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biobiology, prof. E. B. Burlakova. These data form new ideas about the biological effectiveness of chronic exposure to radiation on humans and clearly indicate the incompetence of extrapolating the effects of high doses of ionizing radiation to the region of low doses.
The development of new concepts is important for the formation of balanced development plans nuclear energy and fair social policy towards liquidators Chernobyl disaster and residents of areas contaminated with radionuclides.
When assessing the effect of radiation on human health, it should be borne in mind that ionizing radiation is a cosmogenic factor in the environment. It is well known that the natural background radiation is necessary for the growth, development and existence of various living beings, including mammals. Understanding radiobiological patterns is associated with insight into the phenomenon of life, the connection between living things and space. There are many mysteries in the effects of ionizing radiation, including the positive or negative effects of irradiated biological objects on non-irradiated ones. Of undoubted interest is the idea expressed by A. M. Kuzin in his last note to his employees: “Life, a living body is a metabolizing system of structures at the molecular level that form a single whole thanks to information continuously delivered by secondary, biogenic radiation arising under the influence of atomic radiation natural radioactive background of cosmic and terrestrial origin.”
BIBLIOGRAPHICAL LIST
1. Kudryashov, Yu. B. Radiation biophysics. Ionizing radiation/ Yu. B. Kudryashov. - M.: ed. Moscow University, 2004. - 580 p.
2. Yarmonenko, S. P. Radiobiology of humans and animals / S. P. Yarmolenko, A. A. Vainson. - M.: Higher. school, 2004. - 550 p.
3. Mothersill, C. Low-dose radiation effects: Experimental hematology and the changing paradigm / C. Mothersill, C. Seymour // Experimental Hematology. - 2003. - No. 31. - P. 437-445.
4. Lee, D.E. The effect of radiation on living cells / D. E. Lee. - M.: Gosatomizdat, 1966. - 288 p.
5. Timofeev-Resovsky, N.V. Application of the hit principle in radiobiology / N.V. Timofeev-Resovsky, V.I. Ivanov, V.I. Korogodin. - M.: Atomizdat, 1968. - 228 p.
6. Goncharenko, E. N. Chemical protection against radiation damage / E. N. Goncharenko. - M.: ed. Moscow University, 1985. - 248 p.
7. National report “20 years after the Chernobyl disaster: consequences in the Republic of Belarus and overcoming them” / Committee on the Problems of the Consequences of the Disaster on Chernobyl nuclear power plant under the Council of Ministers of the Republic of Belarus; edited by V. E. Shevchuk, V. L. Guravsky. - 2006. - 112 p.
8. Vozianov, A. Health erections of Chornobyl accident, Eds / A Vozianov, V. Bebeshko, D. Bayka. - Kyiv.: “DIA”, 2003. - 508 p.
9. Kuzin, A. M. Structural-metabolic hypothesis in radiobiology / A. M. Kuzin. - M.: Nauka, 1970. - 170 p.
10. Kuzin, A. M. Structural-metabolic theory in radiobiology / A. M. Kuzin. - M.: Nauka, 1986. - 20 p.
11. Knyazeva, E. N. Foundations of synergetics / E. N. Knyazeva, S. P. Kurdimov. - St. Petersburg: Aletheia Publishing House, 2002. - 31 p.
12. Stepanova, S. I. Biorhythmic aspects of the problem of adaptation / S. I. Stepanova. - M.: Nauka, 1986. - 244 s.
13. Non-monotonicity of the metabolic response of mammalian cells and tissues to the effects of ionizing radiation / I. K. Kolomiytsev [et al.] // Biophysics. - 2002. - T. 47, Issue. 6. - pp. 1106-1115.
14. Kolomiytseva, I. K. Nonmonotonous changes in metabolic parameters of tissues and cells under action ionizing radiation on animals / I. K. Kolomiytseva, T. R. Markevich, L. N. Potekhina // J. Biol. Physics. - 1999. - No. 25. - P. 325-338.
15. Burlakova, E. B. New aspects of the patterns of action of low-intensity irradiation in small doses / E. B. Burlakova, A. N. Goloshchapov, G. P. Zhizhina, A. A. Konradov // Radiats. biology. Radioecology. - 1999. - T. 39. - P. 26-34.
Received 04/18/2008
USE OF EVIDENCE-BASED MEDICINE DATA IN CLINICAL PRACTICE (literature review)
A. L. Kalinin1, A. A. Litvin2, N. M. Trizna1
1Gomel State medical University 2Gomel regional clinical Hospital
A brief overview of the principles of evidence-based medicine and meta-analysis is provided. An important aspect of evidence-based medicine is determining the degree of reliability of information.
Quantitatively pooling data from different clinical studies using meta-analysis allows us to obtain results that cannot be obtained from individual clinical studies. Reading and studying systematic reviews and meta-analyses allows you to more effectively navigate the large number of published articles.
Key words: evidence-based medicine, meta-analysis.
Health and environmental issues
USE OF DATA OF EVIDENCE BASED MEDICINE IN CLINICAL PRACTICE
(literature review)
A. L. Kalinin1, A. A. Litvin2, N. M. Trizna1
1Gomel State Medical University 2Gomel Regional Clinical Hospital
The purpose of the article is the review of principles of evidence based medicine and the meta-analysis. A prominent aspect of evidence based medicine is definition of degree of reliability of the information.
