A Novel Nomogram for Individually Predicting 30-Day Pneumonia Mortality Risk in ILD Patients with Long-Term Use of Glucocorticoid

doi:10.21203/rs.3.rs-5289642/v1

Objective: Long-term glucocorticoid use in patients with interstitial lung disease (ILD) is associated with a significantly increased risk of death within 30-day following pneumonia, indicating poor prognosis. This study aims to identify the risk of mortality after pneumonia onset to optimize treatment strategies and enhance patient management.

Methods: This study retrospectively analyzed ILD pneumonia patient data from DRYAD. Patients were randomly split into training and validation sets. LASSO regression selected predictive factors, and a nomogram model was built. ROC curves and AUCs assessed the model's 30-day mortality prediction. Bootstrap resampling (500 times) on the validation set confirmed the model's robustness with a 95% CI for AUC. The model's calibration and discrimination were evaluated in both sets.

Results: A total of 324 patients with ILD who developed pneumonia were included in this study, among which 82 patients died within 30-day. LASSO regression identified respiratory failure, vasoactive drug use, ventilator use, and lymphocytopenia as predictors for constructing a nomogram model. The model showed good calibration in both training and validation datasets, with AUCs of 0.897 (95% CI: 0.8642-0.9292) and 0.903 (95% CI: 0.8680-0.9321), respectively. Decision curve analysis suggested clinical benefits when the threshold probability was <77%.

Conclusion: The nomogram developed in this study effectively predicts the 30-day mortality risk in patients with ILD following pneumonia, demonstrating strong discrimination and calibration. This provides a valuable tool for optimizing treatment strategies and improving patient outcomes.

interstitial lung disease

long-term use of glucocorticoid

pneumonia

death

nomogram

Interstitial lung disease (ILD) is a group of complex and heterogeneous lung disorders that include more than 200 different types of parenchymal lung diseases [1]. These diseases share common characteristics of widespread fibrosis and/or abnormal inflammatory reactions within the lung parenchyma, severely damaging the structural and functional integrity of the lungs, and potentially leading to irreversible pulmonary fibrosis [2, 3]. As ILD progresses to advanced stages, the extensive pulmonary fibrosis poses a fatal risk to patients, and its diffuse nature makes treatment and management extremely challenging. Particularly during acute exacerbations or in the late stages of the disease, respiratory failure often emerges as a severe complication, with a generally poor prognosis [4].

Despite the increasing research efforts and growing medical focus on clarifying the causes, diagnosis, and treatment methods of ILD in recent years, definitive treatment recommendations have yet to be established. It is worth noting that the rising incidence and prevalence of ILD, driven by worsening environmental factors, has placed an increasing burden on healthcare systems [5, 6]. Currently, glucocorticoids, as the mainstay treatment for ILD, can slow the progression of pulmonary fibrosis due to their immunosuppressive and anti-inflammatory effects. However, long-term use of glucocorticoids can lead to immunosuppression, resulting in negative outcomes such as an increased risk of pneumonia [7, 8]. More concerning is that ILD patients on long-term glucocorticoid therapy have a significantly higher risk of pneumonia compared to the general population, and lung infections play a crucial role in disease progression and mortality in these patients [9, 10].

Although the existence of numerous severity scoring systems for pneumonia, such as the CURB-65 score [11–12], qSOFA ≥ 2 has been proven to be closely associated with mortality among pneumonia patients [13]. With the outbreak of COVID-19, numerous studies have developed predictive models for mortality risk among individuals with COVID-19 [14–15]. However, predictive models for mortality among patients with ILD who develop pneumonia have yet to be reported. Patients with ILD who are chronic users of glucocorticoids have a higher probability of developing pneumonia compared to the general population [16]. Furthermore, long-term glucocorticoid use places these patients in an immunosuppressed state, resulting in more severe pneumonia and a heightened risk of mortality when compared to non-ILD patients. Therefore, the establishment of a predictive model for mortality risk among ILD patients with chronic glucocorticoid use who develop pneumonia is urgently needed.

Our study fills this gap by focusing on ILD patients who develop pneumonia despite receiving long-term glucocorticoid therapy, utilizing the rich clinical data from the DRYAD database. Our goal is to create a more accurate and individualized 30-day mortality risk prediction model. Once established, this model will provide clinicians with a more scientific and personalized risk assessment tool to help optimize treatment plans, reduce adverse events, and improve patient survival rates and quality of life.

Patient enrolment and grouping

The information needed for this study came from previously published research that gathered information on patients who underwent glucocorticoid therapy before being hospitalized for pneumonia between January 1, 2013, and December 31, 2017 [17]. The following were the inclusion criteria: (1) glucocorticoids, either intravenously or orally, prior to admission; (2) pneumonia diagnosis at admission or during hospital stay; and (3) patients sixteen years of age or older. The following were the exclusion criteria: (1) Non-infectious pulmonary diseases such as heart failure, pulmonary embolism, lung cancer, and interstitial lung disease without infection; (2) Not being able to agree to the study's methods. According to the guidelines of the Infectious Diseases Society of America and the American Thoracic Society, pneumonia was diagnosed in hospitalized patients during the study [18, 19]. The diagnosis of pneumoniae is the presence of a new pulmonary infiltrate, as demonstrated by infiltrative changes seen on computed tomography (CT) scans or chest radiography, along with one or more of the following clinical signs: (1) recent onset of coughing, sputum production, or worsening of respiratory symptoms, including the appearance of purulent sputum and possibly chest pain; (2) fever (defined as an axillary temperature of 37.3°C or higher) or hypothermia (defined as an axillary temperature of 36°C or lower); (3) the manifestation of clinical signs indicative of pulmonary consolidation and/or the audible presence of moist rales upon auscultation; or (4) an abnormal white blood cell count, either exceeding 10×10⁹/L or falling below 4×10⁹/L, potentially with a neutrophil preponderance. This retrospective study was approved by the Ethics Committee of China-Japan Friendship Hospital, and centralized cooperation and approval of all participating institutions were organized [17].

The raw data were downloaded from the DRYAD database (https://datadryad.org/stash), which included 716 samples and 127 observations. The first step involved the exclusion of patients without ILD. Subsequently, clinical observations with missing values exceeding 10% were deleted, and multiple imputation was carried out in R language. We filtered clinical variables, including general conditions, comorbidities, biochemical indicators, and medication usage. Finally, data of 324 patients, including 82 patients succumbed to pneumonia within 30 days of infection, whereas 242 patients survived beyond this 30-day period, were retrospectively enrolled and were divided into a training cohort and a validation cohort.

Feature Selection

The least absolute shrinkage and selection operator (LASSO) is a penalized regression method that estimates regression coefficients by maximizing the log-likelihood function while limiting the sum of the absolute values of the regression coefficients. The regression coefficients estimated by LASSO are sparse, with many components being exactly zero. Thus, LASSO automatically removes unnecessary covariates. The LASSO logistic regression algorithm is advantageous in high-dimensional regression. In this study, LASSO was used to identify predictive features in the training dataset. All categorical variables were converted into dummy variables, and the presence or absence of death within 30 days for patients with pneumonia who have undergone long-term glucocorticoid treatment was considered the dependent variable. Ten-fold cross-validation was used to confirm the suitable tuning parameter (λ) for LASSO logistic regression. Finally, the most significant features selected by LASSO were used to construct the logistic regression model.

Constructing the Nomogram and Performance Assessment

This study used descriptive statistics to summarize the baseline characteristics, with quartiles representing categorical variables and mean±standard deviation representing continuous variables. Based on the characteristics of the data distribution, group comparisons were performed using the t-test, Mann-Whitney U test, Pearson chi-squared test, or Fisher's exact test. By comparing the factors related to 30-day mortality (P < 0.01), we conducted LASSO regression analysis and constructed a nomogram. Samples from the validation group were drawn 500 times from the model group. Finally, clinical decision curves were used to assess the clinical application efficiency, and the area under the receiver operating characteristic (ROC) curve (AUC) was utilized to evaluate the discrimination between the model group and the validation group, while calibration was used to assess the goodness of fit of the model.

Validation of the Nomogram

The validation dataset was used to evaluate the performance of the nomogram. A predicted value was calculated for every patient in the validation dataset based on the formula constructed using the training dataset. The ROC curve and area under the curve (AUC) were used to assess the predictive discrimination ability of the nomogram in the validation dataset. Additionally, the model's calibration was evaluated using calibration curves, and the application value of the model was assessed through decision curve analysis.

Statistical Analysis

This study used descriptive statistics to summarize the baseline characteristics of participants. Group comparisons employed t-tests, Mann-Whitney U tests, Pearson chi-squared tests, or Fisher's exact tests to identify significant factors associated with 30-day mortality (P < 0.01). LASSO regression analysis was used to determine predictive features and construct a nomogram. To evaluate model performance, 500 samples were randomly drawn to form a validation group, and the ROC curve and its area under the curve (AUC) were utilized to assess the model's discriminatory ability. Calibration curves were employed to examine the goodness of fit of the model, and decision curve analysis was conducted to assess its clinical applicability.

Clinical Characteristics

A total of 324 interstitial lung disease patients who developed pneumonia and were receiving long-term use of glucocorticoid were screened through the DRYAD database. The present study included 160 male patients and 164 female patients. Among the 342 patients included, 176 were over 60 years of age. 75 were smokers, 104 patients had diabetes, 210 had connective tissue diseases, 65 had idiopathic pulmonary fibrosis, and 40 were diagnosed with coronary heart disease. Notably, there were signifcant differences between the die within 30days and no die within 30days groups regarding 21 relevant features such as smoking, white cell count and persistent lymphocytorenia (all P<0.05), while there were no signifcant diferences regarding the remaining 14 relevant features (all P>0.05).

Feature Selection

LASSO logistic regression analysis was used to construct a prediction model in the training dataset. Using the occurrence of death within 30 days among patients with pneumonia who have been on long-term glucocorticoid treatment as the dependent variable, with all other clinical characteristics serving as predictors. Through ten-fold cross-validation, the tuning parameter λ which is most suitable for LASSO logistic regression analysis is obtained (Figure 2A). Four predictors with non-zero coefficients from the tuning parameters were selected, including the use of vasoactive drugs, resirator failure, ventilator use, and the persistent lymphocytorenia. These were used to construct the regression model (Figure 2B).

Performance Assessment of the Nomogram

After performing Lasso regression analysis, four variables were selected and a nomogram was constructed based on their respective weights. A scale was incorporated at the top of the nomogram, serving to indicate the points assigned to each variable. Each patient was assigned a total point score, which could be translated into a statistical probability of the presence of death within 30 days among patients with pneumonia who have been on long-term glucocorticoid treatment using the scale positioned at the bottom of the nomogram (Figure 3). The ROC curve showed that the nomogram provided good discriminatory power, with an AUC of 0.897 for the training set (Figure 4A). Additionally, the calibration curve of the nomogram demonstrated that the predicted probabilities of death within 30 days for patients were comparable to the actual probabilities in the derivation dataset, indicating no significant deviation from a perfect fit (Figure 5A).

Validation and Clinical Utility of the Nomogram

Perform 500 repeated of sampling as the validation set, the nomogram was validated using the validation dataset, specifically by calculating the individual probability of death within 30 days for patients with pneumonia who have undergone long-term glucocorticoid treatment. The calibration curve demonstrated a strong agreement between the predicted probabilities and the observed outcomes. Furthermore, the nomogram exhibited an AUC of 0.903 in the validation set, indicating its excellent discriminatory ability (Figure 4B). To assess the clinical utility of the nomogram, this study employed the DCA curve. As shown in Figure 5B, when the probability threshold was within the range generated by the nomogram, within 77%, using the nomogram to predict the risk of patient death provided greater benefits compared to either treating all patients or treating none. This underscores the high clinical practicality of the nomogram developed in this study.

In this study, we created a predictive model for the risk of death in patients with ILD receiving long-term glucocorticoid therapy within 30 days of contracting pneumonia. The best features were determined by using Lasso regression analysis to determine the use of ventilators, respiratory failure, vasoactive drug use, and lymphocytopenia. In both the training set (AUC = 0.897) and the validation set (AUC = 0.903), the model showed good predictive ability, indicating its possible clinical utility.

ILD is a collection of diverse lung conditions marked by inflammation and, occasionally, fibrosis [20]. These conditions cause hypoxemia, which can lead to respiratory failure, dyspnea, coughing, abnormal gas exchange, and restrictive physiology, which manifests as decreased lung capacity. Genetic variations, environmental exposures, and systemic diseases are some of the factors that contribute to the pathogenesis of ILD [21, 22]. The incidence of ILD varies greatly between global regions and is trending gradually higher [23, 24].

In the treatment of ILD, glucocorticoids are a crucial therapeutic choice. Although their effectiveness varies depending on the type of ILD, glucocorticoids typically help patients with lung function, clinical symptoms, and inflammatory response reduction. It is commonly acknowledged that one of the first-line therapies for ILD is glucocorticoids [25]. They work by reducing inflammatory reactions and over-stimulating the immune system, which lessens lung damage and enhances the quality of life for patients [26]. The use of glucocorticoids is not risk-free, though. Extended use of glucocorticoids can lead to immunosuppression in patients and a faster course of disease because it can decrease drug sensitivity, raise the risk of infections, and possibly affect the immune system [27]. Predicting death and prognosis in these patients is therefore especially important.

Both congenital and secondary lymphocytopenia can occur; secondary lymphocytopenia frequently results from long-term glucocorticoid use or from recurrent infections [28]. Individuals with immune-mediated lymphocytopenia who receive long-term glucocorticoid therapy for their underlying illness are also more susceptible to lymphocytopenia [29]. As essential immune cells involved in recognizing and getting rid of pathogens like bacteria and viruses, lymphocytes are essential to the immune system [30]. A common symptom of acute respiratory distress syndrome (ARDS), which is frequently linked to severe pneumonia, is respiratory failure. Research has also indicated that the severity of respiratory failure is associated with patient mortality [31, 32]. Respiratory failure is also a common complication in the terminal stage of ILD patients with pneumonia, who may experience exacerbated dyspnea and hypoxemia, necessitating the use of a ventilator to maintain oxygenation and assist ventilation. The presence of respiratory failure and ventilator use may indicate a relatively severe state of ILD or a more severe degree of infection, both of which can raise the risk of death [33]. When severe pneumonia results in septic shock, vasoactive drugs are essential in maintaining basic vital signs in the respiratory, circulatory, and metabolic systems [34]. When pneumonia patients develop hemodynamic instability and shock, the probability of progressing to severe pneumonia increases significantly, further elevating the risk of death [35]. Therefore, these four variables were used as predictors to construct the predictive model in this study. The decision curve analysis showed that if the threshold probability for an individual is less than 77%, using the model developed in this study provides more benefits than either treating all or not treating.

Numerous mortality prediction models for pneumonia, particularly those pertaining to COVID-19, have been published thus far. However, most of them are predictions for general patients, and there are few prediction models for patients with ILD to predict pneumonia. Moreover, the majority of these models employ a substantial number of predictors. For instance, Zhao et al.'s study utilized seven variables—including heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age—as predictors for the mortality risk of COVID-19 patients, encompassing two laboratory tests, two vital signs, two diagnoses, and one patient demographic[36]. In contrast, the prediction model presented in this paper necessitates only four predictors, of which only respiratory failure necessitates laboratory testing. The remaining three predictors are rooted in patients' medical histories and objective factors related to their treatment protocols, offering a more expedient and convenient approach that does not oblige the delay of awaiting test results for prediction.

It is important to recognize the limitations of this study. Firstly, not all patients in the raw data underwent a full set of pathogen tests, suggesting that the detection and diagnosis of pathogens in patients may be incomplete. Secondly, the detection of pathogens was not completed immediately upon admission, and some pathogens may only be detected several days after admission, thereby increasing the risk of nosocomial infections. Moreover, the study was validated using an internal validation set, and the consequences have not been validated with external cases, so caution should be exercised in its clinical application. Additionally, the database was gathered from hospitals in China, and its applicability to other countries requires further validation. But our research findings align with clinical observations and the currently published literature, offering valuable insights into the prevention of mortality and prognosis for patients with ILD who develop pneumonia while undergoing long-term glucocorticoid therapy.

This study established a predictive model with four variables to predict the 30-day mortality rate among patients with ILD who are on long-term glucocorticoid therapy. The nomogram based on this model performed well in predicting the 30-day death risk in such patients.

ILD: Interstitial lung disease; LASSO: Least absolute shrinkage and selection operator; ROC: Receiver operating characteristic; AUC: Areas under the curve; DCA: Decision curve analysis; CI: Confidence interval; CT: Computed tomography.

Contributions:

Research, drafting, and editing of the manuscript were completed by Luying Chen, Kaixiang Zhang and Yajie Zhou. The research, manuscript drafting, and editing were all done by Saibin Wang. The manuscript's editorial changes were made with input from all authors. The final manuscript was read and approved by all authors. Each author has agreed to take full responsibility for all aspects of the work and has contributed enough to it.

Fundings:

This research received no external funding.

Competing interests:

No support from any organization for the submitted work; no financial relationships with any organization that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval:

No ethical approval available.

Data sharing:

The access policy and procedures are available at https://datadryad.org/stash.

Acknowledgements

The authors would like to thank Lijuan Li, et al. for their support in data collection.

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Table 1. Description of population

Variables	Die Within 30days (n=82)	No Die Within 30days (n=242)	P-value
Age > 60 years, n (%)	51 (62.20%)	125 (51.65%)	0.098
Gender, n (%)			0.159
Male	46 (56.10%)	114 (47.11%)
Female	36 (43.90%)	128 (52.89%)
Somke, n(%)	22 (26.83%)	53 (21.90%)	0.038*
Laboratory examination
White cell count, ×10⁹/L (IQR)	9.29 (6.82-12.36)	7.58 (5.86-11.28)	0.007*
Neutrophils count, ×10⁹/L (IQR)	7.78 (5.71-11.05)	6.17 (4.28-9.11)	<0.001*
Lymphocyte count, ×10⁹/L (IQR)	0.66 (0.40-0.98)	0.99 (0.61-1.45)	<0.001*
Hemoglobin, g/L (IQR)	110.00 (97.25-127.00)	117.00 (100.25-132.00)	0.069
Platelet, ×10⁹/L (IQR)	162.00 (111.50-253.25)	197.00 (149.00-251.00)	0.021*
Albumin, g/L (IQR)	31.07 (27.25-35.05)	34.75 (31.00-38.00)	<0.001*
Aspartate aminotransferase, U/L (IQR)	34.50 (23.00-53.00)	24.00 (16.00-37.00)	<0.001*
Alanine aminotransferase, U/L (IQR)	32.00 (20.00-54.10)	26.00 (16.00-46.00)	0.086
Blood urea nitrogen, mmol/L (IQR)	6.83 (5.30-12.41)	6.00 (4.37-8.40)	0.002*
Serum creatinine, mmol/L (IQR)	62.50 (47.95-94.11)	59.65 (48.32-75.17)	0.153
Lactic acid, mol/L (IQR)	1.00 (1.00-1.00)	0.70 (0.00-1.00)	<0.001*
Na⁺, mmol/L (IQR)	135.00 (131.00-139.00)	138.00 (135.00-140.00)	<0.001*
CURB-65 score >1, n (%)	34 (41.46%)	65 (26.86%)	0.013*
Underlying diseases
COPD, n (%)	5 (6.10%)	15 (6.20%)	0.974
Coronary heart disease, n (%)	11 (13.41%)	29 (11.98%)	0.697
Diabetes mellitus, n (%)	25 (30.49%)	79 (32.64%)	0.718
Connective tissue disease, n (%)	57 (69.51%)	153 (63.22%)	0.303
Persistent lymphocytorenia, n (%)	57 (69.51%)	82 (33.88%)	<0.001*
Idiopathic interstitial lung disease, n (%)	15 (18.29%)	50 (20.66%)	0.643
Complications
Resirator failure, n (%)	82 (100.00%)	99 (40.91%)	<0.001*
Treatment
Antibiotics, n (%)	56 (68.29%)	145 (59.92%)	0.177
Antiviral drugs, n (%)	13 (15.85%)	30 (12.40%)	0.425
Sulfanilamide, n (%)	51 (62.20%)	121 (50.00%)	0.056
Ganciclovir, n (%)	56 (68.29%)	121 (50.00%)	0.004*
Antiaspergills, n (%)	48 (58.54%)	99 (40.91%)	0.006*
Antipseudomonas, n (%)	78 (95.12%)	184 (76.03%)	<0.001*
Hihgh dose gulcocorticid, n (%)	44 (53.66%)	87 (35.95%)	0.005*
Ventilation, n (%)	71 (86.59%)	69 (28.51%)	<0.001*
Vasoactive drugs, n (%)	45 (54.88%)	21 (8.68%)	<0.001*
Continuous veno-venous haemofiltration, n (%)	20 (24.39%)	10 (4.13%)	<0.001*
ICU stay, n (%)	68 (82.93%)	81 (33.47%)	<0.001*
Etiological examination
Totale tiology			0.645
CMV, n (%)	10 (12.20%)	29 (11.98%)
PCP, n (%)	7 (8.54%)	12 (4.96%)
Aspergillus, n (%)	4 (4.88%)	18 (7.44%)
Klebsiella pneumoniae, n (%)	1 (1.22%)	9 (3.72%)
Pseudomonas aeruginosa, n (%)	2 (2.44%)	5 (2.07%)
Other pathogens, n (%)	15 (18.29%)	58 (23.97%)
Including two or more, n (%)	20 (24.39%)	45 (18.60%)

CVC: Cytomegalovirus; PCP: Pneumocystis carinii pneumonia. * indicates P < 0.05

No competing interests reported.

A Novel Nomogram for Individually Predicting 30-Day Pneumonia Mortality Risk in ILD Patients with Long-Term Use of Glucocorticoid

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Abstract

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Methods

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Discussion

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