Predicting Readmission at Early Hospitalization Using Electronic Clinical Data
An Early Readmission Risk Score
Bibliographic Data
| ID | 9102561 |
|---|---|
| Authors | Ying P Tabak (0000-0002-5248-716X, Medical Informatics, Becton, Dickinson and Company, corresponding author), Xiaowu Sun (0000-0002-7960-4207, Medical Informatics, Becton, Dickinson and Company, corresponding author), Carlos M Nunez (Medical Informatics, Becton, Dickinson and Company, corresponding author), Vikas Gupta (0000-0003-0975-336X, Medical Informatics, Becton, Dickinson and Company, corresponding author), Richard S Johannes (Medical Informatics, Becton, Dickinson and Company, corresponding author) |
| Year | 2017 |
| Volume | 55 |
| Issue | 3 |
| Pages | 267-275 |
| Publication date | 2017-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0000000000000654 |
| PMID | 27755391 |
| OpenAlex | W2530724435 |
| Language | EN |
| Citations received | 4 |
| References cited | 25 |
BACKGROUND: Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission. METHODS: We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score as an aggregated measure of clinical severity at admission and the number of hospital discharges in the previous 90 days as a measure of disease progression. We then evaluated the administrative data-enhanced model by adding principal and secondary diagnoses and other variables. We examined the c-statistic change when additional variables were added to the model. RESULTS: There were 1,195,640 adult discharges from 70 hospitals with 39.8% male and the median age of 63 years (first and third quartile: 43, 78). The 30-day readmission rate was 11.9% (n=142,211). The early readmission model yielded a graded relationship of readmission and the Acute Laboratory Risk of Mortality Score and the number of previous discharges within 90 days. The model c-statistic was 0.697 with good calibration. When administrative variables were added to the model, the c-statistic increased to 0.722. CONCLUSIONS: Automated clinical data can generate a readmission risk score early at hospitalization with fair discrimination. It may have applied value to aid early care transition. Adding administrative data increases predictive accuracy. The administrative data-enhanced model may be used for hospital comparison and outcome research
Cohort · Confidence interval · Disease · Framingham Risk Score · Hospital readmission · Medical diagnosis · Quartile · Statistic · Statistics · Emergency Medicine · Heart Failure Treatment and Management · Hospital Admissions and Outcomes · Internal Medicine · Medicine · Sepsis Diagnosis and Treatment
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| Unique citing works | 4 |
|---|---|
| Citations per year | 0,57 |
| Citation span | 2019 - 2025 (7) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |