Generally, in RNAseq, we collapse technical replicates. However, by "technical replicate", we generally mean multiple libraries built from the same biological sample. In bulk RNAseq we use replicates to estimate the dispersion. The dispersion between repeated samplings from the same library is approximately zero (unless something has gone wrong), and the variance between them is well estimated by the poisson distribution.
Its not clear from your description whether what you have is repeated samplings from the same biological sample, or repeated biological samplings from the same patient. Thus, its not clear if what you describe as technical replicates are what the bulk-RNAseq analysis world refers to as technical replicates. One might investigate this by plotting the mean vs the variance for genes in each patient. If it seems that mean = variance (approximately), then you should regard them as technical replicates and collapse them. If variance appears to be more than the mean, then you shouldn't.
However, if you don't regard them as technical replicates and collapse them, then you can't just use DESeq2 to do the analysis. You have two sources of variance - patient to patient variation and sample to sample variation, and the samples are nested within the patients. You effectively have a nested, or mixed effects model, which DESeq2 isn't really designed to handle.
You might look to limma-voom, which can model the random effect replicates using its duplicateCorrelation function.
A word of warning. If you are doing a DE of one patient against all others, because that patient has something special about them (e.g. they have the disease, while the others don't). What you will get is genes that are DE in that patient, not genes that are DE between disease and normal, as you have no way to disentangle patient effects from condition effects. Furthermore, because you only have one replicate for condition A on the patient level (patient 1), you will be making the assumption that variance on the patient level is the same in condition A and condition B, and that only the means have changed.