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Frailty Phenotypes and Relations With Surgical Outcomes: A Latent Class Analysis.

ABSTRACT: BACKGROUND:Frailty 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. METHODS:In 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. RESULTS:The 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). CONCLUSIONS:Self-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.

SUBMITTER: Sadiq F

PROVIDER: S-EPMC6931904 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Frailty Phenotypes and Relations With Surgical Outcomes: A Latent Class Analysis.

Sadiq Furqaan F Kronzer Vanessa L VL Wildes Troy S TS McKinnon Sherry L SL Sharma Anshuman A Helsten Daniel L DL Scheier Lawrence M LM Avidan Michael S MS Ben Abdallah Arbi A

Anesthesia and analgesia 20181001 4

<h4>Background</h4>Frailty 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.<h4>Metho ...[more]

PMID: 30113393

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Project description:BackgroundAcute respiratory distress syndrome remains a heterogeneous syndrome for clinicians and researchers difficulting successful tailoring of interventions and trials. To this moment, phenotyping of this syndrome has been approached by means of inflammatory laboratory panels. Nevertheless, the systemic and inflammatory expression of acute respiratory distress syndrome might not reflect its respiratory mechanics and gas exchange.MethodsRetrospective analysis of a prospective cohort of two hundred thirty-eight patients consecutively admitted patients under mechanical ventilation presenting with acute respiratory distress syndrome. All patients received standardized monitoring of clinical variables, respiratory mechanics and computed tomography scans at predefined PEEP levels. Employing latent class analysis, an unsupervised structural equation modelling method, on respiratory mechanics, gas-exchange and computed tomography-derived gas- and tissue-volumes at a PEEP level of 5cmH2O, distinct pulmonary phenotypes of acute respiratory distress syndrome were identified.ResultsLatent class analysis was applied to 54 respiratory mechanics, gas-exchange and CT-derived gas- and tissue-volume variables, and a two-class model identified as best fitting. Phenotype 1 (non-recruitable) presented lower respiratory system elastance, alveolar dead space and amount of potentially recruitable lung volume than phenotype 2 (recruitable). Phenotype 2 (recruitable) responded with an increase in ventilated lung tissue, compliance and PaO2/FiO2 ratio (p < 0.001), in addition to a decrease in alveolar dead space (p < 0.001), to a standardized recruitment manoeuvre. Patients belonging to phenotype 2 (recruitable) presented a higher intensive care mortality (hazard ratio 2.9, 95% confidence interval 1.7-2.7, p = 0.001).ConclusionsThe present study identifies two ARDS phenotypes based on respiratory mechanics, gas-exchange and computed tomography-derived gas- and tissue-volumes. These phenotypes are characterized by distinctly diverse responses to a standardized recruitment manoeuvre and by a diverging mortality. Given multicentre validation, the simple and rapid identification of these pulmonary phenotypes could facilitate enrichment of future prospective clinical trials addressing mechanical ventilation strategies in ARDS.

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:BackgroundSince COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.Research questionDoes the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes?Study design and methodsWe included adult patients (≥ 18 years) positive for laboratory-confirmed SARS-CoV-2 infection from a prospective COVID-19 registry database in the Cleveland Clinic Health System in Ohio and Florida. The patients were split into training and testing sets. Using latent class analysis (LCA), we first identified phenotypic clusters of patients with COVID-19 based on demographics, comorbidities, and presenting symptoms. We then identified subphenotypes of hospitalized patients with additional blood biomarker data measured on hospital admission. The associations of phenotypes/subphenotypes and clinical outcomes were investigated. Multivariable prediction models were established to predict assignment to the LCA-defined phenotypes and subphenotypes and then evaluated on an independent testing set.ResultsWe analyzed data for 20,572 patients. Seven phenotypes were identified on the basis of different profiles of presenting COVID-19 symptoms and existing comorbidities, including the following groups: young, no symptoms; young, symptoms; middle-aged, no symptoms; middle-aged, symptoms; middle-aged, comorbidities; old, no symptoms; and old, symptoms. The rates of inpatient hospitalization for the phenotypes were significantly different (P < .001). Five subphenotypes were identified for the subgroup of hospitalized patients, including the following subgroups: young, elevated WBC and platelet counts; middle-aged, lymphopenic with elevated C-reactive protein; middle-aged, hyperinflammatory; old, leukopenic with comorbidities; and old, hyperinflammatory with kidney dysfunction. The hospital mortality and the times from hospitalization to ICU transfer or death were significantly different (P < .001). The models for predicting the LCA-defined phenotypes and subphenotypes showed high discrimination (concordance index, 0.92 and 0.91).InterpretationHypothesis-free LCA-defined phenotypes and subphenotypes of patients with COVID-19 can be identified. These may help clinical investigators conduct stratified analyses in clinical trials and assist basic science researchers in characterizing the pathobiology of the spectrum of COVID-19 presentations.

Project description: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.

Project description:BackgroundPrevious latent class analysis of adults with acute respiratory distress syndrome (ARDS) identified two phenotypes, distinguished by the degree of inflammation. We aimed to identify phenotypes in children with ARDS in whom developmental differences might be important, using a latent class analysis approach similar to that used in adults.MethodsThis study was a secondary analysis of data aggregated from the Randomized Evaluation of Sedation Titration for Respiratory Failure (RESTORE) clinical trial and the Genetic Variation and Biomarkers in Children with Acute Lung Injury (BALI) ancillary study. We used latent class analysis, which included demographic, clinical, and plasma biomarker variables, to identify paediatric ARDS (PARDS) phenotypes within a cohort of children included in the RESTORE and BALI studies. The association of phenotypes with clinically relevant outcomes and the performance of paediatric data in adult ARDS classification algorithms were also assessed.Findings304 children with PARDS were included in this secondary analysis. Using latent class analysis, a two-class model was a better fit for the cohort than a one-class model (p<0·001). Latent class analysis identified two classes: class 1 (181 [60%] of 304 patients with PARDS) and class 2 (123 [40%] of 304 patients with PARDS), referred to as phenotype 1 and 2 hereafter. Phenotype 2 was characterised by higher concentrations of inflammatory biomarkers, a higher incidence of vasopressor use, and more frequent diagnosis of sepsis, consistent with the adult hyperinflammatory phenotype. All levels of severity of PARDS were observed across both phenotypes. Children with the hyperinflammatory phenotype (phenotype 2) had worse clinical outcomes than those with the hypoinflammatory phenotype (phenotype 1), with a longer duration of mechanical ventilation (median 10·0 days [IQR 6·3-21·0] for phenotype 2 vs 6·6 days [4·1-10·8] for phenotype 1, p<0·0001), and higher incidence of mortality (17 [13·8%] of 123 patients vs four [2·2%] of 181 patients, p=0·0001). When using adult phenotype classification algorithms in children, the soluble tumour necrosis factor receptor-1 (sTNFr1), vasopressor use, and interleukin (IL)-6 variables gave an area under the curve (AUC) of 0·956, and the sTNFr1, vasopressor use, and IL-8 variables gave an AUC of 0·954, compared with the gold standard of latent class analysis.InterpretationLatent class analysis identified two phenotypes in children with ARDS with characteristics similar to those in adults, including worse outcomes among patients with the hyperinflammatory phenotype. PARDS phenotypes should be considered in design and analysis of future clinical trials in children.FundingUS National Institutes of Health.

Dataset Information

Frailty Phenotypes and Relations With Surgical Outcomes: A Latent Class Analysis.

Publications

Frailty Phenotypes and Relations With Surgical Outcomes: A Latent Class Analysis.

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