lyprox.patients.models.Patient

class documentation

class Patient(mixins.LockedDatasetMixin, loggers.ModelLoggerMixin, models.Model):

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The representation of a patient in the database.

Contains some demographic information, as well as patient-specific characteristics that are important in the context of cancer, e.g. HPV status.

This model also ties together the information about the patient's tumor(s) and the lymphatic progression pattern of that patient in the form of a Diagnose model.

Class M_stages Defines the possible M-stages as choice class.
Class N_stages Defines the possible N-stages as choice class.
Class T_stages Defines the possible T-stages as choice class.
Method __str__ Report some patient specifics.
Method get_absolute_url Return the absolute URL for a particular patient.
Method get_diagnoses Return the LNL diagnose(s) of the patient.
Method get_tumors Return the primary tumor(s) of that patient.
Method update_t_stage Update T-stage after new Tumor is added to Patient.
Method validate_unique Make sure the patient has not already been added.
Class Variable age Age in years at the time of diagnosis.
Class Variable alcohol_abuse Was the patient a drinker?
Class Variable dataset Every patient must belong to a dataset entry that manages importing, exporting as well as preventing edits that compromise the integrity of the dataset.
Class Variable diagnose_date Date of histological confirmation with a squamous cell carcinoma.
Class Variable hpv_status Was the patient HPV positive (True) or negative (False)?
Class Variable m_stage Indicates whether or not there are distant metastases.
Class Variable n_stage Categorizes the extend of regional metastases.
Class Variable neck_dissection Did the patient undergo (radical) neck dissection?
Class Variable nicotine_abuse Was the patient a smoker?
Class Variable sex Biological sex of the patient.
Class Variable tnm_edition The edition of the TNM staging system that was used.
Instance Variable stage_prefix 'c' for 'clinical' and 'p' for 'pathological'.
Instance Variable t_stage Stage of the primary tumor. Categorized the tumor by size and infiltration of tissue types.

Inherited from LockedDatasetMixin:

Method delete Raise LockedDatasetError befor deleting if associated dataset is locked.
Method save Raise LockedDatasetError before saving if associated dataset is locked.
Property _must_raise Infer from class if an error should be raised on save/delete.
def __str__(self):

Report some patient specifics.

def get_absolute_url(self):

Return the absolute URL for a particular patient.

def get_diagnoses(self):

Return the LNL diagnose(s) of the patient.

def get_tumors(self):

Return the primary tumor(s) of that patient.

def update_t_stage(self):

Update T-stage after new Tumor is added to Patient.

This Patient method gets called in Tumor.save method. It also updates the patient's stage prefix to that of the tumor with the highest T-category.

def validate_unique(self, exclude=None):

Make sure the patient has not already been added.

Uniqueness is checked by ensuring this patient's combination of sex, age, date of diagnosis, alcohol, nicotine, and HPV status, as well as TNM stage is unique in the dataset.

age =

Age in years at the time of diagnosis.

alcohol_abuse =

Was the patient a drinker?

dataset =

Every patient must belong to a dataset entry that manages importing, exporting as well as preventing edits that compromise the integrity of the dataset.

diagnose_date =

Date of histological confirmation with a squamous cell carcinoma.

hpv_status =

Was the patient HPV positive (True) or negative (False)?

m_stage =

Indicates whether or not there are distant metastases.

n_stage =

Categorizes the extend of regional metastases.

neck_dissection =

Did the patient undergo (radical) neck dissection?

nicotine_abuse =

Was the patient a smoker?

sex =

Biological sex of the patient.

tnm_edition =

The edition of the TNM staging system that was used.

stage_prefix =

'c' for 'clinical' and 'p' for 'pathological'.

t_stage =

Stage of the primary tumor. Categorized the tumor by size and infiltration of tissue types.