Deborah J. Cook

Bio: Deborah J. Cook is an academic researcher from McMaster University. The author has contributed to research in topics: Intensive care & Intensive care unit. The author has an hindex of 173, co-authored 907 publications receiving 148928 citations. Previous affiliations of Deborah J. Cook include McMaster University Medical Centre & Queen's University.

Withholding and withdrawing life support in critical care settings: ethical issues concerning consent

Abstract: The right to refuse medical intervention is well established, but it remains unclear how best to respect and exercise this right in life support. Contemporary ethical guidelines for critical care give ambiguous advice, largely because they focus on the moral equivalence of withdrawing and withholding care without confronting the very real differences regarding who is aware and informed of intervention options and how patient values are communicated and enacted. In withholding care, doctors typically withhold information about interventions judged too futile to offer. They thus retain greater decision-making burden (and power) and face weaker obligations to secure consent from patients or proxies. In withdrawing care, there is a clearer imperative for the doctor to include patients (or proxies) in decisions, share information and secure consent, even when continued life support is deemed futile. How decisions to withhold and withdraw life support differ ethically in their implications for positive versus negative interpretations of patient autonomy, imperatives for consent, definitions of futility and the subjective evaluation of (and submission to) benefits and burdens of life support in critical care settings are explored. Professional reflection is required to respond to trends favouring a more positive interpretation of patient autonomy in the context of life support decisions in critical care. Both the bioethics and critical care communities should investigate the possibilities and limits of growing pressure for doctors to disclose their reasoning or seek patient consent when decisions to withhold life support are made.

How to use diagnostic test articles in the intensive care unit: diagnosing weanability using f/Vt.

Abstract: Medical diagnosis involves generating a set of hypotheses and obtaining information that modifies these hypotheses. Sources of this information include the history, physical examination, and laboratory investigations, all of which function as diagnostic tests. Studies of diagnostic tests are useful when a) the population under study is representative of those to whom we would like to apply the results; b) when an independent, blind comparison is made of the test results with a reference standard; and c) when the reference standard is performed on all patients, rather than restricted to those patients with particular test results. Clinicians can use the data from such high quality studies in the form of sensitivity and specificity, as well as likelihood ratios, which indicate the direction and magnitude of the change in probability of a target condition from pretest to posttest. Study results will be more easily applicable to practice when the performance and interpretation of the test is similar in study and clinical settings. We conduct diagnostic tests primarily to improve the process of patient care and patient outcome, and test ordering behavior ideally reflects these goals.

Somatostatin treatment for cryptosporidial diarrhea in a patient with the acquired immunodeficiency syndrome (AIDS).

Abstract: Excerpt In healthy persons, the coccidioidal protozoan,Cryptosporidium, can inhabit the microvillous border of intestinal epithelial cells and cause an acute self-limited diarrhea. In contrast, the...

Patient autonomy versus parentalism.

Abstract: Several decision-making models have been advanced to characterize the patient-physician relationship. These include the parental model, the informed decision-making model, the interpretive model, and the deliberative model. Although these models are useful to consider in reflecting on the care we gi

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

Abstract: David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.

Abstract: Systematic reviews and meta-analyses have become increasingly important in health care. Clinicians read them to keep up to date with their field,1,2 and they are often used as a starting point for developing clinical practice guidelines. Granting agencies may require a systematic review to ensure there is justification for further research,3 and some health care journals are moving in this direction.4 As with all research, the value of a systematic review depends on what was done, what was found, and the clarity of reporting. As with other publications, the reporting quality of systematic reviews varies, limiting readers' ability to assess the strengths and weaknesses of those reviews. Several early studies evaluated the quality of review reports. In 1987, Mulrow examined 50 review articles published in 4 leading medical journals in 1985 and 1986 and found that none met all 8 explicit scientific criteria, such as a quality assessment of included studies.5 In 1987, Sacks and colleagues6 evaluated the adequacy of reporting of 83 meta-analyses on 23 characteristics in 6 domains. Reporting was generally poor; between 1 and 14 characteristics were adequately reported (mean = 7.7; standard deviation = 2.7). A 1996 update of this study found little improvement.7 In 1996, to address the suboptimal reporting of meta-analyses, an international group developed a guidance called the QUOROM Statement (QUality Of Reporting Of Meta-analyses), which focused on the reporting of meta-analyses of randomized controlled trials.8 In this article, we summarize a revision of these guidelines, renamed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses), which have been updated to address several conceptual and practical advances in the science of systematic reviews (Box 1). Box 1 Conceptual issues in the evolution from QUOROM to PRISMA

Measuring inconsistency in meta-analyses

Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …