Predictive model of persistent choledocholithiasis in patients with acute biliary pancreatitis.

BACKGROUND: Choledocholithiasis causing acute biliary pancreatitis (ABP) may migrate to the duodenum or persist in the common bile duct (CBD). We developed a model for predicting persistent choledocholithiasis (PC) in patients with ABP. METHODS: This retrospective cohort study included 204 patients, age ≥18 years (mean age: 73 years, 65.7% women), admitted for ABP in 2013-2018, with at least a magnetic resonance cholangiopancreatography (MRCP), endoscopic ultrasonography (EUS), and/or endoscopic retrograde cholangiopancreatography (ERCP). Epidemiological, analytical, imaging, and endoscopic variables were compared between patients with and without PC. Multivariate logistic regression analyses were performed to develop a predictive model of PC. RESULTS: Patients underwent MRCP (n=145, 71.1), MRCP and ERCP (n=44, 21.56%), EUS and ERCP (n=1, 0.49%), or ERCP (n=14, 6.86%). PC was detected in 49 patients (24%). PC was strongly associated with CBD dilation, detected in the emergency ultrasound (p<0.001; OR=27; 95% CI: 5.8-185.5), increased blood levels of gamma glutamyl transpeptidase, detected at 72h (p=0.008; OR=3.4; 95% CI: 1.5-8.9); and biliary sludge in the gallbladder (p=0.008; OR=0.03; 95% CI: 0.001-0.3). CONCLUSIONS: The predictive model showed a validated area under the curve (AUC) of 0.858 for detecting PC in patients with ABP. A nomogram was developed based on model results. CONCLUSIONS: The predictive model was highly effective in detecting PC in patients with ABP. Therefore, this model could be useful in clinical practice.

A dynamic simulation model to support reduction in illegal trade within legal wildlife markets.

Sustainable wildlife trade is critical for biodiversity conservation, livelihoods, and food security. Regulatory frameworks are needed to secure these diverse benefits of sustainable wildlife trade. However, regulations limiting trade can backfire, sparking illegal trade if demand is not met by legal trade alone. Assessing how regulations affect wildlife market participants' incentives is key to controlling illegal trade. Although much research has assessed how incentives at both the harvester and consumer ends of markets are affected by regulations, little has been done to understand the incentives of traders (i.e., intermediaries). We built a dynamic simulation model to support reduction in illegal wildlife trade within legal markets by focusing on incentives traders face to trade legal or illegal products. We used an Approximate Bayesian Computation approach to infer illegal trading dynamics and parameters that might be unknown (e.g., price of illegal products). We showcased the utility of the approach with a small-scale fishery case study in Chile, where we disentangled within-year dynamics of legal and illegal trading and found that the majority (∼77%) of traded fish is illegal. We utilized the model to assess the effect of policy interventions to improve the fishery's sustainability and explore the trade-offs between ecological, economic, and social goals. Scenario simulations showed that even significant increases (over 200%) in parameters proxying for policy interventions enabled only moderate improvements in ecological and social sustainability of the fishery at substantial economic cost. These results expose how unbalanced trader incentives are toward trading illegal over legal products in this fishery. Our model provides a novel tool for promoting sustainable wildlife trade in data-limited settings, which explicitly considers traders as critical players in wildlife markets. Sustainable wildlife trade requires incentivizing legal over illegal wildlife trade and consideration of the social, ecological, and economic impacts of interventions.

Un Modelo Dinámico de Simulación para Asistir en la Reducción del Comercio Ilegal dentro de Mercados Legales de Vida Silvestre Resumen El comercio sustentable de vida silvestre es crítico para la conservación de la biodiversidad, los medios de subsistencia y la seguridad alimentaria. Son necesarios marcos regulatorios para asegurar estos diversos beneficios del comercio sustentable de vida silvestre. Sin embargo, las regulaciones que limitan el comercio pueden ser contraproducentes, generando un mercado ilegal si la demanda no se suple solamente con el comercio legal. El análisis de cómo las regulaciones afectan a los incentivos de los participantes del comercio de vida silvestre es de suma importancia para controlar el comercio ilegal. Mientras que muchas investigaciones se han centrado en analizar cómo las regulaciones afectan tanto a quienes consumen como quieren proveen visa silvestre, , poco se ha hecho para entender los incentivos de los intermediarios. Construimos un modelo dinámico de simulación para asistir en la reducción del comercio ilegal de vida silvestre dentro de los mercados legales, enfocándonos en los incentivos que enfrentan los intermediarios para comercializar productos legales o ilegales. Usamos un enfoque de Computación Bayesiana Aproximada para inferir las dinámicas del comercio ilegal y los parámetros que podrían ser desconocidos (p. ej.: el precio de los productos ilegales). Demostramos la utilidad del modelo mediante el caso de estudio de una pesquería de pequeña escala en Chile, en donde desentrañamos las dinámicas del comercio legal e ilegal y estimamos que la mayor parte del pescado comercializado es ilegal. Utilizamos el modelo para analizar el efecto de intervenciones para mejorar la sustentabilidad de la pesquería y para explorar los trade-offs entre metas ecológicas, económicas y sociales. Las simulaciones de escenarios mostraron que incluso incrementos significativos (más del 200%) de parámetros que recreaban intervenciones permitieron solamente mejoras moderadas en la sustentabilidad ecológica y social de la pesquería a un costo económico sustancial. Estos resultados exponen cuán desequilibrados están los incentivos de los intermediarios hacia el comercio de productos ilegales por encima de los legales en esta pesquería. Nuestro modelo proporciona una herramienta innovadora para la promoción del comercio sustentable de vida silvestre en entornos con datos limitados, y considera explícitamente a los intermediarios como actores críticos dentro del comercio de vida silvestre. El comercio sustentable de vida silvestre requiere incentivar el comercio legal sobre el ilegal y la consideración del impacto social, ecológico y económico de las intervenciones.

Towards a predictive model for prevention of the risk of COVID-19 infection.

INTRODUCTION: The scarcity of person-centered applications aimed at developing awareness on the risk posed by the COVID-19 pandemic, stimulates the exploration and creation of preventive tools that are accessible to the population. OBJECTIVE: To develop a predictive model that allows evaluating the risk of mortality in the event of SARS-CoV-2 virus infection. METHODS: Exploration of public data from 16,000 COVID-19-positive patients to generate an efficient discriminant model, evaluated with a score function and expressed by a self-rated preventive interest questionnaire. RESULTS: A useful linear function was obtained with a discriminant capacity of 0.845; internal validation with bootstrap and external validation, with 25 % of tested patients showing marginal differences. CONCLUSION: The predictive model with statistical support, based on 15 accessible questions, can become a structured prevention tool.

INTRODUCCIÓN: La escasez de aplicaciones centradas en la persona y con vistas al desarrollo de la conciencia del riesgo que representa la pandemia de COVID-19 estimula la exploración y creación de herramientas de carácter preventivo accesibles a la población. OBJETIVO: Elaboración de un modelo predictivo que permita evaluar el riesgo de letalidad ante infección por el virus SARS-CoV-2. MÉTODOS: Exploración de datos públicos de 16 000 pacientes positivos a COVID-19, para generar un modelo discriminante eficiente, valorado con una función score y que se expresa mediante un cuestionario autocalificado de interés preventivo. RESULTADOS: Se obtuvo una función lineal útil con capacidad discriminante de 0.845; la validación interna con bootstrap y la externa, con 25 % de los pacientes de prueba, mostraron diferencias marginales. CONCLUSIÓN: El modelo predictivo, basado en 15 preguntas accesibles puede convertirse en una herramienta de prevención estructurada.

Prognostic thirteen-long non-coding RNAs (IncRNAs) could improve the survival prediction of gastric cancer.

OBJECTIVE: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) play important regulatory roles in the tumorigenesis and progression of gastric cancer (GC). The aim of this study was to construct the prognostic predictive model of lncRNAs signature and improve the survival prediction of GC. PATIENTS AND METHODS: The expression profiling of lncRNAs in large GC cohorts was performed from The Cancer Genome Atlas (TCGA) databases using the lncRNAs-mining approach, including training data set (N=160) and testing data set (N=159). A 13-lncRNAs signature significantly associated with overall survival (OS) in the training data set was selected. The prognostic value of this 13-lncRNAs signature was then confirmed in the test validation set and the entire validation set, respectively. RESULTS: Based on lncRNA expression profiling of 319 patients with stomach adenocarcinoma (STAD), prognostic 13-lncRNAs signature was found to be significantly associated with the prognosis of GC. Compared to patients with low-risk scores, patients with high-risk scores had a significantly shorter survival time. Moreover, functional enrichment analysis indicated that this 13-lncRNAs signature was potentially involved in multiple biological processes, such as DNA replication and cell cycle signaling pathway. CONCLUSIONS: The prognostic model of the 13-lncRNAs signature established by our study could improve the survival prediction of GC to a greater extent.

External validation of the EFFECT mortality prediction scale in patients admitted for acute heart failure in Araba, Spain.

AIM: To validate the EFFECT (Enhanced Feedback for Effective Cardiac Treatment) scales, which predict mortality at 1 month and 1 year after admission, in a defined cohort of patients admitted to the Araba University Hospital (HUA) with a diagnosis of acutely decompensated heart failure. METHOD: External validation study of a predictive model, in a retrospective cohort of patients admitted between October 1, 2020 and September 30, 2021. RESULTS: A total of 550 patients were included. The two scales demonstrated good overall discriminatory ability in our series, with an area under ROC (0.755 y 0.756) and values in Brier score (0.094 y 0.194) similar to the original series. Calibration was assessed using the Hosmer-Lemeshow test and calibration plots and was also adequate. All this despite the fact that significant differences were observed in many clinical characteristics between our series and the original one. CONCLUSIONS: The EFFECT scales showed good predictive ability and transportability. The one-month prediction scale was also useful for predicting mortality at one year. For both time periods, mortality was similar in the groups established in the original as low and very low risk.

