Dynamic Predictive Scores for Cardiac Surgery-Associated Agitated Delirium: a single-center retrospective observational study

doi:10.21203/rs.3.rs-143779/v1

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Dynamic Predictive Scores for Cardiac Surgery-Associated Agitated Delirium: a single-center retrospective observational study

https://doi.org/10.21203/rs.3.rs-143779/v1

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Background

Prevention, screening, and early treatment are the mainstays of postoperative delirium management. Score system is an objective and effective tool to stratify potential delirium risk for patients undergoing cardiac surgery

Methods

Patients undergoing cardiac surgery from January 1, 2012, to January 1, 2019, were enrolled in our retrospective study. The patients were divided into a derivation cohort (n = 45,744) and a validation cohort (n = 11,436). The agitated delirium (AD) predictive systems were formulated using multivariate logistic regression analysis at three time points: preoperation, ICU admittance, and 24 hours after ICU admittance.

Results

The prevalence of AD after cardiac surgery in the whole cohort was 3.6% (2,085/57,180). The dynamic scoring system included preoperative LVEF ≤ 45%, serum creatinine > 100 umol/L, emergency surgery, coronary artery disease, hemorrhage volume > 600 mL, intraoperative platelet or plasma use, and postoperative LVEF ≤ 45%. The area under the receiver operating characteristic curve (AUC) values for AD prediction of 0.68 (preoperative), 0.74 (on the day of ICU admission), and 0.75 (postoperative). The Hosmer-Lemeshow test indicated that the calibration of the preoperative prediction model was poor (P = 0.01), whereas that of the pre- and intraoperative prediction model (P = 0.49) and the pre-, intra- and postoperative prediction model (P = 0.35) was good.

Conclusions

Using perioperative data, we developed a dynamic scoring system for predicting the risk of AD following cardiac surgery. The dynamic scoring system may improve early recognition of and interventions for AD.

Cardiothoracic Surgery

adult cardiac surgery

agitated delirium

risk prediction score systems

Delirium is an acute confusional state affecting consciousness, attention, cognition, and perception¹. Patients who undergo cardiac surgery are at high risk for delirium because they are more likely to be older and often have multiple comorbidities, such as hypertension and diabetes². It has been reported that the prevalence of delirium after cardiac surgery varies widely (4%-51%)^3–5. The wide range could be explained by the heterogeneity of the patient population, timing of the assessments, hospital location, delirium subtype, and selected assessment tool. Postoperative delirium, especially after cardiac surgery, can foreshadow poor outcomes⁶. From the patient^’s view, delirium and subsequent sequelae, including cognitive decline, loss of independence, increased costs, and increased mortality, are among the most feared adverse events following cardiac surgery⁷. It has been estimated that approximately $6.9 billion (in American dollars) of medicare hospital expenditures are attributable to delirium⁸. Thus, it is increasingly important for clinicians to have an understanding of postoperative delirium.

Clinically, delirium can be divided into hyperactive, hypoactive, and mixed types⁹. Agitated delirium (AD) syndrome is a disease state characterized by mental status changes in combination with psychomotor agitation, metabolic derangements, and hyperthermia and has the potential to cause death¹⁰. Until now, the definition of AD was derived primarily from clinical criteria, and the diagnosis tends to be subjective¹¹. In 2019, Rood¹² indicated that 7% of intensive care unit (ICU) delirium patients were hyperactive, 36% were mixed type, and 27% were hypoactive. Patients encountering delirium frequently present with hypoactivity and somnolence; however, the agitated and hyperactive subtype is easily recognized. Based on past studies, the cause of delirium is multifactorial, and there are many risk factors that predispose patients to delirium, including older age, dementia (often not recognized clinically), functional disabilities, and the complex interactions of comorbidities¹³. To date, many studies have suggested that the symptoms, etiology, pathophysiology, detection rates, treatment experience and outcomes of different subtypes of delirium are highly heterogeneous¹⁴. Consequently, interest has focused on identifying clinically meaningful subtypes.

Prevention, screening, and early treatment are the mainstays of postoperative delirium management. Though the reported risk factors vary and are not amenable to change, identification of patients with these factors can allow clinicians to direct preventive efforts towards at-risk patients. Delirium remains under-diagnosed in the perioperative setting, but screening and assessment tools are readily available to aid clinicians in identifying delirium. Prediction tools also allow the patient and their family to be better informed about risks. Delirium may be prevented or attenuated when risk stratification tools are used. We sought to derive and validate a dynamic scoring system that could predict the risk of AD following cardiac surgery during the preoperative and early postoperative periods.

Patients

In this retrospective study, we included 58,469 adult patients who underwent cardiac repair at our institute from January 1, 2012, to January 1, 2019. The research was approved by the Ethics Committee of Fuwai Hospital (20191308), and the requirement for written informed consent was waived for all patients. Patients were included if they were aged more than 18 years and had undergone open cardiac surgery (including coronary artery bypass grafting (CABG), valve surgery, great vessel operations, congenital heart disease repair, cardiac tumor surgery and combined surgery). The exclusion criteria were adult patients with previous renal replacement therapy or dialysis (n=70), epilepsy (n=152), shock (n=899), a cardiac assist device (n=5), delirium (n=0), septicemia (n=1), sleep apnea syndrome (n=138), sleep disorder (n=15), or pulmonary embolism (n=25). A total of 1,289 patients were excluded. Finally, the charts of 57,180 patients were enrolled retrospectively (eFigure 1).

Data Collection

All digital clinical data were provided by Fuwai Hospital Information Center. We monitored and then checked the collected perioperative data. The data were checked twice by postgraduates and engineers who work for the Hospital Information Center. A total of 31 candidate variables were directly extracted from the patient medical charts. The definitions of risk variables were mainly referenced from the STS website.

Diagnosis of Agitated Delirium

Following the STS definition, AD was defined as short-lived mental disturbance marked by illness, confusion, and cerebral excitement or patient requirements for medical intervention such as olanzapine and droperidol. The trained postgraduates and engineers were primarily responsible for the decision of AD occurrence (yes/no) after searching and reviewing the clinical charts. They looked for the diagnosis and clinical manifestation of AD within the patient medical records and a history of medical intervention such as olanzapine and droperidol usage. The Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), the best criterion, was not available in the ICU at our institute for the majority of patients, but the agitated subtype was documented by the nursing staff. Furthermore, in the general ward, the treatment of AD with olanzapine and droperidol was determined by clinical personnel in accordance with measurements from a mini-mental test, a confusion assessment method and any evidence of AD and then recorded in the medical chart.

Statistical Analysis

Statistical analysis was performed with SPSS software for Windows (version 25.0. IBM Corp, Armonk, NY). We transformed continuous variables into categorical variables according to clinically meaningful cutoff values or values reported in previous literature. Categorical variables are expressed as a frequency (n) and percentage (%) and were analyzed using Pearson’s chi-square test or Fisher’s exact test as appropriate. When less than 2% of the values were missing for a variable, single imputation was used (defaulted to the most common value of the variable), whereas if more than 2% of the values were missing, the missing values were modeled as unknown.

The data set was randomly divided into a derivation cohort (n=45,744) and a validation cohort (n=11,436). The rationale for using these variables in the scoring model was based on the results of univariate analyses and the clinical relevance of the variables. After univariate analysis, AD predictive systems were formulated using multivariate logistic regression analysis at three time points: preoperation, ICU admittance, and 24 hours after ICU admittance. The calibration of the prediction models was assessed by the Hosmer–Lemeshow goodness-of-fit test, and the discriminatory ability of the models was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). The final prediction scores were the nearest integer to the regression coefficient. The AUC and Hosmer–Lemeshow goodness-of-fit test were applied to estimate the reliability of the prediction scoring systems in the validation cohort.

A total of 57,180 patients (45,744 patients in the derivation cohort and 11,436 in the validation cohort) were retrospectively analyzed during the 7-year period. The prevalence of AD after cardiac surgery in the whole cohort was 3.6% (2085/57180), whereas in the derivation and validation cohorts, it was 3.3% (1504/45744) and 5.1% (581/11436), respectively. The mortality rate in the whole cohort was 0.9% (504/57180), Whereas in the AD cohort, it was 1.4% (29/2085). The baseline clinical characteristics of patients in the derivation and validation cohorts are illustrated in eTable 1 in the Supplement. The perioperative information of the patients in the derivation group is shown in Table 1.

Analysis of the Risk Variables

The details of the preoperative prediction model for AD are shown in eTable 2, whereas the pre- and intraoperative prediction model is shown in eTable 3, and the pre-, intra- and postoperative prediction model is shown in eTable 4. The risk variables contributing to AD were age, male sex, obesity, previous chronic obstructive pulmonary disease (COPD), hypertension, type 2 diabetes, New York Heart Association (NYHA) classification=4, low preoperative left ventricular ejection fraction (LVEF), elevated serum creatinine, emergency surgery, alcohol use, carotid artery stenosis, history of stroke, coronary artery disease, low total protein, type of surgery, intraoperative hemorrhage volume > 600 mL, intraoperative red blood cell (RBC) count, platelet or plasma use and low postoperative LVEF.

Diagnostic Utility of the Prediction Score

1. AD prediction model based on preoperative variables

After using only the preoperative variables in the derivation cohort to construct the AD prediction model, the AUC for AD was 0.68 (95% CI, 0.67, 0.70, Figure 1), and in the validation cohort, the AUC was 0.67 (95% CI, 0.64, 0.69, Figure 1). Nevertheless, the calibration according to the Hosmer-Lemeshow test was poor for this model (P = 0.01). The sensitivity, specificity, positive predictive value, and negative predicted value for predicting the medium- and high-risk groups were 37.5%, 83.1%, 10.6%, and 96.1%, respectively.

2. AD prediction model based on pre- and intraoperative variables

In the derivation data set, the performance of the AD risk prediction model, which was based on pre- and intraoperative variables, was as follows: AUC = 0.74 (95% CI, 0.72, 0.75, Figure 1), indicating good discrimination ability. It was very similar to the validation cohort (AUC = 0.74, 95% CI, 0.72, 0.76). The Hosmer-Lemeshow test showed that the calibration of the prediction model was good (P = 0.49). The sensitivity, specificity, positive predictive value, and negative predicted value for predicting the medium- and high-risk groups were 49.7%, 82.2%, 13.00%, and 96.80%, respectively.

3. AD prediction model based on pre-, intra-, and postoperative variables

The AUC for the AD prediction model with the derivation cohort was 0.75 (95% CI, 0.73, 0.76, Figure 1). The Hosmer-Lemeshow test demonstrated good calibration for the derivation cohort (P = 0.35). The performance with the validation cohort also indicated good discriminability (AUC=0.74, 95% CI, 0.71, 0.76). The sensitivity, specificity, positive predictive value, and negative predicted value for predicting the medium- and high-risk groups were 52.7%, 79.5%, 12.10%, and 96.90%, respectively.

Development of the Prediction Score

Three scoring systems for predicting postoperative AD after cardiac surgery are presented in Table 2. The prediction scoring systems were developed from regression coefficients of data from the derivative cohort patients. Depending on the scoring system, the predicted risks of AD could be grouped into three classifications: low, medium, and high, in accordance with the practically observed incidence of AD (Table 3). In the validation cohorts, the incidences of AD onset predicted by the model were similar to those observed clinically (Figure 2). The risk scores and their associated predictive risks are presented in eTable 5 in the Supplement.

Negative Outcomes

AD was associated with greater medical expenditures and a prolonged length of hospital stay (P < 0.001, eTable 6). AD was also associated with mortality (P =0.011). However, Postoperative AD was not correlated with the risk of pulmonary complications or the requirement of reintubation (P = 0.572, P = 0.496, eTable 7).

Our study developed dynamic scoring systems for predicting the risk of AD following cardiac surgery. The dynamic scoring systems evolved from the perioperative risk variables of a large cohort of cardiac patients, and they can predict the risk of AD following cardiac surgery during the preoperative and early postoperative periods. These dynamic scoring systems were also well validated internally. Additionally, The rationale for exclusion was critical ill patients and frequency of each variable. Although some of these criteria (e.g., shock, dialysis, sepsis) would elevate the risk for delirium, the frequency of those variable for each group was too small.

Delirium, a postoperative complication affecting brain function, can severely affect the quality of patients’ lives, especially in the aging population. Similar to previous investigations², we found that postoperative delirium was associated with negative outcomes. Several authors agree that early detection could prevent or assist in the treatment of delirium¹⁵. Some of the proposed preventive interventions have been shown to have beneficial effects on delirium, such as early mobilization, early extubation and minimization of alarm use and noisiness. Patients undergoing cardiac surgery are at a higher risk of developing postoperative delirium due to several factors, including surgical complexity, comorbidities and age¹⁶. The purpose of this study was to develop and validate a dynamic risk model for predicting the risk of AD after cardiac surgery. On the basis of the prediction model, we created a simple bedside dynamic scoring card that can be used to proactively identify the risk strata (low, medium, or high risk) for AD at three time points (preoperation, ICU admittance, and 24 hours after ICU admittance).

Our research represents a meaningful attempt to prove that preoperative elevated serum creatinine is a risk factor for AD following cardiac surgery. The risk of AD for patients whose preoperative serum creatinine was more than 150 µmol/L was nearly two times higher than that of those whose serum creatinine was less than 70 µmol/L. Katznelson et al¹⁷ found that the estimated odds ratio for patients who had preoperative creatinine levels greater than 150 µmol/L was 2.96 (95% CI 1.90–4.63, P < 0.001), which was similar to our study results. In 2013, Koster et al¹⁸ indicated that the European System for Cardiac Operative Risk Evaluation (EuroSCORE) score predicted the risk of delirium following cardiac surgery, and the EuroSCORE includes creatinine concentrations greater than 200 µmol/L as a risk variable (http://www.euroscore.org/calcold.html). In 2017, Siew et al¹⁹ found that the daily peak serum creatinine value was a risk variable for delirium (OR = 1.35, 95% CI, 1.18–1.55) and coma (OR = 1.44, 95% CI, 1.20–1.74) during critical illness. In 2020, Mossello et al²⁰ indicated that moderate renal impairment was independently associated with delirium among older fracture patients aged 75–84. The association between serum creatinine and delirium requires further analysis^{21, 22}.

Prediction scores in previous investigations were derived from information collected from preoperative variables^17,18. We included intraoperative and postoperative predictors in the prediction score and derived dynamic predictive scores for cardiac surgery-associated AD. Meanwhile, we found that ICU admittance (or after surgery) was the best time point at which to employ the prediction model. The AUC of the pre- and intraoperative prediction model was better than that of the preoperative prediction model (0.74 VS 0.68) and was similar to that of the pre-, intra- and postoperative prediction model (0.74 VS 0.75). Compared with the investigations carried out by Tse et al²³ (n = 679) and Mufti et al²⁴ (n = 5,584), our study has an unprecedentedly larger sample size. Other authors also attempted to develop a predictive model for delirium through a prospective study: Katznelson et al ¹⁷ in 2009 (n = 1,059), Koster et al¹⁸ in 2013 (n = 300), Krzych et al³ in 2014 (n = 5,781), and Kumar et al²⁵ in 2017 (n = 120). However, those investigations were based on a small sample size and not internally validated. Although the preoperative prediction rule for delirium after cardiac surgery developed by Rudolph²⁶ in 2009 was internally validated (AUC in the derivation cohort was 0.74; AUC in the validation cohort was 0.75), it had a tendency to be imprecise and was outdated and even eventually eliminated. Given the differences in delirium definition and race, no statistical comparison can be performed among the above mentioned models.

There were several limitations to our study. First, as a retrospective analysis that is pending prospective validation, our observation data were mainly based on chart extraction. The quality of the acquired data might profoundly impact the results and their interpretation, although the data were checked twice by postgraduates and engineers. Additionally, as in most hospitals wards²⁷, the majority of patients in our institute are not routinely monitored for delirium, but the presence of the agitated subtype is recorded in medical charts. The incidence of AD was low, only 3.6%.This limits the ability to generalize the dynamic scoring systems to the types of delirium other than the agitated subtype. The reason was that we only included the agitated subtype of delirium, which represents only a small proportion of all delirium cases⁹. And the criterion to diagnosis AD was strict. The decision of AD was base on diagnosis, clinical manifestation and a history of medical intervention.

