‘Big data’ is a term for data sets that are so
large or complex that traditional data processing applications are
inadequate. Billions of dollars have been spent on attempts to build predictive
tools from large sets of poorly controlled healthcare metadata.
Companies often sell reports at a physician or facility level based
on various flawed data sources, and comparative websites of ‘publicly
reported data’ purport to educate the public. Physicians should
be aware of concerns and pitfalls seen in such data definitions,
data clarity, data relevance, data sources and data cleaning when
evaluating analytic reports from metadata in health care. Cite this article:
This paper documents the epidemiology of adults (aged more than 18 years) with a calcaneal fracture who have been admitted to hospital in England since 2000. Secondary aims were to document whether publication of the United Kingdom Heel Fracture Trial (UK HeFT) influenced the proportion of patients admitted to hospital with a calcaneal fracture who underwent surgical treatment, and to determine whether there has been any recent change in the surgical technique used for these injuries. In England, the Hospital Episode Statistics (HES) data are recorded annually. Between 2000/01 and 2016/17, the number of adults admitted to an English NHS hospital with a calcaneal fracture and whether they underwent surgical treatment was determined.Aims
Patients and Methods
The extent and depth of routine health care data
are growing at an ever-increasing rate, forming huge repositories
of information. These repositories can answer a vast array of questions.
However, an understanding of the purpose of the dataset used and
the quality of the data collected are paramount to determine the
reliability of the result obtained. This Editorial describes the importance of adherence to sound
methodological principles in the reporting and publication of research
using ‘big’ data, with a suggested reporting framework for future Cite this article:
The aims of this study were to characterize the frequency of
missing data in the National Surgical Quality Improvement Program
(NSQIP) database and to determine how missing data can influence
the results of studies dealing with elderly patients with a fracture
of the hip. Patients who underwent surgery for a fracture of the hip between
2005 and 2013 were identified from the NSQIP database and the percentage
of missing data was noted for demographics, comorbidities and laboratory
values. These variables were tested for association with ‘any adverse
event’ using multivariate regressions based on common ways of handling
missing data.Aims
Patients and Methods
Total knee arthroplasty (TKA) is a major orthopaedic
intervention. The length of a patient's stay has been progressively
reduced with the introduction of enhanced recovery protocols: day-case
surgery has become the ultimate challenge. This narrative review shows the potential limitations of day-case
TKA. These constraints may be social, linked to patient’s comorbidities,
or due to surgery-related adverse events (e.g. pain, post-operative
nausea and vomiting, etc.). Using patient stratification, tailored surgical techniques and
multimodal opioid-sparing analgesia, day-case TKA might be achievable
in a limited group of patients. The younger, male patient without
comorbidities and with an excellent social network around him might
be a candidate. Demographic changes, effective recovery programmes and less invasive
surgical techniques such as unicondylar knee arthroplasty, may increase
the size of the group of potential day-case patients. The cost reduction achieved by day-case TKA needs to be balanced
against any increase in morbidity and mortality and the cost of
advanced follow-up at a distance with new technology. These factors
need to be evaluated before adopting this ultimate ‘fast-track’
approach. Cite this article:
