Next Article in Journal
Alternative Transaxillary Access for Transcatheter Aortic Valve Implantation
Previous Article in Journal
Effectiveness of Ozone Therapy in Non-Surgical Periodontal Treatment: A Meta-Analysis of Topical Applications
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 14
Issue 14
10.3390/jcm14145125
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prevalence, Outcomes and Healthcare Costs of Postoperative ARDS Compared with Medical ARDS

by
Miguel Bardají-Carrillo
1,2,3,
Rocío López-Herrero
1,2,3,4,*,
Mario S. Espinoza-Fernández
1,2,3,
Lucía Alonso-Villalobos
1,2,3,
Rosa Cobo-Zubia
1,
Rosa Prieto-Utrera
1,
Irene Arroyo-Hernantes
1,5,
Esther Gómez-Sánchez
1,2,3,4,
Luigi Camporota
6,7,†,
Jesús Villar
8,9,10,11,† and
Eduardo Tamayo
1,2,3,4,†
1
BioCritic, Group for Biomedical Research in Critical Care Medicine, 47003 Valladolid, Spain
2
Anesthesiology and Critical Care, Clinical University Hospital of Valladolid (HCUV), 47003 Valladolid, Spain
3
Department of Surgery, University of Valladolid, 47003 Valladolid, Spain
4
CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Avenida Monforte de Lemos 5, 28029 Madrid, Spain
5
Department of Research and Innovation, Clinical University Hospital of Valladolid (HCUV), SACYL/IECSCYL, 47003 Valladolid, Spain
6
Department of Adult Critical Care, Guy’s and St Thomas’ NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, UK
7
Centre for Human and Applied Physiological Sciences, King’s College London Strand, London WC2R 2LS, UK
8
CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Avenida Monforte de Lemos 5, 28029 Madrid, Spain
9
Research Unit, Hospital Universitario Dr. Negrín, Fundación Canaria Instituto de Investigación Sanitaria de Canarias, Barranco de la Ballena s/n, 35019 Las Palmas de Gran Canaria, Spain
10
Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 209 Victoria St, Toronto, ON M5B 1T8, Canada
11
Faculty of Health Sciences, Universidad del Atlántico Medio, Carretera de Quilmes 37, Tafira Baja, 35017 Las Palmas, Spain
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
†
These authors contributed equally as senior authors.
J. Clin. Med. 2025, 14(14), 5125; https://doi.org/10.3390/jcm14145125
Submission received: 28 May 2025 / Revised: 13 July 2025 / Accepted: 15 July 2025 / Published: 18 July 2025
(This article belongs to the Section Anesthesiology)
Browse Figures
Versions Notes

Abstract

Background/Objectives: Postoperative acute respiratory distress syndrome (ARDS) is a recognized complication with reported prevalence rates of up to 20% and highly variable mortality. However, there is limited published evidence comparing the outcomes of postoperative ARDS with those of medical ARDS. We aimed to evaluate the prevalence, hospital mortality, and healthcare costs of postoperative ARDS in Spain between 2000 and 2022 and to compare them with those of medical ARDS. Methods: We performed a nationwide, registry-based study of all hospitalizations for postoperative ARDS in Spain between 1 January 2000 and 31 December 2022 using the Minimum Basic Data Set (MBDS) Registry. Results: We identified a total of 93,192 ARDS patients, of which 40,601 had postoperative ARDS. The postoperative ARDS prevalence varied between 0.05 and 0.22%, accounting for 45–50% of total ARDS cases recorded during the study period. Hospital mortality was lower in postoperative ARDS compared with medical ARDS during the first phase (2000–2015) (47.0% vs. 49.9%, p < 0.001) and converged during the second phase (2017–2022) (42.7% vs. 43.2%, p = 0.413). Postoperative ARDS was associated with a longer hospital stay and 1.5 times higher healthcare costs compared with medical ARDS. During the COVID-19 pandemic, mortality rates declined but costs peaked in both groups. The incidence of digestive tract infection was higher in postoperative ARDS. Conclusions: The prevalence of postoperative ARDS remained stable, except during the COVID-19 pandemic, and its hospital mortality declined and equalized with that of medical ARDS. However, the costs associated with postoperative ARDS remained significantly higher.

1. Introduction

Postoperative acute respiratory failure (PARF) and postoperative pulmonary complications (PPCs) are major contributors to morbidity and mortality and are associated with prolonged length of hospital stay (LoS) and increased healthcare costs [1,2]. A leading cause of PARF and PPCs is postoperative acute respiratory distress syndrome (ARDS) [3]. Given that over 230 million surgical procedures are performed annually worldwide [2,4], the impact of postoperative ARDS may be underappreciated.
The risk of developing postoperative ARDS varies depending on the type of surgical procedure. Cardiac, thoracic, vascular, and trauma surgeries carry a higher risk [5]. The reported prevalence ranges from 0.2% to 20% [6]. Emergency surgery is a major risk factor, increasing the likelihood of postoperative ARDS ninefold [5]. The mortality rates associated with postoperative ARDS vary widely [6,7] and appear largely independent of preoperative comorbidities [5].
Giannakoulis et al. [7] reported that postoperative ARDS is associated with lower mortality than medical ARDS. While advancements in intraoperative management and implementation of optimal ventilatory and fluid strategies might contribute to improved outcomes, it is plausible that the prevalence of postoperative ARDS has declined over time [7,8]. There is currently limited evidence comparing ARDS in medical vs. postsurgical patients, and comprehensive data on the epidemiology of postoperative ARDS remain scarce. Moreover, the impact of COVID-19 on the epidemiology of ARDS [9] and its specific effect on postoperative ARDS have not been fully evaluated.
We aimed to evaluate the prevalence, hospital mortality, and healthcare costs of postoperative ARDS in Spain between 2000 and 2022 and to compare them with those of medical ARDS over the same time period. We also evaluated temporal trends, including the potential impact of the COVID-19 pandemic on the outcomes, and examined the clinical features associated with postoperative ARDS.

2. Materials and Methods

2.1. Study Design and Data Source

We performed a nationwide registry-based retrospective study of all ARDS hospitalizations in Spain between 1 January 2000 and 31 December 2022, taking into account that before 2016 only public hospitals were included in the Minimum Basic Data Set (MBDS), while from 2016 onwards both public and private hospitals were included. We analyzed only ARDS patients who received invasive MV [10]. The year 2016 was excluded from the analysis because the Spanish Ministry of Health was unable to provide data for that year due to problems with the implementation of a new model for MBDS [11]. Afterwards, we divided ARDS patients on postoperative ARDS and medical ARDS based on the MBDS data.
Clinical and administrative data were sourced from hospital records in the MBDS of the National Surveillance System for Hospital Data in Spain, provided by the Ministry of Health and published annually with a two-year delay. The MBDS is a clinical and administrative database that includes clinical information recorded at the time of hospital discharge, with an estimated coverage of 99.5% [11,12], providing a robust and consistent foundation for the data. The MBDS includes up to 20 diagnoses, indicating whether the diagnosis was present on admission, as well as 20 therapeutic procedures performed during the hospital stay. The MBDS provides encrypted patient identification numbers, along with information on sex, date of birth, dates of hospital admission and discharge, medical institutions providing the services, diagnosis and procedure codes, and the outcome at discharge, according to the International Classification of Diseases 9th Revision, Clinical Modification (ICD-9-CM) [13] or the International Classification of Diseases 10th Revision, Clinical Modification (ICM-10-CM) [14] depending on the period of the study. The number of surgical interventions performed in Spain from 2010 to 2022, used to calculate the prevalence of postoperative ARDS, was obtained from the Spanish Ministry of Health [15,16], with no data available from previous years.
Data were handled in strict accordance with Spanish confidentiality regulations. This study was approved by the Ethics Committee for Clinical Research of Valladolid East Health Area (#PI-24-399-C). Given the anonymity and nature of the collected data, informed consent was waived.

