Management and prognosis of status epilepticus according to hospital setting: a prospective study.

BACKGROUND: The treatment of status epilepticus (SE) is based on relatively little evidence although several guidelines have been published. A recent study reported a worse SE prognosis in a large urban setting as compared to a peripheral hospital, postulating better management in the latter. The aim of this study was to analyse SE episodes occurring in different settings and address possible explanatory variables regarding outcome, including treatment quality. METHODS: Over six months we prospectively recorded consecutive adults with SE (fit lasting five or more minutes) at the Centre Hospitalier Universitaire Vaudois (CHUV) and in six peripheral hospitals (PH) in the same region. Demographical, historical and clinical variables were collected, including SE severity estimation (STESS score) and adherence to Swiss SE treatment guidelines. Outcome at discharge was categorised as "good" (return to baseline), or "poor" (persistent neurological sequelae or death). RESULTS: Of 54 patients (CHUV: 36; PH 18), 33% had a poor outcome. Whilst age, SE severity, percentage of SE episodes lasting less than 30 minutes and total SE duration were similar, fewer patients had a good outcome at the CHUV (61% vs 83%; OR 3.57; 95% CI 0.8-22.1). Mortality was 14% at the CHUV and 5% at the PH. Most treatments were in agreement with national guidelines, although less often in PH (78% vs 97%, P = 0.04). CONCLUSION: Although not statistically significant, we observed a slightly worse SE prognosis in a large academic centre as compared to smaller hospitals. Since SE severity was similar in the two settings but adherence to national treatment guidelines was higher in the academic centre, further investigation on the prognostic role of SE treatment and outcome determinants is required

Primary care physicians' attitude and reported prescribing behavior for chronic low back pain: An exploratory cross-sectional study.

Recent guidelines for chronic or recurrent low back pain recommend non-pharmacologic treatments as first-line options. The objective of this study was thus to explore the perceived usefulness of several conventional and complementary medicine treatments for chronic or recurrent low back pain by primary care physicians and their reported prescribing behavior. An exploratory cross-sectional study. Primary care physicians of the French-speaking part of Switzerland. Primary care physicians' perceived usefulness of each conventional and complementary medicine treatment and their reported recommendation behavior were considered dependent variables in multivariate logistic regression models. All correlations were computed between binary variables, and phi coefficients were calculated to estimate correlation strengths. 533 primary care physicians answered the questionnaire (response rate: 25.6%). The top 3 conventional treatments most often considered useful by primary care physicians for chronic or recurrent low back pain were physiotherapy (94.8%), nonsteroidal anti-inflammatory drugs (87.9%), and manual therapy (82.5%), whereas the most prescribed conventional treatments were physiotherapy (99.2%), nonsteroidal anti-inflammatory drugs (97.4%), and acetaminophen (94.4%). Osteopathic treatment (78.4%), yoga (69.3%), and therapeutic massage (63.9%) were the complementary medicine treatments most often considered useful by primary care physicians in managing chronic or recurrent low back pain. Being a female physician, younger than 56 years, trained in complementary medicine, or using complementary medicine were all associated with higher perceived usefulness of complementary medicine treatments in general. The most recommended complementary medicine treatments by primary care physicians were osteopathic treatment (87.3%), acupuncture (69.3%), and therapeutic massage (58.7%). Being a female physician, younger than 56, and using complementary medicine were all associated with more complementary medicine recommendation in general. Our results highlight the importance of better understanding the prescribing patterns of primary care physicians for chronic or recurrent low back pain. Considering the frequency and burden of chronic or recurrent low back pain, programs focusing on the most (cost-) effective treatments should be implemented

What differences are detected by superiority trials or ruled out by noninferiority trials? A cross-sectional study on a random sample of two-hundred two-arms parallel group randomized clinical trials

