Has the time come to search for the Wells score 4.0?

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Through the weighting of variables from the case history, from the physical examination or from low-complexity diagnostic techniques, the model classifies patients into a specific risk group. This provides the clinician with a probability for a certain result or prognosis, from which different courses of action (diagnostic and therapeutic) can be derived.<a class="elsevierStyleCrossRef" href="#bib0155">1</a> The clinical models widely used for various medical and surgical diseases allow clinicians to standardize the actions to be performed and thereby reduce clinical variability. In 1995, Wells et al.<a class="elsevierStyleCrossRef" href="#bib0160">2</a> published what would be, with a number of changes, the clinical reference model for the diagnosis of deep venous thrombosis in the lower extremities (DVTLE). This model was composed of 12 predictors that, when combined, established 3 probability groups for experiencing DVTLE: high probability (85.7% of cases confirmed with imaging tests), intermediate probability (32.9% of confirmed cases) and low probability (5.3% of confirmed cases). Two years later,<a class="elsevierStyleCrossRef" href="#bib0165">3</a> the predictors were weighted and reduced to 9, although they were increased back to 10 in 2003.<a class="elsevierStyleCrossRef" href="#bib0170">4</a> Although the 3 probability groups were kept in the 3 versions, a clinical trial<a class="elsevierStyleCrossRef" href="#bib0175">5</a> published the same year reduced them to 2: likely DVT and unlikely DVT. This reduction was performed to improve its practical application by eliminating the uncertainty generated by patients included in the intermediate probability group. Despite the fact that the implementation of the Wells model enabled the adoption of diagnostic (delaying imaging studies for patients with low probability) and therapeutic approaches (starting anticoagulant treatment in patients with high probability),<a class="elsevierStyleCrossRef" href="#bib0180">6</a> it was insufficient per se for diagnosing DVTLE in patients with likely DVT (27.9% [95% CI: 23.9–31.8] of cases confirmed with imaging tests) or for excluding this diagnosis for patients with unlikely DVT (5.5% [95% CI: 3.8–7.6] of cases confirmed with imaging tests).<a class="elsevierStyleCrossRef" href="#bib0175">5</a> Imaging tests were therefore still needed to ultimately confirm or rule out this diagnosis.The D-dimer is a product of fibrin degradation, whose measurement has been placed at the front line in the diagnosis of venous thromboembolism. Although the low positive predictive power of the D-dimer is not very useful for confirming its presence, it has, however, a high negative predictive value. In this respect, the strategy of ruling out DVTLE in patients with low clinical probability or unlikely DV” who present a negative D-dimer has been validated in prospective studies. The strategy is considered safe, cost-effective and has been included in the main clinical practice guidelines on venous thromboembolism.<a class="elsevierStyleCrossRefs" href="#bib0185">7,8</a> In these patients, not performing an imaging technique or an emergency anticoagulant treatment results, at 3 months, in a percentage of venous thromboembolic events similar to that obtained when systematic imaging tests are used to objectively rule out this diagnosis.<a class="elsevierStyleCrossRef" href="#bib0185">7</a> For years now, it has been reported that the implementation of the Wells model in hospital emergency departments (HEDs) has been low,<a class="elsevierStyleCrossRefs" href="#bib0195">9,10</a> primarily due to the complexity of its application.<a class="elsevierStyleCrossRef" href="#bib0155">1</a> In addition to the high number of predictors, we have to consider that a number of them consists of the combination of various clinical variables, which complicates its memorization. Subjectivity and arbitrariness in the interpretation of a number of the items, the use of redundant parameters to describe the same clinical condition and having to combine the results with those obtained after measuring the D-dimer are some of the reasons that make this process unintuitive and difficult to remember,<a class="elsevierStyleCrossRef" href="#bib0205">11</a> especially under high-patient load conditions as usually occurs in HEDs.Although the Wells Scale is the most well-known, it is not the only one developed for the diagnosis of DVTLE in outpatients. In 2005, Oudega et al.<a class="elsevierStyleCrossRef" href="#bib0210">12</a> developed a clinical model similar to the Wells model but using fewer predictors, including the D-dimer. The present study has been designed on the hypothesis that the inclusion of the D-dimer as a predictor of the Wells model could result in a simplification in the number of items, as has already occurred in the 1997 model compared with the original of 1995.Materials and methodsStudy populationWe designed a retrospective observational study using a clinical database of anonymous data from patients who had been studied for the clinical suspicion of DVTLE. The study was conducted in the General Hospital of Specialties of Jaen between 1998 and 2002, with outpatients who consecutively visited the HED for this disease. For this study, we selected the registries with the epidemiological, clinical, exploratory and laboratory data sufficient for applying the Wells model.