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  1. Nov 2022
    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

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

      Deberíamos analizar esto en nuestro estudio.

    2. 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.

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

      statistical analysis for prediction

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

      Podemos nosotros añadir esto a nuestros objetivos?

    5. An exacerbation of COPD was de-fined as a short-course treatment withoral corticosteroids alone or in combi-nation with an antibiotic or as a hos-pital admission due to COPD

      Definición de exacerbación

    6. However, some patients with COPDalso have evidence of low-grade sys-temic inflammation with increased lev-els of such inflammatory biomarkers

      Hay marcadores biológicos que pueden estar elevados en condiciones estables y que pueden estar relacionados con outcomes negativos. Estos son:

      • CRP
      • Fibrinógeno
      • Leucocitos

      Podemos solicitar estos en las visitas? o de verdad solo queremos información de los sensores?

    7. However, when predicting risk of fu-ture exacerbations based on previousevents, the positive predictive value re-mains low,9 indicating that additionaldeterminants of exacerbation suscep-tibility remain to be identified

      Esto contrasta con la información del paper de Mullerova:

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

      Es un argumento interesante a debatir en nuestro estudio

    1. Finally, the ECLIPSE study wasconducted in multiple countries, which may have dif-ferent policies for hospitalizations and may follow(slightly) different treatment strategies. To explore thispossibility, we analyzed the subset of data collectedfrom three countries contributing the majority ofpatients (United States, United Kingdom, and Norway)and did not find significant differences among thesecountries in incidence of hospitalized exacerbations(e-Table 2), suggesting that if anything, this effect islikely small.

      Un análisis que nosotros también podemos hacer

    2. On theother hand, we acknowledge that our results may notbe immediately generalizable to the entire primarycare population of patients with COPD because inour study, patients were recruited mainly from pulmo-nary clinics.

      Una limitación que nosotros también tenemos

    3. In a subgroup of patients without recent hospitalizedexacerbation at baseline, fibrinogen was identified asan additional risk factor of future risk of hospital admis-sions for COPD exacerbations. Inflammatory pathwaysplay a prominent role in exacerbations of COPD.Fibrinogen has been recognized to independently contrib-ute to clinically important outcomes in COPD, includinghospitalization for COPD. 46,47 Our results indicate fibrin-ogen levels are predictive of the future hospitalizedexacerbations in the absence of information on past his-tory of these events. Plasma fibrinogen is under regula-tory qualification as a risk factor for mortality andhospitalization in COPD.48

      Relación del fibrinógeno con las hospitalizaciones por EPOC

    4. The degree of airflow limitation has been shown to cor-relate with the frequency of both moderate and severeexacerbations10 and hospitalized exacerbations.37-39 Inour cohort, we also observed that the rate of hospital-ized exacerbations increased with increasing severity ofairflow limitation ( Fig 2 ). It is important to note, how-ever, that even 19% of patients with moderate airflowlimitation were hospitalized at least once during thefollow-up. Given that these moderate patients are mostlyseen in primary care,40 our results indicate that hospital-ized exacerbations pose a huge health-care and eco-nomic burden across all spectrum of disease severity

      Como se relaciona con el airflow limitation. Remarca que el 19% de los pacientes con moderate airflow limitation fueron hospitalizados al menos una vez, y dado que estos pacientes son la mayoría atendidos en atención primaria, representan una alta carga económica

    5. following an exacerbation 29-32 ; inactivity associatedwith hospitalization may cause muscle mass loss anddysfunction33; comorbidities may develop or worsen dueto treatment side effects (eg, systemic steroids inducinghyperglycemia and/or muscle weakness)34; patients maybe exposed to nosocomial infections35; and hospitaliza-tion may cause and/or aggravate depression.36

      ¿Por qué las hospitalizaciones son negativas para los pacientes con EPOC?

    6. We found that a previous hospitalized exacerbation inthe past year was the strongest predictor of future exac-erbations requiring hospitalization.

      Predictor más importante de exacerbaciones severas

    7. (1) COPD exacerba-tions requiring hospital admission are relatively fre-quent events occurring in about 30% of patients duringthe 3-year follow-up; (2) past history of hospitalizedexacerbations is most predictive of future events, andother risk factors include the severity of airflow limita-tion, poor health status, radiologic evidence of emphy-sema, older age, and presence of systemic inflammation;and (3) a history of hospitalized exacerbations heraldspoor survival

      Resumen de resultados

    8. (1) baseline differences between patients with and withouthospitalized exacerbation during follow-up were tested using analysisof variance or Wilcoxon rank-sum test for continuous variables, andx2 test for categorical variables; (2) the incidence (first hospitalizedexacerbation during the prospective follow-up) and recurrence (secondhospitalized exacerbation during the prospective follow-up) of hos-pitalized exacerbations was summarized as a rate per person per year(PPPY), using a sum of individual patient’s person-time in the studyand standardized per year, accompanied by 95% CIs; (3) factors asso-ciated with first hospitalized (and recurrent) exacerbations during the3-year follow-up, were explored using Cox proportional hazards models,adjusted for a wide range of demographics, and clinical and biologicmarkers.

      Statistical analysis

    9. Subjects were followed-up at 3 months, 6 months, and every 6 monthsthereafter for a maximum of 3 years. All patients had their vital statusconfirmed 3 years after recruitment.Information on COPD exacerbations was collected at scheduled visitsby investigators using the case report forms and based on either sub-jects’ recall of exacerbation events or available medical records for exac-erbation events, supplemented by monthly phone calls. For the purposeof the current analysis, we focused on those exacerbation episodes thatrequired hospital admission (hospitalized exacerbation).

      Seguimiento de pacientes parecido a la propuesta de TOLIFE.

    10. The methodology used in the ECLIPSE study has been described indetail elsewhere.9

      ECLIPSE methodology

      1. Vestbo J , Anderson W , Coxson HO , et al ; ECLIPSE investigators . Evaluation of COPD longitudinally to identify predictive surrogate end-points (ECLIPSE). Eur Respir J. 2008; 31 ( 4 ): 869 - 873 .
    11. Exacerbations of COPD accel-erate disease progression5-7

      Interesante de leer

      1. Tanabe N , Muro S , Hirai T , et al . Impact of exacerbations on emphysema progression in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2011 ; 183 ( 12 ): 1653 - 1659 .

      2. Donaldson GC , Seemungal TA , Bhowmik A , Wedzicha JA . Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease. Thorax. 2002 ; 57 ( 10 ): 847 - 852.

      3. Vestbo J , Edwards LD , Scanlon PD , et al ; ECLIPSE Investigators . Changes in forced expiratory volume in 1 second over time in COPD. N Engl J Med. 2011 ; 365 ( 13 ): 1184 - 1192 .

    1. logisticmultivariate regression tests

      Statistical analysis

    2. xacerbation wasdefined on the basis of symptom-based diagnosis such asincreased cough and sputum production, a change of sputumcolor, and worsening of dyspnea from a stable state andbeyond-normal day-to-day variations, i.e., showing acute onsetand necessitating a change in regular medication, in accor-dance with a previous report [21]. Moderate exacerbationsrequired a prescription for antibiotics and/or systemic corticos-teroids, and severe exacerbations required hospitalization [22].

      Definición de exacerbación

      1. Calverley PM, Anderson JA, Celli B, et al. Salmeterol and fluticasone propionate and survival in chronic obstructive pulmonary disease. N Engl J Med 2007;356:775–89
    1. the CODEX indexis the most useful in predicting survival, hospital read-missions, and a combination of the two in the short andmedium term in patients hospitalized for AECOPD.

      De nuevo, la importancia del CODEX

    2. Multicomponent scales have been developed toimprove prognosis prediction in COPD, and they haveproved to be better predictors of survival than anyisolated variable.

      Lo importante de esto es que podamos hacer las escalas con las medidas de los sensores. Leer la continuación del párrafo. Destaca que el BODE fue desarrollado para pacientes sin comorbilidades, pero que esta el BODEX y el DOSE.

      Si se van a usar los indices es importante que haya una frecuencia de visitas cada 3 meses, por ejemplo, porque se los obtiene a través de variables que hay que medirse en las visitas.

      Pensar en que se quieren hacer modelos predictivos tomando en cuenta solo la información de los sensores. Sería bueno contar con estos indices de manera automática con la información que se obtiene de los sensores.

    3. This newly proposed CODEX index is essentially anevolution of the BODE and BODEX indexes, retain-ing their cutoffs for dyspnea, obstruction, and previousexacerbations, but replacing BMI with comorbiditymeasured using the original Charlson index modifiedby age.

      Importante considerar esta variante de los indices previos.

    4. Second, we reportedthe usefulness of this CODEX index in evaluating therisk of readmission, as well as the composite end point(readmission and/or mortality).

      Evaluar incluir CODEX

    5. Time-dependentvariables from hospital discharge were analyzed with Cox logisticregression and Kaplan-Meier statistics.

      Cox logistic regression

    6. Short- and Medium-term Prognosisin Patients Hospitalized for COPDExacerbation

      Short- and Medium-term Prognosis in Patients Hospitalized for COPD Exacerbation

    7. Full methodology is avail-able elsewhere and is summarized in e-Appendix 1. 16,17

      Methods supplement

    1. The mostcommon statistical method was logistic regression (11 out of 25 different statistical methods analysed)followed by Cox regression (10), and correlation analysis between an index (or a multivariable regressionequation) with the outcome (three). Finally, Poisson regression model, negative binomial regression modeland random forest model were each used once.

      Métodos estadísticos. Leer los papers que están siendo estudiados

      Bertens (29) Ya lo tienes

      Motegi (43) Motegi T, Jones RC, Ishii T, et al. A comparison of three multidimensional indices of COPD severity as predictors of future exacerbations. Int J COPD 2013; 8: 259–271.

      Almagro (23) Almagro P, Soriano JB, Cabrera FJ, et al. Short- and medium-term prognosis in patients hospitalized for COPD exacerbation: the CODEX index. Chest 2014; 145: 972–980.

      Suetomo (48) Suetomo M, Kawayama T, Kinoshita T, et al. COPD assessment tests scores are associated with exacerbated chronic obstructive pulmonary disease in Japanese patients. Respir Investig 2014; 52: 288–295

      Mullerova (45) Müllerova H, Maselli DJ, Locantore N, et al. Hospitalized Exacerbations of COPD. Chest 2015; 147: 999–1007.

      Thomsen (50) Thomsen M, Ingebrigtsen TS, Marott JL, et al. Inflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease. JAMA 2013; 309: 2353–2361.

      Moberg (42) Moberg M, Vestbo J, Martinez G, et al. Validation of the i-BODE index as a predictor of hospitalization and mortality in patients with COPD Participating in pulmonary rehabilitation. COPD 2014; 11: 381–387.

      Takahashi (49) Takahashi T, Muro S, Tanabe N, et al. Relationship between periodontitis-related antibody and frequent exacerbations in chronic obstructive pulmonary disease. PLoS One 2012; 7: e40570.

      Faganello (33) Faganello MM, Tanni SE, Sanchez FF, et al. BODE index and GOLD staging as predictors of 1-year exacerbation risk in chronic obstructive pulmonary disease. Am J Med Sci 2010; 339: 10–14

      Garcia-Aymerich (34) Garcia-Aymerich J, Farrero E, Félez MA, et al. Risk factors of readmission to hospital for a COPD exacerbation: a prospective study. Thorax 2003; 58: 100–105.

      Ko (39) Ko FW, Tam W, Tung AH, et al. A longitudinal study of serial BODE indices in predicting mortality and readmissions for COPD. Respir Med 2011; 105: 266–273.

      Echave (32) Echave-Sustaeta J, Comeche Casanova L, Garcia Lujan R, et al. Prognosis following acute exacerbation of COPD treated with non-invasive mechanical ventilation. Arch Bronconeumol 2010; 46: 405–410.

      Lee (40) Lee SD, Huang MS, Kang J, et al. The COPD assessment test (CAT) assists prediction of COPD exacerbations in high-risk patients. Respir Med 2014; 108: 600–608.

      Moy (44) Moy ML, Teylan M, Danilack VA, et al. An index of daily step count and systemic inflammation predicts clinical outcomes in chronic obstructive pulmonary disease. Ann Am Thorac Soc 2014; 11: 149–157

      Hurst (36) Hurst JR, Vestbo J, Anzueto A, et al. Susceptibility to exacerbation in chronic obstructive pulmonary disease. N Engl J Med 2010; 363: 1128–1138

      Amalakuhan (28) Amalakuhan B, Kiljanek L, Parvathaneni A, et al. A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem. J Community Hosp Intern Med Perspect 2012; 2: 9915.

    2. In order to come up with high-quality prediction models for exacerbations in COPD patients, a standardmethodology for developing the models should be adopted [55]

      Leer cita 55

      Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 2012; 98: 683–690.

    3. Prediction models for exacerbations inpatients with COPD

      Prediction models for exacerbations in patients with COPD



    1. CODEX was designed to predict mortality and hospitalreadmission in 3–12 months after discharge of patients hospi-talized for AECOPD.6

      CODEX Leer

      Almagro P, Soriano JB, Cabrera FJ, et al; Working Group on COPD, Spanish Society of Internal Medicine. Short- and medium-term prognosis in patients hospitalized for COPD exacerbation: the CODEX index. Chest. 2014;145(5):972–980.

    2. BODE2 (body mass index [BMI], airflow obstruction, dyspnea, andexercise capacity), BODEX3 (BMI, airflow obstruction, dyspnea, and previous severeexacerbations), ADO4 (age, dyspnea, and airflow obstruction), and DOSE5 (dyspnea,airflow obstruction, smoking status, and exacerbation frequency

      ¿Se pueden incluir estas variables?

    3. Prediction of short term re-exacerbation inpatients with acute exacerbation of chronicobstructive pulmonary disease

      Prediction of short term re-exacerbation in patients with acute exacerbation of chronic obstructive pulmonary disease

    1. When the recordings met criteria number 1 a red lineappeared on the corresponding day of the patient’s time-score plot (Fig. 2) and an electronic mail message wasautomatically sent to the research team.

      Ideas of how alerts may work

    2. To establish a baseline all patients entered an exacer-bation-free run-in period of 14 days where they recordedsymptom score and lung function as described above.Baseline for each symptom score and FEV 1 was the medianand the mean value respectively of the 14-day run-in periodrecordings

      exacerbation free run in period of 14 days.

      Podemos aplicarlo en nuestro estudio? Hacer pasar a los pacientes por un periodo de 2 semanas sin exacerbaciones y empezar a contar desde ahí.

    3. . AECOPD was, therefore, regarded to be present ifat least one of the following criteria was encountered:1. An increase of at least 1 degree of 2 symptom scoresand/or a decline in FEV 1  10% from baseline for 2successive days.2. A patient presenting with symptoms they felt to bethose of AECOPD and sought help that resulted in thembeing given a course of antibiotics and/or prednisolone.

      Nosotros registraremos los ingresos

    4. Remote daily real-time monitoring in patients withCOPD e A feasibility study using a novel device

      Remote daily real-time monitoring in patients with COPD e A feasibility study using a novel device

    1. Conventional threshold-basedalgorithms, adopted in the majority of reviewed stud-ies (n ¼ 12), show poor performance in early detect-ing respiratory exacerbations or identifying severityand duration in COPD, 13,53 with the best reportedaccuracy being 73% of exacerbations detected. 52 ; bestsensitivity/specificity 66%/93%55 24 h before hospi-talization.

      Estudio donde se creó una alarma.

      1. Pinnock H, Hanley J, McCloughan L, et al. Effective ness of telemonitoring integrated into existing clinical services on hospital admission for exacerbation of chronic obstructive pulmonary disease: researcher blind, multicentre, randomised controlled trial. BMJ 2013; 347: f6070

      2. Halpin DMG, 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.

      3. Sund ZM, Powell T, Greenwood R, et al. Remote daily real-time monitoring in patients with COPD – a feasibility study using a novel device. Respir Med 2009; 103: 1320–1328.

      4. Yanez AM, Guerrero D, Perez De Alejo R, et al. Monitoring breathing rate at home allows early identification of COPD exacerbations. Chest 2012; 142:1524–1529.

    2. Whilst the 7-day recall scoresand the daily diary scores have been found to beequivalent in detecting changes over time of theimpact of COPD symptoms, only daily data seemto be suitable if the outcome of interest is detectingthe onset of exacerbations. 85

      Importante que la medida de los sensores sea diaria. Problemas técnicos tienen que verse por adelantado.

    3. In COPD, physiological measurements have notproved to be able to predict deteriorations, eitherbecause they change late in the time course ofexacerbation, they cannot be measured reliably orbecause therapeutic interventions during the experi-ment alter the outcomes hindering the accuracy ofalgorithms. 17

      Posibles inconvenientes de obtener medidas fisiológicas.

    4. One study used a com-promise solution, 56 whereby the patient with a lowrisk of exacerbation monitored on a weekly basis andwhen risk increased according to the predictivemodel, reporting tasks could be scheduled daily toensure timely detection.

      ¿Se puede hacer esto? Sería importante conversar los posibles problemas técnicos de los sensores

    5. A common limitation in the selected studies was theexamination of a relatively small group of patientswho presented a high rate of exacerbations. Winterperiods were extensively selected for trials becauseexacerbations are more likely than in other sea-sons. 51 Although this may have enabled recruit-ment of ‘at-risk’ populations it may have affectedgeneralizability of results.

      Esto es importante preguntar a Judith

    6. EXACT-PRO was specifically designed and validated fordetecting COPD exacerbation

      La ventaja de exact es que es el único cuestionario validado para detectar exacerbaciones

    7. (EXACT)

      Sería necesario recoger EXACT??

    8. An ‘event-based’ definition of an exacerbation, 70including self-administration of medication orunscheduled visits to emergency units and/or admis-sions, was used in nine studies (n ¼ 6 in COPD andn ¼ 3 in asthma studies). Symptom-based definitionsof exacerbation (e.g. Anthonisen criteria 71 ) were usedin seven COPD studies.

      Criterios de exacerbaciones

    9. Exacerbation criteria

      Diferentes criterios de exacerbaciones

    10. Common classification algorithms for supervisedlearning in the healthcare field include artificialneural networks, 36 decision trees, 37 random forests, 38Bayesian networks, 39 k-nearest neighbors,40 supportvector machines, 41 linear discriminant analysis, 42 k-means clustering 43 and logistic regression. 44

      Haremos un classification. Evaluar la posibilidad de regresión de Cox.

      • Artificial neural networks
      • Decision trees
      • Random forests
      • Bayesian networks
      • k-nearest neighbors
      • Support vector machines
      • Linear discriminant analysis
      • k-means clustering
      • Logistic regression
    11. Use of predictive algorithms in-homemonitoring of chronic obstructivepulmonary disease and asthma:A systematic review

      Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review

    1. n most cases, if censoring isnegligible and the follow-up period clearly defined, logistic regres-sion is used; if censoring is significant or time to event is important,then a survival time approach using a Cox proportional Hazardsmodel is preferred. Other more complex model approaches such asmachine learning or competing risk models exist but are beyond thescope of this chapter [18, 19].

      Métodos estadísticos para predecir variables binarias

    2. To ensure stability of the model coefficients in logistic and Coxregression, an event frequency of at least 10/events per degree offreedom in the model is advised [13]. For example, in a cohort of1000 patients where 100 outcomes have been observed, the pre-diction model should include at most 10 variables. Ratios of lessthan 10 events per variable can result in overfitting of the data,leading to poor generalizability in other patient cohorts. All thesegeneral aspects of study and model specification should bedescribed in the methods to allow assessment of internal validity.


    3. Internal validity refers to the concept that a prediction model mustbe derived from the study sample in such a way that the modelcoefficients accurately reflect the true relationships between thepredictor variables and the outcome of interest. Internal validitytherefore requires that the prediction model be derived from anappropriately structured and assembled cohort.
    4. ClinicalEpidemiology

      Clinical Epidemiology - Springer

    1. Cox proportional hazards modeling would alsohave been a valid approach for risk estimation, but we choselogistic regression analysis because we considered each exac-erbation within our predefined time frame of 2 years to beof equal importance, regardless of whether this exacerbationoccurred early or late in the follow-up period.

      Esto es importante. Comparación entre logística y regresión de cox

    2. We used logistic regression modeling to estimate the riskof occurrence of COPD exacerbations within the proceeding24 months.

      Análisis estadístico

    3. We were not able to include all potential predictorsavailable from the literature, such as exercise capacity, theSt George’s Respiratory Questionnaire, oxygen therapy,and gastroesophageal reflux

      Variables a incluir

    4. Unfortunately, wecould not evaluate the widely accepted BODE index,5 becausewe did not perform a 6-minute walking test in any of ourcohorts.

      Nosotros podemos contar con el BODE?

    5. For both cohorts we used the same “operational” definitionfor exacerbation of COPD, that is, symptomatic deteriorationrequiring pulsed oral steroid use or hospitalization.15–1

      Se pueden utilizar ambas definiciones?

    6. For both cohorts we used the same “operational” definitionfor exacerbation of COPD, that is, symptomatic deteriorationrequiring pulsed oral steroid use or hospitalization.15–1

      Se pueden utilizar ambas definiciones?

    7. Development and validation of a model to predictthe risk of exacerbations in chronic obstructivepulmonary disease

      Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease

    1. An increase in respiratory rates and thepercentage of respiratory cycles triggered by the patients nearly systematically precededexacerbation in patients with COPD treated by home NIV [ 25].

      Características de monitorización a distancia que pueden describir las exacerbaciones

    2. mean decrease of 700 steps per day was associated with an increasein the EXACT score indicating the start of an exacerbation [21 ].

      relación actividad física exacerbaciones

    3. The COPD “frequent exacerbator” phenotype is consistently defined by atleast two treated exacerbations per year and is associated with poor long-term outcomesand an accelerated decline in lung function

      Fenotipo de frequent exacerbator

    4. Remote Monitoring for Prediction and Management of AcuteExacerbations in Chronic Obstructive PulmonaryDisease (AECOPD)

      Remote Monitoring for Prediction and Management of Acute Exacerbations in Chronic Obstructive Pulmonary Disease (AECOPD)

    5. COPD exacerbations are more common in females, patients with car-diometabolic or psychiatric comorbidities—in particular depression—and at the mostsevere spectrum of the disease. A history of prior exacerbations has been demonstrated tohave by far the strongest association with the risk of future exacerbations [7,8]

      sex-related factors that are related to copd exacerbatoins

    1. The primary composite outcome was death or admissionsfrom the baseline data collection until 60 months. Thesecondary outcome was exacerbations from the baselinedata collection until 60 months.

      Sería lo mejor dejar de seguir a los pacientes después de la primera exacerbación?

    2. Prediction of severe exacerbations and mortalityin COPD: the role of exacerbation history andinspiratory capacity/total lung capacity ratio

      Prediction of severe exacerbations and mortality in COPD: the role of exacerbation history and inspiratory capacity/total lung capacity ratio



    1. Seven out of nine studies foundin the literature confirmed the finding that female patientshave a higher number of total exacerbations.15–17,19,21,22,31

      Dato curioso. Sería interesante revisarlo

    2. Five different prediction models for the annual exacerba-tion rate were estimated using negative binomial regression2

      nb regression. Han hecho 5 modelos, pero en el sentido de que han utilizado diferentes variables como predictoras

    3. Participants were asked to use the following definition foran exacerbation: a moderate exacerbation was defined asan increase in symptoms requiring a visit to a health careprovider and a course of antibiotics and/or oral steroids.A severe exacerbation was defined as an exacerbation requir-ing hospitalization.

      Parecido a lo que queremos medir

    4. Prediction models for exacerbations in differentCOPD patient populations: comparing results offive large data sources

      Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources

    1. History of Exacerbations

      Tratar de conseguir esta información

    2. The test set AUROC of 0.86 was relatively high,18–24 butour models may be of limited value for prediction ofsevere exacerbations due to high false positive rate

      Tener cuidado con esto.

    3. These methods included logistic regression with multipleregularization methods (lasso, ridge and elastic net), ran-dom forest and gradient boosted trees models (XGBoost).

      Regularization methods and XGboosted random and boosted trees

    4. This set of models(along with support vector machines and neural networks,which were not taken into consideration being more chal-lenging to interpret) are considered gold standard formachine learning classification studies done on tabulardata. Resampling was applied during cross-validation,making sure that only training folds of each cross-valida-tion iteration are affected, and the effect of resampling istested on the non-resampled test fold in each cross-valida-tion iteration.

      Procesos estadísticos que acompañan a los modelo predictivos.

    5. The prediction period was divided into non-overlap-ping 10-day prediction windows for each patient (Figure3). Before the start of each 10-day prediction window,lookback periods of 10, 30, 60, 90,180, 365 days or theentire patient history were set up depending on the vari-able. For details refer to Supplemental Table 1.

      Esto es muy importante. Esto es una opción de cómo nosotros podríamos evaluar las exacerbaciones

    6. Predicting Hospitalization Due to COPDExacerbations in Swedish Primary Care PatientsUsing Machine Learning – Based on the ARCTICStudy

      Predicting Hospitalization Due to COPD Exacerbations in Swedish Primary Care Patients Using Machine Learning – Based on the ARCTIC Study

    1. Development and Validation of a MultivariablePrediction Model to Identify Acute Exacerbationof COPD and Its Severity for COPD Managementin China (DETECT Study): A Multicenter,Observational, Cross-Sectional Study

      Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study

    2. he outcome was the occurrence of AECOPD (ie occurrence versus no occurrence)

      Muy parecido a lo que sería nuestro outcome. Poner interés en como hacen el modelo predictivo.



    1. Seasonality isknown to affect COPD exacerbations, most frequently occur-ring in the winter months,37–39 which is likely to reflect anincreased prevalence of respiratory infections, reduced immu-nity, altered environmental conditions, and physiologicalresponses during these months.40,41

      effect of seasonality on exacerbacions

    2. Timing of Re-Exacerbation RiskTo understand the timing and dynamics of re-exacerbationrisk, and to evaluate the choice of cut points for early andlate re-exacerbations, Kaplan–Meier cumulative incidencecurves of time to first re-exacerbation amongst all patientsin cohort A were plotted.

