259 research outputs found

    Clinical judgement and therapeutic decision making

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    AbstractClinical decision making is under increased scrutiny due to concerns about the cost and quality of medical care. Variability in physician decision making is common, in part because of deficiencies in the knowledge base, but also due to the difference in physicians' approaches to clinical problem solving. Evaluation of patient prognosis is a critical factor in the selection of therapy, and careful attention to methodology is essential to provide reliable information.Randomized controlled clinical trials provide the most solid basis for the establishment of broad therapeutic principles. Because randomized studies cannot be performed to address every question, observational studies will continue to play a complementary role in the evaluation of therapy. Randomized studies in progress, meta analyses of existing data, and increased use of administrative and collaborative clinical data bases will improve the knowledge base for decision making in the future

    Importance of clinical measures of ischemia in the prognosis of patients with documented coronary artery disease

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    AbstractTo examine the value of clinical measures of ischemia for stratifying prognosis, 5,886 consecutive patients who had symptomatic significant (≄75% stenosis) coronary artery disease were studied. Using the Cox regression model in a randomly selected half of the patients, the prognostically independent clinical variables were weighted and arranged into a simple angina score: angina score = angina course × (1 + daily angina frequency) + ST-T changes, where angina course was equal to 3 if unstable or variant angina was present, 2 if the patient's angina was progressive with nocturnal episodes, 1 if it was progressive without nocturnal symptoms and 0 if it was stable; 6 points were added for the presence of “ischemic” ST-T changes. This angina score was then validated in an independent patient sample.The score was a more powerful predictor of prognosis than was any individual anginal descriptor. Furthermore, the angina score added significant independent prognostic information to the patient's age, sex, coronary anatomy and left ventricular function. Patients with three vessel disease and a normal ventricle (n = 1,233) had a 2 year infarction-free survival rate of 90% with an angina score of 0 and a 68% survival rate with an angina score ≄9. With an ejection fraction <50% and three vessel disease (n = 1,116), the corresponding infarction-free survival figures were 76 and 56%. Thus, a careful summarization of clinical markers of ischemia in the form of an angina score can provide a powerful prognostic tool and may aid clinicians in identifying high risk patients who are candidates for aggressive therapeutic interventions

    Case complexity scores in congenital heart surgery: A comparative study of the Aristotle Basic Complexity score and the Risk Adjustment in Congenital Heart Surgery (RACHS-1) system

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    ObjectiveThe Aristotle Basic Complexity score and the Risk Adjustment in Congenital Heart Surgery system were developed by consensus to compare outcomes of congenital cardiac surgery. We compared the predictive value of the 2 systems.MethodsOf all index congenital cardiac operations at our institution from 1982 to 2004 (n = 13,675), we were able to assign an Aristotle Basic Complexity score, a Risk Adjustment in Congenital Heart Surgery score, and both scores to 13,138 (96%), 11,533 (84%), and 11,438 (84%) operations, respectively. Models of in-hospital mortality and length of stay were generated for Aristotle Basic Complexity and Risk Adjustment in Congenital Heart Surgery using an identical data set in which both Aristotle Basic Complexity and Risk Adjustment in Congenital Heart Surgery scores were assigned. The likelihood ratio test for nested models and paired concordance statistics were used.ResultsAfter adjustment for year of operation, the odds ratios for Aristotle Basic Complexity score 3 versus 6, 9 versus 6, 12 versus 6, and 15 versus 6 were 0.29, 2.22, 7.62, and 26.54 (P < .0001). Similarly, odds ratios for Risk Adjustment in Congenital Heart Surgery categories 1 versus 2, 3 versus 2, 4 versus 2, and 5/6 versus 2 were 0.23, 1.98, 5.80, and 20.71 (P < .0001). Risk Adjustment in Congenital Heart Surgery added significant predictive value over Aristotle Basic Complexity (likelihood ratio χ2 = 162, P < .0001), whereas Aristotle Basic Complexity contributed much less predictive value over Risk Adjustment in Congenital Heart Surgery (likelihood ratio χ2 = 13.4, P = .009). Neither system fully adjusted for the child’s age. The Risk Adjustment in Congenital Heart Surgery scores were more concordant with length of stay compared with Aristotle Basic Complexity scores (P < .0001).ConclusionsThe predictive value of Risk Adjustment in Congenital Heart Surgery is higher than that of Aristotle Basic Complexity. The use of Aristotle Basic Complexity or Risk Adjustment in Congenital Heart Surgery as risk stratification and trending tools to monitor outcomes over time and to guide risk-adjusted comparisons may be valuable

    Evaluation of clinical prediction models (part 1): from development to external validation

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    Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation, as well as exploring heterogeneity, fairness, and generalisability in model performance

    Evaluation of clinical prediction models (part 1):from development to external validation

    Get PDF
    Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation, as well as exploring heterogeneity, fairness, and generalisability in model performance

    Different perceptions of the burden of upper GI endoscopy: an empirical study in three patient groups

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    Background: Few studies have evaluated patients' perceived burden of cancer surveillance tests. Cancer screening and surveillance, however, require a large number of patients to undergo potentially burdensome tests with only some experiencing health gains from it. We investigated the determinants of patients' reported burden of upper gastrointestinal (GI) endoscopy by comparing data from three patient groups. Patients and methods: A total of 476 patients were included: 180 patients under regular surveillance for Barrett esophagus (BE), a premalignant disorder; 214 patients with non-specific upper GI symptoms (NS), and 82 patients recently diagnosed with upper GI cancer (CA). We assessed pain, discomfort and overall burden experienced during endoscopy, symptoms in the week afterwards and psychological distress over time (Hospital Anxiety and Depression scale and Impact of Event Scale). Results: Two-thirds (66%) of patients reported discomfort and overall burden of upper GI endoscopy. Only 23% reported any pain. BE patients reported significantly less discomfort, pain and overall burden than the other patients: those with NS reported more discomfort, CA patients more pain, and both more overall burden. These differences could be statistically explained by the number of previous endoscopies and whether sedation was provided or not, but not by patient characteristics. Conclusion: The perception of upper GI endoscopy varies by patient group, due to potential adaptation after multiple endoscopies and aspects of th

    Evaluation of clinical prediction models (part 1): from development to external validation

    Get PDF
    Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation, as well as exploring heterogeneity, fairness, and generalisability in model performance
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