Analysing the concept of diagnostic inertia in hypertension: a cross-sectional study.

AIMS: The aim of this study was to quantify diagnostic inertia (DI) when the physician fails to diagnose hypertension and determine its associated factors. METHODS: This cross-sectional, observational study involved all patients without a diagnosis of hypertension who had their blood pressure (BP) measured at least three times during the second half of 2010 (N = 48,605). Patients with altered mean BP figures (≥ 140/90 mmHg) were considered to experience DI. Secondary variables: gender, atrial fibrillation, diabetes mellitus, dyslipidemia, cardiovascular disease, age and the physician having attended a cardiovascular training course (ESCARVAL). Associated factors were assessed by multivariate logistic regression analysis. RESULTS: Diagnostic inertia was present in 6450 patients (13.3%, 95% CI: 13.0-13.6%). Factors significantly associated with DI were: male gender (OR = 1.46, 95% CI: 1.37-1.55, p < 0.001), atrial fibrillation (OR = 0.73, 95% CI: 0.58-0.92, p = 0.007), the ESCARVAL cardiovascular course (OR = 0.88, 95% CI: 0.81-0.96, p = 0.005), diabetes mellitus (OR = 0.93, 95% CI: 0.87-0.99, p = 0.016), cardiovascular disease (OR = 0.77, 95% CI: 0.67-0.88, p < 0.001) and older age (years) (18-44→OR = 1; 45-59→OR = 12.45, 95% CI: 11.11-13.94; 60-74→OR = 18.11, 95% CI: 16.30-20.12; ≥ 75→OR = 20.43, 95% CI: 18.34-22.75; p < 0.001). The multivariate model had an area under the ROC curve of 0.81 (95% CI: 0.80-0.81, p < 0.001). CONCLUSIONS: This study will help clinical researchers differentiate between the two forms of DI (interpretation of a positive screening test and interpretation of positive diagnostic criteria). The results found here in patients with hypertension suggest that this problem is prevalent, and that a set of associated factors can explain the outcome well (AUC>0.80).

Differences in the management of hypertension, diabetes mellitus and dyslipidemia between obesity classes.

We did not find any paper that assessed clinical inertia in obese patients. Therefore, no paper has compared the clinical inertia rates between morbidly and nonmorbidly obese patients. A cross-sectional observational study was carried out. We analysed 8687 obese patients ⩾40 years of age who attended their health-care center for a checkup as part of a preventive program. The outcome was morbid obesity. Secondary variables were as follows: failure in the management of high blood pressure (HBP), high blood cholesterol (HBC) and high fasting blood glucose (HFBG); gender; personal history of hypertension, dyslipidemia, diabetes, smoking and cardiovascular disease; and age (years). We analysed the association between failures and morbid obesity by calculating the adjusted odds ratio (OR). Of 8687 obese patients, 421 had morbid obesity (4.8%, 95% confidence interval (CI): 4.4-5.3%). The prevalence rates for failures were as follows: HBP, 34.7%; HBC, 35.2%; and HFBG, 12.4%. Associated factors with morbid obesity related with failures were as follows: failure in the management of HBP (OR=1.42, 95% CI: 1.15-1.74, P=0.001); failure in the management of HBC (OR=0.73, 95% CI: 0.58-0.91, P=0.004); and failure in the management of HFBG (OR=2.24, 95% CI: 1.66-3.03, P<0.001). Morbidly obese patients faced worse management for HBP and HFBG, and better management for HBC. It would be interesting to integrate alarm systems to avoid this problem.

A Tool to Quickly Detect Short Hamstring Syndrome in Boys who Play Soccer.

UNLABELLED: There is a lack of studies of alternative techniques differing from the straight leg raise test (SLR) and the passive knee extension test (PKE) to diagnose short hamstring syndrome (SHS). We built a predictive model with simple parameters to diagnose SHS and implemented it in a mobile app. This cross-sectional study analyzed 85 Spanish boys aged 10-16 years who played soccer in 2012. OUTCOMES: SHS (SLR<70° and/or PKE>15°), and grade II SHS (SLR<60° and/or PKE≥35°). Secondary variables: toe-touch test (TT), body mass index (BMI), age, laterality and number of years registered as part of a federation. A risk table implemented in a mobile app was built to estimate the probability of SHS and grade II SHS according to secondary variables. The area under the ROC curve (AUC) was calculated and we constructed risk groups. Scoring factors for SHS: low TT, younger age and lower BMI. AUC: 0.89 (95% CI: 0.82-0.96, p<0.001). Scoring factors for grade II SHS: younger age, higher BMI, left footed and lower TT. AUC: 0.78 (95% CI: 0.68-0.88, p<0.001). We provide a tool with minimum material but with a high discriminatory power to quickly calculate whether a boy who plays soccer has SHS. The models need validation studies.

Predictive models for all-cause and cardiovascular mortality in type 2 diabetic inpatients. A cohort study.

BACKGROUND: Many authors have analysed premature mortality in cohorts of type 2 diabetic patients, but no analyses have assessed mortality in hospitalised diabetic patients. AIM: To construct predictive models to estimate the likelihood of all-cause mortality and cardiovascular mortality in type 2 diabetic inpatients. DESIGN: Cohort study with follow-up from 2010 to 2014. METHODS: We evaluated mortality in a randomly selected cohort of 112 type 2 diabetic inpatients at the Hospital of Elda (Spain) in 2010-2012. OUTCOMES: all-cause mortality and cardiovascular mortality during the follow-up. Other variables: gender, age, depression, asthma/chronic obstructive pulmonary disease, hypertension, dyslipidemia, insulin, pills, smoking, walking, baseline blood glucose and creatinine. Predictive tables with risk groups were constructed to estimate the likelihood of all-cause mortality and cardiovascular mortality. Calculations were made of the area under the ROC curve (AUC). RESULTS: During the follow-up, 52 inpatients died (46.4%, 95% CI, confidence interval: 37.2-55.7%), 22 because of cardiovascular causes (19.6%, 95% CI: 12.3-27.0%). The mean follow-up time was 2.7 ± 1.5 years. The AUC for the all-cause mortality model was 0.84 (95% CI: 0.77-0.92, p < 0.001). Associated parameters: pills, smoking, walking, gender, insulin and age. The AUC for the cardiovascular mortality model was 0.79 (95% CI: 0.67-0.91, p < 0.001). Associated parameters: age, pills, walking, smoking, depression and insulin. CONCLUSIONS: This study provides tools to predict premature mortality in type 2 diabetic inpatients. However, before their general application they require joint validation by the internal medicine unit, emergency department, primary healthcare unit and endocrinology service to enable better prediction of the prognosis and more adequate decision-taking.

Suitability of antiplatelet therapy in hypertensive patients.

Antiplatelet therapy (AT) is indicated in hypertensive patients with increased cardiovascular risk. The literature about the adequate or inadequate prescription of AT is scarce. We conducted a prospective descriptive study to quantify therapeutic inertia and non-guideline-recommended prescription (NGRP) of AT (aspirinor clopidogrel or both), and to assess associated factors, calculating the adjusted odds ratios (ORs) from multivariate models. In 2007-2009, 712 primary health-care hypertensive patients in a Spanish region were enrolled. Inertia was defined as the lack of an AT prescription, despite being indicated by guidelines, whereas NGRP was defined as AT prescription when there was no guideline recommendation. We also recorded cardiovascular variables. Inertia and NGRP were quantified for primary and secondary prevention. Of 108 patients in secondary prevention, 53 had inertia (49.1%, 95% confidence interval (CI): 39.6-58.5%). Associated profile: female (OR=0.460, P=0.091), no dyslipidemia (OR=0.393, P=0.048), no coronary heart disease (OR=0.215, P=0.001) and high diastolic blood pressure (OR=1.076, P=0.016). In primary prevention, NGRP was present in 69 of 595 patients (11.6%, 95% CI: 9.0-14.2%). Associated profile: male (OR=1.610, P=0.089), smoking (OR=2.055, P=0.045), dyslipidemia (OR=3.227, P<0.001) and diabetes (OR=2.795, P<0.001). Although certain factors were clearly associated with these phenomena much still remains to be learnt.

