Sage Journals: Discover world-class research

Abstract

Background:

This study aims to analyze the risk factors for post-operative pulmonary infection in patients with brain tumors by meta-analysis to provide a reference for its prevention.

Methods:

PubMed, Embase, Web of Science, Cochrane Library, Ovid, and four Chinese databases (CNKI, SinoMed, VIP, and Wanfang databases) were searched for studies covering risk factors of pulmonary infection in patients with brain tumors, limited to the duration from the dates of inception of the respective databases to December 31, 2022. The Newcastle-Ottawa scale was used to assess the evidence. A meta-analysis of the factors affecting the incidence of pulmonary infection was performed using Revman 5.4 software.

Results:

Twelve studies were selected, covering 35,615 patients with brain tumors, among whom pulmonary infection occurred in 1,635 cases with an accumulated incidence of 4.6%, including 38 related risk factors. Meta-analysis results indicated: history of chronic pulmonary disease (odds ratio [OR], 5.74; 95% confidence interval [CI], 1.34–24.51; p = 0.02], diabetes mellitus (OR, 1.58; 95% CI, 1.29–1.95; p < 0.0001), history of cardiovascular disease (OR, 3.97; 95% CI, 2.18–7.24; p < 0.00001), age ≥60 years (OR, 1.55; 95% CI, 1.12–2.15; p = 0.009)], operation time ≥3 hours (OR, 1.03; 95% CI, 1.00–1.05; p = 0.03], Glasgow Coma Scale (GCS) score <13 (OR, 3.5; 95% CI, 1.90–6.46; p < 0.0001), and the American Society of Anesthesiologists classification (ASA) ≥3 (OR, 2.03; 95% CI, 1.68–2.46; p < 0.00001) as independent risk factors.

Conclusions:

History of chronic pulmonary disease, diabetes mellitus, history of cardiovascular disease, age ≥60 years, operation time ≥3 hours, GCS score <13, and the ASA grade ≥3 are independent risk factors for post-operative pulmonary infection in patients with brain tumors, which nursing staff should be aware of.

Brain tumors, also known as intra-cranial tumors or craniocerebral tumors, refer to the nervous system tumors occurring in the craniocerebral cavity, including those originating from the neuroepithelium, peripheral nerves, meninges, and germ cells; tumors of lymphatic and hematopoietic tissues; craniopharyngioma and granulosa cell tumor in the Sella region; and metastatic tumors.¹ The latest global cancer statistics reported 308,000 newly emerging brain tumors and 251,000 deaths in 2020 worldwide.² In the United States, the annual incidence of brain and central nervous system tumors is reported to be 10 per 100,000 cases on average.³ According to cancer statistics in China, 2022, brain tumors accounted for 5.57% of all malignant tumors and 2.91% of the mortality rate of all malignant tumors, ranked as the ninth leading cause of death for malignant tumors.⁴

Surgery has been adopted as the primary treatment for brain tumors,⁵ which alleviates clinical symptoms to the maximum extent by safely removing the tumor, prolonging patient survival, and allowing sufficient tumor specimens for pathologic diagnosis and molecular genetic testing, where the pulmonary infection is revealed as the most common post-operative complication.⁶ Affected by severe illness, central nervous system damage, long-term disorders of consciousness, and bed rest, patients with brain tumors are more likely to exhibit pulmonary infection.⁷ Several extensive sample studies have shown that the rate of pulmonary infection after surgery for brain tumors ranges from 0.6% to 18.29%.^8–10 Pneumonia pathogens pass through the distal bronchus and alveoli, break through the host's defense mechanisms, and multiply in the lungs to cause invasive damage.¹¹

Post-operative pulmonary infection may lead to prolonged mechanical ventilation duration and increased hospital length of stay of patients, resulting in ventilator dependence of patients, increasing the difficulty of disease treatment, affecting the prognosis of patients, resulting in increased mortality, and may also increase the number of intensive care unit (ICU) admissions, patient re-hospitalization rate, patient medical costs, and medical resource consumption.^12,13 A five-year cohort study in Canada showed that of 4,033 patients, 378 patients with post-operative pulmonary infection had a significantly lower five-year overall survival rate than those without pulmonary infection (62.8% vs. 73.8%).¹⁴ As a result, it important to explore the risk factors of post-operative pulmonary infection in patients with brain tumors.

Until now, multiple clinical studies on the risk factors of post-operative pulmonary infection in patients with brain tumors have been performed domestically and abroad, revealing the possible factors referring to operation time, age, Glasgow Coma Score (GCS), diabetes mellitus, invasive procedures, ventilator use, basic diseases, etc.^8,15–17 However, the outcomes and the views among the studies vary. For example, Ren et al.⁸ did not believe age was a risk factor for post-operative pulmonary infection in patients with brain tumors, whereas Zhang et al.¹⁸ came to the opposite conclusion. Deng et al.¹⁹ believed the tumor located in the anterior or lateral brainstem was a risk factor, and Longo et al.¹⁵ found a higher infra-tentorial tumor infection rate. As far as we know, no systematic review or meta-analysis has been conducted to summarize post-operative pulmonary infection risk factors in patients with brain tumors. Herein, we carried out a meta-analysis on the risk factors of pulmonary infection in patients with brain tumors to provide the research basis for clinical prevention of pulmonary infection in patients with brain tumors.

Methods

This review was done according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement (Supplementary Figure S1). The study protocol has been registered under PROSPERO (Ref: CRD42023398999).

Search strategy

Comprehensive research was performed by searching PubMed, Embase, Web of Science, Cochrane Library, Ovid, and the four Chinese databases, including National Knowledge Infrastructure (CNKI), Chinese Biomedical Literatures database (SinoMed), Wanfang Digital Periodicals (WanFang), and Chinese Science and Technology Periodicals (VIP) database, to identify the risk factors for pulmonary infection in patients with brain tumors. The retrieval time was set from the establishment of the database to December 31, 2022. The “brain neoplasms,” “central nervous system neoplasms,” “brain cancers,” “pulmonary infection,” “pneumonia,” “risk factors,” “influence factors,” and “predictor” were used as the search terms, Medical Subject Headings terms, and their combinations in the title/abstract field of the search engines. The languages were limited to English and Chinese. Using PubMed as an example, the specific search strategy is shown in Figure 1.

FIG. 1.

PubMed search strategy.

Inclusion and exclusion criteria

The inclusion criteria were as follows: population aged over 18 years, who were diagnosed with a brain tumor by computed tomography (CT) and magnetic resonance imaging (MRI) and who have received surgery⁵; case control studies or cohort studies; the odds ratio values and 95% confidence interval data obtained from multivariable logistic regression analysis can be extracted or converted; the outcome index was a post-operative pulmonary infection (pulmonary infection occurred within 30 days after surgery), covering diagnostic criteria for pulmonary infection in the study; the “Guidelines for the diagnosis and treatment of hospital-acquired pneumonia and ventilator-associated pneumonia in Chinese adults (2018 edition)¹¹ was used as the diagnostic criteria for pulmonary infection; and the article language was English or Chinese. The exclusion criteria were as follows: duplicate studies; review, systematic review, meeting summary, case reports; literature with incomplete or inconsistent data; lack of full-text literature; the Newcastle-Ottawa scale was lower than seven points.

Data extraction

Two researchers receiving training in evidence-based methodology independently screened the literature and extracted the data according to the inclusion and exclusion criteria. Disagreements on study eligibility were resolved by discussion or consulting another senior researcher until a consensus was reached. The duplication and preliminary screening of the acquired literature were removed using NoteExpress software, and the literature that met the inclusion criteria was re-screened by careful reading to finally determine the literature enrolled. Data of the enrolled studies were extracted, covering title, first author, publication time, country or region, study type, source of sample size, the incidence of pulmonary infection, number of cases in case group and control group, and independent influencing factors, etc.

Quality assessment

The Newcastle-Ottawa scale was utilized to evaluate the methodological quality of each study,²⁰ which was independently completed by two researchers. Disagreements were resolved via discussion or consulting a third researcher until a consensus was reached. The Newcastle-Ottawa scale involved eight items in three sections with nine scores. covering selection (4 items, 1 point each), comparability (1 item, 2 points in total), and outcome domains (3 items, 1 point each). The literature with scores seven points or more were considered high quality.

Statistical analyses

Meta-analysis was performed on the RevMan5.4 software. The odds ratio was used as the effect analysis statistic with its 95% confidence interval provided. The heterogeneity between the enrolled studies was analyzed by the χ² test (α = 0.1), and I² determined the heterogeneity. That p > 0.1 and I² ≤ 50 indicated that the heterogeneity between studies was small, and the fixed effect model was applied for meta-analysis. Instead, the random effects model was applied with statistical heterogeneity existing between the studies. Sensitivity analysis was used to explore the source of heterogeneity. By modulating the effect model, sensitivity analysis for the included literature was performed to evaluate the stability of the combined results. Descriptive analysis was performed to explore the influencing factors of inappropriate meta-analysis. If the number of original studies were 10 or more, the funnel plot was taken to determine the existence of publication bias. A p value <0.05 was considered statistically significant.

Results

Search results

A total of 2,880 articles were retrieved, including 1,591 in English and 1,289 in Chinese. After eliminating duplicate literature, 2,310 articles remained. After reading the title and abstract, the remaining 29 articles were preliminarily screened, and the full text was searched and read. Further exclusion of unable to extract data (5 articles), irrelevant literature (11 articles), and unable to obtain full text (1 article), 12 articles were finally included, including three Chinese articles^8,18,21 and nine English articles.^{6,10,11,15,19–23} The search results are shown in Figure 2.

FIG. 2.

Literature search diagram.

General characteristics of the included studies

Twelve studies were included in the meta-analysis, involving eight case control studies and four cohort studies covering 35,615 patients, among whom 4.6% (1,635) developed post-operative pulmonary infection. All enrolled studies were published from 2012 to 2022, and most after 2019. The general characteristics and methodological quality evaluation results and demographic table of the included studies are listed in Tables 1 –3.

Table 1.

Characteristics of the Included Studies

Author/year	Country	Study design	Infected group	Non-infected group	Incidence (%)	Factors
Ren et al. 2021⁸	China	Retrospective case control study	15	67	18.3	①②③④⑤ 在此处键入公式。
Zhang et al. 2018¹³	China	Retrospective case control study	48	251	16.1	④⑦⑧⑨
Zhao et al. 2022¹⁸	China	Cohort studies	18	372	4.6	⑥⑩⑪⑫
Chen et al. 2021²⁰	China	Cohort studies	50	232	17.7	⑬
Deng et al. 2020¹⁴	China	Retrospective case control study	44	277	13.7	③⑥⑭⑮
Longo et al. 2019¹⁰	United States	Retrospective case control study	1161	28,700	3.9	④⑥⑦⑰⑱⑲⑳㉑㉒㉓㉔㉕㉖
Zuo et al. 2019⁶	China	Retrospective case control study	51	1,105	4.4	⑩㉗㉘㉙㉚㉛
Wang et al. 2017²²	China	Retrospective case control study	29	215	11.9	⑦⑲㉜
Oh et al. 2014²³	United States	Retrospective case control study	6	458	1.3	⑥⑦⑯
Zhang et al. 2022¹⁷	China	Retrospective case control study	24	249	8.8	㉝㉞㉟
Guo et al. 2022¹⁹	China	Cohort studies	77	598	11.4	⑥⑩⑯㉓㊱㊳
Alkins et al. 2021²¹	Canada	Cohort studies	112	1,456	7.1	⑲㊲

1 = number of sputum aspiration times per day; 2 = tracheotomy time; 3 = Glasgow Coma Scale (GCS) score <13; 4 = history of chronic pulmonary disease; 5 = invasive operation; 6 = operation time ≥3 h; 7. age ≥60 years; 8 = intraoperative infusion volume ≥3,000 mL; 9 = endotracheal tube; 10 = history of chronic pulmonary disease; 11 = pulmonary imaging abnormalities; 12 = epilepsy; 13 = systemic inflammatory response index (SIRI); 14 = tumor size; 15 = tumor located in the anterior or lateral brainstem; 16 = estimated blood loss; 17 = gender; 18 = self-care ability; 19 = diabetes mellitus; 20 = dyspnea; 21 = chronic steroid use; 22 = pre-operative white blood cell (WBC) count >2; 23 = American Society of Anesthesiologists (ASA) classification ≥3; 24 = emergent case; 25 = infra-tentorial; 26 = body mass index (BMI) ≥40 kg/m²; 27 = tumor recurrence; 28 = skull base tumors; 29 = blood transfusion; 30 = red blood cell distribution width-standard deviation (RDW-SD); 31 = neutrophil to lymphocyte ratio (NLR); 32 = Koos grade of vestibular schwannoma (VS); 33 = Karnofsky performance score (KPS); 34 = high lactate dehydrogenase (LDH); 35 = mechanical ventilation time; 36 = extraction time of tracheal catheter; 37 = stroke; 38 = anesthesia time.

Table 2.

Quality Assessment of Included Studies

Included studies	Studies population selection	Comparability	Exposure	NOS score
Ren et al. 2021	4	2	3	9
Zhang et al. 2018	4	2	3	9
Zhao et al. 2022	4	1	2	7
Chen et al. 2021	4	2	2	8
Deng et al. 2020	3	2	2	7
Longo et al. 2019	3	2	2	7
Zuo et al. 2019	4	2	3	9
Wang et al. 2017	3	2	3	8
Oh et al. 2014	3	2	2	7
Zhang et al. 2022	3	2	3	8
Guo et al. 2022	3	2	2	7
Alkins et al. 2021	3	2	2	7

NOS = Newcastle-Ottawa scale.

Table 3.

Demographic Table of Pooled Patients

Included studies	Total	Gender		Age
Included studies	Total	Male	Female	Age
Ren et al. 2021	82	46	36	25 ∼ 43
Zhang et al. 2018	299	131	168	65 ∼ 82
Zhao et al. 2022	372	161	211	20 ∼ 86
Chen et al. 2021	282	132	150	18 ∼ 65
Deng et al. 2020	321	110	211	47 ∼ 61
Longo et al. 2019	28700	15211	13489	18 ∼ 85
Zuo et al. 2019	1156	340	816	18 ∼ 85
Wang et al. 2017	244	97	147	18 ∼ 79
Oh et al. 2014	464	133	331	18 ∼ 92
Zhang et al. 2022	273	153	120	34 ∼ 57
Guo et al. 2022	598	145	453	18 ∼ 65
Alkins et al. 2021	1456	657	799	18 ∼ 75

Meta-analysis results

To avoid results bias, the influencing factors mentioned in only one study were excluded, and those in two or more studies were combined. Meta-analysis indicated the history of chronic pulmonary disease, diabetes mellitus, history of cardiovascular disease, age ≥60 years, operation time ≥3 hours, GCS score <13, and the ASA grade ≥3 as the independent risk factors for post-operative pulmonary infection in patients with brain tumors.

History of chronic pulmonary disease

Three studies^8,15,18 reported the relation between the history of chronic pulmonary disease and pulmonary infection after brain tumor surgery, and there was obvious heterogeneity among the studies (p < 0.0001; I² = 90%). Therefore, the random effects model was used for analysis. The results showed that history of chronic pulmonary disease is a risk factor for pulmonary infection in patients with brain tumors (odds ratio [OR], 5.74; 95% confidence interval [CI], 1.34–4.51; p = 0.02). Forest plot is shown in Figure 3A.

FIG. 3.

A: Forest plot for the history of chronic pulmonary disease. B: Forest plot for diabetes mellitus. C: Forest plot for the history of cardiovascular disease. D: Forest plot for age ≥60 years. E: Forest plot for the operation time ≥3 hours. CI = confidence interval; SE = standard error.

Diabetes mellitus

The relation between diabetes mellitus and pulmonary infection was reported in three studies,^15,24,25 displaying acceptable statistical heterogeneity (p = 0.3; I² = 16%). As a result, the fixed-effects model was used for analysis, which revealed diabetes mellitus as a risk factor (OR, 1.58; 95% CI, 1.29–1.95; p < 0.0001). Forest plot is shown in Figure 3B.

History of cardiovascular disease

The relation between the history of cardiovascular disease and pulmonary infection was reported in three studies,^6,10,23 without heterogeneity (p = 0.57; I² = 0%). The fixed effects model was applied, which revealed the history of cardiovascular disease as a risk factor (OR, 3.97; 95% CI, 2.18–7.24; p < 0.00001). Forest plot is shown in Figure 3C.

Age ≥60 years

Four studies^15,18,22,25 examined the effect of age on pulmonary infection, with heterogeneity (p = 0.0005; I² = 83%). The random effects model was used, which indicated age ≥60 years as a risk factor for pulmonary infection in patients with brain tumors (OR, 1.55; 95% CI, (1.12–2.15; p = 0.009. Forest plot is shown in Figure 3D.

Operation time ≥3 hours

Five studies with obvious heterogeneity (p < 0.00001; I² = 93%) were included.^{10,15,19,22,23} Sensitivity analysis indicated no change in heterogeneity, and the random effects model was therefore used for meta-analysis, which indicated the significantly long operation time in patients with pulmonary infection compared with those without infection (OR, 1.03; 95% CI, 1.00–1.05; p = 0.03]. Forest plot is shown in Figure 3E.

Glasgow Coma score <13 and ASA grade ≥3

Two studies^8,14 without heterogeneity (p = 0.71; I² = 0%) investigated whether a GCS score <13 exerted an effect on pulmonary infection. The fixed effects model was applied, revealing a GCS score <13 as a risk factor for pulmonary infection in patients with brain tumors (OR, 3.5; 95% CI, 1.90–6.46; p < 0.0001]. Two studies^15,26 reported the relation between the ASA grade ≥3 and pulmonary infection, with acceptable statistical heterogeneity (p = 0.18; I² = 45%). The fixed effects model was applied, which indicated ASA grade ≥3 as a risk factor (OR, 2.03; 95% CI, 1.68–2.46; p < 0.00001).

Descriptive analysis results

According to the studies enrolled, one study⁸ indicated the daily number of sputum aspirations, tracheotomy time, and invasive operation to exert an impact on post-operative pulmonary infection in patients with brain tumors. The data¹⁸ indicated a higher risk of pulmonary infection associated with brain tumor patients with intra-operative infusion volume ≥3,000 mL and tracheal catheter retention after surgery. One study²³ reported the pulmonary imaging abnormalities and epilepsy as risk factors for post-operative pulmonary infection in patients with brain tumors. One study²⁶ demonstrated the association of patients with high systemic inflammatory response index (SIRI) with higher risk of lung infection. One study¹⁴ pointed out that tumor size and tumor location in the anterior or lateral brain stem were risk factors for post-operative lung infection. Another study¹⁰ indicated gender, self-care ability, dyspnea, long-term steroid use, pre-operative white blood cell (WBC) count ≥12 × 10⁹/L, emergent case, infra-tentorial tumor, and body mass index ≥40 kg/m² as risk factors; One study⁶ demonstrated tumor recurrence, skull base tumor, blood transfusion, red blood cell distribution width-standard deviation (RDW-SD) and neutrophil to lymphocyte ratio (NLR) as risk factors for pulmonary infection. Another study²⁵ reported the Koos grade of vestibular schwannoma (VS) as a risk factor for pulmonary infection. One study²¹ reported the positive correlation of Karnofsky performance score (KPS), lactate dehydrogenase (LDH), and mechanical ventilation duration with the occurrence of pulmonary infection after brain tumor surgery. Another study¹⁰ indicated the extraction time of tracheal catheter and anesthesia time as risk factors. Another study²⁴ reported the stroke as a risk factor for pulmonary infection after brain tumor surgery. Considering the report from a single study without enough data for meta-analysis, the description was qualitative.

Sensitivity analysis and publication bias detection

The fixed effects model and the random effects model, respectively, were used to estimate the combined odds ratio values of the risk factors with high heterogeneity, which gave similar results, indicating the essential reliability of the present study. The Cochrane Handbook for Systematic Reviews 5.1²⁷ suggests that conducting publication bias analysis by funnel plot will achieve higher meaning when the number of studies is not <10. In our study, the number of studies involved in risk factors was <10, so there may be publication bias, and publication bias analysis was not conducted.

Discussion

History of chronic pulmonary disease

The present study indicated a 5.74 times greater risk of developing a pulmonary infection in patients with brain tumors with a history of chronic pulmonary disease compared with those without, which is consistent with previous findings. A study by Daming et al.⁹ based on data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database demonstrated a higher tendency to suffer from post-operative infection in patients with a history of chronic lung diseases because of the reduced airway clearance, increased airway inflammation, and reduced lung compliance, also supported by Lee et al.²⁸ For patients undergoing surgery, the traumatic stress response elicited by surgical trauma will further lead to the decline of immune function and the weakened resistance of patients, making it easy for the invasion and colonization of pathogenic bacteria, and thus promoting the risk of pulmonary infection.²⁹ Therefore, clinical medical staff is should actively encourage patients to undergo lung rehabilitation training, such as lip contraction breathing by lung rehabilitation education, to improve the lung function.³⁰

Diabetes mellitus

Diabetes mellitus is considered a risk factor for post-operative pulmonary infection. An earlier study reported a 6% to 10% increase in the risk of pulmonary infection with every 1 mmol/L increase in blood sugar.³¹ The mechanism may be the inhibited expression of TLR2 and TLR4 in monocytes of patients with diabetes that damages the immune function of patients. In addition, the hypoperfusion and hypoxia environment caused by diabetic autonomic neuropathy and peripheral vascular disease will also affect the aggregation and attachment of immune cells, reducing the anti-infection ability of the body and increasing the risk of post-operative infection.^32,33 Chronic hyperglycemia leads to the increased concentration of liquid glucose on the surface of the airway, which drives the multiplication of bacteria. Altered cell function, bacterial proliferation, and vascular permeability promote the risk of pulmonary infection.^34,35

Poor control of blood sugar or the prolonged duration of diabetes mellitus may aggravate the susceptibility to pneumonia after surgery. Therefore, it is essential to strengthen the pre-operative control of blood glucose in patients with brain tumors complicated by diabetes mellitus. However, fasting or random blood glucose is often used in clinical work for pre-operative assessment and regulation, and it is easy to be affected by many factors such as diet, exercise, pain, traumatic stress, and other transient interference, which is difficult to reflect the blood glucose control level for a period of time, affecting the correct assessment of pre-operative risk and prognosis.³⁶

Hemoglobin A_1c (HbA_1c) is a continuous, slow, and irreversible non-enzymatic glycation product of glucose and hemoglobin in the blood. As a gold standard reflecting the blood glucose control level, HbA_1c can reflect the blood glucose control level of patients in the past two to three months.³⁷ Pre-operative elevated HbA_1c, as a marker of poor glycemic control, is associated with increased peri-operative risk in patients with diabetes, and patients with HbA_1c >6.5% have a higher risk of post-operative pulmonary infection than those with HbA_1c <6.5%.³⁸ Pre-operative assessment of patient's HbA_1c was not analyzed in detail in the original study included in this article. In future clinical studies, more attention should be paid to pre-operative changes in patient's HbA_1c to assess blood glucose control.

History of cardiovascular disease

Consistent with the previous reports, the pooled results of the present study suggest the history of cardiovascular disease as a risk factor for post-operative pulmonary infection. It may trigger or aggravate the instability of hemodynamics, resulting in pulmonary congestion, edema, and infection.³⁹ Studies have evidenced the high risk of developing severe post-operative complications in patients with meningioma who have hypertension or are taking cardiac drugs.⁶ It is suggested that the cardiovascular system status of patients should be evaluated carefully before surgery for brain tumors.⁴⁰

Age

Xiang et al.⁴¹ carried out a large single-center retrospective study and found age >60 years as an independent risk factor for post-operative pulmonary infection. Kunisaki et al.⁴² reported the different incidences of post-operative pulmonary infection between patients >75 years old and those between 45 and 65 years of age (13.3% and 6.3%). Similarly, Yamada et al.⁴³ reported a higher incidence of post-operative pulmonary infection in patients over 85 years old compared with those between 75 and 85 years old (16.7% and 3.3%). These findings are in accordance with the present study, in which the risk of post-operative pulmonary infection is increased with patient age. This could be explained by the decline in respiratory system function, swallowing, and immune function with age in elderly patients with brain tumors, which reduces the resistance to bacterial invasion and poses a higher risk of post-operative lung infection.²⁵ Therefore, effective measures should be taken to prevent pulmonary infection in elderly patients with brain tumors.

Operation time

The higher risk of post-operative vomiting induced by the longer operation time may increase the incidence of post-operative aspiration pneumonia in patients with brain tumors, especially for those in a post-operative coma and impaired swallowing function.^44–46 In addition, prolonged operation time also represents longer tracheal intubation time, which will elevate the risk of post-operative pulmonary infection.⁴⁷ In the present study, the incidence of post-operative pulmonary infection was 1.03 times in patients with operation time of three hours or more compared with those less than three hours. Therefore, it is suggested to identify the high-risk patients to shorten the operation duration so as to reduce the incidence of pulmonary infection after brain tumors, especially for patients with large tumors in the anterior or lateral brainstem.¹⁹

Glasgow Coma Score <13 and ASA grade ≥3

Lower GCS score is associated with a higher risk of post-operative pulmonary infection in patients with brain tumors, which is consistent with previous studies.^48,49 On the one hand, it may be related to the inhibited gag reflex induced by coma that leads to aspiration pneumonia. On the other hand, these patients generally require mechanical ventilation, which may damage the airway and aggravate the possibility of ventilator-associated pneumonia.⁵⁰ By conducting a large single-center observational cohort study, Lioutas et al.⁵¹ found that mechanical ventilation increased the risk of pulmonary infection by more than four times. Endotracheal intubation makes the lower respiratory tract exposed to the outside world, followed by difficulty in cleaning the oral cavity. As a result, the colonizing bacteria multiply in the oropharynx, and the secretions containing colonizing bacteria penetrate the lower respiratory tract through the gap between the air sac and the air tube wall, leading to pulmonary infection. In addition, our meta-analysis also demonstrated ASA grade ≥3 as a critical risk factor for pulmonary infection after brain tumor surgery.

As an effective method to evaluate the health status and surgical risk of patients, ASA classification could contribute to predicting peri-operative risk. The higher ASA grade is associated with the worse general condition of patients and a higher tendency to develop pulmonary infection.^52,53 However, considering the limited amount of studies related to the correlation between these two risk factors, with only two studies included in the meta-analysis for each factor, large-sample, multicenter studies are still required to verify this conclusion.

Limitations

To the best of our knowledge, this is the first meta-analysis of the risk factors for pulmonary infection in patients with brain tumors. However, there are some limitations. First, the studies on the risk factors of pulmonary infection after brain tumor surgery domestically and abroad are limited, so included articles are limited, which makes it insufficient to elucidate the risk factors of pulmonary infection after brain tumor surgery. Second, the risk factors involved in the included studies were numerous but scattered; some risk factors are reported in a few studies, so meta-consolidation cannot be performed, only descriptive analysis. Third, some factors of the meta-analysis were mentioned in only two studies; the authenticity and reliability of the results remain to be discussed. Among the included studies, few were cohort studies, accompanied by the small sample sizes of some included studies. Therefore, more prospective cohort studies with rigorous designs and large sample sizes are recommended to explore the risk factors of post-operative pulmonary infection further in patients undergoing brain tumor surgery in future research.

Conclusions

In summary, this study demonstrated that history of chronic pulmonary disease, diabetes mellitus, cardiovascular disease, age ≥60 years, operation time lasting three or more hours, GCS score <13, and ASA grade ≥3 were independent risk factors for post-operative pulmonary infection in patients with brain tumors. The results can serve as a reference for clinical medical staff to strengthen the evaluation and management of patients undergoing brain tumor surgery and take targeted and effective intervention as early as possible so as to avoid the incidence of post-operative pulmonary infection in patients with brain tumors and improve the clinical outcome. At the same time, because of the limited number of articles included in this study, some risk factors may exert a particular impact on the research results. Future large-sample, multicenter, high-quality research recommended.

Footnotes

Acknowledgments

The authors thank the Guangxi Medical and Health Appropriate Technology Development and Application Project for funding this paper.

Authors' Contributions

Conceptualization (lead): Lan. Conceptualization (supporting): Lei. Formal analysis (lead): Lan. Writing—original draft (lead): Lan. Writing—original draft (supporting): Lei. Writing—review and editing (equal): Lan, Yan Wei, Zhu, Y. Zhang, S. Zhang, Mo, Lei. Software (lead): Yan Wei, Zhu, Y. Zhang, S. Zhang. Methodology (lead): D. Wei.

Funding Information

This work was supported by Guangxi Medical and Health Appropriate Technology Development and application project (grant number: S2020104).

Author Disclosure Statement

This manuscript has no potential conflict of interest to disclose. Because this is a systematic review and meta-analysis, ethical approval and consent to participate are not applicable.

Supplementary Material

References

Ramakrishna

, Hsu

, Mao

, et al. Surgeon annual and cumulative volumes predict early postoperative outcomes after brain tumor resection. World Neurosurg, 2018; 114:e254–e66; doi: 10.1016/j.wneu.2018.02.172

Sung

, Ferlay

, Siegel

, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021; 71(3):209–249; doi: 10.3322/caac.21660

Sorajja

, Moore

, Sample

, et al. Global variation in young adult central nervous system tumor incidence by region, age, and sex from 1988 to 2012. Cancer Epidemiol, 2022; 78:102151; doi: 10.1016/j.canep.2022.102151

Zheng

, Zhang

, Zeng

, et al. Cancer incidence and mortality in China, 2016. J Natl Cancer Cent, 2022; 2(1):1–9; doi: 10.1016/j.jncc.2022.02.002

Bureau of Medical

Administration

, National Health Commission of the People's Republic of

China

, China Anti-Cancer Association Committee of

Glioma

, Society for Neuro-Oncology of China (SNO-China). Glioma diagnosis and treatment guidelines (2022 edition). Chin J Neurosurg, 2022; 38(08):757–777.

Zuo

, Liang

, Li

, et al. A comprehensive study of risk factors for post-operative pneumonia following resection of meningioma. BMC Cancer, 2019; 19(1):100; doi: 10.1186/s12885-019-5271-7

Abbott

TEF

, Fowler

, Dobbs

, et al. Frequency of surgical treatment and related hospital procedures in the UK: A national ecological study using hospital episode statistics. Br J Anaesth, 2017; 119(2):249–257; doi: 10.1093/bja/aex137

Ren

, Wang

, Sun

. Analysis of influencing factors of postoperative pulmonary infection in patients with intracranial tumor. Chin J Practical Med, 2021; 48(5):22–25.

Zhang

, Zhuo

, Yang

, et al. Postoperative pneumonia after craniotomy: incidence, risk factors and prediction with a nomogram. J Hosp Infect, 2020; 105(2):167–175; doi: 10.1016/j.jhin.2020.03.015

10.

Guo

, Shi

, Gao

, et al. Effect of differences in extubation timing on postoperative pneumonia following meningioma resection: A retrospective cohort study. BMC Anesthesiol, 2022; 22(1):296; doi: 10.1186/s12871-022-01836-w

11.

Shi

, Huang

, Zhang

, et al. Chinese guidelines for the diagnosis and treatment of hospital-acquired pneumonia and ventilator-associated pneumonia in adults (2018 edition). J Thorac Dis, 2019; 11(6):2581–2616; doi: 10.21037/jtd.2019.06.09

12.

Hamele

, Stockmann

, Cirulis

, et al. Ventilator-associated pneumonia in pediatric traumatic brain injury. J Neurotrauma. 2016; 33(9):832–839; doi: 10.1089/neu.2015.4004

13.

Zong

, Zhu

. Expert consensus on prevention and control of postoperative pneumonia. Chin J Clin Infect Dis, 2018; 11(1):11–19; doi: 10.3760/cma.j.issn.1674-2397.2018.01.003

14.

Andalib

, Ramana-Kumar

, Bartlett

, et al. Influence of postoperative infectious complications on long-term survival of lung cancer patients: A population-based cohort study. J Thorac Oncol, 2013; 8(5):554–561; doi: 10.1097/JTO.0b013e3182862e7e

15.

Longo

, Agarwal

. Postoperative pulmonary complications following brain tumor resection: A national database analysis. World Neurosurg, 2019; 126:e1147–e1154; doi: 10.1016/j.wneu.2019.03.058

16.

, Ma

, Wang

, et al. Investigation and analysis of pulmonary infection in patients with tracheal intubation in intensive care unit. People's Military Medicine, 2017; 60(03):241–245.

17.

Chen

, Li

, Fang

, et al. Risk factors for malnutrition in stroke patients: A meta-analysis. Clin Nutr, 2019; 38(1):127–135; doi: 10.1016/j.clnu.2017.12.014

18.

Zhang

, Peng

, Yu

, et al. Analysis of pulmonary complications and related factors in elderly patients undergoing elective craniotomy. Chin J Neurosurg, 2018; 34(6):606–609.

19.

Deng

, Wang

, Zhang

. Risk factors for postoperative pneumonia in patients with posterior fossa meningioma after microsurgery. Heliyon, 2020; 6(5):e03880; doi: 10.1016/j.heliyon.2020.e03880

20.

Stang

. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol, 2010; 25(9):603–605; doi: 10.1007/s10654-010-9491-z

21.

Zhang

, Fang

, Lin

, et al. Prognostic value of lactate dehydrogenase in patients with glioma after surgery. J Fujian Med University, 2022; 56(4):342–346; doi: 10.1155/2018/1723184

22.

, Safaee

, Sun

, et al. Surgical risk factors for post-operative pneumonia following meningioma resection. Clin Neurol Neurosurg, 2014; 118:76–79; doi: 10.1016/j.clineuro.2013.12.017

23.

Zhao

, Zhao

, Zhang

, et al. Association between postoperative hypoalbuminemia and postoperative pulmonary imaging abnormalities patients undergoing craniotomy for brain tumors: A retrospective cohort study. Sci Rep. 2022; 12(1):64; doi: 10.1038/s41598-021-00261-2

24.

Alkins

, Newsted

, Nguyen

, et al. Predictors of postoperative complications in vestibular schwannoma surgery: A population-based Study. Otol Neurotol, 2021; 42(7):1067–1073; doi: 10.1097/mao.0000000000003107

25.

Wang

, Li

, Tang

, et al. Risk factors for postoperative pneumonia after microsurgery for vestibular schwannoma. Clin Neurol Neurosurg, 2017; 162:25–28; doi: 10.1016/j.clineuro.2017.06.004

26.

Chen

, Lin

, Pang

, et al. Systemic inflammatory response index improves the prediction of postoperative pneumonia following meningioma resection. Chin Med J (Engl), 2020; 134(6):728–730; doi: 10.1097/cm9.0000000000001298

27.

Higgins

JPT

, Altman

. Assessing risk of bias in included studies. In: Cochrane handbook for systematic reviews of interventions: Cochrane book series. (Higgins J, Green S. eds.) John Wiley & Sons: New York;, 2008:187–241.

28.

Lee

, Lee

, Mamidi

, et al. Patients with chronic obstructive pulmonary disease are at higher risk for pneumonia, septic shock, and blood transfusions after total shoulder arthroplasty. Clin Orthop Relat Res, 2019; 477(2):416–423; doi: 10.1097/CORR.0000000000000531

29.

Chen

, Tang

, Luo

, et al. Risk factors and intervention effect of pulmonary infection in patients after craniocerebral surgery. Chinese J Nosocomiology. 2017; 27(1):120–123.

30.

You

, Bai

, Wu

, et al. Risk factors for pulmonary infection in elderly patients with acute stroke: A meta-analysis. Heliyon. 2022; 8(11):e11664; doi: 10.1016/j.heliyon.2022.e11664

31.

Benfield

, Jensen

, Nordestgaard

. Influence of diabetes and hyperglycaemia on infectious disease hospitalisation and outcome. Diabetologia, 2007; 50(3):549–554; doi: 10.1007/s00125-006-0570-3

32.

Hicks

, Selvin

. Epidemiology of peripheral neuropathy and lower extremity disease in diabetes. Curr Diab Rep, 2019; 19(10):86; doi: 10.1007/s11892-019-1212-8

33.

Kheir

, Tan

, Kheir

, et al. Postoperative blood glucose levels predict infection after total joint arthroplasty. J Bone Joint Surg Am, 2018; 100(16):1423–1431; doi: 10.2106/jbjs.17.01316

34.

, Liu

, Li

, et al. The effects of type 2 diabetes and postoperative pneumonia on the mortality in inpatients with surgery. Diabetes Metab Syndr Obes, 2019; 12:2507–2513; doi: 10.2147/dmso.S232039

35.

López-de-Andrés

, Perez-Farinos

, de Miguel-Díez

, et al. Type 2 diabetes and postoperative pneumonia: An observational, population-based study using the Spanish Hospital Discharge Database, 2001–2015. PLoS One, 2019; 14(2):e0211230; doi: 10.1371/journal.pone.0211230.

