Increased thoracic fluid content is associated with higher risk for pneumonia in patients undergoing maintenance hemodialysis

Abstract

Background

Pneumonia is the most common infectious disease in patients undergoing maintenance hemodialysis (MHD). The aim of this study is to determine the possible predictive value of thoracic fluid content (TFC) for pneumonia in this population.

Method

Clinical data were recorded for 1412 MHD patients who were hospitalized for certain comorbidities or complications. Each patient underwent an impedance cardiography (ICG) examination before next dialysis session after admission. Patients were divided into Having-, Will-have-, and Non-pneumonia groups based on whether they had pneumonia at the time of ICG examination after the admission and within five months after the examination. Hemodynamic parameters and other clinical data were compared and analyzed.

Results

Patients who were going to develop pneumonia were older, and had a higher proportion of diabetes, poorer nutritional status, a higher level of inflammatory, poorer cardiac function, and more fluid volume load than those who did not develop pneumonia. Multivariate binary logistic analysis revealed that for each 1/KΩ increase in TFC and 1 increase in neutrophil-to-lymphocyte ratio (NLR), the risk of the development of pneumonia increased by 3.1% (p ˂ 0.01) and 7.2% (p = 0.035), respectively, whereas for each 1 g/L increase in hemoglobin and 1 g/L increase in serum albumin, the risk of the development of pneumonia decreased by 1.3% (p = 0.034) and 5% (p = 0.048), respectively.

Conclusions

TFC, NLR, hemoglobin, and serum albumin were independent risk factors for the development of pneumonia in MHD patients. Given the advantages of ICG, TFC can be used clinically as a helpful predictor of pneumonia in MHD patients.

Keywords:

• Thoracic fluid content
• pneumonia
• maintenance hemodialysis
• fluid volume overload

Background

Infectious diseases are common comorbidities in patients with end-stage renal disease (ESRD) receiving maintenance hemodialysis (MHD), in whom the mortality due to infections is second only to cardiovascular diseases. Of all infections in this population, pneumonia is the most frequent [1,2]. The incidence of pneumonia in patients undergoing MHD is much higher than that in the general population. It has been reported that the mortality caused by pneumonia in MHD patients was 14–16 times higher compared with the general population, and pneumonia could account for approximately 25–45.5% of deaths from infectious diseases in MHD patients [1,3]. Both the burden of pneumonia and the associated mortality is noteworthy, which encourages the utilization of prophylactic measures, timely diagnosis, and prompt initiation of appropriate treatment.

The susceptibility of MHD patients to pulmonary infections is generally believed to be mainly attributed to the abnormalities of pulmonary function as well as the decline in immunity [1]. However, the association between hydrostatic pulmonary edema and pulmonary infections also seems to warrant more attention, as ESRD patients undergoing MHD may be frequently at risk of pulmonary fluid overload during the inter-dialysis periods, especially in those with poor fluid control, recurrent congestive heart failure, and pulmonary edema. In the pulmonary edema state, bacterial colonization and infection are enhanced, whereas the host bactericidal capacity is diminished, and both of the alterations in the alveolar microenvironment predispose patients to pulmonary infection [4]. Therefore, it is speculated that close monitoring of thoracic fluid volume to warn against the occurrence of overload and even pulmonary edema may have clinical practical significance for preventing pulmonary infection in patients undergoing MHD.

With the development of new hardware and computational algorithms, impedance cardiography (ICG) is becoming more accurate and may provide a noninvasive alternative to hemodynamic monitoring [5,6]. Among the parameters monitored by ICG, thoracic fluid content (TFC) is an indicator for the assessment of changes in chest fluid volume [7]. Although a TFC value cannot be used to distinguish the location of excess fluid, it helps to indicate potential fluid overload, allowing further examination to localize fluid, such as pulmonary edema or pleural effusion. In most individuals, changes in the TFC values reflect altered fluid volume in the pulmonary intravascular and interstitial spaces [8]. Continuous monitoring of TFC has been used clinically to guide the regulation of fluid clearance during continuous renal replacement therapy.

Given that excessive pulmonary fluid is one of the potential risk factors for pulmonary infection, it is worth examining whether TFC value, to some extent, can be used to predict pulmonary infection in patients undergoing MHD. To clarify the predictive role of TFC value in the occurrence of pulmonary infection, we retrospectively investigated the correlation between TFC value and the occurrence of pneumonia in MHD patients. Data from this study indicate that the higher TFC value before dialysis sessions is an independent risk factor for pneumonia and can be used as a predictor of pulmonary infection in this population.

Methods

Patients

Between January 2017 and December 2019, all MHD patients admitted to our Medical Center for Kidney Disease due to some complication or comorbidity underwent an ICG examination prior to the next dialysis session after admission to assess hemodynamic status. After screening with the exclusion criteria, a total of 1412 patients were included in this retrospective study. The exclusion criteria were as follows: (1) dialysis vintage less than three months; (2) accompanied by any advanced malignancy; (3) death within five months after the ICG examination.

The 1412 patients were classified into three groups: 1) Having-pneumonia group, which included 177 patients who already had pneumonia at the time of the ICG examination; 2) Will-Have-pneumonia group, which included 101 patients who did not have pneumonia at the time of the ICG examination but had pneumonia at least once within five months after it; and 3) Non-pneumonia group, included 1134 patients who had no pneumonia at the time of the ICG examination and in the five months after it. The inclusion and grouping of patients are shown in Figure 1. The diagnosis of pneumonia required the demonstration of an infiltrate on chest imaging in a patient with a clinically compatible syndrome, e.g., fever, cough, sputum production, and dyspnea [9].

Figure 1. Flow chart of patient inclusion and grouping in this study.

All procedures in the study were performed in accordance with the ethical standards of the institutional and national research committee and the Declaration of Helsinki. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Nanjing Medical University (Ethical number: 2022KY-143-01).

Impedance cardiography

Bio-Noninvasive Hemodynamic Monitor (BioZ-2011, Medean Medical Equipment Co., Shenzhen, China) was used to noninvasively measure hemodynamic parameters. The monitoring principle has been described in detail in the previous study [10]. Briefly, the monitor used a 2.5 mA at 70 kHz current via atetrapolar system of four sensors, two of which were placed on the neck above the clavicle and two others were placed on the chest at the level of the tip of the xiphoid process. Each member of a pair were 180° apart, both at the top and at the bottom of the thoracic cavity. Voltage pickup electrodes on the sensors bridge the thorax for an axial measurement of voltage when current is introduced through the outer electrodes on each sensor. The algorithm allows the monitor to calculate hemodynamic-related parameters based on variations in thoracic bio-impedance due to changes in blood volume and velocity in the aorta. TFC, cardiac output (CO), cardiac index (CI), systemic vascular resistance index (SVRI), and left cardiac work index (LCWI) were measured and obtained directly from the monitor without using indwelling catheters. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate were measured concurrently. The placement of electrodes on the body surface is presented in Supplemental Figure 1.

Data collection

Collected data of each patient comprised gender, age, body mass index (BMI), the main reason (complication or comorbidity) for this hospitalization, dialysis history including dialysis vintage and frequency and inter-dialytic weight gain (IDWG, calculated as the difference between the pre-HD weight and the weight recorded after the previous session; the average of the sessions in a month was assessed as absolute IDWG, and relative IDWG was absolute IDWG divided by dryweight [11,12].), and medical history. Data from a routine blood test, chest imaging (X-ray or computed tomography), and ultrasonic cardiogram after the admission prior to the next dialysis session were also collected. For the patients admitted to the hospital twice or more between January 2017 and December 2019, only the data from the first admission were collected.

Statistical analysis

Kolmogorov–Smirnov test was used to determine the normal distribution of each variable. Variables with normal distribution were represented by mean ± standard deviation (SD), while variables with non-normal distribution were represented by median (1/4, 3/4). Cross-tabulation was used to compare rates between groups. For the variables with normal distribution, variance analysis was used to analyze the difference between groups, while for the variables with non-normal distribution, Kruskal–Wallis test was used. For variables with proven differences between groups, Bonferroni multiple analysis was further used to analyze the difference between any two groups. Univariate and multivariate logistic regression were used to analyze the risk factors affecting the occurrence of pneumonia, and variance inflation factor (VIF) were used to detect the severity of multicollinearity in all the screened risk factors. Data were analyzed using the special statistical software SPSS version 23 (SPSS Inc. Chicago, IL), and p < 0.05 was considered significant.