Quantitative association of the given various clinical researches by means of the meta-analysis allows to receive results which cannot be received from separate clinical researches. Reading and studying of systematic reviews and results of the meta-analysis allows to be guided more effectively in a significant quantity of published articles.
Key words: evidence based medicine, meta-analysis.
Not a single practicing doctor has sufficient experience to freely navigate the whole variety of clinical situations. You can rely on expert opinions, authoritative guidelines and reference books, but this is not always reliable due to the so-called lag effect: promising medical methods are introduced into practice long after evidence of their effectiveness has been obtained. On the other hand, information in textbooks, manuals and reference books often becomes outdated even before they are published, and the age of the person conducting the treatment experienced doctor negatively correlates with treatment effectiveness.
The half-life of literature reflects the intensity of progress. For medical literature, this period is 3.5 years. Only 1015% of the information published today in the medical press will have scientific value in the future. After all, if we assume that at least 1% of the 4 million articles published annually have something to do with the medical practice of a doctor, he would have to read about 100 articles every day. It is known that only 10-20% of all medical interventions currently used were based on reliable scientific evidence.
The question arises: why don’t doctors put good evidence into practice? It turns out that 75% of doctors do not understand statistics, 70% do not know how to critically evaluate published articles and studies. Currently, in order to practice evidence-based, a doctor must have the knowledge necessary to assess the reliability of the results of clinical trials, have prompt access to various sources of information (primarily international journals), have access to electronic databases (Medline), and speak English.
The purpose of this article is a brief overview of the principles of evidence-based medicine and its component - meta-analysis, which allows you to more quickly navigate the flow of medical information.
The term "Evidence Based Medicine" was first proposed in 1990 by a group of Canadian scientists from McMaster University in Toronto. The term quickly took root in English-language scientific literature, but at that time there was no clear definition of it. Currently, the most common definition is: “Evidence based medicine is a section of medicine based on evidence, involving the search, comparison, synthesis and wide dissemination of the obtained evidence for use in the interests of patients.”
Today, evidence-based medicine (EBM) is new approach, a direction or technology for collecting, analyzing, summarizing and interpreting scientific information. Evidence-based medicine involves the conscientious, explanatory, and common-sense use of the best current knowledge to treat each patient. The main goal of introducing the principles of evidence-based medicine into healthcare practice is to optimize the quality of care medical care in terms of safety, effectiveness, cost and other significant factors.
An important aspect of evidence-based medicine is determining the degree of reliability of information: the results of studies that are used as a basis when compiling systematic reviews. The Center for Evidence-Based Medicine at Oxford has developed the following definitions of the degree of reliability of the information presented:
A. High reliability - information is based on the results of several independent clinical trials (CTs) with concordant results, summarized in systematic reviews.
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B. Moderate reliability - information is based on the results of at least several independent clinical trials with similar objectives.
C. Limited reliability - information is based on the results of one CT.
D. Strict scientific evidence absent (no clinical trials have been conducted) - a certain statement is based on expert opinion.
According to modern estimates, the reliability of evidence from different sources is not the same and decreases in the following order:
1) randomized controlled trial;
2) non-randomized CT with simultaneous control;
3) non-randomized CT with historical control;
4) cohort study;
5) case-control study;
6) crossover CI;
7) results of observations;
8) description of individual cases.
The three “pillars” of reliability in clinical medicine are: random blind sampling of subjects into comparison groups (blind randomization); sufficient sample size; blind control (ideally triple). It must be specially emphasized that the incorrect but widely used term “statistical reliability” with its notorious p Symptoms
In outcome studies, relevant endpoints are often symptoms or measures of functional ability and care needs—what the patient receiving treatment considers important. For example, a patient suffering from an infection who has been given penicillin may pay more attention to the fact that he does not have high temperature and improved general condition than the effect of penicillin on the actual level of infection. In this case, the symptoms and how he feels are seen as a direct assessment of his health—and these are the endpoints that outcome research focuses on. The patient will also likely be interested in the possible side effects associated with penicillin, as well as the cost of treatment. In the case of other diseases such as cancer, it is important clinical result, relevant to the patient, will be the risk of death.
If the study is long-term, “ ” may be used when studying the research results. A surrogate endpoint involves the use of a biomarker to measure an outcome, acting as a replacement for a clinical endpoint measuring the effect of penicillin by testing the reduction in the amount of one type of protein (C-reactive protein) that is always present in the blood. The amount of this protein in the blood healthy person very little, but during acute infection it increases rapidly. Thus, measuring the level of C-reactive protein in the blood is an indirect way of determining the presence of infection in the body, therefore in this case the protein serves as a “biomarker” of infection. A biomarker is a measurable indicator of disease status. This parameter also correlates with the risk of disease onset or progression, or how the prescribed treatment will affect the disease. Every day, the patient's blood is taken for analysis to measure the amount of biomarker in the blood.
It must be emphasized that in order to use a surrogate endpoint for monitoring and surveillance purposes, the marker must be validated or validated in advance. It is necessary to demonstrate that changes in a biomarker are correlated (consistent) with clinical outcome in a particular disease and the effect of treatment.