      Timing and dynamics de las exacerbaciones

    3. The cut-off pointfor early and late re-exacerbations was chosen based on clinicaljudgment, and was informed by an analysis of dailyre-hospitalization risk in the US.18 The 90-day cut-off pointwas also chosen based on clinical development support for anAECOPD therapy. Patients were classed as having nore-exacerbations if they did not re-exacerbate within the180-day period after the index date.

      Considerar que las hospitalizaciones son un equivalente a las exacerbaciones severas ya que estas requieren hospitalizarse. Depende de cómo las vayamos a medir

    4. Re-exacerbations were defined a priori as either early (occur-ring within 1–90 days after the index date) or late (occurringbetween 91–180 days after the index date

      re exacerbaciones

    5. This included start date, end (reso-lution) date, treatment (oral corticosteroid/antibiotics),severity (including hospitalization), and outcome (includ-ing death). Data was collected at scheduled visits (baselineand 3, 6, 12, 18, 24, 30, and 36 months), using an electro-nic case-report form and based on either patients’ recall ofexacerbation events or available medical records forexacerbation events, and was supplemented by monthlyphone calls to ECLIPSE participants

      Puede servir para guiar nuestro diseño

    6. First, we aimed to develop and validatea predictive model capable of identifying factors potentiallypredictive of experiencing early, late, or no re-exacerbationwithin 180 days.

      Ligado al objetivo del CSA

    7. Predicting Re-Exacerbation Timing andUnderstanding Prolonged Exacerbations: AnAnalysis of Patients with COPD in the ECLIPSECohort

      Predicting Re-Exacerbation Timing and Understanding Prolonged Exacerbations: An Analysis of Patients with COPD in the ECLIPSE Cohort

    1. A challenge in COPD is the variation between patients and how to set alarm limits for anindividual patient. Of the 16 articles included in this review, only eight studies (three at high riskof bias, one at low quality and two at moderate quality) [ 5, 13, 14 ,18 – 21, 25] mentioned that they hadcustomised the alarm limits for each individual. Methods used were reported in six out of the eightstudies


    2. Monitoring of Physiological Parameters to PredictExacerbations of Chronic Obstructive PulmonaryDisease (COPD): A Systematic Review

      Monitoring of Physiological Parameters to Predict Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): A Systematic Review

    1. ACCEPT 2¢0: Recalibrating and externally validatingthe Acute COPD exacerbation prediction tool (ACCEPT)

      ACCEPT 2,0: Recalibrating and externally validating the Acute COPD exacerbation prediction tool (ACCEPT)

    1. Prediction of readmission in patientswith acute exacerbation of chronic obstructivepulmonary disease within one yearafter treatment and discharge

      Prediction of readmission in patients with acute exacerbation of chronic obstructive pulmonary disease within one year after treatment and discharge



    1. Exacerbations were defined as subject-reported worsen-ing in respiratory health, requiring therapy with systemic cortico-steroids and/or antibiotics. Frequent exacerbations were definedas 2 or more exacerbations in 1 year, and severe exacerbations weredefined as those leading to hospitalization or emergency room vis-its [9]


    2. Prediction of Acute COPD Exacerbation in theSwiss Multicenter COPD Cohort Study (TOPDOCS)by Clinical Parameters, Medication Use, andImmunological Biomarkers

      Prediction of Acute COPD Exacerbation in the Swiss Multicenter COPD Cohort Study (TOPDOCS) by Clinical Parameters, Medication Use, and Immunological Biomarkers

    1. Prediction of 30-day risk of acuteexacerbation of readmission in elderly patientswith COPD based on support vector machinemodel

      Prediction of 30-day risk of acute exacerbation of readmission in elderly patients with COPD based on support vector machine model

    2. Acute exacerbationreadmission within 30 days due to the short acute exacer-bation cycle, not only severely damages lung function andincreases the risk of death, but also occupies a large num-ber of medical resources [4]

      short exacerbation cycle?

    3. Our study assumes that the SVM model can achieve acertain prediction effect in predicting the risk of readmis-sion in COPD patients, and the results have certain refer-ence value. Therefore, it is proposed to use SVM to builda 30-day acute exacerbation readmission risk predictionmodel for elderly COPD patients, and evaluate its predic-tion effect, so as to provide a basis for early identificationof patients with high risk of readmission in the future.

      SVM for the prediction analysis

    1. Neither the developmental nor the validation datasetsincluded patients with mild (GOLD 1) severity and, assuch, we could not establish the accuracy of predictionsfor this subgroup.

      Mencionar en nuestros estudios

    2. ACCEPT is externally validated in anindependent cohort extending its generalisability beyondtherapeutic clinical trials.

      Esto es algo que podríamos hacer en el CSA2

    3. Azithromycin reduces annual exacer-bation rate by 27%.8


    4. The Acute COPD Exacerbation Prediction Tool (ACCEPT):a modelling study

      The Acute COPD Exacerbation Prediction Tool (ACCEPT):a modelling study

    5. ACCEPT can combine predicted risk with effectestimates from randomised trials to enable personalisedtreatment.

      esto sería interesante recalcar en el ensayo clínico

    6. We used a joint accelerated failure time and logistic modelto characterise rate and severity of exacerbations. We havepreviously published details of this approach elsewhere.14

      análisis estadístico

    7. A 2017 systematic review of clinical prediction modelsfor COPD exacerbations found that only two models18,19 ofthe 27 reviewed reported on any external validation. Whenavailability of predictors and practical applicability werealso considered, none of the models were deemed readyfor clinical implementation.6

      es posible que nuestro estudio sea difícil de aplicar por el uso de sensores

    8. ACCEPT predicts rate and severity of exacerbations.

      Este es el outcome de ACCEPT. Nosotros definitivamente tenemos que hacer una clasificación.

