Evidence–based Neonatology (2006)

After completing this article, readers should be able to: From the time physicians enter medical school to the time they retire from medicine, they are guided by the principle, “Primum Non Nocere,” first do no harm. Although the origins of that phrase are uncertain, it has been widely attributed to Hippocrates who said, “I will prescribe regimens for the good of my patients according to my ability and my judgment and never do harm to anyone.” Fundamental to this concept of being a good physician is the acquisition of medical knowledge to provide the highest quality care. Unlike the practice of neonatology 20 years ago, clinicians today are bombarded with incredible amounts of advice and information (scientific meetings, lectures, journals, peers, and the Internet) and ultimately must decide when and if to incorporate a change into clinical practice. In the 1990s, the phrase “evidence-based medicine” became the watchword for the most reliable source of new information. Meta-analyses often are considered the gold standard because an investigator has taken the time to review a topic critically and used a statistical test to confirm or refute the hypothesis. Although meta-analyses are important, they are an imperfect tool and represent only one of several sources of information for the practicing neonatologist. This review critically examines the strengths and weaknesses of new information and provides a primer on commonly used statistical methods in evidence-based medicine. Table 1 lists common definitions for terms used in this article.If there is a challenge for practicing clinicians in the new millennium, it is filtering and assimilating the enormous amount of information received on a daily basis. New therapies are constantly being recommended for neonates who have a wide variety of problems. Such recommendations range from changes in care practices to prevent physiologic derangements (eg, covering preterm infants in the delivery room to decrease heat loss) to new therapies for life-threatening or disabling conditions (eg, probiotics to prevent necrotizing enterocolitis). Clinicians, however, should be cautious before incorporating a new treatment into their standard practice. For example, in the early 1950s, oxygen was administered freely to treat a variety of conditions, ranging from apnea to prematurity to birth asphyxia. Oxygen was recommended for apnea because of “observations” in the 1940s that supplemental oxygen could improve periodic breathing. Thousands of preterm infants were maintained on incubators containing more than 50% oxygen, resulting in an increase in retinopathy of prematurity (ROP). That practice was discontinued in 1954 after the first multicenter, randomized clinical trial in neonatal medicine demonstrated that curtailment of this practice reduced the incidence of cicatricial ROP. Unfortunately, the practice of restricting supplemental oxygen to less than 40% was adopted (but not tested) and led to an increase in neonatal mortality and spastic diplegia. It is concerning that neonatologists now are faced with the decision of whether to limit the amount of oxygen used for resuscitation in term and near-term infants based on a limited amount of data on long-term outcomes.When deciding whether to incorporate a new treatment into practice, several clinical questions must be addressed:There are four common sources of new information for practitioners: 1) wise clinicians, 2) observational studies, 3) randomized clinical trials, and 4) systematic reviews. Each has advantages and disadvantages and has an important role in the educational process.Much of what most physicians know has been learned from “wise clinicians.” That process begins in medical school and extends through residency and postresidency years. A “wise clinician” needs only to be a little smarter than the person he or she is teaching. For the intern, it might mean a second- or third-year resident; for the senior resident, it might be a fellow or an attending physician. There are two major advantages to learning via this approach. First, wise clinicians can provide valuable information about the subtleties of care not evident in a clinical trial. For example, the literature says that Mycoplasma infections can be treated with erythromycin, but the wise clinician may teach that the compliance of individuals taking erythromycin is suboptimal because of abdominal discomfort. Second, learning from wise clinicians is instantaneous and only requires close proximity of the teacher and student. On the other hand, there are two important disadvantages to this form of education. Wise clinicians are not always so “wise,” and even when they know the correct information, they may not be able to convey it effectively. In addition, the spoken word is more likely to be misinterpreted, passing along erroneous “new knowledge.”In observational studies, clinical practice decisions are made by clinicians and patients based on potential or perceived risks and benefits. The health status of the recipients of the interventions then is observed. If data are not available for a comparison group, the study is descriptive. If data are collected for a comparison group not receiving the intervention of interest, the study is analytical. Analytical studies are classified further by the directionality of the study. In a cohort study, the exposure is the starting point, and exposed and unexposed (or treated and untreated) groups are followed over time to ascertain the health outcome hypothesized to be affected by the treatment. Time can be in the past (retrospective cohort study) or in the present and future (prospective cohort study). In the case-control study, directionality is reversed, and the outcome is the starting point. Subjects with the outcome of interest are identified, unaffected controls are selected, and a history of exposure to the treatment is sought in both groups. Subjects with the health outcome of interest are cases and subjects without the outcome are controls. In the cross-sectional study, the exposure and outcome are ascertained at the same time within a study population.Observational studies have an important role determining the incidence and natural course of a disease or health state and in generating hypotheses about pathogenesis or treatment response. A cause-and-effect relation can be hypothesized based on an observation of an association. This hypothesis can be tested in a randomized clinical trial (RCT). Observational analytical studies are generally less costly, more timely, and include a broader range of patients than do randomized, controlled trials. The latter aspect increases the likelihood that the study results can be generalized to the population to whom the study is purported to apply. The major disadvantage, of course, is that the nonrandom allocation of the intervention increases the likelihood of incorporating bias and confounding into apparent treatment effects.Cohort studies are the best analytical study design for determining the incidence and natural course of a disease or health state. The prospective cohort study is the most appropriate nonexperimental research design to evaluate an intervention when a randomized, controlled trial is not possible, the outcome is relatively common, and the interval between the intervention and outcome is relatively short. Furthermore, the properly designed and executed prospective cohort study is the analytical study type least susceptible to bias. The possibility of selection bias is minimized if the study is population-based, ie, the study includes all members of a population (usually restricted to a geographic area) at risk for the outcome. Controls should be contemporary. The use of historical controls in assessments of intervention is very risky. Among other problems, patients treated in the present tend to receive a variety of new interventions other than the one under investigation, and it is hard to “tease out” the singular effect of the treatment under study. A prospective design and execution of cohort studies minimizes ascertainment bias. Direct and contemporary determination of the intervention under study is preferable to data obtained from subject recall or existing medical records. Criteria for the intervention should be objective, strictly and uniformly applied, and (if possible) quantifiable. Quantification of the intervention allows possible investigation of a dose-response relationship. Finally, the outcome should be identified prospectively, and an a priori hypothesis about its relationship to the treatment should be stated. There is room, however, for studies that do not have an a priori hypothesis but are truly hypothesis-generating. Criteria for assigning outcome should be objective, reliable, and (if possible) quantifiable. If all elements of subjectivity cannot be eliminated, evaluators who are blinded to the exposure should determine outcome. When the reliability of the determination of outcome is imperfect, levels of assurance of the outcome (eg, definite, probable, suspect) should be assigned.To reduce the possibility of confounding, known and potential confounding variables should be measured and their effects appropriately controlled by the experimental design or in analysis of the data. It is not necessary for an extraneous variable to be statistically significantly associated with exposure and outcome for it to confound a relationship. More attention should be paid to the extent to which the odds ratio or relative risk is attenuated by the hypothesized confounder than to statistical the same care must be taken to for variables that are in a between the exposure and the which could an relationship between the exposure and outcome. 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