PROFUGO study protocol: Predictive model for the early diagnosis of anastomotic leak after esophagectomy and gastrectomy.

In esophagogastric surgery, the appearance of an anastomotic leak is the most feared complication. Early diagnosis is important for optimal management and successful resolution. For this reason, different studies have investigated the value of the use of markers to predict possible postoperative complications. Because of this, research and the creation of predictive models that identify patients at high risk of developing complications are mandatory in order to obtain an early diagnosis. The PROFUGO study (PRedictivO Model for Early Diagnosis of anastomotic LEAK after esophagectomy and gastrectomy) is proposed as a prospective and multicenter national study that aims to develop, with the help of artificial intelligence methods, a predictive model that allows for the identification of high-risk cases. of anastomotic leakage and/or major complications by analyzing different clinical and analytical variables collected during the postoperative period of patients undergoing esophagectomy or gastrectomy.

Models to predict bacteremia in the emergency department: a systematic review. / Modelos predictivos de bacteriemia en el servicio de urgencias: revisión sistemática.

OBJECTIVES: Blood cultures are ordered in emergency departments for 15% of patients with suspected infection. The diagnostic yield varies from 2% to 20%. Thirty-day mortality in patients with bacteremia is high, doubling or tripling the rate in patients with the same infection but without bacteremia. Thus, finding an effective model to predict bacteremia that is applicable in emergency departments is an important goal. Shapiro's model is the one traditionally used as a reference internationally. The aim of this systematic review was to compare the predictive power of bacteremia risk models published since 2008, when Shapiro's model first appeared. MATERIAL AND METHODS: We followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, searching in the following databases for articles published between January 2008 and May 31, 2023: PubMed, Web of Science, EMBASE, Lilacs, Cochrane, Epistemonikos, Trip Medical Database, and ClinicalTrials.gov. No language restrictions were specified. The search terms were the following Medical Subject Headings: bacteremia/bacteraemia/blood stream infection, prediction model/clinical prediction rule/risk prediction model, emergencies/emergency/emergency department, and adults. Observational cohort studies analyzing diagnostic yield were included; case-control studies, narrative reviews, and other types of articles were excluded. The Newcastle-Ottawa Scale was used to score quality and risk of bias in the included studies. The results were compared descriptively, without meta-analysis. The protocol was included in the PROSPERO register (CRD42023426327). RESULTS: Twenty studies out of a total of 917 were found to meet the inclusion criteria. The included studies together analyzed 33 182 blood cultures, which detected 5074 cases of bacteremia (15.3%). Eleven studies were of high quality, 7 of moderate quality, and 2 of low quality. The area under the receiver operating characteristic curve (AUC) of Shapiro's model varied from 0.71 to 0.83. Sensitivity was as high as 98%, and specificity ranged from 26% to 69%. Three models with high scores for quality were also supported by both internal and external validation studies: Lee's model (AUC, 0.81; sensitivity 68%; specificity, 81%), the 5MPB-Toledo model (AUC, 0.906 to 0.946), and the MPB-INFURG-SEMES model (AUC, 0.924; sensitivity, 97%; specificity, 76%. CONCLUSION: The 5MPB-Toledo and MPB-INFURG-SEMES are useful for assessing the true risk of bacteremia in patients attended in emergency departments.

OBJETIVO: La obtención de hemocultivos (HC) se realiza en el 15% de los pacientes atendidos con sospecha de infección en los servicios de urgencias (SU) con una rentabilidad diagnóstica variable (2-20%). La mortalidad a 30 días de estos pacientes con bacteriemia es elevada, doble o triple que el resto con el mismo proceso. Así, encontrar un modelo predictivo de bacteriemia eficaz y aplicable en los SU sería muy importante. Clásicamente, el modelo de Shapiro ha sido la referencia en todo el mundo. El objetivo de esta revisión sistemática (RS) es comparar la capacidad para predecir bacteriemia en los SU de los distintos modelos predictivos publicados desde el año 2008 (fecha de publicación del modelo de Shapiro). METODO: Se realiza una RS siguiendo la normativa PRISMA en las bases de datos de PubMed, Web of Science, EMBASE, Lilacs, Cochrane, Epistemonikos, Tripdatabase y ClinicalTrials.gov desde enero de 2008 hasta 31 mayo 2023 sin restricción de idiomas y utilizando una combinación de términos MESH: "Bacteremia/Bacteraemia/Blood Stream Infection", "Prediction Model/Clinical Prediction Rule/Risk Prediction Model", "Emergencies/Emergency/Emergency Department" y "Adults". Se incluyeron estudios de cohortes observacionales (analíticos de rendimiento diagnóstico). Para valorar la calidad del método empleado y el riesgo de sesgos de los artículos incluidos se utilizó la Newcastle-Ottawa Scale (NOS). No se incluyeron estudios de casos y controles, revisiones narrativas y en otros tipos de artículos. No se realizaron técnicas de metanálisis, pero los resultados se compararon narrativamente. El protocolo de la RS se registró en PROSPERO (CRD42023426327). RESULTADOS: Se identificaron 917 artículos y se analizaron finalmente 20 que cumplían los criterios de inclusión. Los estudios incluidos contienen 33.182 HC procesados con 5.074 bacteriemias (15,3%). Once estudios fueron calificados de calidad alta, 7 moderada y 2 baja. El ABC-COR conseguida por el modelo de Shapiro varía de 0,71 a 0,83, con sensibilidad (Se) hasta del 98%, con especificidad (Es) (26% a 69%). Para los tres modelos que tienen validación interna y externa y una buena calidad metodológica, el modelo de Lee consigue un ABC-COR de 0,81 con Se: 68% y Es: 81%, el modelo 5MPB-Toledo consigue un ABC-COR entre 0,91 y 0,95, y el MPB-INFURG-SEMES obtiene una ABC-COR de 0,92 con una Se: 97% y Es: 76%. CONCLUSIONES: Los modelos 5MPB-Toledo y MPB-INFURG-SEMES representan herramientas útiles para la estratificación del riesgo real de bacteriemia en los pacientes atendidos en los SU.