2.2. Study Variables

We included all hospitalized patients in public and private hospitals in Spain with a diagnosis of respiratory distress syndrome (ICD-9-CM codes 518.82 and 518.5 and ICD-10-CM codes J80.*). For the purpose of this study, we also selected the need for mechanical ventilation (MV) (ICD-9 codes 96.70, 96.71, 96.72, and 96.04 and ICD-10 codes 5A1935Z, 5A1945Z, and 5A1955Z). Postoperative patients were defined if they had a surgical intervention date attached. We applied no exclusion criteria. Sepsis was defined using the codes adapted by MacLaren et al. [17], Esper et al. [18], Dombrovskiy et al. [19], and Bateman et al. [20] (Supplementary Tables S1 and S2). The source of infection was defined by codes adapted by Esper et al. [18] and Wang et al. [21] (Supplementary Tables S3 and S4), as well as by organ dysfunction (Supplementary Tables S5 and S6) according to sepsis criteria by Angus [22], adapted by Shen et al. [23] and Bateman et al. [20]. The Charlson Comorbidity Index was calculated from data extracted from the MBDS (Supplementary Tables S7 and S8). All codes were updated to ICD-10-CM by our group [24].
The collected data included age, sex, comorbidities (diabetes, hypertension, heart disease, chronic renal disease, respiratory disease, and neurological disease), site of infection, LoS, and hospital mortality. The study was divided into two periods, corresponding to the change in the ARDS codification due to the transition from ICD-9-CM to ICD-10-CM between 2015 and 2016. The primary outcomes were postoperative ARDS prevalence, hospital mortality, and mean healthcare costs per postoperative ARDS patient. Secondary outcomes included LoS, site of infection, comorbidities, and effects of COVID-19 on epidemiology of postoperative ARDS.

2.3. Statistical Analysis

The postoperative ARDS prevalence was calculated as the proportion of patients who developed ARDS requiring MV after undergoing surgery (excluding day-case surgery, as obtained from the MBDS) relative to the total number of patients who underwent surgery (excluding day-case surgery). Hospital mortality was calculated as the proportion of overall hospital deaths in ARDS patients. Differences among groups were assessed using the chi-square test for categorical variables and the ANOVA test for continuous variables normally distributed, as well as the Kruskal–Wallis test for continuous variables not normally distributed. We used the Kolmogorov–Smirnov test to assess the normality of data. Categorical variables are expressed in percentages, and continuous variables expressed as the mean ± standard deviation (SD). The LoS was obtained as the difference in days between the date of hospital admission and the date of discharge (in survivors) or death (in non-survivors). Day of hospital admission was considered day 0. Discharge on the same day was considered as a 1-day stay. Costs were calculated using diagnosis-related groups (DRGs), which represents a medical–economic entity concerning a set of diseases requiring analogous management resources. The DRG data were extracted from the MBDS. A univariate logistic regression adjusted for age and sex was conducted with postoperative ARDS as the outcome (Supplementary Tables S9 and S10). Variables with p < 0.05 were included in the multivariate analysis. All analyses were performed using the R statistical package, version 4.3.2 [25]. All tests conducted were two-tailed, and p-values <0.05 were considered significant.

3. Results

3.1. Patients’ Characteristics

Table 1 and Table 2 show the characteristics of patients admitted in the period 2000–2015 and in the period 2017–2022, respectively. A total of 93,192 patients were diagnosed with ARDS between 2000 and 2022. Of those, 68,213 between 2000 and 2015 (36,393 medical ARDS and 31,820 postoperative ARDS) and 24,979 between 2017 and 2022 (16,198 medical ARDS and 8781 postoperative ARDS). Surgical patients were older than the medical ARDS patients during the first study period (60.3 ± 19.7 vs. 56.9 ± 20.8 years, p < 0.001). No age difference was observed in the second period. In both periods, ARDS was more common in males. The Charlson Comorbidity Index was significantly higher in surgical compared with medical patients in both periods (0.92 ± 0.98 vs. 0.73 ± 0.86, p < 0.001; 1.25 ± 1.76 vs. 1.01 ± 1.41, p < 0.001). The mean LoS was 12–13 days longer in postoperative ARDS.
Gastrointestinal infections were the most frequent cause of postoperative complications in both study periods [9.9% (n = 3681) vs. 18.1% (n = 5753), p < 0.001; 1.6% (n = 253) vs. 12.3% (n = 1080), p < 0.001], whereas respiratory infections were most frequent in medical patients. Obesity was more prevalent in medical ARDS during the second period [20.7% (n = 3354) vs. 15.0% (n = 1314), p < 0.001]. Sepsis was more frequent in postoperative ARDS patients during the second period [48.88% (n = 7917) vs. 59.06% (n = 5186), p < 0.001]. Extracorporeal membrane oxygenation (ECMO) was used more frequently in postoperative ARDS patients than in medical ARDS (Table 1 and Table 2).
In the multivariate analysis of the first study period (Figure 1A), gastrointestinal infection (OR 1.7, 95%CI 1.6–1.8, p < 0.001) emerged as the strongest factor associated with postoperative ARDS compared with medical ARDS. During the second period of the study (Figure 1B), gastrointestinal infection (OR 5.4, 95%CI 4.7–6.3, p < 0.001) was associated with postoperative ARDS, together with skin infection (OR 2.3, 95%CI 1.8–2.8, p < 0.001). A higher Charlson Index was independently associated with postoperative ARDS in both study periods (OR 1.3, 95%CI 1.3–1.4, p < 0.001; OR 1.0, 95%CI 1.0–1.1, p = 0.006), as well as circulatory source of infection (OR 1.6, 95%CI 1.4–2.0, p < 0.001; OR 1.6, 95%CI 1.2–2.1, p = 0.003).

3.2. Prevalence

Figure 2 shows the proportion of patients with postoperative ARDS from 2000 to 2022. During the first study period, postoperative ARDS accounted for about 45–50% of all ARDS cases, with the exception of 2014 and 2015, during which the proportion dropped to 31.8% and 30.7%, respectively. In the second period, postoperative ARDS represented about 50% of all ARDS cases. This rate declined with the onset of the COVID-19 pandemic, reaching a low of 29.4% in 2021. As the pandemic receded in 2022, the proportion of postoperative ARDS patients rose to 42.4%. The trend in postoperative ARDS prevalence is shown in Figure 3. The prevalence of postoperative ARDS varied in Spain between 0.05% and 0.22%, peaking in 2021.

3.3. Hospital Mortality

The trend in hospital mortality is shown in Figure 4. As reported in Table 1, mortality was higher in medical ARDS than in postoperative ARDS during the first period (49.9% (n = 18,168) vs. 47.0% (n = 14,943), p < 0.001). No difference in mortality was observed between the two groups during the second period (43.2% (n = 7004) vs. 42.7% (n = 3749), p = 0.413). Hospital mortality declined over time. In postoperative ARDS patients, mortality decreased from 50.7% in 2000 to 42.8% in 2022, while in medical ARDS patients, it declined from 57.9% in 2000 to 50.6% in 2022. Postoperative ARDS mortality was higher than medical ARDS mortality in 2017 and 2021, but these differences were not significant.

3.4. Mean Costs per ARDS Patient

The mean healthcare costs per ARDS patient between 2000 and 2022 are shown in Figure 5. Over the course of the study period, the mean cost for postoperative ARDS patients was about 1.5 times higher than for medical patients. Overall costs increased three-fold over 22 years, while the cost for medical ARDS patients increased from EUR 10,353 in 2000 to EUR 33,810 in 2022, whereas in postoperative patients it increased from EUR 15,792 in 2000 to EUR 45,436 in 2022.

3.5. Effect of COVID-19 Pandemic

There was an increase in medical ARDS cases over total ARDS cases (Figure 2). In 2021, there was a decline in medical ARDS hospital mortality to 37.8% and 38.6% in postoperative ARDS hospital mortality, while in 2022 hospital mortality reached rates prior to the pandemic period (Figure 4). Healthcare costs increased during COVID-19 pandemic, peaking in 2021 with EUR 42,030 per medical ARDS patient and EUR 54,787 per postoperative ARDS patient (Figure 5).

4. Discussion

The major findings of our study are the following: (i) the proportion of postoperative ARDS was about 45% of total ARDS (except during the COVID-19 pandemic), with a prevalence (percentage from total surgeries) that varied between 0.05% and 0.22%; (ii) hospital mortality for postoperative ARDS was lower than for medical ARDS during the first period of the study, but it converged with medical ARDS; (iii) healthcare costs for postoperative ARDS were about 1.5 times higher than medical ARDS, aligning with an LoS 1.5 times longer; (iv) during the COVID-19 pandemic, healthcare costs reached their peak, although hospital mortality declined; and (v) gastrointestinal infection emerged as the strongest factor associated with postoperative ARDS.
Giannakoulis et al. [7] reported that ARDS is more common in males and that postoperative ARDS patients are older. However, in the second period, there was no age difference between postoperative and medical ARDS. Those authors also observed that postoperative ARDS patients were more likely to have sepsis as a risk factor and cancer as a comorbidity, as we also found in our study. Abdominal infection was common in postoperative ARDS compared with postsurgical patients without ARDS [26]. The occurrence of aspergillosis increased in our study, likely due to its prevalence as a co-infection during the COVID-19 pandemic [27], as obesity emerged as a frequent comorbidity in medical ARDS, which aligns with its role as a significant risk factor for severe COVID-19 infection [28].
Of note, we found no studies comparing postoperative ARDS epidemiology with medical ARDS epidemiology. As illustrated in Figure 2, postoperative ARDS has historically accounted for 45–50% of ARDS cases. However, this proportion declined to about 30% during the COVID-19 pandemic as a result of the surge in ARDS caused by SARS-CoV-2 infections [29]. The current literature on postoperative ARDS prevalence focuses on specific surgical procedures [6]; for instance, ARDS developed in 7.2% following hepatectomy [30], 0.4–20% after cardiac surgery [31], or 10% in patients with postoperative sepsis [26]. In contrast, studies assessing ARDS prevalence in a general surgical population [5] found a low rate of 0.2%, which aligns closely with our findings. However, the data available from the MBDS did not allow for a detailed assessment of the specific type of surgery performed on each patient. It is important to note that the period 2014–2018 is less representative when assessing postoperative ARDS prevalence; during those years, ARDS cases in Spain declined, largely attributed to the transition from ICD-9-CM to ICD-10-CM coding, a change that has been recognized in the epidemiological reporting of several conditions [32,33].
ARDS mortality has been widely studied, with hospital mortality ranging from 32% to 51%. From 2010, the hospital mortality rate has been about 45% [34]. At the beginning of the 21st century, hospital mortality for medical ARDS in Spain exceeded 55%, while postoperative ARDS had a lower mortality; this difference persisted for the first period of our study (2000–2015). Medical ARDS is known to have a higher 90-day mortality compared with postoperative ARDS, even after adjusting for confounders such as age, lung severity, and use of vasopressors [7]. In recent years, there has also been a notable decline in hospital mortality in both postoperative and medical ARDS patients. Apparently, this decline could coincide with the implementation of intraoperative lung-protective MV strategies [34,35]. Improvements in perioperative management might have contributed to lower postoperative ARDS mortality [7]. Avoiding excessive perioperative transfusions is known to reduce postoperative ARDS development [36], and implementation of intraoperative blood management has reduced transfusion-related complications, including postoperative ARDS [37].
In our study, the period 2014–2018 is less representative due to the lower number of ARDS cases. After 2020, the COVID-19 pandemic plays a key role in ARDS epidemiology, not only for becoming one of the main causes of ARDS [29] but because it was associated with a reduction in elective surgery [38], although this number declined with subsequent SARS-CoV-2 waves [39]. In Spain, during the COVID-19 pandemic emergency surgery decreased by 58.9% and higher morbidity was observed in patients undergoing emergency surgery [40]. Decline in both elective and emergency surgeries, coupled with an increase in comorbidities and advanced disease stages among patients undergoing surgery, could explain the convergence in mortality between medical and postoperative ARDS [41,42]. However, some reports declared no evidence of increased mortality or morbidity for surgeries during COVID-19 pandemic [43,44]. ECMO use in severe ARDS patients is known to reduce mortality [45], and it is a useful tool in post-cardiothoracic surgery [46], although its use in postoperative ARDS has not been widely studied. Most systematic reviews on ECMO in ARDS focus on medical ARDS patients [45,47]. When comparing our two study periods, ECMO use increased in both groups, accompanied by a decline in hospital mortality. However, we cannot ensure that clinicians have followed the multidisciplinary care bundles in all patients collected in the MBDS, so ARDS mortality might have been affected by this fact.
When evaluating the healthcare costs associated with ARDS patients, we found that the costs for postoperative ARDS were consistently about 1.5 times higher than those for medical ARDS patients, which is consistent with findings that the LoS for postoperative ARDS patients is approximately 1.5 times longer. As reported by Park et al. [48], postoperative patients tend to associate a longer ICU stay. Although we did not adjust for inflation in the cost analysis, this increase in healthcare costs cannot be fully explained by the general rise in prices in Spain, which was 68% between 2000 and 2022 [49], while ARDS healthcare costs increased by 100% to 200%. Shari et al. [50] found that compared with ARDS present at admission, hospital-acquired ARDS was more likely to develop in surgical patients and was associated with a longer adjusted hospital stay. As LoS is a well-known determinant of increased healthcare costs [51], this is particularly important given that ICU stay is three times more expensive than the stay in a general ward [52]. Early-warning systems may help reduce healthcare costs by enabling the timely identification of risk factors, which can lead to lower mortality rates and shorter hospital stays [53]. Additionally, lung-protective ventilation has demonstrated clear benefits in improving ARDS outcomes while decreasing associated costs [54].
To reduce the incidence of postoperative ARDS, as well as associated mortality and healthcare costs, integrating predictive analytics into surgical workflows to identify high-risk patients could be highly beneficial. Predictive models have already demonstrated high sensitivity and specificity in forecasting ARDS risk [55].
We acknowledge some limitations, as well as strengths, of this study. Firstly, as with any retrospective analysis, there is a possibility of under-coding variables, leading to incomplete or inaccurate information. This could introduce a potential bias and affect the robustness of our findings. Secondly, we lacked detailed data, including chest radiographs, the ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2), and levels of positive end-expiratory pressure (PEEP), which are essential for confirming ARDS and grading its severity. Thirdly, since MBDS data are anonymous, it is impossible to ascertain whether a patient was hospitalized more than once within the same year, across multiple years, or in different hospitals. Fourthly, the transition from the ICD-9-CM to ICD-10-CM, along with the decline in cases, makes those years less representative. Fifthly, the date of ARDS diagnosis cannot be obtained from the MBDS, as it is not possible to identify whether the ARDS diagnosis was prior to surgery. Sixthly, we acknowledge that during our study period, some changes could have occurred in the perioperative management of critically ill patients that could change the prevalence and mortality of ARDS. Moreover, due to the large sample size of this study, most p-values reached statistical significance. Therefore, the interpretation of the results should focus not only on statistical significance but also on the absolute differences observed among groups, on the magnitude of these differences, as well as on their clinical relevance and effect sizes, as reflected in odds ratios, means, and proportions. Although the “Present on Admission” (POA) indicator has been available in the CMBD since the implementation of the ICD-10-ES in 2016, it was not used in this study due to poor data quality and inconsistent coding patterns observed in our cohort. Following the recommendations by Walraven et al. [56], we addressed the issues posed by administrative databases to the best of our ability, acknowledging certain limitations. However, the major strength of this study is the large number of ARDS patients, which provides high statistical power and enhances the reliability of our analyses. Additionally, the long follow-up period allows for a comprehensive assessment of trends over time. Moreover, the MBDS has an estimated coverage of 99.5% of hospitals in Spain [11,12], so the vast majority of hospitals and patients are recorded in this database, giving consistency to the data. To our knowledge, this is the largest nationwide epidemiological study of ARDS with the longest available follow-up period, offering a clear view of the disease’s trend.

5. Conclusions

The prevalence of postoperative ARDS in the 21st century in Spain ranged between 0.05% and 0.22%, representing 45–50% of all ARDS cases. While postoperative ARDS has historically been associated with lower mortality compared with medical ARDS, recent trends indicate that these rates are converging. Postoperative ARDS incurs costs that are 1.5 times higher than those for medical ARDS, consistent with longer hospital stays. During the COVID-19 pandemic, hospital mortality rates decreased in both groups, though associated costs reached their highest levels. Gastrointestinal infections emerged as the most significant factor associated with postoperative ARDS.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm14145125/s1, Table S1: International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes for sepsis diagnosis; Table S1: International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for sepsis diagnosis; Table S3: International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes for the site of infection; Table S4: International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for the site of infection; Table S5: International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes for acute organ dysfunction; Table S6: International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for acute organ dysfunction; Table S7: International Classification of Diseases, 10th Revision, Clinical Modification (ICD-9-CM) codes for Charlson Index calculation; Table S8: International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for Charlson Index calculation; Table S9: Univariate logistic regression of the first period of the study (2000–2015); Table S10: Univariate logistic regression of the first period of the study (2017–2022).

Author Contributions

M.B.-C.: Conceptualization, methodology, investigation, writing—original draft, writing—review & editing, and visualization. R.L.-H.: Investigation, writing—original draft, and writing—review & editing. M.S.E.-F.: Conceptualization, formal analysis, investigation, and writing—original draft. L.A.-V.: Conceptualization, formal analysis, investigation, and writing—original draft. R.C.-Z.: Conceptualization, formal analysis, investigation, and writing—original draft. R.P.-U.: Conceptualization, formal analysis, investigation, and writing—original draft. I.A.-H.: Formal analysis, investigation, and writing—original draft. E.G.-S.: Methodology, investigation, and writing—original draft. L.C.: Conceptualization, writing—review & editing, and supervision. J.V.: Conceptualization, writing—review & editing, supervision, and funding acquisition. E.T.: Conceptualization, methodology, writing—original draft, writing—review & editing, visualization, supervision, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Instituto de Salud Carlos III (PI21/00917, PI18/01238, CIBERINFEC CB21/13/00051), Junta de Castilla y León (GRS 2546/A/22, GRS 2425/A/ 21, GRS 1922/A/19, GRS 2057/A/19), Consejería de Educación de Castilla y León (VA256P20), and Fundación Ramón Areces (CIVP19A5953). J. Villar was funded by grants from the Instituto de Salud Carlos III (CB06/06/1088, PI 19/00141, AC21-2/00039), ERAPerMed (JTC_2021), The European Regional Development Funds, Fundación Canaria Instituto de Investigación Sanitaria de Canarias, Spain, and Asociación Científica Pulmón y Ventilación Mecánica, Las Palmas, Spain.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Valladolid East Health Area (code PI-24-399-C, 5 June 2024).

Informed Consent Statement

Patient consent was waived due to the anonymity and nature of the collected data.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
ARDSacute respiratory distress syndrome
DRGdiagnosis-related groups
ECMOextracorporeal membrane oxygenation
ICDinternational classification of diseases
ICUintensive care unit
LoSlength of hospital stay
MBDSminimum basic dataset
MVmechanical ventilation
PEEPpositive end-expiratory pressure
PARFpostoperative acute respiratory failure
PPCspostoperative pulmonary complications
SDstandard deviation

References

  1. Lusquinhos, J.; Tavares, M.; Abelha, F. Postoperative Pulmonary Complications and Perioperative Strategies: A Systematic Review. Cureus 2023, 15, e38786. [Google Scholar] [CrossRef]
  2. Stocking, J.C.; Drake, C.; Aldrich, J.M.; Ong, M.K.; Amin, A.; Marmor, R.A.; Godat, L.; Cannesson, M.; Gropper, M.A.; Romano, P.S.; et al. Outcomes and risk factors for delayed-onset postoperative respiratory failure: A multi-center case-control study by the University of California Critical Care Research Collaborative (UC3RC). BMC Anesthesiol. 2022, 22, 146. [Google Scholar] [CrossRef]
  3. Fernandez-Bustamante, A.; Frendl, G.; Sprung, J.; Kor, D.J.; Subramaniam, B.; Martinez Ruiz, R.; Lee, J.-W.; Henderson, W.G.; Moss, A.; Mehdiratta, N.; et al. Postoperative Pulmonary Complications, Early Mortality, and Hospital Stay Following Noncardiothoracic Surgery: A Multicenter Study by the Perioperative Research Network Investigators. JAMA Surg. 2017, 152, 157–166. [Google Scholar] [CrossRef]
  4. Canet, J.; Gallart, L.; Gomar, C.; Paluzie, G.; Vallès, J.; Castillo, J.; Sabaté, S.; Mazo, V.; Briones, Z.; Sanchis, J.; et al. Prediction of postoperative pulmonary complications in a population-based surgical cohort. Anesthesiology 2010, 113, 1338–1350. [Google Scholar] [CrossRef]
  5. Blum, J.M.; Stentz, M.J.; Dechert, R.; Jewell, E.; Engoren, M.; Rosenberg, A.L.; Park, P.K. Preoperative and intraoperative predictors of postoperative acute respiratory distress syndrome in a general surgical population. Anesthesiology 2013, 118, 19–29. [Google Scholar] [CrossRef]
  6. Chen, L.; Zhao, H.; Alam, A.; Mi, E.; Eguchi, S.; Yao, S.; Ma, D. Postoperative remote lung injury and its impact on surgical outcome. BMC Anesthesiol. 2019, 19, 30. [Google Scholar] [CrossRef] [PubMed]
  7. Giannakoulis, V.G.; Papoutsi, E.; Kaldis, V.; Tsirogianni, A.; Kotanidou, A.; Siempos, I.I. Postoperative acute respiratory distress syndrome in randomized controlled trials. Surgery 2023, 174, 1050–1055. [Google Scholar] [CrossRef] [PubMed]
  8. Kor, D.J.; Warner, D.O.; Alsara, A.; Fernández-Pérez, E.R.; Malinchoc, M.; Kashyap, R.; Li, G.; Gajic, O. Derivation and diagnostic accuracy of the surgical lung injury prediction model. Anesthesiology 2011, 115, 117–128. [Google Scholar] [CrossRef] [PubMed]
  9. Hendrickson, K.W.; Peltan, I.D.; Brown, S.M. The Epidemiology of Acute Respiratory Distress Syndrome Before and After Coronavirus Disease 2019. Crit. Care Clin. 2021, 37, 703–716. [Google Scholar] [CrossRef]
  10. Bardají-Carrillo, M.; López-Herrero, R.; Aguilar, G.; Arroyo-Hernantes, I.; Gómez-Sánchez, E.; Camporota, L.; Villar, J.; Tamayo, E. Epidemiological trends of mechanically ventilated acute respiratory distress syndrome in the twenty-first century: A nationwide, population-based retrospective study. J. Intensive Care 2025, 13, 9. [Google Scholar] [CrossRef]
  11. Ministerio de Sanidad—Sanidad en datos—Registro de Altas de los Hospitales del Sistema Nacional de Salud. CMBD. Available online: https://www.sanidad.gob.es/estadEstudios/estadisticas/cmbdhome.htm (accessed on 13 June 2024).
  12. BOE-A-2015-1235 Real Decreto 69/2015, de 6 de Febrero, por el que se Regula el Registro de Actividad de Atención Sanitaria Especializada. Available online: https://www.boe.es/buscar/act.php?id=BOE-A-2015-1235 (accessed on 13 June 2024).
  13. The Web’s Free ICD-9-CM & ICD-10-CM Medical Coding Reference. Available online: http://www.icd9data.com/ (accessed on 13 June 2024).
  14. The Web’s Free 2024 ICD-10-CM/PCS Medical Coding Reference. Available online: https://www.icd10data.com/ (accessed on 13 June 2024).
  15. Ministerio de Sanidad—Sanidad en datos—Intervenciones Quirúgicas Realizadas en Hospitales del Sistema Nacional de Salud (SNS), Frecuentación por 1.000 Habitantes, Porcentaje de Intervenciones de Cirugía Mayor Ambulatoria (C.M.A.) Sobre el Total de Intervenciones y Días de Espera para Intervenciones no Urgentes Según Comunidad Autónoma. Available online: https://www.sanidad.gob.es/estadEstudios/sanidadDatos/tablas/tabla26.htm (accessed on 26 August 2024).
  16. Ministerio de Sanidad—Sanidad en datos—Estadística de Centros Sanitarios de Atención Especializada. Available online: https://www.sanidad.gob.es/estadEstudios/estadisticas/EstabSanitReginInternado/EST_CENT_SANIT_AT_ESP.htm (accessed on 27 August 2024).
  17. MacLaren, R.; Bond, C.A.; Martin, S.J.; Fike, D. Clinical and economic outcomes of involving pharmacists in the direct care of critically ill patients with infections. Crit. Care Med. 2008, 36, 3184. [Google Scholar] [CrossRef]
  18. Esper, A.M.; Moss, M.; Lewis, C.A.; Nisbet, R.; Mannino, D.M.; Martin, G.S. The role of infection and comorbidity: Factors that influence disparities in sepsis. Crit. Care Med. 2006, 34, 2576–2582. [Google Scholar] [CrossRef]
  19. Dombrovskiy, V.Y.; Martin, A.A.; Sunderram, J.; Paz, H.L. Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: A trend analysis from 1993 to 2003. Crit. Care Med. 2007, 35, 1244. [Google Scholar] [CrossRef]
  20. Bateman, B.T.; Schmidt, U.; Berman, M.F.; Bittner, E.A. Temporal Trends in the Epidemiology of Severe Postoperative Sepsis after Elective Surgery: A Large, Nationwide Sample. Anesthesiology 2010, 112, 917–925. [Google Scholar] [CrossRef]
  21. Wang, H.E.; Shapiro, N.I.; Angus, D.C.; Yealy, D.M. National estimates of severe sepsis in United States emergency departments. Crit. Care Med. 2007, 35, 1928. [Google Scholar] [CrossRef] [PubMed]
  22. Angus, D.C.; Linde-Zwirble, W.T.; Lidicker, J.; Clermont, G.; Carcillo, J.; Pinsky, M.R. Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care. Crit. Care Med. 2001, 29, 1303–1310. [Google Scholar] [CrossRef] [PubMed]
  23. Shen, H.-N.; Lu, C.-L.; Yang, H.-H. Epidemiologic Trend of Severe Sepsis in Taiwan From 1997 Through 2006. Chest 2010, 138, 298–304. [Google Scholar] [CrossRef] [PubMed]
  24. López-Herrero, R.; Sánchez-de Prada, L.; Tamayo-Velasco, A.; Lorenzo-López, M.; Gómez-Pesquera, E.; Sánchez-Quirós, B.; de la Varga-Martínez, O.; Gómez-Sánchez, E.; Resino, S.; Tamayo, E.; et al. Epidemiology of bacterial co-infections and risk factors in COVID-19-hospitalized patients in Spain: A nationwide study. Eur. J. Public Health 2023, 33, 675–681. [Google Scholar] [CrossRef]
  25. R: The R Project for Statistical Computing. Available online: https://www.r-project.org/ (accessed on 30 June 2025).
  26. Bardají-Carrillo, M.; Martín-Fernández, M.; López-Herrero, R.; Priede-Vimbela, J.M.; Heredia-Rodríguez, M.; Gómez-Sánchez, E.; Gómez-Pesquera, E.; Lorenzo-López, M.; Jorge-Monjas, P.; Poves-Álvarez, R.; et al. Post-operative sepsis-induced acute respiratory distress syndrome: Risk factors for a life-threatening complication. Front. Med. 2024, 11, 1338542. [Google Scholar] [CrossRef]
  27. López-Herrero, R.; Sánchez-de Prada, L.; Tamayo-Velasco, A.; Heredia-Rodríguez, M.; Bardají Carrillo, M.; Jorge Monjas, P.; de la Varga-Martínez, O.; Resino, S.; Sarmentero-López de Quintana, G.; Gómez-Sánchez, E.; et al. Epidemiology of fungal infection in COVID 19 in Spain during 2020 and 2021: A nationwide study. Sci. Rep. 2024, 14, 5203. [Google Scholar] [CrossRef]
