Usefulness of a Telemedicine Tool TELEA in the Management of the COVID-19 Pandemic

Rey, Ramón Rabuñal; Suárez-Gil, Roi; Golpe, Rafael; Mártinez-García, Mónica; Gómez-Méndez, Raquel; Romay-Lema, Eva; Pérez-López, A.; Rodríguez, Ana Marta Guillén; Bal-Alvaredo, Mercedes

doi:10.1089/tmj.2020.0144

Cited by 33 publications

(20 citation statements)

References 6 publications

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“…Although this study relied on vital sign monitoring without use of VC, it underlines the utility of telemedicine in the response to the COVID-19 pandemic. 25 During the current pandemic, a rapid transition to telemedicine was necessary to guarantee the continued care of our LTx patients while limiting their potential exposure to SARS-CoV-2. The observed drop in patient appointments, virtual or in person, during the pandemic reflects a lag in implementing telemedicine rapidly and with little preparation.…”

Section: Discussionmentioning

confidence: 99%

Video Consultation During the COVID-19 Pandemic: A Single Center's Experience with Lung Transplant Recipients

Kayser

Valtin

Greer

et al. 2021

Telemedicine and e-Health

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Background: The coronavirus disease 2019 (COVID-19) pandemic has disrupted health care systems worldwide. This is due to both to the reallocation of resources toward COVID-19 patients as well as concern for the risk of nosocomial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure. The interruption of routine care is especially problematic for patients with chronic conditions requiring regular follow-up, such as lung transplant (LTx) recipients. Introduction: New methods such as telemedicine are needed to bridge the gap in follow-up care caused by the pandemic. Materials and Methods: A retrospective analysis of video consultations (VCs) in comparison with on-site visits (OSVs) was performed during a 6-week period in an LTx center in Germany. VC included a structured work-up questionnaire and vital sign documentation. Results: During the 6-week study period, 75 VCs were performed for 53 patients and 75 OSVs by 51 patients occurred. By the end of our study period, 77% of physician-patient contacts occurred through VC. Physician-patient consultations were reduced by 47% compared with the equivalent time frame in 2019. In 62% of cases, VC resulted in a concrete clinical decision. One COVID-19 patient in home quarantine was admitted due to respiratory failure detected by VC. Patient satisfaction with VC was high. Discussion: Implementation of VC helped to reduce the need for OSV and thus the risk of SARS-CoV-2 exposure in our patient cohort. This technology can be adopted to provide care for a wide range of chronic illnesses. Conclusions: VC can preserve access to specialist care while reducing SARS-CoV-2 exposure for patients with chronic illnesses during the pandemic.

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Section: Discussionmentioning

confidence: 99%

Video Consultation During the COVID-19 Pandemic: A Single Center's Experience with Lung Transplant Recipients

Kayser

Valtin

Greer

et al. 2021

Telemedicine and e-Health

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“…(i) At home by the TELEA system, a home monitoring platform for monitoring respiratory and heart rate, temperature and SaO 2 . 13 (ii) Patients without internet connection at home were monitored via 2–3 telephone calls daily. If the clinical status of the patient deteriorated, a physician contacted them to decide whether hospitalization was required or not.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a prognostic model based on comorbidities to predict COVID-19 severity: a population-based study

Gude‐Sampedro

Fernández-Merino

Ferreiro

et al. 2020

International Journal of Epidemiology

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Background The prognosis of patients with Covid-19 infection is uncertain. We derived and validated a new risk model for predicting progression to disease severity, hospitalization, admission to intensive care unit (ICU) and mortality in patients with Covid-19 infection (Gal-Covid-19 scores). Methods This is a retrospective cohort study of patients with Covid-19 infection confirmed by reverse transcription polymerase chain reaction (RT-PCR) in Galicia, Spain. Data were extracted from electronic health records of patients, including age, sex and comorbidities according to International Classification of Primary Care codes (ICPC-2). Logistic regression models were used to estimate the probability of disease severity. Calibration and discrimination were evaluated to assess model performance. Results The incidence of infection was 0.39% (10 454 patients). A total of 2492 patients (23.8%) required hospitalization, 284 (2.7%) were admitted to the ICU and 544 (5.2%) died. The variables included in the models to predict severity included age, gender and chronic comorbidities such as cardiovascular disease, diabetes, obesity, hypertension, chronic obstructive pulmonary disease, asthma, liver disease, chronic kidney disease and haematological cancer. The models demonstrated a fair–good fit for predicting hospitalization {AUC [area under the receiver operating characteristics (ROC) curve] 0.77 [95% confidence interval (CI) 0.76, 0.78]}, admission to ICU [AUC 0.83 (95%CI 0.81, 0.85)] and death [AUC 0.89 (95%CI 0.88, 0.90)]. Conclusions The Gal-Covid-19 scores provide risk estimates for predicting severity in Covid-19 patients. The ability to predict disease severity may help clinicians prioritize high-risk patients and facilitate the decision making of health authorities.

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“…Only laboratory-confirmed cases were considered for analysis. Patients with uncomplicated disease, with symptoms of upper airway infection, headache, myalgia, anosmia, dysgeusia or anorexia, but with an SaO 2 > 95% and a respiratory rate < 25 breaths/min, were monitored as follows: (1) At home by the TELEA system 13 , a home monitoring platform that allows to monitor respiratory and heart rate, temperature, and SaO 2 ;…”

mentioning

confidence: 99%

Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients

Gudé

Riveiro

Rodríguez-Núñez

et al. 2020

Sci Rep

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The prognosis of a patient with COVID-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with COVID-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, laboratory, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1152 patients presented with SARS-CoV-2 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 h of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤ 5%, 6–25%, and > 25% exhibited disease progression, respectively. A risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.

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Usefulness of a Telemedicine Tool TELEA in the Management of the COVID-19 Pandemic

Cited by 33 publications

References 6 publications

Video Consultation During the COVID-19 Pandemic: A Single Center's Experience with Lung Transplant Recipients

Video Consultation During the COVID-19 Pandemic: A Single Center's Experience with Lung Transplant Recipients

Development and validation of a prognostic model based on comorbidities to predict COVID-19 severity: a population-based study

Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients

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