A nomogram for predicting the risk of pulmonary fungal infection for patients with pulmonary tuberculosis

doi:10.21203/rs.3.rs-1993043/v1

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A nomogram for predicting the risk of pulmonary fungal infection for patients with pulmonary tuberculosis

https://doi.org/10.21203/rs.3.rs-1993043/v1

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published 07 Feb, 2023

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Background: Pulmonary fungal infection is one of the common complications in patients with pulmonary tuberculosis(PTB).The aim of this study was to construct a nomogram to predict the risk of pulmonary fungal infection in patients with PTB.

Methods: The present case control study retrospectively collected materials of 286 patients affected by PTB and received treatment from 2016.12.6-2021.12.6, in Beijing Chest Hospital, Capital Medical University. As control subjects, patients with sex and address corresponding to those of the case subjects were included in the study at a proportion of 1 controls for every case subject. These 286 patients were randomly divided into a training set and an internal validation set at a ratio of 3:1.Chi-square test and logistic regression analysis were performed in the training set, and a nomogram was developed using selected predictors. Then a bootstrapping procedure was used for internal validation.

Results: Seven variables [illness course, pulmonary avitation, advanced antibiotics were used for at lest 1 week, chemotherapy or immunosuppressants, surgery, bacterial infection in the lungs, hypoproteinemia] were finally validated and used to develop a nomogram. The nomogram showed good discrimination capability for both training set[area under the curve (AUC) =0.860, 95% confidence interval (CI) = 0.811–0.909] and internal validation set(AUC =0.884, 95% CI = 0.799–0.970). Its calibration curves also showed that the probabilities as predicted by the nomogram displayed a satisfied consistence with the actual probability for both training set and internal validation set.

Conclusions: We developed a nomogram that can predict the risk of pulmonary fungal infection in patients with PTB. It showed potential clinical utility.

Pulmonary fungal infection

pulmonary tuberculosis

risk

nomogram

Tuberculosis (TB) is an infectious disease caused by mycobacterium tuberculosis (MTB),of which pulmonary tuberculosis is the most common type,accounting for 80%-90% of all tuberculosis[1-3].Patients with PTB are immunocompromised and susceptible to several complications such as pulmonary fungal infection[6].Pulmonary fungal infection is a respiratory disease caused by various pathogens including yeast species such as candida albicans,cryptococcus neoformans, mold such as aspergillus fumigatus, trichotric, bipolar fungi such as histoplasma, white ground mold, fungi such as pneumocystis.Studies from over the world reported that pulmonary fungal infections complicated with PTB, whose predominant pathogen seems aspergillus spp[4-6].As well as others reported the candida albicans as the most frequent fungi spp [7,10].Whether aspergillosis caused by aspergillus, or candidiasis caused by candida albicans,or other types of pulmonary fungal infections,they all have their similarities in symptoms and imaging with PTB,which can easily lead to missed pulmonary fungal infection causing a high rate of morbidity and mortality[8,31,33,36].A study illustrated that up to 1 million people recovered from tuberculosis develop coinfection of pulmonary fungal infections annually usually misdiagnosed as relapsed PTB[9].At present, some clinicians obviously do not pay enough attention to this situation[32].Therefore, related studies on pulmonary fungal infection complicated with PTB are urgently needed which might make the patients benefit from targeted antifungal and antibacterial chemoprophylaxis[37].At present, there are some explorations of risk factors of pulmonary fungal infection for patients with PTB, but corresponding risk prediction model is scarce even though the nomogram has been regarded as a reliable tool to create a intuitive graph of a statistical predictive model that quantifies the risk of a clinical event[11,12].We took note of this problem and aimed to build a nomogram for predicting the risk of pulmonary fungal infection for patients with PTB.

2.1 study design and patients

A retrospective case control study was designed and conducted to identify

risk factors of pulmonary fungal infection for patients with PTB and develop a predictive model.At first,we retrospectively retrieved 1151 PTB patients who received treatment from 2016.12.6-2021.12.6, in Beijing Chest Hospital,Capital Medical University (including 145 patients with coinfection of pulmonary fungal infection who were classified as case group）in the electronic medical record system,then we randomly selected 450 PTB patients without pulmonary fungal infection as control group from the rest. The two groups of patients were matched according to gender and address characteristics at a proportion of 1 control for every case subject. Finally, there are respectively 143 patients (2 case subjects failed to match were excluded)in case group and control group included in this study.Then these 286 patients were randomly divided into a training set and an internal validation set at a ratio of 3:1. Univariate analysis and multivariate logistic regression analysis were performed in the training set to evaluate the risk factors and construct a predictive model, then a nomogram was developed using selected variables.A bootstrapping procedure was used for internal validation after that.

2.2 Inclusion and exclusion criteria

Case group:eligible patients included those with confirmed pulmonary tuberculosis and pulmonary fungal infection who simultaneously meet the diagnostic criteria of tuberculosis in the ”Health Industry Standards of the People's Republic of China” issued in 2017 [13]and the diagnostic criteria of pulmonary mycosis in the “Expert Consensus on the Diagnosis and Treatment of Pulmonary mycosis” issued by the Infectious Diseases Group of the Respiratory Society of the Chinese Medical Association in 2007[14],as well as,age 16 years or older, hospitalized.The following were excluded:patients with unknown information.

Control group:eligible patients included those with confirmed pulmonary tuberculosis but without pulmonary fungal infection who only meet the diagnostic criteria of tuberculosis in the ”Health Industry Standards of the People's Republic of China” issued in 2017[13] but fail to meet the diagnostic criteria of pulmonary mycosis in the “Expert Consensus on the Diagnosis and Treatment of Pulmonary mycosis” issued by the Infectious Diseases Group of the Respiratory Society of the Chinese Medical Association in 2007[14],as well as,age 16 years or older, hospitalized.The following were excluded:unknown information,suspected pulmonary fungal infection.

2.3 Candidate Predictors

We studied lots of literature on the risk factors of pulmonary fungal infection in PTB patients.And based on data from these prior publications[15-17,35,38-39],the following variables of patients data were collected as candidate predictors:1.demographic features such as age,gender,address;2.personal history such as smoking and alcohol history;3.the features of pulmonary tuberculosis such as category,drug resistance,illness course;4.imaging findings such as pulmonary cavitation,pleural effussion;5.medication use such as regular anti-tuberculosis treatment,advanced antibiotics were used for at lest 1 week,glucocorticoid were used for at lest 2 weeks,chemotherapy or immunosuppressants;6.therapeutic operations such as invasive operation,surgery history;7.comorbidities such as bacterial infection in the lungs,chronic bronchitis or chronic obstructive pulmonary disease or bronchitis or asthma,silicosis,interstitial lung disease,hypohepatia,renal inadequacy,tumor or hematological disease,anemia,hypoproteinemia and diabetes;All the variables were collected at hospital admission as a baseline data except for using advanced antibiotics for at lest 1 week and using glucocorticoid for at lest 2 week. These two excluded factors meant whether it occurred from 6 months prior to hospitalization to hospitalization.

2.4 Statistical analysis

We used SPSS 25.0 and R 4.2.0 for statistical analysis and all analyses were two-tailed, and a P-value of <0.05 was assumed to represent statistical significance. All candidate predictors were categorical variables which were summarized as frequency counts and percent.In the training set,we used Chi-square test or Fisher's exact test as the univariate analysis to compare differences in features between the pulmonary tuberculosis patients with and without coinfection of pulmonary fungal infection.Based on univariate analysis, variables significant at the P < 0.05 level were entered into the multivariate logistic regression model.According to the multivariate logistic regression analysis, significant variables were deemed as predictive variables to establish the predictive model for predicting the risk of pulmonary fungal infection in PTB patients, and then a nomogram was constructed according to the model. The area under the curve (AUC) was calculated to assess the discrimination capacity of the model, and internal validation was performed using bootstrapping with 1000 iterations[16].Additionally, its calibration was assessed by the calibration curve.

A total of 286 patients were included in this study; 143 were case subjects and 143 were control subjects.286 patients were randomly divided into a training set and an internal validation set at a ratio of 3:1.Training set containing 102 case subjects and 112 control subjects were used to development model and nomogram and validation set containing 40 case subjects and 32 control subjects were used to internal validate.Table1 listed the demographic and clinical characteristics of pulmonary tuberculosis patients in the training set and validation set.As you can see in table1,the ration of case subjects to all subjects was comparable between two data sets (47.7% vs 56.9%; P =0.173).

Table1 Clinical characteristics of PTB patients in the training set and validation set

Variable

Training set(n=214)

Validation set（n＝72）

Pulmonary fungal infection[n(%)]

Yes

102(47.7)

112(52.3)

41(56.9)

31(43.1)

0.173

Age[n(%)]

＞65years old

≤65years old

54（25.2）

160（74.8）

16（22.2）

56（77.8）

0.607

Gender[n(%)]

Male

Female

164（76.6）

50（23.4）

48（66.7）

24（33.3）

0.095

Address[n(%)]

North

South

206（96.3）

8（3.7）

71（98.6）

1（1.4）

0.458

Smoke[n(%)]

Yes

89（41.6）

125（58.4）

29（40.3）

43（59.7）

0.845

Drink[n(%)]

Yes

67（31.3）

147（68.7）

22（30.6）

50（69.4）

0.905

Category[n(%)]

Initial

Recurrent

155（72.4）

59（27.6）

16（22.2）

56（77.8）

0.372

Drug resistance[n(%)]

No drug resistance

Single-drug resistance

Multi-drug resistance

178（83.2）

17（7.9）

19（8.9）

59（81.9）

4（5.6）

9（12.5）

0.563

Illness course[n(%)]

≤1year

＞1year，≤5years

＞5years，≤10years

＞10years

104（48.6）

59（27.6）

19（8.9）

32（15.0）

33（45.8）

13（18.1）

14（19.4）

12（16.7）

0.062

Pulmonary cavitation[n(%)]

Yes

126（58.9）

88（41.1）

34（7.2）

38（52.8）

0.085

Pleural effussion[n(%)]

Yes

82（38.3）

132（61.7）

25（34.7）

47（65.3）

0.585

Regular anti-tuberculosis[n(%)]

Yes

121（56.5）

93（43.5）

42（58.3）

30（41.7）

0.791

Advanced antibiotics were used for at lest 1 week[n(%)]

Yes

89（41.6）

125（58.4）

35（48.6）

37（51.4）

0.298

Glucocorticoid were used for at lest 2 weeks[n(%)]

Yes

31（14.5）

183（85.5）

8（11.1）

64（88.9）

0.470

Chemotherapy or immunosuppressants[n(%)]

Yes

10（4.7）

204(95.3)

1（1.4）

71(98.6)

0.210

Invasive operation[n(%)]

Yes

150（70.1）

64（29.9）

48（66.7）

24（33.3）

0.586

Surgery[n(%)]

Yes

81（37.9）

133（62.1）

27（37.5）

45（62.5）

0.958

Bacterial infection in the lungs [n(%)]

Yes

27（12.6）

187（87.4）

12（16.7）

60（83.3）

0.386

Chronic bronchitis/ COPD/Bronchitis/Asthma[n(%)]

Yes

28（13.1）

186（86.9）

8（11.1）

64（88.9）

0.662

Silicosis[n(%)]

Yes

3（1.4）

211（98.6）

4（5.6）

68（94.4）

0.070

Interstitial lung disease[n(%)]

Yes

7（3.3）

207（96.7）

2（2.8）

70（97.2）

1.000

Hypohepatia[n(%)]

Yes

68（31.8）

146（68.2）

18（25.0）

54（75.0）

0.278

Renal inadequacy[n(%)]

Yes

15（7.0）

199（93.0）

1（1.4）

71（98.6）

0.081

Tumor / Hematological disease[n(%)]

Yes

12（5.6）

202（94.4）

5（6.9）

67（93.1）

0.773

Hypoproteinemia[n(%)]

Yes

77（36.0）

137（64.0）

28（38.9）

44（61.1）

0.658

Anemia[n(%)]

Yes

62（29.0）

152（71.0）

21（29.2）

51（70.8）

0.975

Diabetes[n(%)]

Yes

49（22.9）

165（77.1）

19（26.4）

53（73.6）

0.547

3.1 Predictors Entering the Model

The characteristics of PTB patients with and without pulmonary fungal infection in training set were summarized in Table 2. The factors of age,category, illness course,pulmonary cavitation,pleural effussion,advanced antibiotics were used for at lest 1 week,chemotherapy or immunosuppressants,surgery,bacterial infection in the lungs, chronicbronchitis or chronic obstructive pulmonary disease or bronchitis or asthma,hypoproteinemia,anemia were significantly associated with pulmonary fungal infection,as evaluated by univariate analyses.Table 3 shows the results from the multivariate logistic analyses,and the following seven factors were statistically significant : illness course,pulmonary cavitation,advanced antibiotics were used for at lest 1 week,chemotherapy or immunosuppressants,surgery,bacterial infection in the lungs,hypoproteinemia.(Table 2,3)

Table 2 Univariate analysis of PTB complicated by pulmonary fungal infection (training dataset, N=214)

Variable

Case group

(n=102)

Control group

（n＝112）

X²/Fisher

Age[n(%)]

＞65years old

≤65years old

32（31.4）

70（68.6）

22（19.6）

90（80.4）

3.893

0.048

Gender[n(%)]

Male

Female

76（74.5）

26（25.5）

88（78.6）

24（21.4）

0.492

0.483

Address[n(%)]

North

South

97（95.1）

5（4.9）

109（97.3）

3（2.7）

0.483

Smoke[n(%)]

Yes

48（47.1）

54（52.9）

41（36.6）

71（63.4）

2.401

0.121

Drink[n(%)]

Yes

32（31.4）

70（68.6）

35（31.3）

77（68.7）

0.016

0.898

Category[n(%)]

Initial

Recurrent

20（19.6）

82（80.4

39（34.8）

73（65.2）

6.187

0.013

Drug resistance[n(%)]

No drug resistance

Single-drug resistance

Multi-drug resistance

82（80.4）

12（11.8）

8（7.8）

96（85.7）

5（4.5）

11（9.8）

3.999

0.135

Illness course[n(%)]

≤1year

＞1year，≤5years

＞5years，≤10years

＞10years

34（33.3）

32（31.4）

11（10.8）

25（24.5）

70（62.5）

27（24.1）

8（7.1）

7（6.3）

23.067

＜0.001

Pulmonary cavitation[n(%)]

Yes

74（72.5）

28（27.5）

52（46.4）

60（53.6）

15.043

＜0.001

Pleural effussion[n(%)]

Yes

47（46.1）

55（53.9）

35（31.3）

77（68.7）

4.966

0.026

Regular anti-tuberculosis [n(%)]

Yes

63（52.1）

39（41.9）

58（51.8）

54（48.2）

2.163

0.141

Advanced antibiotics were used for at lest 1 week[n(%)]

Yes

65（63.7）

37（36.3）

12（21.4）

24（78.6）

39.314

＜0.001

Glucocorticoid were used for at lest 2 weeks[n(%)]

Yes

19（18.6）

83（81.4）

12（10.7）

100（89.3）

2.698

0.100

Chemotherapy or immunosuppressants[n(%)]

Yes

9（8.8）

93(91.2)

1（0.9）

111(99.1)

0.007

Invasive operation[n(%)]

Yes

69（67.6）

33（32.4）

81（72.3）

31（27.7）

0.556

0.456

Surgery[n(%)]

Yes

47（46.1）

55（53.9）

34（30.4）

78（69.6）

5.609

0.018

Bacterial infection in the lungs[n(%)]

Yes

24（23.5）

78（76.5）

3（2.7）

109（97.3）

21.051

＜0.001

Chronic bronchitis/ COPD/Bronchitis/Asthma[n(%)]

Yes

19（18.6）

83（81.4）

9（8.0）

103（92.0）

5.266

0.022

Silicosis[n(%)]

Yes

3（2.9）

99（97.1）

0（0）

112（100）

0.107

Interstitial lung disease[n(%)]

Yes

3（3.9）

99（97.1）

4（3.6）

108（96.4）

1.000

Hypohepatia[n(%)]

Yes

37（36.3）

65（63.7）

31（27.7）

81（72.3）

1.820

0.177

Renal inadequacy[n(%)]

Yes

8（6.29）

94（93.71）

7（4.90）

105（95.10）

0.208

0.648

Tumor / Hematological disease[n(%)]

Yes

9（8.8）

93（91.2）

3（2.7）

109（97.3）

3.808

0.051

Hypoproteinemia[n(%)]

Yes

56（54.9）

46（45.1）

21（17.9）

91（81.2）

30.289

＜0.001

Anemia[n(%)]

Yes

42（41.2）

60（58.8）

20（17.9）

92（82.1）

14.107

＜0.001

Diabetes[n(%)]

Yes

24（23.5）

78（76.5）

25（22.3）

87（77.7）

0.044

0.834

Table 3 Multivariate analysis of PTB complicated by pulmonary fungal infection(training dataset, N=214)

Variable	β-coefficient	S.E	Wald	Sig	Exp（B）	95%CI
Illness course			13.867	0.003
Illness course（1）	1.381	0.518	7.103	0.008	3.978	1.441-10.980
Illness course（2）	1.430	0.667	4.599	0.032	4.180	1.131-15.450
Illness course（3）	2.431	0.691	12.366	＜0.001	11.368	2.933-44.063
Pulmonary cavitation	1.241	0.405	9.377	0.002	3.460	1.563-7.656
Advanced antibiotics were used for at lest 1 week	1.148	0.396	8.425	0.004	3.153	1.452-6.847
Chemotherapy or immunosuppressants	3.028	1.264	5.736	0.017	20.646	1.733-245.934
Surgery	0.769	0.382	4.041	0.044	2.157	1.019-4.565
Bacterial infection in the lungs	1.894	0.782	5.857	0.016	6.645	1.434-30.799
Hypoproteinemia	1.801	0.593	9.222	0.002	6.054	1.894-19.352

3.2 Establishment and internal validation of the Model and Nomogram

Then we developed a predictive model for predicting the risk of pulmonary

fungal infection for patients with PTB on the basis of the aforementioned 7 predictors. The OR of predictor entering the model was as follows:1 year＜illness course≤5 years,3.978;5years＜illness course≤10 years,4.180;illness course＞10 year,11.368; pulmonary cavitation, 3.460; advanced antibiotics were used for at lest 1 week,3.153 ; chemotherapy or immunosuppressants,20.646; surgery,2.157;bacterial infection in the lungs,6.645 and hypoproteinemia,6.054. As shown in Figure 1, 2,the predictive model showed good discrimination capability for both training[area under the curve (AUC) =0.860, 95% confidence interval (CI) = 0.811–0.909] and internal validation (AUC =0.884, 95% CI = 0.799–0.970) using bootstrapping (resampling = 1000 times).The sensitivity and specificity of the receiver operating characteristic (ROC) curve were 0.777 and 0.804,respectively. The optimal cutoff was 0.5 according to the ROC curve.In addition, its calibration curves also showed that the probabilities as predicted by the predictive model displayed a satisfied consistence with the actual probability for both training set and internal validation set. (Figure 3,4)

To provide physicians with a quantitative tool for individualized prediction of pulmonary fungal infection, a nomogram was constructed according to multivariable logistic regression (Figure 5). The formula based on the model was presented as follows: Logit（P）=-2.970+1.381*illness course（＞year，≤5years）+1.430*illness course（＞5years，≤10years）+2.431*illness course（＞10years）+1.241*pulmonary cavitation（yes=1；no=0）+1.148*advanced antibiotics were used for at lest 1 week（yes=1；no=0）+3.028*chemotherapy or immunosuppressants（yes=1；no=0）+0.769*surgery（yes=1；no=0）+1.894*bacterial infection in the lungs（yes=1；no=0）+1.801*hypoproteinemia（yes=1；no=0).

People are susceptible to secondary fungal infection when their immunity goes down. Tuberculosis as the underlying disease provides a prerequisite for secondary pulmonary fungal infection which may cause pulmonary disability and even death[6,8].But if pulmonary fungal infection diagnosed early can be treated efficiently, and consequently can prevent from serious consequence[31,40].So an accurate risk prediction is imperative for clinicians and PTB patients.Although multiple risk factors have been proposed in prior studies, it is still challenging to predict the occurrence of pulmonary fungal infection for PTB patients. To the best of our knowledge, no model to identify PTB patients who are at risk for pulmonary fungal infection has been reported to date.For these reasons,in the real-world retrospective study, a multivariable model based on easily accessible clinical parameters was developed and internally validated.

This model incorporated illness course,pulmonary cavitation,advanced antibiotics were used for at lest 1 week,chemotherapy or immunosuppressants,surgery,bacterial infection in the lungs,hypoproteinemia.Then a nomogram was developed according to the predictors for facilitating individualized prediction of pulmonary fungal infection.For example,using the nomogram,a PTB patient with pulmonary cavitation and a more than 1year but no more than 5years illness course,has an about 30% predicted risk of pulmonary fungal infection. So it can help clinicians and patients improve clinical decision-making by using this nomogram.

Consistent with the findings of some previous studies[10,15-17,19-25,27-28], our study found illness course,use of advanced antibiotics,use of chemotherapy or immunosuppressants,surgical history and pulmonary bacterial infection as important risk factors of pulmonary fungal infections for PTB patients. In line with our study,Page et al found that pulmonary fungal infection such as pulmonary aspergillosis commonly complicates treated pulmonary tuberculosis with pulmonary cavitation[29,30,34].In other words, pulmonary cavitation was also an independent risk predictor. Although our data also showed that age,recurrent tuberculosis and invasive operation were significant risk factors in the univariate analysis [10,15-17,27], they were subsequently excluded by the logistic multivariate regression. Use of glucocorticoid was identified as potential variable in some studies[10,15-17,25-27],while our findings differ from theirs,probably because of the limited number of cases (n=31) which might need further investigation.Drug resistance and invasive operations were also observed as possible risk factors in some previous researchs[5,10,15-17,27], but they did not show statistical significance in this study.We also found that hypoproteinemia was also an predictor of pulmonary fungal infection for PTB patients, with possible cause that TB,as a chronic wasting disease,will cause reduced protein, which may cause a decrease in immunity and make the patients more susceptible to fungal infection.

However, there were several limitations in our study. Firstly, the training set was randomly selected from the whole PTB patients which may lead to lower evidence strength and failure to show incidence rate.Secondly,this study was a single-center retrospective study , which inevitably suffered from confounding bias.Finally, we only did internal validation to evaluate the discrimination and calibration of the scoring model,an external validation is warranted to confirm the performance of the nomogram.

In conclusion, a predictive model, which is consisted of seven independent risk factors, may empower clinicians and PTB patients with earlier,more precise prediction and better management of pulmonary fungal infection. Its favorable calibration and discrimination might permit it to be suitable for routine clinical practice in most hospitals.

Ethics approval and consent to participate: This study was approved by the Ethics Committee of Beijing Chest Hospital of the Capital Medical University (No.YJS-2021-039) and the study was conducted in accordance with the Helsinki Declaration. The requirement for written informed consent was waived by the Ethics Committee of Beijing Chest Hospital of the Capital Medical University based on the retrospective nature.

Consent for publication:Not applicable.

Availability of data and materials:The datasets generated and/or analysed during the current study are not publicly available due to a privacy-protection policy, but are available from the corresponding author on reasonable request.

Competing interests:The authors declare that they have no competing interests.

Funding:This research did not receive any specific grant from funding agencies among the public, commercial, or not-for-profit sectors.

Authors' contributions:GMQ is the leader of the study and guided the process of the tudy, YHX collected data,analyzed data and wrote the main manuscript text,GL offered guidance of software technology,PY and LFC offered the supervision and LTH checked clinical data.All authors reviewed the manuscript.

Acknowledgements:Not applicable.

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No competing interests reported.

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A nomogram for predicting the risk of pulmonary fungal infection for patients with pulmonary tuberculosis

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