Additional sources- World Health Organization (2008). Where are the patients in decision-making about their own care? Retrieved August 31, 2015, from
There are several definitions of evidence-based medicine:
- This is a new technology for collecting, analyzing, synthesizing and using medical information to make optimal clinical decisions.
- It is the conscious, clear and impartial use of the best available evidence to make decisions about the care of individual patients.
- It is the strengthening of the clinician's traditional skills in diagnosis, treatment, prevention and other areas through the systematic formulation of questions and application mathematical assessments probability and risk.
It should be noted right away that the terms “no evidence,” “not proven,” or “insufficient evidence available” are not the same as “proven no effect” or “proven no benefit.” The wording “not proven” may indicate insufficient knowledge of the problem and the advisability of organizing larger studies or using other methods of collecting information and conducting statistical analysis. At the same time, we must not forget that the reverse wording “proven” may indicate statistical manipulation in the interests of manufacturing companies.
Evidence-based medicine is based on research methods used in epidemiology.
J.M. Last, formulating the modern definition of epidemiology, focuses on individual words in this definition. Thus, “study” should be understood as conducting observational (observational) and experimental studies, testing hypotheses and analyzing results.
“The spread of diseases and factors...” implies the study of the incidence of illness, death, risk factors, patient compliance with doctor’s recommendations, organization of medical care and its effectiveness.
“Target group” is a group with a precise number of people and certain age, gender, social and other characteristics.
Currently modern concept Epidemiology is designated by the term "clinical epidemiology". This term comes from the names of two “parent” disciplines: clinical medicine and epidemiology.
"Clinical" because it strives to answer clinical questions and recommend clinical decisions based on the best evidence.
"Epidemiology" because many of its methods were developed by epidemiologists, and care for a specific patient is considered in the context of the larger population to which the patient belongs.
Clinical epidemiology is a science that makes it possible to make predictions for each individual patient based on the study of the clinical course of the disease in similar cases, using rigorous scientific methods for studying groups of patients to ensure the accuracy of predictions.
The goal of clinical epidemiology is the development and application of clinical observation methods that make it possible to make fair conclusions with a guaranteed assessment of the influence of systematic and random errors. This is a critical approach to obtaining the information physicians need to make sound decisions.
The fundamental method in epidemiology is comparison. It is carried out by mathematical calculations of such quantities as odds ratio, risk ratio for the development of the events under study.
However, before making a comparison, we should understand what we will be comparing with (oranges to oranges, not oranges to steamships), i.e. formulate the task (problem) preceding the start of any research. Most often, the problem is formulated in the form of a question to which an answer must be found.
For example, hypothetically, we (that is, a practicing physician) are presented with a drug that, according to the chemists who synthesized it, should treat the heel. The pharmacological company that put the drug into production also assures in the instructions that the declared effect really takes place.
What can a practitioner do when deciding whether to use a drug?
We exclude the answer “to take the chemists/pharmacologists’ word for it” as trivial and fraught with consequences. Our task is to verify the claimed effect of the drug on the heel by means available to the practicing physician (to confirm or refute, etc.). Of course, we will not test the drug on laboratory mice, volunteers, etc. It is assumed that before “launching into the series,” someone has already done this more or less conscientiously.
In accordance with the problem, we will begin the formation of an array of data used to solve it:
According to the Swedish Council for Evaluation Methodology in Health Care, the strength of evidence from different sources varies depending on the type of study carried out. The type of study performed, according to the international agreement of the Vancouver Group of Biomedical Journal Editors (http://www.icmje.org/), must be carefully described; methods for statistical processing of clinical trial results must also be indicated, conflicts of interest must be declared, the author’s contribution to the scientific result and the possibility of requesting primary information from the author on the results of the study.
To ensure the evidence of the results obtained in research, an “evidence-based”, i.e., adequate to the tasks, research methodology (research design and methods of statistical analysis) (Table 1) must be chosen, which we will take into account when selecting information from the data array.
Table 1. Selection of research methodology depending on the purpose of the study
(for a description of the terms, see the Glossary of Methodological Terms)
| Research objectives | Study design | Statistical analysis methods |
| Estimation of disease prevalence | A one-time study of the entire group (population) using strict criteria disease recognition | Estimation of share, calculation of relative indicators |
| Morbidity assessment | Cohort study | Share estimation, calculation of time series, relative indicators |
| Assessment of disease risk factors | Cohort studies. Case-control studies | Correlation analysis, regression analysis, survival analysis, risk assessment, odds ratio |
| Assessing the impact of environmental factors on people, studying cause-and-effect relationships in the population | Ecological population studies | Correlation, regression analysis, survival analysis, risk assessment (incremental risk, relative risk, incremental population risk, incremental population risk), odds ratio |
| Drawing attention to the unusual course of the disease and the result of treatment | Case description, case series | No |
| Description of current clinical practice findings | Observational (“before and after”) | Mean, standard deviation, paired Student's t test (quantitative data). McNimara test (qualitative data) |
| Testing a new treatment method | Phase I clinical trial (before and after) | Mean, standard deviation, paired Student's t test. McNimara criterion |
| Comparison of two treatments in current clinical practice | Controlled prospective. Randomized (open, blind, double blind). Controlled retrospective. Controlled prospective + retrospective (mixed design) | Student's t test (quantitative data). χ 2 or z test (qualitative features). Kaplan-Meers test (survival) |
| Comparison of new and traditional treatment method | Phase II-IV clinical trials (controlled, prospective or randomized) | Student's t test. χ2 test. Kaplan-Meers test |
Each type of research is characterized by certain rules for collecting and analyzing information. If these rules are followed, any type of research can be called qualitative, regardless of whether they confirm or refute the hypothesis put forward. The statistical methods of analysis used to obtain evidence are presented in more detail in the books by Petri A., Sabin K. “Visual Statistics in Medicine” (M., 2003), Glanz S. “Medical and Biological Statistics” (M., 1999).
