Determining indications for care common to competing guidelines by using classification tree analysis: application to the prevention of venous thromboembolism in medical inpatients.

International audienceBACKGROUND: Substantial variations have been reported in the advice given by competing guidelines addressing the same clinical problem. OBJECTIVE: This study aimed to assess the usefulness of classification tree analysis in comparing competing guidelines. METHOD: The authors implemented a classification tree-growing algorithm on cross-sectional data from 818 patients to determine indications for prophylactic heparin treatment common to 4 competing guidelines disseminated between 1998 and 2000 and addressing the prophylaxis of venous thromboembolism in medical inpatients. RESULTS: The resulting classification tree involved 10 terminal nodes. Its mean accuracy estimated by performing 10-fold cross-validation was 82% (s=3). The guidelines consistently supported prophylactic heparin treatment for 5 indications: a previous episode of deep vein thrombosis or pulmonary embolism, recent paralysis of lower limb(s), congestive heart failure with one or more risk factors, recent myocardial infarction, and malignancy with one or more risk factors. These indications involved 257 patients (31.4%) and were supported by robust scientific evidence. Deep vein thrombosis was detected in 27 of these patients (10.5%). Two consistent negative indications involved 347 patients (42.4%). Deep vein thrombosis was detected in 9 of these patients (2.6%). Three indications involving 214 patients (26.2%) were discordant over the 4 guidelines. CONCLUSION: Classification tree analysis of real patient data is a useful strategy to identify indications common to competing guidelines. These indications should be considered for inclusion when updating guidelines. The findings of recently completed randomized trials have partly resolved the disagreement among the 4 guidelines. This approach may be helpful when developing new guidelines or for identifying topics warranting further complementary clinical trials

Heparin thromboprophylaxis for medical inpatients.

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How to derive and validate clinical prediction models for use in intensive care medicine

International audienceBACKGROUND:Clinical prediction models are formal combinations of historical, physical examination and laboratory or radiographic test data elements designed to accurately estimate the probability that a specific illness is present (diagnostic model), will respond to a form of treatment (therapeutic model) or will have a well-defined outcome (prognostic model) in an individual patient. They are derived and validated using empirical data and used to assist physicians in their clinical decision-making that requires a quantitative assessment of diagnostic, therapeutic or prognostic probabilities at the bedside.PURPOSE:To provide intensivists with a comprehensive overview of the empirical development and testing phases that a clinical prediction model must satisfy before its implementation into clinical practice.RESULTS:The development of a clinical prediction model encompasses three consecutive phases, namely derivation, (external) validation and impact analysis. The derivation phase consists of building a multivariable model, estimating its apparent predictive performance in terms of both calibration and discrimination, and assessing the potential for statistical over-fitting using internal validation techniques (i.e. split-sampling, cross-validation or bootstrapping). External validation consists of testing the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. Impact analysis involves comparative research [i.e. (cluster) randomized trials] to determine whether clinical use of a prediction model affects physician practices, patient outcomes or the cost of healthcare delivery.CONCLUSIONS:This narrative review introduces a checklist of 19 items designed to help intensivists develop and transparently report valid clinical prediction models

D-dimer and duration of anticoagulation.

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Hyponatremia and short-term outcomes in patients with acute pulmonary embolism

BACKGROUND: Hyponatremia, a marker of neurohormonal activation, is associated with poor outcomes in acute cardiorespiratory diseases such as myocardial infarction, right and left ventricular heart failure, and pneumonia. The prognostic value of hyponatremia in patients with acute pulmonary embolism (PE) is unknown. We sought to assess whether hyponatremia at presentation was associated with mortality and hospital readmission in patients hospitalized with PE. METHODS: We studied patient discharges with a primary diagnosis of PE from 185 acute care hospitals in Pennsylvania (1/2000-11/2002). We defined hyponatremia as a serum sodium level ≤135 mmol/l, measured at the time of patient presentation. The study outcomes were 30-day all-cause mortality and hospital readmission. We used random-intercept logistic regression to examine the association between hyponatremia and mortality. We adjusted for baseline patient (race, insurance, severity of illness using the Pulmonary Embolism Severity Index) and hospital characteristics (region, hospital size and teaching status). We used the same approach to examine the association between hyponatremia and readmission among patients who were discharged alive. RESULTS: Among 13,728 patient discharges with PE, 2907 (21.1%) had hyponatremia at the time of presentation. Patients with hyponatremia were older (P<0.001) and more likely to have a history of cancer (P<0.001), heart failure (P<0.001), or chronic lung disease (P=0.002) than patients without hyponatremia. Patients with hyponatremia had a higher unadjusted cumulative 30-day mortality (15.2% vs 8.0%;P<0.001) and readmission rate (15.9% vs 11.8%; P< 0.001) than patients without hyponatremia (Figure). After adjustment for race, insurance, severity of illness, and hospital factors, hyponatremia was associated with a significantly greater odds of death (OR 1.71, 95% CI: 1.50-1.95) and hospital readmission (OR 1.29, 95% CI: 1.14-1.46). CONCLUSIONS: In this large, statewide sample of unselected patients with acute PE, hyponatremia was relatively common and was an independent predictor of short-term mortality and hospital readmission. Given that sodium is a low-cost, easily available laboratory parameter, it may be potentially useful in risk-stratifying patients with PE

Perception and use of the results of patient satisfaction surveys by care providers in a French teaching hospital.

International audienceOBJECTIVE: . The aim of this study was to assess clinical staff's opinions on the results of in-patient satisfaction surveys and their use within the quality improvement process. SETTING: The institution is a 2200-bed teaching hospital of tertiary health care employing 8000 professionals. Patient satisfaction surveys are carried out each year using a validated questionnaire mailed to a random sample of patients. The specific results of each department are sent to the medical and paramedical managers. METHODS: We conducted a questionnaire survey on 500 care providers randomly selected in every medical and surgical department. RESULTS: A total of 261 questionnaires were returned and analysed. Overall, 94% of responders had a favourable opinion of the patient satisfaction surveys. They considered that the patient was able to judge hospital service quality, especially in its relational, organizational, and environmental dimensions. The specific results for the department were less well known than the overall hospital results (60 versus 76%). These results were formally discussed in the department according to 40% of responders; 40% declared that these data resulted in improvement actions and considered that they led to modifications in their behaviour with patients. CONCLUSIONS: Despite a declared interest in satisfaction surveys, the results remain underused by hospital staff and insufficiently discussed within teams

Approximation of the variance gamma model with a finite mixture of normals

We investigate the possibility of approximating the variance gamma distribution with a finite mixture of normals. Therefore, we apply this result to derive a simple historical estimation procedure by means of the Expectation Maximization algorith

Defining The End Of The Circulatory Phase In Humans Undergoing Cardiopulmonary Resuscitation

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