    9. For example, ACCEPT can predict thenumber of exacerbations at a given time period, time tonext exacerbation, and probability of having a specificnumber of non-severe or severe exacerbations within agiven follow-up time (up to 1 year). By contrast, logisticregression models, used in most previous clinicalprediction models, predict the probability of having at leastone exacerbation in a single timeframe.6

      Aquí hay un versus muy claro. Esto hay que estudiarlo a fondo

    10. Outcomes of interest were rates of exacerbations andsevere exacerbations over 1 year.

      tasa de exacerbaciones y exacerbaciones severas en un año

    1. The Association Between Rate and Severity of Exacerbations in ChronicObstructive Pulmonary Disease: An Application of a Joint Frailty-Logistic Model

      The Association Between Rate and Severity of Exacerbations in Chronic Obstructive Pulmonary Disease: An Application of a Joint Frailty-Logistic Model

    2. Prevention, early detection, and appropriate treatment ofexacerbations are a focal point of attention in COPD careand research.
    3. Use of the ubiquitous proportional hazardsmodel, with time to first exacerbation as the outcome, is acommon mode of inference in contemporary clinical trialsof COPD. While it is robust in estimating treatment effectin randomized controlled trials, this analytical method fallsshort of providing other features, such as background rateof exacerbations or the shape of the incidence function, toenable predictions about the rate and (absolute or relative)duration of time to future events for a given patient. Asmentioned by Cox et al. (11), making such informative pre-dictions has been hindered by the widespread use of semi-parametric proportional hazards models.

      desventajas del proportinal hazards

    4. In the present work, we used a joint para-metric recurrent-event and logistic regression model toenable full quantification of exacerbation incidence andseverity and their correlation

      objetivo del análisis estadístico

    1. such as bloodeosinophil count, chronic bronchitis, gastroesophagealreflux, socioeconomic status, and insurance coverageimprove risk prediction remains to be investigated.

      Considerar estas variables a medir.

      • Blood eosinphil count
      • Chronic bronchitis
      • Gastroesophageal reflux
      • SES (cómo lo vamos a medir?)
      • Insurance coverage

      Tal vez no sea lo más importante porque queremos que el modelo esté construído con variables a partir de los sensores

    2. Second, ACCEPT does not advance ourunderstanding of how various risk factors operatein combination. Many variables appear to be simplymarkers of severity rather than biological predictors ofreal risk. The authors address one of these: contrary toestablished literature, current smoking confers reducedrisk of exacerbation, probably because patients withsevere disease and high frequency of exacerbations aremore likely to have quit smoking.

      Dos cosas importantes: - Nosotros tenemos que resaltar que no medidos marcadores de severidad. - Resaltan la posible confusión entre fumar y un riesgo reducido de exacerbaciones

    3. Finally, preventionof severe exacerbations is needed before they occur,and performance of this risk tool in individuals whohave never had an exacerbation is not clear and needsto be tested.

      Cómo evaluaremos a los pacientes que no han tenido exacerbaciones?

    4. COPD exacerbations: finally, a more than ACCEPTable risk score

      COPD exacerbations: finally, a more than ACCEPTable risk score

    5. First, although ACCEPTshowed good-to-excellent discrimination overalland appears superior to exacerbation history alone,improvement in risk prediction was smaller in thosewith previous history of exacerbations than in thosewithout.

      Esto también es importante. Es importante porque nosotros deberíamos dividir el desempeño del modelo según varias poblaciones de interés. Por ejemplo, en este caso, han comparado el desempeño del modelo en población con y sin exacerbaciones previas.

    6. Thus, the major improvement in risk prediction withACCEPT appears to be for severe exacerbations.

      Esto es importante porque es justamente lo que nosotros queremos.

    7. Usinga joint survival–logistic model, this risk tool providesan individualised risk estimate for exacerbations in thesubsequent year and their severity, as well as the rate offuture events.

      Este documento es un comentario al estudio del ACCEPT. Nosotros podemos hacer lo mismo, pero teniendo en cuenta un mes(?) como referencia.

    8. Severe exacerbations are especially important asthey are associated with rapid decline of lung functionand 50% of patients die within 2 years after admissionto hospital.

      Importancia de las exacerbaciones. ¿Por qué las estudiamos?

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

      COPD exacerbations: Prognosis, discharge planning, and prevention

    2. Vitamin D supplementation — Adhering to current guidelines regarding vitamin Dsupplementation in patients with a 25-hydroxyvitamin D level <20 or 30 ng/mL (50 or 75nmol/L) reduces COPD exacerbations in addition to benefits in reducing falls and fractures

      Otra variables que se podría añadir. Revisarlo bien porque el resultado de los metaanálisis son mixtos

    3. Noninvasive ventilation — For patients who require noninvasive ventilation (NIV) during ahospitalization for a COPD exacerbations and who remain hypercapnic, nocturnal NIV athome significantly reduces the risk of rehospitalization

      Importante porque puede afectar a el riesgo de hospitalización y depende de como queramos medir las exacerbaciones

    4. Prophylactic azithromycin

      Para pacientes con exacerbaciones recurrentes (>= a 2 por año) la administación profiláctica de azitromicina puede ayudar a reducir la frecuencia de exacerbaciones.

    5. defined as "an acuteevent characterized by a worsening of the patient's respiratory symptoms that is beyondnormal day-to-day variations and leads to a change in medication

      Definition of exacerbation according to GOLD guidelines


      Sección de prevención de exacerbaciones de COPD en uptodate. Importante leer que la sección comienza con medidas para reducir la exacerbaciones. Estas medidas pueden ser incluidas cómo variables a preguntar.

      Irán señaladas, pero igual: - Smoking cessation - Proper use of medications - Vaccination against seasonal influenza - Vaccination against COVID - Pneumococcal vaccination

    7. Pulmonary rehabilitation

      Posible variables a añadir: tiempo en rehabilitación pulmonar? ha estado en rehabilitación pulmonar en el ultimo año? Más de 6 meses en el último año? Cuántos meses en el último año?

    8. Smoking cessation (see "Overview of smoking cessation management in adults")•Proper use of medications (including inhaler technique) ( table 5 and table 6and table 7 and table 8) (see "The use of inhaler devices in adults")•Vaccination against seasonal influenza (see "Seasonal influenza vaccination inadults")•Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)(see "COVID-19: Vaccines")•Pneumococcal vaccination ( figure 1) (see "Pneumococcal vaccination in adults")

      Posibles variables a añadir.

    1. COPD exacerbations: Management

      COPD exacerbations: Management


      Podemos dividir el tratamiento según la familia de fármacos que esté recibiendo el paciente. Así como la base de datos de Urban Training.

      • Beta adrenergic
      • Muscarinic
      • Oral Glucocorticoid
      • Inhaled glucocorticoid
      • Antimicrobial therapy (antibiotics, antiviral)
    3. More than 80percent of exacerbations of COPD can be managed on an outpatient basis, sometimes afterinitial treatment in the office or emergency department.

      Debido a este hecho: ¿Deberíamos hacer un check list sobre la toma de decisiones para ingresar o no a los pacientes con exacerbaciones? El hecho de que no ingresen a pacientes que debieron, puede aumentar el riesgo de exacerbaciones?

      Variables a considerar para ingresar a un paciente con COPD - Inadequate response to outpatient or emergency department management - Onset of new signs (eg, cyanosis, altered mental status, peripheral edema) - Marked increase in intensity of symptoms over baseline (eg, new onset resting dyspnea) accompanied by increased oxygen requirement or signs of respiratory distress - Severe underlying COPD (eg, forced expiratory volume in one second [FEV ] ≤50 percent of predicted) - History of frequent exacerbations or prior hospitalization for exacerbations - Serious comorbidities including pneumonia, cardiac arrhythmia, heart failure, diabetes mellitus, renal failure, or liver failure - Frailty - Insufficient home support


      ¿Sería bueno crear una variable: viral infections? Y tal vez las demás infecciones virales del COVID.

  2. www.stat.umn.edu www.stat.umn.edu
    1. The point is that the probability (1.3) does not depend on the parameter θ bydefinition of “pivotal quantity.

      Cantidad pivotal

    1. Patients were contacted by telephone by atrained clinical research assistant at months 1, 2, and 3 afterrecruitment and were asked about potential hospitalizationsbecause of ECOPD. Whenever this was identified, hospital,data of admission, and reason for hospitalization were recordedusing standardized questionnaires. The attending pulmonolo-gist of each patient, blinded to the respiratory results, reviewedall available information and validated the episode of ECOPDhospitalization

      llamada de teléfono al mes 1, 2 , 3 después del reclutamiento

    2. First, weanalyzed individual changes in mean respiratory frequency usingtime series analysis of breathing rate in each patient whorequired ECOPD hospitalization during follow-up. This timeseries included 5 baseline days and the 5 days that precededhospitalization. Baseline was defined as the first 5 consecutivefollow-up days with valid recorded data and a minimum com-pliance with oxygen therapy of 4 h/d, before any exacerbationhad occurred. Individual changes were analyzed using the YoungC statistic, which is appropriate for the study of changing trendsin short series with a small number of measures. 16 Second, weanalyzed the change in mean respiratory frequency between the5 baseline days and the 5 days that preceded hospitalization ofall individual time series considered together using analysis ofvariance for repeated measures. Third, we analyzed the discrimi-nating power of the change in the mean respiratory frequency topredict ECOPD hospitalizations using receiver operating char-acteristic (ROC) curves in two different potentially relevantclinical scenarios (increase of breathing rate from baseline to24 or 48 h before hospitalization).

      time series

    3. Patients were contacted by telephone by atrained clinical research assistant at months 1, 2, and 3 afterrecruitment and were asked about potential hospitalizationsbecause of ECOPD. Whenever this was identified, hospital,data of admission, and reason for hospitalization were recordedusing standardized questionnaires. The attending pulmonolo-gist of each patient, blinded to the respiratory results, reviewedall available information and validated the episode of ECOPDhospitalization.

      Llamadas de teléfono 1, 2, 3 días después del reclutamiento