Based on prior knowledge, the feature "n_p_ecg_p_10", which represents the presence or absence of Complete LBBB on ECG at the time of admission to the hospital, can be relevant in determining whether a patient with myocardial infarction complications shows chronic heart failure. Let's analyze the relationship between this feature and the target variable.

To create the dictionary, we will need to examine the values of feature "n_p_ecg_p_10" for each target class: "no" (indicating no chronic heart failure) and "yes" (indicating chronic heart failure).

To accomplish this analysis, we will evaluate the relevance of each feature value to the target class.

```json
{
	"no": ["no"], 
    "yes": ["yes"]
}
```

In this case, since the feature values are categorical, we only include the categories that align with each target class. All other categories are not included in the dictionary since they are hard to predict.

This dictionary shows that the only value of "n_p_ecg_p_10" associated with chronic heart failure is "yes". The absence of Complete LBBB on ECG (value "no") is not indicative of chronic heart failure in this context.