Utilidad del modelo 5MPB-Toledo para predecir bacteriemia en el paciente anciano / Usefulness of the 5MPB-Toledo model to predict bacteremia in the elderly patient

Resumen Objetivo: Analizar la utilidad del modelo predictivo de bacteriemia (5MPB-Toledo) en los mayores de 65 años atendidos por infección en el servicio de urgencias (SU). Material y Método: Estudio observacional prospectivo y multicéntrico de los hemocultivos (HC) obtenidos en pacientes mayores de 65 años atendidos por infección en 66 SU españoles desde el 1 de diciembre de 2019 hasta el 30 de abril de 2020. Se analizó la capacidad predictiva del modelo con el área bajo la curva (ABC) de la característica operativa del receptor (COR) y se calculó el rendimiento diagnóstico de los puntos de corte (PC) del modelo elegido con los cálculos de la sensibilidad, la especificidad, el valor predictivo positivo y el valor predictivo negativo. Resultados: Se incluyeron 2.401 episodios de HC extraídos. De ellos, se consideró como bacteriemia verdadera a 579 (24,11%) y como HC negativo a 1.822 (75,89%). Entre los negativos, 138 (5,74%) se consideraron contaminados. Se categorizó a los pacientes en bajo (0-2 puntos), moderado (3-5 puntos) y alto (6-8 puntos) riesgo, con una probabilidad de bacteriemia de 1,2%, 18,1% y 80,7%, respectivamente. El ABC-COR del modelo tras remuestreo fue de 0,908 (IC 95%: 0,897-0,924). El rendimiento diagnóstico del modelo, considerando un PC ≥ 5 puntos, obtiene una sensibilidad de 94% (IC 95%:92-96), especificidad de 77% (IC 95%:76-79) y un valor predictivo negativo de 97% (IC 95%:96-98). Conclusión: El modelo 5MPB-Toledo es de utilidad para predecir bacteriemia en los mayores de 65 años atendidos en el SU por un episodio de infección.

Abstract Objective: To analyse a risk score to predict bacteremia (MPB5-Toledo) in the patients aged older 65 years seen in the emergency departments (ED) due to infections. Patients and Methods: Prospective and multicenter observational cohort study of the blood cultures (BC) ordered in 66 Spanish ED for patients aged older 65 years seen from December 1, 2019, to April 30, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The prognostic performance for true bacteremia was calculated with the cut-off values chosen for getting the sensitivity, specificity, positive predictive value and negative predictive value. Results: A total of 2.401 blood samples wered cultured. True cases of bacteremia were confirmed in 579 (24.11%). The remaining 1.822 cultures (75.89%) wered negative. And, 138 (5.74%) were judged to be contaminated. Low risk for bacteremia was indicated by a score of 0 to 2 points, intermediate risk by 3 to 5 points, and high risk by 6 to 8 points. Bacteremia in these 3 risk groups was predicted for 1.2%, 18.1%, and 80.7%, respectively. The model´s area under the receiver ope rating characteristic curve was 0.908 (95% CI, 0.897-0.924). The prognostic performance with a model´s cut-off value of ≥ 5 points achieved 94% (95% CI: 92-96) sensitivity, 77% (95% CI: 76-79) specificity, and negative predictive value of 97% (95% CI: 96-98). Conclusion: The 5MPB-Toledo score is useful for predicting bacteremia in the patients aged older 65 years seen in the emergency departments due to infections.

Advance in Prediction for Outcome of Cervical Whiplash-associated Disorders (review) / 中国康复理论与实践

@#Whiplash-associated disorders of the cervical vertebrae develop unevenly after the injury, and the recovery process is complicated and non-linear. It is important to identify factors related with poor recovery in the early stages, to select proper treatment and rehabilitation. However, there are too many prognostic factors related to the clinical outcome, while the Clinical Prediction Rules (CPR) are used to help decision-making. Now there are only Whiplash CPR, Assessment Tool for Whiplash Injuries Developed from A Prospective Observational Study and CPR-like Tool undergone the clinical trials.

Una regla de predicción clínica ¿anticipa el diagnóstico de la faringitis estreptocóccica en niños de 2 a 15 años? / Does a clinical prediction rule anticipate the diagnosis for streptococcal pharyngitis in children aged 2 to 15?

