Ali KARAGÖZ, Hacer Ceren TOKGÖZ, Özgür Yaşar AKBAL, Seda TANYERİ, Süleyman Çağan EFE, Berhan KESKİN, Barkın KÜLTÜRSAY, Cem DOĞAN, İbrahim Halil TANBOĞA, Nihal ÖZDEMİR, Cihangir KAYMAZ, Flora ÖZKALAYCI, Zübeyde BAYRAM

COVID-19 Döneminde, STEMI Azalması Zaman Serisi Modeline Göre Google-Trend Verileri ile Tahmin Edilebilir

Giriş: Koronavirüs hastalığı (COVID-19)’na bağlı ST yükselmeli miyokart enfarktüsü (STEMI) düşüşü gösterilmiş, ancak nedenleri yeterince araştırılmamıştır. Amacımız, COVID pandemisi ile STEMI başvurusu arasında bir ilişki olup olmadığını belirlemek ve Google-Trend (GT) ile ölçülen Covid, Ölüm terimi arama hacmiyle ilişkilendirilebilecek STEMI sayısındaki azalmayı araştırmaktır. Hastalar ve Yöntem: Hastanemize başvuran STEMI verileri ile 01 Mart 2020-21 Mayıs 2020 tarihleri arasında GT arama verilerinde covid ve ölüm araması toplandı, ayrıca STEMI sayımı bir önceki yıl aynı zaman limitiyle karşılaştırıldı. Günlük yeni STEMI olgu sayısı ve GT arama indeksinin kullanımında günlük covid ve ölüm terimiyle göreceli ilişki ile birleştirmek için vektör otoregresif model (VAR) kullanıldı. Bulgular: Bu zaman çerçevesinde hastanemize kabul edilen toplam 302 STEMI toplanmış, bir önceki yıla göre %34 azalma bulunmuştur. Spearman’ın STEMI ile “covid, stemi ve ölüm” trendi arasındaki korelasyon katsayısı, sonuçlar [(-0.560) p< 0.001, (-0.631) p< 0.001, sırasıyla]; model-1 ve model-2 VAR iki değişkenli analiz sonuç katsayısı (sırasıyla -0.532, -2.694); ayrıca COVID ve STEMI’ye ölüm için Granger nedenselliği bulunmuştur. Etki tepki fonksiyonu (IRF), STEMI’nin ölüm ve covid eğilimiyle nispeten büyük bir miktarda azaldığını göstermiştir. Sonuç: COVID-19 döneminde, çalışmamız GT verilerinin kullanımının STEMI olgularının azalmasını öngörebildiğini göstermiştir. Ayrıca, covid ve ölüm GT verileri ve günlük STEMI sayıları arasındaki makul Granger nedensellik ilişkileri mevcuttur. Covid ve Ölüm ile ilgili GT, STEMI hastalarının azalmasını önlemek için sağlık bakımı sunanlar için uygun davranışların geliştirilmesine yön verebilir.

In COVID-19 Era, STEMI Decreasing May Predicted with Google-Trend Data by Time-Series Model

Introduction: The explanation for the decrease of ST elevation myocardial infarction (STEMI) due to Coronavirus Disease (COVID-19) is poorly described. Our goal is to identify whether there is a temporal association between COVID pandemics and STEMI admissions and investigate the decreased number of STEMI could be associated to the search volume of Covid, Death and chest pain, measured by Google-Trend (GT). Patients and Methods: We collected our hospital admission of STEMI data and search of covid, death and chest pain in GT search data between March 01, 2020 and May 21, 2020, also we compared to STEMI count same time-limit in the previous year. We used vector-autoregressive model (VAR) to combine the number of daily new cases STEMI and search index to relative association daily covid, and death term search in using GT. Results: During this time-frame, we collected a total of 302 STEMI admitted to our hospital when compared previous year we found a 34% reduction. Spearman’s correlation-coefficient between covid, stemi, and death trend with STEMI, results [(-0.560) p< 0.001, (-0.631) p< 0.001, respectively]; model-1 and model-2 VAR bivariate analysis results in coefficient (-0.532, -2.694), also we found Granger-causality for covid and death to STEMI. Impulse response functions (IRF) depicted the STEMI decreased a relatively large amount by death and covid trend. Conclusion: In the COVID-19 period, our study showed that the uses of Google-Trend data have predict decreasing STEMI cases. Also, plausible Granger causality relationships between covid, death GT data and daily counts of STEMI. GT about Covid and Death might provide to developing appropriate behaviour of patients with the STEMI for healthcare supporter.

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ISSN: 2149-2972
Yayın Aralığı: Yılda 3 Sayı
Başlangıç: 1990
Yayıncı: Sağlık Bilimleri Üniversitesi, Kartal Koşuyolu Yüksek İhtisas Eğitim ve Araştırma Hastanesi