Nonelective Rehospitalizations and Postdischarge Mortality
Predictive Models Suitable for Use in Real Time
Bibliographic Data
| ID | 9100089 |
|---|---|
| Authors | Gabriel J Escobar (0000-0003-2540-3327, Kaiser Permanente Walnut Creek Medical Center, corresponding author), Arona I Ragins (Kaiser Permanente, corresponding author), Arona Ragins, Peter Scheirer (Kaiser Permanente, corresponding author), Vincent Liu (0000-0003-1987-9521, Kaiser Permanente Santa Clara Medical Center, corresponding author), Jay Robles, J A Muñoz Robles (Kaiser Permanente, corresponding author), Patricia Kipnis (0000-0003-4572-0178, Kaiser Permanente, corresponding author) |
| Year | 2015 |
| Volume | 53 |
| Issue | 11 |
| Pages | 916-923 |
| Publication date | 2015-11-01 |
| 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.0000000000000435 |
| PMID | 26465120 |
| OpenAlex | W2412187680 |
| Language | EN |
| Citations received | 3 |
| References cited | 37 |
BACKGROUND: Hospital discharge planning has been hampered by the lack of predictive models. OBJECTIVE: To develop predictive models for nonelective rehospitalization and postdischarge mortality suitable for use in commercially available electronic medical records (EMRs). DESIGN: Retrospective cohort study using split validation. SETTING: Integrated health care delivery system serving 3.9 million members. PARTICIPANTS: A total of 360,036 surviving adults who experienced 609,393 overnight hospitalizations at 21 hospitals between June 1, 2010 and December 31, 2013. MAIN OUTCOME MEASURE: A composite outcome (nonelective rehospitalization and/or death within 7 or 30 days of discharge). RESULTS: Nonelective rehospitalization rates at 7 and 30 days were 5.8% and 12.4%; mortality rates were 1.3% and 3.7%; and composite outcome rates were 6.3% and 14.9%, respectively. Using data from a comprehensive EMR, we developed 4 models that can generate risk estimates for risk of the combined outcome within 7 or 30 days, either at the time of admission or at 8 AM on the day of discharge. The best was the 30-day discharge day model, which had a c-statistic of 0.756 (95% confidence interval, 0.754-0.756) and a Nagelkerke pseudo-R of 0.174 (0.171-0.178) in the validation dataset. The most important predictors-a composite acute physiology score and end of life care directives-accounted for 54% of the predictive ability of the 30-day model. Incorporation of diagnoses (not reliably available for real-time use) did not improve model performance. CONCLUSIONS: It is possible to develop robust predictive models, suitable for use in real time with commercially available EMRs, for nonelective rehospitalization and postdischarge mortality
Cohort study · Confidence interval · Medical record · MEDLINE · Mortality rate · Retrospective cohort study · Emergency and Acute Care Studies · Emergency Medicine · Heart Failure Treatment and Management · Internal Medicine · Medicine · Sepsis Diagnosis and Treatment
Regression Modeling Strategies
Modern Applied Statistics with S
Risk Prediction Models for Hospital Readmission
Rehospitalizations among Patients in the Medicare Fee-for-Service Program
Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases
Neighborhood Socioeconomic Disadvantage and 30-Day Rehospitalization
Unbiased Recursive Partitioning
Random Forests
Risk-adjusting Hospital Mortality Using a Comprehensive Electronic Record in an Integrated Health Care Delivery System
Access to Clinically-Detailed Patient Information
Using Automated Clinical Data for Risk Adjustment
Risk-Adjusting Hospital Inpatient Mortality Using Automated Inpatient, Outpatient, and Laboratory Databases
Identifying Patients at Increased Risk for Unplanned Readmission
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,43 |
| Citation span | 2019 - 2022 (4) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |