Association between preoperative atrial fibrillation and postoperative delirium in patients with pulmonary hypertension: A secondary analysis of a retrospective cohort study

doi:10.21203/rs.3.rs-2365922/v1

Background

Postoperative delirium is the most common neuropsychiatric disorder in major surgery. Predisposing factors have been identified, but risk factors assessment useful for managing clinical intervention, has not received unanimous consent. This work aims to investigate the relationship between atrial fibrillation and postoperative delirium in patients with pulmonary hypertension in the United States.

Methods

This is a secondary analysis of a retrospective cohort study. Patients with pulmonary hypertension who underwent non-cardiac, non-obstetric surgery at the University of Washington Medical Center from April 2007 to September 2013 were enrolled in the original research. The incidences of postoperative delirium were compared between patients with and without atrial fibrillation. We used multivariable logistic regression, propensity score analysis, and an inverse probability-weighting model to ensure the robustness of our findings.

Results

518 patients with pulmonary hypertension (151 and 367 patients were atrial fibrillation and non-atrial fibrillation, respectively) were included in this study. The overall incidence of postoperative delirium was 3%(16/518) ; it was 1.9% (9/367) and 5.8% (7/151) for non-atrial fibrillation and atrial fibrillation patients, respectively. In the inverse probability-weighting model, atrial fibrillation was associated with high postoperative delirium (OR = 3.02, 95% CI: 1.11–8.19, P = 0.030).

Conclusions

Atrial fibrillation may be associated with increased risk-adjusted postoperative delirium in patients with pulmonary hypertension. Thus, these patients should be adequately assessed, monitored and controlled in early stage.

preoperative atrial fibrillation

postoperative delirium

pulmonary hypertension

non-cardiac surgery

retrospective cohort study

Patients with pulmonary hypertension (PHTN) are presenting with an increasing frequency for elective surgery, and are at elevated risk for postoperative complications^[1]. Postoperative delirium (POD) is a common and serious neurological complication following major surgery. In different populations, the incidence of POD was 3.3% -54.9%^{[2, 3]}. The occurrence of POD lengthens length of stay; intensive care unit stay, 30-day mortality, significant functional decline and a two to three times higher risk of needing care facilities on discharge and higher healthcare costs^[4]. However, its pathophysiology is not clearly understood, analysis of the risk factors remains complex.

Findings from many studies have shown that atrial fibrillation is associated with higher prevalence of global cognitive impairment and an increased risk of POD, and concluded that atrial fibrillation is a non-psychiatric predictor of delirium after cardiac surgery^{[5, 6]}. But these studies remain weak and not able to offer sufficiently clear evidences^[7]. A study of delirium risk factors analysis post proximal femur fracture surgery in elderly found atrial fibrillation was a delirium-related comorbidity^[8]. Another study also found atrial fibrillation was more common in patients who developed POD after cardiac surgery^[9]. However, it is still unclear the correlation of atrial fibrillation and POD in patients with PHTN underwent non-cardiac, non-obstetric surgery.

In present study, we performed a secondary analysis with published data which studied postoperative outcomes in pulmonary hypertension (PHTN) patients^[10]. They may be a high risk population to POD since many factors lead to PHTN, such as smoking, OSA, hypertension, COPD, atrial fibrillation and pulmonary circulation disorders are correlation to POD^{[7, 11, 12]}.

Therefore, we aim to explore the prevalence and the association between preoperative atrial fibrillation and POD in non-cardiac, non-obstetric surgical patients with PHTN. We hypothesize that PHTN patients who had atrial fibrillation preoperatively have a higher risk of POD.

Setting and subjects

A total of 550 adult (age ≥ 18) patients were recruited in the original investigation. Patients with PHTN were considered eligible for our study; PHTN was defined that mean pulmonary artery pressure ≥ 25 mmHg at rest and a pulmonary capillary wedge pressure ≤ 15 mmHg. Poor self-reported function status was defined less than 4 METs. Renal failure was defined serum creatinine > 1.5 mg/dl). 23 patients had missed PASP values, so we excluded them. We further excluded 9 patients had missed data. Finally, 518 patients were included in our study (Fig. 1). All reporting followed the STROBE guidelines. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Demographic And Clinical Characteristics

We collected information about participant demographics (age, sex, ASA, BMI and ), lifestyle (smoking), and the clinical characteristics, including surgery sites and co-morbidity(OSA, ICU bed needed for surgery, procedure length, open surgery, RCRI high risk surgery, systemic hypertension, asthma, COPD, diabetes and renal failure).

