Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 219
Filtrar
Más filtros

Banco de datos
Tipo del documento
Intervalo de año de publicación
1.
Cell ; 170(5): 828-843, 2017 Aug 24.
Artículo en Inglés | MEDLINE | ID: mdl-28841416

RESUMEN

The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution-what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high-definition medicine and project how these technologies will alter the future of medicine.


Asunto(s)
Medicina de Precisión/métodos , Conjuntos de Datos como Asunto , Enfermedad/genética , Monitoreo del Ambiente , Monitores de Ejercicio , Ingeniería Genética , Predisposición Genética a la Enfermedad , Genoma Humano , Encuestas Epidemiológicas , Humanos , Evaluación Nutricional
2.
Circulation ; 146(1): 36-47, 2022 07 05.
Artículo en Inglés | MEDLINE | ID: mdl-35533093

RESUMEN

BACKGROUND: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values to facilitate meaningful recommendations for echocardiography. METHODS: Using 2 232 130 ECGs linked to electronic health records and echocardiography reports from 484 765 adults between 1984 to 2021, we trained machine learning models to predict the presence or absence of any of 7 echocardiography-confirmed diseases within 1 year. This composite label included the following: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction <50%, or interventricular septal thickness >15 mm. We tested various combinations of input features (demographics, laboratory values, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multisite validation trained on 1 site and tested on 10 independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010. RESULTS: Our composite rECHOmmend model used age, sex, and ECG traces and had a 0.91 area under the receiver operating characteristic curve and a 42% positive predictive value at 90% sensitivity, with a composite label prevalence of 17.9%. Individual disease models had area under the receiver operating characteristic curves from 0.86 to 0.93 and lower positive predictive values from 1% to 31%. Area under the receiver operating characteristic curves for models using different input features ranged from 0.80 to 0.93, increasing with additional features. Multisite validation showed similar results to cross-validation, with an aggregate area under the receiver operating characteristic curve of 0.91 across our independent test set of 10 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without preexisting structural heart disease in the year 2010, 11% were classified as high risk and 41% (4.5% of total patients) developed true echocardiography-confirmed disease within 1 year. CONCLUSIONS: An ECG-based machine learning model using a composite end point can identify a high-risk population for having undiagnosed, clinically significant structural heart disease while outperforming single-disease models and improving practical utility with higher positive predictive values. This approach can facilitate targeted screening with echocardiography to improve underdiagnosis of structural heart disease.


Asunto(s)
Cardiopatías , Aprendizaje Automático , Adulto , Ecocardiografía , Electrocardiografía , Cardiopatías/diagnóstico por imagen , Cardiopatías/epidemiología , Humanos , Estudios Retrospectivos
3.
Annu Rev Biomed Eng ; 24: 1-27, 2022 06 06.
Artículo en Inglés | MEDLINE | ID: mdl-34932906

RESUMEN

Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.


Asunto(s)
COVID-19 , Dispositivos Electrónicos Vestibles , Biometría , COVID-19/diagnóstico , Humanos
4.
J Card Fail ; 27(12): 1466-1471, 2021 12.
Artículo en Inglés | MEDLINE | ID: mdl-34428592

RESUMEN

BACKGROUND: Heart failure and sleep-disordered breathing have been increasingly recognized as co-occurring conditions. Their bidirectional relationship warrants investigation into whether heart failure therapy improves sleep and sleep-disordered breathing. We sought to explore the effect of treatment with sacubitril/valsartan on sleep-related endpoints from the AWAKE-HF study. METHODS AND RESULTS: AWAKE-HF was a randomized, double-blind study conducted in 23 centers in the United States. Study participants with heart failure with reduced rejection fraction and New York Heart Association class II or III symptoms were randomly assigned to receive treatment with either sacubitril/valsartan or enalapril. All endpoints were assessed at baseline and after 8 weeks of treatment. Portable sleep-monitoring equipment was used to measure the apnea-hypopnea index, including obstructive and central events. Total sleep time, wake after sleep onset and sleep efficiency were exploratory measures assessed using wrist actigraphy. THE RESULTS WERE AS FOLLOWS: 140 patients received treatment in the double-blind phase (sacubitril/valsartan, n = 70; enalapril, n = 70). At baseline, 39% and 40% of patients randomly assigned to receive sacubitril/valsartan or enalapril, respectively, presented with undiagnosed, untreated, moderate-to-severe sleep-disordered breathing (≥ 15 events/h), and nearly all had obstructive sleep apnea. After 8 weeks of treatment, the mean 4% apnea-hypopnea index changed minimally from 16.3/h to 15.2/h in the sacubitril/valsartan group and from 16.8/h to 17.6/h in the enalapril group. Mean total sleep time was long at baseline and decreased only slightly in both treatment groups at week 8 (-14 and -11 minutes for sacubitril/valsartan and enalapril, respectively), with small changes in wake after sleep onset and sleep efficiency in both groups. CONCLUSIONS: In a cohort of patients with heart failure with reduced rejection fraction who met prescribing guidelines for sacubitril/valsartan, one-third had undiagnosed moderate-to-severe obstructive sleep apnea. The addition of sacubitril/valsartan therapy did not significantly improve sleep-disordered breathing or sleep duration or efficiency. Patients who meet indications for treatment with sacubitril/valsartan should be evaluated for sleep-disordered breathing.


Asunto(s)
Enalapril , Insuficiencia Cardíaca , Aminobutiratos/uso terapéutico , Antagonistas de Receptores de Angiotensina/uso terapéutico , Compuestos de Bifenilo , Combinación de Medicamentos , Enalapril/uso terapéutico , Insuficiencia Cardíaca/tratamiento farmacológico , Humanos , Sueño , Volumen Sistólico , Tetrazoles/uso terapéutico , Valsartán , Vigilia
5.
Europace ; 22(12): 1781-1787, 2020 12 23.
Artículo en Inglés | MEDLINE | ID: mdl-32995870

RESUMEN

AIMS: Screening for asymptomatic atrial fibrillation (AF) could prevent strokes and save lives, but the AF burden of those detected can impact prognosis. New technologies enable continuous monitoring or intermittent electrocardiogram (ECG) snapshots, however, the relationship between AF detection rates and the burden of AF found with intermittent strategies is unknown. We simulated the likelihood of detecting AF using real-world 2-week continuous ECG recordings and developed a generalizable model for AF detection strategies. METHODS AND RESULTS: From 1738 asymptomatic screened individuals, ECG data of 69 individuals (mean age 76.3, median burden 1.9%) with new AF found during 14 days continuous monitoring were used to simulate 30 seconds ECG snapshots one to four times daily for 14 days. Based on this simulation, 35-66% of individuals with AF would be detected using intermittent screening. Twice-daily snapshots for 2 weeks missed 48% of those detected by continuous monitoring, but mean burden was 0.68% vs. 4% in those detected (P < 0.001). In a cohort of 6235 patients (mean age 69.2, median burden 4.6%) with paroxysmal AF during clinically indicated monitoring, simulated detection rates were 53-76%. The Markovian model of AF detection using mean episode duration and mean burden simulated actual AF detection with ≤9% error across the range of screening frequencies and durations. CONCLUSION: Using twice-daily ECG snapshots over 2 weeks would detect only half of individuals discovered to have AF by continuous recordings, but AF burden of those missed was low. A model predicting AF detection, validated using real-world data, could assist development of optimized AF screening programmes.


Asunto(s)
Fibrilación Atrial , Accidente Cerebrovascular , Anciano , Fibrilación Atrial/diagnóstico , Fibrilación Atrial/epidemiología , Electrocardiografía , Electrocardiografía Ambulatoria , Humanos , Tamizaje Masivo , Factores de Riesgo
6.
Circulation ; 137(9): 961-972, 2018 02 27.
Artículo en Inglés | MEDLINE | ID: mdl-29483172

RESUMEN

This publication describes uniform definitions for cardiovascular and stroke outcomes developed by the Standardized Data Collection for Cardiovascular Trials Initiative and the US Food and Drug Administration (FDA). The FDA established the Standardized Data Collection for Cardiovascular Trials Initiative in 2009 to simplify the design and conduct of clinical trials intended to support marketing applications. The writing committee recognizes that these definitions may be used in other types of clinical trials and clinical care processes where appropriate. Use of these definitions at the FDA has enhanced the ability to aggregate data within and across medical product development programs, conduct meta-analyses to evaluate cardiovascular safety, integrate data from multiple trials, and compare effectiveness of drugs and devices. Further study is needed to determine whether prospective data collection using these common definitions improves the design, conduct, and interpretability of the results of clinical trials.


