Assessing the validity of post-discharge readmission and mortality as a composite outcome among newborns in Uganda
Bibliographic record
Abstract
Background: Composite outcomes, which include mortality and readmission rates, are often used in risk prediction models following hospital discharge when event rates for the primary outcome of interest, mortality, are low. However, increased readmission rates may result in decreased mortality making interpretation of the composite outcome difficult. We assess the usefulness of a composite outcome of post-discharge readmission and mortality as a target outcome in this context. Methods: This was a secondary analysis of data collected among mothers and their newborn(s) admitted for delivery at two regional referral hospitals in Uganda. Six-week post-discharge mortality (all-cause) and readmission in newborn infants were analyzed using a competing risk framework. The Sub distribution Hazard Ratios (SHRs) were compared across predictor variables to examine the relationship between the two outcomes. Results: Of the 206 predictors, 81 had a consistent association with both outcomes. These include a higher weight (Mortality SHR: 0.14, Readmission SHR: 0.68) and length of the baby (Mortality SHR: 0.85, Readmission SHR: 0.91). However, 125 variables depicted an association in opposing directions for both outcomes which may be linked to social and financial barriers to care-seeking. These include a travel time to the hospital of greater than 1 hour (Mortality SHR: 1.4, Readmission SHR: 0.28). Conclusion: While mortality is unequivocally a negative outcome, readmission may be a positive outcome, reflecting health seeking, or a negative outcome, reflecting recurrent illness. This directional dichotomy is reflected to varying degrees within different variables. When using a composite outcome for a prediction model, caution should be exercised to ensure that the model identifies individuals at risk of the intended outcomes of interest, rather than merely the proxies used to represent those outcomes. Identifying predictors with a consistent relationship for both outcomes may yield a more optimized and less biased prediction model for use in clinical care. Data Collection Methods: All data were collected at the point of care using encrypted study tablets and these data were then uploaded to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). Following delivery of newborns, written consent was obtained to complete a structured questionnaire in-person and a follow-up questionnaire over the phone six weeks later broadly categorized into the following five domains: 1) social and demographic, 2) pregnancy history and antenatal care, 3) delivery, 4) maternal discharge, and 5) neonatal discharge. Data Processing Methods: The initial cleaned data file was created using R version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). Further processing to obtain the final dataset used for analysis including filtering for exclusion criteria, removing predictors with low incidence, and imputing missing values using multiple imputations were also performed in R in the R scripts titled “MBCO_Analysis_Code_SD.R”. Data Analysis Methods: All analyses were conducted using R version 4.4.0 (R Foundation for Statistical Computing, Vienna, Austria). Libraries used within the script include: tidyverse, Hmisc, reshape2, mice, survival, cmprsk, riskRegression, survminer, ggplot2, ggfortify and gridExtra. Ethics Declaration: This study was approved by Makerere University School of Public Health (MakSPH) Institutional Review Board (SPH-2021-177), the Uganda National Council of Science and Technology (UNCST) in Uganda (HS2174ES) and the University of British Columbia in Canada (H21-03709). This study has been registered at clinicaltrials.gov (NCT05730387). Abbreviations: ANC: Antenatal Care CI: Confidence interval HIV: Human immunodeficiency virus HR: Heart rate JRRH: Jinja Regional Referral Hospital LMIC: Low-middle income country MRRH: Mbarara Regional Referral Hospital OR: Odds ratio PNC: Postnatal care PPD: Postpartum depression Q1: First quartile Q3: Third quartile RR: Respiratory rate SD: Standard deviation SpO2: Oxygen saturation Funding Source(s): Funding was provided by British Columbia Children's Hospital Research Institute Healthy Starts Catalyst Grant: JMA, ACD. Abhroneel Ghosh also received funding from the Mitacs Globalink Research Internship to conduct research with the team at the Institute for Global Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Study Protocol & Supplementary Materials: Smart Discharges for Mom & Baby 2.0: A cohort study to develop prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".