Frailty in perioperative patients in three South African academic hospitals.
Frailty is the depletion of physiological and functional reserve across many organ systems, leading to a diminished capacity to withstand environmental stressors such as surgery.  An independent predictor of mortality, morbidity, complications and increased length of stay perioperatively,  it is likely to become one of the most problematic public health issues and will place a heavy strain on already overburdened healthcare systems.  Although often associated with advanced chronological age, frailty can also affect the non-elderly. Rockwood et al.  found the prevalence of frailty to be 2.0% in individuals aged >30 years,  which is similar to the findings of Kehler et al.  that the prevalence of frailty in Canadians between the ages of 18 and 34 was 1.8-5.3%, depending on the frailty assessment used.
A number of assessment methods have been used as indicators of overall health and physiological status. The American Society of Anaesthesiologists Physical Status (ASA-PS) scoring system is a scale from 1 through 6 in increasing order of risk, with an 'E' to denote emergency surgery, that is assigned to a patient by the anaesthesiologist before surgery.  Originally designed to describe the overall health status of a patient, it has since been used as a risk stratification tool and found to be predictive of postoperative morbidity and mortality.  The Revised Cardiac Risk Index is a simplified version of an earlier index.  It has six predictive factors with one point assigned per factor and with an increasing number of points corresponding to increased rates of perioperative cardiac risk.  Frailty assessments have been shown to be better predictors of postoperative complications, mortality and morbidity in the aged than other preoperative assessments. [15-17]
There are many assessment models that have been used to assess frailty. The two most commonly used models are the frailty phenotype  and the frailty index or deficit accumulation.  Both models have mainly been used for research purposes, and there have been concerns about their usefulness in a clinical setting. Screening tools such as the Clinical Frailty Scale (CFS) from Dalhousie University in Canada were subsequently developed for practical clinical use.  The CFS is a 9-point scale that uses pictographs to depict degrees of functional dependency. The scale ranges from 1 (very fit) to 9 (terminally ill), and a score of [greater than or equal to] 5 suggests frailty. Most studies using any one of the validated frailty measuring tools have been conducted in developed countries on predominantly white patients, with few published studies in the developing world and fewer still in Africa.  One study utilising data from the WHO study on global ageing and adult health found the prevalence of frailty to be 38% in South Africa (SA) and 37.9% in Ghana in adults aged [greater than or equal to] 50 years.  Another SA study compared frailty between HIV-positive and HIV-negative adults.  The mean ages of the HIV-positive and HIV-negative groups were 41.1 years and 42 years, respectively. There was a 19.4% prevalence of frailty in the HIV-positive adults compared with 13.3% in those who were HIV-negative.
Combining risk assessment tools such as the ASA-PS and frailty assessments to improve postoperative risk prediction could increase the probability of correct identification of patients who are likely to suffer adverse intraoperative and postoperative adverse events, and has been suggested by some authors. [5,24] However, combinations could make these tools more complex to use and limit their applicability to a wider population, and are likely to require extensive validation studies before they can be put into practice. Although much has been written about the postoperative adverse outcomes and mortality related to frailty, little is known about how frailty impacts on anaesthetic complications intraoperatively, or on the nature of utilisation of health services such as the intensive care unit (ICU) by patients assessed as frail. Correct risk assessment will identify the most vulnerable and may help facilitate discussions about realistic treatment goals.
To determine the prevalence of frailty and assess the intraoperative implications of frailty in a surgical population in three academic hospitals in Johannesburg, SA. We also assessed the association between frailty and comorbid illnesses and the association between the ASA-PS score and the CFS. Finally, we determined whether participants assessed as frail were more likely to go to a high-care unit or an ICU facility postoperatively.
Approval for the study was obtained from the Human Research Ethics Committee (Medical) of the University of the Witwatersrand (ref. no. M160756). Participants aged 18-90 years presenting for elective surgery at Chris Hani Baragwanath Academic Hospital (CHBAH), Charlotte Maxeke Johannesburg Academic Hospital (CMJAH) and Helen Joseph Hospital (HJH) between mid-November 2016 and mid-March 2017 were included. The study period was effectively 4 months. Combined, these hospitals operate on an average of 2 500 elective cases monthly.
This was a prospective, observational and cross-sectional study that employed convenience sampling. The surgical specialties included were general, gynaecological, orthopaedic, maxillofacial, ear, nose and throat, urological, neurosurgical and vascular. We excluded patients known to be pregnant and those with a diagnosis of mental impairment or dementia without any access to family members for information on frailty.
Frailty was defined using the CFS,  which is a validated 9-point assessment from the Canadian Study on Health and Aging (Table 1). Patients were defined as frail if they scored [greater than or equal to] 5 on the CFS. Copies of the data collection sheet and the CFS were placed in the operating theatre receiving areas at the three sites. Patients presenting for any of the included elective surgeries were identified during the preoperative assessments by the anaesthetists assigned to various elective lists. The anaesthetists included medical officers and registrars at different levels of training. After obtaining written informed consent from the patient or family member, demographic data and clinical data, which included comorbidities and planned procedure, were documented. An ASA-PS score and CFS was assigned on the data collecting sheet. Intraoperative complications and postoperative destination were documented.
All data were entered into a password-protected Microsoft Excel 2016 spreadsheet (Microsoft, USA). Patient-identifiable data were removed. The data were analysed using Stata version 13 (StataCorp, USA). Descriptive statistics were tabulated using means and percentages. The x2 test and Fisher's exact test were used to describe the association between categorical variables. Logistic regression analysis was used to assess factors associated with frailty. Univariate logistic regression analysis was performed for each of the variables. Multivariable logistic regression analysis was performed to adjust for the possible confounding variables. A p-value <0.05 was considered statistically significant for all analyses.
A total of 312 data collection forms were filled in and returned by the attending anaesthetists. We collected data on these 312 patients. Thirteen were excluded in the final analysis (7 patients did not undergo the planned surgery, and 6 forms were not filled in and/or consent was not documented), leaving a total of 299 patients. There were 156 women (52%) and 143 men (48%). The mean (standard deviation) age was 50.6 (15.8) years (range 21-90).
The CFSs assigned ranged from 1 (fit) to 8 (very severely frail). Of the patients, 67 (22%) were classified as frail, with 39 (58%) of these being female and 28 (42%) male. There were more frail women than men, as shown in Table 2. This was not statistically significant (p=0.263).
At CHBAH 27 out of 106 patients (25.4%) were frail, at CMJAH 28 out of 112 (25%) were frail, and at HJH 12 out of 81 (15%) were frail. Of all the patients, 222 (74%) had an ASA-PS score of 1 or 2 whereas 75 (26%) had an ASA-PS score of 3 or 4. Higher ASA-PS scores were strongly associated with frailty relative to an ASA-PS score of 1, with an ASA-PS score of 3 being the most highly associated with frailty (odds ratio (OR) 19.01, 95% confidence interval (CI) 6.870-52.614; p<0.001).
Between the ages of 18 and 34 years, 4% of patients were assessed as frail, in the 35-49 age group 13%, in the 50-64 age group 37%, and in the over-65 age group 45%. Age was significantly associated with frailty (OR 1.06, 95% CI 1.034-1.084; p<0.001. Frailty was also associated with an increased number of comorbidities. Older patients and those with more comorbidities were also more likely to experience intraoperative complications such as hypotension and need for vasopressors (Table 3).
Before adjusting for confounding variables, frailty was significantly associated with an increased incidence of hypotension and desaturation, need for blood transfusion and need for vasopressors. We then adjusted for confounding variables (age, gender, comorbidities) that are related to frailty and intraoperative complications. After adjustment, desaturation was significantly associated with frailty (adjusted odds ratio (aOR) 4.12, 95% CI 1.202-14.139; p=0.024) as well as need for blood transfusion (aOR 5.36, 95% CI 1.50-19.16; p=0.01), as shown in Table 3. Age, gender and comorbidities were not significantly related to frailty in either univariate or multivariable logistic regression analysis when assessing desaturation and need for blood transfusion.
Patients assessed as frail also had an increased likelihood of going to high care postoperatively (OR 3.01, 95% CI 1.073-8.447; p=0.036).
The prevalence of frailty is said to be higher in developing countries than in developed countries, where it ranges from 5.4% to 44% in community-dwelling elderly  and is reported to be between 4.1% and 50.3% in patients of all ages presenting for surgical procedures, depending on the assessment tool used and the population sampled.  The present study found that 22% of patients undergoing elective surgery in three large academic hospitals in Johannesburg were frail. This percentage is lower than our estimate of 30% and is also lower than the reported rates of frailty among the hospitalised elderly. [14,15] Our study, however, was one of the few that included age groups [greater than or equal to] 18 years, and on further analysis it was found that 45% of patients aged >65 were frail, which is on the higher end of prevalence rates generally reported for surgical patients. The distribution of frailty according to type of surgery is shown in Table 4.
Other studies have used the CFS to predict mortality and adverse outcomes. Bagshaw et at.  used the CFS in critically ill adults aged [greater than or equal to] 50 years and showed a prevalence of frailty of 32.8%. They also showed that in-hospital mortality was higher in the frail (32% v. 16% in the non-frail) and that major adverse events were more common (39% v. 29%). Similar to our study, the frail were older and had more comorbidities, which is also consistent with many other studies. [18,27,28] Hewitt et at.  showed that 28% of patients aged [greater than or equal to] 65 in three acute surgical admission units were frail according to the CFS. The frail individuals spent longer in hospital and were more likely to die within 30 and 90 days after surgery.
The present study also demonstrates the ability of the CFS to predict outcomes even when used by clinicians without prior training on how to use the scale. In a comparable study, the CFS was used by junior doctors without any prior training in patients aged >65 years admitted to an acute general medical unit.  In this study, frailty was associated with functional decline (p=0.011) and mortality within 3 months (p=0.012).
There are no current studies with which we can directly compare our study, as most studies have focused on postoperative outcomes.  We could not find any studies that aimed to associate frailty with intraoperative complications, although a number of studies have shown a relationship between intraoperative complications and postoperative outcomes. The need for intraoperative blood transfusion was shown to be associated with increased morbidity and mortality in anaemic surgical patients.  Intraoperative hypotension was demonstrated to be associated with myocardial injury, acute kidney injury and death. 
The present study identifies the intraoperative environment as one associated with a high risk of adverse outcomes in the frail. These increased adverse outcomes in the frail should ultimately emphasise the need for intraoperative vigilance and highlight the significance of frailty for all perioperative physicians, including the anaesthetist and not just the surgeon or intensivist.
Univariate logistic regression analysis showed that frailty was associated with an increased risk of intraoperative complications such as hypotension, desaturation, need for vasopressors and blood transfusion. Frailty was only significantly associated with desaturation and need for blood transfusion after multivariable logistic regression analysis, which is possibly a function of a small sample size, with fewer frail patients in the sample than anticipated. Patients with higher levels of frailty on the CFS were more likely to receive an ASA-PS score of [greater than or equal to] 3, which is in keeping with the findings of Robinson et at.  There was an association between frailty and postoperative destination, with the frail being more likely to go to a high-care unit, but there was no significant association between frailty and ICU admission owing to sample size. The clinical significance of this is unclear.
Although the prevalence of frailty is highest among the elderly, there is a noteworthy prevalence among younger age groups. This prevalence could increase with the increase in communicable and non-communicable diseases such as HIV, tuberculosis and traumatic injury.
Study strengths and limitations
A strength of the study is that it is the only study of its kind to be conducted in SA looking specifically at the effect of frailty on intraoperative complications. The study also included patients from a few of the largest hospitals in sub-Saharan Africa. The patients were largely from a low socioeconomic group and were representative of the patient population that is seen at most SA hospitals, as only a small proportion of the SA population can afford private hospital care.
Limitations include the fact that assessment of frailty in a perioperative setting is confounded by the effects of the presenting surgical condition. To minimise the effects of acute surgical conditions, only patients presenting for elective procedures were included.
Data were documented by different anaesthesia providers at different levels of anaesthetic training, and inter-observer reliability was not assessed. It was, however, important to demonstrate the utility of the CFS as a screening tool in a real-life clinical setting where it was intended for use by non-geriatricians. The CFS has shown good interobserver reliability in a previous study. 
This was a single-centre study with a relatively small sample size. Future larger studies may focus on the association between frailty and postoperative complications, morbidity, length of stay and mortality in an SA surgical population.
The prevalence of frailty was high among surgical patients. Consistent with other studies, frailty was associated with older age and multiple comorbidities. The association between frailty and intraoperative complications found in this study may indicate and help inform areas of further research.
Acknowledgements. We thank the departments of anaesthesiology at CHBAH, CMJAH and HJH, and Dr Innocent Maposa (biostatistician and senior lecturer in the University of the Witwatersrand School of Public Health) for his assistance with statistical calculations.
Author contributions. Research idea conception: GDN and NTNL-G; data collection: NTNL-G; proposal write-up: NTNL-G and GDN; article writeup: NTNL-G and GDN.
Conflicts of interest. None.
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Accepted 7 January 2019.
N T N Leopold-George, (1) MB ChB, DA (SA), FCA (SA); G D Nethathe, (2) MB ChB, DA (SA), FCA (SA), FANZCA, MMed (Anaes), Cert Critical Care (SA), PGDHSE
(1) Specialist Anaesthesiologist, Private Practice, Johannesburg, South Africa
(2) Division of Critical Care, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; and Department of Anaesthesia and Perioperative Medicine, Royal Brisbane and Women's Hospital, Brisbane, Australia
Corresponding author: N TNLeopold-George (email@example.com)
Table 1. Clinical Frailty Scale  1. Very fit Robust, active, energetic and motivated. Commonly exercise. Fittest for their age 2. Well No active disease symptoms, but less fit than category 1 3. Managing well Medical problems are well controlled, but not regularly active 4. Vulnerable Not dependent on others for daily help. Symptoms limit activities 5. Mildly frail More evident slowing. Need help in higher-order instrumental activities of daily living 6. Moderately frail Need help with all outside activities. Need help with instrumental and non- instrumental activities of daily living 7. Severely frail Completely dependent for personal care. Not at high risk of dying 8. Very severely frail Completely dependent. Approaching the end of life 9. Terminally ill Approaching the end of life, or life expectancy <6 months in those who are not otherwise evidently frail Table 2. Baseline characteristics of study participants Age (years), n (%) mean (SD) Overall 299 50.6 (15.8) Frail (CFS [greater 67 (22.4) 62.29 (15.45) than or equal to] 5) Non-frail 232 (77.6) 47.30 (14.32) Gender, n (%) Overall Women 156 (52.2) Men 143 (47.8) Frail (CFS [greater Women 39/67 (58.2) than or equal to] 5) Men 28/67 (41.8) Non-frail Women 117/232 (50.4) Men 115/232 (49.6) SD = standard deviation; CFS = Clinical Frailty Scale. Table 3. Effect of frailty on intraoperative complications Univariate logistic model to assess Factors effect of frailty OR 95% CI p-value Association between frailty and hypothermia Frailty No 1 Yes 2.08 0.99-4.36 0.051 Association between frailty and hypotension Frailty No 1 Yes 1.87 1.08-3.25 0.025 Age category (years) 18-34 1 35-49 3.04 1.23-7.49 0.01 50-64 4.38 1.77-10.81 0.001 [greater than or 8.0 3.06-20.87 0.000 equal to] 65 Sex Female 1 Male 0.81 0.509-1.300 0.394 Comorbidities, n 0 1 1 1.94 1.08-3.50 0.02 2 2.67 1.33-5.34 0.005 3 1.84 0.78-4.35 0.16 4 1.10 0.20-6.06 0.90 Association between frailty and desaturation Frailty No 1 Yes 3.79 1.36-10.54 0.01 Association between frailty and need for blood transfusion Frailty No 1 Yes 3.26 1.13-9.36 0.02 Association between frailty and arrhythmia Frailty No 1 Yes 2.34 0.38-14.35 0.35 Association between frailty and need for vasopressors Frailty No 1 Yes 2.81 1.60-4.91 0.00 Age category (years) 18-34 1 35-49 1.76 0.70-4.46 0.22 50-64 4.18 1.69-10.33 0.002 [greater than or 8 3.06-20.87 0.00 equal to] 65 Sex Female 1 Male 0.79 0.48-1.28 0.34 Comorbidities, n 0 1 1 1.63 0.87-3.84 0.12 2 3.33 1.63-3.77 0.00 3 3.02 1.27-7.14 0.01 4 1.38 0.25-7.61 0.71 Multivariable logistic regression controlling for other variables on effect of frailty Factors on complications aOR 95% CI p-value Association between frailty and hypothermia Frailty No Yes -- -- -- Association between frailty and hypotension Frailty No Yes 1.87 0.81-4.34 0.14 Age category (years) 18-34 35-49 0.80 0.24-2.68 0.72 50-64 1.76 0.54-5.70 0.34 [greater than or 1.65 0.44-6.16 0.45 equal to] 65 Sex Female Male -- -- -- Comorbidities, n 0 1 0.77 0.31-1.90 0.57 2 0.74 0.25-2.16 0.58 3 0.64 0.17-2.38 0.51 4 -- -- -- Association between frailty and desaturation Frailty No Yes 4.21 1.31-13.53 0.01 Association between frailty and need for blood transfusion Frailty No Yes 5.36 1.50-19.16 0.01 Association between frailty and arrhythmia Frailty No Yes -- -- -- Association between frailty and need for vasopressors Frailty No Yes 1.57 0.83-2.98 0.16 Age category (years) 18-34 35-49 1.67 0.64-4.31 0.28 50-64 3.24 1.24-8.47 0.01 [greater than or 6.23 2.17-17.82 0.001 equal to] 65 Sex Female Male 1.03 0.60-1.79 0.89 Comorbidities, n 0 1 1.05 0.52-2.09 0.88 2 1.74 0.77-3.91 0.18 3 1.74 0.66-4.58 0.26 4 0.41 0.06-2.58 0.34 OR = odds ratio; CI = confidence interval: aOR = adjusted odds ratio. Table 4. Distribution of frailty Frail, Non-frail, Total, Type of surgery n (%) n (%) n (%) Orthopaedic 44 (65.7) 56 (24.1) 100 (33.4) General surgery 9 (13.4) 73 (31.5) 82 (27.4) Urology 5 (7.5) 33 (14.2) 38 (12.7) Gynaecology 0 30 (12.9) 30 (10.0) Neurosurgery 3 (4.5) 9 (3.9) 12 (4.0) Ear, nose and throat 2 (3.0) 10 (4.3) 12 (4.0) Vascular 4 (6.0) 6 (2.6) 10 (3.3) Plastics 0 8 (3.4) 8 (2.7) Maxillofacial 0 6 (2.6) 6 (2.0) Not specified 0 1 (0.4) 1 (0.3) Total 67 (100) 232 (100) 299 (100)
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|Author:||Leopold-George, N.T.N.; Nethathe, G.D.|
|Publication:||SAMJ South African Medical Journal|
|Date:||Jul 1, 2019|
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