Boken presenterer en helhetlig strategi for hvordan myndigheter, helsepersonell, industri og forbrukere kan redusere medisinske feil.

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To Err Is Human Building a Safer Health System

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

EASL-EORTC clinical practice guidelines : management of hepatocellular carcinoma

Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.

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Progressive Statistics for Studies in Sports Medicine and Exercise Science

tion’, implying that most patients ‘should’ receive a particular action. In contrast, level 2 guidelines are essentially ‘suggestions’ and are deemed to be ‘weak’ or discretionary, recognising that management decisions may vary in different clinical contexts. Each recommendation was further graded from A to D by the quality of evidence underpinning them, with grade A referring to a high quality of evidence whilst grade D recognised a ‘very low’ evidence base. The overall strength and quality of the supporting evidence is summarised in table 1 . The guidelines focused on 4 key domains: (1) AKI definition, (2) prevention and treatment of AKI, (3) contrastinduced AKI (CI-AKI) and (4) dialysis interventions for the treatment of AKI. The full summary of clinical practice statements is available at www.kdigo.org, but a few key recommendation statements will be highlighted here.

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KDIGO clinical practice guidelines for acute kidney injury.

When more than one statistical test is performed in analysing the data from a clinical study, some statisticians and journal editors demand that a more stringent criterion be used for “statistical significance” than the conventional P<0051 Many well meaning researchers, eager for methodological rigour, comply without fully grasping what is at stake Recently, adjustments for multiple tests (or Bonferroni adjustments) have found their way into introductory texts on medical statistics, which has increased their apparent legitimacy This paper advances the view, widely held by epidemiologists, that Bonferroni adjustments are, at best, unnecessary and, at worst, deleterious to sound statistical inference

#### Summary points

Adjusting statistical significance for the number of tests that have been performed on study data—the Bonferroni method—creates more problems than it solves

The Bonferroni method is concerned with the general null hypothesis (that all null hypotheses are true simultaneously), which is rarely of interest or use to researchers

The main weakness is that the interpretation of a finding depends on the number of other tests performed

The likelihood of type II errors is also increased, so that truly important differences are deemed non-significant

Simply describing what tests of significance have been performed, and why, is generally the best way of dealing with multiple comparisons

Bonferroni adjustments are based on the following reasoning1-3 If a null hypothesis is true (for instance, two treatment groups in a randomised trial do not differ in terms of cure rates), a significant difference (P<005) will be observed by chance once in 20 trials This is the type I error, or α When 20 independent tests are performed (for example, study groups are compared with regard to 20 unrelated variables) and the null hypothesis holds for all 20 comparisons, the chance of at least one test being significant is no longer 005, but 064 …

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What's wrong with Bonferroni adjustments

Effectiveness of a hospital-wide programme to improve compliance with hand hygiene

Transmission of microorganisms from the hands of health care workers is the main source of cross-infection in hospitals and can be prevented by handwashing. In this study, compliance with handwashi...

http://sofos.scsalud.es/sofosdocs/HM6/articulos/a07.pdf

Compliance with handwashing in a teaching hospital. Infection Control Program.

The authors questioned physicians on their beliefs about hand-washing before examining a patient and observed their actual hand-washing practices. Predictors of adherence include being an internist...

/pdf/hand-hygiene-among-physicians-performance-beliefs-and-3rhqar04hy.pdf

Hand Hygiene among Physicians: Performance, Beliefs, and Perceptions

Objective To develop a simple standardized clinical score to stratify emergency ward patients with clinically suspected pulmonary embolism (PE) into groups with a high, intermediate, or low probability of PE to improve and simplify the diagnostic approach. Methods Analysis of a database of 1090 consecutive patients admitted to the emergency ward for suspected PE in whom diagnosis of PE was ruled in or out by a standard diagnostic algorithm. Logistic regression was used to predict clinical parameters associated with PE. Results A total of 296 (27%) of 1090 patients were found to have PE. The optimal estimate of clinical probability was based on 8 variables: recent surgery, previous thromboembolic event, older age, hypocapnia, hypoxemia, tachycardia, band atelectasis, or elevation of a hemidiaphragm on chest x-ray film. A probability score was calculated by adding points assigned to these variables. A cutoff score of 4 best identified patients with low probability of PE. A total of 486 patients (49%) had a low clinical probability of PE (score ≤4), of which 50 (10.3%) had a proven PE. The prevalence of PE was 38% in the 437 patients with an intermediate probability (score of 5-8; n = 437) and 81% in the 63 patients with a high probability (score ≥9). Conclusions This clinical score, based on easily available and objective variables, provides a standardized assessment of the clinical probability of PE. Applying this score to emergency ward patients suspected of having PE could allow a more effective diagnostic process.

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Thomas V. Perneger

Papers

What's wrong with Bonferroni adjustments

Effectiveness of a hospital-wide programme to improve compliance with hand hygiene

Compliance with handwashing in a teaching hospital. Infection Control Program.

Hand Hygiene among Physicians: Performance, Beliefs, and Perceptions

Assessing clinical probability of pulmonary embolism in the emergency ward: a simple score.