Sepsis patients are becoming progressively older and frailer and often require chronic medication. Comorbidities and long-term pharmacological treatments may influence survival outcomes in sepsis. This study aimed to evaluate the association between baseline health status, chronic medication use, and in-hospital mortality among ICU-admitted patients with sepsis.
MethodsRetrospective population-based cohort study of ICU adult patients diagnosed with sepsis. The influence of baseline health status, measured using the adjusted morbidity groups (GMA) score, along with the effect of chronic medication use on post-sepsis survival was analysed. Pre-admission medication exposure was determined from outpatient prescription data for the eight months prior to admission. Multivariate logistic regression identified independent predictors of mortality.
ResultsTwelve thousand ninety-five ICU-admitted patients with a diagnosis of sepsis were identified in the Catalan population database for 2018–2019. The cohort had a mean age of 68.2 years; high comorbidity burden (GMA: 33.2), high dependency on health services; and elevated baseline health expenditures. In 73.5% of cases infection was present at admission. The overall in-hospital mortality rate was 36.4%. Non-survivors exhibited higher comorbidity levels and greater medication use. In multivariate analysis, prior statin use was independently associated with reduced mortality (OR 0.794; 95% CI 0.723–0.872), whereas corticosteroids were linked to increased mortality (OR 1.321; 95% CI 1.108–1.575).
ConclusionComorbidities and chronic medication use significantly influence sepsis survival. Statin use prior to admission was associated with reduced mortality, whereas corticosteroid use correlated with worse outcomes. The GMA score may enhance predictive models, improving resource allocation and clinical decision-making in hospitals. ClinicalTrials.gov: NCT06354452.
Los pacientes con sepsis son cada vez más mayores y frágiles, y con frecuencia requieren medicación crónica. Las comorbilidades y los tratamientos farmacológicos a largo plazo pueden influir en la supervivencia de la sepsis. El objetivo del estudio es evaluar la asociación entre el estado de salud basal, el uso de medicación crónica y la mortalidad intrahospitalaria en pacientes ingresados en UCI con diagnóstico de sepsis.
MétodosEstudio de cohorte retrospectivo, poblacional, de pacientes adultos ingresados en UCI con diagnóstico de sepsis. Se analizó la influencia del estado de salud basal, medido mediante la puntuación de Grupos de Morbilidad Ajustados (GMA), junto con el efecto del uso de medicación crónica sobre la supervivencia tras la sepsis. La exposición a medicación previa al ingreso se determinó a partir de datos de prescripción ambulatoria durante los ocho meses anteriores a la hospitalización. Se empleó una regresión logística multivariante para identificar los predictores independientes de mortalidad.
ResultadosSe identificaron 12.095 pacientes ingresados en UCI con diagnóstico de sepsis en la base de datos poblacional de Cataluña correspondiente al periodo 2018-2019. La cohorte presentaba una edad media de 68,2 años; una alta carga de comorbilidad (GMA: 33,2); una gran dependencia de los servicios sanitarios; y un gasto sanitario basal elevado. En el 73,5% de los casos, la infección estaba presente al ingreso. La tasa de mortalidad intrahospitalaria fue del 36,4%. Los pacientes no supervivientes presentaban mayores niveles de comorbilidad y mayor consumo de medicamentos. En el análisis multivariante, el uso previo de estatinas se asoció de forma independiente con una menor mortalidad (OR 0,794; IC 95%: 0,723-0,872), mientras que el uso de corticoides se asoció con una mayor mortalidad (OR 1,321; IC 95%: 1,108-1,575).
ConclusiónLas comorbilidades y el uso de medicación crónica influyen significativamente en la supervivencia de los pacientes con sepsis. El uso previo de estatinas se asoció con una menor mortalidad, mientras que el uso de corticoides se relacionó con peores resultados. La puntuación de GMA puede mejorar los modelos predictivos, optimizando la asignación de recursos y la toma de decisiones clínicas en los hospitales. ClinicalTrials.gov: NCT06354452.
Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection.1 It is a major global health challenge, accounting for substantial morbidity and mortality worldwide. Its prognosis has changed substantially in recent years, with in-hospital mortality falling by up to 16%,2 and depends on early identification and treatment, previous comorbidities, response to treatment and severity at presentation.
