COVID-19 Outcome

A comparison of excess deaths between populations suggests that active users of the VA health system had similar relative increases in mortality compared with the general US population during the first 10 months of the COVID-19 pandemic 1).


In Tehran, a study revealed a clear disparity in the health outcome of patients infected with COVID-19 between urban and sub-urban areas 2).

The possible risk factors that lead to death in critical inpatients with coronavirus disease 2019 (COVID-19) are not yet fully understood.


The COVID-19 pandemic affected the lifestyle, mood, and chronic diseases management among community-dwelling older adults. Supportive measures and interventions need to be tailored to older adults living in the community 3).


Using two independent patient datasets, Jamshidi et al. designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in the model have increased levels of blood urea nitrogen (BUN), lowered albumin levels, increased creatinine, INR, and red cell distribution width (RDW), along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making 4).


Old age (>70 years), neutrophilia, C-reactive protein greater than 100 mg/L and lactate dehydrogenase over 300 U/L are high-risk factors for mortality in critical patients with COVID-19. Sinus tachycardia and ventricular arrhythmia are independent ECG risk factors for mortality from COVID-19 5).

While the disease itself is often mild, approximately 11% of cases require acute medical care, and this cohort quickly overwhelmed healthcare systems around the world 6).

In anticipation of such a demand, hospitals in many countries quickly stopped all nonurgent visits, procedures, and surgeries, freeing up beds, equipment, and workforce 7)


The mortality rate for COVID-19 is not as high (approximately 2-3%), but its rapid propagation has resulted in the activation of protocols to stop its spread 8).

A total of 174 consecutive patients confirmed with COVID-19 were studied. Demographic data, medical history, symptoms and signs, laboratory findings, chest computed tomography (CT) as well we treatment measures were collected and analyzed.

Guo et al. found that COVID-19 patients without other comorbidities but with diabetes (n=24) were at higher risk of severe pneumonia, the release of tissue injury-related enzymes, excessive uncontrolled inflammation responses and hypercoagulable state associated with dysregulation of glucose metabolism. Furthermore, serum levels of inflammation-related biomarkers such as IL-6, C-reactive protein, serum ferritin, and coagulation index, D-dimer, were significantly higher (p< 0.01) in diabetic patients compared with those without, suggesting that patients with diabetes are more susceptible to an inflammatory storm eventually leading to rapid deterioration of COVID-19.

Data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19. More intensive attention should be paid to patients with diabetes, in case of rapid deterioration 9).


Racism and discrimination in COVID-19 responses 10).


1)
Weinberger DM, Rose L, Rentsch C, Asch SM, Columbo JA, King J Jr, Korves C, Lucas BP, Taub C, Young-Xu Y, Vashi A, Davies L, Justice AC. Excess Mortality Among Patients in the Veterans Affairs Health System Compared With the Overall US Population During the First Year of the COVID-19 Pandemic. JAMA Netw Open. 2023 May 1;6(5):e2312140. doi: 10.1001/jamanetworkopen.2023.12140. PMID: 37155169.
2)
Sohrabi MR, Amin R, Maher A, Hannani K, Alimohammadi H, Zali AR. Urban and sub-urban disparities in health outcomes among patients with COVID-19; a cross-sectional study of 234 418 patients in Iran. BMC Public Health. 2022 May 10;22(1):927. doi: 10.1186/s12889-022-13290-x. PMID: 35538564.
3)
Ding S, Lei Q, Wu W, Xiao Z, Wu Z, Chen M, Chen L. Changes in lifestyle, mood, and disease management among community-dwelling older adults during the COVID-19 pandemic in China. Aging Health Res. 2022 Jan 26:100059. doi: 10.1016/j.ahr.2022.100059. Epub ahead of print. PMID: 35098199; PMCID: PMC8789384.
4)
Jamshidi E, Asgary A, Tavakoli N, Zali A, Setareh S, Esmaily H, Jamaldini SH, Daaee A, Babajani A, Sendani Kashi MA, Jamshidi M, Jamal Rahi S, Mansouri N. Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU. Front Digit Health. 2022 Jan 13;3:681608. doi: 10.3389/fdgth.2021.681608. PMID: 35098205; PMCID: PMC8792458.
5)
Li L, Zhang S, He B, Chen X, Wang S, Zhao Q. Risk factors and electrocardiogram characteristics for mortality in critical inpatients with COVID-19. Clin Cardiol. 2020 Oct 22. doi: 10.1002/clc.23492. Epub ahead of print. PMID: 33094522.
6)
Remuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet. 2020;395(10231):P1225-P1228.
7)
Wong J, Goh QY, Tan Z, et al. Preparing for a COVID-19 pandemic: a review of operating room outbreak response measures in a large tertiary hospital in Singapore. Can J Anaesth. 2020;395:497.
8)
Palacios Cruz M, Santos E, Velázquez Cervantes MA, León Juárez M. COVID-19, a worldwide public health emergency. Rev Clin Esp. 2020 Mar 20. pii: S0014-2565(20)30092-8. doi: 10.1016/j.rce.2020.03.001. [Epub ahead of print] Review. English, Spanish. PubMed PMID: 32204922.
9)
Guo W, Li M, Dong Y, Zhou H, Zhang Z, Tian C, Qin R, Wang H, Shen Y, Du K, Zhao L, Fan H, Luo S, Hu D. Diabetes is a risk factor for the progression and prognosis of COVID-19. Diabetes Metab Res Rev. 2020 Mar 31:e3319. doi: 10.1002/dmrr.3319. [Epub ahead of print] PubMed PMID: 32233013.
10)
Devakumar D, Shannon G, Bhopal SS, Abubakar I. Racism and discrimination in COVID-19 responses. Lancet. 2020 Apr 1. pii: S0140-6736(20)30792-3. doi: 10.1016/S0140-6736(20)30792-3. [Epub ahead of print] PubMed PMID: 32246915.
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