Styrzynski Filip, Zhakparov Damir, Schmid Marco, Roqueiro Damian, Lukasik Zuzanna, Solek Julia, Nowicki Jakub, Dobrogowski Milosz, Makowska Joanna, Sokolowska Milena, Baerenfaller Katja
Department of Rheumatology with Subdepartment of Internal Medicine, Medical University of Lodz, 90-419, Lodz, Poland.
Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Herman-Burchard-Strasse 9, 7265, Davos, Switzerland.
Infect Dis Ther. 2023 Jan;12(1):111-129. doi: 10.1007/s40121-022-00707-8. Epub 2022 Nov 4.
In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome.
This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications.
We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome.
Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.
在当前的新冠疫情中，临床医生需要一套易于管理的决定性参数，用于（i）快速识别新冠病毒2019（SARS-CoV-2）阳性患者，（ii）识别入院时具有致命结局高风险的患者，以及（iii）识别可能致命结局的纵向警示信号。
本比较研究在波兰兹吉尔的玛丽亚·斯克沃多夫斯卡-居里专科省医院的515例患者中进行。研究组包括314例有新冠样症状但检测呈阴性的患者和201例SARS-CoV-2感染检测呈阳性的患者；后者中，72例新冠患者死亡，129例出院。我们用于训练多个机器学习（ML）模型的数据包括入院时和住院期间的临床发现、症状、流行病学风险以及报告的合并症和用药情况。
我们确定了一组八个入院参数：白细胞、抗体合成淋巴细胞、嗜碱性粒细胞/淋巴细胞比值、血小板/中性粒细胞比值、单核细胞/淋巴细胞比值、降钙素原、肌酐和C反应蛋白。使用这些参数构建的医学决策树区分SARS-CoV-2阳性和阴性患者的准确率高达90%-100%。发现入院时年龄较大、降钙素原、C反应蛋白和肌钙蛋白I水平较高且血红蛋白和血小板/中性粒细胞比值较低的新冠患者死于新冠的风险最高。此外，我们确定了C反应蛋白、白细胞和D-二聚体的纵向模式，这些模式可预测疾病结局。
我们的研究提供了一组易于获得的参数，可用于评估SARS-CoV-2感染患者的状况以及入院时和疾病过程中致命疾病结局的风险。