Procedure

We enrolled patients with PHTN with atrial fibrillation who underwent elective non-cardiac, non-obstetric surgery. We obtained and downloaded raw data for free from Dryad data package (https://doi.org/10.5061/dryad.9236ng5) in research of Self-reported functional status predicts postoperative outcomes in non-cardiac surgery patients with pulmonary hypertension (Shah, Aalap C. et al. (2019), which was a retrospective cohort study at the institution of the University of Washington from April 2007 to September 2013.

Postoperative Delirium

The primary outcome was POD. The gold standard of delirium diagnosis was DSM5 (Diagnostic and Statistical Manual of Mental Disorders, 5th edition) ^[10]. However, the assessment approach was not mentioned in the original article. POD might be diagnosed by a psychiatrist or other physician.

Statistical analysis

We conducted a descriptive analysis of all participants. For categorical variables, normal distribution and skewed distribution continuous variables, data are expressed as numbers/percentages, mean ± standard deviation (SD) and median/quartile spacing, respectively. Chi-square test, one-way analysis of variance (ANOVA) and Kruskal Wallis test were used to compare the above continuous variables.

To minimize the potential bias of exposure allocation and confounding, we generated a propensity score to estimate by logistic regression the likelihood that patients had atrial fibrillation exposure. A 1: 1 nearest neighbor matching algorithm was applied using a caliper width of 0.2. The following variables were selected to generate the propensity score: age, sex, ASA, BMI, tobacco, PASP, OSA, ICU bed needed for surgery, procedure length, open surgery, revised cardiac risk index(RCRI) high risk surgery, systemic hypertension, asthma, COPD, diabetes and renal failure, A standardized mean difference (SMD) was used to examine the propensity score matching (PSM) degree. A threshold of less than 0.1 was considered acceptable. In the PSM cohort, we used the estimated propensity scores as weights, an inverse probability weighting (IPW) model was used to generate a weighted cohort ^[13]. To ascertain whether the results were sensitive to the matching method, multivariable logistic regression analysis was performed to assess an independent association after adjusting for all the covariates above.

All analyses were performed using the statistical software package R. version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria) and Free Statistics software versions 1.7.1. A threshold of P < 0.05 (two-sided) was considered statistically significant.

Samples

We identified 518 individuals with pulmonary arterial hypertension who underwent non-cardiac, non-obstetric surgery. Of these patients, 151 (29%) had atrial fibrillation. Figure 1 presents the flow chart of the study patients. After PSM, patient characteristics were balanced across groups (Table 1).

Baseline Characteristics

The demographic characteristics of all participants are presented in Table 1. After PSM, 136 pairs matched. The mean age was 62.9 ± 13.4 years, and 121(44.5%) were women (Table 1)

Primary Outcome

The postoperative delirium were 1.9% (7/373) and 5.8% (9/ 154) for non- atrial fibrillation and atrial fibrillation patients, respectively. IPW demonstrated a significant lower of postoperative delirium in non- atrial fibrillation patients. The OR was 3.02 (95% CI, 1.11–8.19, P = 0.030). The propensity score-matched postoperative delirium for non- atrial fibrillation and atrial fibrillation patients were 2.2% and 6.6%, respectively. In multivariable logistic regression analysis, the OR was 3.26 (95% CI, 1.19–8.92, P = 0.021). Although P > 0.05 with Matching, the OR were consistent with other results. In addition, the sample size was reduced after the propensity score, resulting in an insignificant P-value (Table 2).

Table 1.Baseline characteristics of participants

Characteristic	Unmatched Patients		SMD	Matched Patients		SMD
	Non- atrial fibrillation	atrial fibrillation		Non- atrial fibrillation	atrial fibrillation
	n=367	n=151		n=136	n=136
Age(years)	58.77 (13.88)	64.05 (13.25)	0.389	63.10 (13.72)	62.75 (13.08)	0.026
Female, sex, no (%)	185 (50.4)	59 (39.1)	0.229	62 (45.6)	59 (43.4)	0.044
ASA score, no (%)			0.113			0.065
Ⅱ	33 ( 9.0)	10 ( 6.6)		8 ( 5.9)	10 ( 7.4)
Ⅲ	250 (68.1)	101 (66.9)		96 (70.6)	93 (68.4)
Ⅳ	84 (22.9)	40 (26.5)		32 (23.5)	33 (24.3)
BMI (kg/m²)	32.04 (13.41)	31.23 (8.47)	0.072	32.53 (12.06)	31.76 (8.71)	0.073
PASP (mmHg)	44.06 (12.71)	46.31 (12.84)	0.176	45.06 (11.99)	45.68 (12.68)	0.050
Tobacco, no (%)	185 (50.4)	76 (50.3)	0.002	63 (46.3)	71 (52.2)	0.118
OSA, no (%)	91 (24.8)	42 (27.8)	0.069	38 (27.9)	39 (28.7)	0.016
ICU bed needed for surgery, no(%)	31 ( 8.4)	9 ( 6.0)	0.096	7 ( 5.1)	9 ( 6.6)	0.063
Procedure length (minutes)	117.88 (113.40)	111.53 (113.38)	0.056	126.61 (117.74)	113.86 (113.61)	0.110
Open surgical, no(%)	200 (54.5)	73 (48.3)	0.123	69 (50.7)	68 (50.0)	0.015
RCRI high risk surgery, no(%)	116 (31.6)	37 (24.5)	0.159	29 (21.3)	34 (25.0)	0.087
Systemic hypertension, no(%)	237 (64.6)	105 (69.5)	0.106	96 (70.6)	97 (71.3)	0.016
Asthma, no(%)	48 (13.1)	23 (15.2)	0.062	19 (14.0)	21 (15.4)	0.042
COPD, no(%)	47 (12.8)	24 (15.9)	0.088	14 (10.3)	20 (14.7)	0.134
Diabetes, no(%)	118 (32.2)	31 (20.5)	0.266	35 (25.7)	31 (22.8)	0.069
Renal failure, no(%)	93 (25.3)	34 (22.5)	0.066	34 (25.0)	30 (22.1)	0.069
ASA: American Society of Anesthesiology, BMI: body mass index, PASP: pulmonary artery systolic pressure, ICU: intensive care unit, OSA: obstructive sleep apnea, RCRI: Revised Cardiac Risk Index , COPD: chronic obstructive pulmonary disease

Table 2. Association between atrial fibrillation and postoperative delirium in the crude analysis, multivariable analysis, and propensity-score analysis.

Analysis	POD	P-value
No.of events(%)
No atrial fibrillation	7/373(1.9%)
atrial fibrillation	9/154(5.8%)
crude analysis-odd ratio(95%CI)	3.26 (1.19 ~ 8.92)	0.021
Multivariable analysis-odd ratio(95%CI)^a	3.64 (1.1 ~ 12.11)	0.035
Adjusted for Propensity Score ^b	3.14 (1.08 ~ 9.13)	0.035
With Matching ^c	3.14 (0.83 ~ 11.87)	0.091
With IPTW ^d	3.02 (1.11 ~ 8.19)	0.030
Adjusted covariates: age, sex, ASA, BMI, tobacco, PASP, OSA, ICU bed needed for surgery, procedure length, open surgery, RCRI high risk surgery, systemic hypertension, asthma, COPD, diabetes and renal failure
a Shown is the odd ratio from the multivariable logistic regression model, with adjusted for all covariates in table.
b Shown is the odd ratio from a multivariable logistic regression model with the same strata and covariates, with additional adjustment for the propensity score.
c Shown is the odd ratio from a multivariable logistic regression model with the same strata and covariates with matching according to the propensity score. The analysis included 136 patients(136 who had atrial fibrillation and 136 who had not)
d shown is the primary analysis with an odd ratio from the multivariable logistic regression model with the same strata and covariates with inverse probability weighting according to the propensity score.

Sensitivity analyses

In the full cohort (N = 518), atrial fibrillationter adjustment for all covariates in Table 1, multivariable logistic regression analysis similarly demonstrated that atrial fibrillation was associated with an increased risk for postoperative delirium (OR = 3.64; 95% CI, 1.1–12.11, P = 0.035). We further adjusted for propensity score, the relationship remained stable (OR = 3.14; 95% CI, 1.08–9.13, P = 0.035). (Table 2).

In this retrospective propensity score-matched cohort study, atrial fibrillation with PHTN had a higher risk-adjusted POD, compared with non-atrial fibrillation patients underwent non-cardiac, non-obstetric surgery. This association was reliable in additional models.

In our study, 29% (151/518) of patients with PHTN and atrial fibrillation, which was higher than the proportion of patients reported in previous studies. They reported that 4%-16% of patients with PHTN had atrial fibrillation^[7]. The main reason for this gap may be that with advances in technology, smart devices can easily be used in large-scale population screening for undiagnosed atrial fibrillation. Another reason may be related to obesity. The average BMI of patients in this study was greater than 31 kg/m², which was confirmed in other studies^[14]. Studies found that the occurrence of perioperative atrial fibrillation associated with POD, but most of them focused on cardiac surgery^[15–18].

The incidence of POD in the present study was 5.8%, which was different from the literature reported before, this discrepancy may be due to anesthesia, surgery and study populations^[19–21]. Delirium varies significantly between surgical populations. In high-risk surgery, 36% − 40% of adult patients developed to postoperative delirium, whereas in low-risk surgery, the prevalence of delirium is 5% − 10%^[22]. Most POD studies focused on the elderly, hip and cardiac surgery^{[20, 23, 24]}. However, the patients only recruited were an adult underwent non-cardiac, non-obstetric surgery in this study, and the time and method for POD assessment was not provided in sufficient detail in the original paper^[10], which may lead to the differences in the incidence rate of POD.

It is still unclear the mechanism of atrial fibrillation associated with POD in patients with PHTN. Vascular-mediated cognitive decline is one of the aspects in most studied^[25–28], the rate of cardio-embolic stroke attributable to atrial fibrillation will continue rising with the aging of the population^{[28, 29]}. The simultaneous conditions of atrial fibrillation leading to micro-embolization phenomena could be responsible for the occurrence of delirium^[18]. These micro-emboli could lead to low perfusion of some brain regions and cerebral ischemia, which could represent the mechanisms for the onset of delirium. Published studies have revealed the presence of micro-emboli during cardiac surgery detected by imaging exams such as trans-cranial Doppler, but the results remain contradictory^[30]. Another heart rate variability analysis found that preoperative high-frequency power was significantly lower in delirium patients than in non-delirium patients. Preoperative measurement of heart rate variability may be a useful predictor of delirium^[31]. Therefore, abnormal autonomic activity may also be one of the mechanisms of postoperative delirium.

Fátima R et al. found that atrial fibrillation after surgery was identified as an independent risk factor for delirium after cardiac surgery, but persistent preoperative atrial fibrillation was not a predictor of delirium after intervention^[18]. The occurrence of postoperative atrial fibrillation and its association with delirium favors the hypothesis of cerebral embolism and/or hypotension leading to a reduction of cerebral perfusion that can explain the pathophysiology of delirium^{[32, 33]}. However, We found that preoperative atrial fibrillation is an independent risk factor for POD, and the results of multi-model adjustment after controlling for confounding factors are still robust ( OR = 3.64, 95% CI: 1.1–12.11, P = 0.035). The sensitivity analysis results also support our results (OR = 3.14, 95% CI: 1.08–9.13, P = 0.035). In addition, our results are consistent with previous studies, they believe that preoperative atrial fibrillation is one of the main inducing factors and independent predictors of postoperative delirium^{[15, 16]}, and can even be used as a non-mental predictor of postoperative delirium^[6].

Whatever, we still need high attention to vascular-mediated cognitive decline in patients with atrial fibrillation. Preventing postoperative delirium and cognitive decline in patients with atrial fibrillation is an important goal of ongoing research. Better methods to prevent vascular brain injury will be necessary to prevent an increasing neurologic burden that will accompany the expected increase in the prevalence of atrial fibrillation in the coming decades. What’s more, we need to explore some biomarkers-based risk predictors for early detection of postoperative cognitive injury. In addition, preoperative atrial fibrillation is inevitable. We should pay attention to effective prevention of atrial fibrillation while focusing on prevention of complications. Studies have shown that effective prevention can reduce cardiovascular mortality and hospitalization rates in patients with a history of atrial fibrillation^[34].

We acknowledge limitations with our secondary analysis in a retrospective study. First, the potential for residual confounding may exist, as with all retrospective analyses. We adjusted as many possible confounders as we could and balanced participants in the propensity score-matched cohorts in this study. Second, the study population was comprised only patients with PTHN underwent elective non-cardiac, non-obstetric surgery; therefore, our findings may not be generalizable to other patients. In addition, the study cohort included very few patients with severe PHTN (PASP ≥ 59 mmHg), likely due to the lower prevalence of severe PHTN and the tendency to avoid elective surgery in such high-risk patients. We did not do any subgroup analysis. Nevertheless, our results remained robust and reliable in adjusted multivariable logistic regression analysis. Third, the original study did not include a matched case control cohort of non-PHTN patients, and thus cannot make conclusions about outcome differences between patients with and without PHTN. Finally, we were unable to distinguish the number of patients with postoperative atrial fibrillation due to the lack of a specific description of the type of new postoperative arrhythmias in the original article, so no further analysis of the association between postoperative atrial fibrillation and POD was performed.

Our findings suggested that atrial fibrillation was associated with POD in PHTN patients who underwent elective non-cardiac, non-obstetric surgery. Thus, these patients should be adequately assessed, monitored and controlled in early stage. This association warrants further investigation

ASA = American Society of Anesthesiologists; PASP = pulmonary artery systolic pressure; PHTN = pulmonary hypertension; POD = postoperative delirium; COPD = chronic obstructive pulmonary disease; BMI = body mass index; FS = functional status; METs = metabolic equivalents of task; RCRI = revised cardiac risk index; LOS = length of stay; ICU = intensive care unit; OSA = obstructive sleep apnea; DSM5 = Diagnostic and Statistical Manual of Mental Disorders, 5th edition; SD = standard deviation; SMD = standardized mean difference; ANOVA = analysis of variance; PSM = propensity score matching; IPW = inverse probability weighting

Acknowledgments

We gratefully thanks the Free Statistics team for providing technical assistance and valuable tools for data analysis and visualization. Wenlong Yao of Department of Anesthesiology, Tongji Hospital and Wenzhen Yuan of Department of Oncology Surgery, First Hospital of Lanzhou University for their support of the comments to the manuscript. Thanks all my friends and relatives who helped me.

Authors’ contributions

Hong Zhang designed the study, analyzed the data, wrote and revised the manuscript. Lei Zhang, Lu Gan, Haihui Ning, Bomin Xing and Xiaoxiao Zhang Participated in the research design. Yatao Liu reviewed the manuscript. All authors read and approved the final manuscript.

Funding

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Availability of data and materials

The data will be available for free at Dryad data package (https://doi.org/10.5061/dryad.9236ng5).

Ethics approval and consent to participate

The ethical approval statement was obtained from the University of Washington Institutional Review Board. Because this was a secondary analysis of a retrospective cohort study and patients in the original data are anonymous, the requirement for written informed consent was waived by the First Hospital of Lanzhou University Ethics Committee. All reporting followed the STROBE guidelines. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Consent for publication

Not Applicable.

Competing interests

The authors declared no conflict of interest.

Author details

1Department of Anesthesiology and Operating Theater, the First Hospital of Lanzhou University, No. 1Donggang West Road, Chenguan District, Lanzhou,730013, Gansu, China

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No competing interests reported.

Association between preoperative atrial fibrillation and postoperative delirium in patients with pulmonary hypertension: A secondary analysis of a retrospective cohort study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Setting and subjects

Demographic And Clinical Characteristics

Procedure

Postoperative Delirium

Statistical analysis

Results

Samples

Baseline Characteristics

Primary Outcome

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1