<p>Abstract</p> <p>Background</p> <p>The smallest difference to be detected in superiority trials or the largest difference to be ruled out in noninferiority trials is a key determinant of sample size, but little guidance exists to help researchers in their choice. The objectives were to examine the distribution of differences that researchers aim to detect in clinical trials and to verify that those differences are smaller in noninferiority compared to superiority trials.</p> <p>Methods</p> <p>Cross-sectional study based on a random sample of two hundred two-arm, parallel group superiority (100) and noninferiority (100) randomized clinical trials published between 2004 and 2009 in 27 leading medical journals. The main outcome measure was the smallest difference in favor of the new treatment to be detected (superiority trials) or largest unfavorable difference to be ruled out (noninferiority trials) used for sample size computation, expressed as standardized difference in proportions, or standardized difference in means. Student t test and analysis of variance were used.</p> <p>Results</p> <p>The differences to be detected or ruled out varied considerably from one study to the next; e.g., for superiority trials, the standardized difference in means ranged from 0.007 to 0.87, and the standardized difference in proportions from 0.04 to 1.56. On average, superiority trials were designed to detect larger differences than noninferiority trials (standardized difference in proportions: mean 0.37 versus 0.27, <it>P </it>= 0.001; standardized difference in means: 0.56 versus 0.40, <it>P </it>= 0.006). Standardized differences were lower for mortality than for other outcomes, and lower in cardiovascular trials than in other research areas.</p> <p>Conclusions</p> <p>Superiority trials are designed to detect larger differences than noninferiority trials are designed to rule out. The variability between studies is considerable and is partly explained by the type of outcome and the medical context. A more explicit and rational approach to choosing the difference to be detected or to be ruled out in clinical trials may be desirable.</p

What differences are detected by superiority trials or ruled out by noninferiority trials? A cross-sectional study on a random sample of two-hundred two-arms parallel group randomized clinical trials

BACKGROUND: The smallest difference to be detected in superiority trials or the largest difference to be ruled out in noninferiority trials is a key determinant of sample size, but little guidance exists to help researchers in their choice. The objectives were to examine the distribution of differences that researchers aim to detect in clinical trials and to verify that those differences are smaller in noninferiority compared to superiority trials. METHODS: Cross-sectional study based on a random sample of two hundred two-arm, parallel group superiority (100) and noninferiority (100) randomized clinical trials published between 2004 and 2009 in 27 leading medical journals. The main outcome measure was the smallest difference in favor of the new treatment to be detected (superiority trials) or largest unfavorable difference to be ruled out (noninferiority trials) used for sample size computation, expressed as standardized difference in proportions, or standardized difference in means. Student t test and analysis of variance were used. RESULTS: The differences to be detected or ruled out varied considerably from one study to the next; e.g., for superiority trials, the standardized difference in means ranged from 0.007 to 0.87, and the standardized difference in proportions from 0.04 to 1.56. On average, superiority trials were designed to detect larger differences than noninferiority trials (standardized difference in proportions: mean 0.37 versus 0.27, P = 0.001; standardized difference in means: 0.56 versus 0.40, P = 0.006). Standardized differences were lower for mortality than for other outcomes, and lower in cardiovascular trials than in other research areas. CONCLUSIONS: Superiority trials are designed to detect larger differences than noninferiority trials are designed to rule out. The variability between studies is considerable and is partly explained by the type of outcome and the medical context. A more explicit and rational approach to choosing the difference to be detected or to be ruled out in clinical trials may be desirable

Prevalence, characteristics, and publication of discontinued randomized trials.

IMPORTANCE: The discontinuation of randomized clinical trials (RCTs) raises ethical concerns and often wastes scarce research resources. The epidemiology of discontinued RCTs, however, remains unclear. OBJECTIVES: To determine the prevalence, characteristics, and publication history of discontinued RCTs and to investigate factors associated with RCT discontinuation due to poor recruitment and with nonpublication. DESIGN AND SETTING: Retrospective cohort of RCTs based on archived protocols approved by 6 research ethics committees in Switzerland, Germany, and Canada between 2000 and 2003. We recorded trial characteristics and planned recruitment from included protocols. Last follow-up of RCTs was April 27, 2013. MAIN OUTCOMES AND MEASURES: Completion status, reported reasons for discontinuation, and publication status of RCTs as determined by correspondence with the research ethics committees, literature searches, and investigator surveys. RESULTS: After a median follow-up of 11.6 years (range, 8.8-12.6 years), 253 of 1017 included RCTs were discontinued (24.9% [95% CI, 22.3%-27.6%]). Only 96 of 253 discontinuations (37.9% [95% CI, 32.0%-44.3%]) were reported to ethics committees. The most frequent reason for discontinuation was poor recruitment (101/1017; 9.9% [95% CI, 8.2%-12.0%]). In multivariable analysis, industry sponsorship vs investigator sponsorship (8.4% vs 26.5%; odds ratio [OR], 0.25 [95% CI, 0.15-0.43]; P &lt; .001) and a larger planned sample size in increments of 100 (-0.7%; OR, 0.96 [95% CI, 0.92-1.00]; P = .04) were associated with lower rates of discontinuation due to poor recruitment. Discontinued trials were more likely to remain unpublished than completed trials (55.1% vs 33.6%; OR, 3.19 [95% CI, 2.29-4.43]; P &lt; .001). CONCLUSIONS AND RELEVANCE: In this sample of trials based on RCT protocols from 6 research ethics committees, discontinuation was common, with poor recruitment being the most frequently reported reason. Greater efforts are needed to ensure the reporting of trial discontinuation to research ethics committees and the publication of results of discontinued trials

Higher occurrence of nausea and vomiting after total hip arthroplasty using general versus spinal anesthesia: an observational study.

BACKGROUND: Under the assumption that postoperative nausea and vomiting (PONV) may occur after total hip arthroplasty (THA) regardless of the anesthetic technique used, it is not clear whether general (GA) or spinal (SA) anesthesia has higher causal effect on this occurrence. Conflicting results have been reported. METHODS: In this observational study, we selected all elective THA interventions performed in adults between 1999 and 2008 in a Swiss orthopedic clinic under general or spinal anesthesia. To assess the effect of anesthesia type on the occurrence of PONV, we used the propensity score and matching methods, which allowed us to emulate the design and results of an RCT. RESULTS: Among 3922 procedures, 1984 (51 %) patients underwent GA, of which 4.1 % experienced PONV, and 1938 underwent SA, of which 3.5 % experienced PONV. We found that the average treatment effect on the treated, i.e. the effect of anesthesia type for a sample of individuals that actually received spinal anesthesia compared to individuals who received GA, was ATET = 2.00 % [95 % CI, 0.78-3.19 %], which translated into an OR = 1.97 [95 % CI 1.35; 2.87]. CONCLUSION: This suggests that the type of anesthesia is not neutral regarding PONV, general anesthesia being more strongly associated with PONV than spinal anesthesia in orthopedic surgery

Ruling out coronary heart disease in primary care patients with chest pain: a clinical prediction score

Chest pain raises concern for the possibility of coronary heart disease. Scoring methods have been developed to identify coronary heart disease in emergency settings, but not in primary care. Data were collected from a multicenter Swiss clinical cohort study including 672 consecutive patients with chest pain, who had visited one of 59 family practitioners' offices. Using delayed diagnosis we derived a prediction rule to rule out coronary heart disease by means of a logistic regression model. Known cardiovascular risk factors, pain characteristics, and physical signs associated with coronary heart disease were explored to develop a clinical score. Patients diagnosed with angina or acute myocardial infarction within the year following their initial visit comprised the coronary heart disease group. The coronary heart disease score was derived from eight variables: age, gender, duration of chest pain from 1 to 60 minutes, substernal chest pain location, pain increasing with exertion, absence of tenderness point at palpation, cardiovascular risks factors, and personal history of cardiovascular disease. Area under the receiver operating characteristics curve was of 0.95 with a 95% confidence interval of 0.92; 0.97. From this score, 413 patients were considered as low risk for values of percentile 5 of the coronary heart disease patients. Internal validity was confirmed by bootstrapping. External validation using data from a German cohort (Marburg, n = 774) revealed a receiver operating characteristics curve of 0.75 (95% confidence interval, 0.72; 0.81) with a sensitivity of 85.6% and a specificity of 47.2%. This score, based only on history and physical examination, is a complementary tool for ruling out coronary heart disease in primary care patients complaining of chest pain