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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".