Impact of the 'weekend effect' on hospital-acquired pneumonia after aneurysmal subarachnoid hemorrhage

Pre-operative prognostic nutrition index and post-operative pneumonia in aneurysmal subarachnoid hemorrhage patients

Background and objective

Post-operative pneumonia (POP), a common complication, may be associated with prolonged hospitalization and long-term mortality in aneurysmal subarachnoid hemorrhage (aSAH) patients. This study aimed to explore the association between pre-operative prognostic nutrition index (PNI) and POP in aSAH patients.

Methods

A total of 280 aSAH patients were enrolled from Nanjing Drum Tower Hospital. PNI was calculated as follows: [10 × albumin(gr/dl)] + [0.005 × absolute pre-operative lymphocyte count (per mm³)]. We utilized multivariate analyses, restricted cubic spline, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) to elucidate the role of PNI in POP.

Results

Pre-operative PNI levels in the POP group were higher, compared with the non-POP group (41.0 [39.0, 45.4] vs. 44.4 [40.5, 47.3], P = 0.001). When we included PNI as a categorical variable in the multivariate analysis, the levels of PNI were associated with POP (odds ratio, 0.433; 95% confidence interval, 0.253-0.743; P=0.002). In addition, when we included PNI as a continuous variable in the multivariate analysis, the PNI levels were also associated with POP (odds ratio, 0.942; 95% confidence interval, 0.892-0.994; P = 0.028). The level of albumin was also a predictor of the occurrence of POP, with a lower diagnostic power than PNI [AUC: 0.611 (95% confidence interval, 0.549-0.682; P = 0.001) for PNI vs. 0.584 (95% confidence interval, 0.517-0.650; P = 0.017) for albumin]. Multivariable-adjusted spline regression indicated a linear dose-response association between PNI and POP in aSAH participants (P for linearity = 0.027; P for non-linearity = 0.130). Reclassification assessed by IDI and NRI was significantly improved with the addition of PNI to the conventional model of POP in aSAH patients (NRI: 0.322 [0.089-0.555], P = 0.007; IDI: 0.016 [0.001-0.031], P = 0.040).

Conclusion

The lower levels of pre-operative PNI may be associated with the higher incidence of POP in aSAH patients. Neurosurgeons are supposed to pay more attention to pre-operative nutrition status in aSAH patients.

A predictive model using risk factor categories for hospital-acquired pneumonia in patients with aneurysmal subarachnoid hemorrhage

Objectives

To identify risk factors for hospital-acquired pneumonia (HAP) in patients with aneurysmal subarachnoid hemorrhage (aSAH) and establish a predictive model to aid evaluation.

Methods

The cohorts of 253 aSAH patients were divided into the HAP group (n = 64) and the non-HAP group (n = 189). Univariate and multivariate logistic regression were performed to identify risk factors. A logistic model (Model-Logit) was established based on the independent risk factors. We used risk factor categories to develop a model (Model-Cat). Receiver operating characteristic curves were generated to determine the cutoff values. Areas under the curves (AUCs) were calculated to assess the accuracy of models and single factors. The Delong test was performed to compare the AUCs.

Results

The multivariate logistic analysis showed that the age [p = 0.012, odds ratio (OR) = 1.059, confidence interval (CI) = 1.013-1.107], blood glucose (BG; >7.22 mmol/L; p = 0.011, OR = 2.781, CI = 1.263-6.119), red blood distribution width standard deviation (RDW-SD; p = 0.024, OR = 1.118, CI = 1.015-1.231), and Glasgow coma scale (GCS; p < 0.001, OR = 0.710, CI = 0.633-0.798) were independent risk factors. The Model-Logit was as follows: Logit(P) = -5.467 + 0.057 * Age + 1.023 * BG (>7.22 mmol/L, yes = 1, no = 0) + 0.111 * RDW-SD-0.342 * GCS. The AUCs values of the Model-Logit, GCS, age, BG (>7.22 mmol/L), and RDW-SD were 0.865, 0.819, 0.634, 0.698, and 0.625, respectively. For clinical use, the Model-Cat was established. In the Model-Cat, the AUCs for GCS, age, BG, and RDW-SD were 0.850, 0.760, 0.700, 0.641, and 0.564, respectively. The AUCs of the Model-Logit were insignificantly higher than the Model-Cat (Delong test, p = 0.157). The total points from -3 to 4 and 5 to 14 were classified as low- and high-risk levels, respectively.

Conclusions

Age, BG (> 7.22 mmol/L), GCS, and RDW-SD were independent risk factors for HAP in aSAH patients. The Model-Cat was convenient for practical evaluation. The aSAH patients with total points from 5 to 14 had a high risk for HAP, suggesting the need for more attention during treatment.

Impact of Off-Hour Admission with Subarachnoid Hemorrhage: A Meta-Analysis

OBJECTIVE

This meta-analysis assessed the impact of off-hour hospitalization (weekends, and evenings or nighttime on weekdays) on mortality and morbidity in patients with nontraumatic subarachnoid hemorrhage (SAH).

METHODS

Electronic databases were systematically searched for studies comparing outcomes between patients with nontraumatic SAH hospitalized during off-hour and on-hour periods (daytime on weekdays). The primary outcome was mortality (in-hospital and at different follow-up periods after hospitalization). Secondary outcomes included delays in treatment, and complications. Sensitivity analysis including only studies in which adjusted multivariate analyses were performed for any of the outcomes, and meta-regression controlling for clinically important patient factors, were also performed.

RESULTS

Sixteen studies were included. There was no significant difference in in-hospital mortality (adjusted odds ratio, 1.03; 95% confidence interval [CI], 0.97-1.09; P = 0.30) and at all follow-up periods (7/14 days and 1/3/6 months) after hospitalization between SAH patients who were admitted during off-hour compared with on-hour periods, despite adjusted multivariate meta-analysis being performed. However, patients who were admitted during off-hour periods experienced greater delays from their initial scan to treatment (mean difference, 42.7, 25.2-60.1 hours; P < 0.0001) and had higher rates of pneumonia (odds ratio, 1.65, 1.12-2.44; P = 0.011).

CONCLUSIONS

This meta-analysis has not shown an increased risk of mortality in the short-term and long-term among patients with nontraumatic SAH who were hospitalized during off-hour compared with on-hour periods, despite adjusting for potentially confounding patient factors. The delays to treatment and higher observed rates of pneumonia highlight areas in which hospital services and resources should be targeted during these off-hour periods in patients presenting with nontraumatic SAH.