We assessed a pool of potential frailty risk factors gleaned from prior research, consensus opinion, and practice guidelines for their associations with frailty (Andrew, Mitnitski, & Rockwood, 2008 (link); Cevenini et al., 2013 (link); De Martinis, Franceschi, Monti, & Ginaldi, 2006 (link); Fried et al., 2004 (link); Gobbens et al., 2012 (link); Iwata, Kuzuya, Kitagawa, & Iguchi, 2006 (link)), and the biopsychosocial model and their availability in the EHR. An expert panel consisting of a board-certified gerontology nurse, geriatrician with expertise in frailty, two advanced practice geriatric nurses, and two doctorally prepared nurses with geriatric expertise provided content validity.
We operationalized frailty by the presence or absence of 16 biopsychosocial risk factors drawn from evidence in previous studies to create an FRS (see Table 1). We further defined six of these risk factors by subfactors. The biological risk factors comprised eight symptoms, syndromes, and conditions and four serum biomarkers. Symptoms, syndromes, and conditions included fatigue, weakness, dyspnea, chronic pain, falls (history or admission diagnosis), vision impairment (glaucoma, cataracts, macular degeneration, retinopathy, blindness), urinary incontinence, and nutrition issues (low body mass index, unplanned weight loss, poor appetite). We selected the four biomarkers—CRP, albumin, hemoglobin, and WBC count— based on associations with frailty, availability in the EHR, and common use in practice. CRP, an acute-phase reactant, exerts catabolic effects leading to muscle atrophy, weakness, fatigue, and poor physical performance due to upregulated protein synthesis and decreased synthesis of albumin. Low albumin and hemoglobin are well-established markers of inflammation and frailty that have similar impacts on symptoms and function. Elevated WBC count is associated with inflammation and frailty and has synergistic interactions with CRP. The three psychological risk factors we included were cognition problems (delirium, dementia), depression, and smoking (current). Finally, we included one social support risk factor (single, living alone, caregiver concerns [i.e., concerns about the impact of illness and hospitalization on discharge needs and planning] or being older, disabled, and living alone). We did not include impaired physical function in the score due to perspectives that regard physical function as an outcome of frailty (Sternberg et al., 2011 (link)) and the lack of a valid proxy indicator in the EHR.
To calculate the FRS, we counted each risk factor as yes = 1 if present and no = 0 if not present. For the six risk factors further defined by more than one subfactor (i.e., nutrition issues, falls, vision impairment, fatigue, cognitive problems, social support issues), the presence of at least one subfactor resulted in counting the overall risk factor as present (see Table 1). The biomarker risk factors were operationalized by the categorical abnormal flag, which indicated that the laboratory value fell outside the reference range, high or low. We created an FRS as the unweighted count of risk factors present (theoretical range 0 = 16), where higher scores are indicative of increased frailty. We then used these FRSs to model the outcome variables of time to in-hospital mortality and rehospitalization within 30 days of discharge.