Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence.

Amyotrophic lateral sclerosis (ALS) is a fatal, neurodegenerative motor neuron disease. Although an early diagnosis is crucial to provide adequate care and improve survival, patients with ALS experience a significant diagnostic delay. This study aimed to use real-world data to describe the clinical profile and timing between symptom onset, diagnosis, and relevant outcomes in ALS. Retrospective and multicenter study in 5 representative hospitals and Primary Care services in the SESCAM Healthcare Network (Castilla-La Mancha, Spain). Using Natural Language Processing (NLP), the clinical information in electronic health records of all patients with ALS was extracted between January 2014 and December 2018. From a source population of all individuals attended in the participating hospitals, 250 ALS patients were identified (61.6% male, mean age 64.7 years). Of these, 64% had spinal and 36% bulbar ALS. For most defining symptoms, including dyspnea, dysarthria, dysphagia and fasciculations, the overall diagnostic delay from symptom onset was 11 (6-18) months. Prior to diagnosis, only 38.8% of patients had visited the neurologist. In a median post-diagnosis follow-up of 25 months, 52% underwent gastrostomy, 64% non-invasive ventilation, 16.4% tracheostomy, and 87.6% riluzole treatment; these were more commonly reported (all Ps < 0.05) and showed greater probability of occurrence (all Ps < 0.03) in bulbar ALS. Our results highlight the diagnostic delay in ALS and revealed differences in the clinical characteristics and occurrence of major disease-specific events across ALS subtypes. NLP holds great promise for its application in the wider context of rare neurological diseases.

Patient journey of individuals tested for HCV in Spain: LiverTAI, a retrospective analysis of EHRs through natural language processing.

OBJECTIVES: Limited screening and delays in diagnosis and linkage-to-care are barriers for hepatitis C virus (HCV) elimination. The LiverTAI study focused on patients tested for HCV using AI technologies to describe their demographic and clinical characteristics and pre-testing patient journeys, reflecting clinical practice in hospitals. PATIENTS AND METHODS: LiverTAI is a retrospective, secondary analysis of electronic health records (EHRs) from 6 tertiary Spanish hospitals, extracting unstructured clinical data using natural language processing (NLP) EHRead® technology. Adult subjects with an HCV testing procedure from January 2014 to December 2018 were grouped according to HCV seropositivity and viremia. RESULTS: From 2,440,358 patients, 16,261 patients were tested for HCV (13,602 [83.6%] HCV seronegative; 2659 [16.4%] seropositive). Active HCV viremia appeared in 37.7% (n=1003) of patients, 18.6% (n=494) had negative viremia, and 43.7% (n=1162) unknown viremia. Patient journeys showed core departments (Gastroenterology, Internal Medicine, and Infectious Disease) and others including Emergency perform ample HCV testing in Spanish hospitals, whereas Medical Oncology lags. Patients were PCR-tested and genotyped significantly faster in core departments (p<.001). CONCLUSIONS: Our results highlight hospital departments responsible for HCV testing. However, further testing was sub-optimal during the study period. Therefore, we underscore the need for HCV screening and reflex testing to accelerate diagnosis and linkage-to-care.

Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing.

INTRODUCTION AND OBJECTIVES: Patients with type 2 diabetes (T2D) and stable coronary artery disease (CAD) previously revascularized with percutaneous coronary intervention (PCI) are at high risk of recurrent ischemic events. We aimed to provide real-world insights into the clinical characteristics and management of this clinical population, excluding patients with a history of myocardial infarction (MI) or stroke, using Natural Language Processing (NLP) technology. METHODS: This is a multicenter, retrospective study based on the secondary use of 2014-2018 real-world data captured in the Electronic Health Records (EHRs) of 1,579 patients (0.72% of the T2D population analyzed; n = 217,632 patients) from 12 representative hospitals in Spain. To access the unstructured clinical information in EHRs, we used the EHRead® technology, based on NLP and machine learning. Major adverse cardiovascular events (MACE) were considered: MI, ischemic stroke, urgent coronary revascularization, and hospitalization due to unstable angina. The association between MACE rates and the variables included in this study was evaluated following univariate and multivariate approaches. RESULTS: Most patients were male (72.13%), with a mean age of 70.5±10 years. Regarding T2D, most patients were non-insulin-dependent T2D (61.75%) with high prevalence of comorbidities. The median (Q1-Q3) duration of follow-up was 1.2 (0.3-4.5) years. Overall, 35.66% of patients suffered from at least one MACE during follow up. Using a Cox Proportional Hazards regression model analysis, several independent factors were associated with MACE during follow up: CAD duration (p < 0.001), COPD/Asthma (p = 0.021), heart valve disease (p = 0.031), multivessel disease (p = 0.005), insulin treatment (p < 0.001), statins treatment (p < 0.001), and clopidogrel treatment (p = 0.039). CONCLUSIONS: Our results showed high rates of MACE in a large real-world series of PCI-revascularized patients with T2D and CAD with no history of MI or stroke. These data represent a potential opportunity to improve the clinical management of these patients.

Clinical characteristics and prognostic factors for Crohn's disease relapses using natural language processing and machine learning: a pilot study.

BACKGROUND: The impact of relapses on disease burden in Crohn's disease (CD) warrants searching for predictive factors to anticipate relapses. This requires analysis of large datasets, including elusive free-text annotations from electronic health records. This study aims to describe clinical characteristics and treatment with biologics of CD patients and generate a data-driven predictive model for relapse using natural language processing (NLP) and machine learning (ML). METHODS: We performed a multicenter, retrospective study using a previously validated corpus of CD patient data from eight hospitals of the Spanish National Healthcare Network from 1 January 2014 to 31 December 2018 using NLP. Predictive models were created with ML algorithms, namely, logistic regression, decision trees, and random forests. RESULTS: CD phenotype, analyzed in 5938 CD patients, was predominantly inflammatory, and tobacco smoking appeared as a risk factor, confirming previous clinical studies. We also documented treatments, treatment switches, and time to discontinuation in biologics-treated CD patients. We found correlations between CD and patient family history of gastrointestinal neoplasms. Our predictive model ranked 25 000 variables for their potential as risk factors for CD relapse. Of highest relative importance were past relapses and patients' age, as well as leukocyte, hemoglobin, and fibrinogen levels. CONCLUSION: Through NLP, we identified variables such as smoking as a risk factor and described treatment patterns with biologics in CD patients. CD relapse prediction highlighted the importance of patients' age and some biochemistry values, though it proved highly challenging and merits the assessment of risk factors for relapse in a clinical setting.

Prevalencia y determinantes biomédicos asociados a infecciones post-extracción dental en dos instituciones escuela de estomatología / Prevalence and biomedical determinants associated with post-dental extraction infections in two stomatology school institutions

La presente investigación tuvo como objetivo establecer la prevalencia y los determinantes biomédicos asociados a infecciones post-extracción dental en dos instituciones escuelas de estomatología (Hospital Nacional Rosales y Facultad de Odontología de la Universidad de El Salvador) con los expedientes de pacientes adultos mayores de 18 años de edad a los que se les realizó tratamientos de extracción dental durante el año 2012. Se evaluaron los determinantes biomédicos: edad, el sexo, el estado sistémico y bucal, así como a las indicaciones post-quirúrgicas brindadas a los pacientes y condición de la zona quirúrgica. La población evaluada fue de 729 expedientes clínicos donde de forma aleatoria se seleccionaron 125 para cada institución obteniendo una muestra de 250 unidades de análisis de pacientes adultos mayores de 18 años. Durante la recolección de los datos se fueron revisando los expedientes con una guía de observación diseñada como lista de cotejo para obtener los determinantes biomédicos asociados a infecciones post-extracción dental. Una vez obtenidos los datos se tabularon para realizar las respectivas tablas y gráficas. Posteriormente se realizó la comprobación de hipótesis mediante el Tes-t estadístico Chi-Cuadrado, además se calcularon medidas de asociación y sus intervalos de confianza correspondientes. De acuerdo a los resultados, de la población total atendida el 26% presentó infección de los cuales el 8% presentó una infección establecida, además el sexo femenino es el más numeroso con un 65% (163) y el grupo etáreo más frecuente de 20-29 años, siendo también este grupo el que presentó 31 casos de infección post-extracción dental. De los pacientes sanos el 25.2% presentó signos de infección y de los que presentaron alguna condición sistémicamente solamente el 0.8% presentó signos y síntomas de infección. De 125 pacientes que recibieron antibiótico 36 presentaron infección, y de los 43 que se les dieron técnicas de higiene oral 7 presentaron infección. Según la presencia de infección preoperatoria, no se encontró ningún paciente 100% sano.

To establish the prevalence and bio medical determinants associated to the development of infection post dental extraction in two institutions of School of Dentristryis the objective of the present study. The record of adult patients over 18 year of age, who underwent dental extractions during 2012 were reviewed. The bio medical determinants evaluated were; ago, sex, systemic an oral condition, site of extraction as well as post surgical indications given to patients. The evaluation of 729 clinical records were the total population, from this universe: 125 records from each institution were ramdomly selected, with a 250 analysis units (records), representing adult patient over 18 year old. During the dataq collection, the records were reviewed whitha observation guide, designed as a check list that includes the before mentioned biomedical determinants, associated whit post dental extraction infection. Once the data was collected, the results were tabulated in the respective tables and graphs. Subsequently, hypothesis testings were performed using T-test, Chi-Square. And association measures and their corresponding confidence intervals were alculated. According to obtained data: 26% of attended population had infection (pre or post op?); besides, the female is the most attended population, whit 65%, (163), and the most frecuent age category group was 20-29 years old. This group presented 31 cases of post extraction infection in the surgical site. 25.2% presented infection in the group of patients without systemical compromise and in the group whit systemical compromise only the 0.8% presented without infection. From 125 patients that received antibiotics, (pre or post treatment?): 36 presented infection and from 43 patientes that received oral hygiene techniques: 7 presented infection. The evaluated records reveals that any patient was 100% healthy in their oral condition.