Abstract
The present protocol describes an observational cohort study that was designed to propose a therapeutic scheme and formulate an individualized treatment strategy for frail elderly patients diagnosed with multiple diseases in a Chinese, multicenter setting. Over a 3-year period, we will recruit 30,000 patients from 10 hospitals and collect baseline data including patient demographic information, comorbidity characteristic, FRAIL scale, age-adjusted Charlson comorbidity index (aCCI), relevant blood tests, the results of imaging examination, prescription of drugs, length of hospital stay, number of overall re-hospitalizations and death. Elderly patients (≥ 65 years old) with multimorbidity and receiving hospital care are eligible for this study. Data collection is being performed at baseline and 3, 6, 9 and 12 months after discharge. Our primary analysis was all-cause death, readmission rate and clinical events (including emergency visits, stroke, heart failure, myocardial infarction, tumor, acute chronic obstructive pulmonary disease, etc). The study is approved by the National Key R & D Program of China (2020YFC2004800). Data will be disseminated in manuscripts submitted to medical journals and in abstracts submitted to international geriatric conferences. Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [ChiCTR2200056070].
According to the World Health Organization (WHO) statistics, the elderly population will reach about 2 billion in 2050.[1] The elderly population especially those aged over 65 years have a high burden of comorbidity.[2,3] China has over 150 million adults aged more than 65 accounting for 11.4% of the total population and 18.3% of them were disabled due to comorbidity.[4] With the continuous extension of life expectancy, comorbidity will become a public health problem worldwide and the biggest challenge facing the medical industry in the coming decades.
Comorbidity refers to the concurrent or successive presence of two or more non-communicable chronic diseases and geriatric syndromes.[5] Previous studies showed that comorbidity is closely associated with adverse clinical outcomes including a high rate of death and disability, poor quality of life and excessive medical resources or expenses.[6,7] CCI was widely used to predict short and long-term outcomes in patients with comorbidity.[8] It contains 19 items including myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic lung disease, connective tissue disease, ulcer disease, mild liver disease and diabetes (1 point for each item); hemiplegia, moderate-severe kidney disease, diabetic end organ damage, tumor, leukemia and lymphoma (2 points for each item), moderate or severe liver disease (3 points) and metastatic solid tumor or AIDS (6 points). The existing chronic diseases in comorbidity may be independent or interrelated with each other and aggravates the course of disease progression.[9,10] Patients with high CCI score demonstrated low survival rate and diminished quality of life. In recent years, frailty has attracted great attention in medical and scientific communities and was suggested as a prognostic marker for adverse survival outcomes such as re-hospitalization, disability and death.[11-13] At present, many studies have found that frailty is affected by the number of chronic diseases and is closely related to comorbidity.[14]
Frailty is a clinical state that reduced the physiological reserve of multiple organ system, and eventually, increased the vulnerability and decreased the capability of self-stability.[15] The prevalence of frailty in elderly adults range from 4% to 16%.[16,17] Compared with other geriatric syndromes, patients with frailty not necessarily experience the same symptoms and/or have complaints disturbing the major diseases. Therefore, two dominant operationalizations of frailty were recommend: Frail scale and Frailty Phenotype.[18] Frailty scale is a questionnaire proposed by the International Association of nutrition and aging (IANA) mainly used to screen and evaluate the high-risk physically frail patients in elderly adults. It includes 5 items : exhaustion, decreased endurance, slow gait speed, illness and weight loss. Patients with three or more items were defined as frailty.[19] While Frailty Phenotype mainly focused on multisystem physiologic state and energy dysregulation.[20] Although frailty and comorbidity are completely different, the two concepts overlap at a large level[21] and the interaction between them leads to a vicious circle. Frailty increases the risk of disability and affects the prognosis and quality of life in elderly patients with comorbidity and the frail elderly patients are more likely to suffer from a variety of diseases. Likewise, the diseases of comorbidity may cause the loss of function and physical strength and eventually lead to frailty. The death rate in frail patients with comorbidity is two times higher than that in health people.
Personalized patient management is the most importance strategy for locking the progression and improving the prognosis of comorbidity, especially for frail elderly adults.[22] However, most of the schemes regarding older patient treatment only considered frailty as a predictor of adverse outcomes but do not use it as a separate classification standard for treatment strategy decision-making.[23] Moreover, geriatric syndromes (such as falls, cognitive impairment, dizziness or frailty) that was associated with a high disease burden and had a negatively influence on the progression of diseases are not included in the comorbidity index.[24-26] Therefore, selection of the most appropriate intervention for older population was very difficult. To investigate the relationship between frailty and comorbidity and formulate individualized treatment strategies for elderly adults, we have set-up the Frailty And Comorbidity in Elderly patients (FACE) cohort. The overall objectives of FACE study are: (1) to establish a clinical database and biobank for elder patients with multimorbidity; (2) to analyze the influence of frailty on the prognosis and (3) to optimize individualized treatments for these patients.
METHODS
Trial Design
In this prospective observational trial, undertaken in multi-centers in China from July 2020 to June 2023, 30,000 patients aged 65 years or older with multimorbidity willing to participate in “FACE” study and providing informed consent and epidemiological questionnaires on geriatric comorbidity will do some examinations (Frailty, Physical strength, Fall risk, Anxiety and depression, Comorbidity index) to form an overall view of health of the patients and all patients will be divided into two groups (Frailty group and non-frailty group) according to the FRAIL scale (3 or more criteria). The details of this trial are presented in Table 1.
Table 1. Study summary.
| Study title | Frailty and comorbidity in elderly patients. |
| Running title | FACE study |
| Study design | A multicenter, Chinese observational cohort study |
| Study participants | Elderly adults aged 65 years or older, inpatients with comorbidity |
| Sample size | 30000 patients |
| Study sites | 10 hospitals |
| Planned study period | 3 years |
| Follow-up duration | 12 months |
| Ethics/registration number | Ethics Committee of Beijing Anzhen Hospital, Beijing, China (No. 2021156X). Chinese Clinical Trial Registry (ChiCTR2200056070). |
| Research aims | The first research aim is to establish a clinical database and biobank for elder patients with multimorbidity. The second aim is to analyze the influence of frailty on the prognosis. A further research aim is to optimize individualized treatments for these patients for these patients. |
Ethics Approvals and Registration
Ethical approval for the study has been obtained from the Ethics Committee of Beijing Anzhen Hospital, Beijing, China (No. 2021156X). The trial was registered at the Chinese Clinical Trial Registry (ChiCTR2200056070). Participating sites will obtain informed written consent from patients or their legal representatives in accordance with local regulations and national guidelines. All completed data will be uploaded to the national population health data center.
Recruitment of Patients and Blinding
Eligible patients will be recruited via promotion materials delivered by ten tertiary hospitals from two provinces in China (1 military hospital and 9 affiliated hospitals). Before collecting baseline data and taking peripheral blood samples, the head of each research center will send an informed consent and epidemiological questionnaire to participants. The incomplete epidemiological questionnaire will be recorded. All of the geriatric patients participating to the study with informed consent will be trained by researchers for the study protocol (Frailty group and non-frailty group), risks and benefits of the study and given a briefing session for blood samples preservation which were used to analyze of biomarkers and/or genetic (DNA). Patients who refused to participate in blood sample collection does not affect their participation in the main study. It will be clearly stated that patients are free to withdraw from the study for any reason at any time without prejudice to future clinical treatment, and with no obligation to give the reason for withdrawal. A copy of the signed informed consent will be kept by the patient and the original one will be kept safely with researchers. The researcher will scan all medical records and filed them. Inclusion criteria: (1) written and informed consent, (2) participants aged 65 years or older, (3) no limitation on gender, and (4) Co-occurring two or more diseases. Exclusion criteria: (1) unwilling to participate in the study, (2) unable or unwilling to cooperate with the baseline or follow-up data collection, (3) unsuitable to participate in the study.
Group Allocation
After collecting baseline information and taking related examinations, participants will be allocated to frailty group and non-frailty group. Additionally, the blood samples will be divided into 4 groups (Frailty group, frailty with events group, non-frailty and non-frailty group with events group) according to whether the enrolled patients had frailty and endpoint events during the follow-up period. The baseline will be matched by propensity score and a total of 360 patients (90 patients in each group) were selected to detect the difference in the expression of plasma metabolites biomarkers by liquid chromatography-mass spectrometry (LC – ms/ms) and then analyze the metabonomics related to frailty.
Variables Collected
Baseline data
Prior to allocation, demographic information and comorbidity characteristics will be obtained from participants at baseline. Demographic information for participants will include age, gender, smoking status, ethnic origin and educational level. The questionnaire of comorbidity will mainly include frailty assessment, functional and physical assessment, fall risk assessment, anxiety, and depression assessment, CCI assessment, lifestyle, medical history, clinical diagnosis, treatment information and auxiliary examinations.
Routine inspections
The data of blood, urinary and stool routine test, biochemical tests, coagulation function, blood gas analysis, glycosylated hemoglobin, myocardial injury markers, tumor markers, BNP, thyroid function, immune and inflammatory indicators together with ECG, X-ray, CT, CTA, MRI, B-mode ultrasound, color Doppler ultrasound, pulmonary function, vascular endothelial function, bone mineral density and other examinations of participants will be collected. All tests and examinations are not mandatory items. If the patient only takes one examination, we will collect the corresponding results.
General health status assessment
Frailty: Frailty will be measured by FRAIL scale and be classified as pre-frail (1 or 2 criteria) and frail (3 or more criteria present);[27] Balance: The balance will be measured by Timed-Up-and-Go test (TUGT). The patients who take ≥ 12 seconds to complete the TUG is at risk for falling[28]; Fall Risk: Fall risk will be measured by Morse fall scale (MFS) and be classified as low risk with 0-25 score, medium risk with 25-45 score and high risk with 45-125 scores;[29] Anxiety and depression: Anxiety and depression will be measured by Hospital Anxiety and Depression Scale (HADS).[30] HADS consists 14 items, of which 7 items was applied to assess depression and 7 items was applied to assess anxiety. There are 6 reverse questions including 5 in the depression subscale and 1 in the anxiety subscale. In this study, we will select 9 score as the cut-off score of anxiety or depression with high sensitivity and specificity after referring to the optimal critical point on CCMD-2, SDS and SAS; Comorbidity Index: Comorbidity Index will be measured by aCCI with the following categories: mild complications (aCCI score: 0-1), moderate complications (aCCI score: 2-3) and severe complications (aCCI score ≥ 4).[31]
Blood Samples Collection
Blood samples will be collected in 10000 participants with three tubes of blood samples in each patient including two tubes with EDTA anticoagulant (6 mL) and 1 tube (5 mL) with non-anticoagulant and preserved at 4 ℃ temporarily sent to the laboratory within 30 min. Blood samples will be preserved and used for biomarker detection and genetic analysis. The expense of blood samples colletion, testing and quality control will be provided by researchers. The information of patients will not be disclosed. The staff who will manage the samples in the biological resource bank and the researchers who will use the samples are unable to get the personal information. The collection and storage of biological samples will strictly abide by the principles of confidentiality and safekeeping.
Outcome Measures
Longer term follow-up
In order to measure the outcomes (endpoint events and frailty), adverse events will be checked at month 3, 6, 9, and 12 after discharge via telephone and inpatient or outpatient follow-up. At each follow-up after registration, the researchers will ask the patients whether they had any symptoms since the last follow-up or whether there is a report issued by the health care department that meets the endpoint events. The evaluation schedule will indicate the preferred modalities for each follow-up visit and all outcomes will be collected.
Clinical outcomes
The evaluation of endpoints refers to the technical specification for long-term follow-up of endpoint events in large population cohort (t/cpma 002-2019). The endpoints of this study included all-cause death, re-hospitalization, emergency treatment, and clinical events (including stroke, heart failure, myocardial infarction, tumor, acute COPD, etc.). Among them, cause of death, clinical diagnosis, date of death, and place of death will be recorded in all-cause death events; Clinical diagnosis, hospitalization date, discharge date and diagnosis will be recorded in re-hospitalization events; Clinical diagnosis, medical treatment date and diagnosis will be recorded in emergency treatment events; and clinical diagnosis, onset date, and diagnosis will be recorded in clinical events.
Data Management, Data Protection and Patient Confidentiality
In this trial, group allocation and the outcome data (including baseline and each follow-up) will be carried out via a web-based electronic data collection (EDC) system and a supporting electronic record and electronic signature (ERES) server. The displayed data will not contain personal information. After screening by the head of each research center, eligible participants will perform some physical examinations and be categorized as frailty group and non-frailty group. After group allocation, medical records of these patients will be uploaded as electronic medical case report form (ECRF) with a unique and random ID number in central monitoring system. The user will access the data using a secured password in Duke Clinical Research Institute (DCRI) serves. During the data-entry stage, continuous variables including missingness, inconsistent and “out-of-range” values will be labeled manually or electronically. The data challenge system will generate a report to track the tagged data and automatically resolve it. Eventually, data will be transferred from the EDC server to SAS software for statistical analysis and be cross-checked in SAS. Internal quality check and data audits will be conducted by data managers, statistician and system analyst during the study period. No investigators will be able to modify any data. All data will be kept by a non-investigator (an IT worker) for 3 years after the expiration of the fund. All data will be uploaded on the website of Chinese clinical trial registry after the study.
Sample Size and Power Calculation
The sample size assessment and power calculations are mainly relied on the results of an analysis of UK Biobank participants.[12] In this prospective study, 11 865 (7%) were diagnosed with frailty in patients experiencing more than one chronic disease. Meanwhile, multimorbidity was more common in frail participants than in non-frail participants. On the basis of the primary outcome parameter, the mortality rate in patients with long-term conditions ranged from 2% to 5%. It is estimated that for assessing the prognosis of frail older adults, 10,000 participants (about one third of the total participants) are required to collect blood samples, achieve 80% statistical power (alpha 0.05; two-sided test) and 90% follow-up rate. Therefore, we aimed to recruit 30,000 participants including 12,000 cases with cardiovascular comorbidities (such as coronary heart disease, atrial fibrillation and heart failure) in Beijing Anzhen Hospital, Xuanwu Hospital Capital affiliated to Medical University and Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology , 5000 cases with stroke comorbidities or Alzheimer's disease in Beijing Tiantan Hospital and Beijing Geriatric Hospital, 3000 cases with COPD comorbidities in Beijing Chaoyang Hospital, 3000 cases with digestive comorbidity (such as pneumonia, hepatitis, liver abscesses and urinary tract infections) in China-Japan Friendship Hospital, 2000 cases with cancer comorbidities in Cancer Hospital Chinese Academy of Medical Sciences, 5000 cases with multiple comorbidities (such as coronary heart disease, heart failure, kidney failure, diabetes and hypertension) in General Hospital of the Chinese People’s Liberation Army. Images of tissue samples from 2,000 elderly patients with gastrointestinal diseases will also be collected at Beijing Friendship Hospital affiliated to Capital Medical University.
Statistical Analysis
Categorical variables will be represented as frequencies and percentages, and continuous variables will be represented as means and SD to describe central and discrete trends. The differences in adverse events rate between frailty group and non-frailty group will be analyzed by Cox regression models. Kaplan-Meier curves will be used to estimate the survival rate and log-rank tests will be used to compare the survival rate among groups. The confounding factors of frailty will be calculated by multiple regression analysis and the linear regression model. All statistics will be analyzed using SPSS software version 25.0 (SPSS, Chicago, IL, US). A two-sided alpha-level of 0.05 identified statistically significant differences.
Withdrawal From the Program
Patients will be informed that they are free to withdraw from the study at any time, without needing to provide reasons, those who still stay in the study will be followed up by telephone, letter, e-mail or medical information. Individuals who withdraw from the study will be excluded from the analyses. The acceptable follow-up methods will be recorded if they are provided.
Patient and Public Involvement
Our interest in this area arose in part from the need for a new therapeutic scheme based on clinical database and biobank and eventually formulate an individualized treatment strategy for frail elderly patients diagnosed with multiple diseases. A lay summary of the findings will be sent to the participants after completion of the study. As a multi-center prospective observational cohort study, there will not be any intervention or additional burden upon patients. All participants were invited to comment on the study when the study questionnaire was finalized.
Dissemination
Research findings will be disseminated on national and international levels. Data will be disseminated in manuscripts submitted to medical journals and in abstracts submitted to international geriatric conferences.
DISCUSSION
Comorbidity is common in frail elderly patients with significant physical disability, having a high burden on healthcare. Generally, management of it can be categorized as two stages: diagnosis and treatment. Optimal diagnosis and treatment scheme would maintain the quality of life in frail elderly. However, the complicated patient complaints and biobehavioral mechanisms underlying multi-diseases have made it hard to diagnose accurately and manage effectively. Due to the lack of standardized diagnostic models and treatment processes, we are trying to establish a clinical database and biobank of elderly patients with frailty and comorbidities to develop an individualized treatment strategy.
In fact, comorbidity and frailty has been assessed and studied in some specific diseases, including renal cell carcinoma,[32] COVID-19,[33] and mesenteric ischemia.[34] Frailty reduced the physiological reserve of multiple organ system, and eventually increased the vulnerability, decreased the capability of self-stability and showed a negative impact on the survival outcomes.[35,36] Previous studies has suggest frailty as the predictor of comorbidity. However, almost none guidelines on assessment and management for multimorbidity have yet to take frailty into account. Considering the lack of standardized diagnostic models and treatment processes, establishment of a clinical database and biobank-centered scheme is needed. This is likely the first study to formulate an individualized treatment strategy for frail elderly patients with multimorbidity. There are some potential limitations in our study protocol. Firstly, as an observational study, there might be some uncontrolled confounding factors and biases of selection during the study. Second, not all participants will take blood samples for omics analysis, which may lead to some bias. Third, this study only recruited patients from ten tertiary hospitals in two provinces of China, therefore, the sampling has relative regional limitations.
In conclusion, this study aims to study the effect of frailty on the prognosis of elderly patients with comorbidity. By establishing clinical database and biobank, we hope to develop the personalized and accurate diagnosis and treatment strategies, and establish a management system integrating diagnosis, evaluation, intervention and monitoring, in order to improve the quality of life in elderly patients with comorbidity.
Acknowledgements
None.
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