Pattern of Comorbidities and the Impact on Outcomes in Patients With COVID-19 in Babel Governorate, Iraq, in 2020
Bibliographic record
Abstract
Background Comorbidities pose a major clinical challenge to the care and treatment of patients with COVID-19. Objective This study aimed to evaluate the effects of common comorbidities on the severity, outcome, and length of stay in the hospital for patients with COVID-19 in Babel Governorate, Iraq, in 2020. Methods All laboratory-confirmed cases of COVID-19 in the 2 COVID-19 hospitals in Babel Governorate from March through September 2020 were included. We developed a form to document sociodemographic data, clinical presentation, severity, comorbidities, length of hospital stay, and outcomes. Results A total of 2574 patients were included; 1581 (61.4%) were men. The mean age was 48.7 (SD 16.4) years. There were 1212 (47.1%) severe cases and 489 (19%) critical cases. There were 1543 (59.9%) patients with no comorbidity, 536 (20.9%) patients with 1 comorbidity, and 495 (19.2%) patients with 2 or more comorbidities. The most common comorbidity was diabetes mellitus (643/1599, 25%), followed by hypertension (598/1599, 23.4%). The proportion of severe or critical cases among the patients with comorbidities was 84% (865/1031) compared to 54.1% (836/1543) among the patients with no comorbidity (P<.001). About 12% (125/1031) of patients with comorbidities had a mean hospitalization time >2 weeks compared to the 8% (123/1543) of patients with no comorbidity (P<.001). The case-fatality ratio was 26.4% (272/1031) in patients with comorbidities compared to 10.6% (163/1543) in patients with no comorbidity (P<.001). Conclusions Comorbidity is a significant predictor of serious hospital course and fatal outcomes in patients with COVID-19. Patients with comorbidities must be vigilant with preventive measures and should be prioritized for COVID-19 vaccination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".