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Enregistrement W3165562528 · doi:10.1097/ccm.0000000000005092

Advancing Telehealth-Based Screening for Postintensive Care Syndrome: A Coronavirus Disease 2019 Paradigm Shift*

2021· letter· en· W3165562528 sur OpenAlexaboutno aff
Leslie P. Scheunemann, Timothy D. Girard

Notice bibliographique

RevueCritical Care Medicine · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueIntensive Care Unit Cognitive Disorders
Établissements canadiensnon disponible
Organismes subventionnairesAgency for Healthcare Research and Quality
Mots-clésMedicineTelehealthCoronavirus disease 2019 (COVID-19)PandemicParadigm shiftIntensive care medicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakCoronavirusDiseaseTelemedicineVirologyInternal medicineHealth careInfectious disease (medical specialty)Outbreak

Résumé

récupéré en direct d'OpenAlex

The postintensive care syndrome (PICS)—a label describing new or worsening physical, cognitive, or psychosocial dysfunction that affects critical illness survivors months to years after hospitalization (1)—has been recognized for over 2 decades and impacts over half of critical illness survivors. Not surprisingly, early reports indicate that coronavirus disease 2019 (COVID-19) survivors frequently experience symptoms of PICS (2–4), but these studies did not specifically assess severe COVID-19 survivors for PICS. In this issue of Critical Care Medicine, Martillo et al (5) report the results of a study that did just that. Their Critical Care Recovery Center (CCRC) at Mount Sinai Hospital in New York City used telehealth assessments of patient-reported outcomes to evaluate survivors of severe COVID-19 for PICS 1 month after hospital discharge (5). Eligibility was restricted to those with COVID-19-related critical illness and an ICU length of stay greater than or equal to 7 days who agreed to telehealth follow-up. Patients reported physical and psychologic symptoms using web-based questionnaires, and clinicians assessed cognition using the Telephone Montreal Cognitive Assessment (T-MoCA). Notably, only 45 of 121 patients (37%) eligible at hospital discharge were assessed at 1-month follow-up; over half were not assessed because they were deceased or still in a healthcare facility. Of those assessed, 41 (91%) met criteria for PICS. Physical impairments predominated (87%), but pandemic-related staffing limitations prevented cognitive assessments of one-third of patients. These results fill an important gap by providing the first estimate of the prevalence of PICS among survivors of severe COVID-19. The essential public health message is that critical illness due to COVID-19 is life changing. Non-COVID-19 critical illness survivors have long testified to suffering when their PICS symptoms go unrecognized, unnamed, and untreated (6), yet underrecognition of PICS remains an enormous problem. The outcomes observed by Martillo et al (5) were not dissimilar from those experienced by survivors of non-COVID-19 critical illnesses. Further research is now needed to understand whether and how COVID-19 survivorship has unique features compared with non-COVID-19 critical illness survivorship. Low diffusing capacity for carbon monoxide and other pulmonary function abnormalities, for example, may be more common after severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection (7) and may contribute to physical disability and impaired rehabilitation potential, at least in the short term. Psychologic dysfunction could also be more common in COVID-19 survivors given the novelty of SARS-CoV-2 infection and social isolation experienced during the pandemic. Because the outcomes experienced by COVID-19 survivors are common among survivors of other forms of critical illness, future studies of COVID-19 outcomes should include non-COVID critical illness survivor comparator groups. Post-ICU clinics like Mount Sinai’s CCRC are important hubs for innovation in PICS care. The CCRC impressively conducted this study during the pandemic. Post-ICU clinics often gather data during routine care, creating opportunities for both quality improvement and clinical research. These efforts will benefit from research designed specifically to answer four key questions raised by the current study: 1) when should patients be screened for PICS? 2) What screening measures best assess PICS symptoms? 3) Should the ascertainment of possible risk factors for PICS cast a wider net? 4) And who should be screened for PICS? One-month postdischarge is among the earliest reported follow-up periods but is consistent with many post-ICU clinic practices. Indeed, the optimal timing of screening—which should be influenced by scientific, patient-centered, and pragmatic factors—is unknown. Scientifically, the National Institute on Aging has placed a high priority on understanding resilience to major stressors like critical illness (8). Research is needed to characterize the trajectories of survivors who “bounce back” versus those who do not, knowledge that will inform timing of screening and of interventions. Frequent or prolonged screening assessments are not patient-centered, increasing the burden on patients already exhausted by critical illness (9). Brief, serial assessments during hospital rehabilitation sessions might offer low-burden, high-yield insights into the patient’s functional trajectory and inform postdischarge assessments, but this remains to be investigated. Pragmatically, the goal is to optimize patients’ functional outcomes within resource constraints, which requires considering that early screening may overidentify impairments that will improve without specialty care and/or overwhelm clinic capacity, whereas delayed screening may increase loss to follow-up and/or interim functional loss. The screening approach employed by Martillo et al (5) was reasonable but should be revised as more data become available. Martillo et al (5) are to be commended