Hospital Mortality in the United States following Acute Kidney Injury

Acute kidney injury (AKI) is a common reason for hospital admission and complication of many inpatient procedures. The temporal incidence of AKI and the association of AKI admissions with in-hospital mortality are a growing problem in the world today. In this review, we discuss the epidemiology of AKI and its association with in-hospital mortality in the United States. AKI has been growing at a rate of 14% per year since 2001. However, the in-hospital mortality associated with AKI has been on the decline starting with 21.9% in 2001 to 9.1 in 2011, even though the number of AKI-related in-hospital deaths increased almost twofold from 147,943 to 285,768 deaths. We discuss the importance of the 71% reduction in AKI-related mortality among hospitalized patients in the United States and draw on the discussion of whether or not this is a phenomenon of hospital billing (coding) or improvements to the management of AKI

Development of statistical methodologies and risk models to perform real-time safety monitoring in interventional cardiology

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2006.Vita.Includes bibliographical references (p. 52-56).Post-marketing surveillance of medical pharmaceuticals and devices has received a great deal of media, legislative, and academic attention in the last decade. Among medical devices, these have largely been due to a small number of highly publicized adverse events, some of them in the domain of cardiac surgery and interventional cardiology. Phase three clinical trials for these devices are generally underpowered to detect rare adverse event rates, are performed in near-optimal environments, and regulators face significant pressure to deliver important medical devices to the public in a timely fashion. All of these factors emphasize the importance of systematic monitoring of these devices after being released to the public, and the FDA and other regulatory agencies continue to struggle to perform this duty using a variety of voluntary and mandatory adverse event rate reporting policies. Data quality and comprehensiveness have generally suffered in this environment, and delayed awareness of potential problems. However, a number of mandatory reporting policies combined with improved standardization of data collection and definitions in the field of interventional cardiology and other clinical domains have provided recent opportunities for nearly "real-time" safety monitoring of medical device data.(cont.) Existing safety monitoring methodologies are non-medical in nature, and not well adapted to the relatively heterogeneous and noisy data common in medical applications. A web-based database-driven computer application was designed, and a number of experimental statistical methodologies were adapted from non-medical monitoring techniques as a proof of concept for the utility of an automated safety monitoring application. This application was successfully evaluated by comparing a local institution's drug-eluting stent in-hospital mortality rates to University of Michigan's bare-metal stent event rates. Sensitivity analyses of the experimental methodologies were performed, and a number of notable performance parameters were discovered. In addition, an evaluation of a number of well-validated external logistic regression models, and found that while population level estimation was well-preserved, individual estimation was compromised by application to external data. Subsequently, exploration of an alternative modeling technique, support vector machines, was performed in an effort to find a method with superior calibration performance for use in the safety monitoring application.by Michael E. Matheny.S.M

Incidence and In-Hospital Mortality of Acute Kidney Injury (AKI) and Dialysis Requiring AKI (AKI-D) After Cardiac Catheterization in the National Inpatient Sample

Background: Acute kidney injury (AKI) and dialysis‐requiring AKI (AKI‐D) are common, serious complications of cardiac procedures. Methods and Results: We evaluated 3 633 762 (17 765 214 weighted population) cardiac catheterization or percutaneous coronary intervention (PCI) hospital discharges from the nationally representative National Inpatient Sample to determine annual population incidence rates for AKI and AKI‐D in the United States from 2001 to 2011. Odds ratios for both conditions and associated in‐hospital mortality were calculated for each year in the study period using multiple logistic regression. The number of cardiac catheterization or PCI cases resulting in AKI rose almost 3‐fold from 2001 to 2011. The adjusted odds of AKI and AKI‐D per year among cardiac catheterization and PCI patients were 1.11 (95% CI: 1.10–1.12) and 1.01 (95% CI: 0.99–1.02), respectively. Most importantly, in‐hospital mortality significantly decreased from 2001 to 2011 for AKI (19.6–9.2%) and AKI‐D (28.3–19.9%), whereas odds of associated in‐hospital mortality were 0.50 (95% CI: 0.45–0.56) and 0.70 (95% CI: 0.55–0.93) in 2011 versus 2001, respectively. The population‐attributable risk of mortality for AKI and AKI‐D was 25.8% and 3.8% in 2001 and 41.1% and 6.5% in 2011, respectively. Males and females had similar patterns of AKI increase, although males outpaced females. Conclusions: The Incidence of AKI among cardiac catheterization and PCI patients has increased sharply in the United States, and this should be addressed by implementing prevention strategies. However, mortality has significantly declined, suggesting that efforts to manage AKI and AKI‐D after cardiac catheterization and PCI have reduced mortality

Quantifying the Learning Curve in the Use of a Novel Vascular Closure Device An Analysis of the NCDR (National Cardiovascular Data Registry) CathPCI Registry

ObjectivesThis study sought to quantify the learning curve for the safety and effectiveness of a newly introduced vascular closure device through evaluation of the NCDR (National Cardiovascular Data Registry) CathPCI clinical outcomes registry.BackgroundThe impact of learning on the clinical outcomes complicates the assessment of the safety and efficacy during the early experience with newly introduced medical devices.MethodsWe performed a retrospective analysis of the relationship between cumulative institutional experience and clinical device success, defined as device deployment success and freedom from any vascular complications, for the StarClose vascular closure device (Abbott Vascular, Redwood City, California). Generalized estimating equation modeling was used to develop risk-adjusted clinical success predictions that were analyzed to quantify learning curve rates.ResultsA total of 107,710 procedures used at least 1 StarClose deployment, between January 1, 2006, and December 31, 2007, with overall clinical success increasing from 93% to 97% during the study period. The learning curve was triphasic, with an initial rapid learning phase, followed by a period of declining rates of success, followed finally by a recovery to a steady-state rate of improved device success. The rates of learning were influenced positively by diagnostic (vs. percutaneous coronary intervention) procedure use and teaching status and were affected inversely by annual institutional volume.ConclusionsAn institutional-level learning curve for the initial national experience of StarClose was triphasic, likely indicating changes in patient selection and expansion of number of operators during the initial phases of device adoption. The rate of learning was influenced by several institutional factors, including overall procedural volume, utilization for percutaneous coronary intervention procedures, and teaching status