Results

Characteristics of the study population

The study population consisted of 1412 patients and was divided into three groups: Having-, Will-Have-, and Non-pneumonia, according to the criteria presented in Figure 1. The profiles of all patients in indifferent groups are presented in Tables 1 and 2.

Table 1. Baseline characteristics of the patients in this study.

Characteristics	Having-pneumonia group (n = 177)	Will-Have-pneumonia group (n = 101)	Non-pneumonia group (n = 1134)	p Value
Age (years)	62.07 (14.03)	57.45 (14.94)	51.99 (13.06)	0.025^a, <0.001^b, <0.001^c
Sex, male	110 (62.1)	58 (57.4)	623 (54.9)	0.190
BMI (kg/m^2)	21.90 (19.13, 24.29)	22.01 (19.37, 24.98)	22.06 (20.00, 24.16)	0.732
Dialysis vintage (months)	51 (22, 97)	69 (31, 114)	79 (42, 122)	0.142^a, <0.001^b, 0.365^c
Dialysis frequency				0.960
Two times per week	6 (3.4)	4 (4.0)	43 (3.8)
Three times per week	171 (96.6)	97 (96.0)	1091 (96.2)
Relative IDWG (%)	2.10 (0.90, 4.00)	3.23 (1.88, 5.66)	2.86 (1.47, 4.63)	0.016^a, 0.083^b, 0.301^c
Pleural effusion	96 (54.2)	48 (47.5)	100 (8.8)	0.282^a, <0.001^b, <0.001^c
History of complications
Diabetes	74 (41.8)	26 (25.7)	190 (16.8)	0.007^a, <0.001^b, 0.023^c
Hypertension	163 (92.1)	91 (90.1)	898 (79.2)	0.570^a, <0.001^b, 0.009^c
Cerebral infarction	47 (27.0)	19 (18.8)	125 (11.2)	0.126^a, <0.001^b, 0.023^c
Coronary heart disease	37 (20.9)	16 (15.8)	97 (8.6)	0.302^a, <0.001^b, 0.015^c

HD: hemodialysis; BMI: body mass index; IDWG: interdialytic weight gain.

Data are mean (SD), n (%), or median (1/4, 3/4).

^aHaving-pneumonia group versus Will-Have-pneumonia group;

^bHaving-pneumonia group versus Non-pneumonia group;

^cWill-Have-pneumonia group versus Non-pneumonia group.

Table 2. The main reasons for the hospitalization of MHD patients in this study.

	Total (n = 1412)	Having-pneumonia group (n = 177)	Will-Have-pneumonia group (n = 101)	Non-pneumonia group (n = 1134)
Secondary hyperparathyroidism	645 (45.7)	13 (7.3)	37 (36.6)	595 (52.5)
Problems related to vascular access for HD (except infection)
Catheter	40 (2.8)	5 (2.8)	5 (5.0)	30 (2.6)
Arteriovenous fistula	289 (20.5)	22 (12.4)	21 (20.8)	246 (21.7)
Cardiovascular or cerebrovascular disease
Angiocardiopathy	64 (4.5)	13 (7.3)	8 (7.9)	43 (3.8)
Acute cerebral accident	35 (2.5)	4 (2.3)	8 (7.9)	23 (2.0)
Infection
Pulmonary	94 (6.7)	94 (53.1)	0	0
Vascular access for HD	32 (2.3)	6 (3.4)	4 (4.0)	22 (1.9)
Digestive system	17 (1.2)	4 (2.3)	1 (1.0)	12 (1.1)
Urinary system	9 (0.6)	2 (1.1)	0	7 (0.6)
Sepsis	2 (0.1)	1 (0.6)	0	1 (0.1)
Hemorrhagic disease
Alimentary tract	6 (0.4)	1 (0.6)	1 (1.0)	4 (0.4)
Urinary tract	4 (0.3)	0	0	4 (0.4)
Others	175 (12.4)	12 (6.8)	16 (15.8)	147 (13.0)

Data are n (%).

There was no significant difference in sex ratio, BMI, or dialysis frequency between the three groups. The dialysis vintage in the Having-pneumonia group was shorter than that in the Non-pneumonia group (51 versus 79 months). The relative IDWG did not differ significantly between the Having-pneumonia and Non-pneumonia group (2.10 versus 2.86%) or between the Will-Have-pneumonia and Non-pneumonia group (3.23 versus 2.86%). Compared with the patients in the Non-pneumonia group, the patients in the Having- or Will-Have-pneumonia group were older (62.07 ± 14.03 years or 57.45 ± 14.94 years versus 51.99 ± 13.06 years), had a higher incidence of pleural effusion (54.2% or 47.5% versus 8.8%), and had a higher prevalence of several complications such as hypertension (92.1% or 90.1% versus 79.2%), diabetes (41.8% or 25.7% versus 16.8%), cerebral infarction (27.0% or 18.8% versus 11.2%), and coronary heart disease (20.9% or 15.8% versus 8.6%).

Due to the outstanding expertise of our Medical Center for Kidney Disease in dealing with secondary hyperparathyroidism and handling problems of vascular access for hemodialysis, these were the top two reasons for hospitalization in MHD patients in this study. As shown in Table 2, except for patients admitted for these two reasons, the most common causes of hospitalization in the remaining patients were cardiovascular and cerebrovascular events and infections. Pulmonary infection accounted for the highest proportion of infections requiring hospitalization, followed by vascular access infection, digestive system infection, and urinary system infection. In the Having-pneumonia group, 53.1% of patients were admitted due to pneumonia, while the remaining patients were complicated with pneumonia when admitted for some other complication.

Comparison of examination results between three groups of patients

To identify risk factors associated with the development of pneumonia, we compared parameters related to infection and inflammation, nutritional status, myocardial injury, and cardiac function among the three groups, which are shown in Table 3.

Table 3. Comparison between three groups of the patient in this study.

Parameters	Having-pneumonia group	Will-Have-pneumonia group	Non-pneumonia group	p Value
WBC (×10^9/L)	5.81 (4.65, 7.43)	5.76 (4.58, 7.13)	5.48 (4.42, 6.78)	0.580^a, 0.003^b, 0.911^c
NCP	72.80 (66.20, 79.50)	70.10 (64.75, 74.80)	67.10 (60.78, 72.50)	0.454^a, <0.001^b, 0.001^c
NLR	4.36 (2.99, 6.71)	3.83 (2.85, 5.21)	3.06 (2.28, 4.24)	0.543^a, <0.001^b, <0.001^c
CRP (>8 mg/L)	106 (60.6)	48 (49.0)	284 (26.0)	0.064^a, <0.001^b, <0.001^c
Hemoglobin (g/L)	100 (86, 112)	99 (86, 113)	107 (95, 120)	1.000^a, <0.001^b, <0.001^c
Albumin (g/L)	38.40 (34.70, 41.70)	39.20 (35.95, 42.30)	41.60 (38.30, 44.50)	1.000^a, <0.001^b, <0.001^c
Triglycerides (mmol/L)	1.34 (0.94, 1.74)	1.46 (0.97, 2.45)	1.45 (1.04, 2.11)	0.345^a, 0.037^b, 1.000^c
Total cholesterol (mmol/L)	3.30 (2.88, 4.03)	3.57 (2.98, 4.05)	3.63 (3.12, 4.30)	0.908^a, 0.016^b, 0.885^c
Uric acid (μmol/L)	390.00 (269.50, 451.00)	364.50 (290.25, 456.50)	416.00 (340.75, 481.25)	1.000^a, 0.019^b, 0.096^c
NT-proBNP (pg/mL)	17,106.00 (5274.00, 39,136.00)	12,443.50 (3636.25, 35,070.50)	4386.50 (2074.50, 10,959.00)	0.286^a, <0.001^b, <0.001^c
CK-MB (ng/mL)	2.00 (1.30, 3.20)	1.97 (1.15, 2.90)	1.70 (1.20, 2.70)	0.961^a, 0.013^b, 0.924^c
Troponin T (ng/mL)	0.08 (0.04, 0.12)	0.07 (0.05, 0.12)	0.05 (0.03, 0.08)	1.000^a, <0.001^b, <0.001^c
LVEF (%)	59 (55, 62)	58 (52, 62)	60 (57, 64)	1.000^a, 0.063^b, 0.023^c

WBC: white blood cell count; NCP: neutrophil count percentage; CRP: C-reactive protein; NLR: neutrophil-to-lymphocyte ratio; NT-proBNP: N-terminal B-type natriuretic peptide; LVEF: left ventricular ejection fraction; CK-MB: creatine kinase-MB.

Data are median (1/4, 3/4) or n (%).

^aHaving-pneumonia group versus Will-Have-pneumonia group;

^bHaving-pneumonia group versus Non-pneumonia group;

^cWill-Have-pneumonia group versus Non-pneumonia group.

The count of white blood cells (WBC), the neutrophil count percentage (NCP), the neutrophil-to-lymphocyte ratio (NLR), and the proportion of patients with a C-reactive protein (CRP) greater than 8 mg/L were all higher in the Having-pneumonia group than in the Non-pneumonia group, and there was no difference between the Having-pneumonia group and Will-Have-pneumonia group. The NCP [70.10 (64.75, 74.80) versus 67.10 (60.78, 72.50)], NLR [3.83 (2.85, 5.21) versus 3.06 (2.28, 4.24)], and the proportion of patients with a CRP greater than 8 mg/L (46.0% versus 26.0%) were higher in the Will-Have-pneumonia group than in the Non-pneumonia group, although there was no difference in WBC between the two groups. These data might suggest that more patients in the Will-Have-pneumonia group were in a potentially inflammatory state than those in the Non-pneumonia group.

The levels of hemoglobin, serum albumin, triglyceride, total cholesterol, and uric acid in the Having-pneumonia group were significantly lower than those in the Non-pneumonia group, but there were no significant differences in these indicators between the Having-pneumonia group and Will-Have-pneumonia group. It is worth noting that patients in the Will-Have-pneumonia group had lower levels of hemoglobin [99 (86, 113) g/L versus 107 (95, 120) g/L] and serum albumin [39.20 (35.95, 42.30) g/L versus 41.60 (38.30, 44.50) g/L] than those in the Non-pneumonia group. Therefore, it appears that more patients in the Will-Have-pneumonia group were malnourished than those in the Non-pneumonia group.

The levels of N-terminal B-type natriuretic peptide (NT-proBNP) did not differ in the Having-pneumonia group and Will-Have-pneumonia group, but both were significantly higher than in the Non-pneumonia group. The difference pattern of Troponin T among the three groups was similar to that of NT-proBNP, while the difference pattern of creatine kinase-MB (CK-MB) among the groups was inconsistent with that of Troponin T. There was no difference in the levels of CK-MB between the Will-Have- and non-pneumonia groups. In addition, compared with patients in the Non-pneumonia group, patients in the Will-Have-pneumonia group had a lower left ventricular injection fraction (LVEF). Analysis of these data suggested a higher proportion of cardiovascular damage in the Will-Have-pneumonia group compared to the Non-pneumonia group.

Comparison of measurement results in noninvasive hemodynamics between groups

The results of noninvasive hemodynamic measurement by ICG are shown in Table 4. There was no difference in TFC, CO, CI, SVRI, and LCWI between the Having-pneumonia group and Will-Have-pneumonia group. Compared with the Non-pneumonia group, TFC in both Having-pneumonia groups [39.55 (33.68, 47.90) 1/KΩ versus 34.50 (30.58, 39.43) 1/KΩ, p < 0.001] and Will-Have-pneumonia group [40.40 (34.55, 46.30) 1/KΩ versus 34.50 (30.58, 39.43) 1/KΩ, p < 0.001] were higher. In addition, SVRI was higher, and CI was lower in both groups than in the Non-pneumonia group. CO and LCWI were lower in the Having-pneumonia group than in the Non-pneumonia group, but there was no difference between the Will-have-pneumonia group and the Non-pneumonia group.

Table 4. Data of noninvasive hemodynamic measurement.

Parameters	Having-pneumonia group	Will-Have-pneumonia group	Non-pneumonia group	p Value
TFC (1/KΩ)	39.55 (33.68, 47.90)	40.40 (34.55, 46.30)	34.50 (30.58, 39.43)	1.000^a, <0.001^b, <0.001^c
CO (L/min)	4.20 (3.45, 5.30)	4.50 (3.30, 5.50)	4.70 (3.90, 5.60)	1.000^a, 0.002^b, 0.061^c
CI [L/(min•m^2)]	2.60 (2.20, 3.10)	2.70 (2.10, 3.18)	2.80 (2.40, 3.30)	1.000^a, 0.001^b, 0.031^c
SVRI [dynes/(sec•cm^-5•m^2)]	2982.00 (2351.00, 3755.00)	2845.00 (2429.00, 4177.00)	2682.50 (2212.25, 3381.38)	1.000^a, 0.013^b, 0.007^c
LCWI (kgm/m^2)	3.30 (2.70, 4.00)	3.70 (3.00, 4.60)	3.80 (3.00, 4.80)	0.080^a, <0.001^b, 1.000^c
Heart rate (beats/min)	80 (71, 88)	82 (71, 94)	78 (70, 88)	0.216
Systolic BP (mmHg)	146 (126, 166)	152 (133, 170)	141 (124, 159)	0.324^a, 0.322^b, 0.003^c
Diastolic BP (mmHg)	79 (68, 91)	83 (75, 97)	84 (73, 93)	0.063^a, 0.014^b, 1.000^c

TFC: thoracic fluid content; CO: cardiac output; CI: cardiac index; SVRI: systemic vascular resistance index; LCWI: left cardiac work index.

Data are median (1/4, 3/4).

^aHaving-pneumonia group versus Will-Have-pneumonia group;

^bHaving-pneumonia group versus Non-pneumonia group;

^cWill-Have-pneumonia group versus Non-pneumonia group.

Analysis of synchronously measured heart rate and blood pressure showed that heart rate did not differ between the three groups, with neither SBP nor DBP varying between the Having-pneumonia group and Will-Have-pneumonia group, while the Will-Have-pneumonia group had higher SBP than the Non-pneumonia group.

Analysis of risk factors for pneumonia in patients with MHD

VIF was used for testing the severity of multicollinearity in the variables with both differences between the Will-Have-pneumonia and Non-pneumonia group and no differences between the Having-pneumonia group and Will-Have-pneumonia group, and the appropriate variables were selected for logistic regression analysis as shown in Table 5. Multivariate binary logistic analysis was used to identify risk factors for the development of pneumonia. Variables with a p value of less than 0.05 in univariate analysis, including age, diabetes, NLR, hemoglobin, serum albumin, NT-proBNP, and TFC, were chosen for further binary logistic regression, and forward stepwise regression was utilized to identify risk factors for the development of pneumonia.

Table 5. Logistic regression analysis of risk factors for the development of pneumonia in MHD patients.

	Univariate analysis			Multivariate analysis
Variable	OR	95% CI	p Value	OR	95% CI	p Value
Age (years)	1.031	1.015–1.047	<0.001	1.017	0.998–1.035	0.074
Diabetes	1.722	1.074–2.763	0.024	1.313	0.764–2.255	0.325
NLR	1.118	1.061–1.179	<0.001	1.072	1.005–1.143	0.035
Hemoglobin (g/L)	0.977	0.967–0.988	<0.001	0.987	0.975–0.999	0.034
Albumin (g/L)	0.900	0.864–0.937	<0.001	0.950	0.902–1.000	0.048
NT-proBNP/1000 (pg/mL/1000)	1.006	1.003–1.009	<0.001	1.002	0.998–1.007	0.272
Troponin T (ng/mL)	1.522	0.718–3.224	0.273	–	–	–
TFC (1/KΩ)	1.051	1.030–1.072	<0.001	1.031	1.010-1.053	0.004
CI [L/(min•m^2)]	1.027	0.973–1.083	0.333	–	–	–

OR: odds ratio; 95% CI: 95% confidence interval; NLR: neutrophil–lymphocyte ratio; LVEF: left ventricular ejection fraction; TFC: thoracic fluid content; CI: cardiac index.

As shown in Table 5, for each 1/KΩ increase in TFC and 1 increase in NLR, the risk of the development of pneumonia increased by 3.1% (p ˂ 0.01) and 7.2% (p = 0.035), respectively; whereas for each 1 g/L increase in hemoglobin and 1 g/L increase in serum albumin, the risk of the development of pneumonia decreased by 1.3% (p = 0.034) and 5% (p = 0.048), respectively. These data suggested that the larger values of TFC and NLR and the lower levels of hemoglobin and serum albumin were all independent risk factors for the development of pneumonia in patients undergoing MHD.

Discussion

In this study, we retrospectively analyzed the clinical data of 1412 MHD patients who were hospitalized due to certain complications or comorbidities. In order to identify potential risk factors for developing pneumonia, we divided these patients into Having-, Will-Have-, and Non-pneumonia groups, based on whether they had developed pneumonia at the time of ICG examination after the admission and within five months after the examination. The comparative analysis between the groups showed that among these patients, those who were going to develop pneumonia were older, and had a higher proportion of diabetes, poorer nutritional status, higher levels of inflammatory markers, poorer cardiac function, and more fluid volume load than those who did not develop pneumonia. Further multivariate analysis confirmed that higher TFC and NLR and lower levels of hemoglobin and serum albumin were independent risk factors for the development of pneumonia in this population.

Many previous studies have attempted to explore the potential risk factors of pulmonary infection in MHD patients, and they have shown that older age, combined cardiovascular diseases and diabetes, and poorer nutritional status were associated with the development of pneumonia in this population [13–15]. In this study, older age and diabetes were positively correlated with the occurrence of pneumonia, while the levels of hemoglobin and serum albumin were negatively correlated with it, which is consistent with the previous reports. In addition, NLR has been used as one of infection markers, and it has been reported that NLR was a predictor of mortality not only for MHD patients [16,17], but also for patients hospitalized with community-acquired pneumonia [18]. In this study, the NLR value was higher in the Will-Have-pneumonia group than in the Non-pneumonia group even if the patients in the Will-have-pneumonia group had not have pneumonia when the blood test was performed. And the further analysis confirmed that NLR value was positively correlated with the occurrence of pneumonia, so it could be used as a predictor of pneumonia in MHD patients.

In addition to the factors mentioned above, another important factor associated with the development of pneumonia is heart failure. Previous studies have shown that approximately 40% of MHD patients had varying degrees of cardiac insufficiency [19,20], while cardiac insufficiency and associated fluid volume overload were correlated with the occurrence of pneumonia and all-cause mortality in MHD patients [21–23]. Cardiac function and fluid volume load are closely related and influence each other. Excessive fluid volume load is one of the contributors to heart failure, while cardiac insufficiency can also aggravate fluid overload. In this study, the analysis of the cardiac function and fluid volume load indicators showed that NT-proBNP and TFC were positively correlated with the occurrence of pneumonia, and TFC, but not NT-proBNP, was an independent risk factor for the development of pneumonia in MHD patients. Hence, not entirely consistent with previous studies, our findings suggested that TFC appeared to be more valuable for predicting pneumonia than NT-proBNP in MHD patients.

In clinical practice, the evaluation methods commonly used to determine the variations in fluid volume of patients with MHD mainly include IDWG, the presence of hypertension and edema, as well as intradialytic hypotension, etc. However, due to subjective factors, comorbidities, and nutritional status, these methods are not accurate enough and are only suitable for rough assessment in MHD patients with stable long-term conditions.

