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Impact of postoperative delirium on days alive and at home after surgery: a prospective cohort study

谵妄 前瞻性队列研究 医学 队列 队列研究 外科 麻醉 内科学 重症监护医学
作者
David Kunkel,Margaret Parker,Cameron Casey,Bryan M. Krause,Robert A. Pearce,Richard C. Lennertz,Robert David Sanders
出处
期刊:BJA: British Journal of Anaesthesia [Elsevier BV]
卷期号:127 (6): e205-e207 被引量:4
标识
DOI:10.1016/j.bja.2021.08.027
摘要

Editor—It is unknown if delirium is independently associated with a reduced number of days alive and at home1Bell M. Eriksson L.I. Svensson T. et al.Days at home after surgery: an integrated and efficient outcome measure for clinical trials and quality assurance.EClinicalMedicine. 2019; 11: 18-26Abstract Full Text Full Text PDF PubMed Scopus (36) Google Scholar, 2Myles P.S. More than just morbidity and mortality – quality of recovery and long-term functional recovery after surgery.Anaesthesia. 2020; 75: e143-e150PubMed Google Scholar, 3Jerath A. Austin P.C. Wijeysundera D.N. Days alive and out of hospital: validation of a patient-centered outcome for perioperative medicine.Anesthesiology. 2019; 131: 84-93Crossref PubMed Scopus (48) Google Scholar at 90 days (DAH90) after anaesthesia and surgery, an important patient-centred outcome. We conducted an exploratory analysis of our ongoing single-centre, prospective cohort study of major, non-intracranial surgical patients (see reference list for a full description of the cohort)4Ballweg T. White M. Parker M. et al.Association between plasma tau and postoperative delirium incidence and severity: a prospective observational study.Br J Anaesth. 2021; 126: 458-466Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar, 5Casey C.P. Lindroth H. Mohanty R. et al.Postoperative delirium is associated with increased plasma neurofilament light.Brain. 2019; 143: 47-54Crossref Scopus (33) Google Scholar, 6White M.F. Tanabe S. Casey C. et al.Relationships between preoperative cortical thickness, postoperative electroencephalogram slowing, and postoperative delirium.Br J Anaesth. 2021; 127: 236-244Abstract Full Text Full Text PDF PubMed Scopus (4) Google Scholar who were >65 yr old and followed up at 90 days postoperatively (NCT01980511, NCT03124303). We measured delirium and delirium severity after surgery in consecutive patients who were enrolled in our cohort study; 153 adult patients were recruited at the time of analysis (delirium data were unavailable in three subjects). All patients were anticipated to stay at least 2 days in hospital. The study excluded patients with a documented history of dementia and those residing in a nursing home. All patients underwent general anaesthesia intraoperatively. For up to 4 days postoperatively in the hospital, patients were assessed for delirium twice daily using the Confusion Assessment Method (CAM)/3D-CAM,7Marcantonio E.R. Ngo L.H. O'Connor M. et al.3D-CAM: derivation and validation of a 3-minute diagnostic interview for CAM-defined delirium: a cross-sectional diagnostic test study.Ann Intern Med. 2014; 161: 554-561Crossref PubMed Scopus (188) Google Scholar or the CAM-ICU8Ely E.W. Inouye S.K. Bernard G.R. et al.Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU).JAMA. 2001; 286: 2703-2710Crossref PubMed Scopus (1962) Google Scholar if the patient was intubated. The severity of postoperative delirium was assessed by the Delirium Rating Scale-98 (DRS).9Trzepacz P.T. Mittal D. Torres R. Kanary K. Norton J. Jimerson N. Validation of the Delirium Rating Scale-revised-98: comparison with the delirium rating scale and the cognitive test for delirium.J Neuropsychiatry Clin Neurosci. 2001; 13: 229-242Crossref PubMed Scopus (475) Google Scholar Our primary outcome was a quantile regression3Jerath A. Austin P.C. Wijeysundera D.N. Days alive and out of hospital: validation of a patient-centered outcome for perioperative medicine.Anesthesiology. 2019; 131: 84-93Crossref PubMed Scopus (48) Google Scholar model to identify possible predictors of DAH90 at the 50th and 75th percentiles. The model estimates the effects of independent variables on DAH90 when predicting the median DAH90 (0.5 quantile) or 75th percentile (greater DAH90). The 0.5 and 0.75 quantiles were selected based on the data distribution. All adjusted quantile regression models were then bootstrapped with 10 000 iterations to provide a better estimate of the confidence intervals (CIs) for our findings. We adjusted for patient characteristics (age and sex), cognitive (Trail Making Test B), and perioperative factors (cardiovascular surgery, National Surgical Quality Improvement Project risk of death, and operative time). These latter factors provide an efficient, and relatively comprehensive, assessment of the risk associated with surgery including comorbidities and procedural characteristics. Out of 150 total subjects assessed for delirium, 98 (65.3%) were non-delirious and 52 (34.7%) were delirious postoperatively. Non-delirious subjects had a greater DAH90 (median=87, inter-quartile range=4) than subjects who experienced delirium (median=80, inter-quartile range=22, P<0.001, Mann–Whitney test, Supplementary Fig. 1). The unadjusted median difference was 7.5 days (95% CI 5–13 days [calculated with 5000 bootstrapped iterations]). Changes in peak delirium severity negatively correlated with changes in DAH90 (ρ=−0.63, P<0.001, Spearman correlation, Supplementary Fig. 1). Of the 148 subjects included in the quantile regression (as a result of missing covariates in two subjects), delirium (0.5 quantile [β=−7, 95% CI −13 to −5], 0.75 quantile [β=−5, 95% CI −6 to −3]), and delirium severity (0.5 quantile [β=−0.6, 95% CI −1 to −0.33], 0.75 quantile [β=−3.5, 95% CI −0.4 to −0.2]), were associated with DAH90 in unadjusted analyses. In adjusted analyses, delirium predicted fewer DAH90 at the 0.75 quantile (β=−2.514, 95% CI −4.22 to −1.35, Table 1) but not the 0.5 quantile (β=−3.303, 95% CI −9.07 to −0.69). Operative time was associated with DAH90 at the 0.5 (β=−0.031, 95% CI −0.06 to −0.01) and 0.75 (β=−0.013, 95% CI −0.02 to −0.009) quantiles. The same adjusted model, using delirium severity (peak DRS) instead of delirium, produced similar results (Supplementary Table S1): operative time (β=−0.028, 95% CI −0.05 to −0.01) and peak DRS (β=−0.306, 95% CI −0.71 to −0.12) were predictors of DAH90 at the 0.5 quantile. Additionally, both operative time (β=−0.014, 95% CI −0.02 to −0.009) and peak DRS (β=−0.245, 95% CI −0.35 to −0.17) were strongly associated with DAH90 at the 0.75 quantile.Table 1Bootstrap quantile regression model for perioperative factors, including delirium, associated with DAH90. Note the 50th quantile is the median. The beta coefficient (coeff.) describes the number of days at home and alive related to a unit change of that variable (negative=less, positive=more). CI, confidence interval; NSQIP-D, National Surgical Quality Improvement Project risk score for death; SE, standard error; TMTB, Trail Making Test B.Outcome: days alive and at home at 90 days0.5 QuantileBeta coeff.SEt-valueP-valueCI (2.5%, 97.5%)Age−0.0440.103−0.4230.67(−0.32, 0.08)Sex−0.3701.009−0.3660.72(−2.34, 1.72)Cardiovascular surgery1.6131.3161.2260.22(−0.32, 5.26)NSQIP-D−0.0780.302−0.2590.80(−0.75, 0.55)Operative time−0.0310.010−3.1000.002∗(−0.06, −0.01)Postoperative delirium−3.3032.034−1.6240.11(−9.07, −0.69)Preoperative TMTB−0.0090.017−0.5210.60(−0.06, 0.01)Outcome: days alive and at home at 90 days0.75 QuantileBeta coef.SEt-valueP-valueCI (2.5%, 97.5%)Age−0.0140.045−0.3160.75(−0.15, 0.05)Sex−0.1120.568−0.1960.84(−0.91, 1.26)Cardiovascular surgery0.6480.5021.2920.20(−0.35, 1.78)NSQIP-D−0.0340.161−0.2110.83(−0.27, 0.26)Operative time−0.0130.004−3.809<0.001∗(−0.02, −0.009)Postoperative delirium−2.5140.740−3.399<0.001∗(−4.22, −1.35)Preoperative TMTB−0.0020.006−0.2480.80(−0.02, 0.007)∗ Refers to p-values <0.05. Open table in a new tab ∗ Refers to p-values <0.05. Delirium and delirium severity are independent predictors of reduced days alive and at home at 90 days, an important patient-centred outcome. Future mechanistic studies should investigate if perioperative inflammation, which is considered a major factor in precipitating delirium, independently drives DAH90 or the effect is mediated through delirium. RS is a member of the board of the British Journal of Anaesthesia. The other authors declare that they have no conflicts of interest. The following is the Supplementary data to this article: Download .docx (.15 MB) Help with docx files Multimedia component 1

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