Impact of early acute kidney injury on 30-day mortality in intensive care unit patients with chronic obstructive pulmonary disease: a retrospective study using MIMIC-IV

Introduction

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide, with mortality rates varying depending on the severity of the disease and the patient (1,2). Although the prevalence of COPD ranges from 5% to 17%, acute exacerbations of COPD (AECOPDs) account for 13% of all hospitalized patients (3). AECOPD is a particularly common disease in intensive care units (ICUs) (4). The economic toll of COPD is significant. In 2008, research found that the average cost for COPD-related simple inpatient admissions related to COPD $7,242 (5). Acute kidney injury (AKI) is a common organ dysfunction in critically ill patients. The incidence of AKI in the ICU was reported to be 57.3%. AKI increases the risk of death; The mortality rate among hospitalized patients with AKI is reported to be 26.9% and up to 35% at 90 days (6,7). The severity, duration, and renal recovery of AKI, lower baseline renal function, male, elderly, and comorbidities (diabetes, hypertension, cardiovascular disease, and tumors) all increase the risk of death (8).

AKI is also a recognized serious complication of AECOPD. AKI was previously reported by Fabbian et al. as an independent risk factor for hospital death in patients with AECOPD (9). Barakat et al. showed that the incidence of AKI in COPD patients was 128/100,000 person-years, compared to 1.9% in AECOPD patients (10).

AKI is defined as a rapid decline in kidney function from baseline over a period of hours or days (11). and more specifically, an increased risk of AKI in people with an absolute increase in creatinine levels of at least 26.4 µmol/L and an estimated decrease in potential estimated glomerular filtration rate (eGFR) over 48 hours (12). even in people with mild proteinuria (13-15). Although AKI is defined as reversible, the concept of reversibility is too simplistic. Deterioration of renal function is a common symptom after AKI (11,16).

Therefore, this retrospective study was designed to investigate the effect of AKI within 2 days on 30-day all-cause mortality in patients with COPD based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV) version 2.2 database. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-18/rc).

Methods

Database

Data were obtained from the MIMIC-IV version 2.2, a large, single-center, publicly available critical care database. This database consisted of all hospitalized ICU or emergency department patients admitted to critical care units at Beth Israel Deaconess Medical Center (BIDMC; Boston, MA, USA) between 2008 and 2019. The database consisted of records of demographics, vital signs, laboratory tests, fluid balance, and vital status; documented International Classification of Diseases, 10^th Revision (ICD-10) codes; documented hourly physiological data from bedside monitors confirmed by ICU nurses; and stored written evaluations of radiology films by specialists during the corresponding time period. One author (C.C.) completed the Collaborative Institutional Training Initiative (CITI) Data or Specimens Only Research course, was approved to access the database, and took responsibility for data extraction (certification number 66935858). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The use of the MIMIC-IV database was approved by the Institutional Review Boards of the Massachusetts Institute of Technology and BIDMC, both of which waive the requirement for informed consent for studies involving the MIMIC-IV database; therefore, our current study did not require approval from Yiwu Central Hospital’s ethics committee.

Study population

COPD is a heterogeneous lung disease characterized by progressive airflow obstruction (17). For pulmonary function tests, the forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) ratio <0.7 after inhaled bronchodilators was used to diagnose COPD (18). The identification of COPD was facilitated by the ICD-10 codes (J44, J440, J441, and J449) (19). People admitted between 2008 and 2019 are easily identified in the database.

A flowchart was provided in Figure 1. Our study included 73,181 MIME-IV patients admitted to the hospital, and then excluded 22,261 patients who were admitted to the ICU for a second (or more) hospital admission or a second ICU admission. We only included 14,070 patients with COPD who were admitted to the ICU for the first time between 2008 and 2019. After excluding 11,461 patients admitted with missing data, we finally included 2,609 patients with COPD in our cohort, of whom 1,514 (58.03%) were known to have experienced AKI within 48 hours of ICU admission. AKI was diagnosed according to the Kidney Diseases: Improving Global Outcomes (KDIGO) guidelines as follows: an increase in serum creatinine of ≥0.3 mg/dL within 48 hours, or an increase in serum creatinine to ≥1.5 times baseline levels, or a urine volume of <0.5 mL/(kg·h) for ≥6 hours (20).

Figure 1 Study cohort. Illustration of exclusion and inclusion criteria as utilized to select the final cohort of 2,609 patients. COPD, chronic obstructive pulmonary disease; ICU, intensive care unit; MIMIC-IV, Medical Information Mart for Intensive Care-IV.

Data extraction

Structured Query Language (SQL), PostgreSQL tools (version 9.6), and STATA version 17.0 were used for data extraction and management. Data on age, sex, ethnicity, comorbidities, initial laboratory parameters after ICU admission, two scoring systems including the Sequential Organ Failure Assessment (SOFA) score and the Simplified Acute Physiology Score II (SAPSII) (21), mechanical ventilation (MV) at ICU admission, length of hospital and ICU stay, and date of death of patients were extracted directly or calculated. Comorbidities identified by ICD-9 code included atrial fibrillation (AFIB), coronary artery disease (CAD), congestive heart failure (CHF), diabetes, malignancy, chronic kidney disease, liver disease, stroke, and laboratory parameters included hemoglobin, platelet count, white blood cell (WBC) count, percentage of lymphocytes and neutrophils, neutrophil-to-lymphocyte ratio (NLR), pH, partial pressure of oxygen (PO₂), partial pressure of carbon dioxide (PCO₂), bicarbonate, partial thromboplastin time (PTT), prothrombin time (PT), glucose, urea nitrogen, creatinine, lactate, creatine kinase, creatine kinase isoenzyme (CK-MB), alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), and albumin.

Statistical analysis

Baseline measurements for a normal distribution are expressed as mean ± standard deviation, while data for a non-normal distribution are expressed as median (quartile). Counting data are expressed as frequencies and percentages (%). Multiple interpolation was used for missing data. Patients were divided into two groups according to whether AKI occurred within 2 days. For analysis of baseline characteristics, statistical differences in continuous variables between the four groups were analyzed using one-way analysis of variance or Kruskal-Wallis H test, and categorical variables were analyzed using the Chi-squared test. Multivariate Cox regression analyses were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for death in different glycemic groups. To control for confounding, factors with a P value of less than 0.05 in the univariate analysis were included in the multivariate model analysis. Finally, albumin, anion gap, potassium, granulocytes, oxygen saturation (SO₂), PO₂, PCO₂, WBC, glucose, blood urea nitrogen (BUN), creatinine, calcium, chloride, PT, PTT, ALT, AST, bilirubin, lactate, base excess, myocardial infarction, CHF, cerebrovascular disease, diabetes with complicated complications, severe liver disease, and sepsis were included in the adjusted model. Gender and age are usually required adjustment covariates. Significance of survival was analyzed by the Kaplan-Meier curve and log-rank test. Univariate and multivariate Cox regression models were used to investigate the association between 30-day mortality in patients with 2-day AKI in COPD. Stratified and interaction analyses were used to examine whether developed AKI within 2 days had any effect in different patient comorbidities. All statistical analyses were performed using the R language (https://www.r-project.org; The R Foundation) and Free Statistics software. All P values reported are two-tailed, and P values <0.05 were considered statistically significant.

To further analyze the effects of comorbidities and major treatments on study outcomes, subgroup analyses were performed for comorbidities (including cerebrovascular disease and severe liver disease) and sex to assess the stability of study results.

Results

Population characteristics

Baseline characteristics and laboratory parameters of the study population are shown in Tables 1,2.

Compared with patients without AKI within 2 days, patients with AKI were older [median: 72.7 (65.1, 80.0) vs. 70.6 (63.2, 79.6), P=0.005] and had a higher Simplified Acute Physiology Score III (SAPSIII) score [median: 50.0 (37.0, 67.8) vs. 37.0 (28.0, 48.0), P<0.001], at the same time, The scores of SAPSII, SOFA score, and Outcome and Assessment Information Set (OASIS) Charlson Comorbidity Index were also higher (all P<0.001). Patients who developed AKI within 2 days were more likely to have myocardial infarction (28.6% vs. 20.5%, P<0.001), CHF (48.2% vs. 39.2%, P<0.001), diabetes with complicated complications (18.9% vs. 14.4%, P=0.003), severe liver disease (4.7% vs. 2.5%, P=0.003), and sepsis (60.0% vs. 34.8%, P<0.001). Metastatic solid tumor (6.7% vs. 7.9%, P=0.24) and peripheral vascular disease (18.5% vs. 17.4%, P=0.49) showed no significant difference in the proportion of other diseases (Table 1). Compared with patients who did not develop AKI within 2 days, patients with AKI had a higher percentage of WBCs [median: 14.2 (9.9, 19.3) vs. 11.5 (8.4, 15.7), P<0.001] and percentage of creatinine [median: 1.2 (0.9, 1.9) vs. 0.9 (0.7, 1.3), P<0.001], percentage of PT [median: 14.5 (12.5, 17.3) vs. 12.9 (11.8, 15.3), P<0.001]. In addition, PTT, AST, ALT, total bilirubin, lactate, base excess, granulocytes, etc. (all P<0.001) (Table 2).

Risk factors associated with 30-day all-cause mortality in patients with COPD. The Kaplan-Meier curve in Figure 2 shows poor short-term survival in patients who develop AKI within 2 days of COPD. In addition, we explored the risk factors associated with 30-day all-cause mortality in patients with COPD by Cox univariate analysis, finding that age (HR =1.03, 95% CI: 1.02–1.04, P<0.001), comorbidities (2-day AKI: HR =2.07, 95% CI: 1.6–2.69, P<0.001; severe liver disease: HR =1.9, 95% CI: 1.31–2.74, P<0.001; metastatic solid tumor: HR =1.83, 95% CI: 1.35–2.47, P<0.001) (Table 3). It was associated with 30-day all-cause mortality in COPD patients.

Figure 2 Kaplan-Meier analysis of survival probability of COPD patients based on the presence of occurrence within 2 days. 0, no; 1, yes. AKI, acute kidney injury; COPD, chronic obstructive pulmonary disease.

To control for confounding factors, we take factors with a P value of less than 0.05 in the univariate analysis were entered in the multivariate model analysis. In the end, albumin, anion gap, potassium, granulocytes, SO₂, PO₂, PCO₂, WBC, glucose, BUN, creatinine, calcium, chloride, PT, PTT, ALT, AST, bilirubin, lactate, base excess, myocardial infarction, CHF, cerebrovascular disease, diabetes with complicated complications, severe liver disease, and sepsis were included in the adjusted model. Gender and age are usually mandatory adjustment covariates. Cerebrovascular disease (HR =5.19, 95% CI: 1.49–18.06, P=0.01) was found in the multivariate analysis. Therefore, the occurrence of 2-day AKI (HR =11.02, 95% CI: 1.8–67.39, P=0.009) remains an important risk factor for 30-day all-cause mortality (Table 4).

In addition, sensitivity analyses stratified by sex and cerebrovascular disease were used to evaluate the association between 2-day AKI and 30-day all-cause mortality in patients with COPD, and the results for each subgroup were consistent with those for the overall patient population (Figure 3).

Figure 3 Sensitivity analysis of the association between 2-day AKI and 30-day mortality in patients with COPD. 0, no; 1, yes. AKI, acute kidney injury; CI, confidence interval; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; ref., reference.

Discussion

The study yielded two significant findings. The findings of this study demonstrated that patients suffering from COPD who subsequently developed AKI within a 48-hour period were characterized by advanced age, a greater number of comorbidities, and higher scores on the SAPSIII, SAPSII, SOFA score, and OASIS Charlson Comorbidity Index than those who did not develop AKI within this time frame. Secondly, the present study identified that 2-day AKI functions as an independent risk factor for 30-day all-cause mortality in patients diagnosed with COPD. The findings of this study demonstrate that 58.03% of COPD patients exhibited the development of AKI within a 2-day period, a result that aligns with the findings of previous studies in which 278 patients (55.8%) demonstrated the development of AKI (22). Barakat et al. showed that the incidence of AKI in patients with COPD was 128/100,000 person-years, and the prevalence of AKI in patients with AECOPD was 1.9% (10). Hirayama et al. reported that the incidence of AKI was 7.0% in AECOPD patients (23). Chen et al. indicated that hospital-acquired AKI (HA-AKI) was 7.9% in AECOPD patient (24). Another study reported a higher AKI occurrence in AECOPD patients. Wan et al. illustrated that 1,768 patients were admitted to Nanjing First Hospital with a principal diagnosis of AECOPD. Of these, 377 (21%) patients had AKI (25). Our current results showed a much higher incidence compared to the 1.9% and 7.9% reported in previous studies by Barakat et al. and Chen et al. We tried to explain the difference. Firstly, in recent years, the incidence of AKI was steadily increasing (note that the study by Barakat et al. started in 2004). Therefore, the incidence of AKI was underestimated. Secondly, acute respiratory failure (ARF) was an important factor for AKI, and previous studies reported that the incidence of AKI in patients with ARF ranged from 35% to 62% (26-28). In another predictive model study, AKI was observed in 521 (56.3%) of 926 patients diagnosed with COPD in the validation cohort (29). The extant literature suggests that AKI is one of the most prevalent comorbidities in patients with COPD.

We found that patients with COPD were older, had more comorbidities, and had higher SAPSIII and other scores than those without AKI. Furthermore, the presence of systemic inflammation (30), chronic hypoxemia (31) and hypercapnia (32), COPD patients with AKI have higher WBC, lower PO₂, lower pH, and higher PCO₂. Study explored the potential biomarkers that predict AKI in AECOPD patients and concluded that hypoxia and inflammation might be risk factors for renal injury in patients with AECOPD, serum cystatin C and β2-macroglobulin (β2-MG) could be sensitive indicators for the early detection of renal injury (33). Taken together, the results showed that COPD patients with AKI were in a worse state at ICU admission than patients without AKI.

We further explored the risk factors for 30-day all-cause mortality in patients with COPD. Cox risk model results showed that 2-day AKI was an independent risk factor for 30-day all-cause death in patients with COPD. Alaithan et al. evaluated 119 patients with COPD who were admitted to ICU and found that AKI was identified as an independent risk factor associated with in-hospital mortality (34). Our study is consistent with previously reported studies that in COPD patients, approximately three-quarters of AKI is community-acquired AKI (CA-AKI) and one-quarter HA-AKI. Compared with CA-AKI patients, HA-AKI patients had a greater need for non-invasive MV, higher in-hospital mortality, longer hospital stays, and longer periods of MV. Even after adjusting for other important factors, HA-AKI (as compared to CA-AKI) remained an independent risk factor for death in hospitalized patients (35).

We tried to find out the reasons for the poor prognosis in COPD patients with AKI. First of all, patients with AKI are older than those who develop AKI, and older age may be associated with poor prognosis in these patients, but Xu et al. found that the risk of AKI in the elderly does not increase with age, except for people aged 75 and above. In older adults, the association between AKI and in-hospital death did not increase in an age-dependent manner (36). Secondly, cardiovascular disease is associated with poor prognosis in COPD patients (37), which is consistent with the results of univariate analysis in our study. But after multi-factor Cox regression analysis, we found that cerebrovascular disease had elevated AKI in patients with CODP, and there was a study showing that cerebrovascular disease patients with impaired baseline renal function and more severe stroke are at higher risk of AKI (38). Therefore, we assessed the association between AKI and 30-day all-cause mortality in patients with COPD using sensitivity analyses stratified by sex and cerebrovascular disease, with results for each subgroup consistent with the overall patient results.

There are some limitations to the study. COPD is defined by the ICD-10 code (four) rather than based on spirometry (FEV1/FVC ratio after bronchodilator <0.70) (39), leading to overdiagnosis of COPD (40).

Conclusions

The present study provides evidence that the occurrence of 2-day AKI is an independent risk factor for 30-day all-cause mortality in patients with COPD. These findings underscore the significance of early renal protection in patients with COPD. Nevertheless, it is imperative to acknowledge that the correlation between elevated mortality in COPD patients with AKI remains speculative and necessitates further exploration through meticulously designed studies.

Acknowledgments

We are grateful for the excellent work of the MIMIC-IV team (MIT Computational Physiology Laboratory), who continuously collect bedside data and provide a database for every critical care researcher.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-18/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-18/prf

Funding: This work was supported by the Yiwu Science and Technology Plan Project (No. 24-3-104).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-18/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 use of the MIMIC-IV database was approved by the Institutional Review Boards of the Massachusetts Institute of Technology and BIDMC, both of which waive the requirement for informed consent for studies involving the MIMIC-IV database; therefore, our current study did not require approval from Yiwu Central Hospital’s ethics committee.

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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