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210330P - DISEASE: NATURAL HISTORY, DIAGNOSIS, AND PROGNOSIS

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Presentation prepared by Professor Omar Hasan Kasule MB ChB (MUK), MPH (Harvard), DrPH (Harvard) Professor of Epidemiology and Bioethics, King Fahad Medical City


CLINICAL EPIDEMIOLOGY: DEFINITION

  • Clinical epidemiology is defined as the study of the outcome of a disease and the factors that affect the variation in outcome.
  • Clinical epidemiology applies an epidemiological methodology to day-to-day patient care. It is a bridge between epidemiology and clinical medicine.
  • Clinical epidemiology uses the same concepts, tools, and methods as general epidemiology. The methodology is modified to suit the limited clinical context
  • In clinical epidemiology, the ‘exposures’ are therapy and the etiological factor. The ‘outcomes’ are disease progression, disease complications, and mortality.


CLINICAL EPIDEMIOLOGY: SCOPE

  • Study of the natural history of the disease
  • Definition of abnormality:
  • Definition of risk:
  • Diagnosis (symptoms, signs, and diagnostic procedures):
  • Frequency of disease: incidence and prevalence
  • Assessment of treatment
  • Prognosis (forecast outcome of the disease)
  • Prevention of disease
  • Decision making


NATURAL HISTORY OF ILLNESS:

  • Natural history is the course of disease in patients who are not receiving any therapy or intervention.
  • Knowledge of the natural history is necessary for planning rational treatment strategies.
  • The study designs for measuring natural history are cohort and case-control. The case-control study compares those with an outcome to those without the outcome for symptoms and signs of disease.
  • Interpretation of studies of natural history is complicated by individual variations that make generalizations difficult.
  • Stages of disease on the basis of clinical manifestations are: Essentially normal (low risk), Establishment of a disease-causing agent, Appearance of signs, Appearance of symptoms, Disability, Death

 

DISEASE and ABNORMALITY:

  • The disease is anatomical, biochemical, physiologic, or psychological derangement.
  • Sick (subjective) vs ill (objective)
  • Disease/abnormality as a statistical distribution – the normal curve and 95% interval
  • Statistical abnormality is defined as deviation beyond 2 standard deviations.
  • A condition that is regularly associated with disease or disability is considered an abnormality.
  • Definition of abnormality assumes an underlying treatability; there is no point in regarding a condition as abnormal if nothing can be done about it.

 

DEFINITION OF RISK:

  • Clinical epidemiology investigates disease etiology
  • Clinical epidemiology identifies risks
  • In defining risk, we must evaluate how its contribution to disease and the effect of its removal or alteration.
  • Risk is studied using case reports/series, ecologic studies, cross-sectional studies, case-control studies,
  • We prefer the terms risk and risk indicator to cause of the disease (too presumptive)

 

DIAGNOSIS - 1:

  • Diagnosis is distinguishing the normal from the abnormal, recognizing the group to which the illness belongs(classification), making a differential diagnosis, and identification of syndromes
  • The most often used strategy in clinical diagnosis is the hypothetico-deductive in which a hypothesis is formed from early clues and then history, clinical examination, and diagnostic tests are undertaken to confirm or reject the hypotheses.
  • Diagnosis involves review of physical findings and laboratory data and an assessment of the diagnostic/screening tests (sensitivity, specificity, predictive value, cost effectiveness).
  • Two clinical examiners could disagree for the following reasons: differences in the senses like touch, differences in interpretation, or just sheer incompetence.
  • Epidemiology as the study of the distribution and determinants of disease provides background information needed in clinical diagnosis.
  • Epidemiological knowledge provides prior probabilities for clinical decision-making. The clinician combines his empirical findings with the prior probabilities to reach a diagnosis (this is informal or formal using Bayesian techniques).

 

DIAGNOSIS - 2: DIAGNOSTIC/SCREENING TESTS

  • Diagnostic tests are used for assessing severity, predicting prognosis, estimating likely response to treatment, and determining the actual response to treatment.
  • The definition of what is normal in tests must be very clear as yes/no for dichotomous variables or as a cut-off for continuous variables.
  • The accuracy/precision of tests is based on a gold standard or definitive diagnosis based on histology or laboratory tests
  • Variability in tests may be actual (biological) or apparent (measurement error)
  • We can increase accuracy by repeating a test and averaging the results, using multiple tests in sequence or parallel.
  • Random controlled trials, follow up and case-control study designs can be used to assess the role of diagnostic tests in predicting outcome.
  • Reliability of a test (reproducibility)
  • In both clinical practice and clinical trials, clinical epidemiology helps in the quantitative interpretation of diagnostic and screening tests

 

DIAGNOSIS 3: TEST PARAMETERS

  • Sensitivity = percentage with the disease correctly classified. Specificity = percentage of those without the disease correctly classified
  • True positive (PV) = have the disease and are correctly classified, False positive (FP)=do not have the disease but are classified as diseased, True negative (TN)= do not have the disease and are correctly classified, False negative (FN)= do have the disease but are classified as diseased.
  • False positive (FP) suffer from extra tests and the anxiety of having a disease that does not actually exist. False negative (FN) will ignore warning signs of disease until too late.
  • Diagnostic procedures can be evaluated using PV+ (% of test-positive who actually have the disease), PV-(% of test negative who actually do not have the disease
  • PPV rises with the prevalence
  • The kappa statistic is used to test agreement among tests or observers.

 

DIAGNOSIS - 4: CLINICAL DATA-BASES and AI IN DIAGNOSIS

  • A hospital stores a lot of clinical data about patients. This data may be shared with other hospitals using local area networks (LAN).
  • Databases have been developed with AI capabilities and they can provide much support to the physician who is trying to diagnose a disease.
  • This is done by comparison of the patient's data with several profiles stored in the database.


TREATMENT - 1: STRATEGY:

  • The objective of treatment must be identified: cure vs. palliation.
  • The specific treatment modalities to be used must then be selected.
  • Treatment targets must then be decided: dose, frequency, start, and end.
  • We use randomized clinical trials to determine therapy. The objective of treatment may be palliation or cure.
  • Planning and follow-up of treatment.

TREATMENT - 2: CHOICE OF TREATMENT MODALITY

  • Treatment decisions are based on clinical experience or medical literature.
  • Formal decision analysis techniques using prior probabilities from clinical epidemiological studies can be used.
  • Decision trees are used with decision nodes. The probabilities are from empirical data.
  • The epidemiological methodology can also be used to study patient compliance, formulate guidelines for treatment, and study side effects of treatment.


TREATMENT - 3: EPIDEMIOLOGICAL MEASURES OF TREATMENT

  • Epidemiological measures of treatment are efficacy, effectiveness, safety, the incidence of side effects, incidence of treatment failure, compliance, and functional status.
  • Efficacy = producing the desired/intended results
  • Effectiveness = how successful in producing the desired result
  • Efficiency = producing the desired result at the least expense
  • Incidence rates can be computed for several events: side effects, adverse drug reactions, and treatment failure.
  • The clinical database can be used to predict patient compliance.
  • Functional status is measured using restricted activity days, work loss days, and bed-disability days.
  • Clinical practice guidelines have been developed for many conditions. They are based on the results of clinical trials and epidemiologic studies. The guidelines can be evaluated using specific criteria.


TREATMENT - 4: EPIDEMIOLOGICAL STUDIES OF TREATMENT EFFICACY

  • Randomly controlled trials for measuring treatment or therapeutic efficacy.
  • A common problem with such studies is non-compliance. Non-compliant patients should still be analyzed as part of the group to which they were allocated. 
  • Endpoints may also be difficult to measure and in some cases, we may have to resort to antecedents of endpoints. S
  • Sufficient time must be allowed for that treatment to have an effect before its efficacy is measured.
  • Randomized trials are expensive.
  • Non-randomized trials for measuring treatment efficacy have the disadvantage of bias. The following types of non-randomized trials may be used: comparing non-concurrent population groups, case control studies, follow up studies, and case reports.


TREATMENT - 5: EPIDEMIOLOGICAL STUDY DESIGNS FOR MEASURING SAFETY

  • Case-control,
  • Follow up, a
  • Case reports.
  • Two main issues arise characterization of patients who receive a particular treatment and ascertainment of unintended effects.


PROGNOSIS - 1:

  • Prognosis seeks to foretell the future clinical progress of the disease which may lead to decisions on what treatments to institute or what treatments to stop.
  • Prognosis considers the short-term and long-term consequences of having the disease in question.
  • Diagnosis and prognosis are determined by symptoms and signs, Results of laboratory and other tests, and Patient characteristics (e.g., age and gender).


PROGNOSIS - 2: ASSESSMENT

  • Death: case fatality
  • Survival: 1-year survival, 5-year survival, median survival, relative survival, lifetable, Kaplan-Meier
  • Clinical experience
  • Expert opinion
  • Review of the literature.
  • Comparing the patient's profile to the information in the clinical database
  • Prognostic research is following a cohort; prospective follow up being more accurate
  • Prognostic model


DECISION MAKING

  • Epidemiological information is needed for making decisions on the following aspects of health programs:
  • efficacy,
  • effectiveness,
  • compliance,
  • quality assurance,
  • planning,
  • programming,
  • monitoring,
  • evaluation,
  • community diagnosis.


REFERENCES

  • L.M. Bouter G.A. Zielhuis M.P.A. Zeegers .Textbook of Epidemiology.Houten 2018 page 171-200.
  • David D. Celentano, ScD, MHS and Moyses Szklo, MD, MPH, DrPH  Gordis Epidemiology Elsevier 2019.