328 Matching Annotations
  1. May 2024
    1. Socioeconomic status, lung function and admission to hospitalfor COPD: results from the Copenhagen City Heart Study
    1. The relationship between physical activity and health status in patients withchronic obstructive pulmonary disease following pulmonary rehabilitation
  2. Apr 2024
    1. Are Immigrant Enclaves Healthy Places to Live? The Multi-ethnicStudy of Atherosclerosi
    1. Social Environment and Physical activity:A review of concepts and evidence



  3. Feb 2024
  4. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    1. Strengthening the Reporting of ObservationalStudies in Epidemiology (STROBE): Explanationand Elaboration

      STROBE guidelines

  5. Jul 2023
    1. Commentary: Causal Inferencefor Social Exposures

      Commentary: Causal Inference for Social Exposures



  6. May 2023
    1. Even if the COPD exacerbation resolves, many patients never return to their baseline level of health [5].

      Importante para justificación

    2. COPD exacerbations: Prognosis, discharge planning, and prevention

      COPD exacerbations: Prognosis, discharge planning, and prevention

    1. Does Early Treatment of Exacerbation ImproveOutcome in Chronic Obstructive Pulmonary Disease?

      Does Early Treatment of Exacerbation Improve Outcome in Chronic Obstructive Pulmonary Disease?

    1. Minimum sample size for external validation of a clinicalprediction model with a binary outcome

      Minimum sample size for external validation of a clinical prediction model with a binary outcome

    1. Details on sample size and power calculation

      Sample size


  7. Mar 2023
    1. The term "immortal time" refers to a period of time during which an individual is not at risk of the outcome of interest, either because they have not yet been exposed to the treatment or intervention, or because they have not yet reached a certain point in time when the outcome can occur. During this time, the individual is "immortal" in the sense that they cannot experience the outcome, even if they would have if they had been at risk.

      Definition of immortal time bias

    1. Thesmartwatch and smartphone applications are Android applications that are programmed to collect data fromthe embedded sensors, save the data locally on each device and offload them to the TOLIFE cloud database.

      dataflow to the TOLIFE cloud

    2. Smart One OXI device(MIR company)

      chosen spirometer

    3. Samsung Galaxy Watch

      chosen watch

    4. In particular, raw sensor data collected from thesmartwatch contains information related to patient sounds (microphone), sleep quality (accelerometers,gyroscopes, PPG 1 sensor), walking speed (accelerometer, GPS), heart rate (HR) and heart rate variability(HRV) (PPG sensor, electrodes); oxygen saturation (PPG sensor), physical activity (accelerometer, gyroscope,GPS), social interaction (microphone), daylight exposure (light sensor).

      Variables que recoge el smartwatch

    5. We have then selected non-invasive sensing devices that are ableto detect such COPD-related input/output patterns without or with minimal effort for the patient

      Intro to sensor section



    1. RSFCR can directlymodel non-linear effects and interactions to performaccurate prediction without making any prior assump-tions about the underlying data.

      Importante. Se pueden modelar efectos e interacciones para hacer predicciones predcisas sin la necesidad de cumplir con alguna asunción previa.

    2. Cause‑specific Cox modelRegression on cause-specific hazards is an extension ofthe popular Cox proportional hazards model for CRs

      Cause-specific Cox model

    3. The aims of this manuscript can be summarised as:(i) examination of extensions of PLANNCR method(PLANNCR extended) for the development and vali-dation of prognostic clinical prediction models withcompeting events, (ii) systematic evaluation of model-predictive performance for ML techniques (PLANNCRoriginal, PLANNCR extended, RSFCR) and SM (cause-specific Cox, Fine-Gray) regarding discrimination andcalibration, (iii) investigation of the potential role ofML in contrast to conventional regression methods forCRs in non-complex eSTS data (small/medium samplesize, low dimensional setting), (iv) practical utility of themethods for prediction

      Objetivos del estudio

    4. Nowadays, there is a growing interest in applyingmachine learning (ML) for prediction (diagnosis or prog-nosis) of clinical outcomes [12, 13] which has sparked adebate regarding the added value of ML techniques ver-sus SM in the medical field. Criticism is attributed toML prediction models. Despite no assumptions aboutthe data structure are made, and being able to naturallyincorporate interactions between predictive features,they are prone to overfitting of the training data andthey lack extensive assessment of predictive accuracy(i.e., absence of calibration curves) [14, 15]. On the otherhand, traditional regression methods are consideredstraightforward to use and harder to overfit. That beingsaid, they do make certain (usually strong) assumptionssuch as the proportional hazards over time for the Coxmodel, and require manual pre-specification of interac-tion terms.

      pros and cons about machine learning and traditional regression survival analysis such as KM-SV

    5. In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statisticalmodels (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recentlythere is a growing interest in applying machine learning (ML) for clinical prediction, these techniques have also beenextended to model CRs but literature is limited. Here, our aim is to investigate the potential role of ML versus SM forCRs within non-complex data (small/medium sample size, low dimensional setting).

      Comparison between statistical models and machine learning models for competing risks.

    6. Statistical models versus machinelearning for competing risks: developmentand validation of prognostic models

      Statistical models versus machine learning for competing risks: development and validation of prognostic models

  8. Feb 2023
    1. The most popular non-parametric approach to esti-mate survival in the presence of right censored time-to-event data is the Kaplan-Meier’s methodology (KM)[8]. However, in the presence of CRs, this methodologyoverestimates the probability of failure which might leadto over-treatment of patients

      Explanation of how KM time-to-event can overestimate the probaility of failure which might lead to over-treatent of patients.

      There are two statistical model that can account for CRs such as cause-specific Cox model, and the Fine-Gray sub-distribution hazards regression model.





    2. Manual Cochrane derevisiones sistemáticas deintervenciones

      Manual Cochrane de revisiones sistemáticas de intervenciones



  9. Dec 2022
    1. Characterizing and predicting person-specific,day-to-day, fluctuations in walking behavior

      Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior



    1. Table 1 Daily life data collected and associated source
    2. Questionnaires and interviews will be administered by physicians to patients to acquire information on the socialand environmental determinants of health such as neighborhood quality, family status, perceived emotional andinstrumental support, personal community network characteristics, type and quality of daily received help, anddemographic data. In the project, daily-life patient specific data will be collected from different sensing sources

      Questions that should be added to the questionnaire in T1

    3. R&D


    4. This task will ensure the development of a solid data analysis plan and standard operatingprocedure. Indeed, the data and sensors requirements definition will be fundamental for both clinical datacollection (WP5)

      Por qué es importante para nosotros el trabajo de TCE (WP2)?

    5. Respiratory Medicine

      CP - IMIM




      CP - PRI


      WP1 - WP3 ~ UNIPI

    9. Institute of Clinical Physiology - Health and Psycho-Engineering group

      WP6 - CNR

    10. Tecnologia Fotonica y Bioingenieria-Life Supporting Technologies

      WP4 - UPM

    11. TIME.LEX

      WP7 - Ethics

    12. Cardiovascular, Endocrine-Metabolic Diseases and Aging

      CP - ISS



    1. For example, a benefit–harm analysis forroflumilast as preventive therapy for COPD exacerbationsreported that benefits of roflumilast outweighed itspotential harm when patients have severe exacerbationrisk of at least 22% over a year.24 Using data from thisbenefit–harm analysis, the accompanying web app ofACCEPT can be used to inform therapeutic decisions onuse of roflumilast for a given patient. Another example isin the potential use of preventative daily azithromycintherapy in COPD. Azithromycin reduces annual exacer-bation rate by 27%.8

      Por qué es importante estimar el riesgo?



  10. www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
    1. Determinants and outcomes of physical activityin patients with COPD: a systematic review

      Determinants and outcomes of physical activity in patients with COPD: a systematic review

    1. The Acute COPD Exacerbation Prediction Tool (ACCEPT): development andexternal validation study of a personalised prediction mode

      The Acute COPD Exacerbation Prediction Tool (ACCEPT): development and external validation study of a personalised prediction model - Supplement



  11. Nov 2022
    1. Exploitation, dissemination and communication
    2. Clinical study
    3. TOLIFE concept
    4. TOLIFE - Clinical Study B

      CSB Essentials

    5. TOLIFE - Clinical Study A

      CSA Essentials



      Miembros: Jaume Manero ~ jaume.manero@techedgegroup.com Domenico Romano ~ domenico.romano@techedgegroup.com Fabio Cerioni ~ fabio.cerioni@techedgegroup.com

      Sugerir unirlos a las reuniones de análisis estadístico. También a la Dra. Petrone

    7. Analytics tools

      En resumen, para llegar a obtener los modelos predictivos:

      WP2 1. Task 2.2 liderado por TCE: Study if the main architecture of the analytics tools and the selection of optimal algorithms for TOLIFE purposes. Evaluarán la posibilidad de utilizar machine learning o deep learning. 2. Task 2.4 liderado por TCE: Harán lo mismo para CAT, SGRQ, mMRC and 6WMD 3. Task 2.5 liderado por ISG: Develop the prediction models

    8. Statistical analysis and multi-domain assessment

      Task 6.2 Liderado por CNR: They will evaluate the prediction models?



    1. Lung function didnot decrease significantly during the prodromal period, but byDay 0, PEFR had fallen from baseline by a median 8.6 (IQR 0to 22.9) L/min, FEV1 by 24.0 (IQR 216.1 to 84.3) ml, and FVCby 76.0 (IQR 240.4 to 216.4) ml. The declines in lung func-tion, whether measured by PEFR, FEV1, or FVC, were allhighly significant (p , 0.001). Significantly greater decreasesin PEFR were seen when the exacerbation was associated withsymptoms of increased dyspnea (r 5 20.12 [n 5 449]; p 50.014), colds (r 5 20.09 [n 5 449]; p 5 0.047), or increasedwheeze (r 5 20.12 [n 5 449]; p 5 0.009), but not with othersymptoms.
    2. Before onset of exacerbation there was deterioration inthe symptoms of dyspnea, sore throat, cough, and symptoms of acommon cold (all p , 0.05), but not lung function.

      La función pulmonar no cambia días antes de la exacerbación

    3. Time Course and Recovery of Exacerbations inPatients with Chronic Obstructive Pulmonary Disease

      Time Course and Recovery of Exacerbations in Patients with Chronic Obstructive Pulmonary Disease

    1. The bad news is that this study suggests that the EXACT seems to be relatively insensitive in detectingexacerbation events. Only 34 (27%) out of 128 of London diary card exacerbations exceeded the EXACTthreshold for an exacerbation event (defined as a 12-point increase in EXACT score above baseline for twoconsecutive days or a 9-point increase for three days). Even more worryingly, of the 85 London COPDCohort diary card-defined exacerbations that were treated with oral antibiotics and/or corticosteroids by thestudy team during the 2-year study period, only 34% were picked up using EXACT.

      Bad news for EXACT

    2. The results of the study are certainly mixed. The good news is that mean EXACT scores did increase, asexpected, during exacerbation events relative to the stable state, and that the time taken for EXACT scores toreturn to baseline was significantly correlated to both diary-card symptom recovery time and lung functionrecovery. This information suggests that EXACT can be used to measure the duration of COPDexacerbation events.

      Usar EXACT en ambos estudios. La ventaja es que EXACT da información sobre la severidad y la duración de la exacerbación.

    3. Measuring and quantifying acuteexacerbations of COPD: pitfallsand practicalities

      Measuring and quantifying acute exacerbations of COPD: pitfalls and practicalities



    1. The EXACT has previously been used in conjunction with a personal digital assistant [14, 24] or smartphone[25].

      25 Halpin DM, Laing-Morton T, Spedding S, et al. A randomised controlled trial of the effect of automated interactive calling combined with a health risk forecast on frequency and severity of exacerbations of COPD assessed clinically and using EXACT PRO. Prim Care Respir J 2011; 20: 324–331

    2. Thus, this study has highlightedimportant potential limitations of the EXACT in its ability to independently identify events that were capturedby physician review (HCU) or London COPD cohort diary cards.

      London COPD cohort diary card

    3. Patients completed a paper version of the EXACT at least once under supervision in the clinic and wereinstructed to complete the EXACT diary each evening before bedtime, based on their symptomsexperienced that day.

      How EXACT was administrated

    4. Exacerbation duration was defined as the number of days after onset that worsening symptoms persisted.The last day of recorded worsening symptoms before two consecutive symptom-free days defined the end ofthe exacerbation

      Definición de duración de exacerbación

    5. The exacerbations of chronic pulmonary disease tool (EXACT) is a PRO daily symptom diary developed tocapture frequency, severity and duration of exacerbations in clinical trials of COPD [14].

      Leer EXACT

      14 Leidy NK, Wilcox TK, Jones PW, et al. Standardizing measurement of chronic obstructive pulmonary disease exacerbations. Reliability and validity of a patient-reported diary. Am J Respir Crit Care Med 2011; 183: 323–329

    6. Exacerbation symptoms systematically recorded on daily diary cards accurately detect both reported andunreported exacerbations [6, 12],

      Sería interesante leer:

      6 Seemungal TA, Donaldson GC, Paul EA, et al. Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1998; 157: 1418–1422.

      12 Seemungal TA, Donaldson GC, Bhowmik A, et al. Time course and recovery of exacerbations in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2000; 161: 1608–1613.

