Multidimentional Preoperative Frailty Assessment and Postoperative Complication Risk in Egyptian Geriatric Patients Undergoing Elective Cardiac Surgery

Abstract

Background:

Frailty affects up to 51%of the geriatric population in developing countries which leads to increased morbidity and mortality.

Objective:

To determine the association between pre-operative frailty through multidimentional assessment score, and the incidence of post-operative complications and to validate Robinson score in geriatric Egyptian patients undergoing elective cardiac surgery.

Methods:

We recruited 180 elderly participants aged 60 years old and above, who underwent elective cardiac surgery. They were divided into frail, pre-frail, and non-frail groups after application of Robinson score (which includes cognitive and functional and fall risk assessment, number of comorbidities, and different laboratory data). Type and duration of operations and the presence and severity of complications at days 3 and 7 post-surgery, and the 30-day readmission rate were assessed.

Results:

Operation duration and the occurrence of postoperative complications at days 3 and 7 were lowest in non-frail and highest in the frail group (p < 0.001 for both). Length of hospital stay and 30-day readmission rate also increased in the frail group. A positive, moderate correlation between frailty and blood transfusion (r = 0.405) and functional dependence (r = 0.552) was found at day-3 post-surgery. Finally, logistic regression analysis identified a 6-fold increase in postoperative complications in the frail group (OR = 6).

Conclusion:

Preoperative frailty was associated with higher incidence of postoperative complications among geriatric patients undergoing elective cardiac surgery. Frailty assessment by Robinson score can be considered as an accurate tool to predict postoperative complications during preoperative assessment of elderly patients.

Keywords

Cardiac surgery frailty geriatric postoperative complications Robinson score

INTRODUCTION

Frailty describes a state of heightened vulnerability to poorly resolved homeostasis after experiencing a stressful event. This vulnerability is due to a major geriatric problem leading to a decline in the physiologic reserves of multiple inter-related systems such as the respiratory, cardiovascular, and renal systems [1]. Chronic inflammation, immune activation, endocrine system dysregulation, and impaired skeletal muscle function also have important roles in the development of frailty [2]. Frailty is a major problem in the aging population who are in particular at an increased the risk of delirium, falls, increased length of hospitalizations, and disability [3]. A bidirectional relationship between frailty and cardiac diseases has been described. Heart failure directly contributes to the development of frailty by reducing exercise capacity and skeletal muscle function. Also, patients with heart failure are more susceptible to falls and cognitive impairment than healthy individuals because of reduction of cardiac output and cerebral perfusion, which accelerates the development of frailty [4]. On the other hand, frailty might predispose to myocardial damage by decreasing resistance to stressors such as myocardial ischemia, volume and pressure overload, subsequently leading to de-compensated heart failure [5]. Older adults represent more than 35%of inpatient admission for surgical operations and the rate of surgery increases much with increasing age [6]. Frailty is becoming an independent predictor for adverse outcomes in surgical patients [7], increasing 30-day and one-year mortality in cardiac surgeries [8], so pre-operative assessment is of utmost importance among elderly population, particularly frail patients to identify such vulnerable patients and implement available techniques to reduce complications [9]. There is no gold standard measure for frailty. Fried criteria using a phenotype model [10] is one of the most common measures as well as a cumulative deficit model developed in the Canadian Study of Health and Aging (CSHA), the Frailty index [11]; however, these measures though valuable, are difficult to apply as a part of preoperative assessment because they take a long time. The most commonly used scores for perioperative assessment of cardiac surgeries are the European System for Cardiac Operative Risk Evaluation (EuroSCORE) and the Society of Thoracic Surgery (STS). Some studies found that EuroSCORE may over or underestimate mortality [12, 13]. Dewey et al. [14] described a strong overestimation of the perioperative risk even in high-risk patients when EuroSCORE was applied. On the contrary, STS may underestimate perioperative risk and both do not assess frailty [15]. The comprehensive assessment of frailty (CAF) [8] is a powerful assessment score; however, it is complex and takes a long time, and a simplified form became available instead.

Can we depend on a simple, multi-dimentional and reliable tool or score for preoperative assessment of frailty in geriatric Egyptian patients undergoing cardiac surgery?

METHODS

Study design

A prospective cohort study was performed recruiting180 elderly participants aged≥60 years old (137 males and 43 females) out of 650 patients who underwent elective cardiac surgery during the period between October 2018 and June 2019. Sample size was calculated according to the Research Ethics Committee, Faculty of Medicine, Ain-Shams Univer-sity, and all patients aged 60 years and above were recruited except those meeting exclusion criteria. Subjects were recruited from the inpatients wards of Ain Shams University Hospitals. They were evaluated by Robinson score (discussed in detail in assessment) [16] to assign them to one of the following three groups: Group 1 : 60 non-frail elderly subjects; Group 2 : 60 pre-frail elderly subjects; Group 3 : 60 frail elderly subjects.

Patients were excluded from the study if they met one of the following criteria: 1) Patients undergoing emergent or urgent operations (clinical conditions that mandate surgery within 12 or 12–72 h of admission respectively); 2) Patients with anemia due to acute blood loss (not an indicator of pre-operative frailty); 3) Neurologic or orthopedic problem or severe cognitive impairment interfering with the ability to perform the timed up and go (TUG) test or understand instructions.

Consent

Before assessment, informed consent was taken from all participants or the patient proxy (who knows well the patient’s medical condition and choices be-fore illness); in our study, we recruited 5 dementia patients and their sons, who lived with them, and obtained consent. The study was approved by the Research Ethics Committee, Faculty of Medicine, Ain-Shams University which operated according to guidelines of the International Council on Harmonization and the Islamic Organization for Medical Sciences, the United States Office for Human Res-earch Protections and the United States Code of Federal Regulations.

Assessment

Each study participant was assessed as detailed below:

History taking; including demography (age, smoking, and educational level) and the presence of different comorbidities such as hypertension, diabetes mellitus, heart failure, cerebrovadcular strokes, dementia, etc.

Multidimensional frailty assessment (Robinson score); This method assessed the presence of a series of 7 frailty characteristics before surgery and the scores for each characteristic were compiled to produce a single frailty score. Assessment took less than 5 min. Participants were then ass-igned one of the three groups based on their frailty burden into non-frail if they had 0-1 abnormal characteristics, pre-frail if they had 2 or 3 abnormal characteristics, and frail if they had 4–7 abnormal characteristics. It was performed as follows:

The TUG test: Participant wore their regular clothing. The test began by each participant sitting back in a standard arm chair and the examiner identified a 3 m markline on the floor in front of the chair. The participants were asked to stand up without support, walk in a straight line at a normal pace (they could use walking aids, if needed), turn and walk back to the chair to sit down again. The time was calculated (abnormal score defined as≥15 s) [17].

The Katz Index for activities of daily living (ADL): It addresses the ability of parti-cipants to conduct six basic daily activities (eating, dressing, continence, toileting, bat-hing, and transfer) independently [18] (abnormal score defined as dependence in one or more ADL).

The Mini-Cog test: This cognitive screening test required the participant to listen to and to remember 3 unrelated words then repeat them. The participant was asked then to draw the face of a clock and put the hands on the clock to represent a particular time, typically 10 minutes past 11. Finally the participant recalled the initial three memorized words. The clock drawing is scored either normal (2 points) or abnormal (0 points) and each recalled word is allocated 1 point, generating a maximum possible score out of five [19] (abnormal cognitive levels were defined as a Mini-Cog score≤3).

The Charlson Comorbidity Index: This in-dex categorizes patient comorbidities. Each comorbidity category has a weight (from 1 to 6), and the sum of all the weights provides the single comorbidity score for a patient. A score of zero indicates that no comorbidities were found [20] (a Charlson Comorbidity Index score≥3 indicates abnormal chronic disease burden).

A venous blood sample (5 ml) was drawn aseptically from each subject to measure hemoglobin level (anemia defined as hematocrit level < 35%).

A venous blood sample (5 ml) was drawn aseptically from each subject to measure serum albumin (poor nutrition was defined as a serum albumin level below 3.4 g/dL).

Participants were asked how many times they had fallen in the six-months prior to surgery (a positive score for falling was defined as≥1 fall in the six-months prior to surgery).

Documentation of type and duration of sur-gery; Data was collected by the first author about cardiac surgeries performed during the study period. Surgery duration in minutes and type of operation was documented. Surgeries were div-ided either coronary artery bypass graft (CABG) alone, or valve replacement alone or combination of both.

Presence of post-operative complications; The presence of postoperative complications was assessed twice: on day 3 and day 7 after surgery.

Severity of post-operative complications; It was assessed using the contracted accordion severity grading system form of surgical complications [21] which defines four different levels of severity:

Mild Complications: Required only minor invasive procedures such as insertion of intravenous lines, urinary catheters, and nasogastric tubes, and drainage of wound infections. Physiotherapy and the following drugs were allowed: anti-emetics, antipyretics, analgesics.

Moderate Complications: Required pharmacologic treatment with drugs other than those allowed for minor complications like antibiotics. Blood transfusions and total parenteral nutrition were also included.

Severe Complications: All complications requiring endoscopic or interventional radiologic procedures or re-operation as well as complications resulting in failure of one or more organ systems.

Death: No deaths occurred in the study during the period of follow up.

Calculation of the length of hospital stay and incidence of 30-day readmission; The precise numbers of days were collected together with the number of subjects readmitted after 30 days.

Statistical methods

The collected data was cross-checked, coded, tabulated using statistical package for social science (SPSS 20). p-values indicated the level of significance with, p > 0.05 was considered being non-significant (NS), p < 0.05 being significant (S), p < 0.01being highly significant (HS).

Descriptive statistics

The mean and standard deviation (±SD) were used for parametric numerical data. While the median and interquartile range (IQR) were for non-parametric numerical data. Frequencies and percentages were used of non-numerical data.

Analytical statistics

ANOVA test was used to assess the statistical significance of the difference between more than two study group means. Post Hoc Bonferroni Test was used for comparisons of all possible pairs of group means. Chi-Square test was used to examine the relationship between two qualitative variables. Fisher’s exact test was used to examine the relationship between two qualitative variables when the expected count is less < 5 in > 20%of cells. The Kruskal-Wallis test was used to assess the statistical significance of the difference between more than two study group ordinal variables. Correlation analysis (using Spearman’s rho method) was used to assess the strength of association between two quantitative variables. The correlation coefficient denoted symbolically “r” defines the strength and direction of the linear relationship between two variables. An r≤0.19 was regarded as very weak correlation; 0.2≤r≤0.39 was regarded as weak correlation, 0.40≤r≤0.59 indicated moderate correlation, 0.6≤r≤0.79 indicated strong correlation, and r≥0.8 indicated very strong correlation.

RESULTS

Patient demographics and comorbidities

There is a gradual increase in age from the non-frail, to pre-frail and frail groups (63.1, 64.73, and 66.2, respectively) (p < 0.001) as shown in Table 1. The majority of the study groups were males (number = 137). Education and smoking showed no significant difference between groups. A higher proportion of patients in the frail group suffered from congestive heart failure compared to pre-frail and non- frail group (p 0.001), and diabetes mellitus with end organ damage was more prevalent in the frail and pre-frail group compared to the non-frail group (p 0.02). No difference was found between the three groups regarding other comorbidities.

Table 1
Comparison between study groups regarding demographic data and comorbidities

Frailty score Test of significance

non-frail“0-1” n = 60 pre-frail“2-3” n = 60 frail“≥4” n = 60

Mean/number (N) (SD/%) Mean/N (SD/%) Mean/N (SD/%) p

Gender Male (N/%) 40 (66.7%) 48 (80%) 49 (81.7%) 0.107 ^C

Female (N/%) 20 (33.3%) 12 (20%) 11 (18.3%)

Age (mean/SD) 63.1 (3) 64.73 (2.85) 66.2 (2.08) < 0.001^A

Education (N/%) Illiterate 8 (13.3%) 2 (3.3%) 0 (0%) 0.073 ^F

Can read and write 5 (8.3%) 7 (11.7%) 5 (8.3%)

Below high school 13 (21.7%) 13 (21.7%) 8 (13.3%)

High school 17 (28.3%) 18 (30%) 20 (33.3%)

University or higher 17 (28.3%) 20 (33.3%) 27 (45%)

Smoking 18 (30%) 24 (40%) 29 (48.3%) 0.12 ^C

Comorbidities

MI 14 (23.3%) 19 (31.7%) 20 (33.3%) 0.436

CHF 1 (1.7%) 9 (15%) 15 (25%) 0.001

CVA or TIA 4 (6.7%) 12 (20%) 9 (15%) 0.103

pulmonary ds or asthma 17 (28.3%) 22 (36.7%) 22 (36.7%) 0.538

DM only 21 (35%) 24 (40%) 17 (28.3%) 0.402

DM &end organ damage 4 (6.7%) 13 (21.7%) 15 (25%) 0.02

renal disease 3 (5%) 5 (8.3%) 6 (10%) 0.69^F

mild liver disease 2 (3.3%) 5 (8.3%) 7 (11.7%) 0.268^F

peptic ulcer 7 (11.7%) 4 (6.7%) 7 (11.7%) 0.574

dementia 0 (0%) 2 (3.3%) 3 (5%) 0.373^F

Rheumatic disease 5 (8.3%) 5 (8.3%) 5 (8.3%) 1.00

hypertension 47 (78.3%) 48 (80%) 52 (86.7%) 0.456

depression 2 (3.3%) 1 (1.7%) 5 (8.3%) 0.28^F

	Frailty score		Test of significance
		Mean/number (N) (SD/%)	Mean/N (SD/%)	Mean/N (SD/%)	p
Gender	Male (N/%)	40 (66.7%)	48 (80%)	49 (81.7%)	0.107 ^C
	Female (N/%)	20 (33.3%)	12 (20%)	11 (18.3%)
Age (mean/SD)		63.1 (3)	64.73 (2.85)	66.2 (2.08)	< 0.001^A
Education (N/%)	Illiterate	8 (13.3%)	2 (3.3%)	0 (0%)	0.073 ^F
Can read and write	5 (8.3%)	7 (11.7%)	5 (8.3%)
Below high school	13 (21.7%)	13 (21.7%)	8 (13.3%)
High school	17 (28.3%)	18 (30%)	20 (33.3%)
University or higher	17 (28.3%)	20 (33.3%)	27 (45%)
Smoking		18 (30%)	24 (40%)	29 (48.3%)	0.12 ^C
Comorbidities
MI		14 (23.3%)	19 (31.7%)	20 (33.3%)	0.436
CHF		1 (1.7%)	9 (15%)	15 (25%)	0.001
CVA or TIA		4 (6.7%)	12 (20%)	9 (15%)	0.103
pulmonary ds or asthma		17 (28.3%)	22 (36.7%)	22 (36.7%)	0.538
DM only		21 (35%)	24 (40%)	17 (28.3%)	0.402
DM &end organ damage		4 (6.7%)	13 (21.7%)	15 (25%)	0.02
renal disease		3 (5%)	5 (8.3%)	6 (10%)	0.69^F
mild liver disease		2 (3.3%)	5 (8.3%)	7 (11.7%)	0.268^F
peptic ulcer		7 (11.7%)	4 (6.7%)	7 (11.7%)	0.574
dementia		0 (0%)	2 (3.3%)	3 (5%)	0.373^F
Rheumatic disease		5 (8.3%)	5 (8.3%)	5 (8.3%)	1.00
hypertension		47 (78.3%)	48 (80%)	52 (86.7%)	0.456
depression		2 (3.3%)	1 (1.7%)	5 (8.3%)	0.28^F

MI, myocardial infarction; CHF, congestive heart failure; CVA, cerebrovascular accident; TIA, transient ischemic attack; DM, diabetes mellitus; ^CChi-Square test of significance; ^FFisher’s Exact test of significance; ^AANOVA test ^*Post-hoc Bonferroni test Significant: Between all Groups.

Surgical procedures, durations, and complications

Table 2 shows that the duration of surgical procedures and the occurrence of postoperative complications at days 3 and 7 were lowest in non-frail and highest in the frail group with highly significant difference (p < 0.001 for all comparisons). Although the duration of intensive care unit (ICU) stay and hospital stay show minimal difference between the 3 groups, yet they were still both highly significant when comparing the frail group to pre-frail and non-frail groups (p < 0.001). Finally the 30-day readmission also significantly increased in the frail group and pre-frail compared to non-frail group (p = 0.003).

Table 2

Comparison between study groups regarding operations, ICU, hospital stay and occurrence of postoperative complications

		Frailty score			Test of significance
		non-frail“0-1” n = 60	pre-frail“2-3” n = 60	frail“≥4” n = 60
		Mean/N/Median (SD/%/ interquartile range)	Mean/N/Median (SD/%/ interquartile range)	Mean/N/Median (SD/%/ interquartile range)	p
type of operation	CABG	47 (78.3%)	43 (71.7%)	39 (65%)	0.079^F
	CABG + valve replacement	0 (0%)	4 (6.7%)	7 (11.7%)
	valve replacement	13 (21.7%)	13 (21.7%)	14 (23.3%)
operation duration in min		282.25 (47.91)	296.17 (38.40)	344.58 (51.16)	< 0.001^A
ICU duration in days (range)		2.00 (2 - 2)	2.00 (2 - 3)	3.00 (2 –5)	< 0.001^K
Postoperative complications (total 107 patients)		18 (16.8%)	37 (34.6%)	52 (48.6%)	< 0.001
Postoperative Comp. on Day 3 (total 94 patients)		13 (13.8%)	31 (33%)	50 (53.2%)	< 0.001
Postoperative Comp. on Day 7 (total 54 patients)		7 (13%)	15 (27.8%)	32 (59.2%)	< 0.001
Number of deaths		0 (0%)	0 (0%)	0 (0%)	-
length of hospital stay in days (range)		7 (6 - 7)	7 (8 - 8)	8 (7 –10)	< 0.001^K
30 day readmission		4 (6.7%)	14 (23.3%)	18 (31%)	0.003^C

CABG, coronary artery bypass graft; ICU, intensive care unit; ^FFisher’s Exact test of significance; ^KKruskal-Wallis H test of significance; ^AANOVA test ^*Post-hoc Bonferroni test Significant at: frail group versus (Non-Frail and pre. Frail groups); ^CChi-Square test of significance.

The incidence of postoperative complications in frail patients is associated with five key characteristics of frailty but not surgery type

Each of the seven characteristics of Robinson score was assessed (as shown in Table 3) regarding postoperative complications to detect the infl-uential characteristics. Postoperative complications increased significantly with increasing age (p < 0.001) and were associated with abnormalities in five of the seven characteristics of frailty which includes the Charlson Comorbidity Index score (p < 0.001), Katz score for functional assessment (p = 0.02), the Mini-cog score (p = 0.001), TUG test (p < 0.001), and abnormalities in albumin level (p = 0.004) (falls during the previous 6 months and hemoglobin score were not associated with such complications). Although type of surgery was not associated with complications, longer operative duration and longer stay in the ICU were associated with such complications (p = 0.002,<0.001) respectively.

Table 3

Association between demography, characteristics of frailty, operation and postoperative complications

		Postoperative complications
		No	Yes
		Mean/N (SD/%)	Mean/N (SD/%)	p
Gender	Male	48 (65.8%)	89 (83.2%)	0.007
	Female	25 (34.2%)	18 (16.8%)
Age		63.73 (2.78)	65.33 (2.9)	< 0.001^T
Charlson Comorbidity index	Normal	38 (63.3%)	12 (20.0%)	< 0.001
	Abnormal “≥3”	22 (36.7%)	48 (80.0%)
Charlson index score number		2.58 (0.98)	3.55 (1.21)	< 0.001^T
Katz score	Normal	70 (95.9%)	91 (85%)	0.02
	Abnormal “dependent in “≥1”	3 (4.1%)	16 (15%)
fall in the past 6 months		13 (17.8%)	32 (29.9%)	0.066
miniCog result		3.19 (0.78)	2.73 (0.98)	0.001^T
miniCog score	Normal	64 (87.7%)	70 (65.4%)	0.001
	Abnormal	9 (12.3%)	37 (34.6%)
TUG in seconds		13.81 (2.61)	15.3 (3.25)	0.001^T
TUG score	Normal	54 (74%)	51 (47.7%)	< 0.001
	Abnormal “≥15”	19 (26%)	56 (52.3%)
Hemoglobin level (gm/dl)		12.7 (1.5)	12.16 (1.63)	0.024^T
hemoglobin score	Normal	50 (68.5%)	62 (57.9%)	0.099 ^F
	Anemic “hct < 35%”	23 (31.5%)	45 (42.1%)
serum albumin level (gm/dl)		3.99 (0.5)	3.74 (0.54)	0.002^T
albumin score	Normal	62 (84.9%)	70 (65.4%)	0.004^C
	Abnormal “≤3.4”	11 (15.1%)	37 (34.6%)
type of operation	CABG	57 (78.1%)	72 (67.3%)	0.068
	CABG+valve replacement	1 (1.4%)	10 (9.3%)
	Valve replacement	15 (20.5%)	25 (23.4%)
Operation duration in min		293.08 (50.07)	317.62 (53.09)	0.002^T
ICU duration in days		2 (0)	3.5 (2.03)	< 0.001^T

Cognitive impairment and postoperative complications

Patients developing postoperative complications had lower baseline Mini-cog score as shown in Table 3 (p = 0.001). Also 34.6%of patients with postoperative complications had abnormal Mini-cog score while 12.3%of those without complications had abnormal score (p = 0.001).

