Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital

Maali, Yashar; Perez‐Concha, Oscar; Coiera, Enrico; Roffe, David; Day, Richard O.; Gallego, Blanca

doi:10.1186/s12911-017-0580-8

Cited by 75 publications

(82 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While growing research [ 1 – 7 ] supports that 7-day readmissions are more preventable than 30-day readmissions, 30-day readmissions continue to dominate the readmission prediction space. Few studies explicitly have developed prediction models for 7-day readmissions [ 8 , 12 ]. Herein, we provide empirical evidence that a previously validated, multi-condition 30-day EHR-based readmission risk prediction model can also be used to predict 7-day readmissions.…”

Section: Discussionmentioning

confidence: 99%

“…However, current risk prediction models often only identify patients at risk for 30-day readmission [ 8 – 10 ] and often fail to use electronic health record (EHR) data effectively to allow for real-time operationalization of the model [ 10 , 11 ]. There is a paucity of research developing prediction models for adult 7-day readmissions [ 8 , 12 ], which may be due to federal financial penalties tied to 30-day readmissions. Yet, to our knowledge, no study has investigated if a 30-day model can be reapplied effectively to predict the important subset of 7-day readmissions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

Saleh

Makam

Halm

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8–30 days). We assessed how well a previously validated 30-day EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors. Methods We conducted an observational study on adult hospitalizations from 6 diverse hospitals in North Texas using a 50–50 split-sample derivation and validation approach. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We then compared the discrimination and calibration of the 7-day model to the 30-day model to assess model performance. To examine the changes in the point estimates between the two models, we evaluated the percent changes in coefficients. Results Of 32,922 index hospitalizations among unique patients, 4.4% had a 7-day admission and 12.7% had a 30-day readmission. Our original 30-day model had modestly lower discrimination for predicting 7-day vs. any 30-day readmission (C-statistic of 0.66 vs. 0.69, p ≤ 0.001). Our re-derived 7-day model had similar discrimination (C-statistic of 0.66, p = 0.38), but improved calibration. For the re-derived 7-day model, discharge day factors were more predictive of early readmissions, while baseline characteristics were less predictive. Conclusion A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

Saleh

Makam

Halm

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…In the present study, the overall 30-day readmission rate was higher than that reported in previous studies. 21,22 This could be because, unlike other studies 8,22 that included patients admitted under all specialties, we included only unplanned index medical admissions, which are generally more complicated admissions with a high risk of readmission. 23 Relative to the number of readmissions to the index hospital, the proportion of patients readmitted to a different hospital was less than reported in other studies, 9,12 and presumably local factors have some relevance in the size of this proportion.…”

Section: Discussionmentioning

confidence: 99%

Characteristics and clinical outcomes of index versus non-index hospital readmissions in Australian hospitals: a cohort study

Sharma

Horwood

Hakendorf

et al. 2020

Aust. Health Review

View full text Add to dashboard Cite

Objective Risk factors and clinical outcomes of non-index hospital readmissions (readmissions to a hospital different from the previous admission) have not been studied in Australia. The present study compared characteristics and clinical outcomes between index and non-index hospital readmissions in the Australian healthcare setting. Methods This retrospective cohort study included medical admissions from 2012 to 2016 across all major public hospitals in South Australia. Readmissions within 30 day to all public hospitals were captured using electronic health information system. In-hospital mortality and readmission length of hospital stay (LOS) were compared, along with 30-day mortality and subsequent readmissions among patients readmitted to index or non-index hospitals. Results Of 114105 index admissions, there were 20539 (18.0%) readmissions. Of these, 17519 (85.3%) were index readmissions and 3020 (14.7%) were non-index readmissions. Compared with index readmissions, patients in the non-index readmissions group had a lower Charlson comorbidity index, shorter LOS and fewer complications during the index admission and were more likely to be readmitted with a different diagnosis to the index admission. No difference in in-hospital mortality was observed, but readmission LOS was shorter and 30-day mortality was higher among patients with non-index readmissions. Conclusion A substantial proportion of patients experienced non-index hospital readmissions. Non-index hospital readmitted patients had no immediate adverse outcomes, but experienced worse 30-day outcomes. What is known about the topic? A significant proportion of unplanned hospital readmissions occur to non-index hospitals. North American studies suggest that non-index hospital readmissions are associated with worse outcomes for patients due to discontinuity of care, medical reconciliation and delayed treatment. Limited studies have determined factors associated with non-index hospital readmissions in Australia, but whether such readmissions lead to adverse clinical outcomes is unknown. What does this paper add? In the Australian healthcare setting, 14.7% of patients were readmitted to non-index hospitals. Compared with index hospital readmissions, patients admitted to non-index hospitals had a lower Charlson comorbidity index, a shorter index LOS and fewer complications during the index admission. At the time of readmission there was no differences in discharge summary completion rates between the two groups. Unlike other studies, the present study found no immediate adverse outcomes for patients readmitted to non-index hospitals, but 30-day outcomes were worse than for patients who had an index hospital readmission. What are the implications for practitioners? Non-index hospital readmissions may not be totally preventable due to factors such as ambulance diversions stemming from emergency department overcrowding and prolonged emergency department waiting times. Patients should be advised to re-present to hospital in case they experience recurrence or relapse of a medical condition, and preferably should be readmitted to the same hospital to prevent discontinuity of care.

show abstract

“…23,27 Recent works described its potential in the medical field. [26][27][28][29][30][31][32][33] Analysis Continuous features are reported as the median with the spread reported as interquartile range (IQR). Categorical elements are reported as percentages.…”

Section: Machine Learning Modelmentioning

confidence: 99%

A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score

Klug

Barash

Bechler³

et al. 2019

J GEN INTERN MED

View full text Add to dashboard Cite

Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital

Cited by 75 publications

References 34 publications

Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

Characteristics and clinical outcomes of index versus non-index hospital readmissions in Australian hospitals: a cohort study

A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score

Contact Info

Product

Resources

About