Validation of a predictive model for bacteraemia (MPB5-Toledo) in the patients seen in emergency departments due to infections.

OBJECTIVE: To validate a simple risk score to predict bacteremia (MPB5-Toledo) in patients seen in the emergency departments (ED) due to infections. METHODS: Prospective and multicenter observational cohort study of the blood cultures (BC) ordered in 74 Spanish ED for adults (aged 18 or older) seen from October 1, 2019, to February 29, 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 3.843 blood samples wered cultured. True cases of bacteremia were confirmed in 839 (21.83%). The remaining 3.004 cultures (78.17%) were negative. Among the negative, 172 (4.47%) were judged to be contaminated. Low risk for bacteremia was indicated by a score of 0-2 points, intermediate risk by 3-5 points, and high risk by 6-8 points. Bacteremia in these 3 risk groups was predicted for 1.5%, 16.8%, and 81.6%, respectively. The model's area under the receiver operating characteristic curve was 0.930 (95% CI, 0.916-0.948). The prognostic performance with a model's cut-off value of ≥5 points achieved 94.76% (95% CI: 92.97-96.12) sensitivity, 81.56% (95% CI: 80.11-82.92) specificity, and negative predictive value of 98.24% (95% CI: 97.62-98.70). CONCLUSION: The 5MPB-Toledo score is useful for predicting bacteremia in patients attended in hospital emergency departments for infection.

Usefulness of the 5MPB-Toledo model to predict bacteremia in patients with urinary tract infections in the emergency department.

OBJECTIVE: To analyze the usefulness of a new predictive model of bacteremia (5MPB-Toledo) in patients treated for urinary tract infection (UTI) in the emergency department (ED). METHODS: Prospective and multicenter observational cohort study of the blood cultures (BC) ordered for patients with UTIs in 65 Spanish ED from November 1, 2019, to March 31, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The diagnostic performance was calculated with the chosen cut-off point for sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: A total of 1,499 blood cultures were evaluated. True cases of bacteremia were confirmed in 277 (18.5%). The remaining 1,222 cultures (81.5%) were negative. Ninety-four (6.3%) were considered contaminated. The model's area under the ROC curve was 0.937 (95% CI, 0.926-0.949). The prognostic performance with a model's cut-off value of ≥5 points achieved 97.47% (95% CI, 94.64-98.89) sensitivity, 76.68% (95% CI, 74.18-79.00) specificity, 48.65% (95% CI, 44.42-52.89) positive predictive value and 99.26% (95% CI, 98.41-99.67) negative predictive value. CONCLUSION: The 5MPB-Toledo score is useful for predicting bacteremia in patients with UTIs who visit the ED.

Predicting the response to methylprednisolone pulses in patients with SARS-COV-2 infection.

INTRODUCTION: Treating systemic inflammation caused by SARS-COV 2 (COVID-19) has become a challenge for the clinician. Corticosteroids have been the turning point in the treatment of this disease. Preliminary data from Recovery clinical trial raises hope by showing that treatment with dexamethasone at doses of 6mg/day shows a reduction on morbidity in patients requiring added oxygen therapy. However, both the start day or what kind of corticosteroid, are still questions to be clarified. Since the pandemic beginning, we have observed large differences in the type of corticosteroid, dose and initiation of treatment. Our objective is to assess the predictive capacity of the characteristics of patients treated with methylprednisolone pulses to predict hospital discharge. MATERIALS AND METHODS: We presented a one-center observational study of a retrospective cohort. We included all patients admitted between 03/06/2020 and 05/15/2020 because of COVID-19. We have a total number of 1469 patients, of whom 322 received pulses of methylprednisolone. Previous analytical, radiographic, previous disease data were analyzed on these patients. The univariant analysis was performed using Chi-squared and the T test of Student according to the qualitative or quantitative nature of the variables respectively. For multivariate analysis, we have used binary logistic regression and ROC curves. RESULTS: The analysis resulted statistically significant in dyspnea, high blood pressure, dyslipidemia, stroke, ischemic heart disease, cognitive impairment, solid tumor, C-reactive protein (CRP), lymphopenia and d-dimer within 5 days of admission. Radiological progression and FIO2 input are factors that are associated with a worst prognosis in COVID-19 that receive pulses of methylprednisolone. Multivariate analysis shows that age, dyspnea and C-reactive protein are markers of hospital discharge with an area below the curve of 0.816. CONCLUSIONS: In patients with methylprednisolone pulses, the capacity of the predictive model for hospital discharge including variables collected at 5 days was (area under the curve) 0.816.

Surgical Risk Following Anatomic Lung Resection in Thoracic Surgery: A Prediction Model Derived from a Spanish Multicenter Database. / Riesgo quirúrgico tras resección pulmonar anatómica en cirugía torácica. Modelo predictivo a partir de una base de datos nacional multicéntrica.

INTRODUCTION: The aim of this study was to develop a surgical risk prediction model in patients undergoing anatomic lung resections from the registry of the Spanish Video-Assisted Thoracic Surgery Group (GEVATS). METHODS: Data were collected from 3,533 patients undergoing anatomic lung resection for any diagnosis between December 20, 2016 and March 20, 2018. We defined a combined outcome variable: death or Clavien Dindo grade IV complication at 90 days after surgery. Univariate and multivariate analyses were performed by logistic regression. Internal validation of the model was performed using resampling techniques. RESULTS: The incidence of the outcome variable was 4.29% (95% CI 3.6-4.9). The variables remaining in the final logistic model were: age, sex, previous lung cancer resection, dyspnea (mMRC), right pneumonectomy, and ppo DLCO. The performance parameters of the model adjusted by resampling were: C-statistic 0.712 (95% CI 0.648-0.750), Brier score 0.042 and bootstrap shrinkage 0.854. CONCLUSIONS: The risk prediction model obtained from the GEVATS database is a simple, valid, and reliable model that is a useful tool for establishing the risk of a patient undergoing anatomic lung resection.

Risk models for predicting in-hospital mortality from COVID-19 pneumonia in the elderly. / Modelos de riesgo para la predicción de mortalidad hospitalaria en ancianos con neumonía por COVID-19.

OBJECTIVES: To compare the prognostic value of 3 severity scales: the Pneumonia Severity Index (PSI), the CURB-65 pneumonia severity score, and the Severity Community-Acquired Pneumonia (SCAP) score. To build a new predictive model for in-hospital mortality in patients over the age of 75 years admitted with pneumonia due to the coronavirus disease 2019 (COVID-19). MATERIAL AND METHODS: Retrospective study of patients older than 75 years admitted from the emergency department for COVID-19 pneumonia between March 12 and April 27, 2020. We recorded demographic (age, sex, living in a care facility or not), clinical (symptoms, comorbidities, Charlson Comorbidity Index [CCI]), and analytical (serum biochemistry, blood gases, blood count, and coagulation factors) variables. A risk model was constructed, and the ability of the 3 scales to predict all-cause in-hospital mortality was compared. RESULTS: We included 186 patients with a median age of 85 years (interquartile range, 80-89 years); 44.1% were men. Mortality was 47.3%. The areas under the receiver operating characteristic curves (AUCs) were as follows for each tool: PSI, 0.74 (95% CI, 0.64-0.82); CURB-65 score, 0.71 (95% CI, 0.62-0.79); and SCAP score, 0.72 (95% CI, 0.63-0.81). Risk factors included in the model were the presence or absence of symptoms (cough, dyspnea), the CCI, and analytical findings (aspartate aminotransferase, potassium, urea, and lactate dehydrogenase. The AUC for the model was 0.81 (95% CI, 0.73-0.88). CONCLUSION: This study shows that the predictive power of the PSI for mortality is moderate and perceptibly higher than the CURB-65 and SCAP scores. We propose a new predictive model for mortality that offers significantly better performance than any of the 3 scales compared. However, our model must undergo external validation.

OBJETIVO: Los objetivos son comparar la utilidad pronóstica de tres escalas de gravedad (Pneumonia Severity Index: PSI; CURB-65 scale; Severity Community Acquired Pneumonia Score: SCAP) y diseñar un nuevo modelo predictivo de mortalidad hospitalaria en pacientes mayores de 75 años ingresados por neumonía por COVID-19. METODO: Estudio retrospectivo de pacientes mayores de 75 años ingresados por neumonía por COVID-19 desde el servicio de urgencias entre el 12 de marzo y el 27 de abril de 2020. Se recogieron variables demográficas (edad, sexo, institucionalización), clínicas (síntomas, comorbilidades, índice de Charlson) y analíticas (bioquímica en suero, gasometría, hematimetría, hemostasia). Se derivó un modelo de riesgo y se compararon las escalas de gravedad PSI, CURB-65 y SCAP para predecir la mortalidad intrahospitalaria por cualquier causa. RESULTADOS: Se incluyeron 186 pacientes, con una mediana de edad de 85 años (RIC 80-89), un 44,1% varones. La mortalidad fue del 47,3%. Las escalas PSI, CURB-65 y SCAP tuvieron un área bajo la curva (ABC) de 0,74 (IC 95% 0,64-0,82), 0,71 (IC 95% 0,62-0,79) y 0,72 (IC 95% 0,63-0,81), respectivamente. El modelo predictivo compuesto por la ausencia o presencia de síntomas (tos y disnea), comorbilidad (índice de Charlson) y datos analíticos (aspartato- aminotransferasa, potasio, urea y lactato-deshidrogenasa) tuvo un ABC de 0,81 (IC 95% 0,73-0,88). CONCLUSIONES: Este estudio muestra que la escala PSI tiene una capacidad predictiva de mortalidad moderada, notablemente mejor que las escalas CURB-65 y SCAP. Se propone un nuevo modelo predictivo de mortalidad que mejora significativamente el rendimiento de estas escalas, siendo necesario verificar su validez externa.

Evaluation of Preoperative Clinical and Serological Determinations in Complicated Acute Appendicitis: A Score for Predicting Complicated Appendicitis. / Valoración de parámetros clínicos y analíticos preoperatorios en apendicitis aguda complicada. Score para predecir apendicitis complicada.

BACKGROUND: To analyze whether clinical and analytical parameters differ according to histopathology in cases of acute appendicitis (AA). METHODS: This is a retrospective, observational study including patients (>14 years of age) admitted for suspicion of AA from 1 April 2014 to 31 July 2016. Histopathology was divided into complicated (including perforated and gangrenous AA) and uncomplicated appendicitis (phlegmonous). Sex, age, temperature of patients on admission to the Emergency Department, symptom duration, preoperative white blood cell (WBC) count, neutrophil percentage, mean platelet volume (MPV), platelet distribution width (PDW), C-reactive protein (CRP) and hospital stay were compared in the two groups. RESULTS: Three hundred and thirty-five patients were analyzed, and 284 were included. Appendicitis was uncomplicated in 194 (68.3%) and complicated in 90 (31.7%). Age, symptom duration, neutrophil percentage, CRP and hospital stay were higher in the complicated AA group (P < .05). The mean differences between uncomplicated and complicated AA were: age 13.2 years (95% CI: 8.2-18.2), symptom duration 14.1hours (95% CI: 6.3-21.9), neutrophil percentage 5.0% (95% CI: 3.2-6.8), CRP 73.6mg/l (95% CI: 50.0-97.2) and hospital stay 2.2 days (95% CI: 1.4-3.0), with p<0.05 for all these variables. A model based on the preoperative parameters (age, symptom duration, neutrophil percentage and CRP) was calculated to predict the likelihood of complicated AA. The receiver operating characteristic (ROC) of the model had an area under the curve of 0.80 (95% CI 0.75-0.85). CONCLUSION: This model is able to diagnose complicated AA without the need for imaging techniques, although it must be validated with prospective analysis.

Validation of a predictive model for bacteraemia (MPB5-Toledo) in the patients seen in emergency departments due to infections. / Validación del modelo predictivo de bacteriemia (5MPB-Toledo) en los pacientes atendidos en el servicio de urgencias por infección.

OBJECTIVE: To validate a simple risk score to predict bacteremia (MPB5-Toledo) in patients seen in the emergency departments (ED) due to infections. METHODS: Prospective and multicenter observational cohort study of the blood cultures (BC) ordered in 74 Spanish ED for adults (aged 18 or older) seen from from October 1, 2019, to February 29, 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 3.843 blood samples wered cultured. True cases of bacteremia were confirmed in 839 (21.83%). The remaining 3.004 cultures (78.17%) were negative. Among the negative, 172 (4.47%) 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 1.5%, 16.8%, and 81.6%, respectively. The model's area under the receiver operating characteristic curve was 0.930 (95% CI, 0.916-0.948). The prognostic performance with a model's cut-off value of ≥ 5 points achieved 94.76% (95% CI: 92.97-96.12) sensitivity, 81.56% (95% CI: 80.11-82.92) specificity, and negative predictive value of 98.24% (95% CI: 97.62-98.70). CONCLUSION: The 5MPB-Toledo score is useful for predicting bacteremia in patients attended in hospital emergency departments for infection.

Predictive Model for Allergic Contact Dermatitis in Patients With Hand Eczema. / Modelo predictivo de dermatitis alérgica de contacto en pacientes con eccema de manos.

INTRODUCTION: Patch tests are only indicated for hand eczema when it is diagnosed as chronic. A positive reaction of current relevance requires a change in treatment strategy. Knowing which clinical factors are associated with current relevance would allow tests to be performed sooner. OBJECTIVE: To develop a model for predicting currently relevant patch test positivity in patients with hand eczema only. MATERIAL AND METHODS: Retrospective study of patients with hand eczema only. We collected data on age, sex, time since onset, occupation, and history of atopic dermatitis. We built a predictive logistic regression model and assessed discrimination by computing the area under the receiver operating characteristic curve. RESULTS: We included 262 patients; 66.03% had positive patch tests (28.6% of current relevance). Univariate analysis detected significant associations between positivity of current relevance and employment as a hairdresser-aesthetician, a personal history of atopy, male sex, and a time since onset of over 6 months. Multivariate analysis confirmed employment as a hairdresser-aesthetician as an independent risk factor and male sex and a personal history of atopy as protective factors. The score suggested by the predictive model was 2.316(hairdresser-aesthetician)-1.792(atopic dermatitis)-0.601(male sex). CONCLUSIONS: Occupation, sex, and a history of atopy influence the likelihood of patch test positivity of current relevance in patients with hand eczema in Spain. Our model suggests that a diagnosis of chronic eczema is not necessarily an indication for patch testing. Future studies with larger samples are needed to determine the true usefulness of predictive models in this setting.

Predictive model of in-hospital mortality in left-sided infective endocarditis.

INTRODUCTION AND OBJECTIVES: Infective endocarditis (IE) is a complex disease with high in-hospital mortality. Prognostic assessment is essential to select the most appropriate therapeutic approach; however, international IE guidelines do not provide objective assessment of the individual risk in each patient. We aimed to design a predictive model of in-hospital mortality in left-sided IE combining the prognostic variables proposed by the European guidelines. METHODS: Two prospective cohorts of consecutive patients with left-sided IE were used. Cohort 1 (n=1002) was randomized in a 2:1 ratio to obtain 2 samples: an adjustment sample to derive the model (n=688), and a validation sample for internal validation (n=314). Cohort 2 (n=133) was used for external validation. RESULTS: The model included age, prosthetic valve IE, comorbidities, heart failure, renal failure, septic shock, Staphylococcus aureus, fungi, periannular complications, ventricular dysfunction, and vegetations as independent predictors of in-hospital mortality. The model showed good discrimination (area under the ROC curve=0.855; 95%CI, 0.825-0.885) and calibration (P value in Hosmer-Lemeshow test=0.409), which were ratified in the internal (area under the ROC curve=0.823; 95%CI, 0.774-0.873) and external validations (area under the ROC curve=0.753; 95%CI, 0.659-0.847). For the internal validation sample (observed mortality: 29.9%) the model predicted an in-hospital mortality of 30.7% (95%CI, 27.7-33.7), and for the external validation cohort (observed mortality: 27.1%) the value was 26.4% (95%CI, 22.2-30.5). CONCLUSIONS: A predictive model of in-hospital mortality in left-sided IE based on the prognostic variables proposed by the European Society of Cardiology IE guidelines has high discriminatory ability.

When to perform renal biopsy in patients with type2 diabetes mellitus? Predictive model of non-diabetic renal disease. / ¿Cuándo realizar biopsia renal en pacientes con diabetes mellitus tipo2? Modelo predictivo de enfermedad renal no diabética.

INTRODUCTION: Diabetic nephropathy (DN) is one of the most frequent complications in patients with diabetes mellitus (DM) and its diagnosis is usually established on clinical grounds. However, kidney involvement in some diabetic patients can be due to other causes, and renal biopsy might be needed to exclude them. The aim of our study was to establish the clinical and analytical data that predict DN and no-diabetic renal disease (NDRD), and to develop a predictive model (score) to confirm or dismiss DN. MATERIAL AND METHODS: We conducted a transversal, observational and retrospective study, including renal biopsies performed in type2 DM patients, between 2000 and 2018. RESULTS: Two hundred seven DM patients were included in our study. The mean age was 64.5±10.6 years and 74% were male. DN was found in 126 (61%) of the biopsies and NDRD in 81 (39%). Diabetic retinopathy was presented in 58% of DN patients, but only in 6% of NDRD patients (P<.001). Patients with NDRD were diagnosed of primary glomerulopathies (52%), nephroangiosclerosis (16%), inmunoallergic interstitial nephritis (15%) and vasculitis (8.5%). In the multivariate analysis, retinopathy (OR26.7; 95%CI: 6.8-104.5), chronic ischaemia of lower limbs (OR4,37; 95%CI: 1.33-14.3), insulin therapy (OR3.05; 95%CI: 1.13-8.25), time course of DM ≥10years (OR2.71; 95%CI: 1.1-6.62) and nephrotic range proteinuria (OR2.91; 95%CI: 1.2-7.1) were independent predictors for DN. Microhaematuria defined as ≥10 red blood cells per high-power field (OR0.032; 95%CI: 0.01-0.11) and overweight (OR0.21; 95%CI: 0.08-0.5) were independent predictors of NDRD. According to the predictive model based on the multivariate analysis, all patients with a score >3 had DN and 94% of cases with a score ≤1 had NDRD (score ranked from -6 to 8points). CONCLUSIONS: NDRD is common in DM patients (39%), being primary glomerulonephritis the most frequent ethology. The absence of retinopathy and the presence of microhematuria are highly suggestive of NDRD. The use of our predictive model could facilitate the indication of performing a renal biopsy in DM patients.

Development and validation of a tool for predicting type 2 diabetes in Mexican women of reproductive age.

INTRODUCTION: Diabetes is a worldwide problem with a greater impact in developing countries, where many people are unaware of their risk. In Mexico, women show the greatest risk for T2D. Current risk scores have been developed and validated in predominantly older European cohorts. They are not the best option in Mexican women. The development of a risk model/score in this population would be useful. OBJECTIVE: To develop and validate a risk model and score that incorporates the most relevant risk factors for T2D in Mexican women of reproductive age. METHODS: The study was carried out in two phases, with the first phase being the development of the predictive model and the second phase the validation of the model in a separate independent population. A cohort of Mexican patients of reproductive age ("Derivation Cohort") was used to create the predictive model. It included data on 3161 women. Risk factors for identification were assessed using Cox proportional hazards regression. Finally a score with a range of 0 to 19 points was developed to identify the 2.4 year probability of developing DM2 in Mexican women of reproductive age. RESULTS: 147 new cases of T2D (4.6%) were identified in the Derivation Cohort model, 97 of 925 participants (10.48%) in the validation cohort. The risk factor predictors of T2D were: history of gestational diabetes (HR 2.69, 95% CI 1.10-6.58), BMI (HR 1.03, 95% CI 1.01-1.06), hypertriglyceridemia (HR 1.54, 95% CI 1.11-2.14) and fasting blood glucose (HR 1.06, 95% CI 1.05-1.08), with an AUC of 0.75. The AUC in the validation cohort was 0.91 (95% CI 0.87-0.94). The score had a sensitivity of 73% and specificity of 67% at a cutoff of ≥15. CONCLUSIONS: A predictive model and risk score was developed to detect cases at risk for incident T2D. It was generated using the characteristics of Mexican women of reproductive age. This risk score is a step forward in attempting to address the generational legacy that diabetes in pregnancy could have on women and their children.

Mortality prediction model for patients with bladder urothelial tumor after radical cystectomy. / Modelo predictivo de mortalidad en pacientes con tumor urotelial de vejiga tras cistectomía radical.

OBJECTIVE: Based on preoperative clinical and postoperative pathological variables, we aim to build a prediction model of cancer specific mortality (CSM) at 1, 3, and 5 years for patients with bladder transitional cell carcinoma treated with RC. MATERIAL AND METHODS: Retrospective analysis of 517 patients with diagnosis of cell carcinoma treated by RC (1986-2009). Demographic, clinical, surgical and pathological variables were collected, as well as complications and evolution after RC. Comparative analysis included Chi square test and ANOVA technique. Survival analysis was performed using Kaplan-Meier method and log-rank test. Univariate and multivariate analyses were performed using logistic regression to identify the independent predictors of CSM. The individual probability of CSM was calculated at 1, 3 and 5 years according to the general equation (logistic function). Calibration was obtained by the Hosmer-Lemeshow method and discrimination with the elaboration of a ROC curve (area under the curve). RESULTS: BC was the cause of death in 225 patients (45%). One, three and five-year CSM were 17%, 39.2% and 46.3%, respectively. The pT and pN stages were identified as independent prognostic variables of CSM at 1, 3 and 5 years. Three prediction models were built. The predictive capacity was 70.8% (CI 95% 65-77%, p=.000) for the 1st year, 73.9% (CI95% 69.2-78.6%, p=.000) for the third and 73.2% (CI% 68.5-77.9%, p=.000) for the 5th. CONCLUSIONS: The prediction model allows the estimation of CSM risk at 1, 3 and 5 years, with a reliability of 70.8, 73.9 and 73.2%, respectively.

Development and External Validation of an Early Prognostic Model for Survivors of Out-of-hospital Cardiac Arrest.

INTRODUCTION AND OBJECTIVES: Despite therapeutic hypothermia, unconscious survivors of out-of-hospital cardiac arrest have a high risk of death or poor neurologic function. Our objective was to assess the usefulness of the variables obtained in the early moments after resuscitation in the prediction of 6-month prognosis. METHODS: A multicenter study was performed in 3 intensive cardiac care units. The analysis was done in 153 consecutive survivors of out-of-hospital cardiac arrest who underwent targeted temperature management between January 2007 and July 2015. Significant neurological sequelae at 6 months were considered to be present in patients with Cerebral Performance Categories Scale > 2. An external validation was performed with data from 91 patients admitted to a third hospital in the same time interval. RESULTS: Among the 244 analyzed patients (median age, 60 years; 77.1% male; 50.0% in the context of acute myocardial ischemia), 107 patients (43.8%) survived with good neurological status at 6 months. The prediction model included 5 variables (Shockable rhythm, Age, Lactate levels, Time Elapsed to return of spontaneous circulation, and Diabetes - SALTED) and provided an area under the curve of 0.90 (95%CI, 0.85-0.95). When external validation was performed, the predictive model showed a sensitivity of 73.5%, specificity of 78.6%, and area under the curve of 0.82 (95%CI, 0.73-0.91). CONCLUSIONS: A predictive model that includes 5 clinical and easily accessible variables at admission can help to predict the probability of survival without major neurological damage following out-of-hospital cardiac arrest.