<a class="elsevierStyleCrossRef" href="#bib0175">5</a> We excluded patients who had been anticoagulated in the previous 24h (either prophylactically or therapeutically) and those with questionable or undetermined imaging test results.Diagnostic techniquesThe diagnosis of DVTLE was established after performing a color-complemented compression B-mode Doppler ultrasound (Philips P-700 Duplex Scanner) with a high-resolution probe of 5–7.5MHz in the symptomatic limb. Consecutive descending compressions were performed by an experienced radiologist with the catheter in B mode (two-dimensional image in real-time and grayscale) in the transverse plane from the inguinal ligament to the fibular lateral malleolus. The measurement of the D-dimer in plasma was performed using a quantitative latex technique (Dade-Behring®, Marburg, Germany), an immunofiltration technique (NycoCard® D-Dimer, Nycomed Pharma AS) or a turbidimetric technique (Sysmex CA 1500®, Dade-Behring®, Marburg, Germany). In each of these techniques, we used the normal cut off value indicated by the manufacturer.Statistical analysisEach of the 10 predictors of the Wells model and the result of the D-dimer were encoded as qualitative variables and inserted into a multivariate analysis model with the aim of adjusting the effect of the analytical result on the clinical predictors. The ultrasound presence of DVTLE was set as the dependent variable. We included the constant in the model and calculated the 95% confidence interval of probability (95% CI) for the ratio of advantages (exp [β]). The variables that achieved statistical significance in the multivariate analysis were weighted according to the β regression coefficient. To avoid the use of decimals, we approximated to the closest whole number. To assess the validity of the model achieved, we analyzed its predictive capacity and its calibration. The assessment of the predictive capacity was conducted using the calculation of the area under the curve in relation to the achieved diagnostic efficiency curve. The value of the status variable was considered to be the existence of DVTLE, and the test was directed so that a greater result indicated a more positive test. For the calibration of the model, we used the Hosmer–Lemeshow statistical goodness of fit test. To recreate the original model's 2 probability groups for experiencing DVTLE (likely DVT and unlikely DVT), we examined various cutoff points. For each cutoff point, we analyzed the sensitivity, specificity, positive predictive value and negative predictive value.The results were compared with those from the implementation of the Wells model in the study population. For the assessment of the predictive capacity of the 2 models, we used the χ2 statistic as a test for determining the homogeneity of the areas under the curve. The models’ diagnostic capacity for ruling out DVTLE without performing an imaging test was assessed by comparing the variables “unlikely DVT” (simplified model) and “unlikely DVT and negative D-dimer” (Wells model) with the result obtained after the performance of the ultrasound. For the 2 models, we calculated the negative predictive value, the proportion of false negatives and the percentage of patients who benefited from not performing the imaging technique. The results were expressed with their respective 95% confidence intervals (95% CI). We used the Z statistic as a test for comparing the 2 proportions from the independent samples. In all the tests employed, the level of statistical significance was established for all values of p<0.05. Age was expressed as mean (standard deviation). The statistical analysis was performed with SPSS® 17 software, with an original license, and EPIDAT 3.1® (Government of Galicia).Ethical aspectsThe present study has followed the recommendations for guiding research in humans, adopted by the 18th World Medical Assembly in Helsinki (Finland, 1964) and its subsequent amendments, the latest of which were performed by the 64th General Assembly (Fortaleza, 2013), as well as by the Convention for the Protection of Human Rights and Dignity of the Human Being with Regard to the Application of Biology and Medicine (Oviedo Convention, 1997). The document complies with the legislation established by Organic Law 15/1999 for the Protection of Personal Data, Basic Law 41/2002 Regulating Patient Autonomy and the Rights and Obligations Regarding Clinical Information and Documentation and law 14/2007 of July 3 on biomedical research. We met with the Research Ethics Committee, who, given the study's retrospective characteristics, considered that the study met the ethical requirements required of this type of research.ResultsStudy populationThe database consisted of 861 registries, of which we excluded 284 (33%): 16 (1.9%) for a doubtful or inconclusive ultrasound