Do We Need to Distinguish Thrombolysis and Nonthrombolysis Patients When Applying Stroke-Associated Pneumonia Predicting Scores? An External Validation from a 2-Center Database

High fibrinogen-prealbumin ratio (FPR) predicts stroke-associated pneumonia

OBJECTIVES

Although numerous factors had been found to be associated with stroke-associated pneumonia (SAP), the underlying mechanisms of SAP remain unclear. Fibrinogen-prealbumin ratio (FPR) is a novel indicator that could balance the effects of inflammation and nutrition, which might reflect biological status of patients more comprehensively than other biomarkers. To date, FPR has not been explored in acute ischemic stroke patients. This study aims to explore the relationship between FPR and SAP.

MATERIALS AND METHODS

900 stroke patients participated in this retrospective study and 146 healthy controls were recruited. Fibrinogen and prealbumin were measured within 24 hours on admission. FPR was calculated after dividing fibrinogen (g/L) by prealbumin (mg/L) × 1000. SAP was defined according to the modified Centers for Disease Control criteria.

RESULTS

121 patients were diagnosed with SAP. Log10FPR was higher in stroke patients than healthy controls. In logistic regression analysis, log10FPR was independently associated with SAP (OR 15.568; 95% CI: 3.287-73.732; P=0.001). Moreover, after using ROC curve, the predictive power of "current standard"(defined as A2DS2 plus leukocyte count and log10hs-CRP) plus log10FPR (0.832[0.804-0.857]) was higher than "current standard" (0.811[0.782-0.837], P=0.0944) and A2DS2 plus log10FPR (0.801[0.772-0.828], P=0.0316). No significant difference was found between the predictive power of A2DS2 plus log10FPR and "current standard" (P =0.6342).

CONCLUSION

Higher FPR was observed in stroke patients compared with healthy controls and was significantly associated with SAP. FPR might provide useful clues for timely identification and treatment of SAP.

Comparison of six risk scores for stroke-associated pneumonia in patients with acute ischemic stroke: A systematic review and Bayesian network meta-analysis

Background

Stroke-associated pneumonia (SAP) is one of the major causes of death after suffering a stroke. Several scoring systems have been developed for the early prediction of SAP. However, it is unclear which scoring system is more suitable as a risk prediction tool. We performed this Bayesian network meta-analysis to compare the prediction accuracy of these scoring systems.

Methods

Seven databases were searched from their inception up to April 8, 2022. The risk of bias assessment of included study was evaluated by the QUADAS-C tool. Then, a Bayesian network meta-analysis (NMA) was performed by R 4.1.3 and STATA 17.0 software. The surface under the cumulative ranking curve (SUCRA) probability values were applied to rank the examined scoring systems.

Results

A total of 20 cohort studies involving 42,236 participants were included in this analysis. The results of the NMA showed that AIS-APS had excellent performance in prediction accuracy for SAP than Chumbler (MD = 0.030, 95%CI: 0.004, 0.054), A2DS2 (MD = 0.041, 95% CI: 0.023, 0.059), ISAN (MD = 0.045, 95% CI: 0.022, 0.069), Kwon (MD = 0.077, 95% CI: 0.055, 0.099) and PANTHERIS (MD = 0.082, 95% CI: 0.049, 0.114). Based on SUCRA values, AIS-APS (SUCRA: 99.8%) ranked the highest.

Conclusion

In conclusion, the study found that the AIS-APS is a validated clinical tool for predicting SAP after the onset of acute ischemic stroke.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=292375, identifier: CRD42021292375.

Prediction of stroke-associated pneumonia by the A2DS2, AIS-APS, and ISAN scores: a systematic review and meta-analysis

Background: Different scoring systems (A2DS2, AISAPS, ISAN) have been designed to predict the risk of in-hospital stroke-associated pneumonia (SAP). Studies have assessed the accuracy of these scores for predicting SAP. We performed this meta-analysis to consolidate the evidence on the predictive accuracies for SAP of the A2DS2, AISAPS, and ISAN scores.Materials and methods: We conducted a systematic search for all studies reporting the SAP predictive accuracy of A2DS2, AISAPS, or ISAN scores in the databases of PubMed Central, SCOPUS, MEDLINE, Embase, and Cochrane from inception until December 2020. We used the STATA software for the meta-analysis.Results: We included 19 studies with 35 849 patients. The pooled score sensitivities were 78% (95% CI, 71%-83%) for A2DS2, 79% (95% CI, 77%-81%) for AISAPS, and 79% (95% CI, 77%-81%) for ISAN. The pooled score specificities were 73% (95% CI, 65%-80%) for A2DS2, 74% (95% CI, 69%-79%) for AISAPS, and 74% (95% CI, 69%-79%) for ISAN. We found significant heterogeneity for all the scoring systems based on the chi-square test results and an I² statistic > 75%. We performed meta-regression to explore the source of heterogeneity and found that patient selection (p< 0.05) and reference standards (p< 0.05) in the sensitivity model, index test standards (p< 0.05), flow and timing of tests (p< 0.01) in the specificity model, and mean age (p < 0.001) in the joint model were the source of heterogeneity.Conclusions: To summarize, we found that A2S2, AISAPS and ISAN have moderate predictive accuracy for SAP with A2S2 having a stable cutoff value.