Blood Urea Nitrogen to Serum Albumin Ratio in critically ill patients with aspiration pneumonia : A retrospective cohort study

doi:10.21203/rs.3.rs-4716954/v1

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Blood Urea Nitrogen to Serum Albumin Ratio in critically ill patients with aspiration pneumonia : A retrospective cohort study

https://doi.org/10.21203/rs.3.rs-4716954/v1

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Background: We aimed to investigate the relationship between the blood urea nitrogen (BUN) to serum albumin ratio (BAR) in critically ill patients with aspiration pneumonia (AP).

Methods: This is a retrospective cohort study from the Medical Information Mart for Intensive Care III (MIMIC-III) database, in which patients in ICU were diagnosed with aspiration pneumonia.The receiver operating characteristic (ROC) curve and survival curves were plotted to evaluate predictive performance of BAR.The multivariable Cox regression analysis were used to investigate the relationship between BAR and mortality. And subgroup analyses were stratified by relevant covariates.

Results: A total of 1121 critically ill patients with AP were enrolled in our study.

Patients in 28-day non-survivor group had significantly higher levels of BAR (P<0.001). The area under the curve (AUC) for predicting 28-day mortality, 90-day and 365-day mortality of BAR (0.693, P<0.001; 0.701, P<0.001; 0.703, P<0.001) was superior to that of SOFA scores or CURB-65 scores. Kaplan-Meier curves also showed similar results (P < 0.001). Multivariate cox regression analysis showed that BAR was a significant risk factor predicting 28-day (HR 1.89, 95%CI 1.37-2.60, P<0.001), 90-day (HR 1.76, 95%CI 1.34-2.31, P<0.001) and 365-day (HR 1.50, 95%CI 1.18-1.89, P=0.001) mortality even after adjusting for confounding variables such as age, gender, ethnicity, risk factors, comorbidities, interventions, score system, vital signs and laboratory results. This also showed intuitively in the restricted cubic spline curve. Moreover, subgroup analyses demonstrated a significant association between elevated BAR and 28-day mortality in mostly relevant subclasses.

Conclusions: Easily accessible BAR is a good prognostic predictor for critically ill patients with AP.

Health sciences/Biomarkers/Prognostic markers

Health sciences/Diseases/Respiratory tract diseases

blood urea nitrogen to serum albumin ratio

critical ill

aspiration pneumonia

Aspiration pneumonia (AP) is a common inflammatory disease caused by aspiration of bacteria-rich oropharyngeal or gastrointestinal secretions into the lungs. Epidemiological studies show that AP predominantly affects older people with swallowing difficulties, in whom it is a major cause of death.^1–2 Rough estimates have suggested that AP accounts for 5–15% of community-acquired pneumonia, and carries a high mortality of around 10–33%.^3–4 With the global population expanding and aging, the incidence of AP with risks of severity and mortality will continue increasing. AP has a wide range of clinical features from mild symptoms to sever acute respiratory distress syndrome (ARDS).Though there are several complex scoring systems and new biomarkers to predict the severity and prognosis of patients with AP.^5–6 The exploration for availability of additional AP biomarkers from routine laboratory tests is still meaningful and low-cost, might improve patient management.

Blood urea nitrogen (BUN) and serum albumin are routine biochemical parameters. Previous studies have found that high BUN and hypoalbuminaemia are associated with poor clinical outcome of critically ill patients with pulmonary diseases.^7–8 Investigators further integrate these two parameter together as BUN to serum albumin ratio (BAR), to reduce influencing factors of albumin or BUN alone like liver diseases and kidney reserve. And BRA has showed a better predictive value in many pulmonary diseases such as chronic obstructive pulmonary disease, acute pulmonary embolism, lung cancer,and pneumonia.^9–12 However, there are only a few researches about BAR in patients with AP, with small sample size or only focusing on the emergency patients without severity classification.¹³ Data on BAR in critically ill patients with AP remains scant.Here, we initially investigated the correlation between BAR and prognosis of critically ill patients with AP.

Data Sources

All the data in this retrospective cohort study from the Medical Information Mart for Intensive Care III (MIMIC-III), which was a single-center and freely accessible database and contained 58,976 ICU admissions during 2001 to 2012 in the Beth Israel Deaconess Medical Center in Boston.

This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines and the Helsinki Declaration. Our access to the database was approved by the institutional review boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center after Xia completed the National Institutes of Health’s web-based course and passed the Protecting Human Research Participants exam(no.44913099). Moreover, considering that this was a retrospective study and all patients in this study were extracted from public database, informed consent was waived.

Patients

Adult patients who were diagnosed with aspiration pneumonia using the ninth of International Classification of Diseases [ICD]-9 code (code = 5070, pneumonitis due to inhalation of food and vomit) were included in this study, and only the first admission to the hospital and ICU were considered. While our exclusion criteria were as follows: (1) no BAR measurement on the first day of ICU admission; (2) stayed in the ICU less than 24h; (3) missing > 5% individual data.

Data Extraction

We used the Structured Query Language to extract the data. We extracted or calculated the following variables, including the baseline characteristics (age, gender, ethnicity), the vital signs (temperature, heart rate, respiratory rate, mean arterial pressure, Spo2, glucose), the risk factors leading to aspiration pneumonia (neurological disease, esophageal reflux, dysphagia), the patients’ comorbidity (hypertension, diabetes, coronary disease, chronic pulmonary disease, chronic kidney disease, liver disease, heart failure, tumor, sepsis, acute respiratory distress syndrome), the laboratory parameters (white blood cell, hemoglobin, platelet, creatinine, albumin, blood urea nitrogen, potassium, sodium, chloride, anion gap, bicarbonate), the severity scoring systems (Sequential Organ Failure Assessment, SOFA; Simplified Acute Physiology Score, SAPS II; Confusion, Urea, Respiratory rate, Blood pressure plus age ≥ 65 years, CURB-65), and the treatment received (mechanical ventilation, vasopressor therapy, renal replacement therapy). All data was estimated for all patients within 24h of ICU admission, and only the first parameters were enrolled. The disease were defined by ICD-9 codes.

The BAR (mg/g) was calculated as BUN (mg/dL) divide serum albumin (g/dL). 28-day, 90-day and 365-day mortality were the main clinical endpoints. Data regarding patient death was obtained from Social Security Death Index.

Statistical analysis

For extreme values, we set variables with values below the 1st percentile to the 1st percentile value and values greater than the 99th percentile to the 99th percentile value. All variables had < 5% missing data and these missing data were replaced by their mean values. Continuous variables were expressed as the mean ± SD for normally distributed continuous variables and median (interquartile range) for non-normally distributed continuous variables. Differences between groups were identified using the Student’s t-test or Kruskal–Wallis test. Categorical variables were reported as number (percentage), and comparisons between groups were made using the chi-square test or Fisher’s exact test as appropriate.

We constructed receiver operating characteristic (ROC) curve to compare the predictive performance of BAR, SOFA, SAPS II and CURB-65 in predicting mortality, then determine the best cutoff value of BAR for predicting 28-day mortality, on the basis of which to divided patients into two groups. Kaplan-Meier curves were also applied to assess the differences between the two groups in 365-day survival rate. Moreover, multivariate Cox proportional hazards regression analysis with restricted cubic spline were applied to evaluate the association between BAR and the risk of the mortality. BAR was analyzed both as a continuous and a categorical variable. Variables with P < 0.05 in the univariate analysis and variables with clinical significance were included into the multivariate Cox proportional hazard regression analysis to exclude confounding factors. We also used stratification and interaction analyses to examine the effect of BAR among subgroups using various parameters and comorbidities. Each stratifification was adjusted for baseline characteristics, risk factors, comorbidities and treatment received except for the stratifification factor itself. A p < 0.05 was considered statistically significant. All statistical analyses were performed using Stata/SE 16.0.

Patient Characteristics

A total of 1121 adult patients with aspiration pneumonia in ICU from the MIMIC-III database were included in this study.The baseline characteristics of the patients were summarized in Table 1. 840 were included in the 28-day survivors group and 281 were included in the 28-day non-survivors group. The average age of the participants was 64.52 ± 18.12 years, and females accounted for 37.82%. Most of the participants were caucasian. Patients in 28-day non-survivors group were more likely to be older, have specific host factors, frailty vital signs, higher severity scores and more comorbidities. They had have higher respiratory rate, glucose, white blood cell counts, blood urea nitrogen, BAR, serum creatinine, sodium, chloride, anion gap, SOFA score, SAPS II score, and CURB-65 score. Meanwhile, they had have lower temperature, mean arterial pressure, platelet counts, hemoglobin, serum albumin, bicarbonate. These 28-day non-survivors also tended to have a history of neurovascular disease, hypertension, diabetes, chronic kidney disease, heart failure, tumor, sepsis. Additionally, vasopressor were used more frequently in non-survivors.

Table 1

Comparison of Baseline Characteristics Between the Survivors Group and Non-Survivors Group
Variables	Total cohort N = 1121	28-day survivors N = 840	28-day non-survivors N = 281	P-value
Baseline characteristics
Age, years	64.52 ± 18.12	62.16 ± 18.19	71.59 ± 15.98	< 0.001
Female, n (%)	424(37.82)	313(37.26)	111(39.50)	0.503
Ethnicity, n (%)				0.191
Caucasian	805(71.81)	608(72.38)	197(70.11)
Black	73(6.51)	59(7.02)	14(4.98)
Others	243(21.68)	173(20.60)	70(24.91)
Vital signs within 24h after ICU admission
Temperature, ◦C	36.99 ± 0.79	37.07 ± 0.74	36.73 ± 0.87	< 0.001
HR, beats/min	89.32 ± 16.71	89.47 ± 16.27	88.87 ± 17.98	0.600
RR, beats/min	20.57 ± 4.48	20.29 ± 4.44	21.42 ± 4.48	< 0.001
MAP,mmHg	78.70 ± 11.06	79.19 ± 11.11	77.24 ± 10.82	0.011
Spo2,%	97.28 ± 1.97	97.32 ± 1.92	97.15 ± 2.12	0.218
Glucose, mg/dL	144.62 ± 46.87	142.05 ± 45.44	152.30 ± 50.20	0.002
Risk factor, n (%)
Neurovascular disease	93(8.30)	61(7.26)	32(11.39)	0.030
Esophageal reflux	108(9.63)	77(9.17)	31(11.03)	0.359
Dysphagia	221(19.71)	189(22.50)	32(11.39)	0.001
Comorbidity, n (%)
Hypertension	102(9.10)	64(7.62)	38(13.52)	0.003
Diabetes	246(21.94)	173(20.60)	73(25.98)	0.059
Coronary disease	138(12.31)	103(12.26)	35(12.46)	0.932
Chronic pulmonary disease	205(18.29)	150(17.86)	55(19.57)	0.520
Chronic kidney disease	121(10.79)	78(9.29)	43(15.30)	0.005
Liver disease	122(10.88)	87(10.36)	35(12.46)	0.328
Heart failure	279(24.89)	195(23.21)	84(29.89)	0.025
Tumor	71(6.33)	40(4.76)	31(11.03)	< 0.001
Sepsis	230(20.52)	138(16.43)	92(32.74)	< 0.001
ARDS	107(9.55)	81(9.64)	26(9.25)	0.847
Score system
SOFA	5.99 ± 3.37	5.61 ± 3.18	7.15 ± 3.66	< 0.001
SAPS II	54.13 ± 22.01	50.81 ± 20.62	64.07 ± 23.05	< 0.001
CRUB-65	3.00 ± 1.11	2.84 ± 1.10	3.46 ± 1.00	< 0.001
Laboratory data on the first day after ICU admission
WBC counts, k/uL	12.94 ± 7.03	12.68 ± 6.84	13.71 ± 7.51	0.032
Platelet counts, k/uL	216.58 ± 115.27	220.28 ± 116.06	205.51 ± 112.37	0.063
Hemoglobin, g/dL	11.13 ± 2.08	11.27 ± 2.04	10.71 ± 2.12	< 0.001
Blood urea nitrogen, mg/dl	28.75 ± 22.02	25.45 ± 19.48	38.62 ± 25.92	< 0.001
Serum albumin, g/dL	2.97 ± 0.63	3.01 ± 0.61	2.84 ± 0.67	< 0.001
BAR, mg/g	10.54 ± 9.49	9.13 ± 8.13	14.77 ± 11.78	< 0.001
Serum creatinine, mg/dL	1.39 ± 1.23	1.31 ± 1.23	1.62 ± 1.21	< 0.001
Potassium, mmol/L	4.01 ± 0.73	4.00 ± 0.74	4.05 ± 0.71	0.261
Sodium, mmol/L	140.07 ± 6.12	139.83 ± 5.84	140.79 ± 6.86	0.024
Chloride, mmol/L	106.15 ± 7.26	105.86 ± 7.06	107.00 ± 7.77	0.023
Anion gap, mEq/L	15.07 ± 3.79	14.79 ± 3.54	15.89 ± 4.37	< 0.001
Bicarbonate, mEq/L	22.82 ± 4.74	23.06 ± 4.62	22.10 ± 5.02	0.003
Therapies on the first day after ICU admission, n (%)
Ventilator use	779(69.49)	588(70.00)	191(67.97)	0.523
Renal replacement therapy use	40(3.57)	27(3.21)	13(4.63)	0.269
Vasopressor use	389(34.70)	265(31.55)	124(44.13)	< 0.001

HR, heart rate; RR, respiratory rate; MAP, mean arterial pressure; ARDS, acute respiratory distress syndrome; SOFA, Sequential Organ Failure Assessment; SAPSII, Simplifified Acute Physiology Score; CURB-65, Confusion, Urea, Respiratory rate, Blood pressure plus age ≥ 65 years; WBC, white blood cell; BAR, blood urea nitrogen to serum albumin ratio.

Association Between BAR and AP

The ROC curves generated by variables (BAR, SOFA scores, SAPS II scores and CURB-65 scores) for prediction of mortality are shown in Fig. 1. BAR had a higher area under the curve (AUC) value than that of SOFA scores and CURB-65 scores in prediction of mortality (28-day mortality: BAR AUC = 0.693 (95% CI 0.658–0.727) vs. SOFA AUC = 0.623 (95% CI 0.586–0.660) vs. CURB-65 AUC = 0.653 (95% CI 0.618–0.687), P = 0.004; 90 day mortality: BAR AUC = 0.701 (95% CI 0.670–0.733) vs. SOFA AUC = 0.617 (95% CI 0.583–0.651) vs. CURB-65 AUC = 0.675 (95% CI 0.644–0.706), P < 0.001; 365 day mortality: BAR AUC = 0.703 (95% CI 0.670– 0.733) vs. SOFA AUC = 0.594 (95% CI 0.561–0.6271) vs. CURB-65 AUC = 0.682 (95% CI 0.652–0.712), P < 0.001), and was similar to the SAPS II scores (28-day mortality: vs. SAPS II AUC = 0.718 (95% CI 0.685– 0.750), P = 0.201; 90-day mortality: vs. SAPS II AUC = 0.716 (95% CI 0.685– 0.746), P = 0.400; 365-day mortality: vs. SAPS II AUC = 0.715 (95% CI 0.685– 0.744), P = 0.476) (Fig. 1A-C). Besides, Kaplan–Meier curves showed the 365-day survival difference between the two BAR groups (Fig. 1D). Patients with high BAR levels had significantly higher 365-day mortality risk than those with lower BAR levels (log-rank p < 0.001).

According to ROC curve analysis, the optimal cutoff value of BAR for predicting 28-day mortality was 8.03 mg/g. This cutoff value was then used to classify the patients into low (≤ 8.03) and high (> 8.03) BAR group. The clinical outcomes of the subjects across the BAR are displayed in Table 2. 28-day mortality was 25.07%, 90-day mortality was 33.99%, and 487(43.44%) patients had died after 365-day follow-up. Patients with high BAR levels showed significantly higher rate of ICU, in-hospital, 28-day, 90-day and 365-day mortality, and longer lengths of ICU and hospital stays compared to patients with low BAR levels.

Table 2

Outcomes of subjects across the BAR strata
Variables	All patients N = 1121	BAR ≤ 8.03 N = 600	8.03<BAR N = 521	P-value
Primary outcome
28-day mortality, n (%)	281(25.07)	82(13.67)	199(38.20)	< 0.001
90-day mortality, n (%)	381(33.99)	120(20.00)	261(50.10)	< 0.001
365-day mortality, n (%)	487(43.44)	174(29.00)	313(60.08)	< 0.001
Secondary outcomes
ICU mortality, n (%)	153(13.65)	48(8.00)	105(20.15)	< 0.001
In-hospital mortality, n (%)	247(22.03)	75(12.50)	172(33.01)	< 0.001
Time in ICU, days	4.89[2.74,10.54]	4.77[2.58,9.87]	5.10[2.81,11.21]	0.028
Time in hospital, days	12.28[7.42,21.11]	11.88[6.97,20]	13.02[7.91,22.16]	0.015

In multivariable cox regression analysis, which was shown in Table 3, we further evaluate the significance of BAR in predicting unfavorable outcomes. After adjusting for age, gender, ethnicity, risk factors, comorbidities, interventions, score system, vital signs and laboratory results, we found that high BAR was still associated with higher risks of the 28-day(HR 1.89, 95%CI 1.37–2.60, P < 0.001) ,90-day (HR 1.76, 95%CI 1.34–2.31, P < 0.001) and 365-day (HR 1.50, 95%CI 1.18–1.89, P = 0.001) mortality.

Table 3

Multivariable cox regression analysis of baseline BAR for mortality
Exposure	Non-adjusted		Adjust I		Adjust II		Adjust III
Exposure	HR(95%CI)	p-value	HR(95% CI)	p-value	HR(95% CI)	p-value	HR(95% CI)	p-value
28-day mortality
BAR	1.04(1.03,1.05)	< 0.001	1.03(1.02,1.04)	< 0.001	1.03(1.02,1.04)	< 0.001	1.02(1.00,1.03)	0.008
Low BAR(≤ 8.03)	1.00(Reference)		1.00(Reference)		1.00(Reference)		1.00(Reference)
High BAR(>8.03)	3.23(2.50,4.18)	< 0.001	2.70(2.07,3.52)	< 0.001	2.39(1.81,3.15)	< 0.001	1.89(1.37,2.60)	< 0.001
90-day mortality
BAR	1.04(1.03,1.05)	< 0.001	1.03(1.02,1.04)	< 0.001	1.03(1.02,1.04)	< 0.001	1.02(1.00,1.03)	0.014
Low BAR(≤ 8.03)	1.00(Reference)		1.00(Reference)		1.00(Reference)		1.00(Reference)
High BAR(>8.03)	3.10(2.50,3.85)	< 0.001	2.46(1.97,3.07)	< 0.001	2.24(1.77,2.82)	< 0.001	1.76(1.34,2.31)	< 0.001
365-day mortality
BAR	1.04(1.03,1.04)	< 0.001	1.03(1.02,1.04)	< 0.001	1.03(1.02,1.03)	< 0.001	1.01(1.00,1.02)	0.022
Low BAR(≤ 8.03)	1.00(Reference)		1.00(Reference)		1.00(Reference)		1.00(Reference)
High BAR(>8.03)	2.76(2.29,3.32)	< 0.001	2.14(1.77,2.59)	< 0.001	1.92(1.57,2.34)	< 0.001	1.50(1.18,1.89)	0.001
Adjusted I for age, gender and ethnicity;

Adjusted II for age, gender, ethnicity, neurovascular disease, dysphagia, hypertension, diabete, chronic kidney disease, liver disease, heart failure, sepsis, ARDS, tumor, renal replacement therapy use, vasopressor use;

Adjusted III for age, gender, ethnicity, temperature, RR, MAP, glucose, WBC, hemoglobin, platelet, creatinine, albumin, blood urea nitrogen, potassium, sodium, chloride, anion gap, bicarbonate, SOFA, SAPS II, CURB-65, neurovascular disease, dysphagia, hypertension, diabete, chronic kidney disease, liver disease, heart failure, sepsis, ARDS, tumor, renal replacement therapy use, vasopressor use;

Besides, we used the restricted cubic spline to visually displayed the non-linear relationship between BAR and the 28-day, 90-day, 365-day all-cause mortality in Fig. 2.The results showed that mortality increased significantly as the BAR increased when BAR was low, but mortality increased slowly as the BAR increased if BAR was higher.

Subgroup Analyses

We performed subgroup analysis to explore whether BAR remained a prognostic factor in certain patient subgroups. Figure 3 demonstrated that BAR was an independent prognostic factor in almost subgroups except for patients with neurovascular disease, ARDS, and renal replacement therapy usage. Remarkably, the predictive significance of BAR appeared to be more prominent in patients without sepsis (Pinteraction = 0.019) and less prominent in patients who use renal replacement therapy in the first day after ICU admission (Pinteraction = 0.042).

Our retrospective cohort study demonstrated that BAR was significantly associated with mortality in ICU patients with AP even after adjusting for confounding variables, which also showed intuitively in the restricted cubic spline curve. And the predictive performance of BAR was superior to that of SOFA scores or CURB-65 scores and could be more effective in terms of long term mortality. Furthermore, BAR showed a significant interaction with sepsis in the 28-day mortality of critically ill patients with AP.

Previous studies have reported that BAR was strongly related to poor prognoses in patients with pneumonia, such as community-acquired pneumonia, hospital-acquired pneumonia, Coronavirus disease 2019, pneumocystis pneumonia and AP.^14–17 AP has more poor prognosis than non-aspiration pneumonia.¹⁸ And AP was more frequently needed intensive care therapy, which accounted for 23% severe pneumonia admitted to ICU as reported.¹⁹ There needs more attention to critically ill patients with AP. Findings from our study add evidence in critically ill patients that BAR was a simple and reliable predictor of poor outcomes in AP patients who were admitted to ICU, particularly in long term prognoses such as 90-day mortality and 365-day mortality. Besides, cumbersome scoring systems like SOFA scores, SAPS II scores or CURB-65 scores did not perform better predictive effect than BAR in our study. Given that BAR also had the benefit of being easily calculated, low-cost and not affected by the evaluator’s subjectivity, we suggest that BAR may be a good predictor for the prognosis of critically ill patients with AP instead of cumbersome scoring systems. It may help identify AP patients at high risk of mortality quickly and lead to better clinical management.

However, the underlying mechanisms that may account for the association between BAR and prognosis remains unclear. Elevated BUN or decreased serum albumin levels may attribute to it. BUN is an important parameter reflecting kidney perfusion, protein metabolism and nutritional status. An elevated BUN level has been found to be associated with increased mortality in critically ill patients with pneumonia.^20–21 Serum albumin known as colloid osmotic pressure maintainer, not only represents nutritional status and systemic inflammation,but also plays an important role in the destruction of free oxygen radicals generated from oxidative stress. The poor prognostic significance of hypoalbuminemia has been identified in patients with aspiration pneumonia in studies.²² Our study also showed elevated BUN and low serum albumin levels in 28-day non-survivor. Possible reasons for this may as followed. Firstly, AP is often combined with impaired swallowing, dehydration and malnutrition, which contribute to decreasing serum albumin levels and increasing reabsorption of urea by the kidneys.²³ Secondly, systemic inflammation caused by aspiration pneumonia may elevate protein catabolism and influence albumin synthesis. BAR might be an indicator of nutritional status and systemic inflammation of critically ill patients with AP.

In multivariate analysis, BAR was consistently related to adverse outcomes even after adjusting for confounding factors such as serum creatinine, chronic kidney disease, liver disease, suggesting its prognostic significance was independent of renal function and liver diseases. However, subgroup analysis suggested that the correlation between high BAR and 28-day mortality became non-significant in patients with neurovascular disease, ARDS, and renal replacement therapy usage. We speculated that this result could be related to the few sample sizes with eurovascular disease, ARDS in the total cohort. And the interaction was significant according with or without renal replacement therapy usage,which might connect with the activation of protein catabolism and the filtration of amino acid by renal replacement therapy.

Additionally, there were an interactions between sepsis and BAR on 28-day mortality; that is, BAR was significantly and positively associated with the risk of 28-day mortality in participants without sepsis. Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. ²⁴ Studies have identified high BAR levels in sepsis as a result of systemic inflammation, deficiency of effective circulating blood volume and acute kidney injury.²⁵ This might affect the prognostic senstveness and effectiveness of BAR. Our result suggests that BAR may be a more helpful predictor for AP patients without sepsis and without renal replacement therapy usage, consequently improving management strategies.

Nevertheless, Our study had some limitations. Firstly, we only take the initial BAR value without evaluating the dynamic changes during ICU, although this might provide better prognostic information.Secondly, the MIMIC III databases did not contain sufficient data on weight, diet, serum C-reactive protein, PCT levels, and some other related variables, which may be helpful to explore the underlying mechanisms such as nutrition and inflammation. Lastly, the retrospective nature of this study could have inevitably contained a certain level of selection bias.Therefore, further high-quality prospective multicenter studies are necessary to validate the prognostic value of BAR for critically ill patients with AP and investigate the underlying mechanisms

Author Contribution

All authors contributed to study design.Tingting Xia wrote the main manuscript text and prepared figures and tables.Shu Lei participated in critical revisions of the manuscript. All authors reviewed the manuscript.

Data Availability

Data in our study are available from the corresponding author upon reasonable request.

Data Sharing Statement

All data in our study are available from the corresponding author upon reasonable request.

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Blood Urea Nitrogen to Serum Albumin Ratio in critically ill patients with aspiration pneumonia : A retrospective cohort study

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