ABSTRACT
BackgroundIn COVID-19 severe disease course such as need of intensive care unit (ICU) as well as development of mortality is mainly due to cytokine storm.ObjectivesIn this study, we aimed to evaluate the high dose intravenous anakinra treatment response and outcome in patients with severe and critical COVID-19 compared to standard of care.MethodsThis retrospective observational study was carried out at a tertiary referral center. The study population consisted of two groups as follows;the patients receiving high dose intravenous anakinra (anakinra group) between 01.09.2021 and 01.02.2022 and the patients treated with standard of care (SoC, control group) as historical control group who were hospitalized between 01.07.2021 and 01.09.2021.ResultsAfter the propensity score 1:1 matching 79 patients in anakinra and 79 patients in SoC matched and included into the analysis. Mean±SD patient age was 67.4±16.7 and 67.1±16.3 years in anakinra and SoC group, respectively (p=0.9). Male gender was 38 (48.7 %) in anakinra and 36 (46.2 %) SoC (p=0.8). Overall, ICU admission was in 14.1 % (n=11) and 30.8 % (n=24) (p=0.013;OR: 6.2), intubation in 12.8 % (n=10) and 16.7 % (n=13) patients (p=0.5), 14.1 % (n=11) and 32.1 % (n=25) patients died in anakinra and control group, respectively (p=0.008;OR: 7.1)ConclusionIn our study mortality was lower in patients receiving anakinra compared to SoC. Intravenous high dose anakinra is safe and effective treatment in patients with severe and critical COVID-19.Table 1.Baseline clinical and laboratory features of patients receiving standard of care (SoC) and Anakinra before and after propensity score (PS) matchingBefore PS matchingAfter PS matchingVariablesAnakinra (n=148)SoC (n=114)p value (OR)Anakinra (n=78)SoC (n=78)p value (OR)Age (years) (mean±SD)66.8±1763.1±170.0967.4±16.767.1±16.30.9Gender, male (n, %)78 (52.7)45 (39.5)0.033 (4.5)38 (48.7)36 (46.2)0.8Duration of hospitalization (days) (median, IQR)11 (12)9 (7.3)0.027.5 (9)11 (8)0.01Comorbidities (n, %) Diabetes mellitus41/146 (28.1)39 (34.2)0.318 (23)31 (39.7)0.025 (5) Hypertension84/143 (58.7)64 (56)0.730 (61.5)50 (64)0.7 Coronary heart disease27/143 (19)24 (21)0.718 (23)20 (25.6)0.7 Heart failure18/143 (12.6)23 (20)0.114 (18)20 (25.6)0.24 Chronic renal failure31 (21)6 (5.3)<0.001 (13.06)15 (19)6 (7.7)0.035 (4.5) Chronic obstructive lung disease23/144 (16)19 (16.7)0.914 (18)15 (19)0.8 Dementia15/117 (12.8)2 (1.8)0.001 (10.4)3/61 (5)2 (2.6)0.5 Malignancy16/146 (11)8 (7)0.39 (11.5)6 (7.7)0.4 Immunosuppressive usage18/146 (12.3)2 (1.8)0.001 (10.08)5 (6.5)2 (2.6)0.2Disease severity (n, %) NIH score 3 (severe)57 (38.5)68 (59.6)0.001 (11.5)48 (61.5)44 (56.4)0.5 NIH score 4 (critical)91 (61.5)46 (40.4)30 (38.5)34 (43.6) mcHIS score (mean±SD)3.4±1.22.64±1.5<0.0012.9±13.1±1.30.2PS: Propensity score, SoC: Standard of care, OR: Odds ratio, SD: Standard deviation, IQR: Interquartile range, mcHIS: Modified Covid hyperinflammatory syndrome score, NIH: National Institute Health, ALT: Alanin aminotransferase, AST: Aspartate aminotransferaseTable 2.Outcomes of patients receiving SoC and Anakinra before and after PS matchingBefore PS matchingAfter PS matchingVariables (n, %)Anakinra (n=148)SoC (n=114)p value (OR)Anakinra (n=78)SoC (n=78)p value (OR)Pneumothorax3/134 (2.2)00.25*2/73 (2.7)00.5*Myocardial infarction3/132 (2.3)6 (5.3)0.32/72 (2.8)2/56 (3.6)1Pulmonary embolism4/134 (3)11 (9.6)0.034 (4.8)*3/73 (4.1)7 (9)0.3*Intensive care unit60 (40.5)25 (22)0.001 (10.2)11 (14.1)24 (30.8)0.013 (6.2)Intubation54 (36.5)13 (11.4)<0.001 (21.3)10 (12.8)13 (16.7)0.5Mortality56 (37.8)27 (23.7)0.015 (5.96)11 (14.1)25 (32.1)0.008 (7.1)PS: Propensity score, SoC: Standard of care, OR: Odds ratioREFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone Declared.
ABSTRACT
The new coronavirus (COVID-19) crisis has had a devastating impact across the world. Public administration discipline addresses emergency crisis management in various ways and dimensions. This article seeks answers to the question: "How can AI contribute to crisis management policies to fight against COVID-19 and its impacts?" To this, the techniques and methods of AI in fighting against the COVID-19 virus will be explained in various dimensions. AI can make significant contributions in the preparation, mitigation-prevention, response, and recovery policies in the COVID-19 pandemic crisis. If adopted, AI can be used to find better treatment routes and drug development. Equally, policymakers can benefit from AI as decision support to reach high-quality decisions through fast and accurate data. The paper concludes that governments should create and implement effective AI-based crisis management strategies to fight against the epidemic locally, regionally, nationally, and internationally with a multi-level governance perspective.