result; 26 (3%) for prior anticoagulant treatment; and 242 (28.1%) for lack of clinical data that prevented the application of the Wells scale. The final population included in the study was 577 patients, of whom 312 (54.1%) were women. The mean age of the population was 66.7 (14.2) years, with an age range of 18–100 years. A diagnosis of DVTLE was established in 145 patients (25.1%). The D-dimer was negative in 217 patients (37.6%) of the total sample and in 11 (7.6%) of the patients with confirmed DVTLE. There were no relevant epidemiological or clinical differences between the included and excluded populations in the study.Simplification of the Wells modelOf the 11 variables inserted into the multivariate model, only 4 achieved statistical significance (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>). In the Hosmer–Lemeshow test, the model reached a value of χ2 of 9.79 (8 degrees of freedom; p=0.28). The variables “increase in the diameter of the lower legs” and “increase in the diameter of the entire limb” were weighted with a point (+1) each. The variable “presence of an alternative diagnosis at least as likely as that of DVT” was weighted negatively (–1), whereas the positive result of the D-dimer achieved the greatest weight (+2). Thus, the simplified model weighted the patients between −1 point and +4 points. The cutoff value was established at 0 points, which differentiated patients with low probability of DVTLE (≤0 points) from those with high probability of DVTLE (≥1 point), with high sensitivity and a high negative predictive value (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).<elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia>One hundred and nineteen patients were included in the category “low probability” (20.6% [95% CI: 17.2–24%]), with a 0.8% prevalence of DVTLE in this group (95% CI: 0–4.6%). The percentage of the population classified as high probability was 79.4% (95% CI: 17.2–24%]), with a 31.4% prevalence of DVTLE in this category (95% CI: 27.1–35.8%). The area under the curve of the simplified model was 0.844, with a typical error of 0.02 (95% CI: 0.81–0.879).Behavior of the Wells modelThe area under the curve of the Wells model in our population was 0.751, with a typical error of 0.02 (95% CI: 0.709–0.793). The diagnostic capacity of the strategy “unlikely DVT and negative D-dimer” was similar to that achieved with the simplified model (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). Ninety-one patients were included in this category (15.8% [95% CI: 12.7–18.8%]), with a 1.1% prevalence of DVTLE in this group (95% CI: 0–6%). The percentage of the population susceptible to requiring an imaging study was 84.2% (95% CI: 81.2–87.3%), with a 29.6% prevalence of confirmed DVTLE (95% CI: 25.5–33.8%). The comparison of the areas under the curve for the 2 models showed a greater predictive capacity for the simplified model (0.844 vs. 0.751, p<0.001). Additionally, the percentage of the population in which the performance of an imaging test could be avoided was higher using the simplified model than using the combination of the Wells and D-dimer method (20.6% vs. 15.8%, p=0.039), with a similar percentage of false negatives in the 2 groups (0.84% vs. 1.01%, p=0.599).DiscussionThe inclusion of the D-dimer as an additional predictor in the clinical models developed for the diagnosis of DVTLE has produced good results for other authors.<a class="elsevierStyleCrossRefs" href="#bib0210">12,13</a> In our study, the insertion of the D-dimer into a regression model helps to substantially simplify the Wells model, reducing the number of predictors from 10 to 4. This can be achieved without losing diagnostic capacity, increasing the percentage of patients for whom imaging techniques can be avoided or by compromising safety (percentage of false negatives in the patient group with DVTLE ruled out without imaging techniques). From the original Wells model, the regression analysis only classifies as independent variables the partial increase (lower legs) or total diameter of the symptomatic limb and the presence of an alternative diagnosis. The importance of the first parameter appears obvious. The Wells model reflects this importance in those 2 items and, indirectly, in a third: the presence of edema in the symptomatic limb. Additionally, various published clinical models for the diagnosis of DVTLE that are not the result of some of the versions of the Wells model include some predictors that weigh this situation.<a class="elsevierStyleCrossRefs" href="#bib0210">12–17</a> Regarding the second parameter, one of the most debated issues in the implementation of the Wells model is the inherent subjectivity of the item “other diagnosis at least as probable as that of DVT”, especially if we consider that this predictor has the greatest weight in the model (−2 points). In this regard, given the wide diagnostic differential established by a possible DVTLE, it is reasonable to think that the presence of other processes should be considered. Additionally, this item usually appears in other clinical models, not only for the diagnosis of DVTLE<a class="elsevierStyleCrossRefs" href="#bib0225">15–17</a> but also in the case of pulmonary thromboembolism.<a class="elsevierStyleCrossRefs" href="#bib0155">1,7</a> The Wells model has also been shown to have high correlation, regardless of the experience of the physician who applies it,<a class="elsevierStyleCrossRefs" href="#bib0185">7,18</a> even when used by other healthcare groups.<a class="elsevierStyleCrossRef" href="#bib0245">19</a>One of the disadvantages established a priori by models that include D-dimer as a predictor lies in the increase in diagnostic expenditure. Clinical practice guidelines<a class="elsevierStyleCrossRefs" href="#bib0185">7,8</a> recommended this measurement when the estimated risk of a patient experiencing DVTLE (either using the Wells model or empirically) is not high, given that for patients with high probability the D-dimer result provides no additional information that can lead to a reconsideration of the need to perform an imaging study. However, the model developed by Oudega et al. has shown its cost-effectiveness versus the Wells model, given that the increased cost resulting from the D-dimer measurement is compensated for by a reduction in the number of requested imaging tests, which have a significantly higher cost.<a class="elsevierStyleCrossRef" href="#bib0250">20</a> This situation has also been made clear in our population, in which the simplified model selected a significantly higher percentage of patients for whom the performance of imaging techniques could have been avoided. We should not forget the other reality of VTE diagnosis, which is occasionally far from the directives indicated in the guidelines and that propels a systematic use of the D-dimer measurement. Various studies performed in HEDs have shown that the request for D-dimer measurement is usually independent of the clinical classification of patients with this clinical suspicion.<a class="elsevierStyleCrossRefs" href="#bib0200">10,21,22</a>One of our study's possible limitations lies in the fact that different techniques have been used for D-dimer quantification. However, in a recent meta-analysis<a class="elsevierStyleCrossRef" href="#bib0265">23</a> that evaluated the usefulness of the Wells model in more than 10,000 patients, the results were equally consistent regardless of the type of method used for its reading. Moreover, this situation merely reflects the reality of Spanish HED laboratories,<a class="elsevierStyleCrossRef" href="#bib0270">24</a> which use different types of assays. This in turn reinforces the recommendations of experts, who consider that the diagnostic capacity of the D-dimer is too variable for making clinical decisions based solely on its results.<a class="elsevierStyleCrossRef" href="#bib0275">25</a>In terms of safety, the results obtained with the simplified model are similar to those shown by the original model. In the ruling out of DVTLE without imaging tests, the clinical models whose false negative rate is <2% are considered adequate. This figure is not random and represents the maximum percentage of patients who, during their follow-up in prospective studies, developed DVTLE or pulmonary embolism despite having undergone urgent imaging studies (venography or ultrasonography) that ruled out this diagnosis.<a class="elsevierStyleCrossRefs" href="#bib0185">7,23</a> The percentage of failed diagnoses with the Wells model in our population (1.1% [95% CI: 0–6%]) agrees with the results of the previously mentioned meta-analysis,<a class="elsevierStyleCrossRef" href="#bib0265">23</a> which estimated its failure rate at 1.2% (95% CI: 0.7–1.8%) of patients classified as unlikely DVT and who progress with a negative D-dimer. The differences between the 2 proportions are not statistically significant and, given the high number of patients analyzed, it is reasonable to conclude that its 95% CI is narrower than that encountered in our population.Although the actual prevalence of DVTLE among patients with this suspicion is <25%, neither empirical stratification nor the clinical models manage to rule out the remaining 75%. Although the published figures have varied significantly, the study by Geersing et al.<a class="elsevierStyleCrossRef" href="#bib0265">23</a> placed them at 29% (95% CI: 20–40%). In our population, the percentage of patients who could benefit from a safe ruling out of a DVTLE diagnosis without requiring imaging techniques is somewhat lower than expected, both in the simplified model (20.6%) and in the original model (15.8%). This could be due to the fact that, in the referred study, the mean percentage of patients with confirmed DVTLE was significantly less than that found in our study (18.6% vs. 25.1%, p<0.001). It is known that there is an inverse relationship between the percentage of patients for whom this diagnosis can be ruled out without an imaging test and the actual prevalence of DVTLE in the study population.