  28. Gao, Y.; Ding, M.; Dong, X.; Zhang, J.; Kursat Azkur, A.; Azkur, D.; Gan, H.; Sun, Y.; Fu, W.; Li, W.; et al. Risk factors for severe and critically ill COVID-19 patients: A review. Allergy 2021, 76, 428–455. [Google Scholar] [CrossRef] [PubMed]
  29. Beloncle, F.; Studer, A.; Seegers, V.; Richard, J.-C.; Desprez, C.; Fage, N.; Merdji, H.; Pavlovsky, B.; Helms, J.; Cunat, S.; et al. Longitudinal changes in compliance, oxygenation and ventilatory ratio in COVID-19 versus non-COVID-19 pulmonary acute respiratory distress syndrome. Crit. Care 2021, 25, 248. [Google Scholar] [CrossRef] [PubMed]
  30. Wang, X.; Zhang, H.; Zong, R.; Yu, W.; Wu, F.; Li, Y. Novel models for early prediction and prevention of acute respiratory distress syndrome in patients following hepatectomy: A clinical translational study based on 1,032 patients. Front. Med. 2022, 9, 1025764. [Google Scholar] [CrossRef] [PubMed]
  31. Chen, S.-W.; Chang, C.-H.; Chu, P.-H.; Chen, T.-H.; Wu, V.C.-C.; Huang, Y.-K.; Liao, C.-H.; Wang, S.-Y.; Lin, P.-J.; Tsai, F.-C. Risk factor analysis of postoperative acute respiratory distress syndrome in valvular heart surgery. J. Crit. Care 2016, 31, 139–143. [Google Scholar] [CrossRef]
  32. Hamedani, A.G.; Blank, L.; Thibault, D.P.; Willis, A.W. Impact of ICD-9 to ICD-10 Coding Transition on Prevalence Trends in Neurology. Neurol. Clin. Pract. 2021, 11, e612. [Google Scholar] [CrossRef]
  33. Salemi, J.L.; Tanner, J.P.; Kirby, R.S.; Cragan, J.D. The impact of the ICD-9-CM to ICD-10-CM transition on the prevalence of birth defects among infant hospitalizations in the United States. Birth Defects Res. 2019, 111, 1365. [Google Scholar] [CrossRef]
  34. Máca, J.; Jor, O.; Holub, M.; Sklienka, P.; Burša, F.; Burda, M.; Janout, V.; Ševčík, P. Past and Present ARDS Mortality Rates: A Systematic Review. Respir. Care 2017, 62, 113–122. [Google Scholar] [CrossRef]
  35. Wanderer, J.P.; Ehrenfeld, J.M.; Epstein, R.H.; Kor, D.J.; Bartz, R.R.; Fernandez-Bustamante, A.; Vidal Melo, M.F.; Blum, J.M. Temporal trends and current practice patterns for intraoperative ventilation at U.S. academic medical centers: A retrospective study. BMC Anesthesiol. 2015, 15, 40. [Google Scholar] [CrossRef]
  36. Sanfilippo, F.; Palumbo, G.J.; Bignami, E.; Pavesi, M.; Ranucci, M.; Scolletta, S.; Pelosi, P.; Astuto, M. Acute Respiratory Distress Syndrome in the Perioperative Period of Cardiac Surgery: Predictors, Diagnosis, Prognosis, Management Options, and Future Directions. J. Cardiothorac. Vasc. Anesth. 2022, 36, 1169–1179. [Google Scholar] [CrossRef]
  37. Kiyatkin, M.E.; Mladinov, D.; Jarzebowski, M.L.; Warner, M.A. Patient blood management, anemia, and transfusion optimization across surgical specialties. Anesthesiol. Clin. 2023, 41, 161–174. [Google Scholar] [CrossRef]
  38. Shivkumar, S.; Mehta, V.; Vaddamanu, S.K.; Shetty, U.A.; Alhamoudi, F.H.; Alwadi, M.A.M.; Aldosari, L.I.N.; Alshadidi, A.A.F.; Minervini, G. Surgical Protocols before and after COVID-19—A Narrative Review. Vaccines 2023, 11, 439. [Google Scholar] [CrossRef]
  39. Gasteiger, L.; Abram, J.; Klein, S.; Tscholl, P.; Hell, T.; Putzer, G.; Moser, B.; Joannidis, M.; Martini, J. Impact of COVID-19 on elective, emergency and oncological surgery during the first and the second wave in a tertiary university hospital: Have we learned the lessons? Wien. Klin. Wochenschr. 2022, 134, 868–874. [Google Scholar] [CrossRef] [PubMed]
  40. Cano-Valderrama, O.; Morales, X.; Ferrigni, C.J.; Martín-Antona, E.; Turrado, V.; García, A.; Cuñarro-López, Y.; Zarain-Obrador, L.; Duran-Poveda, M.; Balibrea, J.M.; et al. Acute Care Surgery during the COVID-19 pandemic in Spain: Changes in volume, causes and complications. A multicentre retrospective cohort study. Int. J. Surg. 2020, 80, 157–161. [Google Scholar] [CrossRef] [PubMed]
  41. Alelyani, R.H.; Alghamdi, A.H.; Mahrous, S.M.; Alamri, B.M.; Alhiniah, M.H.; Abduh, M.S.; Aldaqal, S.M. Impact of COVID-19 Pandemic Lockdown on the Prognosis, Morbidity, and Mortality of Patients Undergoing Elective and Emergency Abdominal Surgery: A Retrospective Cohort Study in a Tertiary Center, Saudi Arabia. Int. J. Environ. Res. Public Health 2022, 19, 15660. [Google Scholar] [CrossRef] [PubMed]
  42. Colosimo, C.; Ingram, B.; Weaver, J.; Offner, P.; Yon, J. Increased Complications of Emergent Surgical Procedures During the First Wave of COVID-19. J. Surg. Res. 2023, 286, 16–22. [Google Scholar] [CrossRef]
  43. Chan, S.L.; Zhang, A.Y.; Lam, S.S.W.; Rao, V.; Kanagalingam, D.; Tan, H.K.; Chow, P.K.H.; Mathur, S. The impact of elective surgery postponement during COVID-19 on emergency bellwether procedures in a large tertiary centre in Singapore. Int. J. Qual. Health Care 2024, 36, mzae022. [Google Scholar] [CrossRef]
  44. Yoshida, T.; Chude-Sokei, R.; Araji, T.; Adra, S. Impact of COVID-19 Pandemic Surge on Surgical Outcomes: A Retrospective Study. Am. Surg. 2024, 90, 1224–1233. [Google Scholar] [CrossRef]
  45. Combes, A.; Peek, G.J.; Hajage, D.; Hardy, P.; Abrams, D.; Schmidt, M.; Dechartres, A.; Elbourne, D. ECMO for severe ARDS: Systematic review and individual patient data meta-analysis. Intensive Care Med. 2020, 46, 2048–2057. [Google Scholar] [CrossRef]
  46. Copeland, H.; Levine, D.; Morton, J.; Hayanga, J.W.A. Acute respiratory distress syndrome in the cardiothoracic patient: State of the art and use of veno-venous extracorporeal membrane oxygenation. JTCVS Open 2021, 8, 97–103. [Google Scholar] [CrossRef]
  47. Sanivarapu, R.R.; Osman, U.; Latha Kumar, A. A Systematic Review of Mortality Rates Among Adult Acute Respiratory Distress Syndrome Patients Undergoing Extracorporeal Membrane Oxygenation Therapy. Cureus 2023, 15, e43590. [Google Scholar] [CrossRef]
  48. Park, K.U.; Eichenhorn, M.; DiGiovine, B.; Ritz, J.; Jordan, J.; Rubinfeld, I. Different Harm and Mortality in Critically Ill Medical vs Surgical Patients: Retrospective Analysis of Variation in Adverse Events in Different Intensive Care Units. Perm. J. 2018, 22, 16–189. [Google Scholar] [CrossRef]
  49. Cálculo de Variaciones del Indice de Precios de Consumo. INE. Available online: https://www.ine.es/varipc/verVariaciones.do (accessed on 20 December 2024).
  50. Shari, G.; Kojicic, M.; Li, G.; Cartin-Ceba, R.; Alvarez, C.T.; Kashyap, R.; Dong, Y.; Poulose, J.T.; Herasevich, V.; Garza, J.A.C.; et al. Timing of the onset of acute respiratory distress syndrome: A population-based study. Respir. Care 2011, 56, 576–582. [Google Scholar] [CrossRef]
  51. Hoogervorst-Schilp, J.; Langelaan, M.; Spreeuwenberg, P.; de Bruijne, M.C.; Wagner, C. Excess length of stay and economic consequences of adverse events in Dutch hospital patients. BMC Health Serv. Res. 2015, 15, 531. [Google Scholar] [CrossRef]
  52. Reardon, P.M.; Fernando, S.M.; Van Katwyk, S.; Thavorn, K.; Kobewka, D.; Tanuseputro, P.; Rosenberg, E.; Wan, C.; Vanderspank-Wright, B.; Kubelik, D.; et al. Characteristics, Outcomes, and Cost Patterns of High-Cost Patients in the Intensive Care Unit. Crit. Care Res. Pract. 2018, 2018, 5452683. [Google Scholar] [CrossRef] [PubMed]