Asunto(s)
Enfermedades Cardiovasculares/diagnóstico , Recolección de Datos/normas , Determinación de Punto Final/normas , Accidente Cerebrovascular/diagnóstico , Ensayos Clínicos como Asunto , Humanos , Estados Unidos , United States Food and Drug Administration
7.
JAMA ; 320(2): 146-155, 2018 07 10.
Artículo en Inglés | MEDLINE | ID: mdl-29998336

RESUMEN

Importance: Opportunistic screening for atrial fibrillation (AF) is recommended, and improved methods of early identification could allow for the initiation of appropriate therapies to prevent the adverse health outcomes associated with AF. Objective: To determine the effect of a self-applied wearable electrocardiogram (ECG) patch in detecting AF and the clinical consequences associated with such a detection strategy. Design, Setting, and Participants: A direct-to-participant randomized clinical trial and prospective matched observational cohort study were conducted among members of a large national health plan. Recruitment began November 17, 2015, and was completed on October 4, 2016, and 1-year claims-based follow-up concluded in January 2018. For the clinical trial, 2659 individuals were randomized to active home-based monitoring to start immediately or delayed by 4 months. For the observational study, 2 deidentified age-, sex- and CHA2DS2-VASc-matched controls were selected for each actively monitored individual. Interventions: The actively monitored cohort wore a self-applied continuous ECG monitoring patch at home during routine activities for up to 4 weeks, initiated either immediately after enrolling (n = 1364) or delayed for 4 months after enrollment (n = 1291). Main Outcomes and Measures: The primary end point was the incidence of a new diagnosis of AF at 4 months among those randomized to immediate monitoring vs delayed monitoring. A secondary end point was new AF diagnosis at 1 year in the combined actively monitored groups vs matched observational controls. Other outcomes included new prescriptions for anticoagulants and health care utilization (outpatient cardiology visits, primary care visits, or AF-related emergency department visits and hospitalizations) at 1 year. Results: The randomized groups included 2659 participants (mean [SD] age, 72.4 [7.3] years; 38.6% women), of whom 1738 (65.4%) completed active monitoring. The observational study comprised 5214 (mean [SD] age, 73.7 [7.0] years; 40.5% women; median CHA2DS2-VASc score, 3.0), including 1738 actively monitored individuals from the randomized trial and 3476 matched controls. In the randomized study, new AF was identified by 4 months in 3.9% (53/1366) of the immediate group vs 0.9% (12/1293) in the delayed group (absolute difference, 3.0% [95% CI, 1.8%-4.1%]). At 1 year, AF was newly diagnosed in 109 monitored (6.7 per 100 person-years) and 81 unmonitored (2.6 per 100 person-years; difference, 4.1 [95% CI, 3.9-4.2]) individuals. Active monitoring was associated with increased initiation of anticoagulants (5.7 vs 3.7 per 100 person-years; difference, 2.0 [95% CI, 1.9-2.2]), outpatient cardiology visits (33.5 vs 26.0 per 100 person-years; difference, 7.5 [95% CI, 7.2-7.9), and primary care visits (83.5 vs 82.6 per 100 person-years; difference, 0.9 [95% CI, 0.4-1.5]). There was no difference in AF-related emergency department visits and hospitalizations (1.3 vs 1.4 per 100 person-years; difference, 0.1 [95% CI, -0.1 to 0]). Conclusions and Relevance: Among individuals at high risk for AF, immediate monitoring with a home-based wearable ECG sensor patch, compared with delayed monitoring, resulted in a higher rate of AF diagnosis after 4 months. Monitored individuals, compared with nonmonitored controls, had higher rates of AF diagnosis, greater initiation of anticoagulants, but also increased health care resource utilization at 1 year. Trial Registration: ClinicalTrials.gov Identifier: NCT02506244.


Asunto(s)
Fibrilación Atrial/diagnóstico , Electrocardiografía Ambulatoria/instrumentación , Dispositivos Electrónicos Vestibles , Anciano , Anticoagulantes/uso terapéutico , Fibrilación Atrial/tratamiento farmacológico , Fibrilación Atrial/epidemiología , Estudios de Cohortes , Comorbilidad , Femenino , Recursos en Salud/estadística & datos numéricos , Humanos , Incidencia , Análisis de Intención de Tratar , Masculino , Tamizaje Masivo , Persona de Mediana Edad , Factores de Riesgo , Dispositivos Electrónicos Vestibles/efectos adversos
8.
Med Care ; 55(12): e137-e143, 2017 12.
Artículo en Inglés | MEDLINE | ID: mdl-29135777

RESUMEN

BACKGROUND: Administrative data are frequently used to identify venous thromboembolism (VTE) for research and quality reporting. However, the validity of these codes, particularly in outpatients, has not been well-established. OBJECTIVE: To determine how well International Classification of Diseases, Ninth Revision (ICD-9) codes for VTE predict chart-confirmed acute VTE in inpatient and outpatients. PATIENTS AND METHODS: We selected 4642 adults with an incident ICD-9 diagnosis of VTE between years 2004 and 2010 from the Cardiovascular Research Network Venous Thromboembolism cohort study. Medical charts were reviewed to determine validity of events. Positive predictive values (PPVs) of ICD-9 codes were calculated as the number of chart-validated VTE events divided by the number with specific VTE codes. Analyses were stratified by VTE type [pulmonary embolism (PE), deep venous thrombosis (DVT)], code position (primary, secondary), and setting [hospital/emergency department (ED), outpatient]. RESULTS: The PPV for any diagnosis of VTE was 64.6% for hospital/ED patients and 30.9% for outpatients. Primary diagnosis codes from hospital/ED patients were more likely to represent acute VTE than secondary diagnosis codes (78.9% vs. 44.4%, P<0.001). Primary hospital/ED codes for PE and lower extremity DVT had higher PPV than for upper extremity DVT (89.1%, 74.9%, and 58.1%, respectively). Outpatient codes were poorly predictive of acute VTE: 28.0% for PE and 53.6% for lower extremity DVT. CONCLUSIONS: ICD-9 codes for VTE obtained from outpatient encounters or from secondary diagnosis codes do not reliably reflect acute VTE. More accurate ways of identifying VTE in outpatients are needed before these codes can be adopted for research or policy purposes.


Asunto(s)
Pacientes Internos , Pacientes Ambulatorios , Indicadores de Calidad de la Atención de Salud , Tromboembolia Venosa/diagnóstico , Adulto , Estudios de Cohortes , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estudios de Validación como Asunto , Trombosis de la Vena/diagnóstico
9.
Am Heart J ; 175: 77-85, 2016 May.
Artículo en Inglés | MEDLINE | ID: mdl-27179726

RESUMEN

Efficient methods for screening populations for undiagnosed atrial fibrillation (AF) are needed to reduce its associated mortality, morbidity, and costs. The use of digital technologies, including wearable sensors and large health record data sets allowing for targeted outreach toward individuals at increased risk for AF, might allow for unprecedented opportunities for effective, economical screening. The trial's primary objective is to determine, in a real-world setting, whether using wearable sensors in a risk-targeted screening population can diagnose asymptomatic AF more effectively than routine care. Additional key objectives include (1) exploring 2 rhythm-monitoring strategies-electrocardiogram-based and exploratory pulse wave-based-for detection of new AF, and (2) comparing long-term clinical and resource outcomes among groups. In all, 2,100 Aetna members will be randomized 1:1 to either immediate or delayed monitoring, in which a wearable patch will capture a single-lead electrocardiogram during the first and last 2 weeks of a 4-month period beginning immediately or 4 months after enrollment, respectively. An observational, risk factor-matched control group (n = 4,000) will be developed from members who did not receive an invitation to participate. The primary end point is the incidence of new AF in the immediate- vs delayed-monitoring arms at the end of the 4-month monitoring period. Additional efficacy and safety end points will be captured at 1 and 3 years. The results of this digital medicine trial might benefit a substantial proportion of the population by helping identify and refine screening methods for undiagnosed AF.


