Risk factors for sepsis morbidity in a rural hospital population: A case-control study

Is patients' rurality associated with in-hospital sepsis death in US hospitals?

Background

The focus of this study was to explore the association of patients' rurality and other patient and hospital-related factors with in-hospital sepsis mortality to identify possible health disparities across United States hospitals.

Methods

The National Inpatient Sample was used to identify nationwide sepsis patients (n = 1,977,537, weighted n = 9,887,682) from 2016 to 2019. We used multivariate survey logistic regression models to identify predictors for how patients' rurality is associated with in-hospital death.

Findings

During the study periods, in-hospital death rates among sepsis inpatients continuously decreased (11.3% in 2016 to 9.9% in 2019) for all rurality levels. Rao-Schott Chi-Square tests demonstrated that certain patient and hospital factors had varied in-hospital death rates. Multivariate survey logistic regressions suggested that rural areas, minorities, females, older adults, low-income, and uninsured patients have higher odds of in-hospital mortality. Further, specific census divisions like New England, Middle Atlantic, and East North Central had greater in-hospital sepsis death odds.

Conclusion

Rurality was associated with increased in-hospital sepsis death across multiple patient populations and locations. Further, rurality in New England, Middle Atlantic, and East North Central locations is exceptionally high odds. In addition, minority races in rural areas also have an increased odds of in-hospital death. Therefore, rural healthcare requires a more significant influx of resources and should also include assessing patient-related factors.

SAPS III is superior to SOFA for predicting 28-day mortality in sepsis patients based on Sepsis 3.0 criteria

INTRODUCTION

The discrimination and calibration accuracy of prediction models tends to become poor over time. The performance of predictive models should be reevaluated periodically. The aim of this study was to reassess the discrimination of the six commonly used models for predicting 28-day mortality in patients with sepsis based on the Sepsis 3.0 criteria.

METHODS

Patient data were extracted from the fourth edition of the Medical Information Mart for Critical Care (MIMIC IV) database. The systemic inflammatory response syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS), Logistic Organ Dysfunction System (LODS), and Simplified Acute Physiology Score II (SAPS II) and III (SAPS III) scores were calculated and collected. The area under the receiver operating characteristic curve (AUROC) was used to compare the discrimination abilities of the models using non-parametric Wilcoxon statistics. The Delong method was used to perform pairwise comparisons of the AUROCs of the models. Multiple subgroup analyses for age, body mass index, and sex were performed with regard to the 28-day mortality prediction of the models.

RESULTS

A total of 12 691 patients were included. The mean age of the patients was 65.97 ± 15.77 years; 7673 patients (60.50%) were male. The mean SIRS, SOFA, OASIS, SAPS II, LODS, and SAPS III scores were higher in the non-survivor group than in the survivor group. The discrimination for 28-day mortality with the SAPS III (AUROC 0.812, 95% confidence interval (CI) 0.802-0.822) and LODS (AUROC 0.804, 95% CI 0.743-0.765) models was superior to that of the SIRS (AUROC 0.575, 95% CI 0.562-0.589), SOFA (AUROC 0.612, 95% CI 0.598-0.626), OASIS (AUROC 0.753, 95% CI 0.742-0.764), and SAPS II (AUROC 0.754, 95% CI 0.743-0.765) models. The Youden index of the SAPS III model was 0.484, which was the highest among the models. Subgroup analyses showed similar results to the overall results.

CONCLUSIONS

The discrimination for 28-day mortality with the SAPS III and LODS models was superior to that of the SIRS, SOFA, OASIS, and SAPS II models. The SAPS III model showed the best discrimination capacity for 28-day mortality compared with the other models.

The effect of dobutamine in sepsis: a propensity score matched analysis

BACKGROUND

The use of dobutamine in patients with sepsis is questionable currently. As the benefit of dobutamine in septic patients is unclear, we aimed to evaluate whether the use of dobutamine was associated with decreased hospital mortality in sepsis patients.

METHODS

Based on the analysis of MIMIC III public database, we performed a big-data, real world study. According to the use of dobutamine or not, patients were categorized as the dobutamine group or non dobutamine group.We used propensity score matched (PSM) analysis to adjust for confoundings. The primary outcome was hospital mortality.

RESULTS

In the present study, after screening 38,605 patients, 2826 patients with sepsis were included. 121 patients were in dobutamine group and 2165 patients were in non dobutamine group. Compared with patients in non-dobutamine group, patients in dobutamine group had a lower MAP, higher HR, higher RR, higher severity of illness scores. 72 of 121 patients (59.5%) in the dobutamine group and 754 of 2165 patients (34.8%) in the non-dobutamine group died in the hospital, which resulted in a significant between-group difference (OR 1.56, 95% CI 1.01-2.40; P = 0.000). For the secondary outcomes, patients in dobutamine group received more MV use, more renal replacement therapy use, had longer ICU stay durations and more cardiac arrhythmias than those in non-dobutamine group. After adjusting for confoundings between groups by PSM analysis, hospital mortality was consistently higher in dobutamine group than that in non-dobutamine group (60.2% vs. 49.4%, OR 1.55, 95% CI 1.01-2.37; P = 0.044).

CONCLUSIONS

Among patients with sepsis, our study showed that the use of dobutamine was not associated with decreased hospital mortality. Further large scale, randomized controlled studies are warrented to confirm our findings.

Incidence, prognosis, and risk factors of sepsis-induced cardiomyopathy

BACKGROUND

At present, large-scale studies on the clinical characteristics of sepsis-induced cardiomyopathy (SIC) are lacking.

AIM

To investigate the clinical characteristics of SIC.

METHODS

Based on the analysis of the MIMIC-III public database, we performed a large-scale retrospective study involving sepsis patients who were admitted to the intensive care unit (ICU) and had no concomitant cardiac disease. We used propensity score matching analysis and multivariate logistic regression to ensure the robustness of the results. The primary outcome was hospital mortality, and the secondary outcomes included the number of patients who received mechanical ventilation or renal replacement therapy during their hospital stay, the number of patients administered with vasopressors, the length of ICU stay, and the length of hospital stay.

RESULTS

In the present study, after screening 38605 patients, 3530 patients with sepsis were included. A total of 997 patients met the SIC diagnostic criteria, and the incidence of SIC was 28.20% (95% confidence interval [CI]: 26.80%-29.70%). Compared to patients in the non-SIC group, patients in the SIC group were of older age and had a higher Simplified Acute Physiology Score (SAPS)-I score, SAPS-II score, and Elixhauser comorbidity index (ECI). A total of 367 (36.8%) of 997 patients in the SIC group and 818 (32.3%) of 2533 patients in the non-SIC group died in the hospital, which resulted in a significant between-group difference (odds ratios = 1.22, 95%CI: 1.05-1.42; P = 0.011). For the secondary outcomes, more patients in the SIC group received mechanical ventilation and vasopressors. Multivariate logistic regression analysis showed that age, male sex, ECI, hemoglobin level, diabetes, and mechanical ventilation use on the first day of ICU admission were risk factors for SIC.

CONCLUSION

Compared with non-SIC patients, hospital mortality is higher in SIC patients.

The effect of dobutamine vs milrinone in sepsis: A big data, real-world study

BACKGROUND

The use of dobutamine in patients with sepsis is questionable. Some studies reported milrinone was used as an alternative inotropic agent. We aim to evaluate whether milrinone is better than dobutamine in patients with sepsis.

METHODS

Based on the analysis of MIMIC III public database, we performed a big data, real-world study. According to the use of dobutamine or milrinone, patients were categorised as the dobutamine group or milrinone group. We used propensity score matched (PSM) analysis to adjust for confoundings. The primary outcome was hospital mortality.

RESULTS

In this study, after screening 38 605 patients, 235 patients with sepsis were included. One hundred and eighty-three patients were in the dobutamine group and 52 patients were in the milrinone group. For the primary outcome of hospital mortality, there was no significant between-group difference (73/183 in dobutamine group vs 23/52 in milrinone group, OR 0.84, 95% CI 0.45-1.56; P = .574). After adjusting for confoundings between groups by PSM analysis, hospital mortality was consistent with the overall result (50% vs 41.3%, OR 1.42, 95% CI 0.68-2.97; P = .349). For the secondary outcomes, more patients in milrinone group received RRT use (46.2% vs 22.4%, P = .001), had longer length of ICU stay (20.97 ± 22.84 days vs 11.10 ± 11.54 days, P = .004) and hospital stay (26.14 ± 25.13 days vs 14.51 ± 13.11 days, P = .002) than those in dobutamine group.

CONCLUSIONS

Compared with dobutamine, the use of milrinone did not decrease hospital mortality in patients with sepsis. Furthermore, milrinone was associated with more RRT therapy, longer length of ICU stay and hospital stay than dobutamine.

Indwelling medical device use and sepsis risk at a health professional shortage area hospital: Possible interaction with length of hospitalization

BACKGROUND

We aimed to identify risk factors for sepsis diagnosis and possible interaction with length of hospital stay (LOS) among inpatients at a rural Health Professional Shortage Area hospital.

METHODS

This case-control study examined 600 adult patients (300 cases and 300 controls) admitted to a rural health system in North Carolina between 2012 and 2018. Case selection was based on assignment of ICD-9-CM diagnostic codes for sepsis. Controls were patients with a medical diagnosis other than sepsis during the observational period. Logistic regression was used to model sepsis diagnosis as a function of indwelling medical device use and stratified by LOS.

RESULTS

Indwelling medical device use preadmission and postadmission were significantly associated with increased risk of sepsis diagnosis among patients with extended hospital stays (LOS ≥ 5 days) (odds ratio [OR] = 5.51; 95% confidence interval [CI] = 1.95-15.62; P = .001 and OR = 3.28; 95% CI = 1.24-8.68; P = .017, respectively). Among patients with LOS <5 days, association with sepsis diagnosis was only significant for indwelling medical device use preadmission (OR = 9.61; 95% CI = 3.68-25.08; P < .0001).

CONCLUSIONS

Indwelling medical device use was significantly associated with increased risk of sepsis diagnosis and the risk was higher with longer hospitalization.