Effects of COVID-19 Acute Respiratory Distress Syndrome Intensive Care Unit Survivor Telemedicine Clinic on Patient Readmission, Pain Perception, and Self-Assessed Health Scores: Randomized, Prospective, Single-Center, Exploratory Study
Notice bibliographique
Résumé
BACKGROUND: Post-intensive care syndrome (PICS) affects up to 50% of intensive care unit (ICU) survivors, leading to long-term neurocognitive, psychosocial, and physical impairments. Approximately 80% of COVID-19 pneumonia ICU patients are at elevated risk for developing acute respiratory distress syndrome (ARDS). Survivors of COVID-19 ARDS are at high risk of unanticipated health care utilization postdischarge. This patient group commonly has increased readmission rates, long-term decreased mobility, and poorer outcomes. Most multidisciplinary post-ICU clinics for ICU survivors are in large urban academic medical centers providing in-person consultation. Data are lacking on the feasibility of providing telemedicine post-ICU care for COVID-19 ARDS survivors. OBJECTIVE: We explored the feasibility of instituting a COVID-19 ARDS ICU survivor telemedicine clinic and examined its effect on health care utilization post-hospital discharge. METHODS: This randomized, unblinded, single-center, parallel-group, exploratory study was conducted at a rural, academic medical center. Study group (SG) participants underwent a telemedicine visit within 14 days of discharge, during which a 6-minute walk test (6MWT), EuroQoL 5-Dimension (EQ-5D) questionnaire, and vital signs logs were reviewed by an intensivist. Additional appointments were arranged as needed based on the outcome of this review and tests. The control group (CG) underwent a telemedicine visit within 6 weeks of discharge and completed the EQ-5D questionnaire; additional care was provided as needed based on findings in this telemedicine visit. RESULTS: Both SG (n=20) and CG (n=20) participants had similar baseline characteristics and dropout rate (10%). Among SG participants, 72% (13/18) agreed to pulmonary clinic follow-up, compared with 50% (9/18) of CG participants (P=.31). Unanticipated visits to the emergency department occurred for 11% (2/18) of the SG compared with 6% (1/18) of the CG (>.99). The rate of pain or discomfort was 67% (12/18) in the SG compared with 61% (11/18) in the CG (P=.72). The anxiety or depression rate was 72% (13/18) in the SG versus 61% (11/18; P=.59) in the CG. Participants' mean self-assessed health rating scores were 73.9 (SD 16.1) in the SG compared with 70.6 (SD 20.9) in the CG (P=.59). Both primary care physicians (PCPs) and participants in the SG perceived the telemedicine clinic as a favorable model for postdischarge critical illness follow-up in an open-ended questionnaire regarding care. CONCLUSIONS: This exploratory study found no statistically significant results in reducing health care utilization postdischarge and health-related quality of life. However, PCPs and patients perceived telemedicine as a feasible and favorable model for postdischarge care among COVID-19 ICU survivors to facilitate expedited subspecialty assessment, decrease unanticipated postdischarge health care utilization, and reduce PICS. Further investigation is warranted to determine the feasibility of incorporating telemedicine-based post-hospitalization follow-up for all medical ICU survivors that may show improvement in health care utilization in a larger population.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».