Dataset Information

Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study.

ABSTRACT: OBJECTIVES:We aimed to develop and validate a postoperative delirium (POD) prediction model for patients admitted to the intensive care unit (ICU). DESIGN:A prospective study was conducted. SETTING:The study was conducted in the surgical, cardiovascular surgical and trauma surgical ICUs of an affiliated hospital of a medical university in Heilongjiang Province, China. PARTICIPANTS:This study included 400 patients (?18 years old) admitted to the ICU after surgery. PRIMARY AND SECONDARY OUTCOME MEASURES:The primary outcome measure was POD assessment during ICU stay. RESULTS:The model was developed using 300 consecutive ICU patients and was validated using 100 patients from the same ICUs. The model was based on five risk factors: Physiological and Operative Severity Score for the enumeration of Mortality and morbidity; acid-base disturbance and history of coma, diabetes or hypertension. The model had an area under the receiver operating characteristics curve of 0.852 (95% CI 0.802 to 0.902), Youden index of 0.5789, sensitivity of 70.73% and specificity of 87.16%. The Hosmer-Lemeshow goodness of fit was 5.203 (p=0.736). At a cutoff value of 24.5%, the sensitivity and specificity were 71% and 69%, respectively. CONCLUSIONS:The model, which used readily available data, exhibited high predictive value regarding risk of ICU-POD at admission. Use of this model may facilitate better implementation of preventive treatments and nursing measures.

SUBMITTER: Xing H

PROVIDER: S-EPMC6858207 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study.

Xing Huanmin H Zhou Wendie W Fan Yuying Y Wen Taoxue T Wang Xiaohui X Chang Guangming G

BMJ open 20191112 11

<h4>Objectives</h4>We aimed to develop and validate a postoperative delirium (POD) prediction model for patients admitted to the intensive care unit (ICU).<h4>Design</h4>A prospective study was conducted.<h4>Setting</h4>The study was conducted in the surgical, cardiovascular surgical and trauma surgical ICUs of an affiliated hospital of a medical university in Heilongjiang Province, China.<h4>Participants</h4>This study included 400 patients (≥18 years old) admitted to the ICU after surgery.<h4> ...[more]

PMID: 31722939

Similar Datasets

Project description:BackgroundNo data is available on delirium prediction models in the cardiac intensive care unit (CICU), although preexisting delirium prediction models [PREdiction of DELIRium in ICu patients (PRE-DELIRIC) and Early PREdiction of DELIRium in ICu patients (E-PRE-DELIRIC)] were developed and validated based on a population admitted to the general intensive care unit (ICU). Therefore, we externally validated the usefulness of the PRE-DELIRIC and E-PRE-DELIRIC models and compared their predictive performance in patients admitted to the CICU.MethodsA total of 2,724 patients admitted to the CICU were enrolled between September 2012 and December 2018. Delirium was defined as at least one positive Confusion Assessment Method for the ICU (CAM-ICU) which was screened at least once every 8 h. The PRE-DELIRIC value was calculated within 24 h of CICU admission, and the E-PRE-DELIRIC value was calculated at CICU admission. The predictive performance of the models was evaluated by using the area under the receiver operating characteristic (AUROC) curve, and the calibration slope was assessed graphically by plotting.ResultsDelirium occurred in 677 patients (24.8%) when the patients were assessed thrice daily until 7 days of the CICU stay. The AUROC curve for the prediction of delirium was significantly greater for PRE-DELIRIC values [0.84, 95% confidence interval (CI): 0.82-0.86] than for E-PRE-DELIRIC values (0.79, 95% CI: 0.77-0.80) [z score of -6.24 (p < 0.001)]. Net reclassification improvement for the prediction of delirium increased by 0.27 (95% CI: 0.21-0.32, p < 0.001). Calibration was acceptable in the PRE-DELIRIC model (Hosmer-Lemeshow p = 0.170) but not in the E-PRE-DELIRIC model (Hosmer-Lemeshow p < 0.001).ConclusionAlthough both models have good predictive performance for the development of delirium, even in critically ill cardiac patients, the performance of the PRE-DELIRIC model might be superior to that of the E-PRE-DELIRIC model. Further studies are required to confirm our results and design a specific delirium prediction model for CICU patients.

Project description:BACKGROUND:Accurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation. METHODS:This 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h. RESULTS:In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of -?2.73 (p?<?0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n?=?68) rated the E-PRE-DELIRIC model more feasible. CONCLUSIONS:While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h. TRIAL REGISTRATION:ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

Project description:BACKGROUND:Postoperative delirium is a common complication associated with increased morbidity and mortality. A recently-reported association between intraoperative electroencephalogram suppression and postoperative delirium might be mediated in some patients by a heightened sensitivity to volatile anaesthetics. METHODS:This retrospective cohort study included 618 elective surgery patients with planned intensive care unit admission, who also received intraoperative electroencephalogram monitoring and had delirium assessments documented in the medical record. Sensitivity to volatile anaesthetics was assessed using a mixed effects model predicting the likelihood of electroencephalogram suppression at each time point based on the current end-tidal anaesthetic concentration. Patients with a random intercept above the population median (electroencephalogram suppression at lower anaesthetic concentrations) were classified as having heightened sensitivity to volatile anaesthetics. Delirium was defined as a positive Confusion Assessment Method for the Intensive Care Unit assessment anytime in the first five postoperative days. RESULTS:Postoperative delirium was observed in 162 of 618 patients (26%). Patients who experienced electroencephalogram suppression at lower volatile anaesthetic concentrations had a higher incidence of postoperative delirium [109/309 (35%)] than other patients [53/309 (17%)] [unadjusted odds ratio 2.63; 95% confidence interval (CI), 1.81-3.84, P<0.001]. This association remained significant after adjusting for patient characteristics, surgical variables, and duration of electroencephalogram suppression (adjusted odds ratio 2.13; 95% CI 1.24-3.65, P=0.006). CONCLUSIONS:These data support the hypothesis that patients with electroencephalogram suppression at lower volatile anaesthetic concentrations have an increased incidence of postoperative delirium. Such patients appear to exhibit a phenotype of anaesthetic sensitivity, which might predispose them to adverse cognitive outcomes.

Project description:Importance:Obstructive sleep apnea has been associated with postoperative delirium, which predisposes patients to major adverse outcomes. Positive airway pressure may be an effective intervention to reduce delirium in this population. Objectives:To determine if preoperative obstructive sleep apnea is associated with postoperative incident delirium in the intensive care unit and if preoperative positive airway pressure adherence modifies the association. Design, Setting, and Participants:A retrospective single-center cohort study was conducted at a US tertiary hospital from November 1, 2012, to August 31, 2016, among 7792 patients admitted to an intensive care unit who underwent routine Confusion Assessment Method for the intensive care unit after major surgery. Patients were adults who had undergone a complete preoperative anesthesia assessment, received general anesthesia, underwent at least 1 delirium assessment, were not delirious preoperatively, and had a preoperative intensive care unit stay of less than 6 days. Statistical analysis was conducted from August 20, 2019, to January 11, 2020. Exposures:Self-reported obstructive sleep apnea, billing diagnosis of obstructive sleep apnea, or STOP-BANG (Snoring, Tiredness, Observed Apnea, Blood Pressure, Body Mass Index, Age, Neck Circumference and Gender) questionnaire score greater than 4, as well as self-reported use of preoperative positive airway pressure. Main Outcomes and Measures:Delirium within 7 days of surgery. Results:A total of 7792 patients (4562 men; mean [SD] age, 59.2 [15.3] years) met inclusion criteria. Diagnosed or likely obstructive sleep apnea occurred in 2044 patients (26%), and delirium occurred in 3637 patients (47%). The proportion of patients with incident delirium was lower among those with obstructive sleep apnea than those without (897 of 2044 [44%] vs 2740 of 5748 [48%]; unadjusted risk difference, -0.04; 99% credible interval [CrI], -0.07 to -0.00). Positive airway pressure adherence had minimal association with delirium (risk difference, -0.00; 99% CrI, -0.09 to 0.09). Doubly robust confounder adjustment eliminated the association between obstructive sleep apnea and delirium (risk difference, -0.01; 99% CrI, -0.04 to 0.03) and did not change that of preoperative positive airway pressure adherence (risk difference, -0.00, 99% CrI, -0.07 to 0.07). The results were consistent across multiple sensitivity analyses. Conclusions and Relevance:After risk adjustment, this study found no association between obstructive sleep apnea and postoperative delirium in the context of usual care in the intensive care unit, with 99% CrIs excluding clinically meaningful associations. With limited precision, no association was found between positive airway pressure adherence and delirium. Selection bias and measurement error limit the validity and generalizability of these observational associations; however, they suggest that interventions targeting sleep apnea and positive airway pressure are unlikely to have a meaningful association with postoperative intensive care unit delirium.

Project description:Background In 2020, there were 2,206,771 new cases of lung cancer worldwide, ranking first in mortality rate postoperative delirium (POD) is a common surgical complication and typically occurs 1–3 days after surgery which leads to prolonged hospital stay. The currently available research on POD in elderly patients was mainly carried out in cardiac surgery, orthopedic, and gastroenterology departments. It is necessary to study the incidence and risk factors of delirium in intensive care unit (ICU) elderly patients after lung cancer surgery. Methods This study is a single-center and singe-arm observational study which examined the incidence of and factors affecting POD in elderly patients with lung cancer in ICU at our center from September 2019 to May 2020. 22 relevant variables including arterial oxygen partial pressure, postoperative sedative drug use, and other data were collected. The Confusion Assessment Method of Intensive Care Unit was used to assess the occurrence of POD, the Chinese version of the Richards-Campbell Sleep Questionnaire was used to assess patients’ postoperative sleep quality, and the Mini-Mental State Examination was used to assess patients’ preoperative cognitive level. Univariate and multivariate logistic regression analyses were conducted to identify the factors affecting POD in elderly ICU patients with lung cancer. Results Among the 208 elderly ICU patients who underwent surgery for lung cancer, 32 (15.38%) had POD. The results showed that postoperative blood oxygen level, preoperative cognitive level score and postoperative sleep quality score in delirium group were significantly lower than those in non-delirium group (P values were 0.002, 0.000, 0.000, respectively). The proportion of previous coronary heart disease and postoperative sedation use in delirium group was significantly higher than that in non-delirium group (P=0.008 and 0.008, respectively). Conclusions It was observed that the preoperative Mini-Mental State Examination (MMSE) score and postoperative Richards-Campbell Sleep Questionnaire (RCSQ) score of the delirium group were significantly lower than those of the non-delirium group, and this trend was still observed after adjusting for influencing factors. It is suggested that the difference of cognitive level in elderly lung cancer population has a significant effect on the occurrence of POD. In clinical work, we should pay more attention to patients with preoperative cognitive impairment, hypoxic state, and postoperative sleep disturbance.

Dataset Information

Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study.

Publications

Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets