Development of a nomogram to predict 30-day mortality in patients with chronic obstructive pulmonary disease complicated by sepsis: insights from the Medical Information Mart for Intensive Care (MIMIC-IV) database

Introduction

Chronic obstructive pulmonary disease (COPD) is an inflammatory illness that affects the pulmonary vascular system, lung parenchyma, and airways. It is defined by a persistent and permanent airflow limitation and frequently manifests as symptoms including dyspnea, sputum production, and a persistent cough (1). According to data released by the World Health Organization (WHO) in 2024, COPD is the fourth leading cause of death globally, resulting in 3.5 million deaths in 2021, accounting for approximately 5% of total global deaths (2). The high prevalence of COPD often results in acute exacerbations that necessitate hospitalization, especially in the intensive care unit (ICU) (3), which raises the risk of death and socioeconomic expenses significantly. According to a retrospective analysis, 24% of patients passed away within the first year after being admitted to the hospital, and the probability of dying from any cause or from COPD rose with each additional hospital stay (4,5). The yearly worldwide cost of COPD is expected to surpass that of cardiovascular illnesses by 2030, reaching $4.8 trillion, according to the World Economic Forum (6). As a result, human health depends on the efficient treatment of COPD.

Acute exacerbation in people with COPD can be caused by many reasons, such as physiological changes and increased airway and systemic inflammation (7). One major risk affecting the prognosis of patients with COPD is sepsis, a potentially fatal organ failure brought on by a dysregulated host response to infection and characterized by a systemic inflammatory state in response to infection (8,9). According to cohort research, people with sepsis and COPD are more likely to die, have severe pneumonia, and experience severe deterioration than those without sepsis (10). In a similar vein, sepsis progression may also be impacted by COPD. One of the most prevalent chronic comorbidities in sepsis patients is COPD, which is estimated to affect 13.9% to 19.6% of sepsis patients (11,12). COPD was identified as an independent risk factor for 28-day all-cause death in a retrospective analysis examining the relationship between COPD and 28-day mortality in sepsis patients (13). The coexistence of chronic illnesses affects clinical outcomes and disease development, increasing the socioeconomic burden and the need for healthcare resources. Therefore, we think that improving patient outcomes requires an awareness of the link between sepsis and COPD, an exploration of the pathogenic risk factors that overlap, and an optimization of disease care. Studies specifically predicting survival for COPD patients with sepsis, however, seem to be lacking.

A nomogram is a statistical prediction model-based graphical tool that combines several prognostic indicators into a single model to forecast the likelihood of clinical events and offer a customized risk assessment based on characteristics unique to each patient. Research on customized medicine now frequently uses it as a useful resource (14). Our goal is to guide therapeutic management by creating a nomogram that can predict the probability of death within 30 days in sepsis-affected COPD patients. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2171/rc).

Methods

Database

The Medical Information Mart for Intensive Care (MIMIC-IV) is a large, single-center, publicly accessible database (https://mimic.physionet.org/about/mimic/) created by the Massachusetts Institute of Technology that contains detailed information on about 299,712 hospitalized patients from 2008 to 2019, providing strong data support for clinical research (15,16). It maximizes the use of varied data origins by organizing its data structure in a modular manner and emphasizing data sources. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The MIMIC-IV database has been approved by the Institutional Review Boards at the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. To protect patients’ privacy, all confidential information in the database repository has been deleted.

Study population

Initially, this study included all patients diagnosed with COPD in the ICU based on the International Classification of Diseases, ninth revision (49120, 49121, 49122, 496) and tenth revision (J44, J440, J441, J449) codes (17). Patients diagnosed with sepsis according to the Third International Consensus Definitions for Sepsis (Sepsis-3) were selected: (I) proven infection by positive microbiological culture findings; (II) Sequential Organ Failure Assessment (SOFA) score ≥2 (18). The exclusion criteria were as follows: (I) patients under the age of 18 years; (II) patients who had several hospitalizations prior to their initial ICU admission; and (III) patients who had spent less than 1 day in the ICU. This study comprised a total of 1,832 patients, who were randomly assigned to train and test sets in a 7:3 ratio. The train set was used for the development and construction of the model, including variable selection and parameter estimation; the test set was used for the independent evaluation of the final model’s performance. The primary outcome variable in this study was the all-cause mortality status (survival/death) of patients within 30 days after ICU admission. The flowchart for population inclusion and exclusion is shown in Figure 1.

Figure 1 Flowchart of the study design. APS, Acute Physiology Score; COPD, chronic obstructive pulmonary disease; ICD, International Classification of Diseases; ICU, intensive care unit; LASSO, least absolute shrinkage and selection operator; MIMIC-IV, Medical Information Mart for Intensive Care.

Sample data variables

Patient data was pulled from the database using structured query language (SQL). The data we included encompass: (I) basic information and hospitalization-related details: age, gender, race, weight, marital status, length of stay (LOS); (II) severity scores: Acute Physiology Score III (APS III), SOFA, Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction System (LODS), Oxford Acute Severity of Illness Score (OASIS); (III) comorbidities: congestive heart failure, mild liver disease, malignant cancer, renal disease; (IV) vital signs and laboratory indicators reflecting organ function and physiological metabolic status: (i) respiratory function-related: breath rate, peripheral oxygen saturation (SpO₂), partial pressure of oxygen (PO₂), partial pressure of carbon dioxide (PCO₂), temperature; (ii) circulatory function-related: heart rate, mean blood pressure (MBP); (iii) renal function-related: creatinine, blood urea nitrogen (BUN), urine output; (iv) coagulation function-related: platelet count, international normalized ratio (INR), prothrombin time (PT), partial thromboplastin time (PTT); (v) blood routine-related: white blood cell (WBC), red blood cell (RBC), hemoglobin, hematocrit, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), red cell distribution width (RDW); (vi) electrolyte and metabolism-related: glucose, anion gap, potassium, sodium, chloride, calcium; (vii) acid-base balance-related: pH, base excess; (V) treatment information: invasive ventilation, vasopressor use, antibiotic use, renal replacement therapy (RRT).

Among these, all severity scores were assessed within the first 24 hours after ICU admission. Vital signs and biochemical indicators were collected within the first 24 hours of ICU admission. For indicators with multiple measurements, the most severe value for each indicator was selected as the representative data. Specific data selection details are shown in Table S1. Treatment information was obtained from within 1 day of the patient’s ICU admission (19).

Statistical analysis

In the construction of baseline tables, for continuous data, we used median [interquartile range (IQR)] for description. Since these data were typically non-normally distributed, the Wilcoxon-Mann-Whitney U test was employed for between-group comparisons. For categorical variables, we described them using frequencies and percentages [n (%)] and applied the Chi-squared test for between-group comparisons. A two-tailed P value <0.05 indicated statistical significance. A least absolute shrinkage and selection operator (LASSO) regression model was used on the train set to determine the best risk variables for 30-day mortality in COPD patients with sepsis. LASSO analysis uses a penalty function to compress variable coefficients in the regression model, reducing overfitting and resolving multicollinearity concerns. The variance inflation factor (VIF) was used to find collinearity between variables, with VIF >4 suggesting multicollinearity (20). A multivariate Cox regression model was then constructed to identify independent prognostic variables by selecting the most significant features from the train set using LASSO regression and collinearity results (21). Variables with P<0.05 were added to the nomogram, and a risk score for prognostic factors was created. The C-index and the area under the receiver operating characteristic curve (AUC) were used to assess the model performance. By calibrating curves with 500 resamples to predict 30-day mortality in the test sample, the predictive ability of the nomogram was evaluated. The clinical relevance of the nomogram was evaluated using decision curve analysis (DCA).

SQL queries were used to get data from the MIMIC-IV (version 2.2) database, and R (version 4.4.1) software was used to analyze the data. Mice, tableone, glmnet, survival, regplot, timeROC, rms, and ggDCA were among the R programs used. Two-tailed P values less than 0.05 were deemed statistically significant. Vital signs and biochemical indices variables with missing values greater than 20% of the sample size were eliminated, and the random forest (RF) approach in the ‘mice’ package was used to handle the remaining missing variables. Figure 1 shows the operating procedures of the study.

Results

Baseline characteristics

A total of 1,832 patients with COPD and sepsis were included in this study. The baseline characteristics according to the 30-day survival status are presented in Table 1. The whole population’s median age at admission was 72.33 (IQR, 63.91, 80.51) years, and there were more men (55.8%) than women. In the ICU, the 30-day death rate for patients with sepsis and COPD was 18.9%. According to baseline characteristics, those who passed away within 30 days were more likely to be older, had lower body weight, and higher severity levels (P<0.05). All other data showed significant variations (P<0.05) across vital signs and biochemical indices, with the exception of a few measures, including sodium concentration, hematocrit, chloride concentration, and MCH, that showed no significant differences (Table 1).

Patients were randomly assigned to a train set (n=1,282) and a test set (n=550) at a 7:3 ratio to guarantee a balanced distribution of clinical variables between groups. The results showed no discernible changes in baseline characteristics between the two groups (all P>0.05) (Table S2).

LASSO regression screening results

To find predictors of 30-day all-cause death in COPD patients with sepsis, the 49 extracted variables were evaluated using a LASSO regression approach with 10-fold cross-validation. Based on the data from the train set, 13 possible predictive variables with non-zero coefficients in the LASSO regression model were finally chosen from among these 49 feature variables (Figure 2). APS III, LODS, OASIS, anion gap, pH value, vasopressor, mild liver disease, malignant cancer, age, race, weight, respiration rate, and temperature were among these factors.

Figure 2 LASSO regression plot. (A) Ten-fold cross-validation curve for tuning parameter (lambda) selection. The x-axis represents the log(lambda), and the y-axis represents the binomial deviance. Error bars represent the standard error of the mean. The left vertical dashed line indicates the optimal lambda (lambda.min) at which the model achieves minimum deviance, and the right vertical dashed line indicates the lambda.1se, which is the largest value of lambda such that the error is within one standard error of the minimum. (B) Coefficient profile plot of the 49 candidate variables. Each curve represents the coefficient path of a variable as the log(lambda) decreases. The vertical dashed line is drawn at the optimal lambda (lambda.min) selected by 10-fold cross-validation, which resulted in 13 non-zero coefficients. LASSO, least absolute shrinkage and selection operator.

Multivariate Cox regression analysis of risk factors

Weight, LODS, OASIS, anion gap, pH value, and vasopressor were among the covariates that did not show statistical significance when the aforementioned components were included in a multivariate Cox proportional hazards analysis (Table S3). These factors were also excluded since the VIF test revealed no multicollinearity across the variables (Table S4). The model was then constructed with seven variables: malignant cancer, APS III, temperature, breath rate, age, race, and mild liver disease (Table 2).

Nomogram construction and evaluation

A nomogram was created as shown in Figure 3 using the seven variables obtained from the multivariate regression analysis as predictive factors and taking into account the 30-day death rate in COPD patients with sepsis as the clinical outcome.

Figure 3 Nomogram for predicting 30-day survival probability in participants. The size of the boxes (cyan) represents the difference in the relative proportion of patients in each subgroup. The density plot (grey) of the total points shows its distribution. APS, Acute Physiology Score.

Measures including the C-index, DCA, calibration curve, and receiver operating characteristic (ROC) curve were then used to assess the nomogram’s clinical usefulness and predictive effectiveness. With C-index values of 0.775 for the train set and 0.765 for the test set, the results showed that the prognostic model had high prediction accuracy and discriminative capacity. With an AUC value of 0.794 in the train set (Figure 4A) and 0.788 in the test set (Figure 4B), the ROC curves showed strong predictive ability. Furthermore, there was high agreement between the nomogram predictions and actual observations, as shown by the calibration curves for the train set (Figure 4C) and test set (Figure 4D), which closely matched the diagonal line. A net advantage in predicting the survival status of samples in both the train set (Figure 5A) and the test set (Figure 5B) is shown by the DCA curves in Figure 5.

Figure 4 The ROC curve of the nomogram and calibration curves of the nomogram. (A) ROC curve for the train set. (B) ROC curve for the test set. (C) Calibration curves of participants in the train set. (D) Calibration curves of participants in the test set. The variables entered in the nomogram are the same. The diagonal represents a perfect prediction by an ideal model (B =500 repetitions), respectively. AUC, area under the receiver operating characteristic curve; OS, overall survival; ROC, receiver operating characteristic.

Figure 5 Decision curves of the nomogram. 30-day mortality benefit of the nomogram in the train set (A) and test set (B), respectively. DCA, decision curve analysis.

Discussion

In this study, we used the MIMIC-IV database to predict the 30-day survival of COPD patients who were sepsis-afflicted. The 30-day death rate was shown to be related to age, race, temperature, breath rate, APS III, mild liver disease, and malignant cancer. We created a nomogram that physicians may use to more accurately assess the prognosis of patients with sepsis and COPD to optimize the 30-day death rate in these patients.

Possible pathophysiological connections exist between sepsis and COPD. Patients with COPD have a persistent systemic inflammatory state in addition to pulmonary inflammation. According to the findings of cross-sectional research that examined COPD and inflammatory biomarkers, individuals with COPD had higher levels of fibrinogen, tumor necrosis factor-alpha (TNF-α), interleukin (IL)-6, IL-8, IL-18, and C-reactive protein than people without the disease (22). Another research emphasized the correlation between blood neutrophil levels and the incidence and mortality of acute exacerbations in individuals with COPD (23). Stable COPD patients have a prothrombotic condition in addition to systemic inflammation. In individuals with stable COPD, observational research found lower levels of anticoagulants and higher levels of important coagulation components (24). We hypothesize that individuals with COPD may affect the course of sepsis by influencing systemic inflammation and the prothrombotic state, as immunological dysregulation and coagulation abnormalities are intrinsic characteristics of sepsis (18,25). On the other hand, sepsis may also serve as a catalyst for sudden flare-ups of COPD. Organ dysfunction can result from severe sepsis or septic shock, with the lungs being the most often impacted and susceptible organ (26,27). Under septic conditions, excessive inflammation and apoptosis lead to alveolar epithelial cell destruction and increased epithelial permeability, inducing the release of many pro-inflammatory cytokines (28,29), which promote the progression of COPD.

The onset and progression of COPD are regulated by multiple key factors, which not only determine the initial development of the disease but also significantly influence its rate of progression and risk of acute exacerbations, thereby indirectly affecting outcomes after complicating with sepsis. Smoking is a primary causative factor of COPD. Long-term smoking activates inflammatory cells, disrupts the protease-antiprotease balance, and leads to irreversible lung tissue damage, while also impairing airway mucociliary clearance, thereby increasing the risk of infections and subsequent sepsis (30-32). Additionally, environmental factors such as outdoor PM2.5, indoor biomass fuel exposure, and occupational dust exposure (33,34), genetic factors like α-1 antitrypsin deficiency (35), as well as comorbidities including cardiovascular disease and diabetes (36), may exacerbate airway inflammation, reduce lung functional reserve, or weaken immune function. These factors accelerate COPD progression, making patients more susceptible to severe complications when complicating with sepsis, further increasing mortality risk.

In this investigation, in addition to the traditional markers such as advanced age (37), malignant cancer (38), and APS III (39) being found as independent risk factors for poor prognosis in COPD patients with septicemia, other factors grabbed our interest. For example, breath rate is thought to be highly linked with illness severity in patients with sepsis and COPD. Adults with breath rates of more than 22 breaths per minute have been classified as having a systemic inflammatory response syndrome, which is associated with sepsis (18). A multicenter retrospective analysis of sepsis patients found that breath rate is an accurate predictor of sepsis death (40). During COPD, alterations in respiratory function occur, with inflammatory responses and airway obstruction resulting in decreased forced expiratory volume, limited airflow, impaired gas exchange, decreased tidal volume, and increased breath rate (1).

In this study, we discovered a link between low body temperature and survival in COPD patients with sepsis. Body temperature is a major topic in sepsis research (41). It has been estimated that roughly 60% of sepsis patients in the ICU have fever symptoms (42), whereas approximately 30% may have hypothermia upon arrival (43). High body temperature does not have a major influence on the prognosis of sepsis patients in the ICU. However, low body temperature is significantly associated with a poor prognosis and a greater frequency of ICU-acquired infections, with severity rising as temperature drops (42,44,45). A study on stable COPD patients has found that they had considerably lower body temperatures than healthy controls (46), with lower temperatures being connected with COPD exacerbations and hospital mortality (47). The presence of comorbidities might hasten the course of COPD or sepsis, greatly increasing fatality rates. Mild liver disease is a major cost driver for increasing healthcare expenses in COPD patients (48). Liver disease can affect the synthesis and clearance of circulating cytokines and other mediators, resulting in changes in pulmonary status (49). Liver dysfunction is regarded as an independent risk factor for poor prognosis in sepsis (50,51). The frequency of sepsis-related liver dysfunction is estimated to range between 15% and 34.7% (52,53), with a one-year death rate of up to 47% (52). Race is also thought to have a significant impact on COPD patients with sepsis. Disease incidence and prognosis vary by race due to socioeconomic variations. However, because the classification of race in this experiment is not comprehensive, it will not be elaborated upon in this study.

This study has some drawbacks. First and foremost, our findings are based only on the MIMIC-IV database, with no prospective validation. Future studies should externally evaluate our findings by using our data to establish the nomogram’s stability and performance. Second, the database may not have all the relevant information about patients, resulting in a smaller sample size owing to missing data. Finally, although race was included as a predictive component in the nomogram, the classification was insufficiently specific to offer a thorough study. Subsequent studies should use more extensive and rigorous research methodologies to offer a better explanation.

Conclusions

We created a nomogram that uses age, race, breath rate, body temperature, APS III, mild liver disease, and malignant cancer as markers to predict the 30-day death rate in COPD patients with sepsis. The nomogram has strong discriminative capacity, accuracy, and clinical application, allowing for tailored prognosis prediction for such patients and giving doctors useful references for clinical decision-making.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2171/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2171/prf

Funding: This study was supported by Yangzhou Social Development Project (No. YZ2024094); National key clinical specialty, Financial Appropriations of National {No. 176 [2022]}; Flagship institution of Chinese and Western medicine coordination, Financial Appropriations of National {No. Jiangsu 60 [2023]}; Jiangsu Provincial Medical Key Discipline Cultivation Unit (No. JSDW20221); Management Project of Northern Jiangsu People’s Hospital (No. YYGL202306); and Yangzhou Social Development Project (No. YZ2023105).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2171/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The MIMIC-IV database has been approved by the Institutional Review Boards at the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. To protect patients’ privacy, all confidential information in the database repository has been deleted.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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