    7. An exacerbation was defined as an increase in respiratorysymptoms for two consecutive days, with at least one major symptom (dyspnoea, sputum purulence orsputum volume) plus either another major or a minor symptom (wheeze, cold, sore throat and cough), thefirst of which was defined as the day of onset of the exacerbation.

      Exacerbation assessment

    8. Detection and severity grading of COPDexacerbations using the exacerbations ofchronic pulmonary disease tool (EXACT)

      Detection and severity grading of COPD exacerbations using the exacerbations of chronic pulmonary disease tool (EXACT)

    1. Most models published so far are either Markovmodels, focusing on the cost-effectiveness of the COPDtreatment intervention (13–17), or logistic regression models,predicting the probability of exacerbation within the next24 months (18) or COPD-patient hospital admissions (19)

      13 Spencer M, Briggs AH, Grossman RF, Rance L. Development of an economic model to assess the cost effectiveness of treatment interventions for chronic obstructive pulmonary disease. PharmacoEconomics. 2005;23(6):619–37.

      14 Borg S, Ericsson A, Wedzicha J, Gulsvik A, Lundback B, Donaldson GC, et al. A computer simulation model of the natural history and economic impact of chronic obstructive pulmonary disease. Value Health. 2004;7(2):153–67. https://doi.org/10.1111/j.1524-4733.2004.72318.x.60 Page 10 of 11 The AAPS Journal (2019) 21: 60

      15 Slejko JF, Willke RJ, Ribbing J, Milligan P. Translating pharmacometrics to a pharmacoeconomic model of COPD. Value Health. 2016;19(8):1026–32. https://doi.org/10.1016/ j.jval.2016.07.006.

      16 Hoogendoorn M, Rutten-van Molken MP, Hoogenveen RT, Al MJ, Feenstra TL. Developing and applying a stochastic dynamic population model for chronic obstructive pulmonary disease. Value Health. 2011;14(8):1039–47. https://doi.org/10.1016/.jval.2011.06.008.

      17 Menn P, Leidl R, Holle R. A lifetime Markov model for the economic evaluation of chronic obstructive pulmonary disease. PharmacoEconomics. 2012;30(9):825–40. https://doi.org/10.2165/11591340-000000000-00000.

      18 Bertens LC, Reitsma JB, Moons KG, van Mourik Y, Lammers JW, Broekhuizen BD, et al. Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2013;8:493–9. https://doi.org/10.2147/COPD.S49609.

      19 Montserrat-Capdevila J, Godoy P, Marsal JR, Barbe F. Predictive model of hospital admission for COPD exacerbation. Respir Care. 2015;60(9):1288–94. https://doi.org/10.4187/respcare.04005.

    2. A Novel Method for Analysing Frequent Observations from Questionnairesin Order to Model Patient-Reported Outcomes: Application to EXACT® DailyDiary Data from COPD Patients

      A Novel Method for Analysing Frequent Observations from Questionnaires in Order to Model Patient-Reported Outcomes: Application to EXACT® Daily Diary Data from COPD Patients



    1. In the United States, this means that any tool used in adrug registration trial to evaluate the effect of treatment on frequency, severity, and/orduration of exacerbations must meet the criteria set forth in the Food and Drug Adminis-tration (FDA) draft guidance on patient-reported outcomes

      Entiendo que por esto, nuestro ensayo clínico también tendría que tener un PRO

    2. Diary Cards.

      El EXACT parece ser la mejor opción para recoger información sobre la sintomatología de los pacientes.

    3. Admission to hospital is directly related both to the underlying health of the patient and tohealth policy or coverage within a given country or region. Patients undergoing treatment inregions with relatively liberal admission policies will have more frequent and more“serious” exacerbations, while those in regions with conservative admission policies willhave less frequent and/or fewer “serious” episodes. This bias has serious implications forprevalence estimates in epidemiologic studies, effect estimates in studies examining thelink between exacerbations and disease trajectory, and site selection and treatment out-comes in clinical trials.

      Desventajas del event-based definition

    4. There are a number of relatively serious limitations associated with an event-baseddefinition of exacerbation. First, the initial clinic contact and visit is initiated by the patienton the basis of his or her assessment of the episode. With as many as 50% of exacerbationsunreported (8,9), event-based definitions seriously underestimate exacerbation frequency.

      Desventaja del event-based definition

    5. The definition proposed by the 1999 Aspen Lung Conference that refers to asustained worsening of the patient’s condition, implying an event that lasts at least 24 hours (2)while worsening beyond normal day-to-day variations, seeks to differentiate the severity ofexacerbations from “bad days” or acute, short-term episodes of cough, breathlessness, or othermanifestations within a given day. This group also proposed that mild exacerbations arecharacterized by an increased need for medication (which patients manage themselves),moderate are those for which the patients seek medical assistance, and severe are those in whichthe patient or caregiver recognizes clear and/or rapid deterioration and requires hospitalization.

      Mejor la de las guías gold, no?

    6. In the case of exacerbations of COPD, which are patient-reported events,instruments to detect exacerbations should be based on the conceptual definition and patientdescriptions and experiences of these events and show evidence of reliability, validity, andresponsiveness

      Las exacerbaciones son un Patient-Reported-Event. Importante el uso del EXACT para poder disminuir un poco el porcentaje de subjetividad de los síntomas

    7. (i) understanding the etiology andmechanisms of exacerbations, (ii) determining the impact of exacerbations on the course ofCOPD, and (iii) determining if interventions alter the incidence or the clinical course ofexacerbations.

      Important for the definition

    8. It is important to realize the difference between the definition of a disease and itsdiagnostic criteria (1). The defining characteristics of a disease are the commonproperties specifying the group of abnormal persons on whom the description of thedisease is based. The definition of a disease is important in communication.Diagnostic criteria are features of the disease chosen from its description thatare found by empirical research to best distinguish the disease from others whichresemble it. The diagnostic criteria may or may not include features of the definingcharacteristics and frequently include features that do not appear in the definition (1).

      Definition vs Diagnosis

    9. Recent observations have clearly shown that therapeutic interventionscan, to some extent, prevent exacerbations as well as modify their course. This has created boththe opportunity and the imperative to develop more effective interventions to mitigate theburden of acute exacerbations, which, in turn, has created a need for precise and opera-tionally tractable definitions.
    10. In a multinational cross-sectional interview-based qualitative study by Kessler et al.of 125 patients with moderate to severe COPD, the most common terms patients used whenreferring to a worsening of their condition were “chest infection” (16%; n ¼ 20), “crisis”(16%; n ¼ 20), or an “attack” (6.4%; n ¼ 8) (13). Only two patients understood what theterm “exacerbation” meant.

      Importante conocer cómo los pacientes se refieren a su condición. Reclacar que un mínimo porcentaje de pacientes saben en realidad lo que significa una exacerbación

    11. Chronic ObstructivePulmonary DiseaseExacerbations

      Chronic Obstructive Pulmonary Disease Exacerbations

    1. Data minimisation

      Collect only the data that we need to meet our research objectives.

    2. The GDPR places obligations on both: the ‘data controller’, which ‘alone, or jointly with others, determines the purposes and means ofthe processing of personal data’; and the ‘data processor’, which ‘processes personal data on behalf of the controller’.

      Nosotros seríamos el data controller

    3. It is your responsibility to ensure that your research complies withthe data protection laws in all the Member States in which your research data areprocessed, as well as the GDPR.33 See in particular Articles 9(4), 8 and 89(3) GDPR.

      Pilas con Alemania

    4. The increasing impact of these and other new technologieson our everyday lives and activity is reflected in the letter and spirit of the EU’s 2016 General DataProtection Regulation GDPR)

      letter about the use of nw technologies (artificial intelligence)

    5. Ethics and data protection

      Ethics and data protection



    1. This section concerns research involving goods, software and technologies covered bythe EU Export Control Regulation No 482/2009.

      Los sensores que nosotros utilizamos tienen que ser evaluados por esta regulación?

    2. Pseudonymisation and anonymisation are not the same thing.‘Anonymised’ means that the data has been rendered anonymous in such away that the data subject can no longer be identified (and therefore is nolonger personal data and thus outside the scope of data protection law).‘Pseudonymised’ means to divide the data from its direct identifiers so thatlinkage to a person is only possible with additional information that is heldseparately. The additional information must be kept separately and securelyfrom processed data to ensure non-attribution.

      Diferencias entre anonimización y pseudoanonimización

    3. Collecting personal data (e.g. on religion, sexual orientation, race, ethnicity,etc.) that is not essential to your research may expose you to allegations of‘hidden objectives’ or ‘mission creep’

      Justificar por qué queremos recolectar esta información

    4. Ethics issues checklist

      Ethics issues checklist - Personal data

    5. 1) Declarationconfirmingcompliance withthe laws of thecountry where thedata was collected.

      Aquí hay que especificar que se cumplen con las leyes del país de donde se recoge la información.

    6. name, anidentification number, location data, an online identifier or to one or morefactors specific to the physical, physiological, genetic, mental, economic,cultural or social identity of that natural person (art. 2(a) EU General DataProtection Regulation (GDPR).

      What makes a person identifiable

    7. Participants must be given an informed consent form and detailed information sheetsthat: are written in a language and in terms they can fully understand describe the aims, methods and implications of the research, the nature of theparticipation and any benefits, risks or discomfort that might ensue explicitly state that participation is voluntary and that anyone has the right torefuse to participate and to withdraw their participation, samples or data atany time — without any consequences state how biological samples and data will be collected, protected during theproject and either destroyed or reused subsequently state what procedures will be implemented in the event of unexpected orincidental findings (in particular, whether the participants have the right toknow, or not to know, about any such findings).

      Detalles del consentimiento informado

    8. Does it involveinvasivetechniques (e.g.collection ofhuman cells ortissues, surgicalor medicalinterventions,invasive studieson the brain, TMSetc.)?

      It seems we do not

    9. vulnerableindividuals orgroups?

      How do they define vulnerable?

    10. Ethics issues checklist

      Ethics issues checklist - Research on human beings

    11. How to complete your ethics self-assessment

      How to complete your ethics self-assessment

    1. The PaCO2 is currently obtained invasively by tak-ing blood samples at discrete time instants. To motivate animprovement, this paper justifies PaCO 2 monitoring forCOPD patients continuously and noninvasively by transcuta-neous CO2 measurements (PtcCO2).

      Should we used PaCO2?

    2. Pulse oximeters, however, cannot detect changes in the arte-rial carbon dioxide (CO2) partial pressure (PaCO2), which weargue is one of the most significant parameters for COPDpatients

      Si tenemos medidas con su tiempo (dependiendo de cuanto tiempo), podríamos restar el segundo posterior al anterior y finalmente hacer un promedio de las medidas por hora o por día.

    3. Continuous remote monitoring of COPD patients—justificationand explanation of the requirements and a surveyof the available technologies

      Continuous remote monitoring of COPD patients—justification and explanation of the requirements and a survey of the available technologies

    1. A previousstudy [13] has suggested that symptoms tend to worsen duringthe 7 days immediately before an exacerbation episode.

      13 Donaldson GC, Wedzicha JA. COPD exacerbations .1: Epidemiology. Thorax 2006 Feb;61(2):164-168 [FREE Full text] [doi: 10.1136/thx.2005.041806] [Medline: 16443707]

    2. Exacerbations in Chronic Obstructive Pulmonary Disease:Identification and Prediction Using a Digital Health System

      Exacerbations in Chronic Obstructive Pulmonary Disease: Identification and Prediction Using a Digital Health System

    1. Here, we present a case of COPD exacerbation detected using RPM ina clinical setting. The RPM system (Spire Health, 2021) has been vali-dated for use with chronic respiratory disease patients [9,10] and iscomprised of: (1) Health Tags, undergarment waistband-adhered phys-iologic monitors which include photoplethysmography, activity, andrespiratory force sensors, (2) an in-home stationary device to collect andupload sensor data, and (3) a web dashboard to display patient data andnotifications to clinicians.

      Parece mucho a TOLIFE. Tienen una app y han medido varias variables fisiológicas

      9 Mark Holt, et al., Ambulatory monitoring of respiratory effort using a clothing-adhered biosensor, in: IEEE International Symposium on Medical Measurements and Applications (MeMeA), IEEE, 2018.

      10 Neema Moraveji, et al., Long-term, ambulatory respiratory monitoring of COPD patients using garment-adhered sensors, in: IEEE International Symposium on Medical Measurements and Applications (MeMeA), IEEE, 2019.

    2. Case

      Caso muy interesante. Es lo que queremos que pase en nuestro pacientes. Solo se han evaluado dos variables: RR y PR. Se mide como porcentaje de cambio y se establece un umbral que está descrito en los gráficos.

    3. Seemungal et al. reported a 7-day prodrome prior to diagnosis ofexacerbation [6]. With this in mind, the use of respiratory RPM has thepotential to reduce COPD treatment delays leading to improved care.Increased respiratory rate has demonstrated predictive ability for ex-acerbations of COPD [7,8]. Shah et al. observed an increased respiratoryrate in the 5 days preceding hospitalization for COPD exacerbations,highlighting the window of opportunity for intervention [7].

      Detalles importantes:

      Seemungal determinó un periodo prodrómico de 7 días. Sin embargo, hay que recordar que las exacerbaciones tienen diferentes presentaciones.

      RR parece ser un predictor importante. Se eleva 5 días antes de la hospitalización

      6 T.A. Seemungal, G.C. Donaldson, A. Bhowmik, D.J. Jeffries, J.A. Wedzicha, Time course and recovery of exacerbations in patients with chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med. 161 (5) (2000 May) 1608–1613, https://doi.org/10.1164/ajrccm.161.5.9908022. PMID: 10806163.

      7 S.A. Shah, C. Velardo, A. Farmer, L. Tarassenko, Exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital Health system, J. Med. Internet Res. 19 (3) (2017) e69, https://doi.org/10.2196/ jmir.7207. Published 2017 Mar 7.

      8 A.M. Ya ̃nez, D. Guerrero, R. P ́erez de Alejo, F. Garcia-Rio, J.L. Alvarez-Sala, M. Calle-Rubio, R.M. de Molina, M. Valle Falcones, P. Ussetti, J. Sauleda, E. Z. García, J.M. Rodríguez-Gonz ́alez-Moro, M. Franco Gay, M. Torrent, A. Agustí, Monitoring breathing rate at home allows early identification of COPD exacerbations, Chest 142 (6) (2012) 1524–1529.

    4. Early identification and treatment of COPD exacerbation using remoterespiratory monitoring

      Early identification and treatment of COPD exacerbation using remote respiratory monitoring

    1. Acute changes in lung function (forcedexpiratory volume in 1 s (FEV 1)) or the FEV1/forced vital capacity ratio are not sensitive, and do notcorrelate well with AECOPD [57, 58].

      Acute changes in lung function FEV1 and FEV1/FVC are not sensitive and dote correlate wll with AECOPD

      57 Stevenson NJ, Walker PP, Costello RW, et al. Lung mechanics and dyspnea during exacerbations of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2005; 172: 1510–1516

      58 Parker CM, Voduc N, Aaron SD, et al. Physiological changes during symptom recovery from moderate exacerbations of COPD. Eur Respir J 2005; 26: 420–428.

    2. Studies that have assessed the incidence of symptom-based AECOPDs compared to event-based AECOPDsin the same patients followed over time suggest that observed exacerbation rates are much higher ifsymptom-based definitions are used. The Investigating New Standards for Prophylaxis in ReducingExacerbations (INSPIRE) study compared the incidence of AECOPD using symptom-based definitions anda treatment-based definition and found that the incidence rate was three AECOPDs per patient-year if asymptom-based definition was used and 1.5 AECOPDs per patient-year if an event-based definition wasused, suggesting that 50% of symptom-defined COPD exacerbations are not treated by physicians [27].

      Dato importante. Symptom-based son más diagnosticadas que las event-based.

      27 Wedzicha JA, Calverley PM, Seemungal TA, et al. The prevention of chronic obstructive pulmonary disease exacerbations by salmeterol/fluticasone propionate or tiotropium bromide. Am J Respir Crit Care Med 2008; 177: 19–26.

    3. A further advantage is that validated tools to capture symptom-basedAECOPDs exist and include patient diary cards [18] and the validated Exacerbation of ChronicPulmonary Disease Tool (EXACT) [19].

      18 Quint JK, Donaldson GC, Hurst JR, et al. Predictive accuracy of patient-reported exacerbation frequency in COPD. Eur Respir J 2011; 37: 501–507.

      19 Leidy NK, Murray LT. Patient-reported outcome (PRO) measures for clinical trials of COPD: the EXACT and E-RS. COPD 2013; 10: 393–398.

    4. Advantages and disadvantages of event-based definitions of AECOPD
    5. Advantages and disadvantages of symptom-based definitions of AECOPD
    6. The 2018 GOLD document defines COPD exacerbation as “an acute worsening ofrespiratory symptoms that results in additional therapy”. Exacerbations are classified as 1) mild if they aretreated with short-acting bronchodilators only; 2) moderate if they are treated with short-actingbronchodilators plus antibiotics and/or oral corticosteroids; or 3) severe if the patient visits the emergencyroom or requires hospitalisation because of an exacerbation [16].

      Acoording ot the 2018 GOLD Guideline

    7. Symptom-based definitions rely on patient-reported worsening of respiratory symptoms either to ahealthcare practitioner or within a symptom diary.

      Esto es lo que NO queremos hacer. Tal vez symotom-based hay que descartarlo

    8. What is a COPD exacerbation? Currentdefinitions, pitfalls, challenges andopportunities for improvement

      What is a COPD exacerbation? Current definitions, pitfalls, challenges and opportunities for improvement

    1. ecause of global variability in the available resources to treat patients and local customsaffecting the criteria for hospital visits and admissions, there is substantial variability in reported ECOPD outcomes.(11)

      Importantísimo leer

      • Halpin DMG, Rabe AP, Loke WJ, et al. Epidemiology, Healthcare Resource Utilization, and Mortality of Asthma and COPD in COVID-19: A Systematic Literature Review and Meta-Analyses. J Asthma Allergy 2022; 15: 811-25
    2. Short-termexposure to fine (PM2.5) and coarse (PM10) particulate matter is associated with increased hospitalizations, ER visits,and outpatient visits,(16) as well as increased mortality of COPD exacerbations.(15,17,18)
    3. Currently, exacerbations are classified after the event has occurred as:► Mild (treated with short acting bronchodilators only, SABDs)► Moderate (treated with SABDs and oral corticosteroids ± antibiotics) or► Severe (patient requires hospitalization or visits the emergency room). Severe exacerbations may also beassociated with acute respiratory failure.

      Claramente se describe cuáles son las exacerbaciones severas. Hospitalizaciones o emergencias.

    4. Currently, exacerbations are classified after the event has occurred as:► Mild (treated with short acting bronchodilators only, SABDs)► Moderate (treated with SABDs and oral corticosteroids ± antibiotics) or► Severe (patient requires hospitalization or visits the emergency room). Severe exacerbations may also beassociated with acute respiratory failure.

      Claramente se describe cuáles son las exacerbaciones severas. Hospitalizaciones o emergencias.

    5. Exacerbations of COPD are important events in the management of COPD because they negatively impact healthstatus, rates of hospitalization and readmission, and disease progression.(2,3)
    1. Frequently used definitions and diagnostic criteria for COPD exacerbations

      Exacerbation definitions

    2. Acute exacerbations of chronic obstructive pulmonarydisease: in search of diagnostic biomarkers andtreatable traits

      Acute exacerbations of chronic obstructive pulmonary disease: in search of diagnostic biomarkers and treatable traits



    1. Combined initial COPD assessment

      Punto a destacar: - A partir del 2011, se incluye el CAT y el mMRC para tomar en consideración los PROs para guiar el tratamiento en pacientes con COPD. - The newest change regarding this topic in the 2023 guide, is the modification from the ABCD to the ABE assessment tool. This approach recognizes the clinical relevance of exacerbations, independently of the level of symptoms of the patient. CD are joined and form the E, to highlight Exacerbations. This still has to be validated.

    2. The SGRQ is the most widely documented comprehensive measure; scores < 25 are uncommon in diagnosed COPDpatients(43) and scores ≥ 25 are very uncommon in healthy persons.(44,45) Therefore, it is recommended that a symptomscore equivalent to SGRQ score ≥ 25 should be used as the threshold for considering regular treatment for symptomsincluding breathlessness, particularly since this corresponds to the range of severity seen in patients recruited to thetrials that have provided the evidence base for treatment recommendations.
    3. The CAT™† is an 8-item questionnaire that assesses health status in patients with COPD (Figure).(41) It was developedto be applicable worldwide and validated translations are available in a wide range of languages. The score rangesfrom 0 to 40, correlates very closely with the SGRQ, and has been extensively documented in numerouspublications.(42)
    4. It should be noted that the use of a fixed FEV1/FVC ratio (< 0.7) to define airflowobstruction may result in over-diagnosis of COPD in the elderly,(30,31) and under-diagnosis in young adults,(31

      Importante para el reclutamiento de pacientes

    5. theimpact of disease on the patient’s health status, and the risk of future events

      Los goals del COPD assessment según GOLD

    6. A diagnosis of COPD should be considered in any patient who has dyspnea, chronic cough orsputum production, a history of recurrent lower respiratory tract infections and/or a history ofexposure to risk factors for the disease, but forced spirometry showing the presence of a post-bronchodilator FEV1/FVC < 0.7 is mandatory to establish the diagnosis of COPD


    7. The realization that environmental factors other than tobacco smoking can contribute to COPD,that it can start early in life and affect young individuals, and that there are precursor conditions(Pre-COPD, PRISm

      Importante leer



    1. Another limitation is that our study did not assess potentialmicrobiological pathogens that may have been associated withindividual exacerbations. It is tempting to speculate that suddenexacerbations may be those that are caused by infections (eitherviral or bacterial respiratory tract infections).
    2. We employed a generalised esti-mating equation (GEE) logistic model with an exchangeablewithin-patient correlation structure to account for individualpatients having multiple exacerbations.

      Análisis estadístico

    3. An openingwas defined as the first day of a positive symptom score indi-cating worsening of respiratory symptoms from baseline (ie,a symptom score $1 point)

      Lo que nosotros podríamos hacer, es establecer umbrales para que cuando cierto numero de medidas superen este umbral, salte la alarma.

    4. This prodrome phase is ofgreat interest, as knowledge of exacerbation onsetcan help physicians to time early therapeuticinterventions appropriately. 7


      Wilkinson T, Donaldson GC, Hurst JR, et al. Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2004;169:1298e303.

    5. However, it should be stressed thataction plans that contain only minimal or no patient self-management education have not been shown to reduce urgenthealthcare utilisation for COPD. 12

      Importante recalcar que las intervenciones que no involucran acciones propias del paciente, no reducen la utilización del sistema sanitario

      12 Walters JA, Turnock AC, Walters EH, et al. Action plans with limited patient education only for exacerbations of chronic obstructive pulmonary disease. Cochrane Database Syst Rev 2010;(5):CD005074

    6. Previous studies suggest that prompt treatment ofexacerbations is associated with better clinical outcomes.

      Importante para justificar el hecho de querer predecir exacerbaciones

      7 Wilkinson T, Donaldson GC, Hurst JR, et al. Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2004;169:1298e303.

    7. One implication isthat COPD action plans, with provision of prespecifiedprescriptions for antibiotics and oral steroids, may be appro-priate to ensure prompt and appropriate management of exac-erbations. 10

      Qué podrían hacer los médicos que saben que se enfrentan a una probable exacerbación en los próximo días?

    8. The onset, or prodrome,of COPD exacerbations is a subject that has received very littlestudy to date.

      Algo que deberíamos utilizar para sustentar nuestro estudio

    9. Time course and pattern of COPD exacerbation onset

      Time course and pattern of COPD exacerbation onset

    1. Thefindings show that patients who receive prompt therapy afterthe onset of their exacerbation are likely to recover more rapidlythan those who delay reporting and thus initiation of treatment.Furthermore, patients who habitually fail to seek therapy fortheir exacerbations have poorer health-related quality of lifeand are more likely to be hospitalized for the management ofan exacerbation

      Esto es importante pero cabe recalcar que nosotros queremos intervenir al paciente antes de que tenga la exacerbacion

    2. patients withCOPD often have poor understanding of their disease and symp-toms, with the result that exacerbations are often not reportedto healthcare professionals for treatment (2).

      Importante que el DIT tenga contenido educativo y que pueda servir como una manera de que el paciente participe activamente en con su enfermedad

    3. Early Therapy Improves Outcomes of Exacerbationsof Chronic Obstructive Pulmonary Disease

      Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease



    1. The relative importance of the eosinophilia re-mains to be determined, but several eosinophil products maycause inflammatory damage to the airway (eosinophil peroxi-dase, major basic protein, eosinophil cationic protein, metallo-proteinases, platelet activating factor, and cysteinyl leukotrienes)(75) and, together with histamine, can cause bronchospasm

      Eosinophilia puede causar broncoespasmo en conjunto con otros factores

    2. Patients with mild to moderate COPD exacerbations show anincreased number of eosinophils in their bronchial mucosa (72).Although this suggests an “asthmatic profile,” the observed eo-sinophils are not degranulated (as they would be in asthma) andare not associated with increased IL-5 expression (72).

      Por qué los eosinófilos aumentan y cómo son diferentes a el perfil de los pacientes con asma

    3. The lower airways of 25 to 50% of patients with COPD arecolonized by bacteria, especially noncapsulated Haemophilusinfluenzae, Streptococcus pneumoniae, and Moraxella catarrhalis.

      Ver listado de bacterias

    4. Recent studies have shown that about one-half of COPD exacer-bations are associated with viral infections, the majority of whichare due to rhinovirus (32–36).

      Casi 50% de las exacerbaciones son por infecciones virales, y dentro de ellas, las provocadas por rhinovirus.

    5. Lung function changes, such as decreases in peak expiratoryflow rate (PEFR) or FEV1 immediately before exacerbation, aregenerally small and not useful in predicting exacerbations, butlarger decreases in PEFR are associated with dyspnea, longerrecovery time after exacerbations, and the presence of symptom-atic colds (11).

      Importante. Los cambios en el FEV1 inmediatamente antes de las exacerbaciones no son útiles para predecir exacerbaciones.

      11 Seemungal TA, Donaldson GC, Bhowmik A, Jeffries DJ, Wedzicha JA. Time course and recovery of exacerbations in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2000;161:1608–1613. (lo tenemos)

    6. There have now been several large population studies (8–10)showing that the number and severity of exacerbations are lowerin patients with mild to moderate COPD (FEV1  50% pre-dicted), whereas in severe disease the rate of COPD exacerba-tions may increase to 1.5 to 2.5/patient/yr.

      Nos puede servir como parámetro para elegir a nuestros pacientes, enfocándonos en aquellos que tienen una estadío moderado/grave.

    7. Pathophysiology of Exacerbations of ChronicObstructive Pulmonary Disease

      Pathophysiology of Exacerbations of Chronic Obstructive Pulmonary Disease

    1. Management of COPD exacerbations:a European Respiratory Society/AmericanThoracic Society guideline

      Management of COPD exacerbations: a European Respiratory Society/American Thoracic Society guideline



    1. Weobserved that an established machine-learning method (GB) narrowlyoutperformed other prediction algorithmsand resulted in a prediction model with ahigh discrimination power (AUC = 0.82),which also showed robust calibration in thevalidation data.

      GB machine learning was the best

    2. The primary outcome was the occurrence ofat least one COPD-related hospitalizationwithin the 2-month period after the indexdate (the outcome window).
    3. A history of previous exacerbations isconsidered the best predictor of futureexacerbations and forms the current basis ofrisk stratification in guidelines (22, 23).
      1. Vestbo J, Hurd SS, Agust ́ı AG, Jones PW, Vogelmeier C, Anzueto A, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 2013;187:347–365.

      2. Kerkhof M, Freeman D, Jones R, Chisholm A, Price DB; Respiratory Effectiveness Group. Predicting frequent COPD exacerbations using primary care data. Int J Chron Obstruct Pulmon Dis 2015;10: 2439–2450

    4. In particular, we comparedlogistic regression (LR), random forest (RF),neural network (NN), and gradient-boosting (GB) methods (20).
    5. Adistinct advantage of this approach is thatrisk prediction can be performed remotelyand can be made arbitrarily complex toimprove its accuracy. In this “populationsurveillance” approach, individuals who areidentified as high-risk can then be contactedfor preventive disease management.

      Ventajas de realizar predicciones con datos medidos de manera continua.

    6. Most previous risk-predictionmodels for COPD exacerbations weredeveloped for use at point of care (5–8);however, an alternative approach is to useroutinely collected health data (9).
      1. Collier R. WHO guidelines on ethical public health surveillance. CMAJ 2017;189:E977
    7. Predicting Severe Chronic Obstructive PulmonaryDisease Exacerbations

      Predicting Severe Chronic Obstructive Pulmonary Disease Exacerbations

    1. Prevention of COPD exacerbations:a European Respiratory Society/American Thoracic Society guideline

      Prevention of COPD exacerbations: a European Respiratory Society/American Thoracic Society guideline



    1. Using the definition of exacerba-tion based on health care utilization, they foundthat the degree of airflow obstruction, health-related quality of life, an elevated white-cellcount, and a history of gastroesophageal refluxwere independently associated with increasedexacerbation frequency in the entire cohort;however, a history of previous exacerbationsbest predicted the subsequent occurrence of ex-acerbations in all stages of COPD severity.

      Predictores de COPD

    2. Whendefined on the basis of health care utilization,exacerbations are classified as moderate or, ifhospitalization ensues, as severe, with those epi-sodes managed by patients themselves relegatedto the mild category.3
      1. Celli BR, MacNee W, American Thoracic Society/European3. Respiratory Society Task Force. Standards for the diagnosis and management of patients with COPD: a summary of the ATS/ERS position paper. Eur Respir J 2004;23:932-46

      Podríamos leer uno más actualizado

    3. Frequent Exacerbations of Chronic Obstructive PulmonaryDisease — A Distinct Phenotype?

      Frequent Exacerbations of Chronic Obstructive Pulmonary Disease — A Distinct Phenotype?

    4. Consequently, confirmation that an “exacerba-tion” has actually occurred requires not only theworsening of the patient’s respiratory symptomsbut also the prescription of additional treatmentby a health care provider



    1. Stepwise logisticregression is commonly used for variable selection 16 and this method was prespecified during the study design



    1. In contrast, in this study,we focused on exacerbation risk prediction for discharged COPDpatients, because their health condition is likely to be lessaccessible.
    2. These resultsshowed that physiological and environmental data are morepowerful predictors than questionnaire data.
    3. When only lifestyle or environmental dataare automatically uploaded daily, the system still predictswhether AECOPD will occur within the next 7 days.
    4. For model comparison with machine learning–basedclassification, we selected the following classifiers: decisiontrees [15], random forests [16], k-nearest neighbor clustering[17], linear discriminant analysis, and adaptive boosting [18]
    5. Classification algorithms for this study were selected accordingto previously published studies on COPD such as those of Wanget al [13] and Rahman et al [14].
      1. Wang C, Chen X, Du L, Zhan Q, Yang T, Fang Z. Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease. Comput Methods Programs Biomed 2020 May;188:105267. [doi: 10.1016/j.cmpb.2019.105267] [Medline: 31841787]

      2. Rahman MJ, Nemati E, Rahman MM, Nathan V, Vatanparvar K, Kuang J. Automated assessment of pulmonary patients using heart rate variability from everyday wearables. Smart Health 2020 Mar;15:100081. [doi: 10.1016/j.smhl.2019.100081]

    6. Acute Exacerbation of a Chronic Obstructive Pulmonary DiseasePrediction System Using Wearable Device Data, MachineLearning, and Deep Learning: Development and Cohort Study

      Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study

    1. severe exacerbationswere those that required an emergency departmentvisit or admission to hospital. 3,8–10

      exacerbaciones severas

      3 Vogelmeier CF, Criner GJ, Martinez FJ, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 Report. GOLD Executive Summary. Am J Respir Crit Care Med 2017; 195: 557–82.

      8 Albert RK, Connett J, Bailey WC, et al. Azithromycin for prevention of exacerbations of COPD. N Engl J Med 2011; 365: 689–98.

      9 Criner GJ, Connett JE, Aaron SD, et al. Simvastatin for the prevention of exacerbations in moderate-to-severe COPD. N Engl J Med 2014; 370: 2201–10.

      10 Aaron SD, Vandemheen KL, Fergusson D, et al. Tiotropium in combination with placebo, salmeterol, or fluticasone–salmeterol for treatment of chronic obstructive pulmonary disease: a randomized trial. Ann Intern Med 2007; 146: 545.

    2. In reporting our prediction model, we followedrecommendations set by the Transparent Reporting of aMultivariable Prediction Model for Individual Prognosisor Diagnosis (TRIPOD) statement.

      Es necesarion que nosotros hagamos esto?

      1. Collins GS, Reitsma JB, Altman DG, Moons K. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med 2015; 13: 1
    3. Inclinical practice, a history of two or more exacerbationsand one severe exacerbation per year is used toguide therapeutic choices for exacerbation prevention.3

      Esto es lo que en la práctica clínica se considera para guiar el tratamiento en exacerbaciones

      Vogelmeier CF, Criner GJ, Martinez FJ, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 Report. GOLD Executive Summary. Am J Respir Crit Care Med 2017; 195: 557–82.

    1. Telehealth interventions: remote monitoring and consultationsfor people with chronic obstructive pulmonary disease (COPD)(Review)

      Telehealth interventions: remote monitoring and consultations for people with chronic obstructive pulmonary disease (COPD) (Review)

    1. A Cochrane review [ 31 ] has summarized the impact of remote monitoring technologyfor people with COPD.

      Janjua, S.; Carter, D.; Threapleton, C.J.; Prigmore, S.; Disler, R.T. Telehealth Interventions: Remote Monitoring and Consultations for People with Chronic Obstructive Pulmonary Disease (COPD). Cochrane Database Syst. Rev. 2021, 7, CD013196.



    1. Hospitalized Exacerbations of COPDRisk Factors and Outcomes in the ECLIPSE Cohort

      Hospitalized Exacerbations of COPD Risk Factors and Outcomes in the ECLIPSE Cohort



    1. Inflammatory Biomarkers and Exacerbationsin Chronic Obstructive Pulmonary Disease

      Inflammatory Biomarkers and Exacerbations in Chronic Obstructive Pulmonary Disease

    2. Another potential limitation is thechanges in treatment of COPD over thestudy period.

      Deberíamos analizar esto en nuestro estudio.

    3. The principal finding of this study isthat simultaneously elevated levels ofCRP, fibrinogen, and leukocytes wereassociated with increased risk of fre-quent exacerbations in individuals withstable COPD.

      Lo que demuestra el estudio.

    4. First, we analyzedrisk of having frequent exacerbationsduring the first year of follow-up usinglogistic regression

      statistical analysis for prediction

    5. Frequent ex-acerbations were defined as 2 or moreexacerbations less than 1 year apart.

      Podemos nosotros añadir esto a nuestros objetivos?

    1. A prediction model for COPDreadmissions: catching up, catchingour breath, and improving a nationalproblem

      A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem

    1. In conclusion, our study confirms the obser-vation that exacerbations become more frequentand more severe as the severity of underlyingCOPD increases and shows that the most impor-tant determinant of frequent exacerbations is ahistory of exacerbations.

      Una vez más, la importancia de las exacerbaciones previas

    2. Wedefined frequent exacerbations as two or moreexacerbations in a year because this definition co-incides with current health care utilization crite-ria for frequent exacerbations.

      Definition of frequent exacerbator

    3. The casedefinition of an exacerbation was a functional one,based on the decision by a patient’s primary cli-nician or by study personnel to prescribe antibi-otics or systemic corticosteroids, alone or in com-bination.

      Definition of exacerbation

    4. Susceptibility to Exacerbation in ChronicObstructive Pulmonary Disease

      Susceptibility to Exacerbation in Chronic Obstructive Pulmonary Disease



    1. An Index of Daily Step Count and Systemic Inflammation PredictsClinical Outcomes in Chronic Obstructive Pulmonary Disease

      An Index of Daily Step Count and Systemic Inflammation Predicts Clinical Outcomes in Chronic Obstructive Pulmonary Disease

    2. We show that the combination of a low dailystep count and high CRP or IL-6 level isassociated with an increased rate of AEsand COPD-related hospitalizations

      Biomarcadores con pasos en predecir hospitalizaciones



    1. The COPD assessment test (CAT) assistsprediction of COPD exacerbations inhigh-risk patients

      The COPD assessment test (CAT) assists prediction of COPD exacerbations in high-risk patients



    1. Results ofthe logistic regression and ROC analysis showed that theinfluences of the BODE index and the GOLD stage on exac-erbation risk during the first year of follow-up was similar.

      El efecto predictor del BODE y el GOLD es el mismo sobre las exacerbaciones, pero este efecto fue evaluado en una regresión simple.

    2. Adjusted multiple logistic regression models were alsoperformed, including independent variables associated with exac-erbation (P  0.20) in the univariate analysis

      Statistical analysis for prediction

    3. BODE Index and GOLD Staging as Predictors of 1-YearExacerbation Risk in Chronic Obstructive PulmonaryDisease

      BODE Index and GOLD Staging as Predictors of 1-Year Exacerbation Risk in Chronic Obstructive Pulmonary Disease

    1. Validation of the i-BODE Index as a Predictor of hospitalizationand Mortality in Patients with COPD Participating in PulmonaryRehabilitation

      Validation of the i-BODE Index as a Predictor of hospitalization and Mortality in Patients with COPD Participating in Pulmonary Rehabilitation

    2. Exacerbations were gradedaccording to either: treatment in primary care, emer-gency room visit, or hospitalization. The BODE indexwas a good predictor of both the number and the sever-ity of exacerbations in COPD, especially in those exacer-bations that required hospital admission

      La relación del BODE y las exacerbaciones.

    3. Recently, Williams et al. sug-gested that the incremental shuttle walking test (ISWT)could be substituted for the 6MWT as an alternativemeasure of exercise capacity within the index and intro-duced the i-BODE (4). As field exercise tests, the ISWTand 6MWT are closely related (5, 6), though ISWT isconsidered to be closer to a maximal exercise test (7),whereas the 6MWT reflects a more functional exerciseperformance

      Diferencias entre el ISWT vs 6MWT.

    4. The BODE index has been found to be better than forcedexpiratory volume in 1 second (FEV1 ) in predicting the risk of death and hos-pitalization among patients with COPD (2, 3).

      Relación del BODE index con las hospitalizaciones y la mortalidad. Mejor predictor que el FEV1