BackgroundThe impact of hemoglobin levels and anemia on stroke mortality remains controversial. We aimed to systematically assess this association and quantify the evidence.Methods and ResultsWe analyzed data from a cohort of 8013 stroke patients (mean±SD, 77.81±11.83 years) consecutively admitted over 11 years (January 2003 to May 2015) using a UK Regional Stroke Register. The impact of hemoglobin levels and anemia on mortality was assessed by sex‐specific values at different time points (7 and 14 days; 1, 3, and 6 months; 1 year) using multiple regression models controlling for confounders. Anemia was present in 24.5% of the cohort on admission and was associated with increased odds of mortality at most of the time points examined up to 1 year following stroke. The association was less consistent for men with hemorrhagic stroke. Elevated hemoglobin was also associated with increased mortality, mainly within the first month. We then conducted a systematic review using the Embase and Medline databases. Twenty studies met the inclusion criteria. When combined with the cohort from the current study, the pooled population had 29 943 patients with stroke. The evidence base was quantified in a meta‐analysis. Anemia on admission was found to be associated with an increased risk of mortality in both ischemic stroke (8 studies; odds ratio 1.97 [95% CI 1.57–2.47]) and hemorrhagic stroke (4 studies; odds ratio 1.46 [95% CI 1.23–1.74]).ConclusionsStrong evidence suggests that patients with anemia have increased mortality with stroke. Targeted interventions in this patient population may improve outcomes and require further evaluation.

Stroke-associated pneumonia is not associated with increased long-term mortality, but it is linked with increased mortality up to 1 year, prolonged LOS, and poor functional outcome on discharge. Targeted intervention strategies are required to improve outcomes of SAP patients who survive to hospital discharge.

Evidence is insufficient at present to advocate omission of conventional ligature-based appendix stump closure in favour of any single type of mechanical device over another in uncomplicated appendicitis.

BackgroundRoutinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed.ObjectiveThe objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer.MethodsData pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways.ResultsThe proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information.ConclusionsClinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals.