How to interpret a logistic regression result with continuous variables? I am conducting a regression analysis.
I have a first problem and that is that the database has only an n of 41 (n=29 for one group and n=12 for the second group).
But I have gone ahead with the logistic regression. For this I used the following code:
 model<-glm(grupo ~arrayil2+citometriail2+citometriail4+citomtriail5,
     data = BD,
     family = binomial(link="logit"))

The "group" variable is whether the patient is asthmatic or not and the variables arrayil2, cytometriail2, cytometriail4, cytomtriail5 are continuous values.
TABLA<-logistic.display(model,crude.p.value = TRUE, decimal = 3);TABLA<-as.data.frame(TABLA)

This gives me the following result:

I don't quite understand the result, as I usually perform logistic regressions with a larger n and with categorical variables.
I would need help in interpreting these results and whether they are wrong or not.
Thanks in advance.

As indicated in the comments, your particular model with this particular small data set is uninterpretable. The odds-ratio estimates and confidence intervals (as best as I can tell from the image of the table) seem to be enormous. I suspect that you have something close to perfect separation with only 12 cases in the minority class and 4 continuous predictor variables.
Otherwise, the interpretation of a coefficient for a continuous linearly modeled predictor in logistic regression is straightforward: it's the increase in log-odds of an event per unit change in that continuous predictor.
