Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events.

COVID-19 survival data presents a special situation where not only the time-to-event period is short, but also the two events or outcome types, death and release from hospital, are mutually exclusive, leading to two cause-specific hazard ratios (csHR d and csHR r ). The eventual mortality/release outcome is also analyzed by logistic regression to obtain odds-ratio (OR). We have the following three empirical observations: (1) The magnitude of OR is an upper limit of the csHR d : |log(OR)| ≥ |log(csHR d )|. This relationship between OR and HR might be understood from the definition of the two quantities; (2) csHR d and csHR r point in opposite directions: log(csHR d ) ⋅ log(csHR r ) < 0; This relation is a direct consequence of the nature of the two events; and (3) there is a tendency for a reciprocal relation between csHR d and csHR r : csHR d ∼ 1/csHR r . Though an approximate reciprocal trend between the two hazard ratios is in indication that the same factor causing faster death also lead to slow recovery by a similar mechanism, and vice versa, a quantitative relation between csHR d and csHR r in this context is not obvious. These results may help future analyses of data from COVID-19 or other similar diseases, in particular if the deceased patients are lacking, whereas surviving patients are abundant.

Blood-Type-A is a COVID-19 infection and hospitalization risk in a Turkish cohort.

We have shown in an ethnically homogenous Turkey cohort with more than six thousand cases and 25 thousand controls that ABO blood types that contain anti-A antibody (O and B) are protective against COVID-19 infection and hospitalization, whereas those without the anti-A antibody (A and AB) are risks. The A + AB frequency increases from 54.7 % in uninfected controls to 57.6 % in COVID-19 outpatients, and to 62.5 % in COVID-19 inpatients. The odds-ratio (OR) for lacking of anti-A antibody risk for infection is 1.16 (95 % confidence interval (CI) 1.1-1.22, and Fisher test p-value 1.8 × 10-7). The OR for hospitalization is 1.23 (95 %CI 1.06-1.42, Fisher test p-value 0.005). A linear regression treating controls, outpatients, inpatients as three numerical levels over anti-A antibody leads to a p-value of 5.9 × 10-9. All these associations remain to be statistically significant after conditioning over age, even though age itself is a risk for both infection and hospitalization. We also attempted to correct the potential effect from vaccination, even though vaccination information is not available, by using the date of the data collection as a surrogate to vaccination status. Although no significant association between infection/hospitalization with Rhesus blood system was found, forest plots are used to illustrate possible trends.

A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort.

Having a complete and reliable list of risk factors from routine laboratory blood test for COVID-19 disease severity and mortality is important for patient care and hospital management. It is common to use meta-analysis to combine analysis results from different studies to make it more reproducible. In this paper, we propose to run multiple analyses on the same set of data to produce a more robust list of risk factors. With our time-to-event survival data, the standard survival analysis were extended in three directions. The first is to extend from tests and corresponding p-values to machine learning and their prediction performance. The second is to extend from single-variable to multiple-variable analysis. The third is to expand from analyzing time-to-decease data with death as the event of interest to analyzing time-to-hospital-release data to treat early recovery as a meaningful event as well. Our extension of the type of analyses leads to ten ranking lists. We conclude that 20 out of 30 factors are deemed to be reliably associated to faster-death or faster-recovery. Considering correlation among factors and evidenced by stepwise variable selection in random survival forest, 10 ~ 15 factors seem to be able to achieve the optimal prognosis performance. Our final list of risk factors contain calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia related factors (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration), sodium, potassium, eosinophils, and aspartate aminotransferase.

Alpha variant (B.1.1.7) of SARS-CoV-2 increases fatality-rate for patients under age of 70 years and hospitalization risk overall.

The emergence of new SARS-CoV-2 variants is a challenge to the control of this pandemic. It is therefore important to collect and to analyze data related to the infection caused by different variants. We have obtained more than 3,700 COVID-19 patients between April 2020 and March 2021 from Tokat, Turkey (roughly 3,100 outpatients and close to 600 inpatients) where about 30% were infected with Alpha variant (B.1.1.7). Descriptive statistics was used to characterize different subgroups. Both logistic regression and cause-specific Cox survival analysis of competing-risk was run on inpatients, to examine the impact of Alpha variant on hospitalization, on mortality and on other factors. We observed that the Alpha variant is over-represented in inpatients than outpatients so infection by Alpha variant increases the chance for hospitalization. The impact of Alpha variant on mortality seems to depend on the patient's age. For patients under age of 70, the case-fatality-rate was 0.84% (5.3%) for patients without (with) Alpha variant (Fisher's test P-value = 2.4 × 10-10). For patients above age of 70, the trend is opposite: the case-fatality-rate is 31.5% (13.6%) for patients without (with) Alpha variant (Fisher's test P-value = 0.0016). The two opposite trends would cancel each other, making other analyses such as cause-specific Cox regression and logistic regression non-significant. The Alpha variant increases the risk for hospitalization, increases the case-fatality-rate for lower age group, and decreases the case-fatality-rate for the upper age group. If the increase of case-fatality-rate in not the most senior group holds true, it should provide useful information for a vaccination planning to counter the impact of Alpha variants.