Resumen Introducción: La etiología estreptocóccica de una faringitis debe ser confirmada con métodos de laboratorio para evitar un sobre-tratamiento antimicrobiano, exámenes que agregan costo a la atención del paciente. Los scores diseñados para aplicar en niños y adultos son imperfectos. Objetivo: Desarrollar una regla de predicción clínica para contribuir al diagnóstico de la faringitis estreptocóccica (FE) en niños. Pacientes y Métodos: Se incluyeron 318 pacientes de 2 a 15 años que fueron evaluados por sospecha de FE en el Servicio de Urgencias Pediátricas y la Unidad de Pediatría Ambulatoria de la Red Salud UC-Christus. Se obtuvo un cultivo faríngeo y una prueba rápida de detección de antígeno para Streptococcus pyogenes de cada paciente. Los datos se analizaron para posibles predictores clínicos de FE con análisis de regresión múltiple. Resultados. Setenta y tres casos de FE fueron diagnosticados (23,9%). En el análisis univariado, la fiebre se asoció inversamente con FE (p = 0,002). La odinofagia, las petequias palatinas y la estación del año (otoño e invierno) se asociaron positivamente con FE (p = 0,007, p < 0,001 y p = 0,03 respectivamente). En el análisis de regresión múltiple, los modelos no tuvieron suficiente poder para predecir etiología por S. pyogenes. Conclusión: Los predictores clínicos analizados, incluso los incluidos sistemáticamente en reglas de predicción clínica, no mostraron suficiente poder predictor para incluir o excluir de forma segura la FE en este contexto y, por lo tanto, sería necesario mejorar el acceso a las pruebas de confirmación.

Background: The etiology of a streptococcal pharyngitis must be documented by laboratory techniques to avoid unnecessary antimicrobial treatment, but this strategy increases cost for the patient. Available scores applied in children or adults are imperfect. Aim: To develop a clinical prediction rule to aid the diagnostic process of streptococcal pharyngitis (SP) in children in a low-resource setting. Methods: Three hundred and eighteen patients aged 2 to 15 years who were evaluated for suspected SP at the Pediatric Emergency Department and the Pediatric Ambulatory Unit of Red Salud UC-Christus entered the study. A throat culture and a rapid antigen detection test for Streptococcus pyogenes were obtained from each patient. Data were analyzed for possible clinical predictors of SP with univariate and multiple regression analyses. Results: Seventy-three cases of SP were diagnosed (23.9%). In the univariate analysis, fever was inversely associated with SP (p = 0.002). Odynophagia, palatal petechiae, and season of the year (autumn and winter) were positively associated with SP (p = 0.007, p < 0.001 and p = 0.03 respectively). In multiple regression analysis the models did not have sufficient power to predict streptococcal etiology. Conclusion: Clinical predictors, even those systematically included in clinical prediction rules, did not show sufficient predictive power to safely include or exclude SP in this setting, and thus, it is necessary to improve access to confirmatory tests.

Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the “large p and small n” problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

Applying the bacterial meningitis score in children with cerebrospinal fluid pleocytosis: a single center's experience / 소아과

PURPOSE: The widespread introduction of bacterial conjugate vaccines has decreased the risk of cerebrospinal fluid (CSF) pleocytosis due to bacterial meningitis (BM) in children. However, most patients with CSF pleocytosis are hospitalized and treated with parenteral antibiotics for several days. The bacterial meningitis score (BMS) is a validated multivariate model derived from a pediatric population in the postconjugate vaccine era and has been evaluated in several studies. In the present study, we examined the usefulness of BMS in South Korean patients. METHODS: This study included 1,063 patients with CSF pleocytosis aged between 2 months and 18 years. The BMS was calculated for all patients, and the sensitivity and negative predictive value (NPV) of the test were evaluated. RESULTS: Of 1,063 patients, 1,059 (99.6%) had aseptic meningitis (AM). Only four patients (0.4%) had BM. The majority of patients (98%) had a BMS of or =4. CONCLUSION: To our knowledge, this is the first study to investigate the diagnostic strength of the BMS in South Korea. In our study, the BMS showed 100% sensitivity and 100% NPV. Therefore, we believe that the BMS is a good clinical prediction rule to identify children with CSF pleocytosis who are at a risk of BM.