for using a telehealth screening battery to assess COVID-19 survivors for PICS. This innovation, necessitated by the pandemic, significantly advances practice. Their approach now needs to be refined. That only six patients (20% of those assessed) had cognitive impairment suggests that the T-MoCA may have inadequate sensitivity among COVID-19 survivors given that another recent study of COVID-19 survivors identified cognitive impairment in 42% of patients who had been mechanically ventilated (4), a rate similar to that observed among other populations of critical illness survivors (10). How the other measures performed is unclear. An optimal screening tool should have good sensitivity and reasonable specificity for the various domains of impairment, be efficient and low-cost to administer, and permit comparison between and across groups. Measurement in PICS is hardly a settled issue. Self-reported and observed functions often differ (11,12); whether self-reported screening tools can identify appropriate cases for performance-based assessments is understudied in critical illness survivors. Martillo et al’s (5) use of the Neuro-quality of life (Neuro-QOL) upper extremity and lower extremity and PRO Measurement Information System (PROMIS) Short Form v1.0 Fatigue, both components of the HealthMeasures comprehensive measurement system, provides a key direction for further research. Indeed, using HealthMeasures’ recommended battery of postacute care measures for all the main study outcomes would have been particularly innovative (13). The battery maps to PICS domains and satisfies the aforementioned requirements for efficiency, low cost, and comparability. Most of the battery comes from PROMIS, which is intuitively better suited to critical illness survivors than Neuro-QOL, since the latter was designed for those with neurologic conditions. Furthermore, PROMIS measures are widely available in Research Electronic Data Capture and several commercial electronic health records (EHRs). If integrated into routine care, EHR-based assessments prior to critical illness could provide baselines for longitudinal studies, feasibility and accessibility of screening for PICS could be dramatically enhanced (9), and PICS-related symptoms could efficiently be compared within and across health systems. Thus, determining whether the PROMIS postacute care battery has good sensitivity and adequate specificity to screen for PICS after severe COVID-19 or after critical illness in general is an important goal for future research. Nearly all studies that evaluated risk factors for PICS have focused on biological factors (e.g., comorbidities and severity of illness), most of which are not strong predictors (14). Thus, a broader set of risk factors require consideration. The health inequities of COVID-19 should galvanize attention to social determinants of health (15), which the current study did not address. Telehealth access requires electronic devices and Internet; access problems should spur the healthcare community to advocate for policies addressing this gap. Furthermore, risk factors for PICS (e.g., immobility and delirium) are reduced by processes of care such as the A–F bundle (16,17). Martillo et al (5) acknowledge this in their article but did not measure A–F bundle implementation. Collecting such data is costly but is critical to characterizing and holding ourselves accountable for our contributions to patient outcomes. Finally, Martillo et al (5) focused on patients who spent 7 days or more in the ICU, but length of stay is not a reliable predictor of PICS, and many severe COVID-19 survivors with shorter stays may be at risk. Additionally, 76/121 of eligible patients (63%) were not assessed. Nine (7%) died between discharge and 1-month follow-up. Thirty (25%) were still in long-term acute care hospitals, skilled nursing facilities, or inpatient rehabilitation; presumably, all of these had PICS-related functional impairments. Thirty-seven (31%) declined to participate, were unable to be reached, no-showed, or did not complete study measures. In summary, severe COVID-19 has high ongoing postdischarge mortality, PICS is not limited to those living at home, and many patients either do not prioritize specialized follow-up care or cannot access it. How can we best provide anticipatory guidance, ensure patients leave the hospital with advance directives, and ensure ready access to end-of-life care if desired? Are those who remain in institutions at highest risk for permanent disability? How should health systems allocate resources to care for them? Are the people not assessed bimodally distributed, with some faring well and others faring poorly? What limits participation in follow-up care? How can post-ICU clinics reach more survivors? The list of unanswered questions remains lengthy. In conclusion, Martillo et al (5) have documented PICS symptoms in 91% of severe COVID-19 survivors at 1-month follow-up using telehealth screening that expanded access. Many questions remain about when and how to screen for PICS and whether severe COVID-19 survivors have unique symptoms or trajectories compared with non-COVID-19 critical illness survivors. The main public health message, however, is clear: critical illness, in general, and severe COVID-19, specifically, are life changing. Thus, the critical care community needs to do all it can to prevent the development of PICS; to raise awareness of PICS among patients, families, and primary care providers; and to optimize screening and treatment for PICS among COVID-19 and non-COVID-19 critical illness survivors alike.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,035

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0010,001
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,356
Écart entre enseignants0,319 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2021
Routes d'admission1
Résumé présentoui

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