Health Care Resource Use and Total Mortality After Hospital Admission for Severe COVID-19 Infections During the Initial Pandemic Wave in France: Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Little is known about post-hospital health care resource use (HRU) of patients admitted for severe COVID-19, specifically for the care of patients with postacute COVID-19 syndrome (PACS). OBJECTIVE: A list of HRU domains and items potentially related to PACS was defined, and potential PACS-related HRU (PPRH) was compared between the pre- and post-COVID-19 periods, to identify new outpatient care likely related to PACS. METHODS: A retrospective cohort study was conducted with the French National Health System claims data (SNDS). All patients hospitalized for COVID-19 between February 1, 2020, and June 30, 2020 were described and investigated for 6 months, using discharge date as index date. Patients who died during index stay or within 30 days after discharge were excluded. PPRH was assessed over the 5 months from day 31 after index date to end of follow-up, that is, for the post-COVID-19 period. For each patient, a pre-COVID-19 period was defined that covered the same calendar time in 2019, and pre-COVID-19 PPRH was assessed. Post- or pre- ratios (PP ratios) of the percentage of users were computed with their 95% CIs, and PP ratios>1.2 were considered as "major HRU change." RESULTS: The final study population included 68,822 patients (median age 64.8 years, 47% women, median follow-up duration 179.3 days). Altogether, 23% of the patients admitted due to severe COVID-19 died during the hospital stay or within the 6 months following discharge. A total of 8 HRU domains were selected to study PPRH: medical visits, technical procedures, dispensed medications, biological analyses, oxygen therapy, rehabilitation, rehospitalizations, and nurse visits. PPRs showed novel outpatient care in all domains and in most items, without specificity, with the highest ratios observed for the care of thoracic conditions. CONCLUSIONS: Patients hospitalized for severe COVID-19 during the initial pandemic wave had high morbi-mortality. The analysis of HRU domains and items most likely to be related to PACS showed that new care was commonly initiated after discharge but with no specificity, potentially suggesting that any impact of PACS was part of the overall high HRU of this population after hospital discharge. These purely descriptive results need to be completed with methods for controlling for confusion bias through subgroup analyses. TRIAL REGISTRATION: ClinicalTrials.gov NCT05073328; https://clinicaltrials.gov/ct2/show/NCT05073328.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".