Development and Validation of a Postoperative Delirium Prediction Model for Elderly Orthopedic Patients in the Intensive Care Unit - Hindawi.com
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Hindawi
Journal of Healthcare Engineering
Volume 2021, Article ID 9959077, 6 pages
https://doi.org/10.1155/2021/9959077
Research Article
Development and Validation of a Postoperative Delirium
Prediction Model for Elderly Orthopedic Patients in the Intensive
Care Unit
Gang Wang ,1 Lei Zhang,1 Ying Qi ,2 Guangjian Chen,1 Juan Zhou,1 Huihui Zhu,1
and Yingxin Hao1
1
Department of Critical Care Medicine, Tongji Hospital, Tongji University School of Medicine, Tongji University,
Shanghai 200065, China
2
Department of Economic Management, Yingkou Institute of Technology, Yingkou 115014, Liaoning, China
Correspondence should be addressed to Ying Qi; qyjt76@163.com
Received 5 March 2021; Revised 23 April 2021; Accepted 28 May 2021; Published 8 June 2021
Academic Editor: Yi-Zhang Jiang
Copyright © 2021 Gang Wang et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We developed a prediction model for delirium in elderly patients in the intensive care unit who underwent orthopedic surgery and
then temporally validated its predictive power in the same hospital. In the development stage, we designed a prospective cohort
study, and 319 consecutive patients aged over 65 years from January 2018 to December 2019 were screened. Demographic
characteristics and clinical variables were evaluated, and a final prediction model was developed using the multivariate logistic
regression analysis. In the validation stage, 108 patients were included for temporal validation between January 2020 and June
2020. The effectiveness of the model was evaluated through discrimination and calibration. As a result, the prediction model
contains seven risk factors (age, anesthesia method, score of mini-mental state examination, hypoxia, major hemorrhage, level of
interleukin-6, and company of family members), which had an area under the receiver operating characteristics curve of 0.82 (95%
confidence interval 0.76–0.88) and was stable after bootstrapping. The temporal validation resulted in an area under the curve of
0.80 (95% confidence interval 0.67–0.93). Our prediction model had excellent discrimination power in predicting postoperative
delirium in elderly patients and could assist intensive care physicians with early prevention.
1. Introduction Delirium is one of the most common surgical compli-
cations among older patients, the incidence of which is
Delirium, which is defined as an acute disorder of at- between 15 and 25% after major elective procedures and
tention and cognition, is a common, life-threatening, and approximately 50% after high-risk surgeries such as hip
preventable clinical syndrome in older persons. The fracture repair and cardiac surgery [5]. The cumulative
etiopathogenesis of delirium remains unclear [1]. Delir- incidence of delirium exceeds 75% in patients undergoing
ium often occurs after severe disease, surgery, or hospi- mechanical ventilation in the intensive care unit (ICU) [6].
talization and is often overlooked by clinicians. The Because of its adverse effects on function and quality of life,
development of delirium initiates a serious range of events delirium has significant societal implications for individuals,
culminating in loss of independence, increased morbidity families, communities, and entire healthcare system.
and mortality, institutionalization, and catastrophic To date, there is no convincing evidence that pharma-
medical costs [2, 3]. Each year, more than 2.6 million cological prevention or treatment is effective, and multi-
adults over 65 years develop delirium, accounting for an component nonpharmacological risk factor-controlled
estimated expenditure over $164 billion in annual approaches have proven to be the most effective strategies
healthcare in the United States [4]. for reducing the incidence of delirium [7]. General2 Journal of Healthcare Engineering
preventive measures for all intensive care patients is time- recorded within 2 h after admission to the ICU. Hypoxia was
consuming, and a number of patients may be exposed to defined as SpO2 (oxygen saturation) less than 90% at any
unnecessary risks, such as the adverse effects of drug pro- time during the entire operation. Hypotension was defined
phylaxis. There are two predictive models for intensive care as a patient’s mean arterial pressure less than 65 mmHg at
patients [8, 9], with an area under the receiver operating any time or vasopressor agents were administered, re-
characteristic (AUROC) curve of 0.77 (95% CI, 0.74–0.79) gardless of the duration. Company of family members
and 0.76 (95% CI, 0.73–0.77), respectively. Both prediction implied that at least one family member accompanied the
models, with focus on medical inpatients, had similar patient for more than 6 h during the night. A total loss of
predictive values based on screening with the confusion more than 400 ml of blood was considered a major hem-
assessment method for the ICU (CAM-ICU) [10]. In clinical orrhage. We excluded patients under mechanical ventilation
practice, however, the predictive value is not satisfactory for from our study. Hence, sedative agents (e.g., midazolam or
surgical patients, especially for critical care patients after dexmedetomidine) were not routinely administered unless
orthopedic surgery. The aim of our study was to develop a RASS score was more than 2 points. Continuous patient-
new delirium prediction model for older patients in the ICU controlled intravenous analgesia or regional analgesia was
after orthopedic surgery and validate as well as evaluating its provided to relieve postoperative pain. When necessary, all
effectiveness. enrolled patients were sedated and anesthetized using the
same protocol.
2. Materials and Methods To detect delirium, all consecutive intensive care patients
were screened at least three times daily. When required, for
2.1. Study Design. This was an observational study in which example, after sudden changes in behavior, attention, or
we developed a prediction model for delirium in elderly consciousness, an additional assessment was performed. All
patients in the ICU who underwent orthopedic surgery and research nurses and physicians received training for delir-
then temporally validated it in a second prospective cohort ium assessment, and the use of the CAM-ICU tool before the
in the same hospital. The inclusion criteria and study study was initiated.
protocol were the same for both recruited groups. The study
protocol was approved by the Clinical Research Ethics
Committee of Tongji Hospital (2018-TJDX-176). The study 2.4. Outcome Definition. The main outcome was the de-
protocol, including potential risks and benefits, was velopment of delirium within 24 h during the patients’ stay
explained to patients in person before we obtained written in the ICU. Patients with delirium were defined as those who
informed consent from the patients or their legal had at least one positive CAM-ICU screening during their
representatives. stay in the ICU. In addition, if the clinical presentation
provided signs of delirium without a positive CAM-ICU
screening, or conversely, if the clinical presentation did not
2.2. Study Population for Development and Validation Studies. provide evidence of delirium and the patient had a positive
We conducted a prospective cohort study at Tongji Hospital CAM-ICU result, a senior psychiatrist was informed to
to develop a prediction model. Between January 2018 and confirm the diagnosis of delirium and rule out false negatives
December 2019, a total of 506 elderly patients in the ICU and false positives using the Diagnostic and Statistical
who underwent orthopedic surgery were screened, 319 of Manual of Mental Disorders, fourth edition diagnostic
whom were finally included in the analyses. We included all criteria [11]. All patients with diagnosis confirmed by a
adult patients, aged 65 years or older, who were admitted to psychiatrist were assigned to the delirium group.
the ICU after orthopedic surgery. We excluded patients who
were delirious before admission to the ICU, stayed in the
2.5. Statistical Analysis. In previous studies, the incidence of
ICU for less than 24 h, were mechanically ventilated, had
delirium in intensive care patients was approximately 28%.
serious auditory or visual disorders, were severely mentally
We calculated the sample size based on the need for 10
disabled, were unable to provide informed consent, or had
delirious patients per risk factor plus 5% dropout for the
the compliance rate of the delirium screening less than 80%
prediction development of the model. Consequently, at least
during the ICU stay.
300 patients were required to develop the model. The data
We conducted a second prospective cohort study for
from the temporal validation study for all variables were
temporal validation of the model between January 2020 and
complete. Risk factors were collected as continuous, cate-
June 2020 in the same hospital, and 108 patients were
gorical, or dichotomized variables.
included.
In our study, continuous variables were expressed as
medians with standard deviations (SDs), and categorical
2.3. Risk Factors. We collected demographic variables, mini- data were expressed as percentages. The independent Stu-
mental state examination (MMSE), and Charlson Comor- dent’s t-test or Mann–Whitney U test was used to compare
bidity Index score before admission to the ICU. Information continuous variables when appropriate, and the chi-square
on mode of anesthesia, operative duration, bleeding volume, test or Fisher’s exact test was used to compare categorical
hypotension and hypoxia during the operation, company of variables with or without delirium. We used univariate
family members during a patient’s stay in the ICU, and logistic regression to assess the association between each
serum concentration of interleukin-6 (IL-6) were also potential prognostic determinant and the presence orJournal of Healthcare Engineering 3
absence of delirium. We excluded determinants with a index as 0.56, and at this optimal cutoff point, the sensitivity
prevalence rate below 10%, or with a p value above 0.10, in and specificity were 0.74 and 0.82, respectively.
univariate analysis. With the remaining significant variables
detected by univariate analysis (p value 0.10. Another study found higher IL-6 immunoreactivity in the
Table 2 summarizes the values or percentages of patients brain of patients with delirium, which suggested a close
with each risk factor and the odds’ ratio with a 95% con- association between human brain activity of microglia, IL-
fidence interval for delirium. The remaining seven risk 6, and delirium in elderly patients [17]. To date, there is no
factors entered into the multivariate logistic regression model with IL-6 to predict the incidence of delirium
analysis, and we constructed a final prediction model. The because the level of IL-6 has not been routinely tested in
AUROC curve of the prediction model was 0.82 (95% CI, orthopedic wards or ICUs. During the first few days after
0.76 to 0.88) and did not change noticeably after boot- the operation, patients’ cognitive function decreased
strapping (Figure 1). Based on the ROC analysis of the significantly in patients who received general anesthesia
development population, we calculated the result of Youden (p < 0.001) compared to those who received regional4 Journal of Healthcare Engineering
Table 1: The demographic characteristics and clinical variables of the development and validation cohorts.
Development cohort Validation cohort
Patients (n) 319 108
Male sex (n) (%) 206 (64.6) 65 (60.2)
Age (years) (mean ± SD) 83.1 ± 7.9 80.1 ± 8.4
BMI (kg/m2) (mean ± SD) 23.3 ± 3.0 23.5 ± 2.8
Urgent admission (n) (%) 110 (34.5) 31 (28.7)
MMSE (mean ± SD) 20.1 ± 4.2 20.5 ± 4.1
Charlson comorbidity index (mean ± SD) 7.4 ± 3.1 8.4 ± 3.4
Duration of surgery (min) (mean ± SD) 148.0 ± 52.4 110.4 ± 36.7
Regional anesthesia (n) (%) 166 (52) 58 (53.7)
Hypotension during surgery (n) (%) 85 (26.6) 17 (15.7)
Hypoxia during surgery (n) (%) 42 (13.2) 15 (13.9)
Major hemorrhage (n) (%) 109 (34.2) 37 (34.3)
Level of IL-6 >9pg/ml (n) (%) 112 (35.1) 45 (41.7)
Company of family member (n) (%) 109 (34.2) 43 (39.8)
BMI denotes body mass index, MMSE denotes mini-mental state examination, SD denotes standard deviation, and IL-6 denotes interleukin-6.
Table 2: Univariate logistic regression analyses and the final multivariate logistic regression model in the development cohort.
Univariate Multivariate
Variables Coefficient
OR p 95% CI OR p 95% CI
Age 1.093Journal of Healthcare Engineering 5
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