The degree of “evidence” of information is ranked as follows (descending):
The results of studies performed using simplified methods or methods that do not correspond to the objectives of the study, with incorrectly selected evaluation criteria, can lead to false conclusions.
The use of complex assessment methods reduces the likelihood of an erroneous result, but leads to an increase in so-called administrative costs (data collection, database creation, statistical analysis methods).
For example, in a study by E.N. Fufaeva (2003) found that among patients who had a disability group before surgery, the persistence of disability was registered in all 100%. Among patients who did not have a disability group before cardiac surgery, in 44% of cases a disability group was determined after surgery. Based on this result, it is possible to draw false conclusions that cardiac surgery worsens the quality of life of patients. However, the survey revealed that 70.5% of patients and 79.4% of doctors who observed these patients were satisfied with the results of treatment. Registration of a disability group is due to social reasons (benefits for receiving medications, paying for housing, etc.).
The importance of social protection in matters of ability to work is confirmed by the results of a study conducted in the USA, which did not reveal a clear relationship between the clinical condition (physical illness) of the patient and ability to work.
In order to compare employment indicators after TLBA and CABG, 409 patients were examined (Hlatky M.A., 1998), of which 192 people underwent TLBA and 217 underwent CABG. It was found that patients who underwent TLBA returned to work six weeks faster than patients who underwent CABG. However, in the long term, the influence of such factors as the type of operation turned out to be insignificant. Over the next four years, 157 patients (82%) in the TLBA group and 177 patients (82%) in the CABG group returned to work. The factors that had the strongest impact on the rate of long-term employment were the age of the patient at the start of the study and the degree of coverage of medical care by health insurance.
Thus, medical factors had less influence on long-term employment outcomes than demographic and social factors. The results obtained by Russian and American researchers indicate that some of the traditional and seemingly simple methods Treatment outcome measures are inappropriate for prioritizing and decision-making.
If we include information with a low degree of reliability, then this point in our research must be discussed separately.
The Center for Evidence-Based Medicine in Oxford offers following criteria reliability of medical information:
- High reliability - information is based on the results of several independent clinical trials with concordant results, summarized in systematic reviews.
- Moderate reliability - information is based on the results of at least several independent, similar clinical trials.
- Limited validity - information is based on the results of a single clinical trial.
- There is no rigorous scientific evidence ( clinical trials were not carried out) – a certain statement is based on expert opinion.
This is of course a joke, but every joke has some truth.
Typically, studies that have yielded positive results, such as those that promote a new treatment, are published. If the working hypothesis (task, problem) is not confirmed or does not find a positive solution, then the researcher, as a rule, does not publish the research data. This can be dangerous. So, in the 80s of the twentieth century, a group of authors studied an antiarrhythmic drug. In the group of patients who received it, a high mortality rate was found. The authors regarded this as an accident, and since the development of this antiarrhythmic medicine was terminated, the materials were not published. Later, a similar antiarrhythmic drug, flecainide, caused many deaths 1-2.
________________________
The above algorithm for searching and evaluating evidence was proposed by D.L. Sackett et al (1997). It can be used in any study, even in assessing the influence of moon phases on the growth of telegraph poles.
This article will help you take a more realistic look at the results of scientific and medical research, which we often use when writing our articles, and also better navigate the flow of advertising information that constantly tries to mislead us by appealing to “scientifically proven” results.
“There are three kinds of lies: lies, damned lies and statistics.”
Benjamin Disraeli, British Prime Minister
On the pages of our articles and especially on the forum, we often appeal to evidence-based medicine. What is evidence-based medicine?
Evidence-based medicine - the term describes an approach to medical practice in which decisions about the use of preventive, diagnostic and therapeutic measures are adopted based on the evidence obtained of their effectiveness and safety, and involves the search, comparison, synthesis and wide dissemination of the obtained evidence for use in the interests of patients.
Evidence-based medicine is a set of methodological approaches to conducting clinical research, evaluating and applying their results. In a narrow sense, “evidence-based medicine” is a method (type) medical practice, when the doctor uses only those methods in caring for the patient whose usefulness has been proven in benign studies.
To put it simply, we can say that evidence-based medicine is medicine based on methods whose effectiveness has been proven. The methodological basis of evidence-based medicine is clinical epidemiology - a science that develops clinical research methods that make it possible to make scientifically based conclusions, minimizing the impact of systematic and random errors on research results. And here comes the most main question- what is the criterion for benign research? We will talk about some signs of benign studies in this article.
The main tool of clinical epidemiology is statistics. Statistics, the science that studies techniques for systematic observation of mass phenomena social life of man, the compilation of their numerical descriptions and the scientific processing of these descriptions. It is with the help of biomedical statistics that all the results of any biological and medical research are described and presented to the reader in the form of numbers, tables, graphs, and histograms. And here the main thing is not to fall under the spell of numbers.
Quality of the control groupIf we are talking about percentages, which are often used to describe results, because... they are very indicative, you need to clearly understand what is the starting point, i.e. which is taken as 0%. That is, when they tell you “20% higher,” you immediately ask “compared to what?” If a drug (medicine, cosmetic) is being studied, then you need to know that control groups that did not take this drug at all are long gone. The study must be conducted using a placebo. Placebo is a physiologically inert substance used as a drug, positive healing effect which is associated with the patient’s unconscious psychological expectation. A placebo is not able to act directly on the conditions that the drug is being studied to change. In addition, the term “placebo effect” refers to the very phenomenon of non-drug effects, not only the drug, but, for example, radiation (sometimes various “flashing” devices, “laser therapy”, etc. are used). Lactose is often used as a placebo substance. The degree of manifestation of the placebo effect depends on the suggestibility of the person and the external circumstances of the “treatment”, for example, on the size and bright color pills, the degree of trust in the doctor, the authority of the clinic. And of course, studies in which the drug under investigation is compared with its predecessor or similar competitors cannot be seriously considered.
Evidence of the studyIt is also important to find out what type of research is carried out, which can be found out from the structure of this work. Each type has its own evidentiary weight, according to which a hierarchy of their evidence can be drawn up (listed in ascending order of evidence):
1) description of individual cases;
2) description of a series of cases;
3) retrospective case-control study;
4) analytical one-time study;
5) prospective cohort (population) study;
6) randomized controlled trial of medical interventions (methods of treatment, prevention);
7) meta-analysis - a synthesis of the results of several randomized clinical trials.
Let us give a brief description of the various types of research structure.
Descriptions of individual cases - the most old way medical research. It consists of describing a rare observation, a “classical” case (“classical” cases, by the way, are never frequent) or a new phenomenon. Scientific hypotheses in such research are not put forward or tested. However this method research is also important in medicine, since the description of rare cases or phenomena cannot be underestimated.
A case series is a study that usually includes descriptive statistics of a group of patients selected according to some characteristic. Descriptive studies are used, for example, in epidemiology to study the influence of uncontrollable factors on the occurrence of a disease.
A case-control study is a retrospective study in which, based on archival data or a survey of its participants, groups of these participants (patients) with and without a certain disease are formed, and then the frequency of exposure to a putative risk factor or cause of the disease is retrospectively assessed. Such studies often advance scientific hypotheses rather than test them. The advantage of this type of research is its relative simplicity, low cost and speed of implementation. However, case-control studies are subject to many possible systematic errors (biases). The most significant of them can be considered systematic errors associated with the selection of study participants and systematic errors that arise during measurement.
A cross-sectional study is a descriptive study that includes groups of participants examined once and is conducted to assess the prevalence of a particular outcome, the course of the disease, and the effectiveness of diagnosis. Such studies are relatively simple and inexpensive. The main problem is the difficulty of forming a sample that adequately reflects the typical situation in the studied patient population (representative sample).
Prospective (cohort, longitudinal) study - a study in which a selected cohort of participants is observed for a certain time. First, a cohort (or two cohorts, for example those exposed to a risk factor and those not exposed to it) is identified, and then it (them) is observed and data is collected. This is in contrast to a retrospective study, in which cohorts are identified after data collection. This type of research is used to identify risk factors, prognostic factors, causes of diseases, and to determine the level of morbidity. Prospective studies are very labor-intensive, as they must be carried out over a long period of time, the cohorts must be quite large due to the fact that the events detected (for example, the occurrence of new cases of the disease) are quite rare.
The main problems encountered when conducting a prospective study are:
- the probability of the events being studied depends on the method of sampling (cohorts; for example, observed participants from a risk group are more likely to get sick than participants from an unorganized population);
- when participants drop out during the study, it is necessary to find out whether this is related to the outcome or factor being studied;
- over time, the strength and nature of the influence of the factor being studied may change (for example, the intensity of smoking as a risk factor for the development of coronary disease
- it is necessary to achieve the same volume of examination of the exposure and control groups in order to minimize the possibility of earlier detection of diseases (and therefore a better prognosis) in a more thoroughly examined group.
A randomized trial is a dynamic study of any preventive, diagnostic or therapeutic effects, in which groups are formed by randomly distributing study objects into groups (randomization). The most well-known variant of a randomized study is a clinical trial. A clinical trial is a prospective comparative study of the effectiveness of two or more interventions (therapeutic, preventive) or a diagnostic method, in which groups of subjects are formed using randomization, taking into account inclusion and exclusion criteria. In this case, there is usually a hypothesis that arose before the study regarding the effectiveness of the methods being tested, which is tested during the test.
Meta-analysis is a quantitative analysis of the pooled results of several clinical trials of the same intervention for the same disease. This approach provides greater statistical sensitivity (power) than in each individual study by increasing the sample size. Meta-analysis is used to summarize the results of many trials, often contradicting each other.