Asunto(s)
Enfermedades Asintomáticas/epidemiología , Fibrilación Atrial , Electrocardiografía Ambulatoria/métodos , Tamizaje Masivo , Accidente Cerebrovascular/prevención & control , Anciano , Fibrilación Atrial/diagnóstico , Fibrilación Atrial/epidemiología , Fibrilación Atrial/fisiopatología , Ahorro de Costo , Femenino , Humanos , Incidencia , Masculino , Tamizaje Masivo/economía , Tamizaje Masivo/instrumentación , Tamizaje Masivo/métodos , Persona de Mediana Edad , Evaluación de Procesos y Resultados en Atención de Salud , Factores de Riesgo , Accidente Cerebrovascular/etiología , Telemedicina/métodos , Estados Unidos/epidemiología
10.
Catheter Cardiovasc Interv ; 88(2): 174-81, 2016 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-26332193

RESUMEN

AIM: To evaluate the impact of antithrombotic regimens during the medical phase of treatment among 13,819 patients with non-ST-segment elevation acute coronary syndromes (NSTE-ACS) treated with an early invasive strategy in the acute catheterization and urgent intervention triage strategy (ACUITY) trial. METHODS AND RESULTS: Endpoints included composite major adverse cardiac events (MACE), major bleeding, and net adverse clinical events (NACE; MACE or major bleeding). The median (interquartile range) duration of antithrombin use in the medical only treatment phase was 6.5 (1.8-22.5) hours. MACE, major bleeding, and NACE during the medical only phase occurred in 63 (0.5%), 117 (0.9%), and 178 (1.3%) patients, respectively. MACE rates in the medical-treatment-only phase were not significantly different between the four randomized medical regimens used (heparin alone, bivalirudin alone, heparin plus a glycoprotein IIb/IIIa inhibitor [GPI], and bivalirudin plus GPI) (Ptrend = 0.65). The lowest rates of major bleeding and NACE during the medical treatment phase occurred in patients treated with bivalirudin alone (Ptrend = 0.0006 and Ptrend = 0.0004, respectively). CONCLUSIONS: In patients with NSTE-ACS undergoing an early invasive strategy, treatment with bivalirudin alone significantly reduced major bleeding and improved net clinical outcomes during the upstream medical management phase with comparable rates of MACE. © 2015 Wiley Periodicals, Inc.


Asunto(s)
Síndrome Coronario Agudo/terapia , Anticoagulantes/administración & dosificación , Antitrombinas/administración & dosificación , Puente de Arteria Coronaria , Enoxaparina/administración & dosificación , Hirudinas/administración & dosificación , Infarto del Miocardio sin Elevación del ST/terapia , Fragmentos de Péptidos/administración & dosificación , Intervención Coronaria Percutánea , Inhibidores de Agregación Plaquetaria/administración & dosificación , Síndrome Coronario Agudo/diagnóstico por imagen , Síndrome Coronario Agudo/mortalidad , Anciano , Anticoagulantes/efectos adversos , Antitrombinas/efectos adversos , Puente de Arteria Coronaria/efectos adversos , Puente de Arteria Coronaria/mortalidad , Quimioterapia Combinada , Enoxaparina/efectos adversos , Femenino , Hemorragia/inducido químicamente , Hirudinas/efectos adversos , Humanos , Masculino , Persona de Mediana Edad , Infarto del Miocardio sin Elevación del ST/diagnóstico por imagen , Infarto del Miocardio sin Elevación del ST/mortalidad , Fragmentos de Péptidos/efectos adversos , Intervención Coronaria Percutánea/efectos adversos , Intervención Coronaria Percutánea/mortalidad , Inhibidores de Agregación Plaquetaria/efectos adversos , Proteínas Recombinantes/administración & dosificación , Proteínas Recombinantes/efectos adversos , Factores de Riesgo , Factores de Tiempo , Resultado del Tratamiento
11.
J Med Internet Res ; 18(6): e116, 2016 06 22.
Artículo en Inglés | MEDLINE | ID: mdl-27334418