The demographic profile of sepsis patients has evolved in recent years, with an increasing proportion of elderly and chronically ill individuals.3 Many of these patients require various medications for the treatment of their chronic diseases. However, the impact of these medications on sepsis outcomes remains poorly understood. Among the physiological changes associated with aging and chronic disease, sarcopenia has emerged as a critical yet underrecognized factor influencing sepsis outcomes.4 Sarcopenia is not only prevalent in elderly individuals but is also exacerbated by chronic conditions such as chronic obstructive pulmonary disease (COPD), diabetes, and congestive heart failure. This muscle deterioration leads to increased frailty, impaired immune function, and reduced physiological reserves, potentially exacerbating the body's ability to withstand the metabolic stress of sepsis.
Chronic treatments, including drugs that may be associated with sarcopenia (such as statins, sulphonylureas, and meglitinides), agents with antioxidant effects potentially influencing sarcopenia (e.g., allopurinol), immunoregulators (corticosteroids), antiaggregants (aspirin, clopidogrel, ticagrelor), beta-blockers, antiarrhythmics (digoxin), and non-steroidal anti-inflammatory drugs (NSAIDs), may positively or negatively affect survival.
Despite the high prevalence of multimorbidity and polypharmacy in sepsis patients, current predictive models often fail to adequately account for the impact of chronic drug exposure on patient outcomes. Moreover, existing research has produced inconsistent findings regarding the effect of specific medications on survival, underscoring the need for further investigation into their potential influence.
Leveraging a large population-based cohort, this study investigates the relationship between pre-existing comorbidities, chronic medication use, and sepsis-related in-hospital mortality in critically ill patients, aiming to identify baseline health factors associated with patient outcomes. Understanding the role of chronic medications in sepsis outcomes may enhance risk stratification, inform guide clinical decision-making, and optimize therapeutic strategies for this vulnerable patient population.
MethodsDesignRetrospective population-based observational analysis of a cohort of patients with sepsis using a population-based database over a 2-year period (2018 and 2019).
Setting, data source and patientsAll adult patients (aged 18 years and older) with a discharge diagnosis of infection associated with an new-onset organ failure admitted to ICU in hospitals belonging to the public health system of Catalonia, Spain, were included. Chronic organ failure was not considered a criterion for sepsis.
The data were obtained from the Catalan Health System (CatSalut) Minimum Basic Data Set (CMBD), which contains data compiled from all public acute care hospitals, covering a population of 7.7 million inhabitants. A cohort of approximately 29,000 patients per year was available for study.3
Data entry into the CMBD was systematically validated using an automated system that checked for consistency and identified possible errors. Periodic external audits were conducted to ensure data quality and reliability. The dataset contains demographic and clinical data for patient care episodes, including age, gender, length of hospital stay, one primary diagnosis, up to 14 secondary diagnoses, one primary procedure, up to 19 secondary procedures, and status at discharge, among other items. Mortality was defined as death occurring prior to hospital discharge.
CatSalut also records information on drug prescription and billing for services. In 2011, the Catalan Health Surveillance System (CHSS) database was created to integrate information from the entire public health system, including hospital admissions and healthcare visits, providing a carefully monitored body of population-based health data on morbidity and mortality. The out-of-hospital pharmacy prescriptions before admission, and the patient interactions with primary care or hospital care, were obtained from this CHSS database.
Study outcomes, variables, and definitionsSepsis was defined using the methodology described by Angus et al.,5 which is currently applied as a reference for population-based studies, by combining a diagnosis of infection with those of acute organ failure, sepsis or septic shock. Acute organ dysfunction was identified from the discharge diagnostic codes, which were assigned by the attending clinicians based on clinical and biological criteria. The diagnostic coding system used was ICD-10-CM, which also allowed the identification of the location of the infection, acute organ failure and bloodstream infection. ICU patients were identified based on coded procedures such as mechanical ventilation, continuous renal replacement therapy (CRRT), tracheostomy, or extracorporeal membrane oxygenation (ECMO) (Supplementary Table 1).
Socio-demographic variables stored in the CHSS database were recorded. To analyse outpatient drug prescription practices, a patient was considered to have received prior treatment if a minimum of six packs of a drug had been dispensed during the eight months prior to admission for sepsis. The ATC (Anatomical, Therapeutic, Chemical) drug classification designed by the WHO and updated in 2024 was applied.
Two different categorisation methods were used to capture comorbidity: (a) current and past diagnoses of relevant pathologies collected from CHSS and (b) the adjusted morbidity groups (in Catalan, Grups de Morbiditat Ajustada, or GMA), a population-based health risk assessment tool that assesses the overall burden of comorbidity or patient complexity.6
The GMA classification considers the weighted sum of all chronic conditions, the number of systems affected, and the acute diagnoses that may increase patient complexity. It groups individuals into five health risk categories: baseline risk (healthy stage, including scores up to the 50th percentile of the total population), low risk, percentiles 50–80, moderate risk, percentiles 80–95, high risk, percentiles 95–98, and very high risk, above the 98th percentile.
In addition, health resources use during the year prior to admission was collected from the CHSS database (i.e., primary care visits, hospital admissions, emergency consultations or admissions, outpatient appointments, and health-related expenditure).
Ethical issuesThe study was approved by the Research Ethics Committee of the Hospital General de Granollers (code 2022.040) and by the Research Ethics Committee of the Universitat Internacional de Catalunya (code MED-2023-02). Informed consent was not considered necessary given that the data were anonymised and the confidentiality of all patients was maintained.
The project was registered on ClinicalTrials.gov with identifier: NCT06354452 and was reported according to the Reporting of Studies Conducted using Observational Routinely collected health Data (RECORD) statement, an extension of the strengthening the reporting of observational studies in epidemiology (STROBE) statement.
Statistical analysisContinuous and discrete variables were compared using analysis of variance and the Chi-square test respectively. Multivariate logistic regression, adjusted for other significant variables, was used to analyse in-hospital mortality risk, with the contribution of each factor expressed as an odds ratio (OR) and its 95% confidence interval (CI). The models were created using a “stepwise-backward” approach based on the Bayesian Information Criterion (BIC), in which a full model with all the variables is sequentially reduced, eliminating less relevant variables, eventually generating the main effects model. BIC was selected over the Akaike Information Criterion (AIC) because our relatively large dataset warranted a stricter penalty for model complexity. This favoured a more parsimonious model, enhancing interpretability and reducing the risk of overfitting. While AIC is commonly used in R, BIC was deemed more appropriate for our goal of identifying robust associations. The significance threshold was set at an alpha error of 0.05; all analyses were performed using R statistical software, version R-4.2.0.
ResultsOverall cohort resultsSixty-five hospitals contributed cases to the study. A total of 12,095 patients were admitted to ICU in Catalan hospitals with a diagnosis of sepsis.
Table 1 summarizes the demographics, comorbidities, and prior healthcare utilization of the studied cohort. The cohort had a mean age of 68.2 years and exhibited substantial comorbidities, as reflected by a mean GMA of 33.2. Notably, 3.38% of the participants resided in nursing care homes. The most prevalent comorbidities were chronic obstructive pulmonary disease (38.1%), diabetes mellitus (37.4%), congestive heart failure (33.6%), active neoplasia (31%), chronic renal failure (30.8%), and ischemic heart disease (23.4%). The cohort demonstrated high dependency on healthcare services in the year preceding admission, with an average of 20.6 primary care visits and a mean annual healthcare expenditure of €9449.
Patient demographics, comorbidities, and healthcare utilization in the year prior to admission among ICU patients diagnosed with sepsis.
| OverallN=12,095 | SurvivorsN=7693(63.6%) | Non-survivorsN=4402(36.4%) | p | |
|---|---|---|---|---|
| Demography | ||||
| Women | 4399 (36.4%) | 2837 (36.9%) | 1562 (35.5%) | 0.130 |
| Age, years, mean (SD) | 68.2 (14.2) | 66.9 (14.8) | 70.6 (12.8) | <0.001 |
| Age groups | <0.001 | |||
| 18–44 | 782 (6.47%) | 623 (8.10%) | 159 (3.61%) | |
| 45–64 | 3541 (29.3%) | 2409 (31.3%) | 1132 (25.7%) | |
| 65–74 | 3274 (27.1%) | 2028 (26.4%) | 1246 (28.3%) | |
| 75–84 | 3248 (26.9%) | 1927 (25.0%) | 1321 (30.0%) | |
| >84 | 1250 (10.3%) | 706 (9.18%) | 544 (12.4%) | |
| Patients admitted to nursing homes | 409 (3.38%) | 283 (3.68%) | 126 (2.86%) | 0.019 |
| Comorbidities | ||||
| Adjusted morbidity group (GMA), mean (SD) | 33.2 (18.9) | 31.5 (18.9) | 36.0 (18.7) | <0.001 |
| Risk level (GMA) | <0.001 | |||
| Baseline risk | 245 (2.03%) | 193 (2.51%) | 52 (1.18%) | |
| Low risk | 1018 (8.42%) | 767 (9.97%) | 251 (5.70%) | |
| Moderate risk | 3279 (27.1%) | 2217 (28.8%) | 1062 (24.1%) | |
| High risk | 4284 (35.4%) | 2665 (34.6%) | 1619 (36.8%) | |
| Very high risk | 3269 (27.0%) | 1851 (24.1%) | 1418 (32.2%) | |
| Diabetes | 4522 (37.4%) | 2792 (36.3%) | 1730 (39.3%) | 0.001 |
| Congestive heart failure | 4059 (33.6%) | 2449 (31.8%) | 1610 (36.6%) | <0.001 |
| Chronic obstructive pulmonary disease | 4612 (38.1%) | 2903 (37.7%) | 1709 (38.8%) | 0.244 |
| Depressive disorder | 2384 (19.7%) | 1526 (19.8%) | 858 (19.5%) | 0.663 |
| People living with HIV | 193 (1.60%) | 113 (1.47%) | 80 (1.82%) | 0.163 |
| Ischaemic heart disease | 2833 (23.4%) | 1759 (22.9%) | 1074 (24.4%) | 0.058 |
| Stroke | 2448 (20.2%) | 1473 (19.1%) | 975 (22.1%) | <0.001 |
| Renal failure | 3730 (30.8%) | 2202 (28.6%) | 1528 (34.7%) | <0.001 |
| Liver cirrhosis | 733 (6.06%) | 370 (4.81%) | 363 (8.25%) | <0.001 |
| Dementia | 652 (5.39%) | 395 (5.13%) | 257 (5.84%) | 0.108 |
| Active neoplasia | 3746 (31.0%) | 2176 (28.3%) | 1570 (35.7%) | <0.001 |
| Dependence on health services (year preceding admission), mean (SD) | ||||
| Visits to primary care | 20.6 (24.8) | 19.5 (24.7) | 22.4 (24.9) | <0.001 |
| Hospital admissions | 1.26 (1.70) | 1.17 (1.65) | 1.41 (1.78) | <0.001 |
| Emergency hospital admissions | 0.90 (1.37) | 0.83 (1.33) | 1.01 (1.42) | <0.001 |
| Emergency room consultations | 2.41 (3.66) | 2.27 (3.13) | 2.67 (4.44) | <0.001 |
| Outpatient appointments | 7.36 (8.88) | 6.86 (8.30) | 8.25 (9.76) | <0.001 |
| Outpatient health expenditure,€ | 1969 (2242) | 1790 (1993) | 2280 (2592) | <0.001 |
| Inpatient healthcare expenditure,€ | 4500 (6724) | 4119 (6440) | 5164 (7146) | <0.001 |
| Other healthcare expenditure,€ | 519 (3553) | 379 (2980) | 764 (4368) | <0.001 |
| Overall healthcare expenditure,€ | 9449 (12702) | 8412 (11147) | 11261 (14867) | <0.001 |
Percentages for men are complementary. No statistically significant difference was observed between sexes (p=0.130).
Values are n (%) unless otherwise indicated.
SD: standard deviation; GMA: adjusted morbidity groups; HIV: human immunodeficiency virus.
Table 2 presents data on chronic drug use prior to admission. The most frequently prescribed outpatient medications included statins (28.6%), antiplatelet agents (19.5%), beta-blockers (15.8%), and corticosteroids (5.36%).
Chronic medication use among patients included in the study.
| OverallN=12095 | SurvivorsN=7693(63.6%) | Non-survivorsN=4402(36.4%) | p | |
|---|---|---|---|---|
| Number of chronic medications, mean (SD) | 12.2 (7.52) | 11.7 (7.43) | 13.1 (7.59) | <0.001 |
| Number of pharmacy drug boxes, mean (SD) | 109 (130) | 102 (124) | 119 (137) | <0.001 |
| Medications | ||||
| Statins | 3462 (28.6%) | 2206 (28.7%) | 1256 (28.5%) | 0.884 |
| Meglitinides | 149 (1.23%) | 91 (1.18%) | 58 (1.32%) | 0.575 |
| Sulphonylureas | 239 (1.98%) | 154 (2.00%) | 85 (1.93%) | 0.840 |
| Allopurinol | 420 (3.47%) | 270 (3.51%) | 150 (3.41%) | 0.808 |
| Corticosteroids | 648 (5.36%) | 350 (4.55%) | 298 (6.77%) | <0.001 |
| Antiplatelet agents | 2354 (19.5%) | 1466 (19.1%) | 888 (20.2%) | 0.142 |
| Beta-blockers | 1906 (15.8%) | 1149 (14.9%) | 757 (17.2%) | 0.001 |
| Digoxin | 6 (0.05%) | 2 (0.03%) | 4 (0.09%) | 0.199 |
| Nonsteroidal anti-inflammatory drugs | 352 (2.91%) | 179 (2.33%) | 173 (3.93%) | <0.001 |
Values are n (%) unless otherwise indicated. SD: standard deviation.