Several other indicators for objective assessment of fluid volume are NT-proBNP, inferior vena cava (IVC) collapsibility index, and B-lines in lung ultrasound [24,25]. NT-proBNP is a natriuretic peptide used as a biomarker to aid the diagnosis of heart failure. The elevation of NT-proBNP levels has a dual meaning as a marker of both fluid volume overload and myocardial damage. It has been reported that NT-proBNP was a good predictor of mortality independently of fluid volume overload and dialysis modality in ESRD patients [23]. Echocardiographic assessment of IVC parameters has been used to evaluate excess intravascular volume, but it is challenging to obtain adequate images even for highly trained observers. Lung ultrasound can be used to assess fluid volume load by determining the degree of pulmonary edema through the difference in the degrees of water penetration between the lobules of the lungs and of the alveoli [26,27]. As a qualitative method, it requires experienced operators, which limits its clinical application.

The principle of ICG examination is to measure the hemodynamic parameters, including CO, TFC, SVRI, etc., by analyzing the frequency of electrical pulses through the chest. Compared with other methods for assessment of fluid volume load, the advantages of ICG include: the operation is so simple that short-term trained nurses can operate independently, objective measurement rather than relying on the experience of the operator, noninvasive, reproducible, and can be measured continuously at the bedside. TFC with other parameters examined by ICG has been utilized to determine the fluid volume status and set ultrafiltration volume in patients receiving hemodialysis [8,28]. However, its relationship with pneumonia in MHD patients has not been reported before. To the best of our knowledge, our study suggested for the first time that TFC was an independent risk factor for the occurrence of pneumonia in MHD patients.

Pulmonary edema may predispose patients to pneumonia via several mechanisms that alter the alveolar microenvironment, including enhanced bacterial colonization and infectivity and decreased host bactericidal capacities [4]. Furthermore, a cross-section study of ESRD patients receiving maintenance hemodiafiltration demonstrated that chronic inflammation is very common in MHD patients, while fluid overload and markers of chronic inflammation strongly correlate [29]. An observational cohort study shown that chronic inflammation per se identified people at increased risk of infection death [30]. Thus a higher value of TFC can indicate high-risk patients for infection, particularly pneumonia, and appropriate treatment plans should be developed, especially increasing ultrafiltration to reduce fluid volume overload.

In this study, we found that the dialysis vintage of patients in the Having-pneumonia group was shorter than those in the Non-pneumonia group [51 (22, 97) months versus 79 (42, 122) months, p < 0.001]. A plausible explanation for this phenomenon is that patients who entered regular MHD status for longer had lower levels of inflammation and better nutritional status compared to ESRD patients with shorter dialysis vintage, as similarly reported in previous studies [31–34]. Given the importance of fluid volume control in changing the degree of pneumonia risk in MHD patients shown in this study, this phenomenon also suggested that patients with a longer dialysis vintage may have better volume control than those with ESRD who were new to dialysis [4,19,20,35].

This study has some limitations. First, due to its retrospective design, patients could not be grouped more scientifically, and interference factors could not be completely excluded. Second, this is a single-center study, which may have selection bias and needs to be further confirmed by multi-center studies. Third, TFC values may also be affected by diseases of the heart, lung, and pleura, and further studies are needed to identify other indicators that can be combined to differentiate.

Conclusions

In summary, the analysis results in this study suggested that rapid assessment of fluid load in MHD patients has important clinical value for early screening of potential individuals at risk of pneumonia and early intervention. Given the advantages of ICG examination in assessing fluid volume load, TFC can be used clinically as a helpful predictor of pneumonia in MHD patients.

Ethical approval and consent to participate

This was a retrospective study using clinical data, and it did not involve further invasive intervention, treatment, or costs to patients. The study received a consent exemption and was approved by the ethics committee of the Second Affiliated Hospital of Nanjing Medical University (ethical number: 2022-KY-143-01). All patients’ records were identified and analyzed anonymously. Our study was performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. There was no commercial sponsorship.

Author contributions

WH and LY conceived and designed the study; YQ, JL, JW, and LY collected the data; JY provided guidance and advice; LY and WH analyzed the data; LY and YQ drafted the manuscript; WH revised the manuscript. All the authors have read and approved the manuscript for submission.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Key Medical Talent in Science & Education Health Project of Jiangsu Province [ZDRCC2016006] awarded to WH, and the Science and Technology Development Foundation of Nanjing Medical University [NMUB20210040] awarded to JL.