RESUMEN

BACKGROUND: Active engagement in the management of hypertension is important in improving self-management behaviors and clinical outcomes. Mobile phone technology using wireless monitoring tools are now widely available to help individuals monitor their blood pressure, but little is known about the conditions under which such technology can effect positive behavior changes or clinical outcomes. OBJECTIVE: To study the influence of wireless self-monitoring program and patient activation measures on health behaviors, medication adherence, and blood pressure levels as well as control of blood pressure in hypertensive patients. METHODS: We examined a subset of 95 hypertensive participants from a 6-month randomized controlled trial designed to determine the utility of a wireless self-monitoring program (n=52 monitoring program, n=43 control), which consisted of a blood pressure monitoring device connected with a mobile phone, reminders for self-monitoring, a Web-based disease management program, and a mobile app for monitoring and education, compared with the control group receiving a standard disease management program. Study participants provided measures of patient activation, health behaviors including smoking, drinking, and exercise, medication adherence, and blood pressure levels. We assessed the influence of wireless self-monitoring as a moderator of the relationship between patient activation and health behaviors, medication adherence, and control of blood pressure. RESULTS: Improvements in patient activation were associated with improvements in cigarette smoking (beta=-0.46, P<.001) and blood pressure control (beta=0.04, P=.02). This relationship was further strengthened in reducing cigarettes (beta=-0.60, P<.001), alcohol drinking (beta=-0.26, P=.01), and systolic (beta=-0.27, P=.02) and diastolic blood pressure (beta=-0.34, P=.007) at 6 months among individuals participating in the wireless self-monitoring program. No differences were observed with respect to medication adherence. CONCLUSIONS: Participation in a wireless self-monitoring program provides individuals motivated to improve their health management with an added benefit above and beyond that of motivation alone. Hypertensive individuals eager to change health behaviors are excellent candidates for mobile health self-monitoring.. TRIAL REGISTRATION: ClinicalTrials.gov NCT01975428, https://clinicaltrials.gov/ct2/show/NCT01975428 (Archived by WebCite at http://www.webcitation.org/6iSO5OgOG).


Asunto(s)
Antihipertensivos/uso terapéutico , Conductas Relacionadas con la Salud , Hipertensión/tratamiento farmacológico , Cumplimiento de la Medicación , Participación del Paciente , Autocuidado , Telemedicina/métodos , Anciano , Consumo de Bebidas Alcohólicas , Presión Sanguínea , Determinación de la Presión Sanguínea , Teléfono Celular , Ejercicio Físico , Femenino , Humanos , Masculino , Persona de Mediana Edad , Aplicaciones Móviles , Fumar
12.
Lancet ; 393(10180): 1493, 2019 Apr 13.
Artículo en Inglés | MEDLINE | ID: mdl-30983579
16.
J Med Internet Res ; 17(9): e215, 2015 Sep 14.
Artículo en Inglés | MEDLINE | ID: mdl-26369254

RESUMEN

BACKGROUND: As a result of the digital revolution coming to medicine, a number of new tools are becoming available and are starting to be introduced in clinical practice. OBJECTIVE: We aim to assess health care professional and consumer attitudes toward new medical technology including smartphones, genetic testing, privacy, and patient-accessible electronic health records. METHODS: We performed a survey with 1406 health care providers and 1102 consumer responders. RESULTS: Consumers who completed the survey were more likely to prefer new technologies for a medical diagnosis (437/1102, 39.66%) compared with providers (194/1406, 13.80%; P<.001), with more providers (393/1406, 27.95%) than consumers (175/1102, 15.88%) reporting feeling uneasy about using technology for a diagnosis. Both providers and consumers supported genetic testing for various purposes, with providers (1234/1406, 87.77%) being significantly more likely than consumers (806/1102, 73.14%) to support genetic testing when planning to have a baby (P<.001). Similarly, 91.68% (1289/1406) of providers and 81.22% (895/1102) of consumers supported diagnosing problems in a fetus (P<.001). Among providers, 90.33% (1270/1406) were concerned that patients would experience anxiety after accessing health records, and 81.95% (1149/1406) felt it would lead to requests for unnecessary medical evaluations, but only 34.30% (378/1102; P<.001) and 24.59% (271/1102; P<.001) of consumers expressed the same concerns, respectively. Physicians (137/827, 16.6%) reported less concern about the use of technology for diagnosis compared to medical students (21/235, 8.9%; P=.03) and also more frequently felt that patients owned their medical record (323/827, 39.1%; and 30/235, 12.8%, respectively; P<.001). CONCLUSIONS: Consumers and health professionals differ significantly and broadly in their views of emerging medical technology, with more enthusiasm and support expressed by consumers.


Asunto(s)
Registros Electrónicos de Salud/normas , Pacientes , Médicos/normas , Recolección de Datos , Atención a la Salud , Femenino , Humanos , Masculino , Persona de Mediana Edad , Encuestas y Cuestionarios , Telemedicina
17.
Lancet ; 391(10125): 1013, 2018 03 17.
Artículo en Inglés | MEDLINE | ID: mdl-29565009
18.
19.
J Card Fail ; 20(7): 459-64, 2014 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-24709663

RESUMEN

BACKGROUND: The electronic health record (EHR) contains a tremendous amount of data that if appropriately detected can lead to earlier identification of disease states such as heart failure (HF). Using a novel text and data analytic tool we explored the longitudinal EHR of over 50,000 primary care patients to identify the documentation of the signs and symptoms of HF in the years preceding its diagnosis. METHODS AND RESULTS: Retrospective analysis consisted of 4,644 incident HF cases and 45,981 group-matched control subjects. Documentation of Framingham HF signs and symptoms within encounter notes were carried out with the use of a previously validated natural language processing procedure. A total of 892,805 affirmed criteria were documented over an average observation period of 3.4 years. Among eventual HF cases, 85% had ≥1 criterion within 1 year before their HF diagnosis, as did 55% of control subjects. Substantial variability in the prevalence of individual signs and symptoms were found in both case and control subjects. CONCLUSIONS: HF signs and symptoms are frequently documented in a primary care population as identified through automated text and data mining of EHRs. Their frequent identification demonstrates the rich data available within EHRs that will allow for future work on automated criterion identification to help develop predictive models for HF.


Asunto(s)
Minería de Datos/estadística & datos numéricos , Registros Electrónicos de Salud/estadística & datos numéricos , Insuficiencia Cardíaca/diagnóstico , Insuficiencia Cardíaca/epidemiología , Vigilancia de la Población , Atención Primaria de Salud , Anciano , Anciano de 80 o más Años , Estudios de Casos y Controles , Estudios de Cohortes , Minería de Datos/métodos , Femenino , Humanos , Masculino , Persona de Mediana Edad , Vigilancia de la Población/métodos , Prevalencia , Atención Primaria de Salud/métodos , Estudios Retrospectivos
20.
J Biomed Inform ; 48: 160-70, 2014 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-24370496

RESUMEN

OBJECTIVE: Healthcare analytics research increasingly involves the construction of predictive models for disease targets across varying patient cohorts using electronic health records (EHRs). To facilitate this process, it is critical to support a pipeline of tasks: (1) cohort construction, (2) feature construction, (3) cross-validation, (4) feature selection, and (5) classification. To develop an appropriate model, it is necessary to compare and refine models derived from a diversity of cohorts, patient-specific features, and statistical frameworks. The goal of this work is to develop and evaluate a predictive modeling platform that can be used to simplify and expedite this process for health data. METHODS: To support this goal, we developed a PARAllel predictive MOdeling (PARAMO) platform which (1) constructs a dependency graph of tasks from specifications of predictive modeling pipelines, (2) schedules the tasks in a topological ordering of the graph, and (3) executes those tasks in parallel. We implemented this platform using Map-Reduce to enable independent tasks to run in parallel in a cluster computing environment. Different task scheduling preferences are also supported. RESULTS: We assess the performance of PARAMO on various workloads using three datasets derived from the EHR systems in place at Geisinger Health System and Vanderbilt University Medical Center and an anonymous longitudinal claims database. We demonstrate significant gains in computational efficiency against a standard approach. In particular, PARAMO can build 800 different models on a 300,000 patient data set in 3h in parallel compared to 9days if running sequentially. CONCLUSION: This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data. This platform can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information. This platform is only a first step and provides the foundation for our ultimate goal of building analytic pipelines that are specialized for health data researchers.


Asunto(s)
Registros Electrónicos de Salud , Informática Médica/métodos , Algoritmos , Área Bajo la Curva , Sistemas de Computación , Sistemas de Apoyo a Decisiones Clínicas , Investigación sobre Servicios de Salud , Humanos , Modelos Teóricos , Reproducibilidad de los Resultados , Programas Informáticos , Tennessee , Factores de Tiempo
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA