Frailty Index and its association with the onset of postoperative delirium in older adults undergoing elective surgery

doi:10.21203/rs.3.rs-1991044/v1

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Research Article

Frailty Index and its association with the onset of postoperative delirium in older adults undergoing elective surgery

https://doi.org/10.21203/rs.3.rs-1991044/v1

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Journal Publication

published 11 Feb, 2023

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Background

The association of frailty with postoperative delilrium (POD) is not well known. We therefore analysed this association in older patients undergoing elective surgery.

Methods

Preoperative data was used to build a 30-item frailty index (FI) for participants of the PAWEL-study. Delirium was defined by a combination of I-CAM and chart review. Using logistic regressions models we analysed the association between frailty and POD adjusting for age, sex, smoking, alcohol consumption, education and type of surgery.

Results

Among 701 participants (mean age 77.1, 52.4% male) median FI was 0.27 (Q1 0.20| Q3 0.34), with 528 (75.3%) frail participants (FI ≥ 0.2). Higher median FI were seen in orthopedic than cardiac surgery patients (0.28 versus 0.23), and in women (0.28 versus 0.25 in men). Frail participants showed a higher POD incidence proportion (25.4% versus 17.9% in non-frail). An increased odds for POD was observed in frail versus non-frail participants (OR 2.14 [95% CI 1.33, 3.44], c-statistic 0.71). A 0.1 increment of FI was associated with OR 1.57 [95% CI 1.30, 1.90] (c-statistic 0.72) for POD. No interaction with sex or type of surgery was detected. Adding timed-up-and-go-test and handgrip strength to the FI did not improve discrimination.

Conclusion

Our data showed a significant association between frailty defined through a 30-item FI and POD among older adults undergoing elective surgery. Adding functional measures to the FI did not improve discrimination. Hence, our preoperative 30-item FI can help to identify patients with increased odds for POD.

Trial registration:

PAWEL and PAWEL-R (sub-) study were registered on the German Clinical Trials Register (number DRKS00013311 and DRKS00012797).

frailty

postoperative delirium

elective surgery

Delirium is an acute and fluctuating disorder characterized by impaired alertness and reduced consciousness, also associated with worsening of cognitive functions such as attention, memory, orientation, language and awareness(1). In hospital delirium incidence rates range from 10–82%, with highest rates on ICU and postoperative wards(2). Incident delirium is a common postoperative complication that occurs in up to 46% of cardiac(2), and up to 24% of orthopedic/ visceral surgical patients(3). Delirium has been associated with different adverse clinical outcomes, e.g. postoperative cognitive decline(4), extended length of stay (LOS), institutionalization and a higher mortality rate(5).

Risk factors for delirium are multidimensional and can be classified into predisposing non-modifiable ones like age, stroke or preexisting dementia, and modifiable ones such as drug side effects or environmental factors^,(2, 6, 7). Thus, it is excepted that by addressing modifiable risk factors clinical practitioners especially in surgical departments would be able to optimize the clinical management for those patients identified with high risk of developing postoperative delirium (POD). In a systematic review of Persico et al.(8) frailty has been identified as another predisposing factor for delirium. Frailty is defined as a clinical syndrome with increased vulnerability towards an external stressor leading to increased risk of adverse outcomes(9). However, the operational definition of frailty remains controversial and different explanatory models have been proposed. The so-called model of deficit accumulation allows the calculation of a frailty index (FI), which represents the individual proportion of potential, multidimensional deficits(10).

The association between frailty and delirium has been evaluated mostly in patients admitted to general medicine wards or in mixed populations(11, 12). Literature on patients admitted only to surgical departments is scarce and based mainly on small study populations(13, 14). Based on data from the control phase before intervention in the PAWEL study (“Patientensicherheit, Wirtschaftlichkeit und Lebensqualität” [transl.: “Patient safety, cost-effectiveness, and quality of life”]: reduction of delirium risk and postoperative cognitive dysfunction after elective procedures in older adults)(15), weaim i) to develop a FI, based on the accumulation of deficits, with information collected at admission, and ii) to analyse the association between this FI and the onset of POD in a population of older adults undergoing elective surgery.

Study Population

Briefly, the PAWEL study was conducted in five medical centers in the southwest of Germany between July 2017 and April 2019 with the aim to evaluate an individualized, multi-professional and multimodal delirium and postoperative cognitive dysfunction (POCD) prevention program. Study setting and design have been described in detail in a previous paper(15). A total of 1470 patients (≥ 70 years) who had been admitted for elective surgery with a minimum length of 60 minutes were recruited. For this analysis only data from the control group, defined as PAWEL-R (n = 899 with no delirium intervention), was considered to prevent potential bias due to intervention. Due to missing data for any of the 30 variables considered for the FI, 198 participants had to be excluded. In total, 701 participants could be considered for further analysis. The majority of this group was recruited in orthopedic/ other surgical wards (n = 405, 57.8%), while 296 (42.2%) patients underwent cardiovascular surgery.

Ethical approval for the PAWEL Study was obtained from the Ethics Commission of the Faculty of Medicine of the Eberhard-Karls University and University Hospital Tuebingen (517/2017BO1), Ulm University (425/17) and the Ethics Commission of the University of Potsdam (38/2017). All participants gave written informed consent. PAWEL and PAWEL-R (sub-) study were registered on the German Clinical Trials Register (number DRKS00013311 and DRKS00012797, first regristration November 23rd 2017).

Frailty

A 30-item FI based on the accumulation of deficits was constructed based on Searle et al(16). using preoperatively gathered data (see Table 1). Relevant information for the FI represented following domains: sociodemographic, physical function, sensory perception, comorbidities, self-estimated health/emotions, activities of daily living (ADLs), anthropometrics and laboratory measurements. Considering that the study targeted community-dwelling older adults admitted for an elective surgery, a cut-off of ≥ 0.2 was chosen to identify frail subjects(17).

Table 1

Variables included in the 30-item Frailty Index.
Item	Variable	Coding
Social situation
1	Living alone	0 pt. = no, 1 pt. = yes
Physical function
2	Falls in last 3 months	0 pt. = no, 1 pt. = yes
3	Mobility (MNA)	0 pt. = goes out, 0.5 pt. = able to get out of bed/chair but does not go out, 1 pt. = bed or chair bound
Sensory tests
4	Visual acuity test	0 pt. = correct results, 1 pt. = no correct results
5	Whisper test	0 pt. = both sides correct, 0.5 pt. = one side correct, 1 pt. = none correct
Comorbidities – case history
6	Arterial hypertension	0 pt. = no, 1 pt. = yes
7	Cardiovascular (MI, heart failure, circulatory disorder in feet, arrhythmia, stroke, cerebral hemorrhage, hemiplegic)	0 pt. = no, 1 pt. = yes
8	Neurological (M. Parkinson, seizures)	0 pt. = no, 1 pt. = yes
9	Chronic lung disease	0 pt. = no, 1 pt. = yes
10	Dementia	0 pt. = no, 1 pt. = yes
11	Gastrointestinal ulcer	0 pt. = no, 1 pt. = yes
12	Osteoarthritis	0 pt. = no, 0.5 pt. = hand/other joint, 1 pt. = hand and other joint
13	Urinary incontinence	0 pt. = continent or independent and successful management of urine catheter 0.5 pt. = occasional accident 1 pt. = incontinent (catheterized, unable to manage alone)
14	Tumor	0 pt. = no, 1 pt. = yes
15	Diabetes mellitus	0 pt. = no, 0.5 pt. = without EOD, 1 pt. = EOD
16	Liver disease	0 pt. = no, 0.5 pt. = light, 1 pt. = moderate to severe
Self-estimated health/emotions
17	Subjective memory impairment	0 pt. = no, 1 pt. = yes
18	General health (SF-12)	0 pt. = excellent to good, 0.5 pt. = fair, 1 pt. = poor
19	Moderate activities (SF-12)	0 pt. = not limited at all, 0.5 pt. = limited a little, 1 pt. = limited a lot
20	Climbing several flights of stairs (SF-12)	0 pt. = not limited at all, 0.5 pt. = limited a little, 1 pt. = limited a lot
21	Interference by pain (SF-12)	0 pt. = not at all, 0.25 pt. = a little bit, 0.5 pt. = moderately, 0.75 pt. = quite a bit, 1 pt. = extremely
22	A lot of energy (SF-12)	0 pt. = all/most of the time, 0.5 pt. = a good bit/some of the time, 1 pt. = a little/none of the time
23	Downhearted and blue (SF-12)	0 pt. = none/a little of the time; 0.5 pt. = some of the time; 1 pt. = a good bit/most/all of the time
24	Interference with social contacts (SF-12)	0 pt. = none/a little of the time; 0.5 pt. = some of the time; 1 pt. = most/all of the time
Measurements
25	Weight loss during the last 3 months (MNA)	0 pt. = no, 0.5 pt. = 1–3 kg, 1 pt. = >3 kg
26	GFR (ml/min//1.73m²)	0 pt. = ≥60; 0.5 pt. = 30 to < 60; 1 pt. = <30
27	BMI (kg/m²)	0 pt. = 18.5 to < 30 ; 1 pt. = <18.5 or ≥ 30
28	Anemia	Men: 1 pt. = <13g/dL; Women: 1 pt. = <12g/dL
29	Polypharmacy	1 pt. = ≥5 medications
30	Barthel Index	0 pt. = ≥85; 0.5 pt. = 35–80; 1 pt. = 0–30 points
pt. = points; MNA = Mini nutritional assessment; MI = Myocardial infarction; EOD = end organ damage; SF-12 = Short form 12; GFR = glomerular filtration rate; BMI = body mass index

Variables

Preoperatively assessments were performed with interview-based self-reports by trained assessors containing questions about current living situation, falls, mobility, subjective memory impairment and weight loss. Age, sex, smoking state, alcohol consumption, education and type of surgery were defined as co-variables. Smoking was categorized into non-smoker, ex-smoker and current smoker. An alcohol-score was built based on the self-reported consumption of beer, wine and liquors (options between never = 0 and daily = 5) using the following formula: alcohol score = 0,05 x beer consumption + 0,12 x wine consumption + 0,4 x liquor consumption taking into account the percentage of alcohol of a standard drink(18). Education was categorized into ≤ 10 or > 10 years of education. Comorbidities were evaluated as follows: diabetes mellitus, arterial hypertension, cardiovascular disease (myocardial infarction, heart failure, circulatory disorders in the lower extremities, arrhythmia, stroke and/or cerebral hemorrhage or hemiplegia), chronic lung disease, dementia, osteoarthritis, urinary incontinence, tumor (lymphoma, leukemia, metastasised solid tumor or any tumor disease), neurological diseases (M. Parkinson and/ or seizures) and liver disease. For the emotional and mental state, the seven questions of the SF-12 were considered: General health, moderate activities, climbing several flights of stairs, interference of pain with daily activites, having a lot of energy, feeling downhearted and blue and interference of physical health or emotional problems with social activities(19). Visual and hearing impairment were evaluated with a visual acuity test and a whisper test, respectively. Body mass index (BMI) was calculated using self-reported weight and height. The preoperative creatinine levels were implemented in the CDK-EPI formula(20) to estimate the glomerular filtration rate (GFR). For hemoglobin WHO’s recommended and sex-stratified cut-offs for anemia were used(21). Polypharmacy was present if five or more medications were taken(22). ADLs were assessed using the Barthel Index(23). For secondary analysis timed-up-and-go (TUG) and handgrip strength were considered. TUG reflected participant`s time needed to rise from a chair, walk 3 metres and return into a sitting position(24). Assistive devices (e.g. walking stick) were allowed. Cut-offs for TUG were defined as follows: 0 pt.: < 10 sec., 0.25 pt.: ≥ 10 and < 20 sec., 0.75 pt.: ≥ 20 and < 30 sec., 1 pt.: ≥ 30 sec.(24). Handgrip strength was measured with a JAMAR hand dynamometer testing both hands. The second measurement on the stronger one was considered in the FI. Differentiating by sex a deficit was assumed by < 16kg in women and < 27kg in men(25).

Delirium

The incidence of POD was assessed at bedside by performing a daily International Confusion Assessment Method (I-CAM)(26, 27) during the first postoperative 7 days. In addition a chart review at discharge was performed by trained research physicians applying the DSM-5 delirium criteria as reference standard(28, 29). POD was a combined endpoint allocated by a positive I-CAM on any postoperative day and/or detection of delirium during chart review(15).

