The combination of endocrine therapy and cyclin-dependent kinase (CDK) 4/6 inhibitors is currently one of the preferred first-line regimens for patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative advanced or metastatic breast cancer. CDK 4/6 inhibitors can cause serum creatinine (SCr) elevation and corresponding estimated glomerular filtration rate (eGFR) reduction without affecting kidney function by inhibiting tubular secretory transporters in the kidneys—a phenomenon called pseudo-acute kidney injury (pseudo-AKI). We report a case of a 69-year old woman found to have elevated SCr while on ribociclib for treatment of metastatic breast cancer. Further evaluation with cystatin C-based eGFR and measured GFR by urinary iothalamate clearance revealed values close to her baseline Cr-based eGFR prior to initiation of treatment with ribociclib. She was therefore diagnosed with ribociclib-induced pseudo-AKI. Treatment with ribociclib was continued with steady favorable response. This case highlights that, in addition to true AKI, ribociclib can cause pseudo-AKI. Alternative methods of kidney function assessment may be necessary in patients who develop elevated SCr while on this medication to help discriminate between artifactual and true kidney function impairment. Doing so can help avoid unnecessary work-up and/or premature discontinuation of the treatment.
<b><i>Introduction:</i></b> Sonographic technologies can estimate extravascular lung water (EVLW) in hemodialysis (HD) patients. This study investigated the suitability of a handheld scanner in contrast to a portable scanner for quantifying EVLW in hospitalized patients requiring HD. <b><i>Methods:</i></b> In this prospective study, 54 hospitalized HD patients were enrolled. Bedside lung ultrasound was performed within 30 min before and after dialysis using handheld (phased array transducer, 1.7–3.8 MHz) and portable (curved probe, 5–2 MHz) ultrasound devices. Eight lung zones were scanned for total B-lines number (TBLN). The maximum diameter of inferior vena cava (IVC) was measured. We performed Passing-Bablok regression, Deming regression, Bland-Altman, and logistic regression analysis. <b><i>Results:</i></b> The 2 devices did not differ in measuring TBLN and IVC (<i>p</i> > 0.05), showing a high correlation (<i>r</i> = 0.92 and <i>r</i> = 0.51, respectively). Passing-Bablok regression had a slope of 1.11 and an intercept of 0 for TBLN, and the slope of Deming regression was 1.02 within the CI bands of 0.94 and 1.11 in the full cohort. TBLN was logarithmically transformed for Bland-Altman analysis, showing a bias of 0.06 (TBLN = 1.2) between devices. The slope and intercept of the Deming regression in IVC measurements were 0.77 and 0.46, respectively; Bland-Altman plot showed a bias of −0.07. Compared with predialysis, TBLN significantly (<i>p</i> < 0.001) decreased after dialysis, while IVC was unchanged (<i>p</i> = 0.16). Univariate analysis showed that cardiovascular disease (odds ratio [OR] 8.94 [2.13–61.96], <i>p</i> = 0.002), smoking history (OR 5.75 [1.8–20.46], <i>p</i> = 0.003), and right pleural effusion (OR 5.0 [1.2–25.99], <i>p</i> = 0.03) were strong predictors of EVLW indicated by TBLN ≥ 4. <b><i>Conclusion:</i></b> The lung and IVC findings obtained from handheld and portable ultrasound scanners are comparable and concordant. Cardiovascular disease and smoking history were strong predictors of EVLW. The use of TBLN to assess EVLW in hospitalized HD patients is feasible. Further studies are needed to determine if TBLN can help guide volume removal in HD patients.
Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.
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