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The frail older person does not exist: development of frailty profiles with latent class analysis.

ABSTRACT: BACKGROUND:A fundamental issue in elderly care is targeting those older people at risk and in need of care interventions. Frailty is widely used to capture variations in health risks but there is no general consensus on the conceptualization of frailty. Indeed, there is considerable heterogeneity in the group of older people characterized as frail. This research identifies frailty profiles based on the physical, psychological, social and cognitive domains of functioning and the severity of the problems within these domains. METHODS:This research was a secondary data-analysis of older persons derived from The Older Person and Informal Caregiver Minimum Dataset. Selected respondents were 60 years and older (n =?43,704; 59.6% female). The following variables were included: self-reported health, cognitive functioning, social functioning, mental health, morbidity status, and functional limitations. Using latent class analysis, the population was divided in subpopulations that were subsequently discussed in a focus group with older people for further validation. RESULTS:We distinguished six frailty profiles: relatively healthy; mild physically frail; psychologically frail; severe physically frail; medically frail and multi-frail. The relatively healthy had limited problems across all domains. In three profiles older people mostly had singular problems in either the physical or psychological domain and the severity of the problems differed. Two remaining profiles were multidimensional with a combination of problems that extended to the social and cognitive domains. CONCLUSIONS:Our research provides an empirical base for meaningful frailty profiles. The profiles showed specific patterns underlying the problems in different domains of functioning. The heterogeneous population of frail older people has differing needs and faces different health issues that should be considered to tailor care interventions. Evaluation research of these interventions should acknowledge the heterogeneity of frailty by profiling.

SUBMITTER: Looman WM

PROVIDER: S-EPMC5885355 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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The frail older person does not exist: development of frailty profiles with latent class analysis.

Looman W M WM Fabbricotti I N IN Blom J W JW Jansen A P D APD Lutomski J E JE Metzelthin S F SF Huijsman R R

BMC geriatrics 20180404 1

<h4>Background</h4>A fundamental issue in elderly care is targeting those older people at risk and in need of care interventions. Frailty is widely used to capture variations in health risks but there is no general consensus on the conceptualization of frailty. Indeed, there is considerable heterogeneity in the group of older people characterized as frail. This research identifies frailty profiles based on the physical, psychological, social and cognitive domains of functioning and the severity ...[more]

PMID: 29618334

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Project description:BackgroundLittle is known about the heterogeneous clinical profile of physical frailty and its association with cognitive impairment in older U.S. nursing home (NH) residents.MethodsMinimum Data Set 3.0 at admission was used to identify older adults newly-admitted to nursing homes with life expectancy ≥6 months and length of stay ≥100 days (n = 871,801). Latent class analysis was used to identify physical frailty subgroups, using FRAIL-NH items as indicators. The association between the identified physical frailty subgroups and cognitive impairment (measured by Brief Interview for Mental Status/Cognitive Performance Scale: none/mild; moderate; severe), adjusting for demographic and clinical characteristics, was estimated by multinomial logistic regression and presented in adjusted odds ratios (aOR) and 95% confidence intervals (CIs).ResultsIn older nursing home residents at admission, three physical frailty subgroups were identified: "mild physical frailty" (prevalence: 7.6%), "moderate physical frailty" (44.5%) and "severe physical frailty" (47.9%). Those in "moderate physical frailty" or "severe physical frailty" had high probabilities of needing assistance in transferring between locations and inability to walk in a room. Residents in "severe physical frailty" also had greater probability of bowel incontinence. Compared to those with none/mild cognitive impairment, older residents with moderate or severe impairment had slightly higher odds of belonging to "moderate physical frailty" [aOR (95%CI)moderate cognitive impairment: 1.01 (0.99-1.03); aOR (95%CI)severe cognitive impairment: 1.03 (1.01-1.05)] and much higher odds to the "severe physical frailty" subgroup [aOR (95%CI)moderate cognitive impairment: 2.41 (2.35-2.47); aOR (95%CI)severe cognitive impairment: 5.74 (5.58-5.90)].ConclusionsFindings indicate the heterogeneous presentations of physical frailty in older nursing home residents and additional evidence on the interrelationship between physical frailty and cognitive impairment.

Project description:BackgroundFrailty is an important concept in the care of older adults although controversy remains regarding its defining features and clinical utility. Both the Fried phenotype and the Rockwood deficit accumulation approaches cast frailty as a "burden" without exploring the relative salience of its cardinal markers and their relevance to the patient. New multifactorial perspectives require a reliable assessment of frailty that can validly predict postoperative health outcomes.MethodsIn a retrospective study of 2828 unselected surgical patients, we used item response theory to examine the ability of 32 heterogeneous markers capturing limitations in physical, functional, emotional, and social activity domains to indicate severity of frailty as a latent continuum. Eighteen markers efficiently indicated frailty severity and were then subject to latent class analysis to derive discrete phenotypes. Next, we validated the obtained frailty phenotypes against patient-reported 30-day postoperative outcomes using multivariable logistic regression. Models were adjusted for demographics, comorbidity, type and duration of surgery, and cigarette and alcohol consumption.ResultsThe 18 markers provided psychometric evidence of a single reliable continuum of frailty severity. Latent class analyses produced 3 distinct subtypes, based on patients' endorsement probabilities of the frailty indicators: not frail (49.7%), moderately frail (33.5%), and severely frail (16.7%). Unlike the moderate class, severely frail endorsed emotional health problems in addition to physical burdens and functional limitations. Models adjusting for age, sex, type of anesthesia, and intraoperative factors indicated that severely frail (odds ratio, 1.89; 95% confidence interval, 1.42-2.50) and moderately frail patients (odds ratio, 1.31; 95% confidence interval, 1.03-1.67) both had higher odds of experiencing postoperative complications compared to not frail patients. In a 3-way comparison, a higher proportion of severely frail patients (10.7%) reported poorer quality of life after surgery compared to moderately frail (9.2%) and not frail (8.3%) patients (P < .001). There was no significant difference among these groups in proportions reporting hospital readmission (5.6%, 5.1%, and 3.8%, respectively; P = .067).ConclusionsSelf-report frailty items can accurately discern 3 distinct phenotypes differing in composition and their relations with surgical outcomes. Systematically assessing a wider set of domains including limitations in functional, emotional, and social activities can inform clinicians on what precipitates loss of physiological reserve and profoundly influences patients' lives. This information can help guide the current discussion on frailty and add meaningful clinical tools to the surgical practice.

Project description:BackgroundThe expression of depressive symptoms in older people with cancer is heterogeneous because of specific features of age or cancer comorbidity. We aimed to identify depressive symptom profiles in this population and describe the associated features including survival.Materials and methodsPatients ≥70 years who were referred to geriatric oncology clinics were prospectively included in the ELCAPA study. In this subanalysis, depressive symptoms were used as indicators in a latent class analysis. Multinomial multivariable logistic regression and Cox models examined the association of each class with baseline characteristics and mortality.ResultsFor the 847 complete-case patients included (median age, 79 years; interquartile range, 76-84; women, 47.9%), we identified five depressive symptom classes: "no depression/somatic only" (38.8%), "no depression/pauci-symptomatic" (26.4%), "severe depression" (20%), "mild depression" (11.8%), and "demoralization" (3%). Compared with the no depression/pauci-symptomatic class, the no depression/somatic only and severe depression classes were characterized by more frequent comorbidities with poorer functional status and higher levels of inflammation. "Severe" and "mild" depression classes also featured poorer nutritional status, more medications, and more frequent falls. Severe depression was associated with poor social support, inpatient status, and increased risk of mortality at 1 year (adjusted hazard ratio, 1.62, 95% confidence interval, 1.06-2.48) and 3 years (adjusted hazard ratio, 1.49; 95% confidence interval, 1.06-2.10).ConclusionA data-driven approach based on depressive symptoms identified five different depressive symptom profiles, including demoralization, in older patients with cancer. Severe depression was independently and substantially associated with poor survival.Implications for practiceOlder patients with cancer present with distinct profiles of depressive symptomatology, including different severity levels of depression and the demoralization syndrome. Clinicians should use a systematic assessment of depressive symptoms to adequately highlight these distinct profiles. Geriatric and oncological features are differently associated with these profiles. For instance, severe depression was associated with more frequent comorbidities with poorer functional, poor nutritional status, polypharmacy, frequent falls, inpatient status and poor social support. Also, severe depression was independently and substantially associated with poor survival so that the identification and management of depression should be considered a high priority in this population.

Project description:ImportanceMedically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date.ObjectiveTo define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters.Design, setting, and participantsThis cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles.Main outcomes and measuresComplex patient profiles, 1-year health care utilization, and mortality outcomes by profile.ResultsThe analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management.Conclusions and relevanceThe findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.

Dataset Information

The frail older person does not exist: development of frailty profiles with latent class analysis.

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The frail older person does not exist: development of frailty profiles with latent class analysis.

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