Preventive antibiotic therapy in acute stroke patients: A systematic review and meta-analysis of individual patient data of randomized controlled trials

Westendorp, Willeke F.; Vermeij, Jan Dirk; Smith, Craig J.; Kishore, Amit K.; Hodsoll, John; Kalra, Lalit; Meisel, Andreas; Chamorro, Ángel; Chang, Jason J.; Rezaei, Yousef; Amiri-Nikpour, Mohammad Reza; DeFalco, Fabrizio A.; Switzer, Jeffrey A.; Blacker, David; Dijkgraaf, Marcel G. W.; Nederkoorn, Paul J.; Beek, Diederik van de

doi:10.1177/23969873211056445

Cited by 22 publications

(24 citation statements)

References 30 publications

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“…So far there is no sufficient evidence from clinical trials to demonstrate the effect of dysphagia screening protocols on the prevention of SAP ( 53 ). Meta-analyses of randomized trials have also failed to support the use of preventive antibiotic therapy to decrease the risk of SAP in acute stroke patients ( 54 , 55 ). Furthermore, only weak evidence exists about whether intensified oral hygiene care reduces the risk of SAP ( 56 , 57 ).…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning and natural language processing for predicting stroke-associated pneumonia

Tsai

Hsieh

Sung

2022

Front. Public Health

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BackgroundIdentifying patients at high risk of stroke-associated pneumonia (SAP) may permit targeting potential interventions to reduce its incidence. We aimed to explore the functionality of machine learning (ML) and natural language processing techniques on structured data and unstructured clinical text to predict SAP by comparing it to conventional risk scores.MethodsLinked data between a hospital stroke registry and a deidentified research-based database including electronic health records and administrative claims data was used. Natural language processing was applied to extract textual features from clinical notes. The random forest algorithm was used to build ML models. The predictive performance of ML models was compared with the A2DS2, ISAN, PNA, and ACDD4 scores using the area under the receiver operating characteristic curve (AUC).ResultsAmong 5,913 acute stroke patients hospitalized between Oct 2010 and Sep 2021, 450 (7.6%) developed SAP within the first 7 days after stroke onset. The ML model based on both textual features and structured variables had the highest AUC [0.840, 95% confidence interval (CI) 0.806–0.875], significantly higher than those of the ML model based on structured variables alone (0.828, 95% CI 0.793–0.863, P = 0.040), ACDD4 (0.807, 95% CI 0.766–0.849, P = 0.041), A2DS2 (0.803, 95% CI 0.762–0.845, P = 0.013), ISAN (0.795, 95% CI 0.752–0.837, P = 0.009), and PNA (0.778, 95% CI 0.735–0.822, P < 0.001). All models demonstrated adequate calibration except for the A2DS2 score.ConclusionsThe ML model based on both textural features and structured variables performed better than conventional risk scores in predicting SAP. The workflow used to generate ML prediction models can be disseminated for local adaptation by individual healthcare organizations.

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Section: Discussionmentioning

confidence: 99%

Application of machine learning and natural language processing for predicting stroke-associated pneumonia

Tsai

Hsieh

Sung

2022

Front. Public Health

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“…Preventive antibiotics had not shown any effect neither in reducing the incidence of stroke-associated pneumonia nor decreasing the mortality or improving the proportion of good outcomes within this field for the last decade. A systematic review ( 51 ) made an analysis of preventive antibiotics in patients with different risk score (A2DS2 or ISAN) and found risk scores did not significantly influence treatment response of preventive antibiotic therapy. However, few clinical trials have compared patients with different risk scores.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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BackgroundStroke-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.MethodsSeven 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.ResultsA 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.ConclusionIn 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 registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=292375, identifier: CRD42021292375.

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“…There are several strengths in this meta-analysis. First, compared with previous studies, 7,8,29 this metaanalysis included more RCTs and sample size; thus, the confidence interval of the study and the heterogeneity between studies for each outcome were reduced. Second, we performed a subgroup analysis by time to the first dose of antibiotic drugs.…”

Section: Discussionmentioning

confidence: 99%

“…The previous Cochrane review and meta-analysis revealed that prophylactic antibiotics reduced the incidence of infections and urinary tract infections in post-stroke patients, but couldn't reduce the mortality rate and incidence of pneumonia. 7,8 However, a randomized controlled trial (RCT) recently published afterwards showed that prophylactic antibiotics decreased the mortality rate and the incidence of early-onset ventilatorassociated pneumonia in patients with acute stroke, 9 which were inconsistent with previous RCTs. [10][11][12] Therefore, prophylactic antibiotics in stroke patients remains a controversial issue.…”

Section: 6mentioning

confidence: 99%

The efficacy and safety of prophylactic antibiotics for post-acute stroke infection: a systematic review and meta-analysis

Wang

Tang

et al. 2022

Preprint

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Background: Infections are common complications after stroke and associated with unfavorable outcomes. We evaluated the efficacy and safety of prophylactic antibiotics for post-acute stroke infection. Methods: We searched PubMed, Embase, the Cochrane Library, SinoMed, China National Knowledge Infrastructure, and WanFangData from inception to February 15th, 2022. We calculated the pooled risk ratio (RR) and mean differences (MDs) with 95% confidence interval (CI), evaluated the risk of bias and conducted sensitivity analysis with RevMan version 5.4.1 and Stata version 14.0 software. The overall quality of evidence was evaluated using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Results: Twelve studies (4809 participants) were included in this meta-analysis. There was no significant difference in the mortality rate [RR 1.03 (95% Cl: 0.91-1.16)], pneumonia [RR 0.94 (95% CI: 0.79-1.11)], and the incidence of adverse events between the prophylactic antibiotics and control groups. Prophylactic antibiotics significantly reduced the incidence of infections [RR 0.72 (95% Cl: 0.58-0.89)], and urinary tract infections [RR 0.39 (95% Cl: 0.3-0.49)] in patients with acute stroke. We performed a subgroup analysis and found a decreasing trend in pneumonia in patients with early prophylactic use of antibiotics within 24 hours after admission [RR 0.81 (95%CI: 0.62-1.07)] as compared with those using prophylactic use of antibiotics within 48 hours after admission [RR 0.94 (95%CI: 0.79-1.11)]. Conclusions: Prophylactic antibiotics did not significantly reduce the mortality rate and pneumonia in patients with acute stroke but reduced the incidence of infections and urinary tract infections.

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Preventive antibiotic therapy in acute stroke patients: A systematic review and meta-analysis of individual patient data of randomized controlled trials

Cited by 22 publications

References 30 publications

Application of machine learning and natural language processing for predicting stroke-associated pneumonia

Application of machine learning and natural language processing for predicting stroke-associated pneumonia

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

The efficacy and safety of prophylactic antibiotics for post-acute stroke infection: a systematic review and meta-analysis

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