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.

Hyponatremia is prevalent in acute stroke admissions and is independently associated with higher mortality in patients <75 years.

Whilst stroke-associated pneumonia (SAP) is common and associated with poor outcomes, less is known about the determinants of these adverse clinical outcomes in SAP. To identify the factors that influence mortality and morbidity in SAP. Data for patients with SAP (n = 854) were extracted from a regional Hospital Stroke Register in Norfolk, UK (2003-2015). SAP was defined as pneumonia occurring within 7 days of admission by the treating clinicians. Mutlivariable regression models were constructed to assess factors influencing survival and the level of disability at discharge using modified Rankin Scale [mRS]. Mean (SD) age was 83.0 (8.7) years and ischaemic stroke occurred in 727 (85.0%). Mortality was 19.0% at 30 days and 44.0% at 6 months. Stroke severity assessment using National Institutes of Health Stroke Scale was not recorded in the data set although Oxfordshire Community Stroke Project was Classification. In the multivariable analyses, 30-day mortality was independently associated with age (OR 1.04, 95% CI 1.01-1.07, p = 0.01), haemorrhagic stroke (2.27, 1.07-4.78, p = 0.03) and pre-stroke disability (mRS 4-5 v 0-1: 6.45, 3.12-13.35, p < 0.001). 6-month mortality was independently associated with age (< 0.001), pre-stroke disability (p < 0.001) and certain comorbidities, including the following: dementia (6.53, 4.73-9.03, p < 0.001), lung cancer (2.07, 1.14-3.77, p = 0.017) and previous transient ischemic attack (1.94, 1.12-3.36, p = 0.019). Disability defined by mRS at discharge was independently associated with age (1.10, 1.05-1.16, p < 0.001) and plasma C-reactive protein (1.02, 1.01-1.03, p = 0.012). We have identified non-modifiable determinants of poor prognosis in patients with SAP. Further studies are required to identify modifiable factors which may guide areas for intervention to improve the prognosis in SAP in these patients.

Background: Patients with diabetes mellitus (DM) have been found to be at an increased risk of suffering a stroke. However, research on the impact of DM on stroke outcomes is limited. Objectives: We aimed to examine the influence of DM on outcomes in ischaemic (IS) and haemorrhagic stroke (HS) patients. Methods: We included 608,890 consecutive stroke patients from the Thailand national insurance registry. In-hospital mortality, sepsis, pneumonia, acute kidney injury (AKI), urinary tract infection (UTI) and cardiovascular events were evaluated using logistic regressions. Long-term analysis was performed on first-stroke patients with a determined pathology (n = 398,663) using Royston-Parmar models. Median follow-ups were 4.21 and 4.78 years for IS and HS, respectively. All analyses were stratified by stroke sub-type. Results: Mean age (SD) was 64.3 (13.7) years, 44.9% were female with 61% IS, 28% HS and 11% undetermined strokes. DM was associated with in-hospital death, pneumonia, sepsis, AKI and cardiovascular events (odds ratios ranging from 1.13-1.78, p < 0.01) in both stroke types. In IS, DM was associated with long-term mortality and recurrence throughout the follow-up: HR max (99% CI) at t = 4108 days: 1.54 (1.27, 1.86) and HR (99% CI) = 1.27(1.23,1.32), respectively. In HS, HR max (t = 4108 days) for long-term mortality was 2.10 (1.87, 2.37), significant after day 14 post-discharge. HR max (t = 455) for long-term recurrence of HS was 1.29 (1.09, 1.53), significant after day 116 post-discharge. Conclusions: Regardless of stroke type, DM was associated with in-hospital death and complications, long-term mortality and stroke recurrence.

BackgroundRisk factors for poststroke falls and fractures remain poorly understood. This study aimed to evaluate which factors increased risk of these events after stroke.MethodsData from 7,267 hospitalized stroke patients were acquired from the Norfolk and Norwich University Hospital Stroke Register from 2003–2015. The impacts of multiple patient level and stroke characteristics and comorbidities on post-discharge falls and fractures were assessed. Univariate and multivariable models were constructed, adjusting for multiple confounders, using binary logistic regression for short-term analysis (up to 1-year post-discharge) and Cox-proportional hazard models for longer term analysis (1–3, 3–5, and 0–10 years follow-up).ResultsThe mean age (SD) was 76.3 ± 12.1 years at baseline. 1,138 (15.7%) participants had an incident fall; and 666 (9.2%) an incident fracture during the 10-year follow-up (total person years = 64,447.99 for falls and 67,726.70 for fractures). Half of the sample population were females (50.6%) and the majority had an ischemic stroke (89.8%). After adjusting for confounders: age, sex, previous history of falls, and atrial fibrillation were associated with an increased risk of both falls and fractures during follow-up. Furthermore, chronic kidney disease and hyperlipidemia were associated with an increased risk of falls, while previous stroke/transient ischemic attack increased fracture risk. Total anterior circulation stroke and a prestroke modified Rankin Scale score of 3–5 were associated with decreased risk of both events, with hypertension and cancer decreasing risk of falls only.ConclusionWe identified demographic, stroke-related, and comorbid factors associated with poststroke falls and fracture incidence. Further studies are required to examine and establish the relationship between reversible factors and further explore the role of preventative measures to prevent poststroke falls and fractures.