Participants of the workshop learn the necessary background information and techniques to diagnose Sars-CoV-2 using the mobile diagnostic laboratory. The laboratory is shipped ready to use with all devices, reagents, certificates, and protocols. After one day of preparation together with a local assistant, a five-day course is given where every step is carried out by each participant. Experts accompany the learning process with written teaching materials, video training, virtual live coaching, and short exams to verify the learned content.

1. MOBILE CORONA DIAGNOSTIC LAB
DIAGNOSTIC METHODS
AND
DATA MANAGEMENT

2. ABOUT MYSELF
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 2
M.Sc. in biochemistry (Technical University Munich)
PhD in Experimental Medicine (Helmholtz Zentrum
Munich)
Susanne Pettinger, M.Sc.
contact: susannepettinger@gmail.com
MY EXPERTISE
• Cell culture | microbiological cultures
• Molecular biology | biochemistry | different assays
• Protein expression and purification | protein engineering
• Electron microscopy | fluorescent imaging

3. Block 1: Introduction to diagnostic methods
• Evaluating the quality of diagnostic tests
• Examples: calculating different test parameters
• Developing a diagnostic strategy
BREAK
Block 2: Data Management
• Data management and data integrity
• The ALCOA concept
• Principles of good data management
• The data lifecycle
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 3
SCHEDULE

4. BLOCK 1: INTRODUCTION TO DIAGNOSTIC METHODS

5. WHAT ARE DIAGNOSTIC METHODS?
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 5
• Laboratory tests
• Imaging techniques: X-rays, ultrasound, CT, MRI, PET, …
• Function tests: measures activity of organs or glands
• Pathology & histology
• Physical examination: signs and symptoms
• Medical history of patients

6. EVALUATING THE QUALITY OF LABORATORY TESTS
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 6
• Reproducibility
➢ Does repeating the test produce the same result again?
• Value for the diagnostic strategy
➢ How will a test improve a patient’s diagnosis, treatment, or outcome?
• Accuracy
➢ How does the test perform compared to a reference / standard test?

7. ACCURACY PARAMETERS
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 7
• Sensitivity: proportion of all diseased patients who have a positive test result
➢ sensitivity = true positive / (true positive + false negative)
• Specificity: proportion of all healthy patients with a negative test result from all healthy patients
➢ specificity = true negative / (true negative + false positive)
• Positive predictive value (PPV): probability of being diseased after a positive test result
➢ PPV = true positive / (true positive + false positive)
• Negative predictive value (NPV): probability of being healthy after a negative test result
➢ NPV = true negative / (true negative + false negative)

8. TERMINOLOGY
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 8

9. EXAMPLE 1: CALCULATING DIFFERENT ACCURACY PARAMETERS
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 9
• Test is performed on a population of 100 persons
• Prevalence of tested disease: 50 %
→ half of the population is affected
o 50 persons are healthy (hollow circles)
o 50 persons are diseased (filled circles)

10. EXAMPLE 1: CALCULATING DIFFERENT ACCURACY PARAMETERS
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 10
• Test is performed on a population of 100 persons
• Prevalence of tested disease: 50 %
• Sensitivity: How many sick patients are positive?
Look at test results of DISEASED patients
o 40 true-positive results (TP, green)
➢ Disease was diagnosed correctly
o 10 false-negative results (FN, red)
➢ Disease was not diagnosed, missed cases
o sensitivity = TP / (TP + FN)
= 40 / (40 + 10) * 100
TP: 40 FN: 10

11. EXAMPLE 1: CALCULATING DIFFERENT ACCURACY PARAMETERS
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 11
• Test is performed on a population of 100 persons
• Prevalence of tested disease: 50 %
• Sensitivity: 80 %
• Specificity: How many healthy persons are negative?
Look at test results of HEALTHY persons
o 47 true-negative results (TN, red)
➢ Healthy status was diagnosed correctly
o 3 false-positive results (FP, green)
➢ Disease was incorrectly diagnosed in healthy patients
o specificity = TN / (TN + FP)
= 47 / (47 + 3) * 100
FP: 3 TN: 47

12. EXAMPLE 1: CALCULATING DIFFERENT ACCURACY PARAMETERS
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 12
• Test is performed on a population of 100 persons
• Prevalence of tested disease: 50 %
• Sensitivity: 80 %
• Specificity: 94 %
• Positive PredictiveValue (PPV):
Look only at POSITIVE test results
o 40 true-positive results
o 3 false-positive results
o PPV = TP / (TP + FP)
= 40 / (40 + 3) * 100
IFYOU RECIVE A POSITIVE TEST RESULT:
How likely is it that you are diseased?
OR:
What are the odds that you are healthy despite having a positive test?

13. EXAMPLE 1: CALCULATING DIFFERENT ACCURACY PARAMETERS
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 13
• Test is performed on a population of 100 persons
• Prevalence of tested disease: 50 %
• Sensitivity: 80 %
• Specificity: 94 %
• Positive PredictiveValue (PPV): 93 %
• Negative PredictiveValue (NPV):
Look only at Negative test results
o 47 true-negative results
o 10 false-negative results
o NPV = TN / (TN + FN)
= 47 / (47 + 10) * 100

14. EXAMPLE 1: CALCULATING DIFFERENT ACCURACY PARAMETERS
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 14
• Test is performed on a population of 100 persons
• Prevalence of tested disease: 50 %
• Sensitivity: 80 %
• Specificity: 94 %
• Positive PredictiveValue (PPV): 93 %
• Negative PredictiveValue (NPV): 82 %

15. EXAMPLE 1: CALCULATING DIFFERENT ACCURACY PARAMETERS
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 15
• Test is performed on a population of 100 persons
• Prevalence of tested disease: 50 %
• Sensitivity: 80 %
o Calculated from all diseased patients
• Specificity: 94 %
o Calculated from all healthy patients
• Positive PredictiveValue (PPV): 93 %
o Calculated from all positive test results
• Negative PredictiveValue (NPV): 82 %
o Calculated from all negative test results

16. EXAMPLE 2: THE DYNAMICS IN POSITIVE AND NEGATIVE PREDICTIVEVALUES
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 16
• Same test is performed on a population of 100 persons again after a successful campaign
• Prevalence of tested disease: 50 % → 5 %
• Sensitivity: 80 %
o 80 % of all diseased patients are identified
o 5 positive patients * 80 % = 4 true-positive cases
1 false-negative case

17. EXAMPLE 2: THE DYNAMICS IN POSITIVE AND NEGATIVE PREDICTIVEVALUES
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 17
• Same test is performed on a population of 100 persons again after a successful campaign
• Prevalence of tested disease: 50 % → 5 %
• Sensitivity: 80 %
• Specificity: 94 %
o 94 % of all healthy patients are correctly identified
o 95 negative patients * 94 % = 89 true-negative cases
6 false-positive cases

18. EXAMPLE 2: THE DYNAMICS IN POSITIVE AND NEGATIVE PREDICTIVEVALUES
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 18
• Same test is performed on a population of 100 persons again after a successful campaign
• Prevalence of tested disease: 5 %
• Sensitivity: 80 %
• Specificity: 94 %

19. EXAMPLE 2: THE DYNAMICS IN POSITIVE AND NEGATIVE PREDICTIVEVALUES
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 19
• Same test is performed on a population of 100 persons again after a successful campaign
• Prevalence of tested disease: 5 %
• Sensitivity: 80 %
• Specificity: 94 %
• Positive PredictiveValue: ???
Look only at POSITIVE test results
o 4 true-positive results
o 6 false-positive results
o PPV = TP / (TP + FP)
= 4 / (4 + 6) * 100

20. EXAMPLE 2: THE DYNAMICS IN POSITIVE AND NEGATIVE PREDICTIVEVALUES
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 20
• Same test is performed on a population of 100 persons again after a successful campaign
• Prevalence of tested disease: 5 %
• Sensitivity: 80 %
• Specificity: 94 %
• Positive PredictiveValue: 40 %
• Negative PredictiveValue: ???

21. EXAMPLE 2: THE DYNAMICS IN POSITIVE AND NEGATIVE PREDICTIVEVALUES
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 21
• Same test is performed on a population of 100 persons again after a successful campaign
• Prevalence of tested disease: 5 %
• Sensitivity: 80 %
• Specificity: 94 %
• Positive PredictiveValue: 40 %
• Negative PredictiveValue: ???
Look only at NEGATIVE test results
o 89 true-negative results
o 1 false-negative results
o PPV = TP / (TP + FP)
= 89 / (89 + 1) * 100

22. EXAMPLE 2: THE DYNAMICS IN POSITIVE AND NEGATIVE PREDICTIVEVALUES
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 22
• Same test is performed on a population of 100 persons again after a successful campaign
• Prevalence of tested disease: 5 %
• Sensitivity: 80 %
• Specificity: 94 %
• Positive PredictiveValue: 40 %
• Negative PredictiveValue: 99 %

23. July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 23
EXAMPLE 2: THE DYNAMICS IN POSITIVE AND NEGATIVE PREDICTIVEVALUES
Parameter Example 1 Example 2
Population 100 100
Prevalence 50 % 5 %
Sensitivity 80 % 80 %
Specificity 94 % 94 %
Positive PredictiveValue 93 % 40 %
Negative PredictiveValue 82 % 99 %

24. BIAS AND VARIATION
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 24
• variation in population prevalence
o Statistical effect on how many true-positives or true-negatives will be found
• Reference standard is imperfect
o Incorrect estimates of accuracy
• Verification bias: reference standard
o Reference standard only applied to cases with strong indication (too expensive, invasive, impractical, …)
o New test: number of false-negative is too low, sensitivity is overestimated
• Case mix / variability
o Sensitivity and specificity depend on patient subpopulation
o Differences between male/female, old/young, patients with/without underlying medical condition, …
• Disease severity: How far progressed is the disease?
o Can affect sensitivity and specificity
High POSITIVE predictive value High NEGATIVE predictive value
high prevalence low prevalence

25. DIAGNOSTIC STRATEGIES
July 8th, 2021
MOBILE CORONA DIAGNOSTIC LAB | Diagnostic Methods and Data Management slide 25
• Will running a test improve the patient’s diagnosis, treatment and outcome?
• Triage: series of two or more consecutive tests
➢ Screening large numbers of patients or when second test has high risk of complications
➢ First test has high sensitivity, specificity is not important
➢ Second test has high specificity to improve overall diagnostic accuracy
• Replacing old assays with new ones
➢ New assay is more accurate, less invasive, easier to handle, cheaper, …
• Downstream consequences of diagnostic tests
➢ Effects on overall mortality, time to discharge, cost-effectiveness