Delirium involves a spectrum of presentation ranging from hypo- to hyperactive symptoms and a more inclusive definition is more predictive of 1-year functional survival following cardiac surgery²⁸, but the agitated and hyperactive subtype is easily recognized. Based on past studies, the symptoms, etiology, pathophysiology, detection rates, treatment experience and outcomes of different subtypes of delirium are highly heterogeneous¹⁴. Consequently, interest has focused on identifying clinically meaningful subtypes. Our study is a retrospective analysis and the majority of patients in our institute are not routinely monitored for delirium, hence we can only identify hyperactive subtype. But our sudy is still helpful to further explorations into effective clinical interventions for delirium and reduce the associated adverse effects. Furthermore, our study is characterized by a large sample size, but data about long-term outcomes are unavailable at our institute for the majority of patients. Finally, data were obtained from a single center, and the scoring system needs to be validated in other centers.

Using perioperative data, we developed a dynamic scoring system for AD following cardiac surgery that can be used to flag patient risk. The dynamic scoring systems may improve early recognition of and interventions for AD. This scorecard will contribute to software development and further explorations into effective clinical interventions for delirium.

AD, agitated delirium; ICU, intensive care unit; CABG, coronary artery bypass grafting;CAM-ICU, confusion assessment method for the intensive care unit;ROC, receiver operating characteristic;AUC, area under the receiver operating characteristic curve;COPD, chronic obstructive pulmonary disease;IABP,Intra-aortic ballon pump;NYHA, New York Heart Association ; RBC, red blood cell;LVEF, left ventricular ejection fraction; OR, odds ration; CI, confidence intervals;BMI, body mass index; IQR, interquartile range.

Ethical approval and consent to participate

This study cohort was approved by the Institutional Review of Board (IRB) of Fuwai

Hospital (20191308). Informed consent from the participants was also waived by the IRB because of the retrospective nature.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the

corresponding author on reasonable request.

Competing interests

All authors have nothing to disclose with regard to commercial support.

Funding

Supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019XK320052).

Authors’ contributions

Yu Tian designed the study, collected and analyzed data, drafted the manuscript, and submitted the final version; Yuefu Wang and Wei Zhao drafted the manuscript, designed the study and collected data; Bingyang Ji and Xiaoli Diao designed the study, collected the data, and drafted the manuscript; Chunrong Wang and Weiwei Wang helped study design, supervision, visualization, and writing–reviewing and editing; Yuchen Gao and Sudena Wang provided advice on the data analysis and drafting of the manuscript; Chun Zhou designed the study, coordinated and supervised data collection; Qiaoni Zhang projected administration, coordinated and supervised data collection; Sizhe Gao designed the study, coordinated and supervised data collection;

Xinyi Xu analyzed data, coordinated and supervised data collection; Jia Liu collected and validated data.

Acknowledgement

Special thanks are given to Drs Y. F. Wang who helped much with the manuscript from initial conception until its publication.

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Table 1.Clinical Characteristics of Derivation Cohort(N=45744)(%)
Preoperative variable	No agitated delirium (n=44240，96.7%)	Agitated delirium (n=1504，3.3%)	P Value
Age（year）			<0.001
<60	25056(56.6)	728(48.4)
60-74	17246(39.0)	693(46.1)
≥75	1938(4.4)	83(5.5)
Gender			<0.001
Female	15460(34.9)	307(20.4)
Male	28780(65.1)	1197(79.6)
Obesity（BMI≥30kg/m²）	3454(7.8)	158(10.5)	<0.001
Previous COPD	631(1.4)	37(2.5)	0.001
History of cardiac surgery	1145(2.6)	31(2.1)	0.204
IABP use	43(0.1)	4(0.3)	0.068
Infective endocarditis	338(0.8)	14(0.9)	0.467
Proteinuria	630(1.4)	37(2.5)	0.001
Hypertension	14916(33.7)	592(39.4)	<0.001
Type 2 diabetes	8543(19.3)	327(21.7)	0.019
NYHA classification=4	1087(2.5)	76(5.1)	<0.001
LVEF			<0.001
Normal(>60%)	43347(98.0)	1436(95.5)
Mild damage(46%-60%)	695(1.6)	45(3.0)
Moderate damage(30-45%)	178(0.4)	20(1.3)
Severe damage(<30%)	20(0.0)	3(0.2)
Serum creatinine (umol/L)			<0.001
<70	14547(32.9)	298(19.8)
70-100	25164(56.9)	904(60.1)
101-120	3059(6.9)	172(11.4)
121-150	895(2.0)	60(4.0)
>150	575(1.3)	70(4.7)
Emergency surgery	1817(4.1)	217(14.4)	<0.001
Alcohol user	35829(81.0)	1296(86.2)	<0.001
Tobacco exposure			<0.001
Yes	19388(43.8)	802(53.3)
Unknown	1094(2.5)	17(1.1)
Carotid artery stenosis	1187(2.7)	62(4.1)	0.001
History of stroke	2993(6.8)	136(9.0)	0.001
Coronary artery disease	42651(96.4)	1481(98.5)	<0.001
Albumin(g/L)			<0.001
Normal(35-45)	27271(61.6)	994(66.1)
Abnormal(<35 OR >45)	16628(37.6)	487(32.4)
Unknown	341(0.8)	23(1.5)
Total protein (g/L)			<0.001
>65	33790(76.4)	1021(67.9)
≤65	10111(22.9)	460(30.6)
Unknown	339(0.8)	23(1.5)
Simvastatin use	15405(34.8)	505(33.6)	0.319
Beta-blocker use	23717(53.6)	830(55.2)	0.228
The type of surgery			<0.001
Others *	3107(7.0)	91(6.1)
CABG	19988(45.2)	673(44.7)
Valve Surgery	10159(23.0)	317(21.1)
Great vessel surgery	3374(7.6)	159(10.6)
Combined surgery	7612(17.2)	264(17.6)
Intraoperative variable
Bypass time (min)			0.008
Off-pump surgery	12036(27.2)	393(26.1)
≤120min	22399(50.6)	727(48.3)
>120min	9805(22.2)	384(25.5)
Hemorrhage volume			<0.001
≤600mL	37606(85.0)	1003(66.7)
＞600mL	6634(15.0)	501(33.3)
RBC use	7322(16.6)	434(28.9)	<0.001
Platelet use	2187(4.9)	270(18.0)	<0.001
Plasma use	4744(10.7)	406(27.0)	<0.001
Postoperative variable
LVEF(>60%,reference group)	11655(26.3)	328(21.8)	<0.001
Mild damage(46%-60%)	16959(38.3)	667(44.3)
Moderate damage(30-45%)	2616(5.9)	163(10.8)
Severe damage(<30%)	131(0.3)	11(0.7)
Unknown	12879(29.1)	335(22.3)
Serum creatinine(umol/L)			<0.001
<70	13624(30.8)	364(24.2)
70-100	21243(48.0)	674(44.8)
101-120	3253(7.4)	161(10.7)
121-150	909(2.1)	77(5.1)
>150	252(0.6)	36(2.4)
Unknown	4959(11.2)	192(12.8)
Data presented as numbers and percentages. BMI, body mass index; COPD, chronic obstructive pulmonary disease; IABP,Intra-aortic ballon pump;CABG, coronary artery bypass grafting; LVEF, left ventricular ejection fraction; NYHA, New York Heart Association ; RBC , red blood cell.*included congenital heart disease repair , cardiac tumor surgery.

Table 2. Prediction scores for postoperative agitated delirium after cardiac surgery.
Preoperative scores	Scores
LVEF≤45%	1
Serum creatinine > 100umol/L	1
Emergency surgery	1
Coronary artery disease	1
Pre- and intraoperative scores
LVEF≤45%	1
Serum creatinine > 100umol/L	1
Emergency surgery	1
Coronary artery disease	1
Hemorrhage volume＞600ml	1
Intraoperative platelet use	1
Intraoperative plasma use	1
Pre-, intra- and postoperative scores
Serum creatinine > 100umol/L	1
Emergency surgery	1
Coronary artery disease	1
Hemorrhage volume＞600ml	1
Intraoperative platelet use	1
Intraoperative plasma use	1
Postoperative LVEF≤45%	1
LVEF, left ventricular ejection fraction

Table 3: Risk Stratification of agitated delirium after cardiac surgery at indicated time points
Total score	Preoperative	ICU Admittance	24 Hours after ICU Admittance
0-2	low(<5.0%)	low(<5.0%)	low(<5.0%)
3-4	medium-high(≥5.0%)	medium(5.0%-25%)	medium(5.0%-25%)
5-7		high(>25%)	high(>25%)
Example: if a patient get total scores ≤ 2, his predicted risk of postoperative agitated delirium following cardiac surgery is low (less than 5.0%).

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Dynamic Predictive Scores for Cardiac Surgery-Associated Agitated Delirium: a single-center retrospective observational study

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