Modulation of the Association Between Age and Death by Risk Factor Burden in Critically Ill Patients With COVID-19.

Older age is a key risk factor for adverse outcomes in critically ill patients with COVID-19. However, few studies have investigated whether preexisting comorbidities and acute physiologic ICU factors modify the association between age and death. DESIGN: Multicenter cohort study. SETTING: ICUs at 68 hospitals across the United States. PATIENTS: A total of 5,037 critically ill adults with COVID-19 admitted to ICUs between March 1, 2020, and July 1, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary exposure was age, modeled as a continuous variable. The primary outcome was 28-day inhospital mortality. Multivariable logistic regression tested the association between age and death. Effect modification by the number of risk factors was assessed through a multiplicative interaction term in the logistic regression model. Among the 5,037 patients included (mean age, 60.9 yr [± 14.7], 3,179 [63.1%] male), 1,786 (35.4%) died within 28 days. Age had a nonlinear association with 28-day mortality (p for nonlinearity <0.001) after adjustment for covariates that included demographics, preexisting comorbidities, acute physiologic ICU factors, number of ICU beds, and treatments for COVID-19. The number of preexisting comorbidities and acute physiologic ICU factors modified the association between age and 28-day mortality (p for interaction <0.001), but this effect modification was modest as age still had an exponential relationship with death in subgroups stratified by the number of risk factors. CONCLUSIONS: In a large population of critically ill patients with COVID-19, age had an independent exponential association with death. The number of preexisting comorbidities and acute physiologic ICU factors modified the association between age and death, but age still had an exponential association with death in subgroups according to the number of risk factors present. Additional studies are needed to identify the mechanisms underpinning why older age confers an increased risk of death in critically ill patients with COVID-19.

Treatment Decision Making for Older Kidney Patients during COVID-19.

BACKGROUND AND OBJECTIVES: Coronavirus disease 2019 (COVID-19) disrupted medical care across health care settings for older patients with advanced CKD. Understanding how shared decision making for kidney treatment decisions was influenced by the uncertainty of an evolving pandemic can provide insights for supporting shared decision making through the current and future public health crises. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We performed thematic and narrative analyses of semistructured interviews with patients (CKD stages 4 and 5, age 70+), care partners, and clinicians from Boston, Portland (Maine), San Diego, and Chicago from August to December 2020. RESULTS: We interviewed 76 participants (39 patients, 17 care partners, and 20 clinicians). Among patient participants, 13 (33%) patients identified as Black, and seven (18%) had initiated dialysis. Four themes with corresponding subthemes emerged related to treatment decision making and the COVID-19 pandemic: (1) adapting to changed educational and patient engagement practices (patient barriers to care and new opportunities for telemedicine); (2) reconceptualizing vulnerability (clinician awareness of illness severity increased and limited discussions of patient COVID-19 vulnerability); (3) embracing home-based dialysis but not conservative management (openness to home-based modalities and limited discussion of conservative management and advanced care planning); and (4) satisfaction and safety with treatment decisions despite conditions of uncertainty. CONCLUSIONS: Although clinicians perceived greater vulnerability among older patients CKD and more readily encouraged home-based modalities during the COVID-19 pandemic, their discussions of vulnerability, advance care planning, and conservative management remained limited, suggesting areas for improvement. Clinicians reported burnout caused by the pandemic, increased time demands, and workforce limitations, whereas patients remained satisfied with their treatment choices despite uncertainty. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: Decision Aid for Renal Therapy (DART), NCT03522740.

Subtyping CKD Patients by Consensus Clustering: The Chronic Renal Insufficiency Cohort (CRIC) Study.

BACKGROUND: CKD is a heterogeneous condition with multiple underlying causes, risk factors, and outcomes. Subtyping CKD with multidimensional patient data holds the key to precision medicine. Consensus clustering may reveal CKD subgroups with different risk profiles of adverse outcomes. METHODS: We used unsupervised consensus clustering on 72 baseline characteristics among 2696 participants in the prospective Chronic Renal Insufficiency Cohort (CRIC) study to identify novel CKD subgroups that best represent the data pattern. Calculation of the standardized difference of each parameter used the cutoff of ±0.3 to show subgroup features. CKD subgroup associations were examined with the clinical end points of kidney failure, the composite outcome of cardiovascular diseases, and death. RESULTS: The algorithm revealed three unique CKD subgroups that best represented patients' baseline characteristics. Patients with relatively favorable levels of bone density and cardiac and kidney function markers, with lower prevalence of diabetes and obesity, and who used fewer medications formed cluster 1 (n=1203). Patients with higher prevalence of diabetes and obesity and who used more medications formed cluster 2 (n=1098). Patients with less favorable levels of bone mineral density, poor cardiac and kidney function markers, and inflammation delineated cluster 3 (n=395). These three subgroups, when linked with future clinical end points, were associated with different risks of CKD progression, cardiovascular disease, and death. Furthermore, patient heterogeneity among predefined subgroups with similar baseline kidney function emerged. CONCLUSIONS: Consensus clustering synthesized the patterns of baseline clinical and laboratory measures and revealed distinct CKD subgroups, which were associated with markedly different risks of important clinical outcomes. Further examination of patient subgroups and associated biomarkers may provide next steps toward precision medicine.