Evaluation of an automated safety surveillance system using risk adjusted sequential probability ratio testing

<p>Abstract</p> <p>Background</p> <p>Automated adverse outcome surveillance tools and methods have potential utility in quality improvement and medical product surveillance activities. Their use for assessing hospital performance on the basis of patient outcomes has received little attention. We compared risk-adjusted sequential probability ratio testing (RA-SPRT) implemented in an automated tool to Massachusetts public reports of 30-day mortality after isolated coronary artery bypass graft surgery.</p> <p>Methods</p> <p>A total of 23,020 isolated adult coronary artery bypass surgery admissions performed in Massachusetts hospitals between January 1, 2002 and September 30, 2007 were retrospectively re-evaluated. The RA-SPRT method was implemented within an automated surveillance tool to identify hospital outliers in yearly increments. We used an overall type I error rate of 0.05, an overall type II error rate of 0.10, and a threshold that signaled if the odds of dying 30-days after surgery was at least twice than expected. Annual hospital outlier status, based on the state-reported classification, was considered the gold standard. An event was defined as at least one occurrence of a higher-than-expected hospital mortality rate during a given year.</p> <p>Results</p> <p>We examined a total of 83 hospital-year observations. The RA-SPRT method alerted 6 events among three hospitals for 30-day mortality compared with 5 events among two hospitals using the state public reports, yielding a sensitivity of 100% (5/5) and specificity of 98.8% (79/80).</p> <p>Conclusions</p> <p>The automated RA-SPRT method performed well, detecting all of the true institutional outliers with a small false positive alerting rate. Such a system could provide confidential automated notification to local institutions in advance of public reporting providing opportunities for earlier quality improvement interventions.</p

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance

Funding Information: This work was funded by a grant from the National Heart, Lung, and Blood Institute (NHLBI; grant number 1R01HL149948). The funding agency was not involved in the design of the study, collection and analysis of data, interpretation of results, or writing of the manuscript. Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD

Risk‐Treatment Paradox in the Selection of Transradial Access for Percutaneous Coronary Intervention

Background: Access site complications contribute to morbidity and mortality during percutaneous coronary intervention (PCI). Transradial arterial access significantly lowers the risk of access site complications compared to transfemoral arteriotomy. We sought to develop a prediction model for access site complications in patients undergoing PCI with femoral arteriotomy, and assess whether transradial access was selectively used in patients at high risk for complications. Methods and Results: We analyzed 17 509 patients who underwent PCI without circulatory support from 2008 to 2011 at 5 institutions. Transradial arterial access was used in 17.8% of patients. In those who underwent transfemoral access, 177 (1.2%) patients had access site complications. Using preprocedural clinical and demographic data, a prediction model for femoral arteriotomy complications was generated. The variables retained in the model included: elevated age (P<0.001), female gender (P<0.001), elevated troponin (P<0.001), decreased renal function or dialysis (P=0.002), emergent PCI (P=0.01), prior PCI (P=0.005), diabetes (P=0.008), and peripheral artery disease (P=0.003). The model showed moderate discrimination (optimism‐adjusted c‐statistic=0.72) and was internally validated via bootstrap resampling. Patients with higher predicted risk of complications via transfemoral access were less likely to receive transradial access (P<0.001). Similar results were seen in patients presenting with and without ST‐segment myocardial infarction and when adjusting for individual physician operator. Conclusions: We generated and validated a model for transfemoral access site complications during PCI. Paradoxically, patients most likely to develop access site complications from transfemoral access, and therefore benefit from transradial access, were the least likely to receive transradial access

Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort with External Validation

Background: Acute kidney injury (AKI) occurs frequently after cardiac catheterization and percutaneous coronary intervention. Although a clinical risk model exists for percutaneous coronary intervention, no models exist for both procedures, nor do existing models account for risk factors prior to the index admission. We aimed to develop such a model for use in prospective automated surveillance programs in the Veterans Health Administration. Methods and Results: We collected data on all patients undergoing cardiac catheterization or percutaneous coronary intervention in the Veterans Health Administration from January 01, 2009 to September 30, 2013, excluding patients with chronic dialysis, end‐stage renal disease, renal transplant, and missing pre‐ and postprocedural creatinine measurement. We used 4 AKI definitions in model development and included risk factors from up to 1 year prior to the procedure and at presentation. We developed our prediction models for postprocedural AKI using the least absolute shrinkage and selection operator (LASSO) and internally validated using bootstrapping. We developed models using 115 633 angiogram procedures and externally validated using 27 905 procedures from a New England cohort. Models had cross‐validated C‐statistics of 0.74 (95% CI: 0.74–0.75) for AKI, 0.83 (95% CI: 0.82–0.84) for AKIN2, 0.74 (95% CI: 0.74–0.75) for contrast‐induced nephropathy, and 0.89 (95% CI: 0.87–0.90) for dialysis. Conclusions: We developed a robust, externally validated clinical prediction model for AKI following cardiac catheterization or percutaneous coronary intervention to automatically identify high‐risk patients before and immediately after a procedure in the Veterans Health Administration. Work is ongoing to incorporate these models into routine clinical practice