36.

Cavero-Redondo

, Peleteiro

, Álvarez-Bueno

, et al. Glycated haemoglobin A1c as a risk factor of cardiovascular outcomes and all-cause mortality in diabetic and non-diabetic populations: A systematic review and meta-analysis. BMJ Open. 2017; 7(7):e015949; doi: 10.1136/bmjopen-2017-015949

37.

Lipska

, Krumholz

. Hemoglobin A1c as a surrogate for clinical outcomes in diabetes studies: Reply. JAMA, 2017; 318(2):200–201; doi: 10.1001/jama.2017.7225

38.

Peng

, Li

, Qi

, et al. The influence of preoperative HbA1c level on the clinical prognosis of elderly patients with hip fracture complicated with type 2 diabetes. J Clin Surg, 2021; 29(02):165–168; doi: 10.3969/j.issn.1005-6483.2021.02.020

39.

Ramalho

SHR

, Shah

. Lung function and cardiovascular disease: A link. Trends Cardiovasc Med, 2021; 31(2):93–98; doi: 10.1016/j.tcm.2019.12.009

40.

Wang

, Lu

, Sun

, et al. Pneumonia after cardiovascular surgery: incidence, risk factors and interventions. Front Cardiovasc Med, 2022; 9:911878; doi: 10.3389/fcvm.2022.911878

41.

Xiang

, Jiao

, Si

, et al. Risk factors for postoperative pneumonia: A case-control study. Front Public Health, 2022; 10:913897; doi: 10.3389/fpubh.2022.913897

42.

Kunisaki

, Akiyama

, Nomura

, et al. Comparison of surgical outcomes of gastric cancer in elderly and middle-aged patients. Am J Surg, 2006; 191(2):216–224; doi: 10.1016/j.amjsurg.2005.09.001

43.

Yamada

, Shinohara

, Takeshita

, et al. Postoperative complications in the oldest old gastric cancer patients. Int J Surg, 2013; 11(6):467–471; doi: 10.1016/j.ijsu.2013.04.005

44.

Howard

, Mahoney

, Thurlow

. Respiratory obstruction after posterior fossa surgery. Anaesthesia, 1990; 45(3):222–224; doi: 10.1111/j.1365-2044.1990.tb14689.x

45.

Koivuranta

, Läärä

. A survey of postoperative nausea and vomiting. Anaesthesia, 1998; 53(4):413–414.

46.

DiBardino

, Wunderink

. Aspiration pneumonia: A review of modern trends. J Crit Care, 2015; 30(1):40–48; doi: 10.1016/j.jcrc.2014.07.011

47.

Cai

, Zeng

H-Y

, Shi

Z-H

, et al. Factors influencing delayed extubation after infratentorial craniotomy for tumour resection: A prospective cohort study of 800 patients in a Chinese neurosurgical centre. J Int Med Res, 2013; 41(1):208–217; doi: 10.1177/0300060513475964

48.

Mounier

, Lobo

, Voulgaropoulos

, et al. The multistep road to ventilator-associated lung abscess: A retrospective study of S. aureus ventilator-associated pneumonia. PLoS One. 2017; 12(12):e0189249; doi: 10.1371/journal.pone.0189249

49.

Ding

, Zhang

, Yang

, et al. Incidence, temporal trend and factors associated with ventilator-associated pneumonia in mainland China: a systematic review and meta-analysis. BMC Infect Dis, 2017; 17(1):468; doi: 10.1186/s12879-017-2566-7

50.

Ganie

, Lone

, et al. Lung contusion: A clinico-pathological entity with unpredictable clinical course. Bull Emerg Trauma, 2013; 1(1):7–16.

51.

Lioutas

, Marchina

, Caplan

, et al. Endotracheal intubation and in-hospital mortality after intracerebral hemorrhage. Cerebrovasc Dis, 2018; 45(5–6):270–278; doi: 10.1159/000489273

52.

Mayhew

, Mendonca

, Murthy

BVS

. A review of ASA physical status: Historical perspectives and modern developments. Anaesthesia, 2019; 74(3):373–379; doi: 10.1111/anae.14569

53.

Hackett

, De Oliveira

, Jain

, et al. ASA class is a reliable independent predictor of medical complications and mortality following surgery. Int J Surg, 2015; 18:184–90; doi: 10.1016/j.ijsu.2015.04.079

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.03 MB

0.00 MB

Risk Factors for Post-Operative Pulmonary Infection in Patients With Brain Tumors: A Systematic Review and Meta-Analysis

Abstract

Background:

Methods:

Results:

Conclusions:

Methods

Search strategy

Inclusion and exclusion criteria

Data extraction

Quality assessment

Statistical analyses

Results

Search results

General characteristics of the included studies

Meta-analysis results

History of chronic pulmonary disease

Diabetes mellitus

History of cardiovascular disease

Age ≥60 years

Operation time ≥3 hours

Glasgow Coma score <13 and ASA grade ≥3

Descriptive analysis results

Sensitivity analysis and publication bias detection

Discussion

History of chronic pulmonary disease

Diabetes mellitus

History of cardiovascular disease

Age

Operation time

Glasgow Coma Score <13 and ASA grade ≥3

Limitations

Conclusions

Footnotes

Acknowledgments

Authors' Contributions

Funding Information

Author Disclosure Statement

Supplementary Material

References

Supplementary Material