Clinical effectivenessWhen reading scientific and medical articles, you need to understand for yourself exactly what characteristics were measured during the research process - clinical or biological (biochemical, physiological, genetic, etc.). Let us give one small example about a study of the use of halothane and morphine in open-heart surgery.
Halothane is a drug widely used in general anesthesia. It is strong, easy to use and very reliable. Halothane is a gas that can be administered through a respirator. Entering the body through the lungs, halothane acts quickly and for a short time; therefore, by adjusting the supply of the drug, anesthesia can be quickly controlled. However, halothane has a significant drawback - it inhibits myocardial contractility
and dilates the veins, which leads to a drop in blood pressure (BP). In this regard, it was proposed to use morphine, which does not lower blood pressure, instead of halothane for general anesthesia. Conahan et al. compared halothane and morphine anesthesia in patients undergoing open heart surgery.The study included patients who had no contraindications to either halothane or morphine. The anesthesia method (halothane or morphine) was randomly selected.
The study included 122 patients. Half of the patients used halothane (group 1), and half used morphine (group 2). On average, in patients receiving halothane, the minimum blood pressure was 6.3 mm Hg. Art. lower than in patients receiving morphine. The spread of values is quite large, and the ranges of values overlap greatly. The standard deviation in the halothane group was 12.2 mmHg. Art. in the morphine group - 14.4 mmHg. Art. Statistical analysis showed that the difference was statistically significant, so it can be concluded that morphine lowers blood pressure to a lesser extent than halothane.
As you may recall, Conahan et al. were based on the assumption that morphine depresses blood circulation to a lesser extent than halothane and is therefore preferable for general anesthesia. Indeed, when using morphine, blood pressure and cardiac index were higher than when using halothane, and these differences were statistically significant. However, it is too early to draw conclusions - after all, the differences in operational mortality have not yet been analyzed, and this indicator is the most significant from a practical point of view.
So, among those receiving halothane (group 1), 8 patients out of 61 (13.1%) died, and among those receiving morphine (group 2) - 10 out of 67 (14.9%). The difference is 1.8%. Statistical analysis showed that the difference is statistically insignificant. Therefore, although halothane and morphine act differently on the blood circulation, there is no reason to talk about a difference in surgical lethality. In fact, it can be said that the clinical effects of these two drugs are no different.
This example is very instructive: we have seen how important it is to take into account the outcome of the flow. The body is complex, the effect of any drug is diverse. If the drug has a positive effect on the cardiovascular system, it is possible that it has a negative effect, for example, on the respiratory system. It is difficult to predict which effect will prevail and how this will affect the final result. That is why the effect of a drug on any indicator, be it blood pressure or cardiac index, cannot be considered evidence of its effectiveness until clinical effectiveness has been proven. In other words, we should clearly distinguish between process indicators - all kinds of changes in biochemical, physiological and other parameters that we believe play a positive or negative role - and outcome indicators that have real clinical significance. Thus, changes in blood pressure and cardiac index under the influence of halothane and morphine are process indicators that did not in any way affect the outcome indicator - operational mortality. If we were content with observing process indicators, we would conclude that morphine is better than halothane, although, as it turned out, the choice of anesthetic does not affect mortality at all.
When reading medical publications or listening to the arguments of a supporter of a particular treatment method, you should first of all understand what indicators we are talking about - the process or the result. It is much easier to demonstrate the impact of a certain factor on a process than to find out whether it affects the result. Recording process indicators is usually simple and does not take much time. On the contrary, finding out the result, as a rule, requires painstaking long-term work and is often associated with subjective measurement problems, especially when it comes to quality of life. And yet, when deciding whether the proposed treatment method is necessary, you need to make sure that it has a positive effect on the outcome indicators. Believe me, the patient and his family are primarily concerned with the result, not the process.
ReferencesQuite often, the results of studies that evaluate the effectiveness of the same therapeutic or preventive intervention or diagnostic method for the same disease differ. In this regard, there is a need for a relative assessment of the results of different studies and integration of their results in order to obtain a general conclusion. One of the most popular and rapidly developing methods for system integration of the results of individual scientific research today refers to the technique of meta-analysis.
Meta-analysis is a quantitative analysis of the combined results of ecological and epidemiological studies assessing the impact of the same environmental factor. It involves quantifying the degree of agreement or discrepancy between results obtained across studies.
Introduction
In accordance with the concept of evidence-based medicine, the results of only those clinical studies that are conducted on the basis of the principles of clinical epidemiology, allowing to minimize both systematic errors and random errors (using correct statistical analysis of the data obtained in the study), are recognized as scientifically valid.
The International Epidemiological Association characterizes this type of research as a technique of “combining the results of various scientific works, consisting of a qualitative component (for example, the use of predetermined criteria for inclusion in the analysis, such as completeness of data, absence of obvious shortcomings in the organization of the study, etc.) and a quantitative component (statistical processing of available data)” - meta-analysis technique.
The first meta-analysis in science was conducted by Karl Pearson in 1904. By bringing together studies, he decided to overcome the problem of reduced study power in small samples. By analyzing the results of these studies, he found that meta-analysis could help obtain more accurate research data.
Despite the fact that meta-analysis is now widely used in the field of epidemiology and medical research. Studies using meta-analysis did not appear until 1955. In the 1970s, more sophisticated analytical methods were introduced into educational research by the work of Gene V. Glass, Frank L. Schmidt and John E. Hunter.
The Oxford English Dictionary tells us that the first use of the term was in 1976 by Glass. The basis of this method was developed by such scientists as: Nambury S. Raju, Larry V. Hedges, Harris Cooper, Ingram Olkin, John E. Hunter, Jacob Cohen, Thomas C. Chalmers, and Frank L. Schmidt).
Meta-analysis: a quantitative approach to research
The purpose of meta-analysis is to identify, examine, and explain differences (due to the presence of statistical heterogeneity, or heterogeneity) in the results of studies.
The undoubted advantages of meta-analysis include the possibility of increasing the statistical power of the study, and, consequently, the accuracy of assessing the effect of the analyzed intervention. This allows us to more accurately determine the categories of patients for whom the results obtained are applicable than when analyzing each individual small clinical trial.
A correctly performed meta-analysis involves testing a scientific hypothesis, a detailed and clear presentation of the statistical methods used in the meta-analysis, a sufficiently detailed presentation and discussion of the results of the analysis, as well as the conclusions arising from it. This approach reduces the likelihood of random and systematic errors and allows us to talk about the objectivity of the results obtained.
Approaches to performing meta-analysis
There are two main approaches to performing meta-analysis.
The first is to statistically reanalyze individual studies by collecting primary data from the observations included in the original studies. Obviously, this operation is not always possible.
The second (and main) approach is to summarize published research results on a single problem. Such a meta-analysis is usually performed in several stages, among which the most important are:
· development of criteria for inclusion of original studies in meta-analysis
· assessment of heterogeneity (statistical heterogeneity) of the results of original studies
Conducting the actual meta-analysis (obtaining a generalized estimate of the effect size)
· sensitivity analysis of findings
It should be noted that the stage of determining the range of studies included in a meta-analysis often becomes a source of systematic errors in the meta-analysis. The quality of a meta-analysis depends significantly on the quality of the original studies and articles included.
The main problems when including studies in a meta-analysis include differences between studies in terms of inclusion and exclusion criteria, study design, and quality control.
There is also publication bias positive results research (studies that produce statistically significant results are more likely to be published than those that do not).
Because the meta-analysis is based primarily on published data, special attention should be paid to the underrepresentation of negative results in the literature. Including unpublished results in a meta-analysis also poses a significant challenge, as their quality is unknown due to the fact that they have not been peer-reviewed.
Basic methodsThe choice of analysis method is determined by the type of data being analyzed (binary or continuous) and the type of model (fixed effects, random effects).
Binary data are typically analyzed by calculating the odds ratio (OR), relative risk (RR), or risk difference in matched samples. All of the above indicators characterize the effect of interventions. Presentation of binary data in the form of OR is convenient for use in statistical analysis, but this indicator is quite difficult to interpret clinically. Continuous data are usually ranges of values for the characteristics being studied or unstandardized differences in weighted means across comparison groups if the outcomes were measured in the same way across studies. If the outcomes were assessed differently (for example, on different scales), then the standardized difference in means (the so-called effect size) in the compared groups is used.
One of the first steps of a meta-analysis is to assess the heterogeneity (statistical heterogeneity) of the results of the intervention effect across studies.
To assess heterogeneity, χ2 tests are often used with the null hypothesis of an equal effect in all studies and with a significance level of 0.1 to increase the statistical power (sensitivity) of the test.
The sources of heterogeneity in the results of different studies are considered to be dispersion within studies (caused by random deviations of the results of different studies from a single true fixed effect value), as well as dispersion between studies (caused by differences between the studied samples in the characteristics of patients, diseases, interventions, leading to slightly different effect values - random effects).
If the variance between studies is assumed to be close to zero, then each study is assigned a weight that is inversely proportional to the variance of the result of that study.
The variance within studies is in turn defined as
Where μ - average within studies. If there is zero variance between studies, a fixed (constant) effects model can be used. In this case, it is assumed that the intervention being studied has the same effectiveness across all studies, and the differences found between studies are due only to within-study variance. This model uses the Mantel-Haenszel method.
Mantel-Haenszel method
The table shows the proportions of patients in New York and London who were diagnosed with schizophrenia.
— weighted average of individual odds ratios by group. The Mantel-Haenszel chi-square test for testing the significance of the overall measure of association is based on the weighted average g of the differences between the proportions.
The Mantel-Haenszel chi-square statistic is given by
with 1 degree of freedom.
For a statistic to have a chi-square distribution with 1 degree of freedom, each of the four sums of expected frequencies
must differ by at least 5 from both its minimum and its maximum.
This means that in order to confidently use the chi-square distribution with 1 degree of freedom for statistics, it is not at all necessary to have large marginal frequencies. The number of observations in the table can even be two, as in the case of related pairs. The only thing needed is a sufficiently large number of tables so that each sum of expected frequencies is large.
Other approaches to performing meta-analysis
The random effects model suggests that the effectiveness of the intervention being studied may vary across studies.
This model takes into account variance not only within one study, but also between different studies. In this case, the variance within studies and the variance between studies are summed. The purpose of meta-analyses of continuous data is usually to provide point and interval (95% CI) estimates of the pooled effect of an intervention.
There are also a number of other approaches to performing meta-analysis: Bayesian meta-analysis, cumulative meta-analysis, multivariate meta-analysis, survival meta-analysis.
Bayesian meta-analysis allows one to calculate prior probabilities of an intervention's effectiveness given indirect evidence. This approach is especially effective when the number of studies analyzed is small. It provides a more precise estimate of the effectiveness of an intervention in a random effects model by accounting for between-study variance.
Cumulative meta-analysis - special case Bayesian meta-analysis - a step-by-step procedure for including the results of studies in a meta-analysis one at a time in accordance with some principle (in chronological order, in descending order of methodological quality of the study, etc.). It allows you to calculate prior and posterior probabilities in an iterative manner as studies are included in the analysis.
Regression meta-analysis (logistic regression, weighted least squares regression, Cox model, etc.) is used when there is significant heterogeneity in research results. It allows us to account for the influence of several study characteristics (eg, sample size, drug dose, route of administration, patient characteristics, etc.) on the results of intervention trials. The results of a regression meta-analysis are usually presented as a slope coefficient with a CI.
It should be noted that meta-analyses can be performed to summarize the results not only of controlled trials of medical interventions, but also of cohort studies (eg, risk factor studies). However, one should take into account the high probability of systematic errors.
A special type of meta-analysis is a generalization of estimates of the information content of diagnostic methods obtained in different studies. The purpose of such a meta-analysis is to construct a characteristic curve of the relationship between the sensitivity and specificity of tests (ROC curve) using a weighted linear regression.
Sustainability. After obtaining a generalized estimate of the effect size, it becomes necessary to determine its stability. For this purpose, a so-called sensitivity analysis is performed.
Depending on the specific situation, it can be carried out on the basis of several various methods, For example:
· Inclusion and exclusion from meta-analysis of studies performed at a low methodological level
· Changes in data parameters selected from each study analyzed, for example if any studies report clinical outcomes in the first 2 weeks. disease, and in other studies - about clinical outcomes in the first 3-4 weeks. diseases, then it is permissible to compare clinical outcomes not only for each of these observation periods, but also for a total observation period of up to 4 weeks.
· Exclusion of the largest studies from meta-analysis. If the effect size of a particular intervention under analysis does not change significantly in a sensitivity analysis, then there is reason to believe that the conclusions of the primary meta-analysis are sufficiently justified.
To qualitatively assess the presence of such a systematic error in a meta-analysis, they usually resort to constructing a funnel-shaped scatter plot of the results of individual studies in coordinates (effect size, sample size). When studies are fully identified, this diagram should be symmetrical. At the same time, there are also formal methods for assessing existing asymmetry.
The results of a meta-analysis are usually presented graphically (point and interval estimates of the effect sizes of each of the studies included in the meta-analysis; example in Fig. 1) and in the form of tables with the corresponding statistics.
Conclusion
Currently, meta-analysis is a dynamic, multidimensional system of methods that allows one to combine data from various scientific studies in a theoretically and methodologically convincing way.
Meta-analysis, compared to primary research, requires relatively few resources, which allows clinicians not involved in the research to obtain clinically proven information.
The main condition for using meta-analysis is the availability of the necessary information about the statistical criteria used in the studies reviewed. Without publications reporting the exact values of the required information, the prospects for applying meta-analysis will be very limited. As the availability of such information increases, there will continue to be a real expansion of meta-analytic research and improvement in its methodology.
Thus, a carefully performed meta-analysis may identify areas requiring further research.
List of used literature:
Taldau mete bul dalel darigerliktin aspaby
Turdalieva B.S., Rakhmatullaeva N.U., Ten V.B., Raushanova A.M.,
Musaeva B.A., Omarova D.B.
Asfendiyarov S.Zh. Atyndagi KAZMU
Daleldi medicine ortalygy
Almaty, Kazakhstan
Tuiyn Bir aura boyinsha bagalangan zertteu natizheleri ylgi bir emdik, aldyn alu nemese diagnosticalyk adistin tiimdiligi zhii zhetkilikti ozgeshelenedi.
Әrtүrli zertteulerdiң nәtizhelerinin salystyrmaly bagasy zhane olardyn zhalpylauysh korytyndynynin nәtizheleri osygan baylanysty paida bolatyn kazhettilik kiriguin maksaty.
En әigili zhane zheke gylymi zertteulerdin natizhelerin zhuyelik kiriguiin zhyldam damityn adistemelerin birine bugin meta - taldau adistema jatada.
Meta - taldau - bul ecologtin epidemiology zertteuler birikken natizhelerinin sandyk taldauy - korshagan ortanyin ylgi bir factorin aserinin bagasy. Ol kelisushiliktin dәrezhesi nemese әartүrli zertteu algan natizhelerdin ayyrmashylygynyn sandyk bagasyn eskeredi.
A meta-analysis as a tool for evidence-based medicine
Turdalieva B.S., Rakhmatullayeva N.U., Ten V.B., Raushanova A.M.,
Musaeva B.A., Omarova D.B.
KazNMU of S.D.Asfendiyarov, Almaty, Kazakhstan
Abstract Quite often, the results of studies that evaluate the effectiveness of the same therapeutic or preventive intervention or a diagnostic method for the same disease are different.
