenkova, Natalia Yu. Kurchugina, Nadira A. Kushubakova, Valeriia I. Levankova, Natalia A. Lyubavina, Nadezhda A. Magdeyeva, Konstantin V. Mazalov, Viktoryia I. Majseenko, Aleksandra S. Makarova, Abdirashit M. Maripov, Anastasiia A. Marusina, Evgenii S. Melnikov, Nikita B. Moiseenko, Fazliniso N. Muradova, Rimma G. Muradyan, Shahane N. Musaelian, Anton O. Myshak, Ekaterina S. Nekaeva, Nataliia M. Nikitina, Bela B. Ogurlieva, Alla A. Odegova, Yuliia V. Omarova, Nazgul A. Omurzakova, Shynar O. Ospanova, Ekaterina V. Pahomova, Liviu D. Petrov, Svetlana S. Plastinina, Vera A. Pogrebetskaya, Dmitry S. Polyakov, Ekaterina V. Ponomarenko, Larisa L. Popova, Natalya A. Prokofeva, Inna A. Pudova, Nikita A. Rakov, Abdurakhmon N. Rahimov, Nadezhda A. Rozanova, Saltanat Serikbolkyzy, Andrei A Simonov, Valeriya V. Skachkova, Daria V. Soloveva, Irina A. Soloveva, Alexander K. Subbotin, Irina M. Sukhomlinova, Anastasiia G. Sushilova, Dilnoza R. Tagayeva, Yuliia V. Titojkina, Elena P. Tikhonova, Danil S. Tokmin, Anastasiia A. Tolmacheva, Mariya S. Torgunakova, Ksenia V. Trenogina, Natalia A. Trostianetckaia, Dmitriy A. Trofimov, Alexander A. Tulichev, Assiya T. Tursunova, Nina D. Ulanova, Oleg V. Fatenkov, Olga V. Fedorishina, Tatiana S. Fil, Irina Yu. Fomina, Irina S. Fominova, Irina A. Frolova, Svetlana M. Tsvinger, Vera V. Tsoma, Meerim B. Cholponbaeva, Tatiana I. Chudinovskikh, Olga A. Shevchenko, Tatyana V. Sheshina, Ekaterina A. Shishkina, Konstantin Yu. Shishkov, Stanislav Yu. Sherbakov, and Ekaterina A. Yausheva

Subjects
Adult, Male, Heart Failure, History, SARS-CoV-2, Endocrinology, Diabetes and Metabolism, COVID-19, Coronary Disease, General Medicine, Prognosis, Cardiovascular Diseases, Chronic Disease, Hypertension, Diabetes Mellitus, Humans, Female, Registries, Family Practice, Noncommunicable Diseases
Abstract

Study the impact of various combinations of comorbid original diseases in patients infected with COVID-19 later on the disease progression and outcomes of the new coronavirus infection.The ACTIV registry was created on the Eurasian Association of Therapists initiative. 5,808 patients have been included in the registry: men and women with COVID-19 treated at hospital or at home.gov ID NCT04492384.Most patients with COVID-19 have original comorbid diseases (oCDs). Polymorbidity assessed by way of simple counting of oCDs is an independent factor in negative outcomes of COVID-19. Search for most frequent combinations of 2, 3 and 4 oCDs has revealed absolute domination of cardiovascular diseases (all possible variants). The most unfavorable combination of 2 oCDs includes atrial hypertension (AH) and chronic heart failure (CHF). The most unfavorable combination of 3 oCDs includes AH, coronary heart disease (CHD) and CHF; the worst combination of 4 oCDs includes AH, CHD, CHF and diabetes mellitus. Such combinations increased the risk of lethal outcomes 3.963, 4.082 and 4.215 times respectively.Polymorbidity determined by way of simple counting of diseases may be estimated as a factor in the lethal outcome risk in the acute phase of COVID-19 in real practice. Most frequent combinations of 2, 3 and 4 diseases in patients with COVID-19 primarily include cardiovascular diseases (AH, CHD and CHF), diabetes mellitus and obesity. Combinations of such diseases increase the COVID-19 lethal outcome risk.Цель. Изучить влияние различных комбинаций сопутствующих заболеваний, существовавших у пациентов исходно, до инфицирования вирусом SARS-CoV-2, на тяжесть течения и исходы новой коронавирусной инфекции. Материалы и методы. Регистр АКТИВ создан по инициативе Евразийской ассоциации терапевтов. В регистр включены 5808 пациентов: мужчины и женщины с диагнозом COVID-19, находящиеся на лечении в стационаре или получающие лечение на дому. Регистрация регистра: идентификатор ClinicalTrials.gov: NCT04492384. Результаты. Подавляющее большинство пациентов с COVID-19 имеют исходные сопутствующие заболевания (иСЗ). Полиморбидность, оцененная методом простого подсчета иСЗ, является самостоятельным фактором негативного прогноза больных COVID-19. Поиск наиболее часто встречающихся комбинаций из 2, 3, 4 и более СЗ выявил абсолютное доминирование сердечно-сосудистых заболеваний во всех возможных вариантах. Наиболее неблагоприятной при сочетании 2 иСЗ была комбинация, включающая артериальную гипертензию (АГ) и хроническую сердечную недостаточность (ХСН), при сочетании 3 иСЗ комбинация, включающая АГ, ишемическую болезнь сердца (ИБС), ХСН, и при сочетании 4 иСЗ комбинация, включающая АГ, ИБС, ХСН и сахарный диабет. Наличие таких комбинаций приводило к росту риска летального исхода в 3,963, 4,082, 4,215 раза соответственно. Заключение. Полиморбидность, определенная методом простого подсчета заболеваний, может использоваться в реальной практике как фактор риска летальности в остром периоде COVID-19. В состав наиболее часто встречающихся комбинаций из 2, 3, 4 заболеваний у пациентов с COVID-19 входят преимущественно сердечно-сосудистые заболевания (АГ, ИБС, ХСН), сахарный диабет и ожирение. Комбинации этих заболеваний повышают риск летального исхода при COVID-19.

Published
2021

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