Impacto de la implantación del programa ASIGNA en un servicio de urgencias hospitalario / Impact of using the ASIGNA computer program in a hospital emergency department

Objetivos: Determinar si el programa informático ASIGNA, basado en la asignación de pacientes por equipo enfermero-médico, reduce el tiempo de espera de los usuarios antes de ser valorados por urgenciólogos del Hospital de General Universitario de Elda. Método: Estudio de intervención no aleatorio, realizado en febrero de 2012 y febrero de 2013, tras la instauración del programa ASIGNA. Se determina como variable principal, el tiempo entre el triaje y la valoración por el urgenciólogo, cuantitativamente en minutos y de forma cualitativa mediante el cumplimiento o no de los tiempos determinados para cada color del triaje Manchester. Las variables secundarias fueron: sexo, edad, color de triaje y la cola o densidad de pacientes. Se utiliza el análisis multivariante de regresión lineal múltiple (tiempo triaje -valoración médica en minutos) y regresión logística (tiempo correcto). Se calculan los indicadores de relevancia clínica. Resultados: ASIGNA aumenta el porcentaje de triaje realizado correctamente del 68,2 al 90,6% (p < 0,001), reduce el tiempo triaje -valoración médica de 63,6 min a 25,2 min (p < 0,001) y reduce la cola de pacientes de 23,8 a 18,0 (p < 0,001). Los indicadores de relevancia clínica fueron: reducción del riesgo absoluto de 0,22 (IC 95%: 0,19-0,26, p < 0,001); reducción del riesgo relativo de 0,70 (IC 95%: 0,64-0,76) y números de pacientes a asistir de 5 (IC 95%: 4-6). ASIGNA multiplica por 4 la posibilidad de atender a un paciente que acuda a urgencias en tiempo correcto. Conclusión: La aplicación del programa ASIGNA obtiene una reducción estadísticamente significativa y clínicamente relevante del tiempo entre triaje y valoración médica

Objective: To determine whether the ASIGNA program for assigning patients to nurse-physician teams expedites time until the initial evaluation of a patient by an emergency physician in the Hospital General Universitario de Elda, Spain. Methods: Nonrandomized study of the effect of using the ASIGNA program from February 2012 to February 2013. The main outcomes were times in minutes until triage and until the initial evaluation by an emergency physician, and the qualitative assessment of compliance or not with times considered reasonable for each color-coded priority category in the Manchester triage system. Secondary variables recorded were sex, age, triage color code, and number of patients waiting. Multivariable linear regression analysis was used to study time between triage and physician evaluation in minutes; logistic regression analysis was used to study the associations with reasonable time according to the Manchester triage system. Indicators of clinical relevance were calculated with 95% confidence intervals (CI). Results: The rates of correctly performed triage increased from 68.2% to 90.6% with use of the ASIGNA program (P < .001). Time from triage to physician evaluation decreased from 63.6 minutes to 25.2 minutes (P < .001), and the number of patients waiting decreased from 23.8 to 18.0 (P<.001). Analysis of indicators of clinical relevance showed improvements in both absolute risk (reduction of 0.22; 95% CI, 0.19-0.26; P < .001) and relative risk (reduction of 0.70; 95% CI, 0.64-0.76). The number of waiting patients fell by 5 (95% CI, 4-6) The ASIGNA program made it possible to attend 4-fold more patients within an amount of time considered reasonable. Conclusions: The use of the ASIGNA program leads to statistically significant and clinically relevant reductions in time from triage to physician evaluation