Early prediction of ICU mortality can support clinical decisions, but retrospective notes can contain future-event cues that cause leakage in clinical NLP. We evaluate leakage-aware modeling of Brazilian Portuguese ICU notes for mortality prediction using BRATECA. We compare 24-hour restriction, regex and LLM leakage auditing, neural and TF-IDF representations, and unrestricted-note baselines. In the 24-hour setting, models achieved AUROC 0.78-0.83; unrestricted notes reached around 0.95, indicating outcome-related information in late documentation. Regex found leakage signals in 21.13% of the notes, and the LLM audit identified additional semantic cases. Results highlight the need for temporal control and leakage auditing in clinical NLP.