Statistical Analysis

For descriptive analyses categorical variables were expressed as numbers and percentage, continuous variables as mean (standard deviation) or median (Q1, Q3). We used logistic regression models to evaluate the association between frailty and the onset of delirium using the FI as a continuous variable as well as a categorical one. Subjects with a FI ≥ 0.2 were defined as frail(17). Model 1 adjusted for sex and age; Model 2 adjusted for sex, age, alcohol consumption, smoking, education and type of surgery. We examined the presence of effect modification by sex, and by type of surgery without significant findings (all p-values > 0.2). Odds ratios (OR) with their 95% confidence interval (CI) as well as the calculated c-statistic are reported. In a subsample of 517 participants without missing data for functional measurements we built a 32-items FI enriched with the TUG and handgrip strength. In order to evaluate the impact of adding functional measurements on the predictive value of the FI a secondary analysis was performed contrasting the results of the 30-items versus the 32-items FI. Additional file 1 shows the definition of the study samples. Statistical significance was set to a p-value < 0.05. All calculations were performed using IBM SPSS software version 26. Graphics were made with R software version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria; www.R-project.org).

Baseline characteristics of the study population (n = 701) stratified by frailty are shown in Table 2, with a mean age of 77.1 (SD 4.7) years and 52.4% male participants. Overall 296 (42.2%) patients underwent cardiac and vascular surgery, while 405 (57.8%) patients were treated in orthopedic or other surgical departments. A total of 165 (23.5%) POD were diagnosed, 108 (36.5%) and 57 (14.1%) among cardiac and orthopedic/other surgical patients, respectively (Additional file 2). Compared to cardiac surgery, orthopedic/ other surgical patients were older, more often female and more likely to live alone. They also showed less alcohol consumption, a lower functional status (higher rate of falls in the last 3 months, lower mobility, lower Barthel Index), worse hearing ability, a higher prevalence of dementia, cancer and urine incontinence as well as a poorer self-estimated health. However, they showed less polypharmacy, a better GFR and a lower incidence of POD.

Table 2

Participant’s characteristics stratified by frailty.
			Total (n = 701)	Frailty Status
			Total (n = 701)	Non-frail (n = 173)	Frail (n = 528)
Delirium, n (%)			165 (23.5)	31 (17.9)	134 (25.4)
Type of surgery, n (%)	Cardiac		296 (42.2)	94 (54.3)	202 (38.3)
	Orthopedic		405 (57.8)	79 (45.7)	326 (61.7)
Age (years), mean (SD)			77.1 (4.7)	75.6 (4.5)	77.5 (4.7)
Male, n (%)			367 (52.4)	106 (61.3)	261 (49.4)
Education > 10 years, n (%)			139 (19.8)	47 (27.2)	92 (17.4)
Alcohol score, median (Q1\|Q3)			0.34 (0.12\|0.69)	0.41 (0.17\|0.86)	0.29 (0.05\|0.62)
Living alone, n (%)			193 (27.5)	32 (18.5)	161 (30.5)
Fall in last 3 months, n (%)			106 (15.1)	9 (5.2)	97 (18.4)
Mobility (MNA), n (%)	Goes out		638 (91.0)	172 (99.4)	466 (88.3)
	Gets out of bed/ chair, does not go out		57 (8.1)	1 (0.6)	56 (10.6)
	Bed or chair bound		6 (0.9)	0	6 (1.1)
Comorbidities, n (%)	Dementia		11 (1.6)	0	11 (2.1)
	Tumor		162 (23.1)	21 (12.1)	141 (26.7)
	Urinary incontinence	None	633 (90.3)	167 (96.5)	466 (88.3)
		Occasional	60 (8.6)	6 (3.5)	54 (10.2)
		Incontinent	8 (1.1)	0	8 (1.5)
	Hypertension		533 (76.0)	98 (56.6)	435 (82.4)
	CVD		441 (62.9)	72 (41.6)	369 (69.9)
	Chronic lung disease		80 (11.4)	12 (6.9)	68 (12.9)
	GI ulcer		67 (9.6)	6 (3.5)	61 (11.6)
	Osteoarthritis	Mono	281 (40.1)	58 (33.5)	223 (42.2)
	Osteoarthritis	Poly	122 (17.4)	18 (10.4)	104 (19.7)
	Diabetes mellitus	No EOD	139 (19.8)	20 (11.6)	119 (22.5)
	Diabetes mellitus	With EOD	27 (3.8)	0	27 (5.1)
	Liver disease	Light	40 (5.7)	5 (2.9)	35 (6.6)
	Liver disease	Moderate to severe	16 (2.3)	2 (1.2)	14 (2.7)
Sensory deficits, n (%)	Visual		140 (20.0)	22 (12.7)	118 (22.3)
Sensory deficits, n (%)	Whisper Test	Only one side correct	135 (19.3)	21 (12.1)	114 (21.6)
		None correct	157 (22.4)	28 (16.2)	129 (24.4)
Weight loss, n (%)	1 to 3 kg		133 (19.0)	23 (13.3)	110 (20.8)
Weight loss, n (%)	> 3kg		140 (20.0)	10 (5.8)	130 (24.6)
GFR (ml/min/1.73m²), median (Q1\|Q3)			71.8 (57.2\|85.0)	75.9 (65.8\|86.2)	70.3 (53.7\|83.8)
BMI (kg/m²), median (Q1\|Q3)			26.8 (24.1\|29.9)	25.7 (23.4\|28.1)	27.2 (24.4\|30.4)
Anemia^b, n (%)	Female		72 (21.6)	3 (4.5)	69 (25.8)
Anemia^b, n (%)	Male		114 (31.1)	15 (14.2)	99 (37.9)
Polypharmacy, n (%)			467 (66.6)	69 (39.9)	398 (75.4)
Barthel Index (points), n(%)	≥ 85		636 (90.7)	173 (100.0)	463 (87.7)
	35 to 80		56 (8.0)	0	56 (10.6)
	0 to 30		9 (1.3)	0	9 (1.7)
Subjective memory impairment, n (%)			344 (49.1)	51 (29.5)	293 (55.5)
General health (SF-12), n (%)	Excellent to good		453 (64.6)	159 (91.9)	294 (55.7)
	Fair		208 (29.7)	14 (8.1)	194 (36.7)
	Poor		40 (5.7)	0	40 (7.6)
TUG (sec), median (Q1\|Q3) (n = 555)			12.0 (9.0\|16.0)	10.0 (8.0\|12.5)	12.0 (9.6\|18.0)
Handgrip strength (kg), median (Q1\|Q3)	Female (n = 299)		21.0 (18.0\|25.0)	23.0 (18.0\|26.0)	21.0 (17.0\|24.0)
Handgrip strength (kg), median (Q1\|Q3)	Male (n = 334)		36.0 (30.0\|42.0)	38.0 (32.5\|43.5)	34.0 (30.0\|41.3)
MNA = Mini nutritional assessment; CVD = cardiovasculary disease; GI = gastrointestinal, EOD = endorgan damage; Auditory = both sides incorrect; GFR = glomerular filtration rate; BMI = body mass index; Polypharmacy = ≥ 5 long-term medications; SF-12 = Short form 12; TUG = time-up-and-go-test _a alcohol score = 0.05(beer amount) + 0.12(wine amount) + 0.4*(spirits amount) _b anemia: male haemoglobin < 13 g/dl; female < 12g/dl _c https://kcgeriatrie.de/Assessments_in_der_Geriatrie/Seiten/Bereich_-_Selbstversorgung.aspx

Frailty Index

The FI showed a right-skewed distribution (Additional file 3). We observed a median FI of 0.27 [IQR 0.06, 0.65]. Notably 528 of 701 participants (75.3%) were identified as frail (FI ≥ 0.2). Orthopedic/other surgical patients yielded on average higher FI values (median 0.28; Q1 0.21|Q3 0.36) and were more often identified as frail (80.5%) than cardiac surgery patients (median 0.23; Q1 0.18|Q3 0.30; 68.2% frail). Median FI values were higher for women (median 0.28; Q1 0.21| Q3 0.37) than for men (median 0.25; Q1 0.18 | Q3 0.32) with 79.9% of women identified as frail versus only 71.1% of men (Table 3).

Table 3

Descriptive characteristics for the frailty index stratfied by type of surgery.
	Study population			Cardiac surgery			Orthopedic/other surgery
	Overall	Female	Male	Overall	Female	Male	Overall	Female	Male
	n = 701	n = 334	n = 367	n = 296	n = 93	n = 203	n = 405	n = 241	n = 164
Mean	0.27	0.29	0.26	0.25	0.27	0.24	0.29	0.30	0.28
Standard deviation	0.11	0.11	0.10	0.10	0.10	0.10	0.11	0.11	0.11
Minimum	0.06	0.06	0.07	0.06	0.06	0.07	0.06	0.06	0.07
1. Quartile	0.20	0.21	0.18	0.18	0.19	0.17	0.21	0.21	0.21
Median	0.27	0.28	0.25	0.23	0.26	0.22	0.28	0.30	0.28
3. Quartile	0.34	0.37	0.32	0.30	0.33	0.28	0.36	0.38	0.36
Maximum	0.65	0.57	0.65	0.55	0.55	0.51	0.65	0.57	0.65
Frail (FI ≥ 0.2), n (%)	528 (75.3)	267 (79.9)	261 (71.1)	202 (68.2)	70 (75.3)	132 (65.0)	326 (80.5)	197 (81.7)	129 (78.7)
Non-frail (FI < 0.2), n (%)	173 (24.7)	67 (20.1)	106 (28.9)	94(31.8)	23 (24.7)	71 (35.0)	79 (19.5)	44 (18.3)	35 (21.3)

We observed 134 incident delirium events among frail subjects representing 25.4%, while only 31 deliriums could be detected among non-frail (17.9%). Compared to non-frail, frail participants were older, more often female, less educated, showed lower alcohol consumption and were more likely to live alone. Poorer physical functioning within the frail group was noticed with a higher rate of falls, lower levels of mobility, lower Barthel Index and worse results in TUG and handgrip strength (if available). Comparing comorbidities frail participants were diagnosed more often with arterial hypertension, cardiovascular disease, chronic lung disease, dementia, gastrointestinal ulcer, osteoarthritis, urinary incontinence, tumor, diabetes mellitus and liver disease than non-frail ones. Frail participants had lower visual and hearing abilities, lower GFR and higher rate of polypharmacy, anemia and higher BMI. In general, frail participants rated their health lower in the SF-12 than fit participants (Table 2).

Frailty and postoperative delirium

Model 1 showed a statistically significant association between frailty and POD. Frail participants had higher odds for POD compared to non-frail (OR 1.66 [95% CI 1.06, 2.59]), where a 0.1 increment of the FI was associated with an OR of 1.32 [95% CI 1.12, 1.57] after adjustment for age and sex. After further adjustment in Model 2 including education, smoking, alcohol-score and type of surgery frailty increased the association with POD (OR 2.14 [95% CI 1.33, 3.44]), with an OR of 1.57 [95% CI 1.30, 1.90] for a 0.1 increase of the FI. The highest c-statistic (AUC) of 0.722 was seen in Model 2 with frailty as a continuous variable (Table 4).

Table 4

Logistic regression for the association between frailty and POD (n = 701).
	Model 1_a		Model 2_b
	OR [95% CI]	c-statistic	OR [95% CI]	c-statistic
Frailty (categorical) (FI ≥ 0.2)	1.66 [1.06, 2.59]	0.596	2.14 [1.33, 3.44]	0.705
Frailty Index continuous (increase of 0.1)	1.32 [1.12, 1.57]	0.613	1.57 [1.30, 1.90]	0.722
OR-odds ratio; CI-confidence interval _a adjusted for age and sex _b adjusted for age, sex, education, smoking, alcohol-score and type of surgery

Secondary analysis

We were able to build a 32-item FI including functional parameters such as TUG and handgrip strength for 517 of 701 participants. When compared to the study population this subpopulation was slightly younger with a higher ratio of men, cardiac surgery patients, lower rate of polypharmacy and less reported falls (Additional file 4). No relevant differences in the odds ratios of model 2 as well as in the distribution between the 30-item and the 32-item FI could be detected (Table 5, Additional file 5). After adding parameters for muscle strength to the 30-items FI the OR for those frail (FI ≥ 0.2) went from 1.95 [95% CI 1.17, 3.25] to 1.80 [95% CI 1.11, 2.91] after adjustment for age, sex, education, smoking, alcohol consumption and type of surgery. Furthermore, no improvement was observed in model’s discrimination (both c-statistics 0.69). As a continuous variable, a 0.1 increment of the FI including parameters of muscle strength entailed an OR of 1.66 [95% CI 1.31, 2.09] compared to an OR of 1.61 [95% CI 1.28, 2.02] when using the 30-item FI, without any improvement in the discrimination (both c-statistic 0.72).

Table 5

Secondary analysis: Logistic regression for the association between frailty and POD within the sub-population (n = 517).
	Model 2_a
	Frail versus non-frail		Frailty Index (continuous)
	OR [95% CI]	c-statistic	OR_b [95% CI]	c-statistic
Frailty Index without functionality (30 items)	1.95 [1.17, 3.25]	0.690	1.61 [1.28, 2.02]	0.715
Frailty Index with functionality (32 items)	1.80 [1.11, 2.91]	0.688	1.66 [1.31, 2.09]	0.718
OR-odds ratio; CI-confidence interval _a adjusted for age, sex, education, smoking, alcohol score, type of surgery _bOR for any 0.1 increment in the frailty index

Our data showed an positive association between frailty defined by a 30-item FI based on the accumulation of deficits and the onset of POD among older adults undergoing elective surgery. Our secondary analysis adding measured parameters for muscle strength (TUG and handgrip strength) to the FI did not show an improvement of model’s discrimination. Hence, a 30-item FI based on variables routinely obtained preoperatively is a significant predictor to identify those at high risk for the onset of POD.

The distribution of the FI and prevalence of frailty in our population of older adults undergoing elective surgery is consistent with observations made in literature, with women having on average higher frailty indices than men(30). A recent metaanalysis including studies using different definitions of frailty showed overall an accumulative OR of 2.43 [1.88, 3.14] in frail patients undergoing selective surgery(31). However, literature providing information on the FI in surgical patients is very scarce(31), and usually presented in mixed populations(8, 12). In this context a study including 198 surgical and 1220 general medicine patients described an overall FI mean of 0.32 and a twofold risk for delirium for a FI increment of 0.1 (OR 2.34, [95% CI 2.08, 2.36])(12). A systematic review including 20 studies (n = 5541 participants) and a mixed population with only a small representation of surgical patients showed a significant association of frailty with delirium with a 2.2 fold higher risk for frail patients. Challenges were heterogeneity of the study populations and of the assessments used for the definition of frailty and the identification of delirium(8). Observed differences with respect to the frailty prevalence between only-surgical and mixed populations may be explained by a pre-selection of surgery-eligible patients, where frailty and age may be considered exclusion criteria for elective surgery. Such exclusion of patients may have lead to a selection bias with an overrepresentation of “fitter” patients in the available studies. In this regard, in our study cardiac surgery patients were less likely to be identified as frail than orthopedic/ general surgery patients. A 62.5% frailty prevalence of the cardiac surgery PAWEL population matches observations made in another study using a similar approach with a 35-item FI in patients undergoing cardiac surgery, where 66.2% participants were classified as frail (FI ≥ 0.2). However, the reported OR for POD were noted to be markedly higher than in our analysis (OR 3.98 [95% CI 1.10, 14.38] versus 2.00 [95% CI 1.19, 3.40] (data not shown))(13).

POD is a well-known risk factor for further complications of surgery such as extended length of stay or institutionalization(5). Surgical teams usually get to know their patients at the time of admission, so that an evaluation of frailty based on functional parameters or functional status weeks before surgery, as attempted in the clinical frailty scale, can become a challenge. Taking advantage of the overall information collected at admission to systematically build a FI could help to identify patients with a high-risk for POD, which might benefit from preventive interventions addressing modifiable risk factors and from rescheduling or modifiying treatment approaches(32). Such interventions may include involvement of geriatricians in perioperative patient care as well as delirium-specialised staff, deprescription of pro-delirious medication (e.g. sedative-hypnotic drugs)(2), or other interventions such as the one implemented in the intervention group participating in the PAWEL study^15,(15, 33) .

Strengths and Limitations

Our analysis included a large number of participants (n = 701), had a multicenter setting (5 medical centers) and evaluated different surgical types (cardiac and non-cardiac), allowing a more differentiated analysis. Moreover, two approaches helped to assess precisely postoperative delirium prevalence: I-CAM, a modified version of the commonly used CAM, and a Chart Review, which allowed to detect any delirium that might have been missed by I-CAM. Combining those two methods helped to keep the rate of missed delirium with its fluctuating course low. Because of missing baseline data we were able to build the FI only for 78.0% of the study population. However, we did not detect relevant differences between the excluded and the included population (Additional file 6). Unfortunately, only 57.5% of the population had data for functional physical parameters addressing muscle strength, known to be a good predictor for different outcomes and a surrogate for physical frailty in older adults(34). Therefore we were able to evaluate the predictive value of a 32-item FI including TUG and handgrip strength only in a subsample of 517 patients. This secondary analysis within this subsample showed no further improvement in model’s discrimination after adding them, making the 30-item FI more feasible during preoperative evaluation since it contains mostly routinely gathered data not requiring the performance of extra time-consuming tests. Moreover, it can also be used for bed-bound patients (e.g. for hip surgery or fracture of femoral neck), which is a huge benefit compared to frailty assessments using mobility variables. In addition, and contrary to the clinical frailty scale, which captures a time frame(35), a prior clinical interaction with the patient is not needed in order to build a FI based on patient’s characteristics at the time of admission reinforcing its utility in surgical wards. To which extent our FI would be able to describe a relationship between frailty and delirium in geriatric or internal medicine settings could and should be analysed in further studies.

A 30-item FI based mostly on data, defined as a routine in geriatric patients and collected preoperatively, could serve for the identification of patients undergoing elective surgery being at high risk for the onset of postoperative delirium. Further addition of parameters for muscle strength such as TUG and handgrip strength to the FI did not improve discrimination of the model. Available electronical medical records could support the automatic calculation of a FI in surgical wards, helping surgeons to improve the management of patients at high risk for POD.

ADL activities of daily living

CI confidence interval

FI frailty index

GFR glomerular filtration rate

I-CAM Confusion Assessment Method

ICU Intensive Care Unit

OR Odds ratios

POD postoperative delilrium

TUG timed-up-and-go

Ethics approval and consent to participate

Ethical approval for the PAWEL Study was obtained from the Ethics Commission of the Faculty of Medicine of the Eberhard-Karls University and University Hospital Tuebingen (517/2017BO1), Ulm University (425/17) and the Ethics Commission of the University of Potsdam (38/2017). All participants gave written informed consent. All measurements were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests

Funding

The PAWEL-Study was funded by the Innovationsfonds (Fund of the Federal Joint Committee, Gemeinsamer Bundesausschuss, G-BA; AZ: VF1_2016-201), which had no role in the design of the study and had no role either during its execution, analyses of the data, or in the decision to submit any results.

Authors' contributions

JS and DD worked on the conception and design of the work; analyzed and interpreted the participants data regarding the association of frailty and POD; and drafted the manuscript. UB performed statistical calculations and graphics. All authors contributed to the drafted manuscript, read and approved the final manuscript.

Acknowledgements

We thank all patients and their relatives who were interviewed and assessed, as well as all staff members at the recruiting centers and all members of the PAWEL Study group. The following persons were involved in data acquisition or active treatment at the sites: University Hospital Tübingen, Tübingen, Germany: Visceral Surgery); Rita Binder, Annette Schroth, Marlen Brachthäuser, Anna Giron, and Cindy Boden (Department of Psychiatry and Psychotherapy); Andrea Albrecht (Nursing Department); Heidrun Sturm and Laura Bertram (Institute for General Practice and Interprofessional Care). Klinikum Stuttgart, Stuttgart, Germany: Patricia Sabbah, Irina Tkacenko (Department of Old Age Psychiatry and Psychotherapy); University of Freiburg, Freiburg, Germany: Norbert P. Südkamp and Gunnar Ochs (Department of Orthopedics and Trauma Surgery); Stefan Fichtner-Feigl (Department of General and Visceral Surgery); Hartmut Bürkle (Department of Anesthesiology and Critical Care); Marina Gimbel, Anna Groß, and Damian Sommer (Medical Center). University Hospital Ulm, Ulm, Germany: Christoph Leinert (Department of Neurology). Study center Karlsruhe, Germany: Jan Gerhardus, ZvjezdanaPeric, Julia Pieloth, Saifullah Abubaker, Naser Ghaffari, Christine Neuber, and Adel Yunes (Helios Clinic for Heart Surgery); Janine Kistner, and Elke Wächter (Geriatric Center Karlsruhe, ViDia Christian Clinics Karlsruhe). AOK Baden-Württemberg, Stuttgart, Germany: Julia Frankenhauser-Mannuß and Julia Pick.

Members of the international advisory board who provided input to the study design: Ulf Günther (Department of Anesthesiology, Intensive Care Medicine, and Pain Therapy,

University Hospital Oldenburg, Oldenburg, Germany), George A. Kuchel (University of Connecticut Center on Aging, Farmington, CT, United States), Susan Kurrle (Health Care of

Older People, Faculty of Medicine, University of Sidney, Sidney, Australia), and Yoanna Skrobik (Department of Medicine, McGill University, Montreal, Quebec, Canada).

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No competing interests reported.

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Frailty Index and its association with the onset of postoperative delirium in older adults undergoing elective surgery

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Abstract

Background

Methods

Results

Conclusion

Trial registration:

Introduction

Methods

Study Population

Frailty

Variables

Delirium

Statistical Analysis

Results

Frailty Index

Frailty and postoperative delirium

Secondary analysis

Discussion

Strengths and Limitations

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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