Incidence and Predictors of 30-Day Hospital Readmission Rate Following Percutaneous Coronary Intervention (from the National Heart, Lung, and Blood Institute Dynamic Registry)

Ricciardi, Mark J.; Selzer, Faith; Marroquin, Oscar C.; Holper, Elizabeth M.; Venkitachalam, Lakshmi; Williams, David O.; Kelsey, Sheryl F.; Laskey, Warren K.

doi:10.1016/j.amjcard.2012.07.002

Cited by 27 publications

(21 citation statements)

References 24 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The National Heart, Lung and Blood Institute Dynamic Registry study of 11,000 patients with an acute coronary syndrome (mean age 64 years) found that women, those with several comorbidities, and patients who underwent emergency PCI had a higher risk for being readmitted to the hospital over the next 30 days than respective comparison groups (26). An analysis of more than 500,000 Medicare claims for AMI related hospitalizations identified patients 85 years and older and those with a history of renal disease or heart failure as being at increased risk for 30-day hospital readmissions (27).…”

Section: Discussionmentioning

confidence: 99%

Thirty-day Hospital Readmissions in Patients with Non-ST-segment Elevation Acute Myocardial Infarction

Tisminetzky

McManus

Erskine

et al. 2015

The American Journal of Medicine

View full text Add to dashboard Cite

Background Limited data exist about relatively recent trends in the magnitude and characteristics of patients who are rehospitalized shortly after admission for a non ST-segment elevation acute myocardial infarction (NSTEMI). This observational study describes decade-long trends (1999-2009) in the magnitude and characteristics of patients readmitted to the hospital within 30 days of hospitalization for an incident (initial) episode of NSTEMI. Methods We reviewed the medical records of 2,249 residents of the Worcester (MA) metropolitan area who were hospitalized for an initial NSTEMI in 6 biennial periods between 1999 and 2009 at 3 central MA medical centers. Results The average age of our study population was 72 years, 90% were white, and 46% were women. The proportion of patients who were readmitted to the hospital for any cause within 30 days after discharge for a NSTEMI remained unchanged between 1999 and 2009 (approximately 15%) in both crude and multivariable adjusted analyses. Slight declines were observed for cardiovascular disease-related 30-day readmissions over the ten-year study period. Women, elderly patients, those with multiple chronic comorbidities, a prolonged index hospitalization, and patients who developed heart failure during their index hospitalization were at higher risk for being readmitted within 30-days than respective comparison groups. Conclusions Thirty day hospital readmission rates after hospital discharge for a first NSTEMI remained stable between 1999 and 2009. We identified several groups at higher risk for hospital readmission in whom further surveillance efforts and/or tailored educational and treatment approaches remain needed.

show abstract

Section: Discussionmentioning

confidence: 99%

Thirty-day Hospital Readmissions in Patients with Non-ST-segment Elevation Acute Myocardial Infarction

Tisminetzky

McManus

Erskine

et al. 2015

The American Journal of Medicine

View full text Add to dashboard Cite

show abstract

“…Successful examples of such registries include the National Heart, Lung, and Blood Institute Percutaneous Transluminal Coronary Angioplasty Registry and the National Heart, Lung, and Blood Institute Dynamic Registry. (86) The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) collects and reports postoperative complication rates for many surgical specialties, but currently not for transplant. As complication rates in transplant recipients cannot easily be compared to other patient populations due to the polymorbid nature of end stage organ disease, adding transplant patients to this national data collection effort would allow for individual center and physician feedback, and is likely to provide information that can lead to outcome improvement.…”

Section: Discussionmentioning

confidence: 99%

A meta-analysis of complications following deceased donor liver transplant

McElroy¹,

Daud²,

Davis³

et al. 2014

The American Journal of Surgery

View full text Add to dashboard Cite

Background Liver transplantation is a complex surgery associated with high rates of postoperative complications. While national outcomes data are available, national rates of most complications are unknown. Data Sources A systematic review of the literature reporting rates of postoperative complications between 2002 and 2012 was performed. A cohort of 29,227 deceased donor liver transplant recipients from 74 studies was used to calculate pooled incidences were for 17 major postoperative complications. Conclusions This is the first comprehensive review of postoperative complications after liver transplantation and can serve as a guide for transplant and non-transplant clinicians. Efforts to collect national data on complications, such as through the National Surgical Quality Improvement Program, would improve the ability to provide patients with informed consent, serve as a tool for individual center performance monitoring, and provide a central source against which to measure interventions aimed at improving patient care.

show abstract

“…4 Several studies examining predictors of readmission have recently been described. 3,[5][6][7] BMI indicates body mass index; CABG, coronary artery bypass graft; CHF, congestive heart failure; CP, chest pain; CVD, cerebrovascular disease; GFR, glomerular filtration rate; HMO, health maintenance organization; IABP, intraaortic balloon pump; MI, myocardial infarction; NSTEMI, non-ST-elevation myocardial infarction; PCI, percutaneous coronary intervention; PVD, peripheral vascular disease; and STEMI, ST-elevation myocardial infarction.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have identified risk factors associated with 30-day readmission after PCI. 3,[5][6][7] However, these studies have been limited by a number of factors, including being limited to specific age groups 3 or being derived from single centers. 6 In addition, none has developed and validated a prediction model and associated risk score that could be implemented by clinicians and hospitals to quantify risk of readmission and, thus, help identify high readmission risk patients before discharge.…”

mentioning

confidence: 99%

A Prediction Model to Identify Patients at High Risk for 30-Day Readmission After Percutaneous Coronary Intervention

Wasfy

Rosenfield

Zelevinsky

et al. 2013

Circ: Cardiovascular Quality and Outcomes

View full text Add to dashboard Cite

Background-The Affordable Care Act creates financial incentives for hospitals to minimize readmissions shortly after discharge for several conditions, with percutaneous coronary intervention (PCI) to be a target in 2015. We aimed to develop and validate prediction models to assist clinicians and hospitals in identifying patients at highest risk for 30-day readmission after PCI. Methods and Results-We identified all readmissions within 30 days of discharge after PCI in nonfederal hospitals in Massachusetts between October 1, 2005, and September 30, 2008. Within a two-thirds random sample (Developmental cohort), we developed 2 parsimonious multivariable models to predict all-cause 30-day readmission, the first incorporating only variables known before cardiac catheterization (pre-PCI model), and the second incorporating variables known at discharge (Discharge model). Models were validated within the remaining one-third sample (Validation cohort), and model discrimination and calibration were assessed. Of 36 060 PCI patients surviving to discharge, 3760 (10.4%) patients were readmitted within 30 days. Significant pre-PCI predictors of readmission included age, female sex, Medicare or State insurance, congestive heart failure, and chronic kidney disease. Post-PCI predictors of readmission included lack of β-blocker prescription at discharge, post-PCI vascular or bleeding complications, and extended length of stay. Discrimination of the pre-PCI model (C-statistic=0.68) was modestly improved by the addition of post-PCI variables in the Discharge model (C-statistic=0.69; integrated discrimination improvement, 0.009; P<0.001). Conclusions-These prediction models can be used to identify patients at high risk for readmission after PCI and to target high-risk patients for interventions to prevent readmission.

show abstract

Incidence and Predictors of 30-Day Hospital Readmission Rate Following Percutaneous Coronary Intervention (from the National Heart, Lung, and Blood Institute Dynamic Registry)

Cited by 27 publications

References 24 publications

Thirty-day Hospital Readmissions in Patients with Non-ST-segment Elevation Acute Myocardial Infarction

Thirty-day Hospital Readmissions in Patients with Non-ST-segment Elevation Acute Myocardial Infarction

A meta-analysis of complications following deceased donor liver transplant

A Prediction Model to Identify Patients at High Risk for 30-Day Readmission After Percutaneous Coronary Intervention

Contact Info

Product

Resources

About