Table 3 summarizes the origin and type of infections, as well as the patient's condition at the time of initial hospital admission, including the frequency of organ failure. Community-acquired infections accounted for 73.5% of cases. The most common sources of infection were the respiratory tract (41.4%), genitourinary tract (21.7%), unspecified origins (18.1%), and digestive tract (16.3%). Bacteraemia was identified in 41.8% of cases. Among organ failures at admission, respiratory failure was the most frequent (56.8%), followed by renal failure (50.9%), cardiovascular failure (42.4%), neurological failure (16%), and haematological failure (12.6%).
Origin, type of infection and type of organ failure at the time of index admission of patients included in the study.
| OverallN=12095 | SurvivorsN=7693 | Non-survivorsN=4402 | p | |
|---|---|---|---|---|
| Characteristics of infection | ||||
| Community-acquired infection | 8893 (73.5%) | 5558 (72.2%) | 3335 (75.8%) | <0.001 |
| Number of infections, mean (SD) | 1.21 (0.49) | 1.23 (0.51) | 1.19 (0.47) | <0.001 |
| Bacteraemia | 5058 (41.8%) | 2719 (35.3%) | 2339 (53.1%) | <0.001 |
| Type of infection | ||||
| Digestive | 1975 (16.3%) | 1168 (15.2%) | 807 (18.3%) | <0.001 |
| Respiratory | 5011 (41.4%) | 3204 (41.6%) | 1807 (41.0%) | 0.533 |
| Central nervous system | 187 (1.55%) | 113 (1.47%) | 74 (1.68%) | 0.405 |
| Genitourinary | 2628 (21.7%) | 1918 (24.9%) | 710 (16.1%) | <0.001 |
| Soft tissue | 512 (4.23%) | 323 (4.20%) | 189 (4.29%) | 0.840 |
| Endocarditis | 266 (2.20%) | 146 (1.90%) | 120 (2.73%) | 0.003 |
| Infection of devices* | 1564 (12.9%) | 1128 (14.7%) | 436 (9.90%) | <0.001 |
| Others | 358 (2.96%) | 243 (3.16%) | 115 (2.61%) | 0.099 |
| Not specified | 2193 (18.1%) | 1221 (15.9%) | 972 (22.1%) | <0.001 |
| Organ failure** | ||||
| Cardiovascular failure | 5130 (42.4%) | 2685 (34.9%) | 2445 (55.5%) | <0.001 |
| Respiratory failure | 6865 (56.8%) | 4184 (54.4%) | 2681 (60.9%) | <0.001 |
| Neurological failure | 1939 (16.0%) | 1250 (16.2%) | 689 (15.7%) | 0.404 |
| Haematological failure | 1527 (12.6%) | 798 (10.4%) | 729 (16.6%) | <0.001 |
| Liver failure | 466 (3.85%) | 141 (1.83%) | 325 (7.38%) | <0.001 |
| Renal failure | 6154 (50.9%) | 3595 (46.7%) | 2559 (58.1%) | <0.001 |
Values are n (%) unless otherwise indicated.
“Infection of devices” refers to infections associated with the presence of medical devices, as coded using ICD-10-CM diagnosis codes, as listed in Supplementary Table 1. This includes infections related to vascular catheters, urinary catheters, implantable devices (e.g., pacemakers, joint prostheses), and other indwelling medical hardware.
Comparisons between survivors and non-survivors revealed significant differences (Table 1). Non-survivors had significantly higher GMA scores (36.0 vs. 31.5; <0.001) compared to survivors. Additionally, non-survivors exhibited higher rates of prior diagnoses, including congestive heart failure (36.6%), active neoplasia (35.7%), and chronic renal failure (34.7%) (p<0.001).
Non-survivors group also demonstrated greater dependency on healthcare services before admission, as evidenced by more frequent visits to general practitioners, increased hospital admissions, and higher emergency room utilization. Consequently, they incurred a significantly higher overall healthcare expenditure (Table 1). Furthermore, non-survivors had higher medication usage, with significantly greater consumption of corticosteroids and NSAIDs compared to survivors (Table 2).
In terms of sepsis presentation, non-survivors exhibited a significantly higher rate of bacteraemia compared to survivors (53.1% vs. 35.3%; p=0.0001) (Table 3). Differences were also observed in the source of sepsis, with non-survivors presenting more frequently with digestive infections, while survivors had higher rates of urinary tract infections. As expected, non-survivors experienced more organ failure at onset of sepsis, with respiratory failure (60.9%), renal failure (58.1%), and cardiovascular failure (55.5%) being the most common.
Table 4 highlights differences in hospital stay duration between survivors and non-survivors. The overall mean hospital stay was 27.1 days, with survivors experiencing a significantly longer mean stay (31.4 days) compared to non-survivors (19.7 days). Non-survivors exhibited a higher proportion of short and moderate hospital stays, whereas survivors were more likely to have prolonged stays, likely due to intensive treatment requirements and extended recovery periods.
Comparison of length of stay between survivors and non-survivors admitted ICU.
| OverallN=12095 | SurvivorsN=7693 | Non-survivorsN=4402 | p | |
|---|---|---|---|---|
| Patients included in the study | 12095 (20.3%) | 7693 (15.8%) | 4402 (39.9%) | <0.001 |
| Length of stay, days, mean (SD) | 27.1 (32.4) | 31.4 (34.1) | 19.7 (27.8) | <0.001 |
| Length of stay | <0.001 | |||
| <3 days | 1033 (8.54%) | 249 (3.24%) | 784 (17.8%) | |
| 3–7 days | 1622 (13.4%) | 769 (10.00%) | 853 (19.4%) | |
| 8–14 days | 2472 (20.4%) | 1610 (20.9%) | 862 (19.6%) | |
| >14 days | 6968 (57.6%) | 5065 (65.8%) | 1903 (43.2%) | |
Values are n (%) unless otherwise indicated. SD: standard deviation.
Multivariate analysis demonstrated that a higher disease burden, as reflected by elevated GMA scores, was strongly associated with an increased risk of in-hospital mortality (Table 5). Specific comorbidities, including cirrhosis and active neoplasia, were also significant risk factors for mortality. In contrast, community-acquired sepsis was associated with a reduced risk of mortality compared to nosocomial sepsis. Additionally, the presence of bacteraemia increased mortality risk by 18.8%.
Multivariate analysis of risk factors associated with mortality among patients included in the study.
| Variable | beta | OR | CI95 |
|---|---|---|---|
| Age | 0.029 | 1.029 | 1.026–1.033 |
| Bacteremia | 0.264 | 1.302 | 1.177–1.441 |
| Morbidity indexes | |||
| Adjusted morbidity group (GMA) | |||
| Baseline risk | 0.000 | 1.000 | |
| Low risk | 0.142 | 1.152 | 0.800–1.660 |
| Moderate risk | 0.275 | 1.317 | 0.930–1.865 |
| High risk | 0.321 | 1.378 | 0.965–1.968 |
| Very high risk | 0.498 | 1.646 | 1.130–2.398 |
| Morbidity | |||
| Liver cirrhosis | 0.461 | 1.586 | 1.342–1.874 |
| Active neoplasia | 0.196 | 1.216 | 1.113–1.328 |
| Chronic medication | |||
| Number of chronic medications | 0.014 | 1.014 | 1.006–1.021 |
| Statins | −0.230 | 0.794 | 0.723–0.872 |
| Allopurinol | −0.202 | 0.817 | 0.657–1.016 |
| Corticosteroids | 0.279 | 1.321 | 1.108–1.575 |
| Dependence on health services | |||
| Emergency hospital admissions | 0.038 | 1.039 | 1.004–1.075 |
| Patients admitted to nursing homes | −0.373 | 0.689 | 0.548–0.866 |
| Organ failure during admission | |||
| Cardiovascular failure | 0.662 | 1.938 | 1.755–2.141 |
| Respiratory failure | 0.597 | 1.816 | 1.655–1.981 |
| Neurological failure | 0.210 | 1.234 | 1.103–1.379 |
| Hematological failure | 0.362 | 1.436 | 1.273–1.620 |
| Liver failure | 1.221 | 3.390 | 2.721–4.223 |
| Renal failure | 0.327 | 1.387 | 1.275–1.508 |
| Source of infection | |||
| Central nervous system | 0.543 | 1.721 | 1.254–2.362 |
| Genitourinary | −0.519 | 0.595 | 0.536–0.661 |
| Endocarditis | 0.421 | 1.524 | 1.173–1.979 |
| Infection of devices | −0.473 | 0.623 | 0.548–0.709 |
The use of corticosteroids was identified as an independent risk factor for mortality (OR 1.321; 95% CI 1.108–1.575). Conversely, the use of statins (OR 0.794; 95% CI 0.723–0.872) was associated with a protective effect against mortality.
DiscussionThis population study of patients admitted to ICU with sepsis highlights that prognosis is associated with advanced age and higher rates of comorbidities, and that chronic medication with statins reduces sepsis-related in-hospital mortality, while the use of corticosteroids is associated with a worse prognosis.
The observed increase in sepsis-related mortality with advancing age and comorbidities2,3 may be attributed to the deterioration of anatomical and functional structures, such as epithelial barriers and thymic atrophy, combined with an altered immune response. This phenomenon, known as immunosenescence, refers to the age-associated degeneration and remodeling of immune organ structures, which impairs both innate and adaptive immune functions, thereby increasing susceptibility to infections and sepsis.7 Advanced age appears to alter the body's immune response to infection through multiple complex pathways (e.g., decreased cytokine production, altered Toll-like receptor expression and function, changes in adaptive immunity related to decreased T-cell function or production of lower affinity antibodies).8 Emerging evidence suggests that certain interventions may mitigate or even reverse the effects of immunosenescence.8,9 These include lifestyle modifications, such as physical exercise and dietary adjustments, as well as the use of specific medications like metformin and statins. Additionally, immunotherapeutic strategies, such as the administration of interleukin-7 (IL-7), show promise in addressing immune dysfunction in this context.9
This study observed high levels of chronic medication use in patients with sepsis, closely associated with advanced age and the presence of comorbidities. The interaction between chronic treatments and the immune response to infections warrants further investigation. Notably, chronic statin use was associated with improved outcomes in sepsis patients, consistent with previous studies suggesting a potential link between statins and better sepsis prognosis.10,11 Statins exert effects beyond cholesterol biosynthesis inhibition. By reducing mevalonate synthesis through the inhibition of HMG-CoA reductase, statins modulate the production of cytokines and chemokines, conferring anti-inflammatory, immunomodulatory, antithrombotic, and antioxidant properties, as well as protecting endothelial function. Unlike therapies targeting specific inflammatory mediators, statins appear to regulate the overall magnitude of the inflammatory response.12 Several studies have highlighted the antioxidant effects of statins in sepsis,13 as well as their potential anti-infective properties. Interestingly, statins were initially developed from fungi with the aim of creating new antibiotics.14 Some statins may exhibit bacteriostatic properties and synergistic effects when combined with antibiotics, though the underlying mechanisms remain unclear.15
Despite these promising findings, the role of statins in infections is not yet fully defined, and reviews and meta-analyses have produced inconclusive results.16 As a result, the generalized use of statins as adjuvant therapy in sepsis or critically ill patients remains controversial. A recent umbrella review,17 suggested that statin use may reduce mortality in sepsis patients, although the evidence was categorized as weak. The review emphasized the need for well-designed future studies to better clarify the extent of this protective effect. Moreover, the beneficial effects of statins in reducing mortality during acute cardiovascular events are well-documented,18 underscoring their potential to provide protective effects in other critical conditions, such as sepsis.
Nevertheless, research examining the utilisation of statins in patients with pneumonia, sepsis, and bacteraemia have yielded inconsistent results. It is important to recognize that sepsis encompasses a wide range of clinical presentations and treatments, making it challenging to attribute reduced mortality to a single factor.19 To better understand the precise effects of statins in sepsis, it is essential to consider the infection source and even the specific type of statin, as certain statins, such as simvastatin, atorvastatin, and rosuvastatin, have demonstrated antibacterial activity.20
Statins have also shown beneficial effects in mechanically ventilated patients, both when used prior to ICU admission and as an adjuvant therapy during treatment.21
The relationship between statin use and sarcopenia remains controversial, with studies yielding conflicting results. Statins have been implicated in muscle-related side effects ranging from mild myalgia to severe myopathies. Mechanistically, statins may contribute to sarcopenia by inducing mitochondrial dysfunction, increasing protein degradation, impairing muscle regeneration, and promoting inflammation.22 Some studies suggest an association between statin use and muscle mass loss, particularly in older adults and those on high doses, while other population-based studies or randomized controlled trials provide mixed findings, with some showing no significant effect.23 Some authors report no significant association or even suggest a protective effect of statins on muscle function in specific populations.24–26 The variability in outcomes likely stems from differences in study design, patient characteristics, and statin types.
In summary, this investigation underscores a clinically relevant association between chronic statin use and improved sepsis prognosis.
The adverse effects of corticosteroids on the immune response in sepsis are well-documented.27,28 Glucocorticoids suppress immune function by inhibiting T-cell activity, reducing pro-inflammatory cytokine production, and impairing macrophage function.29 Chronic corticosteroid use results in immunosuppression and has been associated with increased hospital mortality, contributing to severe complications such as acute respiratory distress syndrome (ARDS), septic shock, and multi-organ failure.30
Finally, this research highlights the significant influence of comorbidities on sepsis outcomes, with patients demonstrating a high disease burden as assessed using the GMA score. The findings revealed that patients classified as high or very high risk according to the GMA score were not only more frequently admitted with sepsis but also experienced the highest mortality rates. The GMA score demonstrated a strong correlation with poor sepsis prognosis, underscoring their potential utility as automated tools for identifying high-risk patients. This index offers valuable support for risk stratification and prognosis prediction in the clinical management of sepsis.
This study has several limitations. First, it is a retrospective epidemiological study based on data from hospital discharges and the previous out-of-hospital pharmacy register. While a validated methodology was employed for case definition, there is a potential for bias arising from inconsistencies in hospital coding. However, the accuracy of both clinical and coded data is regularly audited by health authorities. As with most population-based studies utilizing large databases, the availability of variables was limited. To address this limitation, a multivariate approach was applied. Nevertheless, important baseline severity scores, such as the Sequential Organ Failure Assessment (SOFA) or the Acute Physiology and Chronic Health Evaluation II (APACHE II), as well as detailed information on ICU interventions, were not available. Additionally, the retrospective design allows for the identification of associations but does not establish causality. Another limitation is that critically ill patients admitted to the ICU were not explicitly coded as such in the CMBD database and had to be identified indirectly through specific procedure codes, which may have led to underestimation or misclassification. Regarding the analysis of medication prior to the sepsis episode, the study examined medication groups rather than specific drugs, which may have limited the precision of the findings. Among the strengths of the study are the large sample size, the inclusion of data from a diverse range of hospitals, and the robust methodology applied to data collection and analysis. These factors enhance the generalizability of the results to other settings and populations, probably providing valuable insights into the epidemiology and outcomes of sepsis.
This population-based study found that older age and the presence of comorbidities significantly influence survival outcomes in patients discharged with a diagnosis of sepsis. Furthermore, chronic treatments prior to hospital admission appear to play a critical role in shaping sepsis prognosis. Specifically, chronic statin use was associated with reduced sepsis-related mortality, while corticosteroid use was linked to worse outcomes. These findings highlight the importance of considering both patient comorbidities and pre-existing medication regimens when evaluating factors associated with sepsis prognosis, some of which may hold value in future predictive models.
CRediT authorship contribution statementConceptualization: RI, JMB, EV, JCY; Methodology: EV, DM, JCY; Formal analysis and interpretation of data: RI, JMB, EV, DM, JCY; Supervision: JMB, JCY; Writing – original draft: RI, JMB, JCY; Writing – review and editing: RI, JMB, EV, DM, JCY.
Ethical approvalThe study was approved by the Clinical Research Ethics Committee of Hospital General de Granollers, with identifier code NCT06354452. The need for informed consent and the provision of an information sheet were waived by the Research Ethics Committee due to the nature of the study, in which data were routinely collected as part of a quality improvement program; due to the use of anonymous clinical data in the analysis, and because their extraction for the study was covered by the general admission consent provided by the patient.
Consent for publicationNot applicable.
Declaration of generative AI and AI-assisted technologies in the writing processWe declare that no artificial intelligence tools were used in the conduct of the research or the development of the manuscript.
FundingThis study received no external funding. CatSalut is supported by public funding from the Catalan Health Service, Department of Health, Generalitat de Catalunya.
Declaration of competing interestsAll authors declare no conflict of interest relevant to this article. All authors submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.
Data availabilityThe research data originate from a restricted-access population database that is not publicly available and belongs to the Catalan Health Service, Department of Health, Generalitat de Catalunya. All data will be made available on reasonable request.
The authors thank Michael Maudsley for his help with the English.
The following are the supplementary data to this article:
ICD-10-CM codes used in the study: for types of infection, acute organ dysfunction, and procedures used in to identify patients with sepsis requiring ICU admission.