Correlation between frailty and postoperative complications

Table 4 shows no correlation between frailty score and severity of postoperative complications except for requirements for blood transfusion and functional dependence on day 3 (r = 0.405, r = 0.552) respectively, which had a moderate correlation. Logistic regression analysis in Table 5 was done to find the most powerful predictor for postoperative complications; including significant factors in previous tables. It shows 6-fold increase in postoperative complications in the frail group (OR = 6), a 3-fold increase in postoperative complications in patients with an abnormal Charlson Comorbidity Index score (OR = 3.03) and 4.45-fold increase in postoperative complications with prolonged hospital stay (OR = 4.45) and no effect for age was found.

Table 4

Correlation between frailty and the severity of Post-operative complications on day 3 and 7

Postoperative complications	Frailty score	Frailty score
	Spearman’s rho Day 3		Spearman’s rho Day 7
DVT	0.190	0.001
MI	0.107	–
Arrhythmia	0.156	0.131
Respiratory complication	0.235	0.003
Wound complication	0.136	0.186
Systemic sepsis	0.211	0.092
Delirium	0.352	0.131
Blood transfusion	0.405	–
CVA or coma > 24 h	0.175	0.139
Pain	–0.001	0.001
IV support	0.208	–
	PPV	NPV	Sensitivity	Specificity
Occurrence of complications	48.6%	54.2%	86.7%	89%
Complications at day 3	53.2%	88.4%	83.3%	63.3%
Complications at day 7	59.3%	77.8%	53.3%	81.7%

DVT, deep venous thrombosis; MI, myocardial infarction; CVA, cerebrovascular accident; IV, intravenous; PPV, positive predictive value; NPV, negative predictive value. Severity of postoperative complication was assessed according to the contracted accordion severity grading system form of surgical complications.

Table 5

Logistic regression of postoperative complications

	p	(Odds Ratio) OR	95%C.I. for OR
Length of hospital stay	< 0.001	4.45	2.32 –8.54
Charlson Comorbidity	0.009	3.03	1.32 –6.96
Index score (ref. Normal score)
Frailty (ref. Normal or prefrail)	0.001	6.00	2.03 –17.76
Age	0.673	1.03	0.89 –1.19

DISCUSSION

More than half of cardiac surgeries are being per-formed in patients aged 75 years and older with elderly patients showing increased risk of prolonged hospitalization, postoperative complications, and mortality [22]. Thus, a comprehensive preoperative assessment is essential to determine the relative risk and benefit of the surgical intervention in such patients [15].

Frailty assessments have been developed as tools to identify the patient physiologic functioning capacity; which is of utmost importance before undergoing major surgeries [23]. The study examined the effect of frailty on postoperative outcomes after cardiac surgery and used Robinson score [16], which is an easy and reliable measure that can be incorporated into perioperative assessment of elderly patients afterwards.

Postoperative complications of cardiac surgeries increased in frail patients diagnosed by Robinson score exceeding healthy and prefrail population with good sensitivity and specificity. The positive predictive value (PPV) for complications at days 3 and 7 is more than 50%with negative predictive value (NPV) reaching up to 88%. Our study findings agreed with those of the systemic review performed by Rocha et al., 2017 [24] who studied the ability of six different frailty scales (FI, clinical frailty scale, comprehensive geriatric assessment, Robinson score, Marshall score, and TUG) to predict postoperative complications. The review included 15 studies and combined 3743 patients; eight studies reported association between frailty and postoperative complications. Multi-domain frailty scales, including Robinson score, were the best in detection of complications and mortality followed by frailty phenotype (FI) and then the single frailty scale (TUG). The Robinson score is an easy, reliable, and valid score to assess preoperative frailty in elderly Egyptian patients undergoing cardiac surgery and also to detect postoperative complications.

Comparison between the three studied groups shows increasing age, higher duration of operation, longer ICU and hospital stays, and a higher 30-day readmission rate in the frail group compared to the other two groups. Similarly, Dasgupta et al. [25] found that frail older adults, with mean age 77, undergoing elective orthopedic surgery were more likely to have post-operative complications, an increased length of hospitalization compared to non-frail pat-ients. Similar findings were also reported by Makary et al. [26] who studied the effect of frailty on surgical outcome in patients aged≥65 years undergoing elective surgeries.

We found that postoperative complications were significantly associated with preoperative increasing age, male sex, smoking status, frailty, functional dependence in ADLs, decreased physical performance, and increased Charlson Comorbidity Index score. Conversely, postoperative complications were not associated with preoperative incidence of falls or abnormal hemoglobin score. Additionally we also found higher incidence of postoperative complications in patients who underwent a prolonged operative duration, had a prolonged ICU stay, and /or increased length of hospital stay with subsequent increased 30-day readmission rate. The incidence of postoperative complications was not associated with the type of surgery performed. Similar findings were reported by Fukuse et al. [27] who studied patients≥60 years of age undergoing thoracic surgery and found that preoperative dependence in ADLs and cognition were associated with higher postoperative complications.

Postoperative complications were also associated with lower baseline cognitive functions. Culley and collegues also performed Mini-cog test for 211 patients with elective hip or knee arthroplasty, aged≥65 years and found that patients with a Mini-Cog score of≤2 had a higher incidence of postoperative delirium and prolonged hospital stay than patients with higher scores [28]. Robinson and collegues studied elderly patients undergoing cardiothoracic, urology, or vascular surgery and reported that cognitive impairment was associated with the occurrence of one or more postoperative complications, prolonged hospital stay, and 6-month mortality [29]. Therefore, the inclusion of routine preoperative assessment of cognitive functions in elderly patients is important for predicting the prognosis of patients after surgery.

As we found significant correlations between postoperative complications and various factors in the current study, we performed logistic regression to highlight the most influential factors to predict postoperative complications in our patient cohorts. Through logistic regression, postoperative complications showed 6-fold increase in frail subjects, 3-fold increase in subjects with abnormal Charlson Comorbidity Index scores, and 4.45-fold increase in participants who endured prolonged hospital stays. Age was not associated with increase risk of postoperative complications. Frailty had the most powerful effect on postoperative complications.

4.1 Limitations of the study

The sample may not be representative of all Egyptians. It is a monocentric study and further multicenter studies are needed with application of Robinson score in different types of surgeries in geriatric patients. Recruitment took several months because elective cardiac surgeries are generally performed in younger population who are healthier with less comorbidies. Also young old people only were represented because cardiac surgeries in Egypt are difficult in older population, with increasing rates of morbidity and mortality.

CONCLUSION

We report that preoperative frailty is associated with longer operative duration, longer duration of ICU stay, longer length of hospital stay, higher 30-day readmission rate, and higher incidence of postoperative complications in elders undergoing elective cardiac surgery. Preoperative cognitive impairment is associated also with postoperative complications. Frailty assessment (including cognitive assessment) should be applied as an essential part of preoperative assessment in geriatric patients. Robinson score can be considered as an accurate tool implemented in preoperative assessment of such patients.

DISCLOSURE STATEMENT

Authors’ disclosures available online (https://www.j-alz.com/manuscript-disclosures/20-1479r1).

References

Strandberg

, Sirola

, Pitkala

, Tilvis

, Strandberg

, Stenholm

(2012) Association of midlife obesity and cardiovascular risk with old age frailty: A 26-year follow-up of initially healthy men. Int J Obes (Lond) 36, 1153–1157.

Yao

, Li

, Leng

(2011) Inflammation and immune system alterations in frailty. Clin Geriatr Med 27, 79–87.

Eeles

, White

, O’Mahony

, Bayer

, Hubbard

(2012) The impact of frailty and delirium on mortality in older inpatients. Age Ageing 41, 412–416.

Singh

, Stewart

, White

(2014) Importance of frailty in patients with cardiovascular disease. Eur Heart J 35, 1726–1731.

Afilalo

, Alexander

, Mack

, Maurer

, Green

, Allen

, Popma

, Ferrucci

, Forman

(2014) Frailty assessment in the cardiovascular care of older adults. J Am Coll Cardiol 63, 747–762.

Hall

, DeFrances

, Williams

, Golosinskiy

, Schwartzman

(2010) National Hospital Discharge Survey: 2007 summary. Natl Health Stat Report, pp. 1-20, 24.

Partridge

, Harari

, Dhesi

(2012) Frailty in the older surgical patient: A review. Age Aging 41, 142–147.

Sündermann

, Dademasch

, Rastan

, Praetorius

, Rodriguez

, Walther

, Mohr

, Falk

(2011) One-year follow-up of patients undergoing elective cardiac surgery assessed with the Comprehensive Assessment of Frailty test and its simplified form. Interact Cardiovasc Thorac Surg 13, 119–123; discussion 123.

Sieber

, Barnett

(2011) Preventing postoperative complications in the elderly. Anesthesiol Clin 29, 83–97.

10.

Fried

, Tangen

, Walston

, Newman

, Hirsch

, Gottdiener

, Seeman

, Tracy

, Kop

, Burke

, McBurnie

; Cardiovascular Health Study Collaborative Research Group (2001) Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci 56, M146–M156.

11.

Rockwood

, Mitnitski

(2011) Frailty defined by deficit accumulation and geriatric medicine defined by frailty. Clin Geriatr Med 27, 17–26.

12.

Gogbashian

, Sedrakyan

, Treasure

(2004) EuroSCORE: A systematic review of international performance. Eur J Cardiothorac Surg 25, 695–700.

13.

Nashef

, Roques

, Hammill

, Peterson

, Michel

, Grover

, Wyse

, Ferguson

(2002) Validation of European System for Cardiac Operative Risk Evaluation (EuroSCORE) in North American cardiac surgery. Eur J Cardiothorac Surg 22, 101–105.

14.

Dewey

, Brown

, Ryan

, Herbert

, Prince

, Mack

(2008) Reliability of risk algorithms in predicting early and late operative outcomes in high-risk patients undergoing aortic valve replacement. J Thorac Cardiovasc Surg 135, 180–187.

15.

Wendt

, Osswald

, Kayser

, Thielmann

, Tossios

, Massoudy

, Kamler

, Jakob

(2009) Society of Thoracic Surgeons score is superior to the EuroSCORE determining mortality in high risk patients undergoing isolated aortic valve replacement. Ann Thorac Surg 88, 468–474; discussion 474–465.

16.

Robinson

, Wu

, Pointer

, Dunn

, Cleveland

JC Jr

, Moss

(2013) Simple frailty score predicts postoperative complications across surgical specialties. Am J Surg 206, 544–550.

17.

Podsiadlo

, Richardson

(1991) The timed “Up & Go”: A test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 39, 142–148.

18.

Katz

(1983) Assessing self-maintenance: Activities of daily living, mobility and instrumental activities of daily living. J Am Geriatr Soc 31, 721–726.

19.

Borson

, Scanlan

, Chen

, Ganguli

(2003) The Mini-Cog as a screen for dementia: Validation in a population-based sample. J Am Geriatr Soc 51, 1451–1454.

20.

Charlson

, Pompei

, Ales

, MacKenzie

(1987) A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 40, 373–383.

21.

Strasberg

, Linehan

, Hawkins

(2009) The accordion severity grading system of surgical complications. Ann Surg 250, 177–186.

22.

Green

, Woglom

, Genereux

, Daneault

, Paradis

, Schnell

, Hawkey

, Maurer

, Kirtane

, Kodali

, Moses

, Leon

, Smith

, Williams

(2012) The impact of frailty status on survival after transcatheter aortic valve replacement in older adults with severe aortic stenosis: A single-center experience. JACC Cardiovasc Interv 5, 974–981.

23.

Clegg

, Young

, Iliffe

, Rikkert

, Rockwood

(2013) Frailty in elderly people. Lancet 381, 752–762.

24.

Rocha

, Marmelo

, Leite-Moreira

, Moreira-Gonçalves

(2017) Clinical utility of frailty scales for the prediction of postoperative complications: Systematic review and meta-analysis. Rev Port Cir Cardiotorac Vasc 24, 132.

25.

Dasgupta

, Rolfson

, Stolee

, Borrie

, Speechley

(2009) Frailty is associated with postoperative complications in older adults with medical problems. Arch Gerontol Geriatr 48, 78–83.

26.

Makary

, Segev

, Pronovost

, Syin

, Bandeen-Roche

, Patel

, Takenaga

, Devgan

, Holzmueller

, Tian

, Fried

(2010) Frailty as a predictor of surgical outcomes in older patients. J Am Coll Surg 210, 901–908.

27.

Fukuse

, Satoda

, Hijiya

, Fujinaga

(2005) Importance of a comprehensive geriatric assessment in prediction of complications following thoracic surgery in elderly patients. Chest 127, 886–891.

28.

Culley

, Flaherty

, Fahey

, Rudolph

, Javedan

, Huang

, Wright

, Bader

, Hyman

, Blacker

, Crosby

(2017) Poor performance on a preoperative cognitive screening test predicts postoperative complications in older orthopedic surgical patients. Anesthesiology 127, 765–774.

29.

Robinson

, Wu

, Pointer

, Dunn

, Moss

(2012) Preoperative cognitive dysfunction is related to adverse postoperative outcomes in the elderly. J Am Coll Surg 215, 12–17.