Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients

Donzé, Jacques; Aujesky, Drahomir; Williams, Deborah H.; Schnipper, Jeffrey L.

doi:10.1001/jamainternmed.2013.3023

Cited by 471 publications

(504 citation statements)

References 26 publications

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“…The model performed moderately well on the validation dataset and outperformed a model derived from Elixhauser comorbidities and the recently introduced HOSPITAL score. 14 This approach has considerable potential to tailor preventative care for AMI patients, to improve institutional performance and to decrease the cost burden associated with AMI.…”

Section: Discussionmentioning

confidence: 99%

“…21 Diagnosis codes from the previous year were used to construct the comorbidities. The second baseline is the recently introduced predictive scoring system HOSPITAL, 14 which has been shown to perform moderately well in predicting readmissions.…”

Section: Comparatorsmentioning

confidence: 99%

“…We defined an admission to be unplanned if the patient was admitted through the emergency department (ED). The EMR model was compared with the model derived from Elixhauser comorbidities and a recently described 14 simple predictive scoring system (HOSPITAL) that was developed to predict unplanned admissions in patients admitted with acute medical conditions.…”

Section: Introductionmentioning

confidence: 99%

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Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Rana¹,

Tran²,

Luo³

et al. 2014

Aust. Health Review

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Objective. Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations.Methods. The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation.Results. The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78; 95% confidence interval (CI) 0.71-0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72; 95% CI 0.66-0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission.Conclusions. Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions.What is known about the topic? Many clinical and demographic risk factors are known for hospital readmissions following acute myocardial infarction, including multivessel disease, high baseline heart rate, hypertension, diabetes, obesity, chronic obstructive pulmonary disease and psychiatric morbidity. However, combining these risk factors into indices for predicting readmission had limited success. A recent study reported a C-statistic of 0.73 for predicting 30-day readmissions. In a recent American study, a simple seven-factor score was shown to predict hospital readmissions among medical patients. What does this paper add? This paper presents a way to predict readmissions following myocardial infarction using routinely collected administrative data. The model performed better than the recently described HOSPITAL score and a model derived from Elixhauser comorbidities. Moreover, the model uses only data generally available in most hospitals. What are the implications for practitioners? Routine hospital data available at discharges can be used to tailor preventative care for AMI patients, to improve institutional performance and to decrease the cost burden associated with AMI.

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Section: Discussionmentioning

confidence: 99%

Section: Comparatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Rana¹,

Tran²,

Luo³

et al. 2014

Aust. Health Review

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“…Irrespective of differences in the frequency of low‐risk survivors in these different investigations, which are likely because of the varying follow‐up periods, and in sociodemographic and clinical characteristics of the study populations (most notably, age and years under study), further investigation of this important subgroup of patients is warranted. A considerable proportion of patients are considered to be at low risk for major adverse clinical events, during either the short‐ or longer‐term 18. Less aggressive follow‐up plans and clinical care may be needed.…”

Section: Discussionmentioning

confidence: 99%

“…Less aggressive follow‐up plans and clinical care may be needed. Although we believe that all patients should receive high‐quality transitional care after hospital discharge for an acute manifestation of a chronic underlying condition, certain interventions that have been shown to be successful are resource intensive and costly 18. One way to make the best use of these limited resources, in the current healthcare environment, may be to reserve them for patients most likely to benefit from these resources.…”

Section: Discussionmentioning

confidence: 99%

Magnitude and Characteristics of Patients Who Survived an Acute Myocardial Infarction

Tisminetzky

Wang

Gurwitz

et al. 2017

JAHA

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BackgroundThe purpose of this study was to describe the magnitude and characteristics of patients who did not experience any significant major adverse cardiovascular event early (within 6 weeks) and late (during the first year) after hospital discharge for an acute myocardial infarction (AMI).Methods and ResultsData from 12 243 patients discharged after an AMI from 233 sites across the United States in the TRANSLATE‐ACS (Treatment With ADP Receptor Inhibitors: Longitudinal Assessment of Treatment Patterns and Events After Acute Coronary Syndrome) study were analyzed. Multivariable adjusted regression analyses modeled factors associated with 6‐week and 1‐year survivors who did not experience a recurrent AMI, stroke, unplanned coronary revascularization, or rehospitalization for unstable angina/chest pain during these time periods. The average age of this study population was 60.0 years, 72.0% were men, and 87.9% were white. In this population, 92.4% were classified as early low‐risk survivors and 76.3% were classified as late low‐risk survivors of an AMI. Factors associated with being an early and late postdischarge survivor included being male and having single‐vessel coronary artery disease at the patient's index hospitalization. Patients who were not first seen with any chronic health condition, had an index hospital stay of ≤3 days, and had high baseline quality‐of‐life scores were more likely to be late low‐risk survivors.ConclusionsIdentifying low‐risk survivors of an AMI may permit healthcare providers to focus more intensive efforts and interventions on those at higher risk of experiencing adverse cardiovascular events during the postdischarge transition period.Clinical Trial RegistrationURL: http://www.clinicaltrials.gov. Unique identifier: NCT01088503.

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Complexity Science and the Readmission Dilemma

Marks

2013

JAMA Intern Med

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Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients

Cited by 471 publications

References 26 publications

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Magnitude and Characteristics of Patients Who Survived an Acute Myocardial Infarction

Complexity Science and the Readmission Dilemma

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