A retrospective study of perioperative clinical seizures and epilepsy in children after operation for CHD

This study investigated the incidence and risk factors of perioperative clinical seizure and epilepsy in children after operation for CHD. We included 777 consecutive children who underwent operation from January 2013 to December 2016 at Kanagawa Children’s Medical Center, Kanagawa, Japan. Perinatal, perioperative, and follow-up medical data were collected. Elastic net regression and mediation analysis were performed to investigate risk factors of perioperative clinical seizure and epilepsy. Anatomic CHD classification was performed based on the preoperative echocardiograms; cardiac surgery was evaluated using Risk Adjustment in Congenital Heart Surgery 1. Twenty-three (3.0%) and 15 (1.9%) patients experienced perioperative clinical seizure and epilepsy, respectively. Partial regression coefficient with epilepsy as the objective variable for anatomical CHD classification, Risk Adjustment in Congenital Heart Surgery 1, and the number of surgeries was 0.367, 0.014, and 0.142, respectively. The proportion of indirect effects on epilepsy via perioperative clinical seizure was 22.0, 21.0, and 33.0%, respectively. The 15 patients with epilepsy included eight cases with cerebral infarction, two cases with cerebral haemorrhage, and three cases with hypoxic-ischaemic encephalopathy; white matter integrity was not found. Anatomical complexity of CHD, high-risk cardiac surgery, and multiple cardiac surgeries were identified as potential risk factors for developing epilepsy, with a low rate of indirect involvement via perioperative clinical seizure and a high rate of direct involvement independently of perioperative clinical seizure. Unlike white matter integrity, stroke and hypoxic-ischaemic encephalopathy were identified as potential factors for developing epilepsy.

Did the Hospital Readmissions Reduction Program Reduce Readmissions? An Assessment of Prior Evidence and New Estimates

In this article, we provide a comprehensive, empirical assessment of the hypothesis that the Hospital Readmissions Reduction Program (HRRP) affected hospital readmissions. In doing so, we provide evidence as to the validity of prior empirical approaches used to evaluate the HRRP and we present results from a previously unused approach to study this research question—a regression-kink design. Results of our analysis document that the empirical approaches used in most prior research assessing the efficacy of the HRRP often lack internal validity. Therefore, results from these studies may not be informative about the causal consequences of the HRRP. Results from our regression-kink analysis, which we validate, suggest that the HRRP had little effect on hospital readmissions. This finding contrasts with the results of most prior studies, which report that the HRRP significantly reduced readmissions. Our finding is consistent with conceptual considerations related to the assumptions underlying HRRP penalty: in particular, the difficulty of identifying preventable readmissions, the highly imperfect risk adjustment that affects the penalty determination, and the absence of proven tools to reduce readmissions.

How Long-Term Care Quality Assurance Measures Address Dementia in Australia, England, Japan, and the United States

The prevalence of people with dementia living in long-term care (LTC) is high and rising internationally, and the need to improve LTC for people with dementia is widely recognized. In some countries, LTC quality assurance programs use quantitative measures of LTC quality, and international bodies emphasize the importance of person-centered care and healthy ageing outcomes. To better understand how LTC quality assurance programs address dementia, programs were reviewed in four countries—Australia, England, Japan, and the United States. Quality measures from each program were identified (n = 38) and examined to determine how they address dementia. Most measures did not address dementia, but four risk-adjusted for dementia (antipsychotic use, fractures, falls, mobility), one was dementia-specific (dementia/delirium hospitalizations), and one excluded people with dementia (losing bowel/bladder control). The other 32 measures were calculated equally regardless of the prevalence of dementia among LTC residents. Overall, LTC quality measurement differs internationally, but few measures address dementia. When dementia is addressed in quality measure calculations, it is most often as a risk-adjustor. Risk adjustment can help with attributing performance on these measures to the LTC setting rather than to the types of residents that the setting serves, but risk adjustment factors also are highly amenable to fraud, and thus require ongoing monitoring. Although LTC quality assessment programs and measures can help ensure people with dementia have access to quality LTC, adoption of measures that are meaningful to people with dementia—including measures of person-centered care and healthy ageing outcomes—remains needed.

Measuring patient safety in New Zealand public hospitals

<p>In New Zealand the Ministry of Health recognises quality of care as an integral part of a high performing health system and identifies patient safety as one of the key dimensions of quality. Over recent years a greater emphasis has been placed on improving patient safety mostly as a result of increased awareness around the frequency of medical error and resulting economic cost. However tools used to measure patient safety are limited. In particular the use of hospital administrative data to measure patient safety is scarce and existing safety measures often ignore one of the major issues confronting comparative analyses of hospital safety, risk adjustment to control for the differences in populations hospitals serve. The objective of this research is to develop comparable measures of patient safety for New Zealand public hospitals. It uses risk adjustment strategies applied to the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs) with New Zealand hospital administrative data, the National Minimum Dataset 2001 to 2009. The research employs econometric techniques to address risk adjustment of the PSIs, utilising existing AHRQ models but adapting and re-estimating them with New Zealand administrative data. The findings from the research indicate that to use the AHRQ PSIs as measures of hospital patient safety in New Zealand, risk adjustment should first be employed to ensure measures are comparable across hospitals and over time. Overall, although the impact of risk adjustment appears to be minor, it has relevance and this should be recognised. Relative hospital performance is affected by risk adjustment. In particular, it has the greatest impact on those hospitals with poor rankings. The research takes us a step closer to being able to confidently measure patient safety and quality of care in New Zealand public hospitals in an innovative way.</p>

Measuring patient safety in New Zealand public hospitals

<p>In New Zealand the Ministry of Health recognises quality of care as an integral part of a high performing health system and identifies patient safety as one of the key dimensions of quality. Over recent years a greater emphasis has been placed on improving patient safety mostly as a result of increased awareness around the frequency of medical error and resulting economic cost. However tools used to measure patient safety are limited. In particular the use of hospital administrative data to measure patient safety is scarce and existing safety measures often ignore one of the major issues confronting comparative analyses of hospital safety, risk adjustment to control for the differences in populations hospitals serve. The objective of this research is to develop comparable measures of patient safety for New Zealand public hospitals. It uses risk adjustment strategies applied to the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs) with New Zealand hospital administrative data, the National Minimum Dataset 2001 to 2009. The research employs econometric techniques to address risk adjustment of the PSIs, utilising existing AHRQ models but adapting and re-estimating them with New Zealand administrative data. The findings from the research indicate that to use the AHRQ PSIs as measures of hospital patient safety in New Zealand, risk adjustment should first be employed to ensure measures are comparable across hospitals and over time. Overall, although the impact of risk adjustment appears to be minor, it has relevance and this should be recognised. Relative hospital performance is affected by risk adjustment. In particular, it has the greatest impact on those hospitals with poor rankings. The research takes us a step closer to being able to confidently measure patient safety and quality of care in New Zealand public hospitals in an innovative way.</p>

Abstract 11372: A Risk-Adjustment Model for Patients Presenting to Hospitals with Out-of-Hospital Cardiac Arrest and ST-Elevation Myocardial Infarction

Background: Given the diversity of patients resuscitated from out-of-hospital cardiac arrest (OHCA) complicated by STEMI, adequate risk adjustment is needed to account for potential differences in case-mix to reflect the quality of percutaneous coronary intervention. Objectives: We sought to build a risk-adjustment model of in-hospital mortality outcomes for patients with OHCA and STEMI requiring emergent angiography. Methods: Within the Cardiac Arrest Registry to Enhance Survival, we included adult patients with OHCA and STEMI who underwent angiography within 2 hours from January 2013 to December 2019. Using pre-hospital patient and arrest characteristics, multivariable logistic regression models were developed for in-hospital mortality. We then described model calibration, discrimination, and variability in patients’ unadjusted and adjusted mortality rates. Results: Of 2,999 hospitalized patients with OHCA and STEMI who underwent emergent angiography (mean age 61.2 ±12.0, 23.1% female, 64.6% white), 996 (33.2%) died. The final risk-adjustment model for mortality included higher age, unwitnessed arrest, non-shockable rhythms, not having sustained return of spontaneous circulation upon hospital arrival, and higher total resuscitation time on scene ( C -statistic, 0.804 with excellent calibration). The risk-adjusted proportion of patients died varied substantially and ranged from 7.8% at the 10 th percentile to 74.5% at the 90 th percentile (Figure). Conclusions: Through leveraging data from a large, multi-site registry of OHCA patients, we identified several key factors for better risk-adjustment for mortality-based quality measures. We found that STEMI patients with OHCA have highly variable mortality risk and should not be considered as a single category in public reporting. These findings can lay the foundation to build quality measures to further optimize care for the patient with OHCA and STEMI.