Declining Intensive Care Unit Mortality of COVID-19: A Multi-Center Study
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) mortality has waned significantly over time; however, factors contributing towards this reduction largely remain unidentified. The purpose of this study was to evaluate the trend in mortality at our large tertiary academic health system and factors contributing to this trend. Methods: This is a retrospective cohort study of intensive care unit (ICU) patients diagnosed with COVID-19 between March and August 2020 admitted across 14 hospitals in the Philadelphia area. Collected data included demographics, comorbidities, admission risk of mortality score, laboratory values, medical interventions, survival outcomes, hospital and ICU length of stay (LOS) and discharge disposition. Chi-square (? 2 ) test, Fisher exact test, Cochran-Mantel-Haenszel method, multinomial logistic regression models, independent sample t -test, Mann-Whitney U test and one-way analysis of variance (ANOVA) were used. Results: A total of 1,204 patients were included. Overall mortality was 39%. Mortality declined significantly from 46% in March to 14% in August 2020 (P < 0.05). The most common underlying comorbidities were hypertension (60.2%), diabetes mellitus (44.7%), dyslipidemia (31.6%) and congestive heart failure (14.7%). Hydroxychloroquine (HCQ) use was more commonly associated with the patients who died, while the use of remdesivir, tocilizumab, steroids and duration of these medications were not significantly different. Peak values of ferritin, lactate dehydrogenase (LDH), C-reactive protein (CRP) and D-dimer levels were significantly higher in patients who died (P < 0.05). The mean hospital LOS was significantly longer in the patients who survived compared to the patients who died (18 vs. 12, P < 0.05). Conclusions: The mortality of patients admitted to our ICU system significantly decreased over time. Factors that may have contributed to this may be the result of a better understanding of COVID-19 pathophysiology and treatments. Further research is needed to elucidate the factors contributing to a reduction in the mortality rate for this patient population. J Clin Med Res. 2021;13(3):184-190 doi: https://doi.org/10.14740/jocmr4452
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".