Implications of a Race Term in GFR Estimates Used to Predict AKI After Coronary Intervention

Percutaneous Coronary Intervention as a Non-Surgical Treatment for Ischemic Mitral Regurgitation in Patients with Multi-Vessel Coronary Artery Disease

BACKGROUND Multi-vessel coronary artery disease (MVD) represents a severe type of coronary artery disease (CAD). Ischemic mitral regurgitation (IMR) is a common mechanical complication in patients with CAD. This study aimed to retrospectively investigate the efficacy of percutaneous coronary intervention (PCI) on moderate/severe IMR in patients with MVD. MATERIAL AND METHODS Clinical data were collected from 15 patients who underwent successful treatment for MVD combined with moderate/severe IMR through the PCI procedure and achieved complete revascularization between January 2014 and December 2022. Cardiac structural and functional parameters were assessed through echocardiographic evaluations. Color flow recordings of MR jets were obtained through an enlarged view of the 4-chamber cut, and the diagnosis of MR was categorized into mild (<4 cm²), moderate (4-8 cm²), and severe (>8 cm²), based on the MR area. RESULTS The common features of the selected cases were advanced age, low body weight, and renal insufficiency. Cardiac echocardiography revealed an augmentation in the left atrial anteroposterior diameter and left ventricular internal diameter at end-systole after PCI, while the left ventricle internal diameter in diastole, left ventricular ejection fraction, and left ventricular fractional shortening were comparable to preoperative values. All patients had moderate/severe MR preoperatively, and MR improved at 1 month (2.73±0.69) and 12 months (2.26±0.58) after PCI. CONCLUSIONS In cases of MVD accompanied by moderate/severe IMR, undergoing PCI can spare certain elderly patients with low body weight and renal insufficiency from high-risk surgery, alleviating the severity of MR without undergoing mitral valve intervention.

The Impact of Health Care Algorithms on Racial and Ethnic Disparities : A Systematic Review

BACKGROUND

There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities.

PURPOSE

To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities.

DATA SOURCES

Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023.

STUDY SELECTION

Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms.

DATA EXTRACTION

Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension.

DATA SYNTHESIS

Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non-race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques.

LIMITATION

Results are mostly based on modeling studies and may be highly context-specific.

CONCLUSION

Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes.

PRIMARY FUNDING SOURCE

Agency for Healthcare Quality and Research.