  53. Bardají-Carrillo, M.; Martín-Fernández, M.; López-Herrero, R.; Priede-Vimbela, J.M.; Arroyo-Hernantes, I.; Cobo-Zubia, R.; Prieto-Utrera, R.; Gómez-Sánchez, E.; Villar, J.; Tamayo, E. Chest radiographs in acute respiratory distress syndrome: An Achilles’ heel of the Berlin criteria? Front. Med. 2025, 12, 1554752. [Google Scholar] [CrossRef] [PubMed]
  54. Fernando, S.M.; Fan, E.; Rochwerg, B.; Burns, K.E.A.; Brochard, L.J.; Cook, D.J.; Walkey, A.J.; Ferguson, N.D.; Hough, C.L.; Brodie, D.; et al. Lung-Protective Ventilation and Associated Outcomes and Costs Among Patients Receiving Invasive Mechanical Ventilation in the ED. Chest 2021, 159, 606–618. [Google Scholar] [CrossRef] [PubMed]
  55. Xiong, Y.; Gao, Y.; Qi, Y.; Zhi, Y.; Xu, J.; Wang, K.; Yang, Q.; Wang, C.; Zhao, M.; Meng, X. Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: A systematic review and meta-analysis. BMC Med. Inform. Decis. Mak. 2025, 25, 44. [Google Scholar] [CrossRef]
  56. van Walraven, C.; Austin, P. Administrative database research has unique characteristics that can risk biased results. J. Clin. Epidemiol. 2012, 65, 126–131. [Google Scholar] [CrossRef]
Jcm 14 05125 g001
Figure 1. Box plot of the multivariate analysis of the factors associated with postoperative acute respiratory distress syndrome (ARDS): (A) multivariate analysis of the first period of the study (2000–2015); (B) multivariate analysis of the second period of the study (2017–2022).
Figure 1. Box plot of the multivariate analysis of the factors associated with postoperative acute respiratory distress syndrome (ARDS): (A) multivariate analysis of the first period of the study (2000–2015); (B) multivariate analysis of the second period of the study (2017–2022).
Jcm 14 05125 g001
Jcm 14 05125 g002
Figure 2. Evolution of the percentage of ARDS patients that are postoperative ARDS from total ARDS patients from 2000 to 2022.
Figure 2. Evolution of the percentage of ARDS patients that are postoperative ARDS from total ARDS patients from 2000 to 2022.
Jcm 14 05125 g002
Jcm 14 05125 g003
Figure 3. Evolution of postoperative ARDS prevalence (percentage from total surgeries) between 2011 and 2022.
Figure 3. Evolution of postoperative ARDS prevalence (percentage from total surgeries) between 2011 and 2022.
Jcm 14 05125 g003
Jcm 14 05125 g004
Figure 4. Evolution of medical ARDS and postoperative ARDS hospital mortality between 2000 and 2022 in Spain.
Figure 4. Evolution of medical ARDS and postoperative ARDS hospital mortality between 2000 and 2022 in Spain.
Jcm 14 05125 g004
Jcm 14 05125 g005
Figure 5. Evolution of the mean cost per medical ARDS and postoperative ARDS patient between 2000 and 2022 in Spain.
Figure 5. Evolution of the mean cost per medical ARDS and postoperative ARDS patient between 2000 and 2022 in Spain.
Jcm 14 05125 g005
Table 1. Patient characteristics during the first period of the study (2000–2015).
Table 1. Patient characteristics during the first period of the study (2000–2015).
Medical ARDS
(n = 36,393)		Postoperative ARDS
(n = 31,820)		p-Value
Characteristics
Sex (male) [% (n)]65.1% (n = 23,700)65.0% (n = 20,692)0.045
Age (years) [mean (SD)]56.9 (20.8)60.3 (19.7)<0.001
Charlson Index [mean (SD)]0.7 (0.9)0.9 (1.0)<0.001
Comorbidities [% (n)]
Diabetes mellitus10.3% (n = 3747)10.0% (n = 3166)0.138
Obesity4.9% (n = 1768)4.9% (1545)1
Chronic respiratory diseases12.6% (n = 4598)11.9% (3777)0.002
Arterial hypertension19.4% (n = 7046)21.5% (6834)<0.001
Ischemic heart diseases5.7% (n = 2057)5.7% (1814)0.797
Cancer14.6% (n = 5305)22.5% (7160)<0.001
HIV1.9% (n = 685)0.5% (164)<0.001
Hepatic diseases4.7% (n = 1695)3.4% (1082)<0.001
Renal diseases10.6% (n = 3874)19.2% (6089)<0.001
Systemic candidiasis1.0% (n = 361)1.6% (492)<0.001
General candidiasis6.9% (n = 2527)7.9% (2517)<0.001
Aspergillosis1.1% (n = 401)0.6% (202)<0.001
Influenza2.3% (n = 819)0.6% (179)<0.001
Sites of infection [% (n)]
Central nervous system1.2% (n = 439)1.0% (314)0.006
Circulatory0.6% (n = 210)0.9% (294)<0.001
Digestive9.9% (n = 3618)18.1% (5753)<0.001
Genitourinary9.0% (n = 3284)8.6% (2732)0.045
Respiratory46.0% (n = 16,737)37.4% (11,897)<0.001
Skin2.1% (747)3.5% (1127)<0.001
Others14.6% (5296)25.2% (8026)<0.001
Outcomes
LoS (days) [mean (SD)]29.7 (36.02)42.7 (45.83)<0.001
Death [% (n)]49.9% (18,168)47.0% (14,943)<0.001
Sepsis [% (n)]66.2% (24,091)66.0% (21,006)0.622
ECMO [% (n)]0.2% (63)0.4% (116)<0.001
Continuous variables are represented as the mean and standard deviation (SD); categorical variables are represented as the percentage (%) and number (n). p values in bold indicate statistical significance. ARDS: acute respiratory distress syndrome; ECMO: extracorporeal membrane oxygenation; HIV: human immunodeficiency virus; LoS: length of stay.
Table 2. Patient characteristics during the first period of the study (2017–2022).
Table 2. Patient characteristics during the first period of the study (2017–2022).
Medical ARDS
(n = 16,198)		Postoperative ARDS
(n = 8781)		p-Value
Characteristics
Sex (male) [% (n)]68.8% (n = 11,138)67.5% (n = 5926)0.048
Age (years) [mean (SD)]60.4 (14.26)60.0 (15.86)0.061
Charlson Index [mean (SD)]1.0 (1.4)1.3 (1.8)<0.001
Comorbidities [% (n)]
Diabetes mellitus22.5% (n = 3649)18.3% (n = 1605)<0.001
Obesity20.7% (n = 3354)15.0% (n = 1314)<0.001
Chronic respiratory diseases13.8% (n = 2241)12.3% (n = 1082)<0.001
Arterial hypertension35.5% (n = 5749)28.4% (n = 2490)<0.001
Ischemic heart diseases1.4% (n = 226)2.8% (n = 248)<0.001
Cancer6.8% (n = 1103)12.2% (n = 1075)<0.001
HIV0.9% (n = 146)0.5% (n = 42)<0.001
Hepatic diseases9.5% (n = 1530)9.4% (n = 828)0.945
Renal diseases6.7% (n = 1077)6.8% (n = 595)0.721
Systemic candidiasis1.4% (n = 229)2.7% (n = 239)<0.001
General candidiasis12.1% (n = 1952)15.4% (n = 1350)<0.001
Aspergillosis4.3% (n = 695)3.7% (n = 327)0.033
Influenza3.2% (n = 517)2.5% (n = 215)<0.001
Sites of infection [% (n)]
Central nervous system0.2% (n = 29)0.4% (n = 37)<0.001
Circulatory0.6% (n = 93)1.1% (n = 97)<0.001
Digestive1.6% (n = 253)12.3% (n = 1080)<0.001
Genitourinary9.0% (n = 3284)8.6% (n = 2732)0.045
Respiratory85.3% (n = 13,822)65.8% (n = 5775)<0.001
Skin1.0% (n = 166)3.1% (n = 269)<0.001
Others15.3% (n = 2485)22.0% (n = 1934)<0.001
Outcomes
LoS (days) [mean (SD)]33.3 (29.0)45.6 (39.7)<0.001
Death [% (n)]43.2% (n = 7004)42.7% (n = 3749)0.413
Sepsis [% (n)]48.9% (n = 7917)59.1% (n = 5186)<0.001
ECMO [% (n)]2.2% (n = 350)3.8% (n = 335)<0.001
Continuous variables are represented as the mean and standard deviation (SD); categorical variables are represented as the percentage (%) and number (n). p values in bold indicate statistical significance. ARDS: acute respiratory distress syndrome; ECMO: extracorporeal membrane oxygenation; HIV: human immunodeficiency virus; LoS: length of stay.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bardají-Carrillo, M.; López-Herrero, R.; Espinoza-Fernández, M.S.; Alonso-Villalobos, L.; Cobo-Zubia, R.; Prieto-Utrera, R.; Arroyo-Hernantes, I.; Gómez-Sánchez, E.; Camporota, L.; Villar, J.; et al. Prevalence, Outcomes and Healthcare Costs of Postoperative ARDS Compared with Medical ARDS. J. Clin. Med. 2025, 14, 5125. https://doi.org/10.3390/jcm14145125

AMA Style

Bardají-Carrillo M, López-Herrero R, Espinoza-Fernández MS, Alonso-Villalobos L, Cobo-Zubia R, Prieto-Utrera R, Arroyo-Hernantes I, Gómez-Sánchez E, Camporota L, Villar J, et al. Prevalence, Outcomes and Healthcare Costs of Postoperative ARDS Compared with Medical ARDS. Journal of Clinical Medicine. 2025; 14(14):5125. https://doi.org/10.3390/jcm14145125

Chicago/Turabian Style

Bardají-Carrillo, Miguel, Rocío López-Herrero, Mario S. Espinoza-Fernández, Lucía Alonso-Villalobos, Rosa Cobo-Zubia, Rosa Prieto-Utrera, Irene Arroyo-Hernantes, Esther Gómez-Sánchez, Luigi Camporota, Jesús Villar, and et al. 2025. "Prevalence, Outcomes and Healthcare Costs of Postoperative ARDS Compared with Medical ARDS" Journal of Clinical Medicine 14, no. 14: 5125. https://doi.org/10.3390/jcm14145125

APA Style

Bardají-Carrillo, M., López-Herrero, R., Espinoza-Fernández, M. S., Alonso-Villalobos, L., Cobo-Zubia, R., Prieto-Utrera, R., Arroyo-Hernantes, I., Gómez-Sánchez, E., Camporota, L., Villar, J., & Tamayo, E. (2025). Prevalence, Outcomes and Healthcare Costs of Postoperative ARDS Compared with Medical ARDS. Journal of Clinical Medicine, 14(14), 5125. https://doi.org/10.3390/jcm14145125

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop