key: cord-0976058-0q11dtpy
authors: Crafa, Andrea; Cannarella, Rossella; Condorelli, Rosita A.; Mongioì, Laura M.; Barbagallo, Federica; Aversa, Antonio; La Vignera, Sandro; Calogero, Aldo E.
title: Corrigendum to: Influence of 25‑hydroxy-cholecalciferol levels on SARS-CoV-2 infection and COVID-19 severity: A systematic review and meta-analysis [EClinicalMedicine 37 (2021) 100,967]
date: 2021-10-20
journal: EClinicalMedicine
DOI: 10.1016/j.eclinm.2021.101168
sha: 7ebf9d027f51a2df0d1f68c40f7815ff4d433e71
doc_id: 976058
cord_uid: 0q11dtpy

[This corrects the article DOI: 10.1016/j.eclinm.2021.100967.].

additional analysis after excluding not only the two withdrawn preprints but also a third one originally included in the analysis (45). Because of the exclusion of the 3 pre-prints (original article references 39, 45, 55), the flowchart of the included studies was modified (Fig. 1) . Table 1, showing the characteristics of the included studies,  and Table 2 , concerning the quality analysis of the studies, were also updated after exclusion of the 3 pre-prints (original article references 39, 45, 55).

Analysis of serum 25(OH)D levels in SARS-CoV2-positive versus negative patients, and also analysis of patients with infection discharged versus those who died from the disease, were not performed, since pre-prints (original article references 39, 45, 55) were not included for these outcomes in the original meta-analysis.

Regarding analysis related to 25(OH)D levels in patients with severe or non-severe COVID-19 (original article Fig. 3) , after exclusion of the pre-prints referenced originally as 39 and 45, 10 studies assessing this outcome remained. Specifically, the new analysis confirmed that 25(OH)D levels were clearly lower in the 492 patients with severe disease compared to the 817 patients with a non-severe course of the disease [MD À5.50 (À8.86, À2.14); p = 0.001] (Fig. 3A) . After exclusion of the two pre-prints mentioned above, high interstudy heterogeneity was still found (Chi2 P < 0.00001, I2=93%) (Fig. 3B) . After the removal of the studies by Luo and colleagues (original article reference 34), and Jain and colleagues (original article reference 42), identified as a source of heterogeneity at the Funnel Plot, the analysis showed homogeneity of the remaining studies (Chi2 P = 0.86, I2=0%) maintaining the statistical significance [MD À4.80 (À6.27, À3.32); p < 0.00001].

Also, the analysis of the risk of severe COVID-19 in patients with VDD (original article Fig. 5 ) did not change after the exclusion of pre-print reference 39. This outcome was assessed on data extracted from 10 studies. The study by Cereda and colleagues (original article reference 52) was considered twice since it evaluated both the percentage of patients with severe pneumonia and patients admitted to the intensive care units as an outcome of severity. The study by Jain and colleagues (original article reference 42) was also considered twice since they assessed the risk of infection severity both in patients with 25(OH)D<20 ng/ml and then in patients with levels below 10 ng/ml. The new statistical analysis confirmed that patients with VDD had a higher risk of a severe disease course than patients without deficiency [OR 3.78 (1.77, 8.06); p = 0.0006], regardless of the cut-off values considered to establish the efficiency (Fig. 5A) . The Funnel plot showed that the heterogeneity found (Chi2 P < 0.00001, I2=85%) was attributable to the studies Jain and colleagues' (original article reference 42) and Hernandez and coworkers' (original article reference 32) (Fig. 5B) . Once the data from these studies were excluded, heterogeneity was no longer observed (Chi2 P = 0.53, I2=0%) and the risk of developing a severe course of the disease in VDD patients remained significant [OR 2.47 (1.80, 3.37); p < 0.00001].

Finally, the analysis of the risk of mortality in patients with VDD (original article supplementary Fig. 2 ) also remained unchanged after the exclusion of the pre-print reference 55. Indeed, the analysis of the remaining 8 studies confirmed the absence of a significant increase in mortality risk in patients with VDD compared to patients with adequate 25(OH)D levels [OR 1.74 [0.84, 3 .59]; p = 0.14] regardless of the cut-off values considered for deficiency (supplementary Fig. 2A) . Heterogeneity between studies was found (Chi2 P < 0.03, I2=55%), and its origin was due to the study by Jain and colleagues (42) (Supplementary Fig. 2B ). When this was excluded from the In conclusion, the results of this new analysis showed no difference compared to the original one. Therefore, the inclusion of preprints did not affect the results of our meta-analysis. After the exclusion of pre-prints, we may still hypothesize a role for low 25(OH)D levels in the risk of SARS-CoV-2 infection and the development of more severe forms of COVID-19.

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.eclinm.2021.101168. Forest plot of studies that assessed the risk of a severe course of disease in subjects with 25(OH)D values below or above a specified cut-off. The different cut-offs used by the studies allowed for subgroup analysis. Studies using cut-off values higher than those established by the Endocrine Society for the diagnosis of Vitamin D Deficiency (<20 ng/ml) were not included. Panel B. Funnel plot showing the source of heterogeneity of studies that evaluated the risk of a severe course of disease in subjects with 25(OH)D below or above a specified cut-off.

Was the study population clearly specified and defined?

Was the participation rate of eligible persons at least 50%?

Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for being in the study pre-specified and applied uniformly to all participants?

Was a sample size justification, power description, or variance and effect estimates provided?

(s) of interest measured prior to the outcome(s) being measured? 7. Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed? 8. For exposures that can vary in amount or level

Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? 10. Was the exposure(s) assessed more than once over time?

Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? 12. Were the outcome assessors blinded to the exposure status of participants? 13. Was loss to follow-up after baseline 20% or less?

Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure(s) and outcome(s)? References

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