<a class="elsevierStyleCrossRef" href="#bib0280">26</a> Moreover, the percentage of patients with negative D-dimer in our population was also lower, which directly implies a reduction in the number of patients who could benefit from this strategy.Although the use of the Wells model provides a savings that significantly exceeds 100 €/patient,<a class="elsevierStyleCrossRefs" href="#bib0285">27,28</a> the model has been criticized for not being able to demonstrate greater efficacy than the classical empiric method,<a class="elsevierStyleCrossRef" href="#bib0240">18</a> presenting less efficacy in patients with distal DVTLE, advanced age or cancer<a class="elsevierStyleCrossRef" href="#bib0265">23</a>; and its lack of validation in pregnant women, patients with recurring thrombosis and in patients who have been anticoagulated.<a class="elsevierStyleCrossRef" href="#bib0185">7</a> However, its application enables the standardization of the assessment of patients, with greater reproducibility and better results in the negative predictive value and in the number of additional tests requested.<a class="elsevierStyleCrossRef" href="#bib0295">29</a> Difficulty with memorization is one of the most important factors when implementing clinical models<a class="elsevierStyleCrossRef" href="#bib0205">11</a> and represents the main handicap of the Wells model. Although the proliferation of computerized tools in recent years (smart phones, palmtops, tablets, office computers, etc.) could remedy the complex implementation of this model,<a class="elsevierStyleCrossRef" href="#bib0200">10</a> the results do not seem to have improved.<a class="elsevierStyleCrossRef" href="#bib0300">30</a> Although our study's retrospective design limits the extrapolation of the results, we believe that they represent a first (small) step toward simplifying the Wells model. The development of lines of research that, with a prospective design, analyze this issue could result in the more extensive use of the Wells model in HEDs, which would lead to a more effective use of healthcare resources, greater diagnostic flexibility and less discomfort for patients with this clinical suspicion.<elsevierMultimedia ident="tb0005"></elsevierMultimedia>Conflicts of interestThe authors declare that they have no conflicts of interest." "textoCompletoSecciones" => array:1 [ "secciones" => array:10 [ 0 => array:3 [ "identificador" => "xres519454" "titulo" => "Abstract" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Background" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Objective" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Patients and methods" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Results" ] 4 => array:2 [ "identificador" => "abst0025" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec539977" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres519453" "titulo" => "Resumen" "secciones" => array:5 [ 0 => array:2 [ "identificador" => "abst0030" "titulo" => "Antecedentes" ] 1 => array:2 [ "identificador" => "abst0035" "titulo" => "Objetivo" ] 2 => array:2 [ "identificador" => "abst0040" "titulo" => "Pacientes y métodos" ] 3 => array:2 [ "identificador" => "abst0045" "titulo" => "Resultados" ] 4 => array:2 [ "identificador" => "abst0050" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec539978" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Background" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Materials and methods" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Study population" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Diagnostic techniques" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Statistical analysis" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Ethical aspects" ] ] ] 6 => array:3 [ "identificador" => "sec0035" "titulo" => "Results" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0040" "titulo" => "Study population" ] 1 => array:2 [ "identificador" => "sec0045" "titulo" => "Simplification of the Wells model" ] 2 => array:2 [ "identificador" => "sec0050" "titulo" => "Behavior of the Wells model" ] ] ] 7 => array:2 [ "identificador" => "sec0055" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0070" "titulo" => "Conflicts of interest" ] 9 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2014-05-23" "fechaAceptado" => "2014-10-27" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec539977" "palabras" => array:5 [ 0 => "D-dimer" 1 => "Deep vein thrombosis" 2 => "Clinical decision rules" 3 => "Wells score" 4 => "Thromboembolic disease" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec539978" "palabras" => array:5 [ 0 => "Dímero D" 1 => "Enfermedad tromboembólica venosa" 2 => "Modelos clínicos" 3 => "Escala de Wells" 4 => "Trombosis venosa profunda" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "BackgroundWells score for deep vein thrombosis presents problems for implementation in the hospital emergencies, mainly due to the complexity of its enforcement. ObjectiveTo assess whether the inclusion of D-dimer as a predictor might lead to a simplification of this clinical decision rule. Patients and methodsA database of deep vein thrombosis patients was studied by logistic regression model in which the 10 predictors in the Wells score and the dimer D were included. The diagnosis was made with compression ultrasonography with Doppler signal. D-dimer was determined by a quantitative method of latex, a technique of immunofiltration or a turbidimetric technique. Results577 patients (54.1% women) were studied, with a mean age of 66.7 (14.2) years. 25.1% were diagnosed with deep vein thrombosis. Only four variables were independent, building a weighted model with greater predictive ability (area under the curve) than the original model (0.844 vs. 0.751, p<0.001). Both models showed an acceptable safety, with a similar rate of failure (0.8% vs. 1%). The simplified model allowed selecting a higher percentage of patients who could have benefited from the non-performance of the imaging test (20.6% vs. 15.8%, p=0.039). ConclusionsThe introduction of D-dimer in a regression model simplifies the Wells score and maintains the same efficacy and safety, which could improve its implementation in the hospital emergencies." "secciones" => array:5 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Background" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Objective" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Patients and methods" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Results" ] 4 => array:2 [ "identificador" => "abst0025" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "AntecedentesEl modelo de Wells para la trombosis venosa profunda presenta problemas para su implementación en las áreas de urgencias hospitalarias debido, fundamentalmente, a la complejidad de su aplicación. ObjetivoEvaluar si la inclusión del dímero D como un predictor podría repercutir en una simplificación de dicho modelo. Pacientes y métodosSobre una base de datos retrospectiva de pacientes estudiados por trombosis venosa profunda se aplicó un modelo de regresión logística en el que se incluyeron los 10 predictores del modelo de Wells y el resultado del dímero D. El diagnóstico se realizó con una ecografía de compresión con señal Doppler. El dímero D se determinó mediante una técnica cuantitativa de látex, una técnica de inmunofiltración o una técnica turbidimétrica. ResultadosSe estudiaron 577 pacientes (mujeres: 54,1%) con una edad media de 66,7 (14,2) años y un porcentaje de trombosis venosa profunda del 25,1%. Solo 4 variables resultaron independientes, construyéndose un modelo ponderado con una mayor capacidad predictiva (área bajo la curva) que el modelo original (0,844 vs. 0,751, p<0,001). Ambos modelos mostraron una seguridad aceptable, con una tasa de fracasos similar (0,8% vs. 1%). El modelo simplificado permitió seleccionar a un mayor porcentaje de pacientes en los que podría no haberse realizado la prueba de imagen (20,6% vs. 15,8%, p=0,039). ConclusionesLa introducción del dímero D en un modelo de regresión permite simplificar el modelo de Wells y mantener su misma eficacia y seguridad, lo que podría mejorar su implementación en las áreas de urgencias hospitalarias." "secciones" => array:5 [ 0 => array:2 [ "identificador" => "abst0030" "titulo" => "Antecedentes" ] 1 => array:2 [ "identificador" => "abst0035" "titulo" => "Objetivo" ] 2 => array:2 [ "identificador" => "abst0040" "titulo" => "Pacientes y métodos" ] 3 => array:2 [ "identificador" => "abst0045" "titulo" => "Resultados" ] 4 => array:2 [ "identificador" => "abst0050" "titulo" => "Conclusiones" ] ] ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "Please cite this article as: Rosa-Jiménez F, Rosa-Jiménez A, Lozano-Rodríguez A, Martín-Moreno P, Hinojosa-Martínez MD, Montijano-Cabrera ÁM. ¿Ha llegado el momento de buscar la escala de Wells 4.0? Rev Clin Esp. 2015;215:258–264." ] ] "multimedia" => array:3 [ 0 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Clinical predictors \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Coefficient β \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Exp β [95% CI] \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">p \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Active cancer (past 6 months) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−0.103 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.109 (0.389–3.157) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.847 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Recent paralysis, paresis or immobility with leg casts \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−0.237 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.268 (0.414–3.884) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.678 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Recently bedridden (>3 days) or major surgery (<12 weeks) requiring general or regional anesthesia \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−23.555 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Previous documented DVT \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−0.085 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.088 (0.605–1.957) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.777 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Localized pain in the deep vein system \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.077 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.925 (0.479–1.790) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.818 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Increased diameter throughout the limb \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.487 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.205 (0.119–0.351) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Increased diameter of the lower legs (>3cm compared with the contralateral) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.364 (0.182–0.728) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.004 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Pitting edema (limited to the symptomatic limb) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.33 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.719 (0.439–1.176) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.189 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Collateral superficial veins (nonvericose) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−0.253 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1.288 (0.129–12.884) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.829 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">D-dimer \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2.272 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0.103 (0.052–0.205) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Alternative diagnosis at least as probable as that of DVT \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">−1.467 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4.793 (2.537–9.057) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab838197.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Logistic regression model." ] ] 1 => array:7 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "Results expressed in % (95% CI)." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Simplified model \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Original model \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Number of variables \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">10 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"></td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">D-dimer \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Included in the model \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">To apply in a 2nd step \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Sensitivity \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">99.3 (97.6–100) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">99.3 (97.6–100) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Specificity \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">27.3 (23–31.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">20.8 (14.9–24.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Positive predictive value \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">31.4 (27.1–35.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">29.6 (25.5–33.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Negative predictive value \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">99.2 (97.1–100) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">98.9 (96.2–100) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"></td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">Patients with low probability \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">20.6 (17.2–24) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">15.8 (12.7–18.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " colspan="3" align="left" valign="top"></td></tr><tr title="table-row"><td class="td" title="table-entry " align="left" valign="top">DVT in patients with low probability \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">0.8 (0–4.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">1.1 (0–6) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab838198.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Comparison of the original model and the simplified model." ] ] 2 => array:5 [ "identificador" => "tb0005" "tipo" => "MULTIMEDIATEXTO" "mostrarFloat" => false "mostrarDisplay" => true "texto" => array:1 [ "textoCompleto" => "What we know?The Wells clinical model enables us to establish the categories of “likely” and “unlikely” DVT. However, its use in hospital emergency departments is low due to its complexity. The measurement of the D-dimer has a high negative predictive value and enables us to rule out DVT in patients with an “unlikely” classification.What this article provides?In this study, we assessed whether the inclusion of the D-dimer as a predictor could simplify the Wells model and therefore achieve better implementation in emergency departments. The authors show that the modification of this model from 10 to 4 variables, which include the reading of the D-dimer, maintains both the efficacy and safety of the diagnosis of lower limb DVT compared with the use of the classical model.The Editors" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:30 [ 0 => array:3 [ "identificador" => "bib0155" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Clinical decision rules in venous thromboembolism" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "G. Le Gal" 1 => "M. Carrier" 2 => "M. 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