Clinical Predictors of Subarachnoid Hemorrhage in Patients with Acute Non-traumatic Headache in Emergency Department

PURPOSE: Missing subarachnoid hemorrhage (SAH) can cause catastrophic results. We aimed to find clinical factors for predicting SAH in neurologically intact patients with acute non-traumatic headache visiting the emergency department (ED). METHODS: This was a retrospective chart review study. Data were collected from September 2006 until October 2011. We included patients aged over 16 with acute non-traumatic headache who had brain imaging work up results during ED visits. Information on candidate clinical predictor variables was obtained from previous reports, and the outcome was confirmed SAH in brain imaging work up or cerebrospinal fluid study. We found the predictors for SAH through multivariable analysis with variables chosen in univariable analysis considering clinical application. Then we simulated possible SAH prediction scoring models using receiver operating characteristic (ROC) analysis and assessed model fit through the Hosmer-Lemeshow test. RESULTS: A total of 3294 patients were enrolled. Seven clinical characteristics were proven for relation of SAH; age, visiting emergency department within six hours from symptom onset time, visiting mode, vomiting, neck pain or neck stiffness, blood pressure, and respiratory rate. We constructed six available SAH prediction scoring models. The area under the ROC curves of each model ranged from 0.810 to 0.834 and all simulated models were good-fit. With these models, we can expect to reduce unnecessary computed tomography use. CONCLUSION: Seven clinical predictors could be helpful in selection of high risk patients of SAH. The proposed SAH prediction models using these characteristics will have to be tested prospectively for external validation.

Clinical Efficacy of Entecavir and Factors Predicting Long-Term Treatment Response in Nucleoside-Naive Patients with Chronic Hepatitis B / 대한내과학회지

BACKGROUND/AIMS: The aims of this study were to characterize the treatment response to entecavir and to examine factors affecting that response. METHODS: A total of 77 nucleoside-naive patients with chronic hepatitis B who had received entecavir (0.5 mg daily) for at least 48 weeks were consecutively enrolled between March 2007 and March 2011. The rates of virological response (hepatitis B virus [HBV] DNA < 116 copies/mL), biochemical response (alanine aminotransferase < or = upper limit of normal), hepatitis B e antigen (HBeAg) loss, and seroconversion were retrospectively analyzed. RESULTS: The cumulative rates of virological response at 12, 24, 48, 96, and 144 weeks were 59.7%, 82%, 88.3%, 89.6%, and 93.1%, respectively; biochemical response rates were 51.9%, 74%, 84.4%, 94.8%, and 98.3%, respectively; HBeAg loss rates were 10.5%, 18.4%, 28.9%, 36.8%, and 47.4%, respectively; and HBeAg seroconversion rates were 7.9%, 18.4%, 21.1%, 28.9%, and 39.5%, respectively. In multivariate analysis, independent predictors associated with HBV DNA polymerase chain reaction (PCR) negativity were the absence of HBeAg at baseline (p = 0.006) and early virological response (HBV DNA < 2,000 copies/mL after 12 weeks of therapy; p = 0.027). In univariate analysis, early virological response was an independent factor predicting HBeAg loss (p = 0.001). CONCLUSIONS: Entecavir induced excellent biochemical and virological responses in nucleoside-naive patients with chronic hepatitis B. Early virological response was an independent factor predicting HBV PCR negativity and HBeAg loss, and can be used to predict long-term treatment response to entecavir.

A clinical prediction rule to identify patients with tuberculosis at high risk for HIV co-infection.

Background & objective: Many patients presenting with tuberculosis (TB) have underlying human immunodeficiency virus (HIV) co-infection. Routine HIV testing, however, is not a component of the national TB control programme in India. We sought to derive and validate a clinical prediction rule, based on clinical and laboratory parameters, to identify patients at high risk for HIV co-infection among those treated for active TB. Methods: Case records of adult patients with active TB treated between 1997 and 2003 at the All India Institute of Medical Sciences hospital, New Delhi were retrospectively reviewed. The data set was randomly split into a training set and a testing set. First a clinical prediction rule was derived by multivariable logistic regression on the training set and was subsequently validated on the testing set. Results: The study group comprised 1074 patients [training set 711 (66%), HIV co-infected 66 (9%); testing set 363 (34%), HIV co-infected 30 (8%)]. In the training set, male gender [odds ratio (95% CI) 5.31(1.52- 18.61)], axillary lymphadenopathy [9.71 (3.24-29.10)], anaemia [7.56 (2.48-23.05)], hypoalbuminaemia [3.67(1.31-10.26)], and reduced triceps skinfold thickness [2.91(0.95-8.89)] were independently associated with HIV co-infection. In the testing set, presence of any two of these five features was 94 per cent (95% CI 84-100%) sensitive and 54 per cent (49-60%) specific for predicting HIV co-infection; negative predictive value was 99 per cent (98-100%). Area under the receiver-operating characteristic curve was 0.93 (0.86-1.0) in the testing set. Interpretation & conclusions: A simple clinical prediction rule based on clinical and laboratory parameters could be used to identify a subgroup of patients, among those treated for active TB in a hospital setting, for targeted HIV testing.