College aantekeningen

From Clinical Trials To Big Data Research - volledige collegeaantekeningen.

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Rijksuniversiteit Groningen (RuG)

Dit document omvat alle colleges (behalve de introduction) wat betreft de elective From Clinical Trials to big data research.

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Geupload op 18 februari 2021
Aantal pagina's 67
Geschreven in 2020/2021
Type College aantekeningen
Docent(en) Eelco hak
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From clinical trials to big data
Lecture 1 – introduction – 1-02

Lecture 2 – diagnostic and prognostic research 1-02
Prediction research → diagnostic and prognosis research
Challenges in clinical practice
- What is wrong with this patietnt?
o Diagnostic knowledge
- Why is this patient ill?
o Etiologic knowledge
- What will happen if I don’t intervene?
o Prognostic knowledge
- What will be the effects of an intervention?
o Therapeutic (influencing prognostic!) knowledge
- DEPTh model → Diagnostic Etiologic Prognostic Therapeutic model

Influenca H1N1 pandemic
→ het kost veel geld. Als er een te kort is aan de hoeveelheid vaccinaties, moet je kiezen aan wie je wel
een vaccinatie geeft en aan wie niet. Wat je dan moet doen, is een predictive chance opstellen:
RISK-PREDICTION role: the higher the number of points, the higher the chance that in the coming epidemic
you will die or be hospitalized.

How do you determine the amount of points for a specific disease? → mathematically based on the
regression model with an estimate → betas associated with the determinance → the higher the beta, the
higher the points.

,y-axis: probability of the disease, estimated. Low probability → no treat.
In between: continue diagnostic tests to change probability; disease uncertain
80% of complication → start treatment
Where the points A and B are depend on the prognosis of the disease; whether it can quickly lead to a
complication. E.g. corona can go very fast, so we have to aggresively treat.
It also depends on the benefits (how sure it helps), costs and side effects of the treatment. Cancer
treatments are expensive, so we have to be really sure that we need to start the treatment. We are not
going to treat if there is a probability of complication which is lower than 15%.
Benefits and side effects next available test.

Diagnostic research
Diagnosis in practice = basis medical care
- Directs (medical drug) treatment
- Indication of a patient’s prognosis
Scientific diagnostic research should serve practice
- Describe (follow) practice in study question, design and analysis
- Test should follow clinical practice (availability)
- Diagnostic outcome should be measured by a golden standard → definitely yes or definitely no, the
research we are interested in.

Making a diagnosis starts with patients having a clinical problem (sign and symptoms)
- Symptom/ sign → suspected of having particular disease
- Helps the medical doctor to rule out/ in a certain disease
o Young female with swollen, red leg → DVT?
o Women with palpable node in breast → breast cancer?
o Child with neck stifness → meningitis?
o Sore throat and fever, dec 2020 → corona?
Symptoms and signs are important in the diagnosis.

Diagnosis practice
- Young man with acute cough and fever → what disease could it be?
- Possible diseases → differential diagnosis
o Upper respiratory tract infection
o Bronchitis
o Pneumonia → needs direct treatment
o Pulmonary embolism
o Other? → we must be sure that it is not the diases.
- Patient with differential diagnoses (DD) and target diagnosis (TD)
- TD = one not to miss (most severe or probable)

, - In our case TD = pneumonia (or corona)
- Before any test is done, we would like to know the chance that they have pneuminia. The prior
probability =
o Pr(pneumonia)= Pr(TD)
▪ Often Pr(TD) too low to treat everybody and too high to send a patient home. We
want to be sure that it is not pneumomia.
- Aim: which results truly contribute to estimation diagnostic probability. We need to estimate the
probability of pneumonia better.
o The less the better → weighing predictive value vs. measurement burden and costs
We want to make sure that we go from A to B → that we aim to treat and give antibiotics.
How are we going to do this? (Go from A to B?)
→ diagnostic testing!
- 24 years of age, male sex, athlete
- Antecedents: upper respiratory tract infections
- Dyspnoea/ cough/ sweating
- Fever: 39.2 celsius
- Morning sputum (‘somewhat yellowish’) → indication of pneumonia
- ‘some’ crepitations/ rhales left
- Tachycardia: 112/ minute → too fast heartbeat.
→ you remain in doubt. What do you do next? → we still are not sure whether it is pneumonia or not.
You need to send him to the hospital to do some additional testing.
Additional diagnostic test → are they available?
- Lung function?
- CRP? → C-reactive protein; indicator for pneumonia when too high.
- Chest X-ray?
- Something else?

What do we do in research? We measure the same as the GP, and we use a reference test (pathology,
imaging, microbiology).
- Commonly hierarchical process
o Potential determinants:
o History (H) + physical examination (PE)
o Simple lab (blood CRP) /urine)
o Imaging
o Functional tests/ extensive lab

Test research
So, we are doing a research in 200 patients with a reference test 40 people who actually have pneumonia.
- Using the reference test 40 out of 200 patients with suspected pneumonia actually have
pneumonia
o Pneumonia (culture positive) n=40
o Culture negative n=160
Pr(TD)= Pr(pneumonia)=0,20 (40/200), the chance of the patients that will have pneumonia, we need to be
sure that the tests are as close as possible to the reference tests.
In test research, we want to evaluate whether an index test usefully approximates the reference test.

, Two types of validity that we measure → sensitivity and specificity from the reference tests.
Sensitivity is how many on the basis of this positive test are we going to detect among all the diseases →
a/k1.
Specificity is how many of the true negatives are also stated as negative by the test, which is d/k 2.

Test characteristics: positive predictive value: if we look at the positive tests, what is the chance that you
actually have pneumonia? → a/r1.
Negative predictive value: c/r2.

As sensitive and as specific possible is needed.

Study design for diagnostic research
Cross sectional → no follow-up.
Determinants and outcome measured at ‘same’ moment → at one period of time, there needs to be a
diagnostic research.

Prognostic research
Prognosis → basis for medical treatment. It determines how aggressive the treatment will be.
If I have a certain disease/ risk factor, what is my diagnosis?
Do I need (extra, more aggressive) treatment?

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