[Python] Get subset of dataframe following a condition from a dictionary

I am trying to find instances of where three columns may exceed some number that is stored in a dictionary. I know this code works, but I think there is a better more pythonic way of doing this.

l = []
for index, row in df_usage.iterrows():
item_id = row['item_id']
try:
if (
row['start_count'] > historical_orders_dict[item_id]
or row['end_count'] > historical_orders_dict[item_id]
or row['received_total'] > historical_orders_dict[item_id]
):
l.append(df_usage.loc[[index], :])
except:
pass
df_too_many_consuables = pd.concat(l).reset_index(drop=True)
df_too_many_consuables.shape


Here is an example

historical_orders_dict = {
'10346': 28644.99,
'10877': 28979.99,
'10695': 6200.0,
'70020': 1960.0,
'40265': 57300.0,
'91524': 9750.0,
'60022': 200.0,
'10210': 156.0,
'11040': 49350.0}

data = {
'item_id': ['10346', '10877', '10695', '70020', '40265', '91524',
'60022', '10210','11040'],
'start_count': [100000000, 2, 3, 4, 5, 6, 7, 8, 9],
'end_count': [10, 11, 12, 13, 14, 15, 16, 17, 18],
'received_total': [19, 20, 21, 22, 23, 24, 46, 45, 34]}
df_usage = pd.DataFrame(data=data)

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