Predictors of Mortality in Home Health Care Service: Data from Saudi Arabia
Bibliographic record
Abstract
Objective: This study aimed to 1) report the prevalence of chronic conditions among Saudi people receiving long-term home health care (HHC) services, 2) identify the predictors of mortality among individuals receiving long-term HHC services, and 3) study the association between frailty and poor health outcomes among HHC users. Design: Retrospective cross-sectional descriptive study. Setting and Participants: A total of 555 participants were recruited from HHC services at King Saud University Medical City (KSUMC), Riyadh, Saudi Arabia. We collected the data from electronic health records (EHR), patient charts, and caregiver interviews for 555 participants included in HHC program from the year 2019 to 2022. Methods: Only individuals fulfilling the HHC program’s eligibility criteria were included to the study. A total of 555 participants were included in the analysis. We assessed the functional performance by the Katz activity of daily living and Bristol Activity of Daily Living Scale (BADLS). A trained health care provider assessed frailty using the Clinical Frailty Scale (CFS). We calculated the means and frequency to describe the prevalence of chronic conditions and variables of interest. A Chi-square test or independent-samples t -test was run to determine if there were differences between the alive and deceased individuals. A binary logistic regression model was performed to predict mortality of HHC service recipients. Results: The mean age for deceased individuals in HHC was 78.3 years. Over twenty percent of individuals receiving HHC services were readmitted to the hospital. We found that the strongest predictors for mortality were pressure ulcers with an odds ratio of 3.75 and p-value of < 0.0001, and the Clinical Frailty Scale, which had an odds ratio of 1.69 and p-value of 0.002, using multivariate regression analysis. Conclusions and Implications: In conclusion, our study found that pressure ulcers and frailty are the strongest predictors of mortality for individuals receiving home health care services. Keywords: home health care, Saudi Arabia, predictors of mortality
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".